System

Abstract

To provide a system for improving the accuracy of a palm AI system by using generative AI to artificially generate palm image datasets that are used to train an authenticator model.SOLUTION: A system for improving the accuracy of a palm identification system includes means for training a biometric model using a generated palm image database, means for generating a palm image database using a generation AI, means for obtaining and identifying a user's palm image, and means for uniquely generating a palm ID to interface with various service providers.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Palm authentication has the advantage of being less prone to false positives than other biometric authentication methods, and is highly reliable for intentional authentication. However, due to a lack of palm image data, sufficient AI training is difficult, and there have been challenges in putting this technology into practical use. In addition, there has been a lack of a platform for linking users' palm IDs to various services, making it difficult to provide seamless services. [Means for solving the problem]

[0005] This invention provides a system that improves the accuracy of palm authentication systems by artificially generating palm image data using generative AI and using that data to train an AI model. It also builds a platform that allows users to send palm images via a smartphone app, generate a palm ID on the server side, and link that ID with various service providers. This allows users to use their palm as an ID and seamlessly access a variety of services.

[0006] "Generative AI" is an artificial intelligence technology for artificially generating palm image data.

[0007] A "palm image database" is a database for storing generated or collected palm image data.

[0008] A "biometric model" is an AI model trained to identify individuals using specific biometric characteristics.

[0009] A "palm ID" is a unique identifier generated based on the characteristic points of a user's palm.

[0010] A "database" is a system or structure for efficiently storing and retrieving information.

[0011] "Feature points" are specific points on the palm that can be used for identification, such as lines, patterns, or shapes.

[0012] A "server" is a computer system that receives, processes, and stores data from users and optionally transmits the results.

[0013] A "user" is an individual who provides a palm image to the system and receives authentication in order to use various services.

[0014] A "service provider" is an organization or company that provides various services (point cards, payment systems, pass cards, etc.).

[0015] "API" stands for Application Program Interface, a set of rules for how different software systems communicate with each other. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The following describes an embodiment of the present invention. This system generates palm image data using a generative AI and trains a biometric authentication model based on that data. It also provides a platform for linking a user's palm as an ID with various services.

[0038] System Configuration

[0039] 1. Palm image data generation part

[0040] server

[0041] The server launches the generation AI, which generates palm image data based on the set parameters. The generated palm image data is stored in a database. The generation AI has the ability to increase the variations in palm shapes and patterns, allowing for the creation of a wide variety of data.

[0042] 2. Training the palm recognition AI model

[0043] server

[0044] The server retrieves palm image data from the database and performs preprocessing, which includes adjusting the image size, removing noise, and adjusting contrast. Feature points are extracted from the preprocessed image data and used to train an AI model. Once trained, the AI ​​model is used for authentication in the production environment.

[0045] 3. Palm ID registration section

[0046] User

[0047] The user uses the smartphone app to take a picture of their palm using the camera function, and the app then sends the image to the server.

[0048] server

[0049] The server analyzes the received palm image, extracts feature points, generates a unique palm ID based on the feature points, associates the ID with the user profile, and stores it in a database.

[0050] 4. Palm ID and service integration

[0051] User

[0052] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[0053] server

[0054] Based on the user's selection, the server will link the Teno-ID with various service provider accounts via API, allowing Teno-ID to be used as a means of authentication for each service.

[0055] 5. Authentication Process

[0056] User

[0057] For example, when checking in at a hotel, a user can hold their palm over a terminal at the front desk.

[0058] Terminal

[0059] The front terminal captures a palm image and sends it to the server.

[0060] server

[0061] The server analyzes the received palm image, extracts the palm ID, and compares it with a database. Based on the comparison result, the server obtains the user's reservation information and sends it to the terminal at the front desk. The terminal then completes the user's check-in and issues an electronic room key.

[0062] Specific examples

[0063] Example 1: Age verification at a convenience store

[0064] User

[0065] When a user purchases alcohol at a convenience store, they hold their palm over a palm authentication terminal installed in the store.

[0066] Terminal

[0067] The device takes a palm image and sends the palm ID to the server.

[0068] server

[0069] The server checks the palm ID against a database to obtain the user's age. Once the age verification is complete, it sends the results to the device, which then displays the results and allows the user to purchase alcohol.

[0070] Example 2: Public transport ride

[0071] User

[0072] Users hold their palm over a palm authentication terminal at the public transportation ticket gate.

[0073] Terminal

[0074] The device captures a palm image and sends the palm ID to the server.

[0075] server

[0076] The server checks the palm ID against a database and obtains the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[0077] In this way, the palm authentication system of the present invention generates palm image data using generative AI and trains a biometric authentication model to achieve highly accurate palm authentication. Furthermore, by registering the user's palm as an ID and providing a platform that links with various services, convenience and security can be improved.

[0078] The processing flow will be explained below.

[0079] Palm image data generation using generative AI

[0080] Step 1:

[0081] server

[0082] The server starts the generation AI and sets parameters regarding the number and variations of palm image data to be generated.

[0083] Step 2:

[0084] server

[0085] The generation AI generates palm image data with different palm shapes and patterns based on set parameters.

[0086] Step 3:

[0087] server

[0088] The generated palm image data is stored in a database and is labeled and classified.

[0089] Training the palm recognition AI model

[0090] Step 1:

[0091] server

[0092] The server retrieves palm image data from the database and performs preprocessing on the images, which includes resizing, noise reduction, and contrast adjustment.

[0093] Step 2:

[0094] server

[0095] A specific algorithm is applied to extract feature points from the preprocessed image data.

[0096] Step 3:

[0097] server

[0098] The extracted feature data is used to train an AI model, and deep learning algorithms are used to improve the model's accuracy.

[0099] Step 4:

[0100] server

[0101] The trained AI model is saved and made available for authentication processing.

[0102] Palm ID registration

[0103] Step 1:

[0104] User

[0105] The user launches the app on their smartphone and uses the camera function to take a picture of their palm.

[0106] Step 2:

[0107] User

[0108] The captured palm image is sent from the app to the server.

[0109] Step 3:

[0110] server

[0111] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID.

[0112] Step 4:

[0113] server

[0114] The generated palm ID is associated with the user profile and stored in a database.

[0115] Linking palm ID with services

[0116] Step 1:

[0117] User

[0118] Users open the app on their smartphone and select the service they want to link their palm ID to, such as a point card, payment system, or pass card.

[0119] Step 2:

[0120] server

[0121] Based on the user's selection, the server calls an API to link the palm ID with a specific service account.

[0122] Step 3:

[0123] server

[0124] Communicate with the service provider via API and update the link information between your palm ID and service account.

[0125] Authentication Process

[0126] Step 1:

[0127] User

[0128] When a user uses the service, for example when checking into a hotel, they hold their palm over a terminal at the front desk.

[0129] Step 2:

[0130] Terminal

[0131] The front terminal captures a palm image and sends it to the server.

[0132] Step 3:

[0133] server

[0134] The server analyzes the received palm image and identifies the corresponding palm ID by extracting feature points.

[0135] Step 4:

[0136] server

[0137] Based on the palm ID, the user's reservation information is retrieved from the database and the authentication results are sent to the terminal.

[0138] Step 5:

[0139] Terminal

[0140] The terminal notifies the user of successful authentication and provides appropriate services (e.g., issuing an electronic room key).

[0141] Age verification at convenience stores

[0142] Step 1:

[0143] User

[0144] When a user purchases alcohol at a convenience store, they hold their hand over a palm authentication terminal.

[0145] Step 2:

[0146] Terminal

[0147] The device captures a palm image and sends the palm ID to the server.

[0148] Step 3:

[0149] server

[0150] The server compares the palm ID with a database to confirm the user's age information.

[0151] Step 4:

[0152] server

[0153] Once age verification is complete, the results will be sent to the device.

[0154] Step 5:

[0155] Terminal

[0156] The device will display the results and allow the purchase of alcohol if necessary.

[0157] Example 1

[0158] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] In modern society, there is a growing demand for simple yet highly accurate biometric authentication systems. However, conventional biometric authentication systems have had problems with the time and cost required to collect and process individual image data. Furthermore, there are limitations to the accuracy of authentication, resulting in frequent misidentifications and missed recognitions. Furthermore, information management is complicated when linking with multiple services, and there is a lack of a mechanism for automatically authenticating users. The present invention aims to solve these problems.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0161] In this invention, the server includes means for training a biometric authentication model using the generated palm image data, means for generating palm image data using the generating artificial intelligence, means for acquiring and identifying a user's palm image, means for uniquely generating a palm identifier and linking it with various service providers, means for generating diverse palm image data, means for performing preprocessing on the palm image data such as noise removal and contrast adjustment, and means for deploying the generated biometric authentication model in a production environment. This enables highly accurate and versatile palm authentication, allowing users to use various services automatically and safely without hassle.

[0162] "Generated palm image data" is digital image data of the palm generated using artificial intelligence.

[0163] "Means for training a biometric authentication model" refers to a method and device that uses acquired palm image data to adjust and train the parameters of an artificial intelligence model.

[0164] "Generative AI" refers to algorithms or software that generate new palm image data based on set parameters.

[0165] "Means for acquiring an image of a user's palm and identifying it" refers to a method and apparatus for taking a picture of a user's palm using a device such as a camera, analyzing the image, and uniquely identifying it.

[0166] A "palm identifier" is unique identification information generated based on specific feature points extracted from a captured palm image.

[0167] "Various service providers" are external service providers, such as payment systems and point card systems, that users access and authenticate using palm identifiers.

[0168] The "means for generating a variety of palm image data" refers to a method and device for generating a plurality of palm images with different shapes and patterns using artificial intelligence.

[0169] "Means for performing pre-processing such as noise removal and contrast adjustment" refers to a method and device for processing acquired palm image data to improve image quality and recognition accuracy.

[0170] The "means for deploying the generated biometric authentication model in a production environment" refers to a method and apparatus for incorporating a trained artificial intelligence model into an actual operational system and setting it up so that it can be used for authentication processing.

[0171] System Configuration

[0172] 1. Palm image data generation part

[0173] server

[0174] The server starts the generative AI and generates palm image data. Specifically, the server uses "StyleGAN" or a similar algorithm as the generative AI. The server sets parameters such as palm size, pattern complexity, and number of variations, and generates palm image data through the generative AI. This generated palm image data is stored in a database. For example, a prompt might be: "To generate palm image data, generate a palm image based on the set parameters. Increase the variations in palm shape and pattern to create diverse data."

[0175] 2. Training the palm recognition AI model

[0176] server

[0177] The server retrieves palm image data from the database and performs preprocessing. This preprocessing uses the OpenCV library to resize the image, remove noise, and adjust the contrast. Furthermore, algorithms such as SURF and SIFT are used to extract feature points. Based on the preprocessed image data and feature point data, an AI model is trained using TensorFlow or PyTorch. The trained model is saved in the SavedModel format and deployed to the production environment via an API server (e.g., Flask).

[0178] 3. Palm ID registration section

[0179] User

[0180] Users take a palm image using a dedicated smartphone app called "CustomPalmID App." Using the smartphone camera, palm images are captured.

[0181] server

[0182] The server receives the palm image sent by the user and extracts the feature points, which are then analyzed using SURF and SIFT techniques to generate a unique palm identifier in the form of a UUID, which is then associated with the corresponding user profile and stored in a database.

[0183] 4. Palm ID and service integration

[0184] User

[0185] Using the smartphone app, the user selects the service (e.g., payment system, point card system, pass card, etc.) they want to link their palm ID to. Using the "ServiceConnector App."

[0186] server

[0187] The server associates the palm ID with the service provider's account based on the user's selection. By linking the palm ID with the service provider's account using the OAuth 2.0 protocol, the server makes it possible to use the palm ID as a means of authentication for various services.

[0188] 5. Authentication Process

[0189] User

[0190] When checking in at a hotel, a user holds their palm over a palm authentication terminal, such as one installed at the front desk.

[0191] Terminal

[0192] The terminal captures a palm image and transmits the image data to a server.

[0193] server

[0194] The server analyzes the received palm image, extracts a palm identifier, and compares it with a database. Based on the comparison result, the server obtains the user's reservation information and sends it to the terminal. After authentication is complete, the terminal issues an electronic room key and provides it to the user.

[0195] Specific examples

[0196] Example 1: Age verification at a convenience store

[0197] User

[0198] When a user purchases alcohol at a convenience store, they hold their palm over a palm authentication terminal installed in the store.

[0199] Terminal

[0200] The terminal takes a palm image and transmits the palm identifier to the server.

[0201] server

[0202] The server compares the palm identifier with a database to obtain the user's age information. Once the age verification is complete, it sends the result to the terminal. The terminal then displays the authentication result and allows the user to purchase alcohol.

[0203] Example 2: Public transport ride

[0204] User

[0205] The user holds their palm over a palm authentication terminal at the ticket gate of a public transport facility.

[0206] Terminal

[0207] The terminal captures a palm image and transmits the palm identifier to the server.

[0208] server

[0209] The server checks the palm identifier against a database to obtain the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[0210] In this way, the palm authentication system of the present invention generates palm image data using generative AI and trains a biometric authentication model to achieve highly accurate palm authentication. Furthermore, by registering the user's palm as an identifier and linking it to various services, convenience and security can be improved.

[0211] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0212] System program processing flow

[0213] Step 1:

[0214] The server launches the generative AI. Specifically, it instantiates the generative AI software and prepares it to generate palm images. The input data used are generative parameters (e.g., palm shape, pattern complexity, number of variations, etc.). The output is an initialized generative AI instance.

[0215] Step 2:

[0216] The server sets the parameters for palm image generation. Specifically, it reads parameters such as palm size, pattern complexity, and number of variations from a configuration file or user input. The input is the configuration parameters, and the output is command data that is input to the generation AI based on those parameters.

[0217] Step 3:

[0218] The server generates palm image data using a generative AI. The generative AI (e.g., "StyleGAN") uses an algorithm to generate palm images according to set parameters. The input is the set parameters and an initialized generative AI instance, and the output is the generated palm image data.

[0219] Step 4:

[0220] The server saves the generated palm image data in a database. It connects to a database management system (e.g., MySQL) and stores the generated palm image data files. The input is the generated palm image data, and the output is the identification information of the data saved in the database.

[0221] Step 5:

[0222] The server retrieves palm image data from the database. It connects to the database and searches for and retrieves stored palm image data. The input is the database connection information and the search query, and the output is the retrieved palm image data.

[0223] Step 6:

[0224] The server performs preprocessing of the image data. Specifically, it uses the "OpenCV" library to adjust the image size, remove noise, and adjust the contrast. The input is palm image data, and the output is the preprocessed palm image data.

[0225] Step 7:

[0226] The server extracts feature points from the preprocessed palm image using the "SURF" or "SIFT" algorithm. The input is the preprocessed palm image data, and the output is the feature point data.

[0227] Step 8:

[0228] The server trains the AI ​​model. Using TensorFlow and PyTorch, the AI ​​model learns the feature point data and palm image data. The input is the feature point data and palm image data, and the output is the trained AI model.

[0229] Step 9:

[0230] The server saves the trained AI model and places it in the production environment. The model file is saved in the "SavedModel" format and deployed using an API server (e.g., "Flask"). The input is the trained AI model, and the output is an AI model instance that runs in the production environment.

[0231] Step 10:

[0232] The user takes a palm image using a dedicated app. Using the "CustomPalmID App," the palm image is acquired with the smartphone camera. The input is the camera image, and the output is the captured palm image data.

[0233] Step 11:

[0234] The user sends the captured palm image data to the server. Using the send function within the app, a POST request is made to the endpoint with the image data. The input is the palm image data, and the output is the image data being uploaded to the server.

[0235] Step 12:

[0236] The server analyzes the received palm image and extracts feature points using a specific algorithm (e.g., "SURF" or "SIFT"). The input is palm image data, and the output is feature point data.

[0237] Step 13:

[0238] The server generates a palm identifier and associates it with the user profile. It generates an identifier in UUID format, associates it with the corresponding user profile, and stores it in a database. The input is minutiae data and user profile information, and the output is the generated palm identifier.

[0239] Step 14:

[0240] Users use the app to link their palm ID to a service. Using the "ServiceConnector App," they select the service they want to link. The input is the palm ID and the service information to link to, and the output is the link setting information.

[0241] Step 15:

[0242] The server links the palm identifier with the service provider account. This is done via API using a protocol such as OAuth 2.0. The input is the palm identifier and the service provider account information, and the output is the linked account information.

[0243] Step 16:

[0244] A user holds their palm over a palm authentication terminal, such as when checking in at a hotel. The input is a palm image, and the output is an authentication request.

[0245] Step 17:

[0246] The terminal captures the palm image and sends it to the server. The palm image is acquired using an image capture device and sent to the server. The input is the palm image, and the output is the transmission of image data to the server.

[0247] Step 18:

[0248] The server analyzes the palm image, extracts a palm identifier, and matches it with a database. It uses an algorithm to match it with an existing palm identifier database. The input is the palm image data, and the output is the matching result.

[0249] Step 19:

[0250] The server obtains the user's reservation information based on the matching result and sends it to the terminal. The server also obtains user information from the reservation system based on the matching result. The input is the matching result and a reservation system inquiry request, and the output is the transmission of reservation information.

[0251] Step 20:

[0252] After authentication is complete, the terminal issues an electronic room key and provides it to the user. The room key information is displayed on the terminal's display device. The input is the verification result and reservation information, and the output is the electronic room key information.

[0253] The above are the specific processing steps of this palm authentication system.

[0254] (Application example 1)

[0255] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0256] Until now, there have been technical and cost challenges in the widespread adoption of highly accurate biometric authentication systems. Furthermore, there has been a lack of security authentication systems that combine user convenience and safety. In particular, more reliable authentication methods are needed for access control in places where high security is required, such as offices and data centers.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0258] In this invention, the server includes a means for training a biometric authentication model using the generated palm image database, a means for generating palm image data using a generation AI, a means for acquiring and identifying a user's palm image, a means for uniquely generating a palm ID and linking it with various service providers, and a security means for capturing and authenticating palm images using a smart device or robot. This allows users to easily perform palm authentication with high accuracy using a smart device or robot. This satisfies high security requirements and improves the reliability of access control systems, particularly those used in offices, data centers, etc.

[0259] "Generative AI" is an artificial intelligence technology used to generate image data of a user's palm, and has the ability to create a variety of palm shapes and patterns.

[0260] The "palm image database" is a database for storing and managing palm image data generated by the generative AI, and is used to train biometric authentication models.

[0261] A "biometric authentication model" is an artificial intelligence model trained to verify a user's identity based on palm image data, and performs highly accurate authentication based on feature points.

[0262] "Palm ID" is an identification number that is uniquely generated using the feature points of a palm image and is used to link with various services.

[0263] A "smart device" is a device that has the function of acquiring an image of a user's palm and performing authentication, and includes smartphones and corresponding access control devices.

[0264] A "robot" is an autonomous or remotely controlled device used to capture palm images and perform authentication processes, and is used for security management.

[0265] "Minutes" are unique data points extracted from palm images that are used to train and authenticate biometric models.

[0266] To implement this invention, a system based on the following procedures is required.

[0267] System Configuration

[0268] 1. Palm image data generation part

[0269] server

[0270] The server generates various palm image data using generative AI, which applies technologies such as StyleGAN. The generated palm image data is stored in a dedicated database.

[0271] 2. Training the palm recognition AI model

[0272] server

[0273] The server trains the biometric authentication model using preprocessed palm image data. Preprocessing includes noise removal, size adjustment, contrast adjustment, etc., and then extracts feature points. Training is performed using TensorFlow and PyTorch.

[0274] 3. Palm ID registration section

[0275] User

[0276] The user uses the camera function of a smartphone app to take an image of their palm, which is then sent to the server via the app.

[0277] server

[0278] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is associated with the user profile and stored in a database.

[0279] 4. Palm ID and service integration

[0280] User

[0281] Through the app, users can select the services they want to link their palm ID to, including a wide range of services such as security authentication, financial services, and access control.

[0282] server

[0283] Based on the user's selection, the server configures the palm ID to link with various service provider accounts via API.

[0284] 5. Authentication Process

[0285] User

[0286] Users hold their palm over a palm authentication terminal at locations where security authentication is required, such as at the entrance to a data center or an office access control device.

[0287] Terminal

[0288] A palm image is captured using a smart device or robot and sent to a server.

[0289] server

[0290] The server analyzes the received palm image, compares the palm ID with a database, and sends an access permission or denial result to the device based on the comparison result.

[0291] Example: Access control at a data center

[0292] This system is used in situations where secure access control is required in corporate data centers.

[0293] 1. User: Scans palm at data center entrance.

[0294] 2. Device: Captures a palm image and sends it to the server.

[0295] 3. Server: Generates palm ID and checks it against the database.

[0296] 4. Authentication result: Based on the verification result, access permission is notified to the terminal and the door opens automatically.

[0297] Prompt Sentence Examples

[0298] "Generate a palm ID based on the palm image shown below and verify it against the database for authentication."

[0299] The above procedure makes it possible to implement the present invention, and realize a highly accurate and user-friendly palm authentication system, which can meet the high security requirements of offices, data centers, etc.

[0300] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0301] Step 1:

[0302] To use the palm authentication system, a user launches the smartphone app and takes a picture of their palm.

[0303] Input: User's palm image

[0304] Output: Palm image data

[0305] How it works: The user activates the camera function of the smartphone app and takes a picture of their palm. This image data is then saved in the application.

[0306] Step 2:

[0307] The smartphone app sends the captured palm image to a server.

[0308] Input: Palm image data

[0309] Output: Image data transmission request to the server

[0310] How it works: An HTTP POST request containing palm image data is sent to the server, with the image data encrypted if necessary.

[0311] Step 3:

[0312] The server receives the palm image data and performs preprocessing.

[0313] Input: Palm image data

[0314] Output: Preprocessed image data

[0315] Operation: After receiving the palm image data, the server performs preprocessing such as noise reduction, size adjustment, contrast adjustment, etc. The image is converted into a standardized format.

[0316] Step 4:

[0317] The server extracts feature points from the preprocessed palm image.

[0318] Input: Preprocessed image data

[0319] Output: feature point data

[0320] How it works: A feature point extraction algorithm is applied to the preprocessed image data. Feature points are extracted using a deep learning model such as CNN.

[0321] Step 5:

[0322] The server generates a palm ID based on the feature point data and stores it in a database.

[0323] Input: feature point data

[0324] Output: Palm ID, registration request to database

[0325] Operation: A palm ID is generated from the feature point data, and it is associated with the user profile and saved in a database. SQL queries are used for the saving process.

[0326] Step 6:

[0327] The user holds their palm over an authentication terminal at a specific security authentication point (e.g., the entrance to a data center).

[0328] Input: User's palm image

[0329] Output: Authentication start request

[0330] How it works: The authentication process begins when the user holds their palm over the authentication device's camera.

[0331] Step 7:

[0332] The device captures a palm image and sends it to the server.

[0333] Input: User's palm image

[0334] Output: Image data transmission request to the server

[0335] How it works: The device's camera captures a palm image and sends it to a server. The image data is encrypted before being transferred.

[0336] Step 8:

[0337] The server analyzes the palm image and compares it with a database.

[0338] Input: Palm image data

[0339] Output: Authentication result (allow or deny)

[0340] How it works: The server analyzes the palm image and matches it with the palm ID stored in the database. Based on the match, authentication is either granted or denied.

[0341] Step 9:

[0342] The terminal receives the authentication result and notifies the user.

[0343] Input: Authentication result

[0344] Output: Authentication notification (allowed or denied)

[0345] Operation: The terminal that receives the authentication result from the server allows access if it is permitted and automatically opens the door. If it is denied, it notifies the user.

[0346] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0347] The following describes an embodiment of the present invention. The system includes a generation AI for generating palm image data, a means for training a biometric authentication model based on the palm image, a means for acquiring and identifying a user's palm image, a means for generating a palm ID and linking it with various service providers, and an emotion engine for recognizing and analyzing the user's emotions.

[0348] System Configuration

[0349] 1. Palm image data generation part

[0350] server

[0351] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with various palm shapes and patterns and stores them in a database.

[0352] 2. Training the palm recognition AI model

[0353] server

[0354] The server retrieves palm image data from the database, pre-processes the image, extracts feature points from the pre-processed image, and trains an AI model. The trained model is used for high-precision authentication.

[0355] 3. Palm ID registration section

[0356] User

[0357] The user launches the app on their smartphone and uses the camera to take a picture of their palm, which is then sent to the server.

[0358] server

[0359] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is then associated with the user profile and stored in a database.

[0360] 4. Linking Tenohira ID with services

[0361] User

[0362] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[0363] server

[0364] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, and communicates with the service provider through the API to update the link information.

[0365] 5. Emotion recognition part

[0366] User

[0367] When a user takes a palm image using a smartphone app, the emotion engine collects data such as the user's facial expressions and voice, which allows it to analyze the user's emotional state in real time.

[0368] server

[0369] The server analyzes the acquired emotional data and associates it with the user's palm ID. The emotional data is stored in a database and will be used to provide future services.

[0370] 6. Authentication Process

[0371] User

[0372] For example, when checking into a hotel, a user holds their palm over a terminal at the front desk.

[0373] Terminal

[0374] The front terminal captures a palm image and sends it to the server.

[0375] server

[0376] The server analyzes the received palm image and compares the palm ID with a database. It retrieves the user's reservation information and sends the authentication result to the terminal. If authentication is successful, the appropriate service (e.g., issuing an electronic room key) is provided.

[0377] Specific examples

[0378] Example 1: Age verification at a convenience store

[0379] User

[0380] When a user purchases alcohol at a convenience store, they hold their hand over a palm authentication terminal.

[0381] Terminal

[0382] The device captures a palm image and sends it to the server.

[0383] server

[0384] The server checks the palm ID against a database to confirm the user's age. Once the verification is complete, it sends the results to the device, which then displays the results and allows the user to purchase alcohol.

[0385] Example 2: Public transport

[0386] User

[0387] Users hold their hand over a palm authentication terminal at the public transportation ticket gate.

[0388] Terminal

[0389] The device captures a palm image and sends it to the server.

[0390] server

[0391] The server compares the palm ID with the database and obtains the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[0392] Example 3: Providing services using user emotion recognition

[0393] User

[0394] The user takes a picture of their palm with their smartphone and sends emotional information to the server via the app.

[0395] server

[0396] The server analyzes the emotion data and uses that information to provide the user with appropriate services (e.g., a customized shopping experience). If the emotion is determined to be "fun," relevant promotions or coupons can be offered.

[0397] In this way, the palm authentication system of the present invention uses generative AI to generate palm image data and train a biometric authentication model. It also uses an emotion engine to acquire and analyze emotion data, allowing it to provide personalized services to users. This system improves convenience and security, resulting in a better user experience.

[0398] The processing flow will be explained below.

[0399] Palm image data generation using generative AI

[0400] Step 1:

[0401] server

[0402] Start the generation AI and set parameters regarding the number and variations of palm image data to be generated.

[0403] Step 2:

[0404] server

[0405] Palm image data with different palm shapes and patterns is generated based on the set parameters.

[0406] Step 3:

[0407] server

[0408] The generated palm image data is stored in a database and is labeled and classified.

[0409] Training the palm recognition AI model

[0410] Step 1:

[0411] server

[0412] Palm image data is acquired from the database and preprocessed, including size adjustment, noise removal, and contrast adjustment.

[0413] Step 2:

[0414] server

[0415] A specific algorithm is applied to extract feature points from the preprocessed image data.

[0416] Step 3:

[0417] server

[0418] The extracted feature data is used to train an AI model, and a deep learning algorithm is used to improve the model's accuracy.

[0419] Step 4:

[0420] server

[0421] The trained AI model is saved and made available for authentication processing.

[0422] Palm ID registration

[0423] Step 1:

[0424] User

[0425] Launch the app on your smartphone and use the camera to take a picture of your palm.

[0426] Step 2:

[0427] User

[0428] The captured palm image is sent from the app to the server.

[0429] Step 3:

[0430] server

[0431] The received palm image is analyzed, feature points are extracted, and a unique palm ID is generated.

[0432] Step 4:

[0433] server

[0434] The generated palm ID is associated with the user profile and stored in a database.

[0435] Linking palm ID with services

[0436] Step 1:

[0437] User

[0438] Open the app on your smartphone and select the service you want to link your Palm ID to, such as a point card, payment system, or pass card.

[0439] Step 2:

[0440] server

[0441] Based on the user's selection, an API is called to link the palm ID with a specific service account.

[0442] Step 3:

[0443] server

[0444] Communicate with the service provider via API and update the link information between your palm ID and service account.

[0445] emotion recognition part

[0446] Step 1:

[0447] User

[0448] When a palm image is taken using a smartphone app, the emotion engine collects data such as the user's facial expressions and voice.

[0449] Step 2:

[0450] server

[0451] The acquired data is analyzed to determine the user's emotional state in real time.

[0452] Step 3:

[0453] server

[0454] The emotion data is associated with a user profile and stored in a database.

[0455] Step 4:

[0456] server

[0457] Settings are made to provide customized services based on the user's emotional data.

[0458] Authentication Process

[0459] Step 1:

[0460] User

[0461] For example, when checking in at a hotel, you hold your palm over the terminal at the front desk.

[0462] Step 2:

[0463] Terminal

[0464] The front terminal captures a palm image and sends it to the server.

[0465] Step 3:

[0466] server

[0467] The received palm image is analyzed and the palm ID is compared with a database.

[0468] Step 4:

[0469] server

[0470] The user's reservation information is obtained based on the palm ID, and the authentication results are sent to the terminal.

[0471] Step 5:

[0472] Terminal

[0473] Notify the user that authentication was successful and provide appropriate services (e.g., issue an electronic room key).

[0474] Age verification at convenience stores

[0475] Step 1:

[0476] User

[0477] When checking in at Costco, hold your palm over the terminal at the front desk.

[0478] Step 2:

[0479] Terminal

[0480] The front terminal captures a palm image and sends it to the server.

[0481] Step 3:

[0482] server

[0483] Feature points are extracted from the received palm image and a palm ID is generated.

[0484] Step 4:

[0485] server

[0486] The generated palm ID is matched to obtain the corresponding user's age information.

[0487] Step 5:

[0488] server

[0489] Once the confirmation is received, the authentication result is sent to the terminal.

[0490] Step 6:

[0491] Terminal

[0492] The device will display the results and allow the alcohol purchase.

[0493] Example 2

[0494] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0495] In conventional biometric authentication systems, achieving high-precision authentication and improving the user experience requires a wide range of individual technologies, and the integration of these technologies is complex. Furthermore, services based on emotion recognition are not considered, making it difficult to provide personalized services to users. This results in reduced authentication accuracy and a lower quality of user experience, making it difficult to improve convenience and safety.

[0496] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0497] In this invention, the server includes means for training a biometric authentication model using the generated palm image database, means for generating palm image data using a generative AI model, means for acquiring and identifying a user's palm image, means for extracting feature points to generate a unique palm ID and linking it with various service providers, and means for analyzing the user's emotional state using an emotion engine. This enables a unified process from palm image data generation to authentication, service linkage, and even personalized service provision based on emotion recognition.

[0498] The "generated palm image database" is a database that stores various palm images generated by a generative AI model and their associated metadata.

[0499] A "biometric model" is an artificial intelligence model trained to identify users based on palm image data.

[0500] A "generative AI model" is a model that uses a generative artificial intelligence algorithm to generate a variety of palm image data.

[0501] The "means for acquiring an image of the user's palm" refers to a means for a user to use a camera on a smartphone or terminal to take an image of the palm of their hand and acquire that image.

[0502] "Feature points" are points with specific visual characteristics, such as lines or patterns on the palm, and by extracting these, individual palms can be uniquely identified.

[0503] A "palm ID" is a unique identifier generated based on feature points extracted from an image of a user's palm.

[0504] "Means for linking with various service providers" refers to means for linking with other services (such as point cards and payment systems) using the palm ID.

[0505] An "emotion engine" is a system or algorithm that analyzes a user's emotional state from facial expressions, voice, etc., and generates the results as data.

[0506] "Means for associating with user profile and storing in a database" refers to a means for linking the generated palm ID and emotion data to the user's individual profile information and storing this in a database.

[0507] The present invention is an integrated biometric authentication system that includes palm image data generation, authentication, service linkage, and even emotion recognition-based service provision.

[0508] Palm image data generation part

[0509] server

[0510] The server starts a generative AI model (e.g., DALL·E) to generate various palm image data. The number and variation of images to be generated are determined by setting a prompt for the generative AI model.

[0511] example:

[0512] Prompt: "Generate 100 palm images with different palm patterns."

[0513] The generated images are stored in a database and each is given a unique ID.

[0514] Palm recognition AI model training part

[0515] server

[0516] The server retrieves palm image data from the database and performs preprocessing on the image. This preprocessing includes image resizing, noise removal, and grayscale conversion. Feature points are extracted from the preprocessed image and used to train an AI model (e.g., a deep learning model using TensorFlow or PyTorch). The trained model is then used for biometric authentication.

[0517] Palm ID registration section

[0518] User

[0519] The user launches the app on their smartphone and uses the camera to take a picture of their palm, which is then sent to the server via the app.

[0520] server

[0521] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is linked to the user profile and stored in a database.

[0522] Palm ID and service integration

[0523] User

[0524] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[0525] server

[0526] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, communicates with the service provider through the API, and updates the linking information in the database.

[0527] emotion recognition part

[0528] User

[0529] When a user takes a palm image with their smartphone, the emotion engine collects the user's facial expressions and voice data.

[0530] server

[0531] The server analyzes the acquired emotional data and associates the emotional information with the user's palm ID. The emotional data is stored in a database and used when providing services.

[0532] Authentication Process

[0533] User

[0534] Users hold their hand over a palm authentication terminal when checking in at a hotel, verifying their age at a convenience store, or using public transportation.

[0535] Terminal

[0536] The device captures a palm image and sends it to the server.

[0537] server

[0538] The server analyzes the received palm image and matches it with the palm ID in its database. If the match is successful, it provides the corresponding service (e.g., issuing an electronic room key or verifying age).

[0539] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0540] Step 1: Generate palm image data

[0541] server

[0542] Input: prompt for setting the generative AI model

[0543] Prompt: "Generate 100 palm images with different palm patterns."

[0544] Specific operation: The server launches a generative AI model and generates palm image data based on the prompt. The generative AI model (e.g., DALL·E) generates images with various palm shapes and patterns and stores them in a database. Each generated image is assigned a unique ID.

[0545] Output: Palm image data (with saved unique ID)

[0546] Step 2: Preprocessing palm image data

[0547] server

[0548] Input: Palm image data

[0549] Specific operation: The server retrieves palm image data from the database and performs preprocessing on the image. This preprocessing includes noise removal, resizing, grayscale conversion, etc. These processes are performed using open source libraries (e.g., OpenCV).

[0550] Output: Preprocessed image data

[0551] Step 3: Extracting feature points

[0552] server

[0553] Input: Preprocessed image data

[0554] Specific operation: The server extracts feature points from the preprocessed image. It uses a feature point extraction algorithm (e.g., SIFT or ORB) to identify the lines and patterns of the palm. The extracted feature points are stored in a database.

[0555] Output: feature point data

[0556] Step 4: Training the AI ​​model

[0557] server

[0558] Input: feature point data

[0559] How it works: The server uses the feature point data to train an AI model. The model is trained using a deep learning framework (e.g., TensorFlow or PyTorch). During training, the dataset is divided into training data and validation data.

[0560] Output: A trained AI model

[0561] Step 5: Take a palm image

[0562] User

[0563] Input: None (user operation)

[0564] Specific operation: The user launches the smartphone app and uses the camera function to take an image of their palm. The app's camera function is then invoked to capture the palm image.

[0565] Output: Palm image

[0566] Step 6: Send a palm image

[0567] User

[0568] Input: Palm image

[0569] Specific operation: The captured palm image is sent to the server via the app, and the app calls an API endpoint to upload the palm image to the server.

[0570] Output: Palm image sent to the server

[0571] Step 7: Generate Palm ID

[0572] server

[0573] Input: Received palm image

[0574] Specific operation: The server analyzes the received palm image, extracts feature points, and generates a unique palm ID based on the extracted feature points. The palm ID is then associated with the user profile and stored in a database.

[0575] Output: Generated palm ID

[0576] Step 8: Select and integrate services

[0577] User

[0578] Input: None (user operation)

[0579] Specific operation: Through the app, the user selects the service (e.g., point card, payment system, pass card, etc.) they want to link their palm ID to. They select the service using the app's UI.

[0580] Output: Selected service information

[0581] server

[0582] Input: Selected service information, Palm ID

[0583] Specific operation: Based on the user's selection, the server calls the API to link the palm ID with a specific service account, and updates the linking information with the service provider through the API.

[0584] Output: Linked service information

[0585] Step 9: Acquire and analyze emotion data

[0586] User

[0587] Input: None (user operation)

[0588] How it works: When a user takes a palm image with the app, the emotion engine collects the user's facial expression and voice data. The data is captured using the smartphone's camera and microphone.

[0589] Output: facial expression data, voice data

[0590] server

[0591] Input: facial expression data, voice data

[0592] Specific operation: The server analyzes the acquired emotion data and associates the emotion information with the user's palm ID. It extracts emotion information from the collected data and stores it in a database.

[0593] Output: Emotional information

[0594] Step 10: Authentication Process

[0595] User

[0596] Input: None (user operation)

[0597] Specific operation: When checking in at a hotel, verifying age at a convenience store, or using public transportation, a user holds their hand over a palm authentication terminal, which then uses the terminal's camera to capture an image of their palm.

[0598] Output: Captured palm image

[0599] Terminal

[0600] Input: Palm image

[0601] Specific operation: The device sends the captured image to the server, and calls an API endpoint to upload the palm image to the server.

[0602] Output: Palm image sent to the server

[0603] server

[0604] Input: Palm image sent to the server

[0605] Specific operation: The server analyzes the received palm image and compares it with the palm ID in the database. If authentication is successful, it provides the corresponding service (e.g., issuing an electronic room key or verifying age).

[0606] Output: Authentication results and services provided

[0607] (Application example 2)

[0608] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0609] While conventional palm authentication technologies require improved authentication accuracy and security, they have the problem of being unable to consider the user's emotional state. Furthermore, due to a lack of efficient means for real-time authentication and emotion analysis, measures against unauthorized access and security risks are insufficient. Therefore, a new palm authentication system is needed that can simultaneously strengthen security and analyze the user's emotional state.

[0610] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for training a biometric authentication model using the generated palm image database, means for generating palm image data using a generation AI, means for acquiring and identifying a user's palm image, means for uniquely generating a palm ID and linking it with various service providers, means for acquiring and analyzing the user's emotion data using an emotion analysis engine, and means for capturing palm images in real time using a camera built into the tool and performing authentication and emotion analysis. This improves authentication accuracy and enables palm authentication and emotion analysis in real time.

[0611] text

[0612] "Generative AI" is an artificial intelligence technology for generating image data of a user's palm.

[0613] The "palm image database" is a database that collects image data related to the shape and pattern of the palm.

[0614] A "biometric authentication model" is an artificial intelligence model for identifying individuals using palm images.

[0615] A "palm ID" is an identification code that is uniquely generated based on each user's palm image.

[0616] An "emotion analysis engine" is an engine that acquires and analyzes emotional data from the user's facial expressions, voice, etc.

[0617] "Cameras built into tools" are cameras built into smart devices or wearable devices.

[0618] "Real-time capture" is a method of instantly acquiring images or data at the current time.

[0619] "Authentication" is the process of verifying that a user is a legitimate user of a system.

[0620] A "service provider" is a company or organization that provides various services.

[0621] A "database" is a system for efficiently storing and managing structured data.

[0622] "Feature points" are important points or patterns in a palm image, and are data used for personal identification.

[0623] "User Profile" means a collection of information and data relating to each User.

[0624] A specific embodiment for carrying out the present invention will be described below: This system generates image data of a user's palm and is capable of performing palm authentication and emotion analysis in real time.

[0625] System Configuration

[0626] 1. Palm image data generation part

[0627] server

[0628] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with various palm shapes and patterns and stores them in a palm image database.

[0629] 2. Training the palm recognition AI model

[0630] server

[0631] The server retrieves palm image data from the palm image database and pre-processes the images. It extracts feature points from the pre-processed images and trains a biometric authentication model. The trained model is used for high-precision authentication.

[0632] 3. Palm ID registration section

[0633] User

[0634] The user uses the camera function of a smart device to take an image of their palm, which is then sent to the server via the application.

[0635] server

[0636] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is then associated with the user profile and stored in a database.

[0637] 4. Linking Tenohira ID with services

[0638] User

[0639] Through the application, users select the services they want to link their palm ID to (e.g., point cards, payment systems, access control systems, etc.).

[0640] server

[0641] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, and communicates with the service provider through the API to update the link information.

[0642] 5. Emotion recognition part

[0643] User

[0644] When a user uses a smart device to capture a palm image, the emotion analysis engine captures data such as facial expressions and voice, allowing for real-time analysis of the user's emotional state.

[0645] server

[0646] The server analyzes the emotion data and associates it with the user's palm ID. The emotion data is stored in a database and will be used to provide future services.

[0647] 6. Authentication Process

[0648] User

[0649] When authentication is required (e.g., entering or leaving a building, logging in to a computer), the user holds their palm over the camera on their smart device.

[0650] Camera-equipped devices

[0651] The camera-equipped device captures palm images and sends them to a server.

[0652] server

[0653] The server analyzes the palm image and compares the palm ID with the database. At the same time, it performs emotion analysis and determines the appropriate security status. If authentication is successful, the specified service (e.g., access permission, login permission) is provided.

[0654] Hardware and software used

[0655] Hardware: Smart glasses (Google Glass, Vuzix Blade, etc.), smartphones, servers, and devices with built-in cameras

[0656] Software: Python, TensorFlow, OpenCV, Sentiment Analysis Engine

[0657] Specific examples

[0658] If the user uses smart glasses:

[0659] The camera built into the smart glasses captures palm images in real time, and the captured images are sent to a server via the smart glasses application for palm ID authentication and emotion analysis.

[0660] Example prompt sentence:

[0661] "I would like to build a system that uses palm images to authenticate users and simultaneously recognize emotions. Specifically, please explain using 2023 technology how smart glasses can be used to analyze palm images and prevent unauthorized access."

[0662] By implementing the invention in accordance with this aspect, it is possible to improve authentication accuracy and perform palm authentication and emotion analysis in real time, thereby reducing security risks and improving the user experience.

[0663] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0664] text

[0665] Step 1:

[0666] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with a variety of palm shapes and patterns and stores it in a database. The input to this process is the parameters (number, variations) given to the generation AI, and the output is the generated palm image data.

[0667] Step 2:

[0668] The server retrieves palm image data from the palm image database and performs preprocessing on the images, which includes image resizing and noise removal. The input is palm image data, and the output is a preprocessed palm image.

[0669] Step 3:

[0670] The server extracts feature points from the preprocessed palm image and uses them to train a biometric model. The input is the preprocessed palm image, and the output is the trained biometric model.

[0671] Step 4:

[0672] The user takes a palm image using the camera on their smart device. The captured palm image is sent to the server by the application. The input is the palm image taken by the user, and the output is the image data sent to the server.

[0673] Step 5:

[0674] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID. The input is the palm image sent by the user, and the output is the generated palm ID.

[0675] Step 6:

[0676] The server associates the generated palm ID with the user profile and stores it in a database. The input is the generated palm ID and the corresponding user profile, and the output is the updated database entry.

[0677] Step 7:

[0678] The user selects the service with which they want to link their palm ID through the application. The input is the user's selection and palm ID, and the output is the selected service information.

[0679] Step 8:

[0680] Based on the user's selection, the server calls an API to link the palm ID with a specific service account. The input is the user's selected service and palm ID, and the output is an update of the linkage information.

[0681] Step 9:

[0682] When a user holds their palm over the camera of their smart device in a situation requiring authentication, the camera captures an image of the palm and sends it to the server. The input is the palm held over the camera, and the output is the captured image of the palm sent to the server.

[0683] Step 10:

[0684] The server analyzes the captured palm image and compares the palm ID with a database. It also performs emotion analysis and determines the appropriate security status. The input is the captured palm image, and the output is the authentication result and emotion analysis result.

[0685] Step 11:

[0686] If authentication is successful, the server provides the specified service (e.g., access permission, login permission). The inputs are the authentication result and instructions to the service provider, and the output is the provision of the service.

[0687] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0688] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0689] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0690] [Second embodiment]

[0691] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0692] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0693] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0694] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0695] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0696] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0697] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0698] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0699] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0700] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0701] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0702] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0703] The following describes an embodiment of the present invention. This system generates palm image data using a generative AI and trains a biometric authentication model based on that data. It also provides a platform for linking a user's palm as an ID with various services.

[0704] System Configuration

[0705] 1. Palm image data generation part

[0706] server

[0707] The server launches the generation AI, which generates palm image data based on the set parameters. The generated palm image data is stored in a database. The generation AI has the ability to increase the variations in palm shapes and patterns, allowing for the creation of a wide variety of data.

[0708] 2. Training the palm recognition AI model

[0709] server

[0710] The server retrieves palm image data from the database and performs preprocessing, which includes adjusting the image size, removing noise, and adjusting contrast. Feature points are extracted from the preprocessed image data and used to train an AI model. Once trained, the AI ​​model is used for authentication in the production environment.

[0711] 3. Palm ID registration section

[0712] User

[0713] The user uses the smartphone app to take a picture of their palm using the camera function, and the app then sends the image to the server.

[0714] server

[0715] The server analyzes the received palm image, extracts feature points, generates a unique palm ID based on the feature points, associates the ID with the user profile, and stores it in a database.

[0716] 4. Palm ID and service integration

[0717] User

[0718] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[0719] server

[0720] Based on the user's selection, the server will link the Teno-ID with various service provider accounts via API, allowing Teno-ID to be used as a means of authentication for each service.

[0721] 5. Authentication Process

[0722] User

[0723] For example, when checking in at a hotel, a user can hold their palm over a terminal at the front desk.

[0724] Terminal

[0725] The front terminal captures a palm image and sends it to the server.

[0726] server

[0727] The server analyzes the received palm image, extracts the palm ID, and compares it with a database. Based on the comparison result, the server obtains the user's reservation information and sends it to the terminal at the front desk. The terminal then completes the user's check-in and issues an electronic room key.

[0728] Specific examples

[0729] Example 1: Age verification at a convenience store

[0730] User

[0731] When a user purchases alcohol at a convenience store, they hold their palm over a palm authentication terminal installed in the store.

[0732] Terminal

[0733] The device takes a palm image and sends the palm ID to the server.

[0734] server

[0735] The server checks the palm ID against a database to obtain the user's age. Once the age verification is complete, it sends the results to the device, which then displays the results and allows the user to purchase alcohol.

[0736] Example 2: Public transport ride

[0737] User

[0738] Users hold their palm over a palm authentication terminal at the public transportation ticket gate.

[0739] Terminal

[0740] The device captures a palm image and sends the palm ID to the server.

[0741] server

[0742] The server checks the palm ID against a database and obtains the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[0743] In this way, the palm authentication system of the present invention generates palm image data using generative AI and trains a biometric authentication model to achieve highly accurate palm authentication. Furthermore, by registering the user's palm as an ID and providing a platform that links with various services, convenience and security can be improved.

[0744] The processing flow will be explained below.

[0745] Palm image data generation using generative AI

[0746] Step 1:

[0747] server

[0748] The server starts the generation AI and sets parameters regarding the number and variations of palm image data to be generated.

[0749] Step 2:

[0750] server

[0751] The generation AI generates palm image data with different palm shapes and patterns based on set parameters.

[0752] Step 3:

[0753] server

[0754] The generated palm image data is stored in a database and is labeled and classified.

[0755] Training the palm recognition AI model

[0756] Step 1:

[0757] server

[0758] The server retrieves palm image data from the database and performs preprocessing on the images, which includes resizing, noise reduction, and contrast adjustment.

[0759] Step 2:

[0760] server

[0761] A specific algorithm is applied to extract feature points from the preprocessed image data.

[0762] Step 3:

[0763] server

[0764] The extracted feature data is used to train an AI model, and deep learning algorithms are used to improve the model's accuracy.

[0765] Step 4:

[0766] server

[0767] The trained AI model is saved and made available for authentication processing.

[0768] Palm ID registration

[0769] Step 1:

[0770] User

[0771] The user launches the app on their smartphone and uses the camera function to take a picture of their palm.

[0772] Step 2:

[0773] User

[0774] The captured palm image is sent from the app to the server.

[0775] Step 3:

[0776] server

[0777] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID.

[0778] Step 4:

[0779] server

[0780] The generated palm ID is associated with the user profile and stored in a database.

[0781] Linking palm ID with services

[0782] Step 1:

[0783] User

[0784] Users open the app on their smartphone and select the service they want to link their palm ID to, such as a point card, payment system, or pass card.

[0785] Step 2:

[0786] server

[0787] Based on the user's selection, the server calls an API to link the palm ID with a specific service account.

[0788] Step 3:

[0789] server

[0790] Communicate with the service provider via API and update the link information between your palm ID and service account.

[0791] Authentication Process

[0792] Step 1:

[0793] User

[0794] When a user uses the service, for example when checking into a hotel, they hold their palm over a terminal at the front desk.

[0795] Step 2:

[0796] Terminal

[0797] The front terminal captures a palm image and sends it to the server.

[0798] Step 3:

[0799] server

[0800] The server analyzes the received palm image and identifies the corresponding palm ID by extracting feature points.

[0801] Step 4:

[0802] server

[0803] Based on the palm ID, the user's reservation information is retrieved from the database and the authentication results are sent to the terminal.

[0804] Step 5:

[0805] Terminal

[0806] The terminal notifies the user of successful authentication and provides appropriate services (e.g., issuing an electronic room key).

[0807] Age verification at convenience stores

[0808] Step 1:

[0809] User

[0810] When a user purchases alcohol at a convenience store, they hold their hand over a palm authentication terminal.

[0811] Step 2:

[0812] Terminal

[0813] The device captures a palm image and sends the palm ID to the server.

[0814] Step 3:

[0815] server

[0816] The server compares the palm ID with a database to confirm the user's age information.

[0817] Step 4:

[0818] server

[0819] Once age verification is complete, the results will be sent to the device.

[0820] Step 5:

[0821] Terminal

[0822] The device will display the results and allow the purchase of alcohol if necessary.

[0823] Example 1

[0824] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0825] In modern society, there is a growing demand for simple yet highly accurate biometric authentication systems. However, conventional biometric authentication systems have had problems with the time and cost required to collect and process individual image data. Furthermore, there are limitations to the accuracy of authentication, resulting in frequent misidentifications and missed recognitions. Furthermore, information management is complicated when linking with multiple services, and there is a lack of a mechanism for automatically authenticating users. The present invention aims to solve these problems.

[0826] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0827] In this invention, the server includes means for training a biometric authentication model using the generated palm image data, means for generating palm image data using the generating artificial intelligence, means for acquiring and identifying a user's palm image, means for uniquely generating a palm identifier and linking it with various service providers, means for generating diverse palm image data, means for performing preprocessing on the palm image data such as noise removal and contrast adjustment, and means for deploying the generated biometric authentication model in a production environment. This enables highly accurate and versatile palm authentication, allowing users to use various services automatically and safely without hassle.

[0828] "Generated palm image data" is digital image data of the palm generated using artificial intelligence.

[0829] "Means for training a biometric authentication model" refers to a method and device that uses acquired palm image data to adjust and train the parameters of an artificial intelligence model.

[0830] "Generative AI" refers to algorithms or software that generate new palm image data based on set parameters.

[0831] "Means for acquiring an image of a user's palm and identifying it" refers to a method and apparatus for taking a picture of a user's palm using a device such as a camera, analyzing the image, and uniquely identifying it.

[0832] A "palm identifier" is unique identification information generated based on specific feature points extracted from a captured palm image.

[0833] "Various service providers" are external service providers, such as payment systems and point card systems, that users access and authenticate using palm identifiers.

[0834] The "means for generating a variety of palm image data" refers to a method and device for generating a plurality of palm images with different shapes and patterns using artificial intelligence.

[0835] "Means for performing pre-processing such as noise removal and contrast adjustment" refers to a method and device for processing acquired palm image data to improve image quality and recognition accuracy.

[0836] The "means for deploying the generated biometric authentication model in a production environment" refers to a method and apparatus for incorporating a trained artificial intelligence model into an actual operational system and setting it up so that it can be used for authentication processing.

[0837] System Configuration

[0838] 1. Palm image data generation part

[0839] server

[0840] The server starts the generative AI and generates palm image data. Specifically, the server uses "StyleGAN" or a similar algorithm as the generative AI. The server sets parameters such as palm size, pattern complexity, and number of variations, and generates palm image data through the generative AI. This generated palm image data is stored in a database. For example, a prompt might be: "To generate palm image data, generate a palm image based on the set parameters. Increase the variations in palm shape and pattern to create diverse data."

[0841] 2. Training the palm recognition AI model

[0842] server

[0843] The server retrieves palm image data from the database and performs preprocessing. This preprocessing uses the OpenCV library to resize the image, remove noise, and adjust the contrast. Furthermore, algorithms such as SURF and SIFT are used to extract feature points. Based on the preprocessed image data and feature point data, an AI model is trained using TensorFlow or PyTorch. The trained model is saved in the SavedModel format and deployed to the production environment via an API server (e.g., Flask).

[0844] 3. Palm ID registration section

[0845] User

[0846] Users take a palm image using a dedicated smartphone app called "CustomPalmID App." Using the smartphone camera, palm images are captured.

[0847] server

[0848] The server receives the palm image sent by the user and extracts the feature points, which are then analyzed using SURF and SIFT techniques to generate a unique palm identifier in the form of a UUID, which is then associated with the corresponding user profile and stored in a database.

[0849] 4. Palm ID and service integration

[0850] User

[0851] Using the smartphone app, the user selects the service (e.g., payment system, point card system, pass card, etc.) they want to link their palm ID to. Using the "ServiceConnector App."

[0852] server

[0853] The server associates the palm ID with the service provider's account based on the user's selection. By linking the palm ID with the service provider's account using the OAuth 2.0 protocol, the server makes it possible to use the palm ID as a means of authentication for various services.

[0854] 5. Authentication Process

[0855] User

[0856] When checking in at a hotel, a user holds their palm over a palm authentication terminal, such as one installed at the front desk.

[0857] Terminal

[0858] The terminal captures a palm image and transmits the image data to a server.

[0859] server

[0860] The server analyzes the received palm image, extracts a palm identifier, and compares it with a database. Based on the comparison result, the server obtains the user's reservation information and sends it to the terminal. After authentication is complete, the terminal issues an electronic room key and provides it to the user.

[0861] Specific examples

[0862] Example 1: Age verification at a convenience store

[0863] User

[0864] When a user purchases alcohol at a convenience store, they hold their palm over a palm authentication terminal installed in the store.

[0865] Terminal

[0866] The terminal takes a palm image and transmits the palm identifier to the server.

[0867] server

[0868] The server compares the palm identifier with a database to obtain the user's age information. Once the age verification is complete, it sends the result to the terminal. The terminal then displays the authentication result and allows the user to purchase alcohol.

[0869] Example 2: Public transport ride

[0870] User

[0871] The user holds their palm over a palm authentication terminal at the ticket gate of a public transport facility.

[0872] Terminal

[0873] The terminal captures a palm image and transmits the palm identifier to the server.

[0874] server

[0875] The server checks the palm identifier against a database to obtain the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[0876] In this way, the palm authentication system of the present invention generates palm image data using generative AI and trains a biometric authentication model to achieve highly accurate palm authentication. Furthermore, by registering the user's palm as an identifier and linking it to various services, convenience and security can be improved.

[0877] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0878] System program processing flow

[0879] Step 1:

[0880] The server launches the generative AI. Specifically, it instantiates the generative AI software and prepares it to generate palm images. The input data used are generative parameters (e.g., palm shape, pattern complexity, number of variations, etc.). The output is an initialized generative AI instance.

[0881] Step 2:

[0882] The server sets the parameters for palm image generation. Specifically, it reads parameters such as palm size, pattern complexity, and number of variations from a configuration file or user input. The input is the configuration parameters, and the output is command data that is input to the generation AI based on those parameters.

[0883] Step 3:

[0884] The server generates palm image data using a generative AI. The generative AI (e.g., "StyleGAN") uses an algorithm to generate palm images according to set parameters. The input is the set parameters and an initialized generative AI instance, and the output is the generated palm image data.

[0885] Step 4:

[0886] The server saves the generated palm image data in a database. It connects to a database management system (e.g., MySQL) and stores the generated palm image data files. The input is the generated palm image data, and the output is the identification information of the data saved in the database.

[0887] Step 5:

[0888] The server retrieves palm image data from the database. It connects to the database and searches for and retrieves stored palm image data. The input is the database connection information and the search query, and the output is the retrieved palm image data.

[0889] Step 6:

[0890] The server performs preprocessing of the image data. Specifically, it uses the "OpenCV" library to adjust the image size, remove noise, and adjust the contrast. The input is palm image data, and the output is the preprocessed palm image data.

[0891] Step 7:

[0892] The server extracts feature points from the preprocessed palm image using the "SURF" or "SIFT" algorithm. The input is the preprocessed palm image data, and the output is the feature point data.

[0893] Step 8:

[0894] The server trains the AI ​​model. Using TensorFlow and PyTorch, the AI ​​model learns the feature point data and palm image data. The input is the feature point data and palm image data, and the output is the trained AI model.

[0895] Step 9:

[0896] The server saves the trained AI model and places it in the production environment. The model file is saved in the "SavedModel" format and deployed using an API server (e.g., "Flask"). The input is the trained AI model, and the output is an AI model instance that runs in the production environment.

[0897] Step 10:

[0898] The user takes a palm image using a dedicated app. Using the "CustomPalmID App," the palm image is acquired with the smartphone camera. The input is the camera image, and the output is the captured palm image data.

[0899] Step 11:

[0900] The user sends the captured palm image data to the server. Using the send function within the app, a POST request is made to the endpoint with the image data. The input is the palm image data, and the output is the image data being uploaded to the server.

[0901] Step 12:

[0902] The server analyzes the received palm image and extracts feature points using a specific algorithm (e.g., "SURF" or "SIFT"). The input is palm image data, and the output is feature point data.

[0903] Step 13:

[0904] The server generates a palm identifier and associates it with the user profile. It generates an identifier in UUID format, associates it with the corresponding user profile, and stores it in a database. The input is minutiae data and user profile information, and the output is the generated palm identifier.

[0905] Step 14:

[0906] Users use the app to link their palm ID to a service. Using the "ServiceConnector App," they select the service they want to link. The input is the palm ID and the service information to link to, and the output is the link setting information.

[0907] Step 15:

[0908] The server links the palm identifier with the service provider account. This is done via API using a protocol such as OAuth 2.0. The input is the palm identifier and the service provider account information, and the output is the linked account information.

[0909] Step 16:

[0910] A user holds their palm over a palm authentication terminal, such as when checking in at a hotel. The input is a palm image, and the output is an authentication request.

[0911] Step 17:

[0912] The terminal captures the palm image and sends it to the server. The palm image is acquired using an image capture device and sent to the server. The input is the palm image, and the output is the transmission of image data to the server.

[0913] Step 18:

[0914] The server analyzes the palm image, extracts a palm identifier, and matches it with a database. It uses an algorithm to match it with an existing palm identifier database. The input is the palm image data, and the output is the matching result.

[0915] Step 19:

[0916] The server obtains the user's reservation information based on the matching result and sends it to the terminal. The server also obtains user information from the reservation system based on the matching result. The input is the matching result and a reservation system inquiry request, and the output is the transmission of reservation information.

[0917] Step 20:

[0918] After authentication is complete, the terminal issues an electronic room key and provides it to the user. The room key information is displayed on the terminal's display device. The input is the verification result and reservation information, and the output is the electronic room key information.

[0919] The above are the specific processing steps of this palm authentication system.

[0920] (Application example 1)

[0921] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0922] Until now, there have been technical and cost challenges in the widespread adoption of highly accurate biometric authentication systems. Furthermore, there has been a lack of security authentication systems that combine user convenience and safety. In particular, more reliable authentication methods are needed for access control in places where high security is required, such as offices and data centers.

[0923] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0924] In this invention, the server includes a means for training a biometric authentication model using the generated palm image database, a means for generating palm image data using a generation AI, a means for acquiring and identifying a user's palm image, a means for uniquely generating a palm ID and linking it with various service providers, and a security means for capturing and authenticating palm images using a smart device or robot. This allows users to easily perform palm authentication with high accuracy using a smart device or robot. This satisfies high security requirements and improves the reliability of access control systems, particularly those used in offices, data centers, etc.

[0925] "Generative AI" is an artificial intelligence technology used to generate image data of a user's palm, and has the ability to create a variety of palm shapes and patterns.

[0926] The "palm image database" is a database for storing and managing palm image data generated by the generative AI, and is used to train biometric authentication models.

[0927] A "biometric authentication model" is an artificial intelligence model trained to verify a user's identity based on palm image data, and performs highly accurate authentication based on feature points.

[0928] "Palm ID" is an identification number that is uniquely generated using the feature points of a palm image and is used to link with various services.

[0929] A "smart device" is a device that has the function of acquiring an image of a user's palm and performing authentication, and includes smartphones and corresponding access control devices.

[0930] A "robot" is an autonomous or remotely controlled device used to capture palm images and perform authentication processes, and is used for security management.

[0931] "Minutes" are unique data points extracted from palm images that are used to train and authenticate biometric models.

[0932] To implement this invention, a system based on the following procedures is required.

[0933] System Configuration

[0934] 1. Palm image data generation part

[0935] server

[0936] The server generates various palm image data using generative AI, which applies technologies such as StyleGAN. The generated palm image data is stored in a dedicated database.

[0937] 2. Training the palm recognition AI model

[0938] server

[0939] The server trains the biometric authentication model using preprocessed palm image data. Preprocessing includes noise removal, size adjustment, contrast adjustment, etc., and then extracts feature points. Training is performed using TensorFlow and PyTorch.

[0940] 3. Palm ID registration section

[0941] User

[0942] The user uses the camera function of a smartphone app to take an image of their palm, which is then sent to the server via the app.

[0943] server

[0944] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is associated with the user profile and stored in a database.

[0945] 4. Palm ID and service integration

[0946] User

[0947] Through the app, users can select the services they want to link their palm ID to, including a wide range of services such as security authentication, financial services, and access control.

[0948] server

[0949] Based on the user's selection, the server configures the palm ID to link with various service provider accounts via API.

[0950] 5. Authentication Process

[0951] User

[0952] Users hold their palm over a palm authentication terminal at locations where security authentication is required, such as at the entrance to a data center or an office access control device.

[0953] Terminal

[0954] A palm image is captured using a smart device or robot and sent to a server.

[0955] server

[0956] The server analyzes the received palm image, compares the palm ID with a database, and sends an access permission or denial result to the device based on the comparison result.

[0957] Example: Access control at a data center

[0958] This system is used in situations where secure access control is required in corporate data centers.

[0959] 1. User: Scans palm at data center entrance.

[0960] 2. Device: Captures a palm image and sends it to the server.

[0961] 3. Server: Generates palm ID and checks it against the database.

[0962] 4. Authentication result: Based on the verification result, access permission is notified to the terminal and the door opens automatically.

[0963] Prompt Sentence Examples

[0964] "Generate a palm ID based on the palm image shown below and verify it against the database for authentication."

[0965] The above procedure makes it possible to implement the present invention, and realize a highly accurate and user-friendly palm authentication system, which can meet the high security requirements of offices, data centers, etc.

[0966] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0967] Step 1:

[0968] To use the palm authentication system, a user launches the smartphone app and takes a picture of their palm.

[0969] Input: User's palm image

[0970] Output: Palm image data

[0971] How it works: The user activates the camera function of the smartphone app and takes a picture of their palm. This image data is then saved in the application.

[0972] Step 2:

[0973] The smartphone app sends the captured palm image to a server.

[0974] Input: Palm image data

[0975] Output: Image data transmission request to the server

[0976] How it works: An HTTP POST request containing palm image data is sent to the server, with the image data encrypted if necessary.

[0977] Step 3:

[0978] The server receives the palm image data and performs preprocessing.

[0979] Input: Palm image data

[0980] Output: Preprocessed image data

[0981] Operation: After receiving the palm image data, the server performs preprocessing such as noise reduction, size adjustment, contrast adjustment, etc. The image is converted into a standardized format.

[0982] Step 4:

[0983] The server extracts feature points from the preprocessed palm image.

[0984] Input: Preprocessed image data

[0985] Output: feature point data

[0986] How it works: A feature point extraction algorithm is applied to the preprocessed image data. Feature points are extracted using a deep learning model such as CNN.

[0987] Step 5:

[0988] The server generates a palm ID based on the feature point data and stores it in a database.

[0989] Input: feature point data

[0990] Output: Palm ID, registration request to database

[0991] Operation: A palm ID is generated from the feature point data, and it is associated with the user profile and saved in a database. SQL queries are used for the saving process.

[0992] Step 6:

[0993] The user holds their palm over an authentication terminal at a specific security authentication point (e.g., the entrance to a data center).

[0994] Input: User's palm image

[0995] Output: Authentication start request

[0996] How it works: The authentication process begins when the user holds their palm over the authentication device's camera.

[0997] Step 7:

[0998] The device captures a palm image and sends it to the server.

[0999] Input: User's palm image

[1000] Output: Image data transmission request to the server

[1001] How it works: The device's camera captures a palm image and sends it to a server. The image data is encrypted before being transferred.

[1002] Step 8:

[1003] The server analyzes the palm image and compares it with a database.

[1004] Input: Palm image data

[1005] Output: Authentication result (allow or deny)

[1006] How it works: The server analyzes the palm image and matches it with the palm ID stored in the database. Based on the match, authentication is either granted or denied.

[1007] Step 9:

[1008] The terminal receives the authentication result and notifies the user.

[1009] Input: Authentication result

[1010] Output: Authentication notification (allowed or denied)

[1011] Operation: The terminal that receives the authentication result from the server allows access if it is permitted and automatically opens the door. If it is denied, it notifies the user.

[1012] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1013] The following describes an embodiment of the present invention. The system includes a generation AI for generating palm image data, a means for training a biometric authentication model based on the palm image, a means for acquiring and identifying a user's palm image, a means for generating a palm ID and linking it with various service providers, and an emotion engine for recognizing and analyzing the user's emotions.

[1014] System Configuration

[1015] 1. Palm image data generation part

[1016] server

[1017] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with various palm shapes and patterns and stores them in a database.

[1018] 2. Training the palm recognition AI model

[1019] server

[1020] The server retrieves palm image data from the database, pre-processes the image, extracts feature points from the pre-processed image, and trains an AI model. The trained model is used for high-precision authentication.

[1021] 3. Palm ID registration section

[1022] User

[1023] The user launches the app on their smartphone and uses the camera to take a picture of their palm, which is then sent to the server.

[1024] server

[1025] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is then associated with the user profile and stored in a database.

[1026] 4. Linking Tenohira ID with services

[1027] User

[1028] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[1029] server

[1030] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, and communicates with the service provider through the API to update the link information.

[1031] 5. Emotion recognition part

[1032] User

[1033] When a user takes a palm image using a smartphone app, the emotion engine collects data such as the user's facial expressions and voice, which allows it to analyze the user's emotional state in real time.

[1034] server

[1035] The server analyzes the acquired emotional data and associates it with the user's palm ID. The emotional data is stored in a database and will be used to provide future services.

[1036] 6. Authentication Process

[1037] User

[1038] For example, when checking into a hotel, a user holds their palm over a terminal at the front desk.

[1039] Terminal

[1040] The front terminal captures a palm image and sends it to the server.

[1041] server

[1042] The server analyzes the received palm image and compares the palm ID with a database. It retrieves the user's reservation information and sends the authentication result to the terminal. If authentication is successful, the appropriate service (e.g., issuing an electronic room key) is provided.

[1043] Specific examples

[1044] Example 1: Age verification at a convenience store

[1045] User

[1046] When a user purchases alcohol at a convenience store, they hold their hand over a palm authentication terminal.

[1047] Terminal

[1048] The device captures a palm image and sends it to the server.

[1049] server

[1050] The server checks the palm ID against a database to confirm the user's age. Once the verification is complete, it sends the results to the device, which then displays the results and allows the user to purchase alcohol.

[1051] Example 2: Public transport

[1052] User

[1053] Users hold their hand over a palm authentication terminal at the public transportation ticket gate.

[1054] Terminal

[1055] The device captures a palm image and sends it to the server.

[1056] server

[1057] The server compares the palm ID with the database and obtains the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[1058] Example 3: Providing services using user emotion recognition

[1059] User

[1060] The user takes a picture of their palm with their smartphone and sends emotional information to the server via the app.

[1061] server

[1062] The server analyzes the emotion data and uses that information to provide the user with appropriate services (e.g., a customized shopping experience). If the emotion is determined to be "fun," relevant promotions or coupons can be offered.

[1063] In this way, the palm authentication system of the present invention uses generative AI to generate palm image data and train a biometric authentication model. It also uses an emotion engine to acquire and analyze emotion data, allowing it to provide personalized services to users. This system improves convenience and security, resulting in a better user experience.

[1064] The processing flow will be explained below.

[1065] Palm image data generation using generative AI

[1066] Step 1:

[1067] server

[1068] Start the generation AI and set parameters regarding the number and variations of palm image data to be generated.

[1069] Step 2:

[1070] server

[1071] Palm image data with different palm shapes and patterns is generated based on the set parameters.

[1072] Step 3:

[1073] server

[1074] The generated palm image data is stored in a database and is labeled and classified.

[1075] Training the palm recognition AI model

[1076] Step 1:

[1077] server

[1078] Palm image data is acquired from the database and preprocessed, including size adjustment, noise removal, and contrast adjustment.

[1079] Step 2:

[1080] server

[1081] A specific algorithm is applied to extract feature points from the preprocessed image data.

[1082] Step 3:

[1083] server

[1084] The extracted feature data is used to train an AI model, and a deep learning algorithm is used to improve the model's accuracy.

[1085] Step 4:

[1086] server

[1087] The trained AI model is saved and made available for authentication processing.

[1088] Palm ID registration

[1089] Step 1:

[1090] User

[1091] Launch the app on your smartphone and use the camera to take a picture of your palm.

[1092] Step 2:

[1093] User

[1094] The captured palm image is sent from the app to the server.

[1095] Step 3:

[1096] server

[1097] The received palm image is analyzed, feature points are extracted, and a unique palm ID is generated.

[1098] Step 4:

[1099] server

[1100] The generated palm ID is associated with the user profile and stored in a database.

[1101] Linking palm ID with services

[1102] Step 1:

[1103] User

[1104] Open the app on your smartphone and select the service you want to link your Palm ID to, such as a point card, payment system, or pass card.

[1105] Step 2:

[1106] server

[1107] Based on the user's selection, an API is called to link the palm ID with a specific service account.

[1108] Step 3:

[1109] server

[1110] Communicate with the service provider via API and update the link information between your palm ID and service account.

[1111] emotion recognition part

[1112] Step 1:

[1113] User

[1114] When a palm image is taken using a smartphone app, the emotion engine collects data such as the user's facial expressions and voice.

[1115] Step 2:

[1116] server

[1117] The acquired data is analyzed to determine the user's emotional state in real time.

[1118] Step 3:

[1119] server

[1120] The emotion data is associated with a user profile and stored in a database.

[1121] Step 4:

[1122] server

[1123] Settings are made to provide customized services based on the user's emotional data.

[1124] Authentication Process

[1125] Step 1:

[1126] User

[1127] For example, when checking in at a hotel, you hold your palm over the terminal at the front desk.

[1128] Step 2:

[1129] Terminal

[1130] The front terminal captures a palm image and sends it to the server.

[1131] Step 3:

[1132] server

[1133] The received palm image is analyzed and the palm ID is compared with a database.

[1134] Step 4:

[1135] server

[1136] The user's reservation information is obtained based on the palm ID, and the authentication results are sent to the terminal.

[1137] Step 5:

[1138] Terminal

[1139] Notify the user that authentication was successful and provide appropriate services (e.g., issue an electronic room key).

[1140] Age verification at convenience stores

[1141] Step 1:

[1142] User

[1143] When checking in at Costco, hold your palm over the terminal at the front desk.

[1144] Step 2:

[1145] Terminal

[1146] The front terminal captures a palm image and sends it to the server.

[1147] Step 3:

[1148] server

[1149] Feature points are extracted from the received palm image and a palm ID is generated.

[1150] Step 4:

[1151] server

[1152] The generated palm ID is matched to obtain the corresponding user's age information.

[1153] Step 5:

[1154] server

[1155] Once the confirmation is received, the authentication result is sent to the terminal.

[1156] Step 6:

[1157] Terminal

[1158] The device will display the results and allow the alcohol purchase.

[1159] Example 2

[1160] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1161] In conventional biometric authentication systems, achieving high-precision authentication and improving the user experience requires a wide range of individual technologies, and the integration of these technologies is complex. Furthermore, services based on emotion recognition are not considered, making it difficult to provide personalized services to users. This results in reduced authentication accuracy and a lower quality of user experience, making it difficult to improve convenience and safety.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1163] In this invention, the server includes means for training a biometric authentication model using the generated palm image database, means for generating palm image data using a generative AI model, means for acquiring and identifying a user's palm image, means for extracting feature points to generate a unique palm ID and linking it with various service providers, and means for analyzing the user's emotional state using an emotion engine. This enables a unified process from palm image data generation to authentication, service linkage, and even personalized service provision based on emotion recognition.

[1164] The "generated palm image database" is a database that stores various palm images generated by a generative AI model and their associated metadata.

[1165] A "biometric model" is an artificial intelligence model trained to identify users based on palm image data.

[1166] A "generative AI model" is a model that uses a generative artificial intelligence algorithm to generate a variety of palm image data.

[1167] The "means for acquiring an image of the user's palm" refers to a means for a user to use a camera on a smartphone or terminal to take an image of the palm of their hand and acquire that image.

[1168] "Feature points" are points with specific visual characteristics, such as lines or patterns on the palm, and by extracting these, individual palms can be uniquely identified.

[1169] A "palm ID" is a unique identifier generated based on feature points extracted from an image of a user's palm.

[1170] "Means for linking with various service providers" refers to means for linking with other services (such as point cards and payment systems) using the palm ID.

[1171] An "emotion engine" is a system or algorithm that analyzes a user's emotional state from facial expressions, voice, etc., and generates the results as data.

[1172] "Means for associating with user profile and storing in a database" refers to a means for linking the generated palm ID and emotion data to the user's individual profile information and storing this in a database.

[1173] The present invention is an integrated biometric authentication system that includes palm image data generation, authentication, service linkage, and even emotion recognition-based service provision.

[1174] Palm image data generation part

[1175] server

[1176] The server starts a generative AI model (e.g., DALL·E) to generate various palm image data. The number and variation of images to be generated are determined by setting a prompt for the generative AI model.

[1177] example:

[1178] Prompt: "Generate 100 palm images with different palm patterns."

[1179] The generated images are stored in a database and each is given a unique ID.

[1180] Palm recognition AI model training part

[1181] server

[1182] The server retrieves palm image data from the database and performs preprocessing on the image. This preprocessing includes image resizing, noise removal, and grayscale conversion. Feature points are extracted from the preprocessed image and used to train an AI model (e.g., a deep learning model using TensorFlow or PyTorch). The trained model is then used for biometric authentication.

[1183] Palm ID registration section

[1184] User

[1185] The user launches the app on their smartphone and uses the camera to take a picture of their palm, which is then sent to the server via the app.

[1186] server

[1187] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is linked to the user profile and stored in a database.

[1188] Palm ID and service integration

[1189] User

[1190] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[1191] server

[1192] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, communicates with the service provider through the API, and updates the linking information in the database.

[1193] emotion recognition part

[1194] User

[1195] When a user takes a palm image with their smartphone, the emotion engine collects the user's facial expressions and voice data.

[1196] server

[1197] The server analyzes the acquired emotional data and associates the emotional information with the user's palm ID. The emotional data is stored in a database and used when providing services.

[1198] Authentication Process

[1199] User

[1200] Users hold their hand over a palm authentication terminal when checking in at a hotel, verifying their age at a convenience store, or using public transportation.

[1201] Terminal

[1202] The device captures a palm image and sends it to the server.

[1203] server

[1204] The server analyzes the received palm image and matches it with the palm ID in its database. If the match is successful, it provides the corresponding service (e.g., issuing an electronic room key or verifying age).

[1205] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1206] Step 1: Generate palm image data

[1207] server

[1208] Input: prompt for setting the generative AI model

[1209] Prompt: "Generate 100 palm images with different palm patterns."

[1210] Specific operation: The server launches a generative AI model and generates palm image data based on the prompt. The generative AI model (e.g., DALL·E) generates images with various palm shapes and patterns and stores them in a database. Each generated image is assigned a unique ID.

[1211] Output: Palm image data (with saved unique ID)

[1212] Step 2: Preprocessing palm image data

[1213] server

[1214] Input: Palm image data

[1215] Specific operation: The server retrieves palm image data from the database and performs preprocessing on the image. This preprocessing includes noise removal, resizing, grayscale conversion, etc. These processes are performed using open source libraries (e.g., OpenCV).

[1216] Output: Preprocessed image data

[1217] Step 3: Extracting feature points

[1218] server

[1219] Input: Preprocessed image data

[1220] Specific operation: The server extracts feature points from the preprocessed image. It uses a feature point extraction algorithm (e.g., SIFT or ORB) to identify the lines and patterns of the palm. The extracted feature points are stored in a database.

[1221] Output: feature point data

[1222] Step 4: Training the AI ​​model

[1223] server

[1224] Input: feature point data

[1225] How it works: The server uses the feature point data to train an AI model. The model is trained using a deep learning framework (e.g., TensorFlow or PyTorch). During training, the dataset is divided into training data and validation data.

[1226] Output: A trained AI model

[1227] Step 5: Take a palm image

[1228] User

[1229] Input: None (user operation)

[1230] Specific operation: The user launches the smartphone app and uses the camera function to take an image of their palm. The app's camera function is then invoked to capture the palm image.

[1231] Output: Palm image

[1232] Step 6: Send a palm image

[1233] User

[1234] Input: Palm image

[1235] Specific operation: The captured palm image is sent to the server via the app, and the app calls an API endpoint to upload the palm image to the server.

[1236] Output: Palm image sent to the server

[1237] Step 7: Generate Palm ID

[1238] server

[1239] Input: Received palm image

[1240] Specific operation: The server analyzes the received palm image, extracts feature points, and generates a unique palm ID based on the extracted feature points. The palm ID is then associated with the user profile and stored in a database.

[1241] Output: Generated palm ID

[1242] Step 8: Select and integrate services

[1243] User

[1244] Input: None (user operation)

[1245] Specific operation: Through the app, the user selects the service (e.g., point card, payment system, pass card, etc.) they want to link their palm ID to. They select the service using the app's UI.

[1246] Output: Selected service information

[1247] server

[1248] Input: Selected service information, Palm ID

[1249] Specific operation: Based on the user's selection, the server calls the API to link the palm ID with a specific service account, and updates the linking information with the service provider through the API.

[1250] Output: Linked service information

[1251] Step 9: Acquire and analyze emotion data

[1252] User

[1253] Input: None (user operation)

[1254] How it works: When a user takes a palm image with the app, the emotion engine collects the user's facial expression and voice data. The data is captured using the smartphone's camera and microphone.

[1255] Output: facial expression data, voice data

[1256] server

[1257] Input: facial expression data, voice data

[1258] Specific operation: The server analyzes the acquired emotion data and associates the emotion information with the user's palm ID. It extracts emotion information from the collected data and stores it in a database.

[1259] Output: Emotional information

[1260] Step 10: Authentication Process

[1261] User

[1262] Input: None (user operation)

[1263] Specific operation: When checking in at a hotel, verifying age at a convenience store, or using public transportation, a user holds their hand over a palm authentication terminal, which then uses the terminal's camera to capture an image of their palm.

[1264] Output: Captured palm image

[1265] Terminal

[1266] Input: Palm image

[1267] Specific operation: The device sends the captured image to the server, and calls an API endpoint to upload the palm image to the server.

[1268] Output: Palm image sent to the server

[1269] server

[1270] Input: Palm image sent to the server

[1271] Specific operation: The server analyzes the received palm image and compares it with the palm ID in the database. If authentication is successful, it provides the corresponding service (e.g., issuing an electronic room key or verifying age).

[1272] Output: Authentication results and services provided

[1273] (Application example 2)

[1274] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1275] While conventional palm authentication technologies require improved authentication accuracy and security, they have the problem of being unable to consider the user's emotional state. Furthermore, due to a lack of efficient means for real-time authentication and emotion analysis, measures against unauthorized access and security risks are insufficient. Therefore, a new palm authentication system is needed that can simultaneously strengthen security and analyze the user's emotional state.

[1276] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for training a biometric authentication model using the generated palm image database, means for generating palm image data using a generation AI, means for acquiring and identifying a user's palm image, means for uniquely generating a palm ID and linking it with various service providers, means for acquiring and analyzing the user's emotion data using an emotion analysis engine, and means for capturing palm images in real time using a camera built into the tool and performing authentication and emotion analysis. This improves authentication accuracy and enables palm authentication and emotion analysis in real time.

[1277] text

[1278] "Generative AI" is an artificial intelligence technology for generating image data of a user's palm.

[1279] The "palm image database" is a database that collects image data related to the shape and pattern of the palm.

[1280] A "biometric authentication model" is an artificial intelligence model for identifying individuals using palm images.

[1281] A "palm ID" is an identification code that is uniquely generated based on each user's palm image.

[1282] An "emotion analysis engine" is an engine that acquires and analyzes emotional data from the user's facial expressions, voice, etc.

[1283] "Cameras built into tools" are cameras built into smart devices or wearable devices.

[1284] "Real-time capture" is a method of instantly acquiring images or data at the current time.

[1285] "Authentication" is the process of verifying that a user is a legitimate user of a system.

[1286] A "service provider" is a company or organization that provides various services.

[1287] A "database" is a system for efficiently storing and managing structured data.

[1288] "Feature points" are important points or patterns in a palm image, and are data used for personal identification.

[1289] "User Profile" means a collection of information and data relating to each User.

[1290] A specific embodiment for carrying out the present invention will be described below: This system generates image data of a user's palm and is capable of performing palm authentication and emotion analysis in real time.

[1291] System Configuration

[1292] 1. Palm image data generation part

[1293] server

[1294] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with various palm shapes and patterns and stores them in a palm image database.

[1295] 2. Training the palm recognition AI model

[1296] server

[1297] The server retrieves palm image data from the palm image database and pre-processes the images. It extracts feature points from the pre-processed images and trains a biometric authentication model. The trained model is used for high-precision authentication.

[1298] 3. Palm ID registration section

[1299] User

[1300] The user uses the camera function of a smart device to take an image of their palm, which is then sent to the server via the application.

[1301] server

[1302] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is then associated with the user profile and stored in a database.

[1303] 4. Linking Tenohira ID with services

[1304] User

[1305] Through the application, users select the services they want to link their palm ID to (e.g., point cards, payment systems, access control systems, etc.).

[1306] server

[1307] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, and communicates with the service provider through the API to update the link information.

[1308] 5. Emotion recognition part

[1309] User

[1310] When a user uses a smart device to capture a palm image, the emotion analysis engine captures data such as facial expressions and voice, allowing for real-time analysis of the user's emotional state.

[1311] server

[1312] The server analyzes the emotion data and associates it with the user's palm ID. The emotion data is stored in a database and will be used to provide future services.

[1313] 6. Authentication Process

[1314] User

[1315] When authentication is required (e.g., entering or leaving a building, logging in to a computer), the user holds their palm over the camera on their smart device.

[1316] Camera-equipped devices

[1317] The camera-equipped device captures palm images and sends them to a server.

[1318] server

[1319] The server analyzes the palm image and compares the palm ID with the database. At the same time, it performs emotion analysis and determines the appropriate security status. If authentication is successful, the specified service (e.g., access permission, login permission) is provided.

[1320] Hardware and software used

[1321] Hardware: Smart glasses (Google Glass, Vuzix Blade, etc.), smartphones, servers, and devices with built-in cameras

[1322] Software: Python, TensorFlow, OpenCV, Sentiment Analysis Engine

[1323] Specific examples

[1324] If the user uses smart glasses:

[1325] The camera built into the smart glasses captures palm images in real time, and the captured images are sent to a server via the smart glasses application for palm ID authentication and emotion analysis.

[1326] Example prompt sentence:

[1327] "I would like to build a system that uses palm images to authenticate users and simultaneously recognize emotions. Specifically, please explain using 2023 technology how smart glasses can be used to analyze palm images and prevent unauthorized access."

[1328] By implementing the invention in accordance with this aspect, it is possible to improve authentication accuracy and perform palm authentication and emotion analysis in real time, thereby reducing security risks and improving the user experience.

[1329] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1330] text

[1331] Step 1:

[1332] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with a variety of palm shapes and patterns and stores it in a database. The input to this process is the parameters (number, variations) given to the generation AI, and the output is the generated palm image data.

[1333] Step 2:

[1334] The server retrieves palm image data from the palm image database and performs preprocessing on the images, which includes image resizing and noise removal. The input is palm image data, and the output is a preprocessed palm image.

[1335] Step 3:

[1336] The server extracts feature points from the preprocessed palm image and uses them to train a biometric model. The input is the preprocessed palm image, and the output is the trained biometric model.

[1337] Step 4:

[1338] The user takes a palm image using the camera on their smart device. The captured palm image is sent to the server by the application. The input is the palm image taken by the user, and the output is the image data sent to the server.

[1339] Step 5:

[1340] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID. The input is the palm image sent by the user, and the output is the generated palm ID.

[1341] Step 6:

[1342] The server associates the generated palm ID with the user profile and stores it in a database. The input is the generated palm ID and the corresponding user profile, and the output is the updated database entry.

[1343] Step 7:

[1344] The user selects the service with which they want to link their palm ID through the application. The input is the user's selection and palm ID, and the output is the selected service information.

[1345] Step 8:

[1346] Based on the user's selection, the server calls an API to link the palm ID with a specific service account. The input is the user's selected service and palm ID, and the output is an update of the linkage information.

[1347] Step 9:

[1348] When a user holds their palm over the camera of their smart device in a situation requiring authentication, the camera captures an image of the palm and sends it to the server. The input is the palm held over the camera, and the output is the captured image of the palm sent to the server.

[1349] Step 10:

[1350] The server analyzes the captured palm image and compares the palm ID with a database. It also performs emotion analysis and determines the appropriate security status. The input is the captured palm image, and the output is the authentication result and emotion analysis result.

[1351] Step 11:

[1352] If authentication is successful, the server provides the specified service (e.g., access permission, login permission). The inputs are the authentication result and instructions to the service provider, and the output is the provision of the service.

[1353] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1354] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1355] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1356] [Third embodiment]

[1357] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1358] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1359] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1360] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1361] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1362] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1363] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1364] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1365] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1366] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1367] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1368] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1369] The following describes an embodiment of the present invention. This system generates palm image data using a generative AI and trains a biometric authentication model based on that data. It also provides a platform for linking a user's palm as an ID with various services.

[1370] System Configuration

[1371] 1. Palm image data generation part

[1372] server

[1373] The server launches the generation AI, which generates palm image data based on the set parameters. The generated palm image data is stored in a database. The generation AI has the ability to increase the variations in palm shapes and patterns, allowing for the creation of a wide variety of data.

[1374] 2. Training the palm recognition AI model

[1375] server

[1376] The server retrieves palm image data from the database and performs preprocessing, which includes adjusting the image size, removing noise, and adjusting contrast. Feature points are extracted from the preprocessed image data and used to train an AI model. Once trained, the AI ​​model is used for authentication in the production environment.

[1377] 3. Palm ID registration section

[1378] User

[1379] The user uses the smartphone app to take a picture of their palm using the camera function, and the app then sends the image to the server.

[1380] server

[1381] The server analyzes the received palm image, extracts feature points, generates a unique palm ID based on the feature points, associates the ID with the user profile, and stores it in a database.

[1382] 4. Palm ID and service integration

[1383] User

[1384] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[1385] server

[1386] Based on the user's selection, the server will link the Teno-ID with various service provider accounts via API, allowing Teno-ID to be used as a means of authentication for each service.

[1387] 5. Authentication Process

[1388] User

[1389] For example, when checking in at a hotel, a user can hold their palm over a terminal at the front desk.

[1390] Terminal

[1391] The front terminal captures a palm image and sends it to the server.

[1392] server

[1393] The server analyzes the received palm image, extracts the palm ID, and compares it with a database. Based on the comparison result, the server obtains the user's reservation information and sends it to the terminal at the front desk. The terminal then completes the user's check-in and issues an electronic room key.

[1394] Specific examples

[1395] Example 1: Age verification at a convenience store

[1396] User

[1397] When a user purchases alcohol at a convenience store, they hold their palm over a palm authentication terminal installed in the store.

[1398] Terminal

[1399] The device takes a palm image and sends the palm ID to the server.

[1400] server

[1401] The server checks the palm ID against a database to obtain the user's age. Once the age verification is complete, it sends the results to the device, which then displays the results and allows the user to purchase alcohol.

[1402] Example 2: Public transport ride

[1403] User

[1404] Users hold their palm over a palm authentication terminal at the public transportation ticket gate.

[1405] Terminal

[1406] The device captures a palm image and sends the palm ID to the server.

[1407] server

[1408] The server checks the palm ID against a database and obtains the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[1409] In this way, the palm authentication system of the present invention generates palm image data using generative AI and trains a biometric authentication model to achieve highly accurate palm authentication. Furthermore, by registering the user's palm as an ID and providing a platform that links with various services, convenience and security can be improved.

[1410] The processing flow will be explained below.

[1411] Palm image data generation using generative AI

[1412] Step 1:

[1413] server

[1414] The server starts the generation AI and sets parameters regarding the number and variations of palm image data to be generated.

[1415] Step 2:

[1416] server

[1417] The generation AI generates palm image data with different palm shapes and patterns based on set parameters.

[1418] Step 3:

[1419] server

[1420] The generated palm image data is stored in a database and is labeled and classified.

[1421] Training the palm recognition AI model

[1422] Step 1:

[1423] server

[1424] The server retrieves palm image data from the database and performs preprocessing on the images, which includes resizing, noise reduction, and contrast adjustment.

[1425] Step 2:

[1426] server

[1427] A specific algorithm is applied to extract feature points from the preprocessed image data.

[1428] Step 3:

[1429] server

[1430] The extracted feature data is used to train an AI model, and deep learning algorithms are used to improve the model's accuracy.

[1431] Step 4:

[1432] server

[1433] The trained AI model is saved and made available for authentication processing.

[1434] Palm ID registration

[1435] Step 1:

[1436] User

[1437] The user launches the app on their smartphone and uses the camera function to take a picture of their palm.

[1438] Step 2:

[1439] User

[1440] The captured palm image is sent from the app to the server.

[1441] Step 3:

[1442] server

[1443] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID.

[1444] Step 4:

[1445] server

[1446] The generated palm ID is associated with the user profile and stored in a database.

[1447] Linking palm ID with services

[1448] Step 1:

[1449] User

[1450] Users open the app on their smartphone and select the service they want to link their palm ID to, such as a point card, payment system, or pass card.

[1451] Step 2:

[1452] server

[1453] Based on the user's selection, the server calls an API to link the palm ID with a specific service account.

[1454] Step 3:

[1455] server

[1456] Communicate with the service provider via API and update the link information between your palm ID and service account.

[1457] Authentication Process

[1458] Step 1:

[1459] User

[1460] When a user uses the service, for example when checking into a hotel, they hold their palm over a terminal at the front desk.

[1461] Step 2:

[1462] Terminal

[1463] The front terminal captures a palm image and sends it to the server.

[1464] Step 3:

[1465] server

[1466] The server analyzes the received palm image and identifies the corresponding palm ID by extracting feature points.

[1467] Step 4:

[1468] server

[1469] Based on the palm ID, the user's reservation information is retrieved from the database and the authentication results are sent to the terminal.

[1470] Step 5:

[1471] Terminal

[1472] The terminal notifies the user of successful authentication and provides appropriate services (e.g., issuing an electronic room key).

[1473] Age verification at convenience stores

[1474] Step 1:

[1475] User

[1476] When a user purchases alcohol at a convenience store, they hold their hand over a palm authentication terminal.

[1477] Step 2:

[1478] Terminal

[1479] The device captures a palm image and sends the palm ID to the server.

[1480] Step 3:

[1481] server

[1482] The server compares the palm ID with a database to confirm the user's age information.

[1483] Step 4:

[1484] server

[1485] Once age verification is complete, the results will be sent to the device.

[1486] Step 5:

[1487] Terminal

[1488] The device will display the results and allow the purchase of alcohol if necessary.

[1489] Example 1

[1490] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1491] In modern society, there is a growing demand for simple yet highly accurate biometric authentication systems. However, conventional biometric authentication systems have had problems with the time and cost required to collect and process individual image data. Furthermore, there are limitations to the accuracy of authentication, resulting in frequent misidentifications and missed recognitions. Furthermore, information management is complicated when linking with multiple services, and there is a lack of a mechanism for automatically authenticating users. The present invention aims to solve these problems.

[1492] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1493] In this invention, the server includes means for training a biometric authentication model using the generated palm image data, means for generating palm image data using the generating artificial intelligence, means for acquiring and identifying a user's palm image, means for uniquely generating a palm identifier and linking it with various service providers, means for generating diverse palm image data, means for performing preprocessing on the palm image data such as noise removal and contrast adjustment, and means for deploying the generated biometric authentication model in a production environment. This enables highly accurate and versatile palm authentication, allowing users to use various services automatically and safely without hassle.

[1494] "Generated palm image data" is digital image data of the palm generated using artificial intelligence.

[1495] "Means for training a biometric authentication model" refers to a method and device that uses acquired palm image data to adjust and train the parameters of an artificial intelligence model.

[1496] "Generative AI" refers to algorithms or software that generate new palm image data based on set parameters.

[1497] "Means for acquiring an image of a user's palm and identifying it" refers to a method and apparatus for taking a picture of a user's palm using a device such as a camera, analyzing the image, and uniquely identifying it.

[1498] A "palm identifier" is unique identification information generated based on specific feature points extracted from a captured palm image.

[1499] "Various service providers" are external service providers, such as payment systems and point card systems, that users access and authenticate using palm identifiers.

[1500] The "means for generating a variety of palm image data" refers to a method and device for generating a plurality of palm images with different shapes and patterns using artificial intelligence.

[1501] "Means for performing pre-processing such as noise removal and contrast adjustment" refers to a method and device for processing acquired palm image data to improve image quality and recognition accuracy.

[1502] The "means for deploying the generated biometric authentication model in a production environment" refers to a method and apparatus for incorporating a trained artificial intelligence model into an actual operational system and setting it up so that it can be used for authentication processing.

[1503] System Configuration

[1504] 1. Palm image data generation part

[1505] server

[1506] The server starts the generative AI and generates palm image data. Specifically, the server uses "StyleGAN" or a similar algorithm as the generative AI. The server sets parameters such as palm size, pattern complexity, and number of variations, and generates palm image data through the generative AI. This generated palm image data is stored in a database. For example, a prompt might be: "To generate palm image data, generate a palm image based on the set parameters. Increase the variations in palm shape and pattern to create diverse data."

[1507] 2. Training the palm recognition AI model

[1508] server

[1509] The server retrieves palm image data from the database and performs preprocessing. This preprocessing uses the OpenCV library to resize the image, remove noise, and adjust the contrast. Furthermore, algorithms such as SURF and SIFT are used to extract feature points. Based on the preprocessed image data and feature point data, an AI model is trained using TensorFlow or PyTorch. The trained model is saved in the SavedModel format and deployed to the production environment via an API server (e.g., Flask).

[1510] 3. Palm ID registration section

[1511] User

[1512] Users take a palm image using a dedicated smartphone app called "CustomPalmID App." Using the smartphone camera, palm images are captured.

[1513] server

[1514] The server receives the palm image sent by the user and extracts the feature points, which are then analyzed using SURF and SIFT techniques to generate a unique palm identifier in the form of a UUID, which is then associated with the corresponding user profile and stored in a database.

[1515] 4. Palm ID and service integration

[1516] User

[1517] Using the smartphone app, the user selects the service (e.g., payment system, point card system, pass card, etc.) they want to link their palm ID to. Using the "ServiceConnector App."

[1518] server

[1519] The server associates the palm ID with the service provider's account based on the user's selection. By linking the palm ID with the service provider's account using the OAuth 2.0 protocol, the server makes it possible to use the palm ID as a means of authentication for various services.

[1520] 5. Authentication Process

[1521] User

[1522] When checking in at a hotel, a user holds their palm over a palm authentication terminal, such as one installed at the front desk.

[1523] Terminal

[1524] The terminal captures a palm image and transmits the image data to a server.

[1525] server

[1526] The server analyzes the received palm image, extracts a palm identifier, and compares it with a database. Based on the comparison result, the server obtains the user's reservation information and sends it to the terminal. After authentication is complete, the terminal issues an electronic room key and provides it to the user.

[1527] Specific examples

[1528] Example 1: Age verification at a convenience store

[1529] User

[1530] When a user purchases alcohol at a convenience store, they hold their palm over a palm authentication terminal installed in the store.

[1531] Terminal

[1532] The terminal takes a palm image and transmits the palm identifier to the server.

[1533] server

[1534] The server compares the palm identifier with a database to obtain the user's age information. Once the age verification is complete, it sends the result to the terminal. The terminal then displays the authentication result and allows the user to purchase alcohol.

[1535] Example 2: Public transport ride

[1536] User

[1537] The user holds their palm over a palm authentication terminal at the ticket gate of a public transport facility.

[1538] Terminal

[1539] The terminal captures a palm image and transmits the palm identifier to the server.

[1540] server

[1541] The server checks the palm identifier against a database to obtain the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[1542] In this way, the palm authentication system of the present invention generates palm image data using generative AI and trains a biometric authentication model to achieve highly accurate palm authentication. Furthermore, by registering the user's palm as an identifier and linking it to various services, convenience and security can be improved.

[1543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1544] System program processing flow

[1545] Step 1:

[1546] The server launches the generative AI. Specifically, it instantiates the generative AI software and prepares it to generate palm images. The input data used are generative parameters (e.g., palm shape, pattern complexity, number of variations, etc.). The output is an initialized generative AI instance.

[1547] Step 2:

[1548] The server sets the parameters for palm image generation. Specifically, it reads parameters such as palm size, pattern complexity, and number of variations from a configuration file or user input. The input is the configuration parameters, and the output is command data that is input to the generation AI based on those parameters.

[1549] Step 3:

[1550] The server generates palm image data using a generative AI. The generative AI (e.g., "StyleGAN") uses an algorithm to generate palm images according to set parameters. The input is the set parameters and an initialized generative AI instance, and the output is the generated palm image data.

[1551] Step 4:

[1552] The server saves the generated palm image data in a database. It connects to a database management system (e.g., MySQL) and stores the generated palm image data files. The input is the generated palm image data, and the output is the identification information of the data saved in the database.

[1553] Step 5:

[1554] The server retrieves palm image data from the database. It connects to the database and searches for and retrieves stored palm image data. The input is the database connection information and the search query, and the output is the retrieved palm image data.

[1555] Step 6:

[1556] The server performs preprocessing of the image data. Specifically, it uses the "OpenCV" library to adjust the image size, remove noise, and adjust the contrast. The input is palm image data, and the output is the preprocessed palm image data.

[1557] Step 7:

[1558] The server extracts feature points from the preprocessed palm image using the "SURF" or "SIFT" algorithm. The input is the preprocessed palm image data, and the output is the feature point data.

[1559] Step 8:

[1560] The server trains the AI ​​model. Using TensorFlow and PyTorch, the AI ​​model learns the feature point data and palm image data. The input is the feature point data and palm image data, and the output is the trained AI model.

[1561] Step 9:

[1562] The server saves the trained AI model and places it in the production environment. The model file is saved in the "SavedModel" format and deployed using an API server (e.g., "Flask"). The input is the trained AI model, and the output is an AI model instance that runs in the production environment.

[1563] Step 10:

[1564] The user takes a palm image using a dedicated app. Using the "CustomPalmID App," the palm image is acquired with the smartphone camera. The input is the camera image, and the output is the captured palm image data.

[1565] Step 11:

[1566] The user sends the captured palm image data to the server. Using the send function within the app, a POST request is made to the endpoint with the image data. The input is the palm image data, and the output is the image data being uploaded to the server.

[1567] Step 12:

[1568] The server analyzes the received palm image and extracts feature points using a specific algorithm (e.g., "SURF" or "SIFT"). The input is palm image data, and the output is feature point data.

[1569] Step 13:

[1570] The server generates a palm identifier and associates it with the user profile. It generates an identifier in UUID format, associates it with the corresponding user profile, and stores it in a database. The input is minutiae data and user profile information, and the output is the generated palm identifier.

[1571] Step 14:

[1572] Users use the app to link their palm ID to a service. Using the "ServiceConnector App," they select the service they want to link. The input is the palm ID and the service information to link to, and the output is the link setting information.

[1573] Step 15:

[1574] The server links the palm identifier with the service provider account. This is done via API using a protocol such as OAuth 2.0. The input is the palm identifier and the service provider account information, and the output is the linked account information.

[1575] Step 16:

[1576] A user holds their palm over a palm authentication terminal, such as when checking in at a hotel. The input is a palm image, and the output is an authentication request.

[1577] Step 17:

[1578] The terminal captures the palm image and sends it to the server. The palm image is acquired using an image capture device and sent to the server. The input is the palm image, and the output is the transmission of image data to the server.

[1579] Step 18:

[1580] The server analyzes the palm image, extracts a palm identifier, and matches it with a database. It uses an algorithm to match it with an existing palm identifier database. The input is the palm image data, and the output is the matching result.

[1581] Step 19:

[1582] The server obtains the user's reservation information based on the matching result and sends it to the terminal. The server also obtains user information from the reservation system based on the matching result. The input is the matching result and a reservation system inquiry request, and the output is the transmission of reservation information.

[1583] Step 20:

[1584] After authentication is complete, the terminal issues an electronic room key and provides it to the user. The room key information is displayed on the terminal's display device. The input is the verification result and reservation information, and the output is the electronic room key information.

[1585] The above are the specific processing steps of this palm authentication system.

[1586] (Application example 1)

[1587] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1588] Until now, there have been technical and cost challenges in the widespread adoption of highly accurate biometric authentication systems. Furthermore, there has been a lack of security authentication systems that combine user convenience and safety. In particular, more reliable authentication methods are needed for access control in places where high security is required, such as offices and data centers.

[1589] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1590] In this invention, the server includes a means for training a biometric authentication model using the generated palm image database, a means for generating palm image data using a generation AI, a means for acquiring and identifying a user's palm image, a means for uniquely generating a palm ID and linking it with various service providers, and a security means for capturing and authenticating palm images using a smart device or robot. This allows users to easily perform palm authentication with high accuracy using a smart device or robot. This satisfies high security requirements and improves the reliability of access control systems, particularly those used in offices, data centers, etc.

[1591] "Generative AI" is an artificial intelligence technology used to generate image data of a user's palm, and has the ability to create a variety of palm shapes and patterns.

[1592] The "palm image database" is a database for storing and managing palm image data generated by the generative AI, and is used to train biometric authentication models.

[1593] A "biometric authentication model" is an artificial intelligence model trained to verify a user's identity based on palm image data, and performs highly accurate authentication based on feature points.

[1594] "Palm ID" is an identification number that is uniquely generated using the feature points of a palm image and is used to link with various services.

[1595] A "smart device" is a device that has the function of acquiring an image of a user's palm and performing authentication, and includes smartphones and corresponding access control devices.

[1596] A "robot" is an autonomous or remotely controlled device used to capture palm images and perform authentication processes, and is used for security management.

[1597] "Minutes" are unique data points extracted from palm images that are used to train and authenticate biometric models.

[1598] To implement this invention, a system based on the following procedures is required.

[1599] System Configuration

[1600] 1. Palm image data generation part

[1601] server

[1602] The server generates various palm image data using generative AI, which applies technologies such as StyleGAN. The generated palm image data is stored in a dedicated database.

[1603] 2. Training the palm recognition AI model

[1604] server

[1605] The server trains the biometric authentication model using preprocessed palm image data. Preprocessing includes noise removal, size adjustment, contrast adjustment, etc., and then extracts feature points. Training is performed using TensorFlow and PyTorch.

[1606] 3. Palm ID registration section

[1607] User

[1608] The user uses the camera function of a smartphone app to take an image of their palm, which is then sent to the server via the app.

[1609] server

[1610] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is associated with the user profile and stored in a database.

[1611] 4. Palm ID and service integration

[1612] User

[1613] Through the app, users can select the services they want to link their palm ID to, including a wide range of services such as security authentication, financial services, and access control.

[1614] server

[1615] Based on the user's selection, the server configures the palm ID to link with various service provider accounts via API.

[1616] 5. Authentication Process

[1617] User

[1618] Users hold their palm over a palm authentication terminal at locations where security authentication is required, such as at the entrance to a data center or an office access control device.

[1619] Terminal

[1620] A palm image is captured using a smart device or robot and sent to a server.

[1621] server

[1622] The server analyzes the received palm image, compares the palm ID with a database, and sends an access permission or denial result to the device based on the comparison result.

[1623] Example: Access control at a data center

[1624] This system is used in situations where secure access control is required in corporate data centers.

[1625] 1. User: Scans palm at data center entrance.

[1626] 2. Device: Captures a palm image and sends it to the server.

[1627] 3. Server: Generates palm ID and checks it against the database.

[1628] 4. Authentication result: Based on the verification result, access permission is notified to the terminal and the door opens automatically.

[1629] Prompt Sentence Examples

[1630] "Generate a palm ID based on the palm image shown below and verify it against the database for authentication."

[1631] The above procedure makes it possible to implement the present invention, and realize a highly accurate and user-friendly palm authentication system, which can meet the high security requirements of offices, data centers, etc.

[1632] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1633] Step 1:

[1634] To use the palm authentication system, a user launches the smartphone app and takes a picture of their palm.

[1635] Input: User's palm image

[1636] Output: Palm image data

[1637] How it works: The user activates the camera function of the smartphone app and takes a picture of their palm. This image data is then saved in the application.

[1638] Step 2:

[1639] The smartphone app sends the captured palm image to a server.

[1640] Input: Palm image data

[1641] Output: Image data transmission request to the server

[1642] How it works: An HTTP POST request containing palm image data is sent to the server, with the image data encrypted if necessary.

[1643] Step 3:

[1644] The server receives the palm image data and performs preprocessing.

[1645] Input: Palm image data

[1646] Output: Preprocessed image data

[1647] Operation: After receiving the palm image data, the server performs preprocessing such as noise reduction, size adjustment, contrast adjustment, etc. The image is converted into a standardized format.

[1648] Step 4:

[1649] The server extracts feature points from the preprocessed palm image.

[1650] Input: Preprocessed image data

[1651] Output: feature point data

[1652] How it works: A feature point extraction algorithm is applied to the preprocessed image data. Feature points are extracted using a deep learning model such as CNN.

[1653] Step 5:

[1654] The server generates a palm ID based on the feature point data and stores it in a database.

[1655] Input: feature point data

[1656] Output: Palm ID, registration request to database

[1657] Operation: A palm ID is generated from the feature point data, and it is associated with the user profile and saved in a database. SQL queries are used for the saving process.

[1658] Step 6:

[1659] The user holds their palm over an authentication terminal at a specific security authentication point (e.g., the entrance to a data center).

[1660] Input: User's palm image

[1661] Output: Authentication start request

[1662] How it works: The authentication process begins when the user holds their palm over the authentication device's camera.

[1663] Step 7:

[1664] The device captures a palm image and sends it to the server.

[1665] Input: User's palm image

[1666] Output: Image data transmission request to the server

[1667] How it works: The device's camera captures a palm image and sends it to a server. The image data is encrypted before being transferred.

[1668] Step 8:

[1669] The server analyzes the palm image and compares it with a database.

[1670] Input: Palm image data

[1671] Output: Authentication result (allow or deny)

[1672] How it works: The server analyzes the palm image and matches it with the palm ID stored in the database. Based on the match, authentication is either granted or denied.

[1673] Step 9:

[1674] The terminal receives the authentication result and notifies the user.

[1675] Input: Authentication result

[1676] Output: Authentication notification (allowed or denied)

[1677] Operation: The terminal that receives the authentication result from the server allows access if it is permitted and automatically opens the door. If it is denied, it notifies the user.

[1678] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1679] The following describes an embodiment of the present invention. The system includes a generation AI for generating palm image data, a means for training a biometric authentication model based on the palm image, a means for acquiring and identifying a user's palm image, a means for generating a palm ID and linking it with various service providers, and an emotion engine for recognizing and analyzing the user's emotions.

[1680] System Configuration

[1681] 1. Palm image data generation part

[1682] server

[1683] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with various palm shapes and patterns and stores them in a database.

[1684] 2. Training the palm recognition AI model

[1685] server

[1686] The server retrieves palm image data from the database, pre-processes the image, extracts feature points from the pre-processed image, and trains an AI model. The trained model is used for high-precision authentication.

[1687] 3. Palm ID registration section

[1688] User

[1689] The user launches the app on their smartphone and uses the camera to take a picture of their palm, which is then sent to the server.

[1690] server

[1691] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is then associated with the user profile and stored in a database.

[1692] 4. Linking Tenohira ID with services

[1693] User

[1694] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[1695] server

[1696] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, and communicates with the service provider through the API to update the link information.

[1697] 5. Emotion recognition part

[1698] User

[1699] When a user takes a palm image using a smartphone app, the emotion engine collects data such as the user's facial expressions and voice, which allows it to analyze the user's emotional state in real time.

[1700] server

[1701] The server analyzes the acquired emotional data and associates it with the user's palm ID. The emotional data is stored in a database and will be used to provide future services.

[1702] 6. Authentication Process

[1703] User

[1704] For example, when checking into a hotel, a user holds their palm over a terminal at the front desk.

[1705] Terminal

[1706] The front terminal captures a palm image and sends it to the server.

[1707] server

[1708] The server analyzes the received palm image and compares the palm ID with a database. It retrieves the user's reservation information and sends the authentication result to the terminal. If authentication is successful, the appropriate service (e.g., issuing an electronic room key) is provided.

[1709] Specific examples

[1710] Example 1: Age verification at a convenience store

[1711] User

[1712] When a user purchases alcohol at a convenience store, they hold their hand over a palm authentication terminal.

[1713] Terminal

[1714] The device captures a palm image and sends it to the server.

[1715] server

[1716] The server checks the palm ID against a database to confirm the user's age. Once the verification is complete, it sends the results to the device, which then displays the results and allows the user to purchase alcohol.

[1717] Example 2: Public transport

[1718] User

[1719] Users hold their hand over a palm authentication terminal at the public transportation ticket gate.

[1720] Terminal

[1721] The device captures a palm image and sends it to the server.

[1722] server

[1723] The server compares the palm ID with the database and obtains the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[1724] Example 3: Providing services using user emotion recognition

[1725] User

[1726] The user takes a picture of their palm with their smartphone and sends emotional information to the server via the app.

[1727] server

[1728] The server analyzes the emotion data and uses that information to provide the user with appropriate services (e.g., a customized shopping experience). If the emotion is determined to be "fun," relevant promotions or coupons can be offered.

[1729] In this way, the palm authentication system of the present invention uses generative AI to generate palm image data and train a biometric authentication model. It also uses an emotion engine to acquire and analyze emotion data, allowing it to provide personalized services to users. This system improves convenience and security, resulting in a better user experience.

[1730] The processing flow will be explained below.

[1731] Palm image data generation using generative AI

[1732] Step 1:

[1733] server

[1734] Start the generation AI and set parameters regarding the number and variations of palm image data to be generated.

[1735] Step 2:

[1736] server

[1737] Palm image data with different palm shapes and patterns is generated based on the set parameters.

[1738] Step 3:

[1739] server

[1740] The generated palm image data is stored in a database and is labeled and classified.

[1741] Training the palm recognition AI model

[1742] Step 1:

[1743] server

[1744] Palm image data is acquired from the database and preprocessed, including size adjustment, noise removal, and contrast adjustment.

[1745] Step 2:

[1746] server

[1747] A specific algorithm is applied to extract feature points from the preprocessed image data.

[1748] Step 3:

[1749] server

[1750] The extracted feature data is used to train an AI model, and a deep learning algorithm is used to improve the model's accuracy.

[1751] Step 4:

[1752] server

[1753] The trained AI model is saved and made available for authentication processing.

[1754] Palm ID registration

[1755] Step 1:

[1756] User

[1757] Launch the app on your smartphone and use the camera to take a picture of your palm.

[1758] Step 2:

[1759] User

[1760] The captured palm image is sent from the app to the server.

[1761] Step 3:

[1762] server

[1763] The received palm image is analyzed, feature points are extracted, and a unique palm ID is generated.

[1764] Step 4:

[1765] server

[1766] The generated palm ID is associated with the user profile and stored in a database.

[1767] Linking palm ID with services

[1768] Step 1:

[1769] User

[1770] Open the app on your smartphone and select the service you want to link your Palm ID to, such as a point card, payment system, or pass card.

[1771] Step 2:

[1772] server

[1773] Based on the user's selection, an API is called to link the palm ID with a specific service account.

[1774] Step 3:

[1775] server

[1776] Communicate with the service provider via API and update the link information between your palm ID and service account.

[1777] emotion recognition part

[1778] Step 1:

[1779] User

[1780] When a palm image is taken using a smartphone app, the emotion engine collects data such as the user's facial expressions and voice.

[1781] Step 2:

[1782] server

[1783] The acquired data is analyzed to determine the user's emotional state in real time.

[1784] Step 3:

[1785] server

[1786] The emotion data is associated with a user profile and stored in a database.

[1787] Step 4:

[1788] server

[1789] Settings are made to provide customized services based on the user's emotional data.

[1790] Authentication Process

[1791] Step 1:

[1792] User

[1793] For example, when checking in at a hotel, you hold your palm over the terminal at the front desk.

[1794] Step 2:

[1795] Terminal

[1796] The front terminal captures a palm image and sends it to the server.

[1797] Step 3:

[1798] server

[1799] The received palm image is analyzed and the palm ID is compared with a database.

[1800] Step 4:

[1801] server

[1802] The user's reservation information is obtained based on the palm ID, and the authentication results are sent to the terminal.

[1803] Step 5:

[1804] Terminal

[1805] Notify the user that authentication was successful and provide appropriate services (e.g., issue an electronic room key).

[1806] Age verification at convenience stores

[1807] Step 1:

[1808] User

[1809] When checking in at Costco, hold your palm over the terminal at the front desk.

[1810] Step 2:

[1811] Terminal

[1812] The front terminal captures a palm image and sends it to the server.

[1813] Step 3:

[1814] server

[1815] Feature points are extracted from the received palm image and a palm ID is generated.

[1816] Step 4:

[1817] server

[1818] The generated palm ID is matched to obtain the corresponding user's age information.

[1819] Step 5:

[1820] server

[1821] Once the confirmation is received, the authentication result is sent to the terminal.

[1822] Step 6:

[1823] Terminal

[1824] The device will display the results and allow the alcohol purchase.

[1825] Example 2

[1826] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1827] In conventional biometric authentication systems, achieving high-precision authentication and improving the user experience requires a wide range of individual technologies, and the integration of these technologies is complex. Furthermore, services based on emotion recognition are not considered, making it difficult to provide personalized services to users. This results in reduced authentication accuracy and a lower quality of user experience, making it difficult to improve convenience and safety.

[1828] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1829] In this invention, the server includes means for training a biometric authentication model using the generated palm image database, means for generating palm image data using a generative AI model, means for acquiring and identifying a user's palm image, means for extracting feature points to generate a unique palm ID and linking it with various service providers, and means for analyzing the user's emotional state using an emotion engine. This enables a unified process from palm image data generation to authentication, service linkage, and even personalized service provision based on emotion recognition.

[1830] The "generated palm image database" is a database that stores various palm images generated by a generative AI model and their associated metadata.

[1831] A "biometric model" is an artificial intelligence model trained to identify users based on palm image data.

[1832] A "generative AI model" is a model that uses a generative artificial intelligence algorithm to generate a variety of palm image data.

[1833] The "means for acquiring an image of the user's palm" refers to a means for a user to use a camera on a smartphone or terminal to take an image of the palm of their hand and acquire that image.

[1834] "Feature points" are points with specific visual characteristics, such as lines or patterns on the palm, and by extracting these, individual palms can be uniquely identified.

[1835] A "palm ID" is a unique identifier generated based on feature points extracted from an image of a user's palm.

[1836] "Means for linking with various service providers" refers to means for linking with other services (such as point cards and payment systems) using the palm ID.

[1837] An "emotion engine" is a system or algorithm that analyzes a user's emotional state from facial expressions, voice, etc., and generates the results as data.

[1838] "Means for associating with user profile and storing in a database" refers to a means for linking the generated palm ID and emotion data to the user's individual profile information and storing this in a database.

[1839] The present invention is an integrated biometric authentication system that includes palm image data generation, authentication, service linkage, and even emotion recognition-based service provision.

[1840] Palm image data generation part

[1841] server

[1842] The server starts a generative AI model (e.g., DALL·E) to generate various palm image data. The number and variation of images to be generated are determined by setting a prompt for the generative AI model.

[1843] example:

[1844] Prompt: "Generate 100 palm images with different palm patterns."

[1845] The generated images are stored in a database and each is given a unique ID.

[1846] Palm recognition AI model training part

[1847] server

[1848] The server retrieves palm image data from the database and performs preprocessing on the image. This preprocessing includes image resizing, noise removal, and grayscale conversion. Feature points are extracted from the preprocessed image and used to train an AI model (e.g., a deep learning model using TensorFlow or PyTorch). The trained model is then used for biometric authentication.

[1849] Palm ID registration section

[1850] User

[1851] The user launches the app on their smartphone and uses the camera to take a picture of their palm, which is then sent to the server via the app.

[1852] server

[1853] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is linked to the user profile and stored in a database.

[1854] Palm ID and service integration

[1855] User

[1856] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[1857] server

[1858] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, communicates with the service provider through the API, and updates the linking information in the database.

[1859] emotion recognition part

[1860] User

[1861] When a user takes a palm image with their smartphone, the emotion engine collects the user's facial expressions and voice data.

[1862] server

[1863] The server analyzes the acquired emotional data and associates the emotional information with the user's palm ID. The emotional data is stored in a database and used when providing services.

[1864] Authentication Process

[1865] User

[1866] Users hold their hand over a palm authentication terminal when checking in at a hotel, verifying their age at a convenience store, or using public transportation.

[1867] Terminal

[1868] The device captures a palm image and sends it to the server.

[1869] server

[1870] The server analyzes the received palm image and matches it with the palm ID in its database. If the match is successful, it provides the corresponding service (e.g., issuing an electronic room key or verifying age).

[1871] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1872] Step 1: Generate palm image data

[1873] server

[1874] Input: prompt for setting the generative AI model

[1875] Prompt: "Generate 100 palm images with different palm patterns."

[1876] Specific operation: The server launches a generative AI model and generates palm image data based on the prompt. The generative AI model (e.g., DALL·E) generates images with various palm shapes and patterns and stores them in a database. Each generated image is assigned a unique ID.

[1877] Output: Palm image data (with saved unique ID)

[1878] Step 2: Preprocessing palm image data

[1879] server

[1880] Input: Palm image data

[1881] Specific operation: The server retrieves palm image data from the database and performs preprocessing on the image. This preprocessing includes noise removal, resizing, grayscale conversion, etc. These processes are performed using open source libraries (e.g., OpenCV).

[1882] Output: Preprocessed image data

[1883] Step 3: Extracting feature points

[1884] server

[1885] Input: Preprocessed image data

[1886] Specific operation: The server extracts feature points from the preprocessed image. It uses a feature point extraction algorithm (e.g., SIFT or ORB) to identify the lines and patterns of the palm. The extracted feature points are stored in a database.

[1887] Output: feature point data

[1888] Step 4: Training the AI ​​model

[1889] server

[1890] Input: feature point data

[1891] How it works: The server uses the feature point data to train an AI model. The model is trained using a deep learning framework (e.g., TensorFlow or PyTorch). During training, the dataset is divided into training data and validation data.

[1892] Output: A trained AI model

[1893] Step 5: Take a palm image

[1894] User

[1895] Input: None (user operation)

[1896] Specific operation: The user launches the smartphone app and uses the camera function to take an image of their palm. The app's camera function is then invoked to capture the palm image.

[1897] Output: Palm image

[1898] Step 6: Send a palm image

[1899] User

[1900] Input: Palm image

[1901] Specific operation: The captured palm image is sent to the server via the app, and the app calls an API endpoint to upload the palm image to the server.

[1902] Output: Palm image sent to the server

[1903] Step 7: Generate Palm ID

[1904] server

[1905] Input: Received palm image

[1906] Specific operation: The server analyzes the received palm image, extracts feature points, and generates a unique palm ID based on the extracted feature points. The palm ID is then associated with the user profile and stored in a database.

[1907] Output: Generated palm ID

[1908] Step 8: Select and integrate services

[1909] User

[1910] Input: None (user operation)

[1911] Specific operation: Through the app, the user selects the service (e.g., point card, payment system, pass card, etc.) they want to link their palm ID to. They select the service using the app's UI.

[1912] Output: Selected service information

[1913] server

[1914] Input: Selected service information, Palm ID

[1915] Specific operation: Based on the user's selection, the server calls the API to link the palm ID with a specific service account, and updates the linking information with the service provider through the API.

[1916] Output: Linked service information

[1917] Step 9: Acquire and analyze emotion data

[1918] User

[1919] Input: None (user operation)

[1920] How it works: When a user takes a palm image with the app, the emotion engine collects the user's facial expression and voice data. The data is captured using the smartphone's camera and microphone.

[1921] Output: facial expression data, voice data

[1922] server

[1923] Input: facial expression data, voice data

[1924] Specific operation: The server analyzes the acquired emotion data and associates the emotion information with the user's palm ID. It extracts emotion information from the collected data and stores it in a database.

[1925] Output: Emotional information

[1926] Step 10: Authentication Process

[1927] User

[1928] Input: None (user operation)

[1929] Specific operation: When checking in at a hotel, verifying age at a convenience store, or using public transportation, a user holds their hand over a palm authentication terminal, which then uses the terminal's camera to capture an image of their palm.

[1930] Output: Captured palm image

[1931] Terminal

[1932] Input: Palm image

[1933] Specific operation: The device sends the captured image to the server, and calls an API endpoint to upload the palm image to the server.

[1934] Output: Palm image sent to the server

[1935] server

[1936] Input: Palm image sent to the server

[1937] Specific operation: The server analyzes the received palm image and compares it with the palm ID in the database. If authentication is successful, it provides the corresponding service (e.g., issuing an electronic room key or verifying age).

[1938] Output: Authentication results and services provided

[1939] (Application example 2)

[1940] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1941] While conventional palm authentication technologies require improved authentication accuracy and security, they have the problem of being unable to consider the user's emotional state. Furthermore, due to a lack of efficient means for real-time authentication and emotion analysis, measures against unauthorized access and security risks are insufficient. Therefore, a new palm authentication system is needed that can simultaneously strengthen security and analyze the user's emotional state.

[1942] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for training a biometric authentication model using the generated palm image database, means for generating palm image data using a generation AI, means for acquiring and identifying a user's palm image, means for uniquely generating a palm ID and linking it with various service providers, means for acquiring and analyzing the user's emotion data using an emotion analysis engine, and means for capturing palm images in real time using a camera built into the tool and performing authentication and emotion analysis. This improves authentication accuracy and enables palm authentication and emotion analysis in real time.

[1943] text

[1944] "Generative AI" is an artificial intelligence technology for generating image data of a user's palm.

[1945] The "palm image database" is a database that collects image data related to the shape and pattern of the palm.

[1946] A "biometric authentication model" is an artificial intelligence model for identifying individuals using palm images.

[1947] A "palm ID" is an identification code that is uniquely generated based on each user's palm image.

[1948] An "emotion analysis engine" is an engine that acquires and analyzes emotional data from the user's facial expressions, voice, etc.

[1949] "Cameras built into tools" are cameras built into smart devices or wearable devices.

[1950] "Real-time capture" is a method of instantly acquiring images or data at the current time.

[1951] "Authentication" is the process of verifying that a user is a legitimate user of a system.

[1952] A "service provider" is a company or organization that provides various services.

[1953] A "database" is a system for efficiently storing and managing structured data.

[1954] "Feature points" are important points or patterns in a palm image, and are data used for personal identification.

[1955] "User Profile" means a collection of information and data relating to each User.

[1956] A specific embodiment for carrying out the present invention will be described below: This system generates image data of a user's palm and is capable of performing palm authentication and emotion analysis in real time.

[1957] System Configuration

[1958] 1. Palm image data generation part

[1959] server

[1960] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with various palm shapes and patterns and stores them in a palm image database.

[1961] 2. Training the palm recognition AI model

[1962] server

[1963] The server retrieves palm image data from the palm image database and pre-processes the images. It extracts feature points from the pre-processed images and trains a biometric authentication model. The trained model is used for high-precision authentication.

[1964] 3. Palm ID registration section

[1965] User

[1966] The user uses the camera function of a smart device to take an image of their palm, which is then sent to the server via the application.

[1967] server

[1968] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is then associated with the user profile and stored in a database.

[1969] 4. Linking Tenohira ID with services

[1970] User

[1971] Through the application, users select the services they want to link their palm ID to (e.g., point cards, payment systems, access control systems, etc.).

[1972] server

[1973] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, and communicates with the service provider through the API to update the link information.

[1974] 5. Emotion recognition part

[1975] User

[1976] When a user uses a smart device to capture a palm image, the emotion analysis engine captures data such as facial expressions and voice, allowing for real-time analysis of the user's emotional state.

[1977] server

[1978] The server analyzes the emotion data and associates it with the user's palm ID. The emotion data is stored in a database and will be used to provide future services.

[1979] 6. Authentication Process

[1980] User

[1981] When authentication is required (e.g., entering or leaving a building, logging in to a computer), the user holds their palm over the camera on their smart device.

[1982] Camera-equipped devices

[1983] The camera-equipped device captures palm images and sends them to a server.

[1984] server

[1985] The server analyzes the palm image and compares the palm ID with the database. At the same time, it performs emotion analysis and determines the appropriate security status. If authentication is successful, the specified service (e.g., access permission, login permission) is provided.

[1986] Hardware and software used

[1987] Hardware: Smart glasses (Google Glass, Vuzix Blade, etc.), smartphones, servers, and devices with built-in cameras

[1988] Software: Python, TensorFlow, OpenCV, Sentiment Analysis Engine

[1989] Specific examples

[1990] If the user uses smart glasses:

[1991] The camera built into the smart glasses captures palm images in real time, and the captured images are sent to a server via the smart glasses application for palm ID authentication and emotion analysis.

[1992] Example prompt sentence:

[1993] "I would like to build a system that uses palm images to authenticate users and simultaneously recognize emotions. Specifically, please explain using 2023 technology how smart glasses can be used to analyze palm images and prevent unauthorized access."

[1994] By implementing the invention in accordance with this aspect, it is possible to improve authentication accuracy and perform palm authentication and emotion analysis in real time, thereby reducing security risks and improving the user experience.

[1995] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1996] text

[1997] Step 1:

[1998] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with a variety of palm shapes and patterns and stores it in a database. The input to this process is the parameters (number, variations) given to the generation AI, and the output is the generated palm image data.

[1999] Step 2:

[2000] The server retrieves palm image data from the palm image database and performs preprocessing on the images, which includes image resizing and noise removal. The input is palm image data, and the output is a preprocessed palm image.

[2001] Step 3:

[2002] The server extracts feature points from the preprocessed palm image and uses them to train a biometric model. The input is the preprocessed palm image, and the output is the trained biometric model.

[2003] Step 4:

[2004] The user takes a palm image using the camera on their smart device. The captured palm image is sent to the server by the application. The input is the palm image taken by the user, and the output is the image data sent to the server.

[2005] Step 5:

[2006] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID. The input is the palm image sent by the user, and the output is the generated palm ID.

[2007] Step 6:

[2008] The server associates the generated palm ID with the user profile and stores it in a database. The input is the generated palm ID and the corresponding user profile, and the output is the updated database entry.

[2009] Step 7:

[2010] The user selects the service with which they want to link their palm ID through the application. The input is the user's selection and palm ID, and the output is the selected service information.

[2011] Step 8:

[2012] Based on the user's selection, the server calls an API to link the palm ID with a specific service account. The input is the user's selected service and palm ID, and the output is an update of the linkage information.

[2013] Step 9:

[2014] When a user holds their palm over the camera of their smart device in a situation requiring authentication, the camera captures an image of the palm and sends it to the server. The input is the palm held over the camera, and the output is the captured image of the palm sent to the server.

[2015] Step 10:

[2016] The server analyzes the captured palm image and compares the palm ID with a database. It also performs emotion analysis and determines the appropriate security status. The input is the captured palm image, and the output is the authentication result and emotion analysis result.

[2017] Step 11:

[2018] If authentication is successful, the server provides the specified service (e.g., access permission, login permission). The inputs are the authentication result and instructions to the service provider, and the output is the provision of the service.

[2019] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2020] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2021] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2022] [Fourth embodiment]

[2023] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2024] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[2025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2026] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2027] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2028] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2030] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2031] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2032] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[2033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2034] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2036] The following describes an embodiment of the present invention. This system generates palm image data using a generative AI and trains a biometric authentication model based on that data. It also provides a platform for linking a user's palm as an ID with various services.

[2037] System Configuration

[2038] 1. Palm image data generation part

[2039] server

[2040] The server launches the generation AI, which generates palm image data based on the set parameters. The generated palm image data is stored in a database. The generation AI has the ability to increase the variations in palm shapes and patterns, allowing for the creation of a wide variety of data.

[2041] 2. Training the palm recognition AI model

[2042] server

[2043] The server retrieves palm image data from the database and performs preprocessing, which includes adjusting the image size, removing noise, and adjusting contrast. Feature points are extracted from the preprocessed image data and used to train an AI model. Once trained, the AI ​​model is used for authentication in the production environment.

[2044] 3. Palm ID registration section

[2045] User

[2046] The user uses the smartphone app to take a picture of their palm using the camera function, and the app then sends the image to the server.

[2047] server

[2048] The server analyzes the received palm image, extracts feature points, generates a unique palm ID based on the feature points, associates the ID with the user profile, and stores it in a database.

[2049] 4. Palm ID and service integration

[2050] User

[2051] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[2052] server

[2053] Based on the user's selection, the server will link the Teno-ID with various service provider accounts via API, allowing Teno-ID to be used as a means of authentication for each service.

[2054] 5. Authentication Process

[2055] User

[2056] For example, when checking in at a hotel, a user can hold their palm over a terminal at the front desk.

[2057] Terminal

[2058] The front terminal captures a palm image and sends it to the server.

[2059] server

[2060] The server analyzes the received palm image, extracts the palm ID, and compares it with a database. Based on the comparison result, the server obtains the user's reservation information and sends it to the terminal at the front desk. The terminal then completes the user's check-in and issues an electronic room key.

[2061] Specific examples

[2062] Example 1: Age verification at a convenience store

[2063] User

[2064] When a user purchases alcohol at a convenience store, they hold their palm over a palm authentication terminal installed in the store.

[2065] Terminal

[2066] The device takes a palm image and sends the palm ID to the server.

[2067] server

[2068] The server checks the palm ID against a database to obtain the user's age. Once the age verification is complete, it sends the results to the device, which then displays the results and allows the user to purchase alcohol.

[2069] Example 2: Public transport ride

[2070] User

[2071] Users hold their palm over a palm authentication terminal at the ticket gate of a public transport facility.

[2072] Terminal

[2073] The device captures a palm image and sends the palm ID to the server.

[2074] server

[2075] The server checks the palm ID against a database and obtains the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[2076] In this way, the palm authentication system of the present invention generates palm image data using generative AI and trains a biometric authentication model to achieve highly accurate palm authentication. Furthermore, by registering the user's palm as an ID and providing a platform that links with various services, convenience and security can be improved.

[2077] The processing flow will be explained below.

[2078] Palm image data generation using generative AI

[2079] Step 1:

[2080] server

[2081] The server starts the generation AI and sets parameters regarding the number and variations of palm image data to be generated.

[2082] Step 2:

[2083] server

[2084] The generation AI generates palm image data with different palm shapes and patterns based on set parameters.

[2085] Step 3:

[2086] server

[2087] The generated palm image data is stored in a database and is labeled and classified.

[2088] Training the palm recognition AI model

[2089] Step 1:

[2090] server

[2091] The server retrieves palm image data from the database and performs preprocessing on the images, which includes resizing, noise reduction, and contrast adjustment.

[2092] Step 2:

[2093] server

[2094] A specific algorithm is applied to extract feature points from the preprocessed image data.

[2095] Step 3:

[2096] server

[2097] The extracted feature data is used to train an AI model, and deep learning algorithms are used to improve the model's accuracy.

[2098] Step 4:

[2099] server

[2100] The trained AI model is saved and made available for authentication processing.

[2101] Palm ID registration

[2102] Step 1:

[2103] User

[2104] The user launches the app on their smartphone and uses the camera function to take a picture of their palm.

[2105] Step 2:

[2106] User

[2107] The captured palm image is sent from the app to the server.

[2108] Step 3:

[2109] server

[2110] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID.

[2111] Step 4:

[2112] server

[2113] The generated palm ID is associated with the user profile and stored in a database.

[2114] Linking palm ID with services

[2115] Step 1:

[2116] User

[2117] Users open the app on their smartphone and select the service they want to link their palm ID to, such as a point card, payment system, or pass card.

[2118] Step 2:

[2119] server

[2120] Based on the user's selection, the server calls an API to link the palm ID with a specific service account.

[2121] Step 3:

[2122] server

[2123] Communicate with the service provider via API and update the link information between your palm ID and service account.

[2124] Authentication Process

[2125] Step 1:

[2126] User

[2127] When a user uses the service, for example when checking into a hotel, they hold their palm over a terminal at the front desk.

[2128] Step 2:

[2129] Terminal

[2130] The front terminal captures a palm image and sends it to the server.

[2131] Step 3:

[2132] server

[2133] The server analyzes the received palm image and identifies the corresponding palm ID by extracting feature points.

[2134] Step 4:

[2135] server

[2136] Based on the palm ID, the user's reservation information is retrieved from the database and the authentication results are sent to the terminal.

[2137] Step 5:

[2138] Terminal

[2139] The terminal notifies the user of successful authentication and provides appropriate services (e.g., issuing an electronic room key).

[2140] Age verification at convenience stores

[2141] Step 1:

[2142] User

[2143] When a user purchases alcohol at a convenience store, they hold their hand over a palm authentication terminal.

[2144] Step 2:

[2145] Terminal

[2146] The device captures a palm image and sends the palm ID to the server.

[2147] Step 3:

[2148] server

[2149] The server compares the palm ID with a database to confirm the user's age information.

[2150] Step 4:

[2151] server

[2152] Once age verification is complete, the results will be sent to the device.

[2153] Step 5:

[2154] Terminal

[2155] The device will display the results and allow the purchase of alcohol if necessary.

[2156] Example 1

[2157] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2158] In modern society, there is a growing demand for simple yet highly accurate biometric authentication systems. However, conventional biometric authentication systems have had problems with the time and cost required to collect and process individual image data. Furthermore, there are limitations to the accuracy of authentication, resulting in frequent misidentifications and missed recognitions. Furthermore, information management is complicated when linking with multiple services, and there is a lack of a mechanism for automatically authenticating users. The present invention aims to solve these problems.

[2159] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2160] In this invention, the server includes means for training a biometric authentication model using the generated palm image data, means for generating palm image data using the generating artificial intelligence, means for acquiring and identifying a user's palm image, means for uniquely generating a palm identifier and linking it with various service providers, means for generating diverse palm image data, means for performing preprocessing on the palm image data such as noise removal and contrast adjustment, and means for deploying the generated biometric authentication model in a production environment. This enables highly accurate and versatile palm authentication, allowing users to use various services automatically and safely without hassle.

[2161] "Generated palm image data" is digital image data of the palm generated using artificial intelligence.

[2162] "Means for training a biometric authentication model" refers to a method and device that uses acquired palm image data to adjust and train the parameters of an artificial intelligence model.

[2163] "Generative AI" refers to algorithms or software that generate new palm image data based on set parameters.

[2164] "Means for acquiring an image of a user's palm and identifying it" refers to a method and apparatus for taking a picture of a user's palm using a device such as a camera, analyzing the image, and uniquely identifying it.

[2165] A "palm identifier" is unique identification information generated based on specific feature points extracted from a captured palm image.

[2166] "Various service providers" are external service providers, such as payment systems and point card systems, that users access and authenticate using palm identifiers.

[2167] The "means for generating a variety of palm image data" refers to a method and device for generating a plurality of palm images with different shapes and patterns using artificial intelligence.

[2168] "Means for performing pre-processing such as noise removal and contrast adjustment" refers to a method and device for processing acquired palm image data to improve image quality and recognition accuracy.

[2169] The "means for deploying the generated biometric authentication model in a production environment" refers to a method and apparatus for incorporating a trained artificial intelligence model into an actual operational system and setting it up so that it can be used for authentication processing.

[2170] System Configuration

[2171] 1. Palm image data generation part

[2172] server

[2173] The server starts the generative AI and generates palm image data. Specifically, the server uses "StyleGAN" or a similar algorithm as the generative AI. The server sets parameters such as palm size, pattern complexity, and number of variations, and generates palm image data through the generative AI. This generated palm image data is stored in a database. For example, a prompt might be: "To generate palm image data, generate a palm image based on the set parameters. Increase the variations in palm shape and pattern to create diverse data."

[2174] 2. Training the palm recognition AI model

[2175] server

[2176] The server retrieves palm image data from the database and performs preprocessing. This preprocessing uses the OpenCV library to resize the image, remove noise, and adjust the contrast. Furthermore, algorithms such as SURF and SIFT are used to extract feature points. Based on the preprocessed image data and feature point data, an AI model is trained using TensorFlow or PyTorch. The trained model is saved in the SavedModel format and deployed to the production environment via an API server (e.g., Flask).

[2177] 3. Palm ID registration section

[2178] User

[2179] Users take a palm image using a dedicated smartphone app called "CustomPalmID App." Using the smartphone camera, palm images are captured.

[2180] server

[2181] The server receives the palm image sent by the user and extracts the feature points, which are then analyzed using SURF and SIFT techniques to generate a unique palm identifier in the form of a UUID, which is then associated with the corresponding user profile and stored in a database.

[2182] 4. Palm ID and service integration

[2183] User

[2184] Using the smartphone app, the user selects the service (e.g., payment system, point card system, pass card, etc.) they want to link their palm ID to. Using the "ServiceConnector App."

[2185] server

[2186] The server associates the palm ID with the service provider's account based on the user's selection. By linking the palm ID with the service provider's account using the OAuth 2.0 protocol, the server makes it possible to use the palm ID as a means of authentication for various services.

[2187] 5. Authentication Process

[2188] User

[2189] When checking in at a hotel, a user holds their palm over a palm authentication terminal, such as one installed at the front desk.

[2190] Terminal

[2191] The terminal captures a palm image and transmits the image data to a server.

[2192] server

[2193] The server analyzes the received palm image, extracts a palm identifier, and compares it with a database. Based on the comparison result, the server obtains the user's reservation information and sends it to the terminal. After authentication is complete, the terminal issues an electronic room key and provides it to the user.

[2194] Specific examples

[2195] Example 1: Age verification at a convenience store

[2196] User

[2197] When a user purchases alcohol at a convenience store, they hold their palm over a palm authentication terminal installed in the store.

[2198] Terminal

[2199] The terminal takes a palm image and transmits the palm identifier to the server.

[2200] server

[2201] The server compares the palm identifier with a database to obtain the user's age information. Once the age verification is complete, it sends the result to the terminal. The terminal then displays the authentication result and allows the user to purchase alcohol.

[2202] Example 2: Public transport ride

[2203] User

[2204] The user holds their palm over a palm authentication terminal at the ticket gate of a public transport facility.

[2205] Terminal

[2206] The terminal captures a palm image and transmits the palm identifier to the server.

[2207] server

[2208] The server checks the palm identifier against a database to obtain the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[2209] In this way, the palm authentication system of the present invention generates palm image data using generative AI and trains a biometric authentication model to achieve highly accurate palm authentication. Furthermore, by registering the user's palm as an identifier and linking it to various services, convenience and security can be improved.

[2210] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2211] System program processing flow

[2212] Step 1:

[2213] The server launches the generative AI. Specifically, it instantiates the generative AI software and prepares it to generate palm images. The input data used are generative parameters (e.g., palm shape, pattern complexity, number of variations, etc.). The output is an initialized generative AI instance.

[2214] Step 2:

[2215] The server sets the parameters for palm image generation. Specifically, it reads parameters such as palm size, pattern complexity, and number of variations from a configuration file or user input. The input is the configuration parameters, and the output is command data that is input to the generation AI based on those parameters.

[2216] Step 3:

[2217] The server generates palm image data using a generative AI. The generative AI (e.g., "StyleGAN") uses an algorithm to generate palm images according to set parameters. The input is the set parameters and an initialized generative AI instance, and the output is the generated palm image data.

[2218] Step 4:

[2219] The server saves the generated palm image data in a database. It connects to a database management system (e.g., MySQL) and stores the generated palm image data files. The input is the generated palm image data, and the output is the identification information of the data saved in the database.

[2220] Step 5:

[2221] The server retrieves palm image data from the database. It connects to the database and searches for and retrieves stored palm image data. The input is the database connection information and the search query, and the output is the retrieved palm image data.

[2222] Step 6:

[2223] The server performs preprocessing of the image data. Specifically, it uses the "OpenCV" library to adjust the image size, remove noise, and adjust the contrast. The input is palm image data, and the output is the preprocessed palm image data.

[2224] Step 7:

[2225] The server extracts feature points from the preprocessed palm image using the "SURF" or "SIFT" algorithm. The input is the preprocessed palm image data, and the output is the feature point data.

[2226] Step 8:

[2227] The server trains the AI ​​model. Using TensorFlow and PyTorch, the AI ​​model learns the feature point data and palm image data. The input is the feature point data and palm image data, and the output is the trained AI model.

[2228] Step 9:

[2229] The server saves the trained AI model and places it in the production environment. The model file is saved in the "SavedModel" format and deployed using an API server (e.g., "Flask"). The input is the trained AI model, and the output is an AI model instance that runs in the production environment.

[2230] Step 10:

[2231] The user takes a palm image using a dedicated app. Using the "CustomPalmID App," the palm image is acquired with the smartphone camera. The input is the camera image, and the output is the captured palm image data.

[2232] Step 11:

[2233] The user sends the captured palm image data to the server. Using the send function within the app, a POST request is made to the endpoint with the image data. The input is the palm image data, and the output is the image data being uploaded to the server.

[2234] Step 12:

[2235] The server analyzes the received palm image and extracts feature points using a specific algorithm (e.g., "SURF" or "SIFT"). The input is palm image data, and the output is feature point data.

[2236] Step 13:

[2237] The server generates a palm identifier and associates it with the user profile. It generates an identifier in UUID format, associates it with the corresponding user profile, and stores it in a database. The input is minutiae data and user profile information, and the output is the generated palm identifier.

[2238] Step 14:

[2239] Users use the app to link their palm ID to a service. Using the "ServiceConnector App," they select the service they want to link. The input is the palm ID and the service information to link to, and the output is the link setting information.

[2240] Step 15:

[2241] The server links the palm identifier with the service provider account. This is done via API using a protocol such as OAuth 2.0. The input is the palm identifier and the service provider account information, and the output is the linked account information.

[2242] Step 16:

[2243] A user holds their palm over a palm authentication terminal, such as when checking in at a hotel. The input is a palm image, and the output is an authentication request.

[2244] Step 17:

[2245] The terminal captures the palm image and sends it to the server. The palm image is acquired using an image capture device and sent to the server. The input is the palm image, and the output is the transmission of image data to the server.

[2246] Step 18:

[2247] The server analyzes the palm image, extracts a palm identifier, and matches it with a database. It uses an algorithm to match it with an existing palm identifier database. The input is the palm image data, and the output is the matching result.

[2248] Step 19:

[2249] The server obtains the user's reservation information based on the matching result and sends it to the terminal. The server also obtains user information from the reservation system based on the matching result. The input is the matching result and a reservation system inquiry request, and the output is the transmission of reservation information.

[2250] Step 20:

[2251] After authentication is complete, the terminal issues an electronic room key and provides it to the user. The room key information is displayed on the terminal's display device. The input is the verification result and reservation information, and the output is the electronic room key information.

[2252] The above are the specific processing steps of this palm authentication system.

[2253] (Application example 1)

[2254] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2255] Until now, there have been technical and cost challenges in the widespread adoption of highly accurate biometric authentication systems. Furthermore, there has been a lack of security authentication systems that combine user convenience and safety. In particular, more reliable authentication methods are needed for access control in places where high security is required, such as offices and data centers.

[2256] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2257] In this invention, the server includes a means for training a biometric authentication model using the generated palm image database, a means for generating palm image data using a generation AI, a means for acquiring and identifying a user's palm image, a means for uniquely generating a palm ID and linking it with various service providers, and a security means for capturing and authenticating palm images using a smart device or robot. This allows users to easily perform palm authentication with high accuracy using a smart device or robot. This satisfies high security requirements and improves the reliability of access control systems, particularly those used in offices, data centers, etc.

[2258] "Generative AI" is an artificial intelligence technology used to generate image data of a user's palm, and has the ability to create a variety of palm shapes and patterns.

[2259] The "palm image database" is a database for storing and managing palm image data generated by the generative AI, and is used to train biometric authentication models.

[2260] A "biometric authentication model" is an artificial intelligence model trained to verify a user's identity based on palm image data, and performs highly accurate authentication based on feature points.

[2261] "Palm ID" is an identification number that is uniquely generated using the feature points of a palm image and is used to link with various services.

[2262] A "smart device" is a device that has the function of acquiring an image of a user's palm and performing authentication, and includes smartphones and corresponding access control devices.

[2263] A "robot" is an autonomous or remotely controlled device used to capture palm images and perform authentication processes, and is used for security management.

[2264] "Minutes" are unique data points extracted from palm images that are used to train and authenticate biometric models.

[2265] To implement this invention, a system based on the following procedures is required.

[2266] System Configuration

[2267] 1. Palm image data generation part

[2268] server

[2269] The server generates various palm image data using generative AI, which applies technologies such as StyleGAN. The generated palm image data is stored in a dedicated database.

[2270] 2. Training the palm recognition AI model

[2271] server

[2272] The server trains the biometric authentication model using preprocessed palm image data. Preprocessing includes noise removal, size adjustment, contrast adjustment, etc., and then extracts feature points. Training is performed using TensorFlow and PyTorch.

[2273] 3. Palm ID registration section

[2274] User

[2275] The user uses the camera function of a smartphone app to take an image of their palm, which is then sent to the server via the app.

[2276] server

[2277] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is associated with the user profile and stored in a database.

[2278] 4. Palm ID and service integration

[2279] User

[2280] Through the app, users can select the services they want to link their palm ID to, including a wide range of services such as security authentication, financial services, and access control.

[2281] server

[2282] Based on the user's selection, the server configures the palm ID to link with various service provider accounts via API.

[2283] 5. Authentication Process

[2284] User

[2285] Users hold their palm over a palm authentication terminal at locations where security authentication is required, such as at the entrance to a data center or an office access control device.

[2286] Terminal

[2287] A palm image is captured using a smart device or robot and sent to a server.

[2288] server

[2289] The server analyzes the received palm image, compares the palm ID with a database, and sends an access permission or denial result to the device based on the comparison result.

[2290] Example: Access control at a data center

[2291] This system is used in situations where secure access control is required in corporate data centers.

[2292] 1. User: Scans palm at data center entrance.

[2293] 2. Device: Captures a palm image and sends it to the server.

[2294] 3. Server: Generates palm ID and checks it against the database.

[2295] 4. Authentication result: Based on the verification result, access permission is notified to the terminal and the door opens automatically.

[2296] Prompt Sentence Examples

[2297] "Generate a palm ID based on the palm image shown below and verify it against the database for authentication."

[2298] The above procedure makes it possible to implement the present invention, and realize a highly accurate and user-friendly palm authentication system, which can meet the high security requirements of offices, data centers, etc.

[2299] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2300] Step 1:

[2301] To use the palm authentication system, a user launches the smartphone app and takes a picture of their palm.

[2302] Input: User's palm image

[2303] Output: Palm image data

[2304] How it works: The user activates the camera function of the smartphone app and takes a picture of their palm. This image data is then saved in the application.

[2305] Step 2:

[2306] The smartphone app sends the captured palm image to a server.

[2307] Input: Palm image data

[2308] Output: Image data transmission request to the server

[2309] How it works: An HTTP POST request containing palm image data is sent to the server, with the image data encrypted if necessary.

[2310] Step 3:

[2311] The server receives the palm image data and performs preprocessing.

[2312] Input: Palm image data

[2313] Output: Preprocessed image data

[2314] Operation: After receiving the palm image data, the server performs preprocessing such as noise reduction, size adjustment, contrast adjustment, etc. The image is converted into a standardized format.

[2315] Step 4:

[2316] The server extracts feature points from the preprocessed palm image.

[2317] Input: Preprocessed image data

[2318] Output: feature point data

[2319] How it works: A feature point extraction algorithm is applied to the preprocessed image data. Feature points are extracted using a deep learning model such as CNN.

[2320] Step 5:

[2321] The server generates a palm ID based on the feature point data and stores it in a database.

[2322] Input: feature point data

[2323] Output: Palm ID, registration request to database

[2324] Operation: A palm ID is generated from the feature point data, and it is associated with the user profile and saved in a database. SQL queries are used for the saving process.

[2325] Step 6:

[2326] The user holds their palm over an authentication terminal at a specific security authentication point (e.g., the entrance to a data center).

[2327] Input: User's palm image

[2328] Output: Authentication start request

[2329] How it works: The authentication process begins when the user holds their palm over the authentication device's camera.

[2330] Step 7:

[2331] The device captures a palm image and sends it to the server.

[2332] Input: User's palm image

[2333] Output: Image data transmission request to the server

[2334] How it works: The device's camera captures a palm image and sends it to a server. The image data is encrypted before being transferred.

[2335] Step 8:

[2336] The server analyzes the palm image and compares it with a database.

[2337] Input: Palm image data

[2338] Output: Authentication result (allow or deny)

[2339] How it works: The server analyzes the palm image and matches it with the palm ID stored in the database. Based on the match, authentication is either granted or denied.

[2340] Step 9:

[2341] The terminal receives the authentication result and notifies the user.

[2342] Input: Authentication result

[2343] Output: Authentication notification (allowed or denied)

[2344] Operation: The terminal that receives the authentication result from the server allows access if it is permitted and automatically opens the door. If it is denied, it notifies the user.

[2345] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2346] The following describes an embodiment of the present invention. The system includes a generation AI for generating palm image data, a means for training a biometric authentication model based on the palm image, a means for acquiring and identifying a user's palm image, a means for generating a palm ID and linking it with various service providers, and an emotion engine for recognizing and analyzing the user's emotions.

[2347] System Configuration

[2348] 1. Palm image data generation part

[2349] server

[2350] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with various palm shapes and patterns and stores them in a database.

[2351] 2. Training the palm recognition AI model

[2352] server

[2353] The server retrieves palm image data from the database, pre-processes the image, extracts feature points from the pre-processed image, and trains an AI model. The trained model is used for high-precision authentication.

[2354] 3. Palm ID registration section

[2355] User

[2356] The user launches the app on their smartphone and uses the camera to take a picture of their palm, which is then sent to the server.

[2357] server

[2358] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is then associated with the user profile and stored in a database.

[2359] 4. Linking Tenohira ID with services

[2360] User

[2361] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[2362] server

[2363] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, and communicates with the service provider through the API to update the link information.

[2364] 5. Emotion recognition part

[2365] User

[2366] When a user takes a palm image using a smartphone app, the emotion engine collects data such as the user's facial expressions and voice, which allows it to analyze the user's emotional state in real time.

[2367] server

[2368] The server analyzes the acquired emotional data and associates it with the user's palm ID. The emotional data is stored in a database and will be used to provide future services.

[2369] 6. Authentication Process

[2370] User

[2371] For example, when checking into a hotel, a user holds their palm over a terminal at the front desk.

[2372] Terminal

[2373] The front terminal captures a palm image and sends it to the server.

[2374] server

[2375] The server analyzes the received palm image and compares the palm ID with a database. It retrieves the user's reservation information and sends the authentication result to the terminal. If authentication is successful, the appropriate service (e.g., issuing an electronic room key) is provided.

[2376] Specific examples

[2377] Example 1: Age verification at a convenience store

[2378] User

[2379] When a user purchases alcohol at a convenience store, they hold their hand over a palm authentication terminal.

[2380] Terminal

[2381] The device captures a palm image and sends it to the server.

[2382] server

[2383] The server checks the palm ID against a database to confirm the user's age. Once the verification is complete, it sends the results to the device, which then displays the results and allows the user to purchase alcohol.

[2384] Example 2: Public transport

[2385] User

[2386] Users hold their hand over a palm authentication terminal at the public transportation ticket gate.

[2387] Terminal

[2388] The device captures a palm image and sends it to the server.

[2389] server

[2390] The server compares the palm ID with the database and obtains the user's transportation pass information. If boarding is permitted, the terminal opens the ticket gate.

[2391] Example 3: Providing services using user emotion recognition

[2392] User

[2393] The user takes a picture of their palm with their smartphone and sends emotional information to the server via the app.

[2394] server

[2395] The server analyzes the emotion data and uses that information to provide the user with appropriate services (e.g., a customized shopping experience). If the emotion is determined to be "fun," relevant promotions or coupons can be offered.

[2396] In this way, the palm authentication system of the present invention uses generative AI to generate palm image data and train a biometric authentication model. It also uses an emotion engine to acquire and analyze emotion data, allowing it to provide personalized services to users. This system improves convenience and security, resulting in a better user experience.

[2397] The processing flow will be explained below.

[2398] Palm image data generation using generative AI

[2399] Step 1:

[2400] server

[2401] Start the generation AI and set parameters regarding the number and variations of palm image data to be generated.

[2402] Step 2:

[2403] server

[2404] Palm image data with different palm shapes and patterns is generated based on the set parameters.

[2405] Step 3:

[2406] server

[2407] The generated palm image data is stored in a database and is labeled and classified.

[2408] Training the palm recognition AI model

[2409] Step 1:

[2410] server

[2411] Palm image data is acquired from the database and preprocessed, including size adjustment, noise removal, and contrast adjustment.

[2412] Step 2:

[2413] server

[2414] A specific algorithm is applied to extract feature points from the preprocessed image data.

[2415] Step 3:

[2416] server

[2417] The extracted feature data is used to train an AI model, and a deep learning algorithm is used to improve the model's accuracy.

[2418] Step 4:

[2419] server

[2420] The trained AI model is saved and made available for authentication processing.

[2421] Palm ID registration

[2422] Step 1:

[2423] User

[2424] Launch the app on your smartphone and use the camera to take a picture of your palm.

[2425] Step 2:

[2426] User

[2427] The captured palm image is sent from the app to the server.

[2428] Step 3:

[2429] server

[2430] The received palm image is analyzed, feature points are extracted, and a unique palm ID is generated.

[2431] Step 4:

[2432] server

[2433] The generated palm ID is associated with the user profile and stored in a database.

[2434] Linking palm ID with services

[2435] Step 1:

[2436] User

[2437] Open the app on your smartphone and select the service you want to link your Palm ID to, such as a point card, payment system, or pass card.

[2438] Step 2:

[2439] server

[2440] Based on the user's selection, an API is called to link the palm ID with a specific service account.

[2441] Step 3:

[2442] server

[2443] Communicate with the service provider via API and update the link information between your palm ID and service account.

[2444] emotion recognition part

[2445] Step 1:

[2446] User

[2447] When a palm image is taken using a smartphone app, the emotion engine collects data such as the user's facial expressions and voice.

[2448] Step 2:

[2449] server

[2450] The acquired data is analyzed to determine the user's emotional state in real time.

[2451] Step 3:

[2452] server

[2453] The emotion data is associated with a user profile and stored in a database.

[2454] Step 4:

[2455] server

[2456] Settings are made to provide customized services based on the user's emotional data.

[2457] Authentication Process

[2458] Step 1:

[2459] User

[2460] For example, when checking in at a hotel, you hold your palm over the terminal at the front desk.

[2461] Step 2:

[2462] Terminal

[2463] The front terminal captures a palm image and sends it to the server.

[2464] Step 3:

[2465] server

[2466] The received palm image is analyzed and the palm ID is compared with a database.

[2467] Step 4:

[2468] server

[2469] The user's reservation information is obtained based on the palm ID, and the authentication results are sent to the terminal.

[2470] Step 5:

[2471] Terminal

[2472] Notify the user that authentication was successful and provide appropriate services (e.g., issue an electronic room key).

[2473] Age verification at convenience stores

[2474] Step 1:

[2475] User

[2476] When checking in at Costco, hold your palm over the terminal at the front desk.

[2477] Step 2:

[2478] Terminal

[2479] The front terminal captures a palm image and sends it to the server.

[2480] Step 3:

[2481] server

[2482] Feature points are extracted from the received palm image and a palm ID is generated.

[2483] Step 4:

[2484] server

[2485] The generated palm ID is matched to obtain the corresponding user's age information.

[2486] Step 5:

[2487] server

[2488] Once the confirmation is received, the authentication result is sent to the terminal.

[2489] Step 6:

[2490] Terminal

[2491] The device will display the results and allow the alcohol purchase.

[2492] Example 2

[2493] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2494] In conventional biometric authentication systems, achieving high-precision authentication and improving the user experience requires a wide range of individual technologies, and the integration of these technologies is complex. Furthermore, services based on emotion recognition are not considered, making it difficult to provide personalized services to users. This results in reduced authentication accuracy and a lower quality of user experience, making it difficult to improve convenience and safety.

[2495] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2496] In this invention, the server includes means for training a biometric authentication model using the generated palm image database, means for generating palm image data using a generative AI model, means for acquiring and identifying a user's palm image, means for extracting feature points to generate a unique palm ID and linking it with various service providers, and means for analyzing the user's emotional state using an emotion engine. This enables a unified process from palm image data generation to authentication, service linkage, and even personalized service provision based on emotion recognition.

[2497] The "generated palm image database" is a database that stores various palm images generated by a generative AI model and their associated metadata.

[2498] A "biometric model" is an artificial intelligence model trained to identify users based on palm image data.

[2499] A "generative AI model" is a model that uses a generative artificial intelligence algorithm to generate a variety of palm image data.

[2500] The "means for acquiring an image of the user's palm" refers to a means for a user to use a camera on a smartphone or terminal to take an image of the palm of their hand and acquire that image.

[2501] "Feature points" are points with specific visual characteristics, such as lines or patterns on the palm, and by extracting these, individual palms can be uniquely identified.

[2502] A "palm ID" is a unique identifier generated based on feature points extracted from an image of a user's palm.

[2503] "Means for linking with various service providers" refers to means for linking with other services (such as point cards and payment systems) using the palm ID.

[2504] An "emotion engine" is a system or algorithm that analyzes a user's emotional state from facial expressions, voice, etc., and generates the results as data.

[2505] "Means for associating with user profile and storing in a database" refers to a means for linking the generated palm ID and emotion data to the user's individual profile information and storing this in a database.

[2506] The present invention is an integrated biometric authentication system that includes palm image data generation, authentication, service linkage, and even emotion recognition-based service provision.

[2507] Palm image data generation part

[2508] server

[2509] The server starts a generative AI model (e.g., DALL·E) to generate various palm image data. The number and variation of images to be generated are determined by setting a prompt for the generative AI model.

[2510] example:

[2511] Prompt: "Generate 100 palm images with different palm patterns."

[2512] The generated images are stored in a database and each is given a unique ID.

[2513] Palm recognition AI model training part

[2514] server

[2515] The server retrieves palm image data from the database and performs preprocessing on the image. This preprocessing includes image resizing, noise removal, and grayscale conversion. Feature points are extracted from the preprocessed image and used to train an AI model (e.g., a deep learning model using TensorFlow or PyTorch). The trained model is then used for biometric authentication.

[2516] Palm ID registration section

[2517] User

[2518] The user launches the app on their smartphone and uses the camera to take a picture of their palm, which is then sent to the server via the app.

[2519] server

[2520] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is linked to the user profile and stored in a database.

[2521] Palm ID and service integration

[2522] User

[2523] Through the app, users select the service they want to link their palm ID to (e.g., point card, payment system, pass card, etc.).

[2524] server

[2525] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, communicates with the service provider through the API, and updates the linking information in the database.

[2526] emotion recognition part

[2527] User

[2528] When a user takes a palm image with their smartphone, the emotion engine collects the user's facial expressions and voice data.

[2529] server

[2530] The server analyzes the acquired emotional data and associates the emotional information with the user's palm ID. The emotional data is stored in a database and used when providing services.

[2531] Authentication Process

[2532] User

[2533] Users hold their hand over a palm authentication terminal when checking in at a hotel, verifying their age at a convenience store, or using public transportation.

[2534] Terminal

[2535] The device captures a palm image and sends it to the server.

[2536] server

[2537] The server analyzes the received palm image and matches it with the palm ID in its database. If the match is successful, it provides the corresponding service (e.g., issuing an electronic room key or verifying age).

[2538] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2539] Step 1: Generate palm image data

[2540] server

[2541] Input: prompt for setting the generative AI model

[2542] Prompt: "Generate 100 palm images with different palm patterns."

[2543] Specific operation: The server launches a generative AI model and generates palm image data based on the prompt. The generative AI model (e.g., DALL·E) generates images with various palm shapes and patterns and stores them in a database. Each generated image is assigned a unique ID.

[2544] Output: Palm image data (with saved unique ID)

[2545] Step 2: Preprocessing palm image data

[2546] server

[2547] Input: Palm image data

[2548] Specific operation: The server retrieves palm image data from the database and performs preprocessing on the image. This preprocessing includes noise removal, resizing, grayscale conversion, etc. These processes are performed using open source libraries (e.g., OpenCV).

[2549] Output: Preprocessed image data

[2550] Step 3: Extracting feature points

[2551] server

[2552] Input: Preprocessed image data

[2553] Specific operation: The server extracts feature points from the preprocessed image. It uses a feature point extraction algorithm (e.g., SIFT or ORB) to identify the lines and patterns of the palm. The extracted feature points are stored in a database.

[2554] Output: feature point data

[2555] Step 4: Training the AI ​​model

[2556] server

[2557] Input: feature point data

[2558] How it works: The server uses the feature point data to train an AI model. The model is trained using a deep learning framework (e.g., TensorFlow or PyTorch). During training, the dataset is divided into training data and validation data.

[2559] Output: A trained AI model

[2560] Step 5: Take a palm image

[2561] User

[2562] Input: None (user operation)

[2563] Specific operation: The user launches the smartphone app and uses the camera function to take an image of their palm. The app's camera function is then invoked to capture the palm image.

[2564] Output: Palm image

[2565] Step 6: Send a palm image

[2566] User

[2567] Input: Palm image

[2568] Specific operation: The captured palm image is sent to the server via the app, and the app calls an API endpoint to upload the palm image to the server.

[2569] Output: Palm image sent to the server

[2570] Step 7: Generate Palm ID

[2571] server

[2572] Input: Received palm image

[2573] Specific operation: The server analyzes the received palm image, extracts feature points, and generates a unique palm ID based on the extracted feature points. The palm ID is then associated with the user profile and stored in a database.

[2574] Output: Generated palm ID

[2575] Step 8: Select and integrate services

[2576] User

[2577] Input: None (user operation)

[2578] Specific operation: Through the app, the user selects the service (e.g., point card, payment system, pass card, etc.) they want to link their palm ID to. They select the service using the app's UI.

[2579] Output: Selected service information

[2580] server

[2581] Input: Selected service information, Palm ID

[2582] Specific operation: Based on the user's selection, the server calls the API to link the palm ID with a specific service account, and updates the linking information with the service provider through the API.

[2583] Output: Linked service information

[2584] Step 9: Acquire and analyze emotion data

[2585] User

[2586] Input: None (user operation)

[2587] How it works: When a user takes a palm image with the app, the emotion engine collects the user's facial expression and voice data. The data is captured using the smartphone's camera and microphone.

[2588] Output: facial expression data, voice data

[2589] server

[2590] Input: facial expression data, voice data

[2591] Specific operation: The server analyzes the acquired emotion data and associates the emotion information with the user's palm ID. It extracts emotion information from the collected data and stores it in a database.

[2592] Output: Emotional information

[2593] Step 10: Authentication Process

[2594] User

[2595] Input: None (user operation)

[2596] Specific operation: When checking in at a hotel, verifying age at a convenience store, or using public transportation, a user holds their hand over a palm authentication terminal, which then uses the terminal's camera to capture an image of their palm.

[2597] Output: Captured palm image

[2598] Terminal

[2599] Input: Palm image

[2600] Specific operation: The device sends the captured image to the server, and calls an API endpoint to upload the palm image to the server.

[2601] Output: Palm image sent to the server

[2602] server

[2603] Input: Palm image sent to the server

[2604] Specific operation: The server analyzes the received palm image and compares it with the palm ID in the database. If authentication is successful, it provides the corresponding service (e.g., issuing an electronic room key or verifying age).

[2605] Output: Authentication results and services provided

[2606] (Application example 2)

[2607] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2608] While conventional palm authentication technologies require improved authentication accuracy and security, they have the problem of being unable to consider the user's emotional state. Furthermore, due to a lack of efficient means for real-time authentication and emotion analysis, measures against unauthorized access and security risks are insufficient. Therefore, a new palm authentication system is needed that can simultaneously strengthen security and analyze the user's emotional state.

[2609] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for training a biometric authentication model using the generated palm image database, means for generating palm image data using a generation AI, means for acquiring and identifying a user's palm image, means for uniquely generating a palm ID and linking it with various service providers, means for acquiring and analyzing the user's emotion data using an emotion analysis engine, and means for capturing palm images in real time using a camera built into the tool and performing authentication and emotion analysis. This improves authentication accuracy and enables palm authentication and emotion analysis in real time.

[2610] text

[2611] "Generative AI" is an artificial intelligence technology for generating image data of a user's palm.

[2612] The "palm image database" is a database that collects image data related to the shape and pattern of the palm.

[2613] A "biometric authentication model" is an artificial intelligence model for identifying individuals using palm images.

[2614] A "palm ID" is an identification code that is uniquely generated based on each user's palm image.

[2615] An "emotion analysis engine" is an engine that acquires and analyzes emotional data from the user's facial expressions, voice, etc.

[2616] "Cameras built into tools" are cameras built into smart devices or wearable devices.

[2617] "Real-time capture" is a method of instantly acquiring images or data at the current time.

[2618] "Authentication" is the process of verifying that a user is a legitimate user of a system.

[2619] A "service provider" is a company or organization that provides various services.

[2620] A "database" is a system for efficiently storing and managing structured data.

[2621] "Feature points" are important points or patterns in a palm image, and are data used for personal identification.

[2622] "User Profile" means a collection of information and data relating to each User.

[2623] A specific embodiment for carrying out the present invention will be described below: This system generates image data of a user's palm and is capable of performing palm authentication and emotion analysis in real time.

[2624] System Configuration

[2625] 1. Palm image data generation part

[2626] server

[2627] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with various palm shapes and patterns and stores them in a palm image database.

[2628] 2. Training the palm recognition AI model

[2629] server

[2630] The server retrieves palm image data from the palm image database and pre-processes the images. It extracts feature points from the pre-processed images and trains a biometric authentication model. The trained model is used for high-precision authentication.

[2631] 3. Palm ID registration section

[2632] User

[2633] The user uses the camera function of a smart device to take an image of their palm, which is then sent to the server via the application.

[2634] server

[2635] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID, which is then associated with the user profile and stored in a database.

[2636] 4. Linking Tenohira ID with services

[2637] User

[2638] Through the application, users select the services they want to link their palm ID to (e.g., point cards, payment systems, access control systems, etc.).

[2639] server

[2640] Based on the user's selection, the server calls an API to link the palm ID to a specific service account, and communicates with the service provider through the API to update the link information.

[2641] 5. Emotion recognition part

[2642] User

[2643] When a user uses a smart device to capture a palm image, the emotion analysis engine captures data such as facial expressions and voice, allowing for real-time analysis of the user's emotional state.

[2644] server

[2645] The server analyzes the emotion data and associates it with the user's palm ID. The emotion data is stored in a database and will be used to provide future services.

[2646] 6. Authentication Process

[2647] User

[2648] When authentication is required (e.g., entering or leaving a building, logging in to a computer), the user holds their palm over the camera on their smart device.

[2649] Camera-equipped devices

[2650] The camera-equipped device captures palm images and sends them to a server.

[2651] server

[2652] The server analyzes the palm image and compares the palm ID with the database. At the same time, it performs emotion analysis and determines the appropriate security status. If authentication is successful, the specified service (e.g., access permission, login permission) is provided.

[2653] Hardware and software used

[2654] Hardware: Smart glasses (Google Glass, Vuzix Blade, etc.), smartphones, servers, and devices with built-in cameras

[2655] Software: Python, TensorFlow, OpenCV, Sentiment Analysis Engine

[2656] Specific examples

[2657] If the user uses smart glasses:

[2658] The camera built into the smart glasses captures palm images in real time, and the captured images are sent to a server via the smart glasses application for palm ID authentication and emotion analysis.

[2659] Example prompt sentence:

[2660] "I would like to build a system that uses palm images to authenticate users and simultaneously recognize emotions. Specifically, please explain using 2023 technology how smart glasses can be used to analyze palm images and prevent unauthorized access."

[2661] By implementing the invention in accordance with this aspect, it is possible to improve authentication accuracy and perform palm authentication and emotion analysis in real time, thereby reducing security risks and improving the user experience.

[2662] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2663] text

[2664] Step 1:

[2665] The server starts the generation AI and sets the number and variations of palm image data. The generation AI generates image data with a variety of palm shapes and patterns and stores it in a database. The input to this process is the parameters (number, variations) given to the generation AI, and the output is the generated palm image data.

[2666] Step 2:

[2667] The server retrieves palm image data from the palm image database and performs preprocessing on the images, which includes image resizing and noise removal. The input is palm image data, and the output is a preprocessed palm image.

[2668] Step 3:

[2669] The server extracts feature points from the preprocessed palm image and uses them to train a biometric model. The input is the preprocessed palm image, and the output is the trained biometric model.

[2670] Step 4:

[2671] The user takes a palm image using the camera on their smart device. The captured palm image is sent to the server by the application. The input is the palm image taken by the user, and the output is the image data sent to the server.

[2672] Step 5:

[2673] The server analyzes the received palm image, extracts feature points, and generates a unique palm ID. The input is the palm image sent by the user, and the output is the generated palm ID.

[2674] Step 6:

[2675] The server associates the generated palm ID with the user profile and stores it in a database. The input is the generated palm ID and the corresponding user profile, and the output is the updated database entry.

[2676] Step 7:

[2677] The user selects the service with which they want to link their palm ID through the application. The input is the user's selection and palm ID, and the output is the selected service information.

[2678] Step 8:

[2679] Based on the user's selection, the server calls an API to link the palm ID with a specific service account. The input is the user's selected service and palm ID, and the output is an update of the linkage information.

[2680] Step 9:

[2681] When a user holds their palm over the camera of their smart device in a situation requiring authentication, the camera captures an image of the palm and sends it to the server. The input is the palm held over the camera, and the output is the captured image of the palm sent to the server.

[2682] Step 10:

[2683] The server analyzes the captured palm image and compares the palm ID with a database. It also performs emotion analysis and determines the appropriate security status. The input is the captured palm image, and the output is the authentication result and emotion analysis result.

[2684] Step 11:

[2685] If authentication is successful, the server provides the specified service (e.g., access permission, login permission). The inputs are the authentication result and instructions to the service provider, and the output is the provision of the service.

[2686] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2687] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2688] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2689] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2690] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2691] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2692] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2693] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2694] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2695] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2696] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2697] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2698] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2699] 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.

[2700] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2701] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2702] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2703] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2704] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2705] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disc...

Claims

1. means for training a biometric model using the generated palm image database; A means for generating palm image data using a generation AI; means for acquiring and identifying a palm image of a user; A method for generating a unique palm ID and linking it with various service providers, A system including:

2. A means for a user to take an image of their palm and transmit the image to a server; A means for extracting feature points from a palm image and generating a palm ID; a means for associating the generated palm ID with a user profile and storing the same in a database; The system of claim 1 , comprising:

3. A means for preprocessing palm image data and extracting feature points; a means for training an AI model using the extracted feature point data; A means of performing highly accurate palm authentication using a trained AI model, The system of claim 1 , comprising:

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