system

Abstract

We provide the system. [Solution] A device for acquiring user biometric authentication data, An information processing device for analyzing the aforementioned biometric authentication data and performing user authentication, A management mechanism for controlling user access based on the results of the aforementioned authentication, A means of managing access to a specific area via a smart device using biometric authentication, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, services using AI agents have become widespread, and users obtain or manage various information through AI agents. However, there is a risk that others may impersonate the user and misuse these AI agents to illegally obtain the user's personal information. Preventing leakage of personal information and unauthorized access due to such impersonation and making the use of AI agents safer is a very important issue.

Means for Solving the Problems

[0005] The present invention provides a system that uses a device to acquire the user's biometric authentication data to collect individual biometric information such as the user's face and voice, and uses a server to analyze this data to verify the user's identity. By controlling access to the AI ​​agent based on the authentication result, unauthorized access is prevented. Furthermore, if authentication fails, additional authentication means are introduced to ensure a higher level of security. This makes it possible to use the AI ​​agent with peace of mind.

[0006] "Biometric data" refers to data that records an individual's physical characteristics as digital information and is used to authenticate its uniqueness.

[0007] A "device" refers to a physical apparatus or component designed to perform a specific function, and in this context, it refers to a device for acquiring biometric data.

[0008] A "server" is a computer system that processes requests from multiple clients in order to provide specific functions or services over a network.

[0009] "Access control" refers to the process of allowing or denying access to or use of a particular resource or service.

[0010] "Authentication" is the process of proving that a specific person is who they claim to be, and it is based on biometric data. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention relates to an embodiment of a system for securely managing interactions with an AI agent using biometric authentication data. This system mainly consists of three elements: a terminal, a server, and a user.

[0033] terminal

[0034] The device has devices for acquiring biometric data, such as a camera and microphone. When a user wishes to interact with the AI ​​agent, the device uses the camera to capture an image of the user's face or the microphone to record the user's voice. This biometric data is sent to a server for authentication.

[0035] server

[0036] The server plays a central role in analyzing biometric authentication data sent from the terminal. Based on the received data, it verifies that it matches the registered user information and determines whether authentication was successful. If authentication is successful, the server instructs the terminal to access the AI ​​agent. Conversely, if authentication fails, the server instructs the terminal to use additional authentication methods (e.g., entering a text password).

[0037] User

[0038] Users access the AI ​​agent through their device. Biometric authentication using the camera and microphone is used during access to prevent unauthorized access. If a user fails the initial authentication, they follow the server's instructions and perform additional identity verification steps.

[0039] As a concrete example, consider a scenario where a user wants to interact with an AI agent about their daily tasks using a device. In this case, the device captures the user's face with its camera and sends it to a server. The server then compares this image data with previously registered facial data to determine whether authentication is successful. If authentication is successful, the user can perform their tasks through the AI ​​agent. This system makes it possible to use the AI ​​agent safely and efficiently.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user opens the application on their device to activate the AI ​​agent. The device displays the user interface and detects the access request.

[0043] Step 2:

[0044] The device prepares to acquire the user's biometric data using its built-in camera and microphone. This involves capturing the user's face or recording their voice.

[0045] Step 3:

[0046] The device sends the acquired biometric authentication data (facial image or voice data) to the server.

[0047] Step 4:

[0048] The server activates an algorithm to analyze the received biometric authentication data. It then verifies that the data matches the registered user database.

[0049] Step 5:

[0050] The server determines the biometric authentication result and sends the result to the terminal.

[0051] Step 6:

[0052] If authentication is successful, the device will be granted access to the AI ​​agent, allowing the user to begin the conversation.

[0053] Step 7:

[0054] If authentication fails, the server prompts the terminal for additional authentication methods. The terminal then displays a password entry screen to the user.

[0055] Step 8:

[0056] The user enters the requested password, and the device sends that data back to the server.

[0057] Step 9:

[0058] The server verifies that the password matches and sends the final authentication result to the device. If authentication is successful, the process proceeds to step 6, and the user begins interacting with the AI ​​agent.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] This solution addresses the challenge of improving authentication processes in information systems to prevent unauthorized access while enabling users to safely and efficiently utilize interactive systems.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes a processing unit for analyzing biometric information and performing authentication, means for issuing an instruction to grant access to an interactive system upon successful authentication, and means for instructing the acquisition of additional authentication information upon authentication failure. This enables efficient access management while ensuring security.

[0064] "Biometric information" is a general term for characteristic data such as visual and auditory information that is acquired in order to identify an individual.

[0065] "Electronic devices" refer to equipment and devices used to acquire and process information.

[0066] A "processing device" is a general term for a device that analyzes received data and makes decisions based on the results.

[0067] "Authentication" refers to the process of using received biometric information to verify whether that information belongs to the registered user.

[0068] An "interactive system" is a system that provides responses based on information entered by the user.

[0069] "Instructions" refer to commands or messages sent to prompt specific actions or the input of additional information.

[0070] A "generative algorithm" refers to a computational method used to create new information based on specific data.

[0071] This system uses biometric information to ensure security when users access an interactive system through a terminal. The terminal is equipped with devices such as a camera and microphone, which are used to acquire visual and auditory biometric information. The software operated on the terminal utilizes existing libraries such as OpenCV and TENSORFLOW® to perform high-precision data collection and processing.

[0072] When a user attempts to access the system, the device uses its camera to capture an image of the user's face or its microphone to record their voice. This data is securely transmitted to the server using encryption technologies such as SSL / TLS. On the server, a dedicated algorithm is used to analyze whether the transmitted biometric information matches the registered information. If authentication is successful, the server sends an instruction to the device granting access to the interactive system. If authentication fails, the server requests additional authentication, such as entering a password.

[0073] As a concrete example, when a user uses an AI assistant to manage their schedule, the device performs facial recognition, and the results are processed on a server. This system allows users to efficiently utilize information systems while securely protecting their data. An example of a prompt to be entered when using a generative AI model is, "Please describe in detail the operation of the access management system based on user authentication."

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

[0075] Step 1:

[0076] The device acquires biometric information when a user requests access to an interactive system. Specifically, it captures a facial image with a camera and records audio with a microphone. This input data is temporarily stored on the device. Importantly, settings are adjusted to ensure sufficient resolution and sound quality.

[0077] Step 2:

[0078] The device sends the acquired biometric information to the server using a highly reliable encryption protocol (e.g., SSL / TLS). The input is temporary data stored on the device, and the output is an encrypted data packet. This encryption prevents third parties from accessing the data during transmission.

[0079] Step 3:

[0080] The server uses a processing unit to analyze biometric information received from the terminal. The received biometric information is analyzed using a dedicated algorithm (e.g., face recognition using TensorFlow). The input is decrypted biometric information, and the output is the authentication result (success or failure).

[0081] Step 4:

[0082] The server sends the authentication result obtained through analysis to the terminal. If authentication is successful, the server sends an instruction to the terminal granting access to the interactive system. In this process, the authentication result is used as input, and the instruction granting access is generated as the corresponding output.

[0083] Step 5:

[0084] The terminal receives instructions from the server, displays a successful authentication message to the user, and prepares to access the interactive system. If authentication fails, the user is prompted for additional authentication (such as entering a password). This prevents unauthorized access while ensuring smooth access for legitimate users.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In modern society, where much information is digitized and strict access control is required, the effective operation of security systems based on biometric authentication is a challenge. In particular, access management using smart devices requires balancing both usability and security. Conventional technologies may not be able to provide sufficient security and usability due to limitations of the devices themselves or the authentication technology.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes a device for acquiring user biometric authentication data, an information processing device for analyzing the biometric authentication data and authenticating the user, a management mechanism for controlling user access based on the authentication result, and means for managing access to a specific area via a smart device using biometric authentication. This makes it possible for users to easily manage their access while improving security.

[0090] "Biometric data" refers to data obtained as digital information about an individual's physical characteristics and used in the authentication process.

[0091] An "information processing device" is a device that analyzes received data and performs various processes based on the results.

[0092] A "management mechanism" is a mechanism that controls the operation of the entire system, and in particular, manages user access rights.

[0093] A "smart device" is a portable electronic device equipped with internet communication capabilities and various sensors, possessing a range of functions.

[0094] "Access control" is a function that allows users to review, grant, or restrict their permissions to specific information or areas.

[0095] To implement this invention, it is necessary to construct a system with the following elements. First, the user uses a smart device. The smart device is equipped with a camera and a microphone for acquiring biometric authentication data. Specifically, smart glasses such as Google® Glass® are envisioned. When the user attempts to access a specific service or area, the smart glasses acquire facial image data with the camera and record voice data with the microphone.

[0096] Next, the biometric authentication data acquired by the terminal is sent to an information processing device. This information processing device could be a server on AWS® or Google Cloud Platform. The server uses OpenCV to analyze facial image data and CMU Sphinx or similar tools for voice authentication. This analysis verifies whether the user is a registered individual.

[0097] If authentication is successful, the management system will use a smart device to grant the user access to a specific area. For example, this could include access to a room where confidential company information is stored.

[0098] This system allows users to securely access information and areas based on visual and auditory characteristics. It provides users with quick and intuitive operation while ensuring security.

[0099] As a concrete example, when entering a company meeting room, the user undergoes biometric authentication through smart glasses. If authentication is successful, the door automatically opens, and the user can enter the meeting room.

[0100] An example of a prompt from a generated AI model is: "Based on the following text, please explain how to use an AI agent dialogue management system with biometric authentication in security services. Please include specific examples."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The device acquires image and audio data of the user's face through a smart device. The camera captures the user's face, and the microphone records the user's voice. This biometric data is acquired from sensors and stored as initial data.

[0104] Step 2:

[0105] The terminal transmits the acquired biometric authentication data to the information processing device. During this process, the biometric data is encrypted and securely sent to the server. Input data is protected by communication protocols for security purposes.

[0106] Step 3:

[0107] The server analyzes the received biometric authentication data. Feature points are extracted from facial image data using OpenCV, and speech recognition is performed on audio data using CMU Sphinx. As part of the data processing, the server obtains the feature vectors necessary for authentication and compares them with registered data to obtain the authentication result as output.

[0108] Step 4:

[0109] The server determines whether user authentication is successful based on the analysis results. If successful, it sends an access permission to the terminal as output via the management mechanism, notifying the user's smart device. If it fails, it sends an instruction to the terminal requesting additional authentication as output.

[0110] Step 5:

[0111] Users can access specific services and areas based on authentication results from the server. This ensures that only authorized users can access information and areas, allowing for safe and efficient use.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention provides a system that combines biometric authentication and emotion recognition. This system enables users to receive safer and more personalized services using an AI agent.

[0114] terminal

[0115] The device is equipped with the ability to acquire user biometric data using its camera and microphone. When a user uses the AI ​​agent, the device collects facial image data using its camera or acquires voice data using its microphone. This data is used in the initial process of user authentication, as well as for subsequent emotion recognition.

[0116] server

[0117] The server receives biometric authentication data sent from the terminal and first authenticates the user. If this authentication is successful, the emotion engine then operates and estimates the user's emotions from the user's facial image data or voice data. The emotion engine uses a specific algorithm to analyze the data and identify the user's emotional state. This information is used by the AI ​​agent to provide more appropriate and personalized responses.

[0118] User

[0119] When users access services through an AI agent, they undergo biometric authentication and emotion recognition processes via their device. For example, when a user makes a normal inquiry, the emotion engine determines whether the user is stressed or calm, and the AI ​​agent responds accordingly, improving the user experience.

[0120] As a concrete example, consider a scenario where a user receives customer support using an AI agent. In this case, the device captures the user's face and sends it to the server. After successful authentication, the server can analyze the user's facial expressions using an emotion engine to detect signs of stress. Based on the detected emotions, the AI ​​agent selects a calmer and more reassuring response. This is expected to improve the overall quality of service, going beyond mere authentication.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The user opens an application on their device to activate the AI ​​agent. The device displays a user interface and prompts the user to prepare for biometric authentication.

[0124] Step 2:

[0125] The device uses its camera to capture an image of the user's face or its microphone to record audio. The acquired biometric data is then sent to a server.

[0126] Step 3:

[0127] The server executes an algorithm to analyze the biometric authentication data it receives. It compares this data with a registered database to verify user authentication.

[0128] Step 4:

[0129] The server determines the authentication result and sends the result to the terminal.

[0130] Step 5:

[0131] If authentication is successful, the server uses an emotion engine to analyze the user's emotional state. This is done based on facial images and voice data. The server determines the emotional state and returns that information to the device.

[0132] Step 6:

[0133] The device instructs the AI ​​agent to optimize user responses based on emotional information received from the server. The service the user experiences is then adjusted to match the recognized emotions.

[0134] Step 7:

[0135] Users access the service through an AI agent, which provides emotionally responsive responses and suggestions as needed.

[0136] Step 8:

[0137] If authentication fails, the server will prompt the device for additional verification methods. The user can then attempt authentication again using methods such as entering a password.

[0138] This process allows users to safely interact with AI agents and receive appropriate responses to their individual emotional states.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] Traditional authentication systems only verify the user's identity and do not consider the user's emotional state when providing services. This results in a uniform and unpersonalized user experience, which is a significant challenge. This raises concerns that users may not receive the support they need, potentially leading to a decline in service quality.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes a device for acquiring the user's biometric information, processing means for analyzing the biometric information and authenticating the user, and analysis means for estimating the emotional state from the biometric information. This makes it possible to personalize responses to the user's emotions and provide a more personalized user experience.

[0144] A "user" is an individual or entity that utilizes a system or service.

[0145] "Biometric information" refers to data that represents unique characteristics of the human body, such as facial images and voice recordings used to identify an individual.

[0146] A "device" refers to equipment used to acquire biometric information from a user, and includes devices such as cameras and microphones.

[0147] "Processing means" refers to methods and devices for analyzing biometric information and authenticating users.

[0148] "Analysis means" refers to algorithms and processes for estimating a user's emotional state using biometric information.

[0149] A "dialogue tool" is an element that generates personalized responses based on the user's emotional state and interacts with the user.

[0150] "Output means" refers to an interface for presenting the system's generated response to the user, and includes displays and audio output devices.

[0151] This invention relates to a system that uses biometric information to authenticate users, recognize their emotions, and provide personalized conversations.

[0152] terminal

[0153] The device is equipped with a mechanism to acquire the user's biometric information. Specifically, it uses input devices such as a camera and microphone to acquire the user's facial image and voice. This allows data about the individual to be collected simply by the user standing in front of the device.

[0154] server

[0155] The server receives biometric information transmitted from the terminal and first performs authentication. The authentication process uses algorithms as processing means to compare against a registered database. Deep learning technology and facial recognition technology are applied in this process to enable highly accurate authentication. Furthermore, after successful authentication, the server performs emotion analysis. Emotion analysis is performed on acquired facial images and voice data, and the user's emotional state is estimated using a deep learning model. For example, a generative AI model is used to evaluate changes in facial expressions and voice intonation.

[0156] This information is used as a dialogue mechanism to generate personalized responses based on prompts created using a generative AI model. The server generates a prompt such as, "Provide information in a calm tone so that the user can relax," and sends it to the AI ​​agent. Through this process, the system can create personalized responses based on emotion recognition and provide high-quality service tailored to the user's needs.

[0157] User

[0158] Users can enjoy personalized services through the device. When a user approaches the device, biometric information is quickly acquired and analyzed, and an AI agent provides optimal information and responses based on the results. This allows users to feel more secure and have a more satisfying experience.

[0159] This system offers a novel service model that combines biometric authentication with emotion recognition, thereby improving the user experience.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The device acquires the user's biometric information through its camera and microphone. Input includes facial image data and voice data. In this step, the device temporarily stores the collected data in its internal memory.

[0163] Step 2:

[0164] The device transmits the acquired biometric information to the server. The input consists of facial image data and voice data stored on the device, and the output is this data, which is encrypted and sent to the server over the network. During this process, protocols such as SSL are used to ensure the security of the data.

[0165] Step 3:

[0166] The server uses the received biometric information to authenticate the user. The input consists of facial image data and voice data, and the output is the authentication result. In this step, data analysis using deep learning technology is performed to compare the received data with existing user templates stored in the database.

[0167] Step 4:

[0168] The server analyzes the emotions of a user based on their biometric information after successful authentication. The input consists of facial image data and audio data, while the output is information about the user's emotional state. This step utilizes a generative AI model to analyze subtle changes in facial expressions and vocal intonation. As a result of the analysis, an emotion label, such as "the user is nervous," is generated.

[0169] Step 5:

[0170] The server generates a prompt message based on the emotional state and sends it to the AI ​​agent. The input is emotional state information, and the output is the generated prompt message. In this step, the server performs the action of creating a specific prompt message (e.g., "Recommend actions to help the user relax").

[0171] Step 6:

[0172] The AI ​​agent generates a personalized response based on the received prompt and sends it to the terminal. The input is the prompt, and the output is an optimized response for the user. Here, natural language processing techniques are used to provide information in a format that is easy for humans to understand.

[0173] Step 7:

[0174] The terminal presents the AI ​​agent's response received from the server to the user. The input is the AI ​​agent's response data, and the output is the information provided to the user through display or audio output. In this step, the terminal performs specific actions such as displaying a message on the screen or playing audio guidance.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] In recent years, there has been a growing need to simultaneously authenticate users and understand their emotional state, but existing systems struggle to do this efficiently. Furthermore, there are limitations to providing personalized experiences tailored to each user's emotional state. This results in problems such as unoptimized user experiences, including customer support.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes a device for acquiring a user's biometric authentication information, an information processing device for analyzing the biometric authentication information and authenticating the user, an emotion analysis device for determining the user's emotional state based on the authentication result, and a corresponding control means for personalizing the user's experience based on the emotional state. This makes it possible to quickly and accurately authenticate the user and understand their emotional state, and to provide a personalized experience.

[0180] "User" refers to a person who uses the system or their individual attributes.

[0181] "Biometric information" refers to data based on physical characteristics that enable the identification of a user, and includes facial images and acoustic data.

[0182] "Device" refers to hardware used to obtain biometric authentication information from a user.

[0183] An "information processing device" refers to an electronic device or software used to analyze acquired biometric authentication information and authenticate the user.

[0184] An "emotion analysis device" refers to a device or program that determines a user's emotional state based on the user's authentication results.

[0185] "Response control means" refers to methods and devices for personalizing the user experience provided by the system according to the user's emotional state.

[0186] The system for carrying out this invention comprises a device for acquiring and analyzing a user's biometric authentication information, and corresponding control means. A specific form thereof is shown below.

[0187] The device includes hardware for acquiring biometric authentication information such as image data and audio data of the user's face. Examples of hardware used include smartphones and smart glasses, which are equipped with high-performance cameras and microphones.

[0188] The server receives biometric authentication information transmitted from the terminal and performs user authentication using an information processing device. This information processing device incorporates biometric analysis software libraries such as OpenCV and TensorFlow.

[0189] After authentication is complete, the emotion analysis device operates and determines the user's emotional state based on a specific algorithm. This process utilizes cloud services specialized in emotion analysis, such as Microsoft® Azure® Emotion API and Google Cloud Vision.

[0190] The acquired user emotional state is used by response control mechanisms to provide users with an individualized experience. For example, when a user contacts customer support, an appropriate response is selected according to their emotional state. Specifically, a user who is feeling angry will be responded to in a calmer tone.

[0191] An example of a prompt message is as follows:

[0192] "You are a customer support agent whose role is to reassure users who are feeling angry or anxious. You need to advise them on how users are expressing their complaints and what specific actions are necessary."

[0193] This can improve the quality of personalized experiences.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The device acquires the user's biometric authentication information. Inputs include facial images captured by the camera and acoustic data collected by the microphone. This data is collected in real time and prepared for transmission to the server.

[0197] Step 2:

[0198] The server analyzes biometric authentication information received from the terminal. The input consists of a facial image and audio data transmitted from the terminal. An information processing device is used to analyze this data and authenticate the user. Once the analysis is complete, the authentication result is output and used for the next process.

[0199] Step 3:

[0200] The server operates the emotion analysis device based on the authentication results. The input consists of the user's facial image and audio data. An emotion analysis algorithm is applied to estimate the user's emotional state from this data. After analysis, the user's emotional state is output.

[0201] Step 4:

[0202] The server transmits the user's emotional state to the response control system. The input is the user's emotional state obtained from the server. Based on this state, prompts are provided to the generative AI model, which generates responses that personalize the user experience. The output is the optimized user response.

[0203] Step 5:

[0204] Users receive a personalized user experience through their device. Input includes user interactions sent from the server. This allows users to comfortably access customer support and other services.

[0205] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0206] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0207] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0211] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0212] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0213] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0214] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0215] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0216] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0217] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0218] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0219] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0220] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0221] This invention relates to an embodiment of a system for securely managing interactions with an AI agent using biometric authentication data. This system mainly consists of three elements: a terminal, a server, and a user.

[0222] terminal

[0223] The device has devices for acquiring biometric data, such as a camera and microphone. When a user wishes to interact with the AI ​​agent, the device uses the camera to capture an image of the user's face or the microphone to record the user's voice. This biometric data is sent to a server for authentication.

[0224] server

[0225] The server plays a central role in analyzing biometric authentication data sent from the terminal. Based on the received data, it verifies that it matches the registered user information and determines whether authentication was successful. If authentication is successful, the server instructs the terminal to access the AI ​​agent. Conversely, if authentication fails, the server instructs the terminal to use additional authentication methods (e.g., entering a text password).

[0226] User

[0227] Users access the AI ​​agent through their device. Biometric authentication using the camera and microphone is used during access to prevent unauthorized access. If a user fails the initial authentication, they follow the server's instructions and perform additional identity verification steps.

[0228] As a concrete example, consider a scenario where a user wants to interact with an AI agent about their daily tasks using a device. In this case, the device captures the user's face with its camera and sends it to a server. The server then compares this image data with previously registered facial data to determine whether authentication is successful. If authentication is successful, the user can perform their tasks through the AI ​​agent. This system makes it possible to use the AI ​​agent safely and efficiently.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The user opens the application on their device to activate the AI ​​agent. The device displays the user interface and detects the access request.

[0232] Step 2:

[0233] The device prepares to acquire the user's biometric data using its built-in camera and microphone. This involves capturing the user's face or recording their voice.

[0234] Step 3:

[0235] The device sends the acquired biometric authentication data (facial image or voice data) to the server.

[0236] Step 4:

[0237] The server activates an algorithm to analyze the received biometric authentication data. It then verifies that the data matches the registered user database.

[0238] Step 5:

[0239] The server determines the biometric authentication result and sends the result to the terminal.

[0240] Step 6:

[0241] If authentication is successful, the device will be granted access to the AI ​​agent, allowing the user to begin the conversation.

[0242] Step 7:

[0243] If authentication fails, the server prompts the terminal for additional authentication methods. The terminal then displays a password entry screen to the user.

[0244] Step 8:

[0245] The user enters the requested password, and the device sends that data back to the server.

[0246] Step 9:

[0247] The server verifies that the password matches and sends the final authentication result to the device. If authentication is successful, the process proceeds to step 6, and the user begins interacting with the AI ​​agent.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] This solution addresses the challenge of improving authentication processes in information systems to prevent unauthorized access while enabling users to safely and efficiently utilize interactive systems.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes a processing unit for analyzing biometric information and performing authentication, means for issuing an instruction to grant access to an interactive system upon successful authentication, and means for instructing the acquisition of additional authentication information upon authentication failure. This enables efficient access management while ensuring security.

[0253] "Biometric information" is a general term for characteristic data such as visual and auditory information that is acquired in order to identify an individual.

[0254] "Electronic devices" refer to equipment and devices used to acquire and process information.

[0255] A "processing device" is a general term for a device that analyzes received data and makes decisions based on the results.

[0256] "Authentication" refers to the process of using received biometric information to verify whether that information belongs to the registered user.

[0257] An "interactive system" is a system that provides responses based on information entered by the user.

[0258] "Instructions" refer to commands or messages sent to prompt specific actions or the input of additional information.

[0259] A "generative algorithm" refers to a computational method used to create new information based on specific data.

[0260] This system uses biometric information to ensure security when users access an interactive system through a terminal. The terminal is equipped with devices such as a camera and microphone, which are used to acquire visual and auditory biometric information. The software operated on the terminal utilizes existing libraries such as OpenCV and TensorFlow to perform high-precision data collection and processing.

[0261] When a user attempts to access the system, the device uses its camera to capture an image of the user's face or its microphone to record their voice. This data is securely transmitted to the server using encryption technologies such as SSL / TLS. On the server, a dedicated algorithm is used to analyze whether the transmitted biometric information matches the registered information. If authentication is successful, the server sends an instruction to the device granting access to the interactive system. If authentication fails, the server requests additional authentication, such as entering a password.

[0262] As a concrete example, when a user uses an AI assistant to manage their schedule, the device performs facial recognition, and the results are processed on a server. This system allows users to efficiently utilize information systems while securely protecting their data. An example of a prompt to be entered when using a generative AI model is, "Please describe in detail the operation of the access management system based on user authentication."

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The device acquires biometric information when a user requests access to an interactive system. Specifically, it captures a facial image with a camera and records audio with a microphone. This input data is temporarily stored on the device. Importantly, settings are adjusted to ensure sufficient resolution and sound quality.

[0266] Step 2:

[0267] The device sends the acquired biometric information to the server using a highly reliable encryption protocol (e.g., SSL / TLS). The input is temporary data stored on the device, and the output is an encrypted data packet. This encryption prevents third parties from accessing the data during transmission.

[0268] Step 3:

[0269] The server uses a processing unit to analyze biometric information received from the terminal. The received biometric information is analyzed using a dedicated algorithm (e.g., face recognition using TensorFlow). The input is decrypted biometric information, and the output is the authentication result (success or failure).

[0270] Step 4:

[0271] The server sends the authentication result obtained through analysis to the terminal. If authentication is successful, the server sends an instruction to the terminal granting access to the interactive system. In this process, the authentication result is used as input, and the instruction granting access is generated as the corresponding output.

[0272] Step 5:

[0273] The terminal receives instructions from the server, displays a successful authentication message to the user, and prepares to access the interactive system. If authentication fails, the user is prompted for additional authentication (such as entering a password). This prevents unauthorized access while ensuring smooth access for legitimate users.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In modern society, where much information is digitized and strict access control is required, the effective operation of security systems based on biometric authentication is a challenge. In particular, access management using smart devices requires balancing both usability and security. Conventional technologies may not be able to provide sufficient security and usability due to limitations of the devices themselves or the authentication technology.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes a device for acquiring user biometric authentication data, an information processing device for analyzing the biometric authentication data and authenticating the user, a management mechanism for controlling user access based on the authentication result, and means for managing access to a specific area via a smart device using biometric authentication. This makes it possible for users to easily manage their access while improving security.

[0279] "Biometric data" refers to data obtained as digital information about an individual's physical characteristics and used in the authentication process.

[0280] An "information processing device" is a device that analyzes received data and performs various processes based on the results.

[0281] A "management mechanism" is a mechanism that controls the operation of the entire system, and in particular, manages user access rights.

[0282] A "smart device" is a portable electronic device equipped with an Internet communication function and various sensors, having various functions.

[0283] "Access management" is a function that verifies the user's authority to specific information or areas and permits or restricts it.

[0284] To implement this invention, it is necessary to construct a system with the following elements. First, the user uses a smart device. The smart device is equipped with a camera and a microphone for acquiring biometric data. Specifically, smart glasses such as Google Glass are assumed. When the user attempts to access a specific service or area, the smart glasses acquire face image data with the camera and record voice data with the microphone.

[0285] Next, the biometric data acquired by the terminal is transmitted to the information processing device. As a specific example of this information processing device, servers on AWS or Google Cloud Platform are applicable. The server analyzes the face image data using OpenCV and uses CMU Sphinx etc. for voice authentication. By this analysis, it is confirmed whether the user is a registered person.

[0286] If the authentication is successful, the management agency permits the user to have access rights to a specific area using the smart device. For example, entering a room where confidential information within a company is stored corresponds to this.

[0287] This system enables the user to safely access information and areas based on visual and voice characteristics. It provides the user with quick and intuitive operations while ensuring security.

[0288] As a specific example, when entering a conference room within the company, the user undergoes biometric authentication through the smart glasses. If the authentication is successful, the door automatically opens and the user can enter the conference room.

[0289] An example of a prompt from a generated AI model is: "Based on the following text, please explain how to use an AI agent dialogue management system with biometric authentication in security services. Please include specific examples."

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The device acquires image and audio data of the user's face through a smart device. The camera captures the user's face, and the microphone records the user's voice. This biometric data is acquired from sensors and stored as initial data.

[0293] Step 2:

[0294] The terminal transmits the acquired biometric authentication data to the information processing device. During this process, the biometric data is encrypted and securely sent to the server. Input data is protected by communication protocols for security purposes.

[0295] Step 3:

[0296] The server analyzes the received biometric authentication data. Feature points are extracted from facial image data using OpenCV, and speech recognition is performed on audio data using CMU Sphinx. As part of the data processing, the server obtains the feature vectors necessary for authentication and compares them with registered data to obtain the authentication result as output.

[0297] Step 4:

[0298] The server determines whether user authentication is successful based on the analysis results. If successful, it sends an access permission to the terminal as output via the management mechanism, notifying the user's smart device. If it fails, it sends an instruction to the terminal requesting additional authentication as output.

[0299] Step 5:

[0300] Based on the authentication result from the server, the user can access specific services or areas. As a result, only legitimate users can access information and areas, enabling safe and efficient use.

[0301] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0302] The present invention provides a system that combines biometric authentication and emotion recognition. This system enables users to receive safer and more personalized services using an AI agent.

[0303] Terminal

[0304] The terminal has the ability to acquire the user's biometric authentication data using a camera or a microphone. When the user uses the AI agent, the terminal uses the camera to collect face image data or the microphone to acquire voice data. This data is utilized in the first process of user authentication and also in subsequent emotion recognition.

[0305] Server

[0306] The server receives the biometric authentication data transmitted from the terminal and first authenticates the user. If this authentication is successful, then the emotion engine operates to estimate the emotion from the user's face image data or voice data. The emotion engine performs data analysis using a specific algorithm to identify the user's emotional state. This information is utilized for the AI agent to provide more appropriate and personalized responses.

[0307] User

[0308] When users access services through an AI agent, they undergo biometric authentication and emotion recognition processes via their device. For example, when a user makes a normal inquiry, the emotion engine determines whether the user is stressed or calm, and the AI ​​agent responds accordingly, improving the user experience.

[0309] As a concrete example, consider a scenario where a user receives customer support using an AI agent. In this case, the device captures the user's face and sends it to the server. After successful authentication, the server can analyze the user's facial expressions using an emotion engine to detect signs of stress. Based on the detected emotions, the AI ​​agent selects a calmer and more reassuring response. This is expected to improve the overall quality of service, going beyond mere authentication.

[0310] The following describes the processing flow.

[0311] Step 1:

[0312] The user opens an application on their device to activate the AI ​​agent. The device displays a user interface and prompts the user to prepare for biometric authentication.

[0313] Step 2:

[0314] The device uses its camera to capture an image of the user's face or its microphone to record audio. The acquired biometric data is then sent to a server.

[0315] Step 3:

[0316] The server executes an algorithm to analyze the biometric authentication data it receives. It compares this data with a registered database to verify user authentication.

[0317] Step 4:

[0318] The server determines the authentication result and sends the result to the terminal.

[0319] Step 5:

[0320] If authentication is successful, the server uses an emotion engine to analyze the user's emotional state. This is done based on facial images and voice data. The server determines the emotional state and returns that information to the device.

[0321] Step 6:

[0322] The device instructs the AI ​​agent to optimize user responses based on emotional information received from the server. The service the user experiences is then adjusted to match the recognized emotions.

[0323] Step 7:

[0324] Users access the service through an AI agent, which provides emotionally responsive responses and suggestions as needed.

[0325] Step 8:

[0326] If authentication fails, the server will prompt the device for additional verification methods. The user can then attempt authentication again using methods such as entering a password.

[0327] This process allows users to safely interact with AI agents and receive appropriate responses to their individual emotional states.

[0328] (Example 2)

[0329] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0330] Traditional authentication systems only verify the user's identity and do not consider the user's emotional state when providing services. This results in a uniform and unpersonalized user experience, which is a significant challenge. This raises concerns that users may not receive the support they need, potentially leading to a decline in service quality.

[0331] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0332] In this invention, the server includes a device for acquiring the user's biometric information, processing means for analyzing the biometric information and authenticating the user, and analysis means for estimating the emotional state from the biometric information. This makes it possible to personalize responses to the user's emotions and provide a more personalized user experience.

[0333] A "user" is an individual or entity that utilizes a system or service.

[0334] "Biometric information" refers to data that represents unique characteristics of the human body, such as facial images and voice recordings used to identify an individual.

[0335] A "device" refers to equipment used to acquire biometric information from a user, and includes devices such as cameras and microphones.

[0336] "Processing means" refers to methods and devices for analyzing biometric information and authenticating users.

[0337] "Analysis means" refers to algorithms and processes for estimating a user's emotional state using biometric information.

[0338] A "dialogue tool" is an element that generates personalized responses based on the user's emotional state and interacts with the user.

[0339] "Output means" refers to an interface for presenting the system's generated response to the user, and includes displays and audio output devices.

[0340] This invention relates to a system that uses biometric information to authenticate users, recognize their emotions, and provide personalized conversations.

[0341] terminal

[0342] The device is equipped with a mechanism to acquire the user's biometric information. Specifically, it uses input devices such as a camera and microphone to acquire the user's facial image and voice. This allows data about the individual to be collected simply by the user standing in front of the device.

[0343] server

[0344] The server receives biometric information transmitted from the terminal and first performs authentication. The authentication process uses algorithms as processing means to compare against a registered database. Deep learning technology and facial recognition technology are applied in this process to enable highly accurate authentication. Furthermore, after successful authentication, the server performs emotion analysis. Emotion analysis is performed on acquired facial images and voice data, and the user's emotional state is estimated using a deep learning model. For example, a generative AI model is used to evaluate changes in facial expressions and voice intonation.

[0345] This information is used as a dialogue mechanism to generate personalized responses based on prompts created using a generative AI model. The server generates a prompt such as, "Provide information in a calm tone so that the user can relax," and sends it to the AI ​​agent. Through this process, the system can create personalized responses based on emotion recognition and provide high-quality service tailored to the user's needs.

[0346] User

[0347] Users can enjoy personalized services through the device. When a user approaches the device, biometric information is quickly acquired and analyzed, and an AI agent provides optimal information and responses based on the results. This allows users to feel more secure and have a more satisfying experience.

[0348] This system offers a novel service model that combines biometric authentication with emotion recognition, thereby improving the user experience.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The device acquires the user's biometric information through its camera and microphone. Input includes facial image data and voice data. In this step, the device temporarily stores the collected data in its internal memory.

[0352] Step 2:

[0353] The device transmits the acquired biometric information to the server. The input consists of facial image data and voice data stored on the device, and the output is this data, which is encrypted and sent to the server over the network. During this process, protocols such as SSL are used to ensure the security of the data.

[0354] Step 3:

[0355] The server uses the received biometric information to authenticate the user. The input consists of facial image data and voice data, and the output is the authentication result. In this step, data analysis using deep learning technology is performed to compare the received data with existing user templates stored in the database.

[0356] Step 4:

[0357] The server analyzes the emotions of a user based on their biometric information after successful authentication. The input consists of facial image data and audio data, while the output is information about the user's emotional state. This step utilizes a generative AI model to analyze subtle changes in facial expressions and vocal intonation. As a result of the analysis, an emotion label, such as "the user is nervous," is generated.

[0358] Step 5:

[0359] The server generates a prompt message based on the emotional state and sends it to the AI ​​agent. The input is emotional state information, and the output is the generated prompt message. In this step, the server performs the action of creating a specific prompt message (e.g., "Recommend actions to help the user relax").

[0360] Step 6:

[0361] The AI ​​agent generates a personalized response based on the received prompt and sends it to the terminal. The input is the prompt, and the output is an optimized response for the user. Here, natural language processing techniques are used to provide information in a format that is easy for humans to understand.

[0362] Step 7:

[0363] The terminal presents the AI ​​agent's response received from the server to the user. The input is the AI ​​agent's response data, and the output is the information provided to the user through display or audio output. In this step, the terminal performs specific actions such as displaying a message on the screen or playing audio guidance.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0366] In recent years, there has been a growing need to simultaneously authenticate users and understand their emotional state, but existing systems struggle to do this efficiently. Furthermore, there are limitations to providing personalized experiences tailored to each user's emotional state. This results in problems such as unoptimized user experiences, including customer support.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes a device for acquiring a user's biometric authentication information, an information processing device for analyzing the biometric authentication information and authenticating the user, an emotion analysis device for determining the user's emotional state based on the authentication result, and a corresponding control means for personalizing the user's experience based on the emotional state. This makes it possible to quickly and accurately authenticate the user and understand their emotional state, and to provide a personalized experience.

[0369] "User" refers to a person who uses the system or their individual attributes.

[0370] "Biometric information" refers to data based on physical characteristics that enable the identification of a user, and includes facial images and acoustic data.

[0371] "Device" refers to hardware used to obtain biometric authentication information from a user.

[0372] An "information processing device" refers to an electronic device or software used to analyze acquired biometric authentication information and authenticate the user.

[0373] An "emotion analysis device" refers to a device or program that determines a user's emotional state based on the user's authentication results.

[0374] "Response control means" refers to methods and devices for personalizing the user experience provided by the system according to the user's emotional state.

[0375] The system for carrying out this invention comprises a device for acquiring and analyzing a user's biometric authentication information, and corresponding control means. A specific form thereof is shown below.

[0376] The device includes hardware for acquiring biometric authentication information such as image data and audio data of the user's face. Examples of hardware used include smartphones and smart glasses, which are equipped with high-performance cameras and microphones.

[0377] The server receives biometric authentication information transmitted from the terminal and performs user authentication using an information processing device. This information processing device incorporates biometric analysis software libraries such as OpenCV and TensorFlow.

[0378] After authentication is complete, the emotion analysis device operates and determines the user's emotional state based on a specific algorithm. This process utilizes cloud services specialized in emotion analysis, such as Microsoft Azure Emotion API and Google Cloud Vision.

[0379] The acquired user emotional state is used by response control mechanisms to provide users with an individualized experience. For example, when a user contacts customer support, an appropriate response is selected according to their emotional state. Specifically, a user who is feeling angry will be responded to in a calmer tone.

[0380] An example of a prompt message is as follows:

[0381] "You are a customer support agent whose role is to reassure users who are feeling angry or anxious. You need to advise them on how users are expressing their complaints and what specific actions are necessary."

[0382] This can improve the quality of personalized experiences.

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The device acquires the user's biometric authentication information. Inputs include facial images captured by the camera and acoustic data collected by the microphone. This data is collected in real time and prepared for transmission to the server.

[0386] Step 2:

[0387] The server analyzes biometric authentication information received from the terminal. The input consists of a facial image and audio data transmitted from the terminal. An information processing device is used to analyze this data and authenticate the user. Once the analysis is complete, the authentication result is output and used for the next process.

[0388] Step 3:

[0389] The server operates the emotion analysis device based on the authentication results. The input consists of the user's facial image and audio data. An emotion analysis algorithm is applied to estimate the user's emotional state from this data. After analysis, the user's emotional state is output.

[0390] Step 4:

[0391] The server transmits the user's emotional state to the response control system. The input is the user's emotional state obtained from the server. Based on this state, prompts are provided to the generative AI model, which generates responses that personalize the user experience. The output is the optimized user response.

[0392] Step 5:

[0393] Users receive a personalized user experience through their device. Input includes user interactions sent from the server. This allows users to comfortably access customer support and other services.

[0394] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0395] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0400] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0401] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0402] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0403] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0404] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0405] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0406] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0407] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0408] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0409] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0410] This invention relates to an embodiment of a system for securely managing interactions with an AI agent using biometric authentication data. This system mainly consists of three elements: a terminal, a server, and a user.

[0411] terminal

[0412] The device has devices for acquiring biometric data, such as a camera and microphone. When a user wishes to interact with the AI ​​agent, the device uses the camera to capture an image of the user's face or the microphone to record the user's voice. This biometric data is sent to a server for authentication.

[0413] server

[0414] The server plays a central role in analyzing biometric authentication data sent from the terminal. Based on the received data, it verifies that it matches the registered user information and determines whether authentication was successful. If authentication is successful, the server instructs the terminal to access the AI ​​agent. Conversely, if authentication fails, the server instructs the terminal to use additional authentication methods (e.g., entering a text password).

[0415] User

[0416] Users access the AI ​​agent through their device. Biometric authentication using the camera and microphone is used during access to prevent unauthorized access. If a user fails the initial authentication, they follow the server's instructions and perform additional identity verification steps.

[0417] As a concrete example, consider a scenario where a user wants to interact with an AI agent about their daily tasks using a device. In this case, the device captures the user's face with its camera and sends it to a server. The server then compares this image data with previously registered facial data to determine whether authentication is successful. If authentication is successful, the user can perform their tasks through the AI ​​agent. This system makes it possible to use the AI ​​agent safely and efficiently.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The user opens the application on their device to activate the AI ​​agent. The device displays the user interface and detects the access request.

[0421] Step 2:

[0422] The device prepares to acquire the user's biometric data using its built-in camera and microphone. This involves capturing the user's face or recording their voice.

[0423] Step 3:

[0424] The device sends the acquired biometric authentication data (facial image or voice data) to the server.

[0425] Step 4:

[0426] The server activates an algorithm to analyze the received biometric authentication data. It then verifies that the data matches the registered user database.

[0427] Step 5:

[0428] The server determines the biometric authentication result and sends the result to the terminal.

[0429] Step 6:

[0430] If authentication is successful, the device will be granted access to the AI ​​agent, allowing the user to begin the conversation.

[0431] Step 7:

[0432] If authentication fails, the server prompts the terminal for additional authentication methods. The terminal then displays a password entry screen to the user.

[0433] Step 8:

[0434] The user enters the requested password, and the device sends that data back to the server.

[0435] Step 9:

[0436] The server verifies that the password matches and sends the final authentication result to the device. If authentication is successful, the process proceeds to step 6, and the user begins interacting with the AI ​​agent.

[0437] (Example 1)

[0438] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0439] This solution addresses the challenge of improving authentication processes in information systems to prevent unauthorized access while enabling users to safely and efficiently utilize interactive systems.

[0440] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0441] In this invention, the server includes a processing unit for analyzing biometric information and performing authentication, means for issuing an instruction to grant access to an interactive system upon successful authentication, and means for instructing the acquisition of additional authentication information upon authentication failure. This enables efficient access management while ensuring security.

[0442] "Biometric information" is a general term for characteristic data such as visual and auditory information that is acquired in order to identify an individual.

[0443] "Electronic devices" refer to equipment and devices used to acquire and process information.

[0444] A "processing device" is a general term for a device that analyzes received data and makes decisions based on the results.

[0445] "Authentication" refers to the process of using received biometric information to verify whether that information belongs to the registered user.

[0446] An "interactive system" is a system that provides responses based on information entered by the user.

[0447] "Instructions" refer to commands or messages sent to prompt specific actions or the input of additional information.

[0448] A "generative algorithm" refers to a computational method used to create new information based on specific data.

[0449] This system uses biometric information to ensure security when users access an interactive system through a terminal. The terminal is equipped with devices such as a camera and microphone, which are used to acquire visual and auditory biometric information. The software operated on the terminal utilizes existing libraries such as OpenCV and TensorFlow to perform high-precision data collection and processing.

[0450] When a user attempts to access the system, the device uses its camera to capture an image of the user's face or its microphone to record their voice. This data is securely transmitted to the server using encryption technologies such as SSL / TLS. On the server, a dedicated algorithm is used to analyze whether the transmitted biometric information matches the registered information. If authentication is successful, the server sends an instruction to the device granting access to the interactive system. If authentication fails, the server requests additional authentication, such as entering a password.

[0451] As a concrete example, when a user uses an AI assistant to manage their schedule, the device performs facial recognition, and the results are processed on a server. This system allows users to efficiently utilize information systems while securely protecting their data. An example of a prompt to be entered when using a generative AI model is, "Please describe in detail the operation of the access management system based on user authentication."

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The device acquires biometric information when a user requests access to an interactive system. Specifically, it captures a facial image with a camera and records audio with a microphone. This input data is temporarily stored on the device. Importantly, settings are adjusted to ensure sufficient resolution and sound quality.

[0455] Step 2:

[0456] The device sends the acquired biometric information to the server using a highly reliable encryption protocol (e.g., SSL / TLS). The input is temporary data stored on the device, and the output is an encrypted data packet. This encryption prevents third parties from accessing the data during transmission.

[0457] Step 3:

[0458] The server uses a processing unit to analyze biometric information received from the terminal. The received biometric information is analyzed using a dedicated algorithm (e.g., face recognition using TensorFlow). The input is decrypted biometric information, and the output is the authentication result (success or failure).

[0459] Step 4:

[0460] The server sends the authentication result obtained through analysis to the terminal. If authentication is successful, the server sends an instruction to the terminal granting access to the interactive system. In this process, the authentication result is used as input, and the instruction granting access is generated as the corresponding output.

[0461] Step 5:

[0462] The terminal receives instructions from the server, displays a successful authentication message to the user, and prepares to access the interactive system. If authentication fails, the user is prompted for additional authentication (such as entering a password). This prevents unauthorized access while ensuring smooth access for legitimate users.

[0463] (Application Example 1)

[0464] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0465] In modern society, where much information is digitized and strict access control is required, the effective operation of security systems based on biometric authentication is a challenge. In particular, access management using smart devices requires balancing both usability and security. Conventional technologies may not be able to provide sufficient security and usability due to limitations of the devices themselves or the authentication technology.

[0466] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0467] In this invention, the server includes a device for acquiring user biometric authentication data, an information processing device for analyzing the biometric authentication data and authenticating the user, a management mechanism for controlling user access based on the authentication result, and means for managing access to a specific area via a smart device using biometric authentication. This makes it possible for users to easily manage their access while improving security.

[0468] "Biometric data" refers to data obtained as digital information about an individual's physical characteristics and used in the authentication process.

[0469] An "information processing device" is a device that analyzes received data and performs various processes based on the results.

[0470] A "management mechanism" is a mechanism that controls the operation of the entire system, and in particular, manages user access rights.

[0471] A "smart device" is a portable electronic device equipped with internet communication capabilities and various sensors, possessing a range of functions.

[0472] "Access control" is a function that allows users to review, grant, or restrict their permissions to specific information or areas.

[0473] To implement this invention, it is necessary to construct a system with the following elements. First, the user uses a smart device. The smart device is equipped with a camera and a microphone for acquiring biometric authentication data. Specifically, smart glasses such as Google Glass are assumed. When the user attempts to access a specific service or area, the smart glasses acquire facial image data with the camera and record voice data with the microphone.

[0474] Next, the biometric authentication data acquired by the terminal is sent to an information processing device. This information processing device could be a server on AWS or Google Cloud Platform. The server uses OpenCV to analyze facial image data and CMU Sphinx or similar tools for voice authentication. This analysis verifies whether the user is a registered individual.

[0475] If authentication is successful, the management system will use a smart device to grant the user access to a specific area. For example, this could include access to a room where confidential company information is stored.

[0476] This system allows users to securely access information and areas based on visual and auditory characteristics. It provides users with quick and intuitive operation while ensuring security.

[0477] As a concrete example, when entering a company meeting room, the user undergoes biometric authentication through smart glasses. If authentication is successful, the door automatically opens, and the user can enter the meeting room.

[0478] An example of a prompt from a generated AI model is: "Based on the following text, please explain how to use an AI agent dialogue management system with biometric authentication in security services. Please include specific examples."

[0479] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0480] Step 1:

[0481] The device acquires image and audio data of the user's face through a smart device. The camera captures the user's face, and the microphone records the user's voice. This biometric data is acquired from sensors and stored as initial data.

[0482] Step 2:

[0483] The terminal transmits the acquired biometric authentication data to the information processing device. During this process, the biometric data is encrypted and securely sent to the server. Input data is protected by communication protocols for security purposes.

[0484] Step 3:

[0485] The server analyzes the received biometric authentication data. Feature points are extracted from facial image data using OpenCV, and speech recognition is performed on audio data using CMU Sphinx. As part of the data processing, the server obtains the feature vectors necessary for authentication and compares them with registered data to obtain the authentication result as output.

[0486] Step 4:

[0487] The server determines whether user authentication is successful based on the analysis results. If successful, it sends an access permission to the terminal as output via the management mechanism, notifying the user's smart device. If it fails, it sends an instruction to the terminal requesting additional authentication as output.

[0488] Step 5:

[0489] Users can access specific services and areas based on authentication results from the server. This ensures that only authorized users can access information and areas, allowing for safe and efficient use.

[0490] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0491] This invention provides a system that combines biometric authentication and emotion recognition. This system enables users to receive safer and more personalized services using an AI agent.

[0492] terminal

[0493] The device is equipped with the ability to acquire user biometric data using its camera and microphone. When a user uses the AI ​​agent, the device collects facial image data using its camera or acquires voice data using its microphone. This data is used in the initial process of user authentication, as well as for subsequent emotion recognition.

[0494] server

[0495] The server receives biometric authentication data sent from the terminal and first authenticates the user. If this authentication is successful, the emotion engine then operates and estimates the user's emotions from the user's facial image data or voice data. The emotion engine uses a specific algorithm to analyze the data and identify the user's emotional state. This information is used by the AI ​​agent to provide more appropriate and personalized responses.

[0496] User

[0497] When users access services through an AI agent, they undergo biometric authentication and emotion recognition processes via their device. For example, when a user makes a normal inquiry, the emotion engine determines whether the user is stressed or calm, and the AI ​​agent responds accordingly, improving the user experience.

[0498] As a concrete example, consider a scenario where a user receives customer support using an AI agent. In this case, the device captures the user's face and sends it to the server. After successful authentication, the server can analyze the user's facial expressions using an emotion engine to detect signs of stress. Based on the detected emotions, the AI ​​agent selects a calmer and more reassuring response. This is expected to improve the overall quality of service, going beyond mere authentication.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] The user opens an application on their device to activate the AI ​​agent. The device displays a user interface and prompts the user to prepare for biometric authentication.

[0502] Step 2:

[0503] The device uses its camera to capture an image of the user's face or its microphone to record audio. The acquired biometric data is then sent to a server.

[0504] Step 3:

[0505] The server executes an algorithm to analyze the biometric authentication data it receives. It compares this data with a registered database to verify user authentication.

[0506] Step 4:

[0507] The server determines the authentication result and sends the result to the terminal.

[0508] Step 5:

[0509] If authentication is successful, the server uses an emotion engine to analyze the user's emotional state. This is done based on facial images and voice data. The server determines the emotional state and returns that information to the device.

[0510] Step 6:

[0511] The device instructs the AI ​​agent to optimize user responses based on emotional information received from the server. The service the user experiences is then adjusted to match the recognized emotions.

[0512] Step 7:

[0513] Users access the service through an AI agent, which provides emotionally responsive responses and suggestions as needed.

[0514] Step 8:

[0515] If authentication fails, the server will prompt the device for additional verification methods. The user can then attempt authentication again using methods such as entering a password.

[0516] This process allows users to safely interact with AI agents and receive appropriate responses to their individual emotional states.

[0517] (Example 2)

[0518] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] Traditional authentication systems only verify the user's identity and do not consider the user's emotional state when providing services. This results in a uniform and unpersonalized user experience, which is a significant challenge. This raises concerns that users may not receive the support they need, potentially leading to a decline in service quality.

[0520] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0521] In this invention, the server includes a device for acquiring the user's biometric information, processing means for analyzing the biometric information and authenticating the user, and analysis means for estimating the emotional state from the biometric information. This makes it possible to personalize responses to the user's emotions and provide a more personalized user experience.

[0522] A "user" is an individual or entity that utilizes a system or service.

[0523] "Biometric information" refers to data that represents unique characteristics of the human body, such as facial images and voice recordings used to identify an individual.

[0524] A "device" refers to equipment used to acquire biometric information from a user, and includes devices such as cameras and microphones.

[0525] "Processing means" refers to methods and devices for analyzing biometric information and authenticating users.

[0526] "Analysis means" refers to algorithms and processes for estimating a user's emotional state using biometric information.

[0527] A "dialogue tool" is an element that generates personalized responses based on the user's emotional state and interacts with the user.

[0528] "Output means" refers to an interface for presenting the system's generated response to the user, and includes displays and audio output devices.

[0529] This invention relates to a system that uses biometric information to authenticate users, recognize their emotions, and provide personalized conversations.

[0530] terminal

[0531] The device is equipped with a mechanism to acquire the user's biometric information. Specifically, it uses input devices such as a camera and microphone to acquire the user's facial image and voice. This allows data about the individual to be collected simply by the user standing in front of the device.

[0532] server

[0533] The server receives biometric information transmitted from the terminal and first performs authentication. The authentication process uses algorithms as processing means to compare against a registered database. Deep learning technology and facial recognition technology are applied in this process to enable highly accurate authentication. Furthermore, after successful authentication, the server performs emotion analysis. Emotion analysis is performed on acquired facial images and voice data, and the user's emotional state is estimated using a deep learning model. For example, a generative AI model is used to evaluate changes in facial expressions and voice intonation.

[0534] This information is used as a dialogue mechanism to generate personalized responses based on prompts created using a generative AI model. The server generates a prompt such as, "Provide information in a calm tone so that the user can relax," and sends it to the AI ​​agent. Through this process, the system can create personalized responses based on emotion recognition and provide high-quality service tailored to the user's needs.

[0535] User

[0536] Users can enjoy personalized services through the device. When a user approaches the device, biometric information is quickly acquired and analyzed, and an AI agent provides optimal information and responses based on the results. This allows users to feel more secure and have a more satisfying experience.

[0537] This system offers a novel service model that combines biometric authentication with emotion recognition, thereby improving the user experience.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The device acquires the user's biometric information through its camera and microphone. Input includes facial image data and voice data. In this step, the device temporarily stores the collected data in its internal memory.

[0541] Step 2:

[0542] The device transmits the acquired biometric information to the server. The input consists of facial image data and voice data stored on the device, and the output is this data, which is encrypted and sent to the server over the network. During this process, protocols such as SSL are used to ensure the security of the data.

[0543] Step 3:

[0544] The server uses the received biometric information to authenticate the user. The input consists of facial image data and voice data, and the output is the authentication result. In this step, data analysis using deep learning technology is performed to compare the received data with existing user templates stored in the database.

[0545] Step 4:

[0546] The server analyzes the emotions of a user based on their biometric information after successful authentication. The input consists of facial image data and audio data, while the output is information about the user's emotional state. This step utilizes a generative AI model to analyze subtle changes in facial expressions and vocal intonation. As a result of the analysis, an emotion label, such as "the user is nervous," is generated.

[0547] Step 5:

[0548] The server generates a prompt message based on the emotional state and sends it to the AI ​​agent. The input is emotional state information, and the output is the generated prompt message. In this step, the server performs the action of creating a specific prompt message (e.g., "Recommend actions to help the user relax").

[0549] Step 6:

[0550] The AI ​​agent generates a personalized response based on the received prompt and sends it to the terminal. The input is the prompt, and the output is an optimized response for the user. Here, natural language processing techniques are used to provide information in a format that is easy for humans to understand.

[0551] Step 7:

[0552] The terminal presents the AI ​​agent's response received from the server to the user. The input is the AI ​​agent's response data, and the output is the information provided to the user through display or audio output. In this step, the terminal performs specific actions such as displaying a message on the screen or playing audio guidance.

[0553] (Application Example 2)

[0554] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] In recent years, there has been a growing need to simultaneously authenticate users and understand their emotional state, but existing systems struggle to do this efficiently. Furthermore, there are limitations to providing personalized experiences tailored to each user's emotional state. This results in problems such as unoptimized user experiences, including customer support.

[0556] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0557] In this invention, the server includes a device for acquiring a user's biometric authentication information, an information processing device for analyzing the biometric authentication information and authenticating the user, an emotion analysis device for determining the user's emotional state based on the authentication result, and a corresponding control means for personalizing the user's experience based on the emotional state. This makes it possible to quickly and accurately authenticate the user and understand their emotional state, and to provide a personalized experience.

[0558] "User" refers to a person who uses the system or their individual attributes.

[0559] "Biometric information" refers to data based on physical characteristics that enable the identification of a user, and includes facial images and acoustic data.

[0560] "Device" refers to hardware used to obtain biometric authentication information from a user.

[0561] An "information processing device" refers to an electronic device or software used to analyze acquired biometric authentication information and authenticate the user.

[0562] An "emotion analysis device" refers to a device or program that determines a user's emotional state based on the user's authentication results.

[0563] "Response control means" refers to methods and devices for personalizing the user experience provided by the system according to the user's emotional state.

[0564] The system for carrying out this invention comprises a device for acquiring and analyzing a user's biometric authentication information, and corresponding control means. A specific form thereof is shown below.

[0565] The device includes hardware for acquiring biometric authentication information such as image data and audio data of the user's face. Examples of hardware used include smartphones and smart glasses, which are equipped with high-performance cameras and microphones.

[0566] The server receives biometric authentication information transmitted from the terminal and performs user authentication using an information processing device. This information processing device incorporates biometric analysis software libraries such as OpenCV and TensorFlow.

[0567] After authentication is complete, the emotion analysis device operates and determines the user's emotional state based on a specific algorithm. This process utilizes cloud services specialized in emotion analysis, such as Microsoft Azure Emotion API and Google Cloud Vision.

[0568] The acquired user emotional state is used by response control mechanisms to provide users with an individualized experience. For example, when a user contacts customer support, an appropriate response is selected according to their emotional state. Specifically, a user who is feeling angry will be responded to in a calmer tone.

[0569] An example of a prompt message is as follows:

[0570] "You are a customer support agent whose role is to reassure users who are feeling angry or anxious. You need to advise them on how users are expressing their complaints and what specific actions are necessary."

[0571] This can improve the quality of personalized experiences.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The device acquires the user's biometric authentication information. Inputs include facial images captured by the camera and acoustic data collected by the microphone. This data is collected in real time and prepared for transmission to the server.

[0575] Step 2:

[0576] The server analyzes biometric authentication information received from the terminal. The input consists of a facial image and audio data transmitted from the terminal. An information processing device is used to analyze this data and authenticate the user. Once the analysis is complete, the authentication result is output and used for the next process.

[0577] Step 3:

[0578] The server operates the emotion analysis device based on the authentication results. The input consists of the user's facial image and audio data. An emotion analysis algorithm is applied to estimate the user's emotional state from this data. After analysis, the user's emotional state is output.

[0579] Step 4:

[0580] The server transmits the user's emotional state to the response control system. The input is the user's emotional state obtained from the server. Based on this state, prompts are provided to the generative AI model, which generates responses that personalize the user experience. The output is the optimized user response.

[0581] Step 5:

[0582] Users receive a personalized user experience through their device. Input includes user interactions sent from the server. This allows users to comfortably access customer support and other services.

[0583] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0584] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0585] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0589] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0590] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0591] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0592] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0593] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0594] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0595] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0596] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0597] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0598] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0599] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0600] This invention relates to an embodiment of a system for securely managing interactions with an AI agent using biometric authentication data. This system mainly consists of three elements: a terminal, a server, and a user.

[0601] terminal

[0602] The device has devices for acquiring biometric data, such as a camera and microphone. When a user wishes to interact with the AI ​​agent, the device uses the camera to capture an image of the user's face or the microphone to record the user's voice. This biometric data is sent to a server for authentication.

[0603] server

[0604] The server plays a central role in analyzing biometric authentication data sent from the terminal. Based on the received data, it verifies that it matches the registered user information and determines whether authentication was successful. If authentication is successful, the server instructs the terminal to access the AI ​​agent. Conversely, if authentication fails, the server instructs the terminal to use additional authentication methods (e.g., entering a text password).

[0605] User

[0606] Users access the AI ​​agent through their device. Biometric authentication using the camera and microphone is used during access to prevent unauthorized access. If a user fails the initial authentication, they follow the server's instructions and perform additional identity verification steps.

[0607] As a concrete example, consider a scenario where a user wants to interact with an AI agent about their daily tasks using a device. In this case, the device captures the user's face with its camera and sends it to a server. The server then compares this image data with previously registered facial data to determine whether authentication is successful. If authentication is successful, the user can perform their tasks through the AI ​​agent. This system makes it possible to use the AI ​​agent safely and efficiently.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The user opens the application on their device to activate the AI ​​agent. The device displays the user interface and detects the access request.

[0611] Step 2:

[0612] The device prepares to acquire the user's biometric data using its built-in camera and microphone. This involves capturing the user's face or recording their voice.

[0613] Step 3:

[0614] The device sends the acquired biometric authentication data (facial image or voice data) to the server.

[0615] Step 4:

[0616] The server activates an algorithm to analyze the received biometric authentication data. It then verifies that the data matches the registered user database.

[0617] Step 5:

[0618] The server determines the biometric authentication result and sends the result to the terminal.

[0619] Step 6:

[0620] If authentication is successful, the device will be granted access to the AI ​​agent, allowing the user to begin the conversation.

[0621] Step 7:

[0622] If authentication fails, the server prompts the terminal for additional authentication methods. The terminal then displays a password entry screen to the user.

[0623] Step 8:

[0624] The user enters the requested password, and the device sends that data back to the server.

[0625] Step 9:

[0626] The server verifies that the password matches and sends the final authentication result to the device. If authentication is successful, the process proceeds to step 6, and the user begins interacting with the AI ​​agent.

[0627] (Example 1)

[0628] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] This solution addresses the challenge of improving authentication processes in information systems to prevent unauthorized access while enabling users to safely and efficiently utilize interactive systems.

[0630] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0631] In this invention, the server includes a processing unit for analyzing biometric information and performing authentication, means for issuing an instruction to grant access to an interactive system upon successful authentication, and means for instructing the acquisition of additional authentication information upon authentication failure. This enables efficient access management while ensuring security.

[0632] "Biometric information" is a general term for characteristic data such as visual and auditory information that is acquired in order to identify an individual.

[0633] "Electronic devices" refer to equipment and devices used to acquire and process information.

[0634] A "processing device" is a general term for a device that analyzes received data and makes decisions based on the results.

[0635] "Authentication" refers to the process of using received biometric information to verify whether that information belongs to the registered user.

[0636] An "interactive system" is a system that provides responses based on information entered by the user.

[0637] "Instructions" refer to commands or messages sent to prompt specific actions or the input of additional information.

[0638] A "generative algorithm" refers to a computational method used to create new information based on specific data.

[0639] This system uses biometric information to ensure security when users access an interactive system through a terminal. The terminal is equipped with devices such as a camera and microphone, which are used to acquire visual and auditory biometric information. The software operated on the terminal utilizes existing libraries such as OpenCV and TensorFlow to perform high-precision data collection and processing.

[0640] When a user attempts to access the system, the device uses its camera to capture an image of the user's face or its microphone to record their voice. This data is securely transmitted to the server using encryption technologies such as SSL / TLS. On the server, a dedicated algorithm is used to analyze whether the transmitted biometric information matches the registered information. If authentication is successful, the server sends an instruction to the device granting access to the interactive system. If authentication fails, the server requests additional authentication, such as entering a password.

[0641] As a concrete example, when a user uses an AI assistant to manage their schedule, the device performs facial recognition, and the results are processed on a server. This system allows users to efficiently utilize information systems while securely protecting their data. An example of a prompt to be entered when using a generative AI model is, "Please describe in detail the operation of the access management system based on user authentication."

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] The device acquires biometric information when a user requests access to an interactive system. Specifically, it captures a facial image with a camera and records audio with a microphone. This input data is temporarily stored on the device. Importantly, settings are adjusted to ensure sufficient resolution and sound quality.

[0645] Step 2:

[0646] The device sends the acquired biometric information to the server using a highly reliable encryption protocol (e.g., SSL / TLS). The input is temporary data stored on the device, and the output is an encrypted data packet. This encryption prevents third parties from accessing the data during transmission.

[0647] Step 3:

[0648] The server uses a processing unit to analyze biometric information received from the terminal. The received biometric information is analyzed using a dedicated algorithm (e.g., face recognition using TensorFlow). The input is decrypted biometric information, and the output is the authentication result (success or failure).

[0649] Step 4:

[0650] The server sends the authentication result obtained through analysis to the terminal. If authentication is successful, the server sends an instruction to the terminal granting access to the interactive system. In this process, the authentication result is used as input, and the instruction granting access is generated as the corresponding output.

[0651] Step 5:

[0652] The terminal receives instructions from the server, displays a successful authentication message to the user, and prepares to access the interactive system. If authentication fails, the user is prompted for additional authentication (such as entering a password). This prevents unauthorized access while ensuring smooth access for legitimate users.

[0653] (Application Example 1)

[0654] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] In modern society, where much information is digitized and strict access control is required, the effective operation of security systems based on biometric authentication is a challenge. In particular, access management using smart devices requires balancing both usability and security. Conventional technologies may not be able to provide sufficient security and usability due to limitations of the devices themselves or the authentication technology.

[0656] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0657] In this invention, the server includes a device for acquiring user biometric authentication data, an information processing device for analyzing the biometric authentication data and authenticating the user, a management mechanism for controlling user access based on the authentication result, and means for managing access to a specific area via a smart device using biometric authentication. This makes it possible for users to easily manage their access while improving security.

[0658] "Biometric data" refers to data obtained as digital information about an individual's physical characteristics and used in the authentication process.

[0659] An "information processing device" is a device that analyzes received data and performs various processes based on the results.

[0660] A "management mechanism" is a mechanism that controls the operation of the entire system, and in particular, manages user access rights.

[0661] A "smart device" is a portable electronic device equipped with internet communication capabilities and various sensors, possessing a range of functions.

[0662] "Access control" is a function that allows users to review, grant, or restrict their permissions to specific information or areas.

[0663] To implement this invention, it is necessary to construct a system with the following elements. First, the user uses a smart device. The smart device is equipped with a camera and a microphone for acquiring biometric authentication data. Specifically, smart glasses such as Google Glass are assumed. When the user attempts to access a specific service or area, the smart glasses acquire facial image data with the camera and record voice data with the microphone.

[0664] Next, the biometric authentication data acquired by the terminal is sent to an information processing device. This information processing device could be a server on AWS or Google Cloud Platform. The server uses OpenCV to analyze facial image data and CMU Sphinx or similar tools for voice authentication. This analysis verifies whether the user is a registered individual.

[0665] If authentication is successful, the management system will use a smart device to grant the user access to a specific area. For example, this could include access to a room where confidential company information is stored.

[0666] This system allows users to securely access information and areas based on visual and auditory characteristics. It provides users with quick and intuitive operation while ensuring security.

[0667] As a concrete example, when entering a company meeting room, the user undergoes biometric authentication through smart glasses. If authentication is successful, the door automatically opens, and the user can enter the meeting room.

[0668] An example of a prompt from a generated AI model is: "Based on the following text, please explain how to use an AI agent dialogue management system with biometric authentication in security services. Please include specific examples."

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The device acquires image and audio data of the user's face through a smart device. The camera captures the user's face, and the microphone records the user's voice. This biometric data is acquired from sensors and stored as initial data.

[0672] Step 2:

[0673] The terminal transmits the acquired biometric authentication data to the information processing device. During this process, the biometric data is encrypted and securely sent to the server. Input data is protected by communication protocols for security purposes.

[0674] Step 3:

[0675] The server analyzes the received biometric authentication data. Feature points are extracted from facial image data using OpenCV, and speech recognition is performed on audio data using CMU Sphinx. As part of the data processing, the server obtains the feature vectors necessary for authentication and compares them with registered data to obtain the authentication result as output.

[0676] Step 4:

[0677] The server determines whether user authentication is successful based on the analysis results. If successful, it sends an access permission to the terminal as output via the management mechanism, notifying the user's smart device. If it fails, it sends an instruction to the terminal requesting additional authentication as output.

[0678] Step 5:

[0679] Users can access specific services and areas based on authentication results from the server. This ensures that only authorized users can access information and areas, allowing for safe and efficient use.

[0680] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0681] This invention provides a system that combines biometric authentication and emotion recognition. This system enables users to receive safer and more personalized services using an AI agent.

[0682] terminal

[0683] The device is equipped with the ability to acquire user biometric data using its camera and microphone. When a user uses the AI ​​agent, the device collects facial image data using its camera or acquires voice data using its microphone. This data is used in the initial process of user authentication, as well as for subsequent emotion recognition.

[0684] server

[0685] The server receives biometric authentication data sent from the terminal and first authenticates the user. If this authentication is successful, the emotion engine then operates and estimates the user's emotions from the user's facial image data or voice data. The emotion engine uses a specific algorithm to analyze the data and identify the user's emotional state. This information is used by the AI ​​agent to provide more appropriate and personalized responses.

[0686] User

[0687] When users access services through an AI agent, they undergo biometric authentication and emotion recognition processes via their device. For example, when a user makes a normal inquiry, the emotion engine determines whether the user is stressed or calm, and the AI ​​agent responds accordingly, improving the user experience.

[0688] As a concrete example, consider a scenario where a user receives customer support using an AI agent. In this case, the device captures the user's face and sends it to the server. After successful authentication, the server can analyze the user's facial expressions using an emotion engine to detect signs of stress. Based on the detected emotions, the AI ​​agent selects a calmer and more reassuring response. This is expected to improve the overall quality of service, going beyond mere authentication.

[0689] The following describes the processing flow.

[0690] Step 1:

[0691] The user opens an application on their device to activate the AI ​​agent. The device displays a user interface and prompts the user to prepare for biometric authentication.

[0692] Step 2:

[0693] The device uses its camera to capture an image of the user's face or its microphone to record audio. The acquired biometric data is then sent to a server.

[0694] Step 3:

[0695] The server executes an algorithm to analyze the biometric authentication data it receives. It compares this data with a registered database to verify user authentication.

[0696] Step 4:

[0697] The server determines the authentication result and sends the result to the terminal.

[0698] Step 5:

[0699] If authentication is successful, the server uses an emotion engine to analyze the user's emotional state. This is done based on facial images and voice data. The server determines the emotional state and returns that information to the device.

[0700] Step 6:

[0701] The device instructs the AI ​​agent to optimize user responses based on emotional information received from the server. The service the user experiences is then adjusted to match the recognized emotions.

[0702] Step 7:

[0703] Users access the service through an AI agent, which provides emotionally responsive responses and suggestions as needed.

[0704] Step 8:

[0705] If authentication fails, the server will prompt the device for additional verification methods. The user can then attempt authentication again using methods such as entering a password.

[0706] This process allows users to safely interact with AI agents and receive appropriate responses to their individual emotional states.

[0707] (Example 2)

[0708] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0709] Traditional authentication systems only verify the user's identity and do not consider the user's emotional state when providing services. This results in a uniform and unpersonalized user experience, which is a significant challenge. This raises concerns that users may not receive the support they need, potentially leading to a decline in service quality.

[0710] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0711] In this invention, the server includes a device for acquiring the user's biometric information, processing means for analyzing the biometric information and authenticating the user, and analysis means for estimating the emotional state from the biometric information. This makes it possible to personalize responses to the user's emotions and provide a more personalized user experience.

[0712] A "user" is an individual or entity that utilizes a system or service.

[0713] "Biometric information" refers to data that represents unique characteristics of the human body, such as facial images and voice recordings used to identify an individual.

[0714] A "device" refers to equipment used to acquire biometric information from a user, and includes devices such as cameras and microphones.

[0715] "Processing means" refers to methods and devices for analyzing biometric information and authenticating users.

[0716] "Analysis means" refers to algorithms and processes for estimating a user's emotional state using biometric information.

[0717] A "dialogue tool" is an element that generates personalized responses based on the user's emotional state and interacts with the user.

[0718] "Output means" refers to an interface for presenting the system's generated response to the user, and includes displays and audio output devices.

[0719] This invention relates to a system that uses biometric information to authenticate users, recognize their emotions, and provide personalized conversations.

[0720] terminal

[0721] The device is equipped with a mechanism to acquire the user's biometric information. Specifically, it uses input devices such as a camera and microphone to acquire the user's facial image and voice. This allows data about the individual to be collected simply by the user standing in front of the device.

[0722] server

[0723] The server receives biometric information transmitted from the terminal and first performs authentication. The authentication process uses algorithms as processing means to compare against a registered database. Deep learning technology and facial recognition technology are applied in this process to enable highly accurate authentication. Furthermore, after successful authentication, the server performs emotion analysis. Emotion analysis is performed on acquired facial images and voice data, and the user's emotional state is estimated using a deep learning model. For example, a generative AI model is used to evaluate changes in facial expressions and voice intonation.

[0724] This information is used as a dialogue mechanism to generate personalized responses based on prompts created using a generative AI model. The server generates a prompt such as, "Provide information in a calm tone so that the user can relax," and sends it to the AI ​​agent. Through this process, the system can create personalized responses based on emotion recognition and provide high-quality service tailored to the user's needs.

[0725] User

[0726] Users can enjoy personalized services through the device. When a user approaches the device, biometric information is quickly acquired and analyzed, and an AI agent provides optimal information and responses based on the results. This allows users to feel more secure and have a more satisfying experience.

[0727] This system offers a novel service model that combines biometric authentication with emotion recognition, thereby improving the user experience.

[0728] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0729] Step 1:

[0730] The device acquires the user's biometric information through its camera and microphone. Input includes facial image data and voice data. In this step, the device temporarily stores the collected data in its internal memory.

[0731] Step 2:

[0732] The device transmits the acquired biometric information to the server. The input consists of facial image data and voice data stored on the device, and the output is this data, which is encrypted and sent to the server over the network. During this process, protocols such as SSL are used to ensure the security of the data.

[0733] Step 3:

[0734] The server uses the received biometric information to authenticate the user. The input consists of facial image data and voice data, and the output is the authentication result. In this step, data analysis using deep learning technology is performed to compare the received data with existing user templates stored in the database.

[0735] Step 4:

[0736] The server analyzes the emotions of a user based on their biometric information after successful authentication. The input consists of facial image data and audio data, while the output is information about the user's emotional state. This step utilizes a generative AI model to analyze subtle changes in facial expressions and vocal intonation. As a result of the analysis, an emotion label, such as "the user is nervous," is generated.

[0737] Step 5:

[0738] The server generates a prompt message based on the emotional state and sends it to the AI ​​agent. The input is emotional state information, and the output is the generated prompt message. In this step, the server performs the action of creating a specific prompt message (e.g., "Recommend actions to help the user relax").

[0739] Step 6:

[0740] The AI ​​agent generates a personalized response based on the received prompt and sends it to the terminal. The input is the prompt, and the output is an optimized response for the user. Here, natural language processing techniques are used to provide information in a format that is easy for humans to understand.

[0741] Step 7:

[0742] The terminal presents the AI ​​agent's response received from the server to the user. The input is the AI ​​agent's response data, and the output is the information provided to the user through display or audio output. In this step, the terminal performs specific actions such as displaying a message on the screen or playing audio guidance.

[0743] (Application Example 2)

[0744] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] In recent years, there has been a growing need to simultaneously authenticate users and understand their emotional state, but existing systems struggle to do this efficiently. Furthermore, there are limitations to providing personalized experiences tailored to each user's emotional state. This results in problems such as unoptimized user experiences, including customer support.

[0746] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0747] In this invention, the server includes a device for acquiring a user's biometric authentication information, an information processing device for analyzing the biometric authentication information and authenticating the user, an emotion analysis device for determining the user's emotional state based on the authentication result, and a corresponding control means for personalizing the user's experience based on the emotional state. This makes it possible to quickly and accurately authenticate the user and understand their emotional state, and to provide a personalized experience.

[0748] "User" refers to a person who uses the system or their individual attributes.

[0749] "Biometric information" refers to data based on physical characteristics that enable the identification of a user, and includes facial images and acoustic data.

[0750] "Device" refers to hardware used to obtain biometric authentication information from a user.

[0751] An "information processing device" refers to an electronic device or software used to analyze acquired biometric authentication information and authenticate the user.

[0752] An "emotion analysis device" refers to a device or program that determines a user's emotional state based on the user's authentication results.

[0753] "Response control means" refers to methods and devices for personalizing the user experience provided by the system according to the user's emotional state.

[0754] The system for carrying out this invention comprises a device for acquiring and analyzing a user's biometric authentication information, and corresponding control means. A specific form thereof is shown below.

[0755] The device includes hardware for acquiring biometric authentication information such as image data and audio data of the user's face. Examples of hardware used include smartphones and smart glasses, which are equipped with high-performance cameras and microphones.

[0756] The server receives biometric authentication information transmitted from the terminal and performs user authentication using an information processing device. This information processing device incorporates biometric analysis software libraries such as OpenCV and TensorFlow.

[0757] After authentication is complete, the emotion analysis device operates and determines the user's emotional state based on a specific algorithm. This process utilizes cloud services specialized in emotion analysis, such as Microsoft Azure Emotion API and Google Cloud Vision.

[0758] The acquired user emotional state is used by response control mechanisms to provide users with an individualized experience. For example, when a user contacts customer support, an appropriate response is selected according to their emotional state. Specifically, a user who is feeling angry will be responded to in a calmer tone.

[0759] An example of a prompt message is as follows:

[0760] "You are a customer support agent whose role is to reassure users who are feeling angry or anxious. You need to advise them on how users are expressing their complaints and what specific actions are necessary."

[0761] This can improve the quality of personalized experiences.

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The device acquires the user's biometric authentication information. Inputs include facial images captured by the camera and acoustic data collected by the microphone. This data is collected in real time and prepared for transmission to the server.

[0765] Step 2:

[0766] The server analyzes biometric authentication information received from the terminal. The input consists of a facial image and audio data transmitted from the terminal. An information processing device is used to analyze this data and authenticate the user. Once the analysis is complete, the authentication result is output and used for the next process.

[0767] Step 3:

[0768] The server operates the emotion analysis device based on the authentication results. The input consists of the user's facial image and audio data. An emotion analysis algorithm is applied to estimate the user's emotional state from this data. After analysis, the user's emotional state is output.

[0769] Step 4:

[0770] The server transmits the user's emotional state to the response control system. The input is the user's emotional state obtained from the server. Based on this state, prompts are provided to the generative AI model, which generates responses that personalize the user experience. The output is the optimized user response.

[0771] Step 5:

[0772] Users receive a personalized user experience through their device. Input includes user interactions sent from the server. This allows users to comfortably access customer support and other services.

[0773] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0774] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0775] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0776] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0777] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0778] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0779] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0780] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0781] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0782] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0783] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0784] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0785] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0786] 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.

[0787] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0788] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0789] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0790] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0791] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0792] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0793] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0794] The following is further disclosed regarding the embodiments described above.

[0795] (Claim 1)

[0796] A device for acquiring the user's biometric authentication data,

[0797] A server for analyzing the aforementioned biometric authentication data to authenticate the user,

[0798] Control means for controlling user access based on the results of the authentication,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the user's biometric authentication data is image data of a face.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the user's biometric authentication data is voice data.

[0804] "Example 1"

[0805] (Claim 1)

[0806] Electronic devices for acquiring biological information,

[0807] A processing device for analyzing the aforementioned biometric information and performing authentication,

[0808] A means of issuing an instruction to grant access to the interactive system upon successful authentication,

[0809] A means of instructing the system to obtain additional authentication information in the event of authentication failure,

[0810] A function that provides output information using a generative algorithm,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the aforementioned biological information is visual data.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the aforementioned biological information is auditory data.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] A device for acquiring user biometric authentication data,

[0819] An information processing device for analyzing the aforementioned biometric authentication data and performing user authentication,

[0820] A management mechanism for controlling user access based on the results of the aforementioned authentication,

[0821] A means of managing access to a specific area via a smart device using biometric authentication,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the user's biometric authentication data is facial image data and is acquired by the smart device.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the user's biometric authentication data is voice data and is analyzed by the management mechanism.

[0827] "Example 2 of combining an emotion engine"

[0828] (Claim 1)

[0829] A device for acquiring the user's biometric information,

[0830] Processing means for analyzing the aforementioned biometric information and authenticating the user,

[0831] An analytical means for estimating emotional state from the aforementioned biological information,

[0832] A dialogue means for generating an individualized response based on the aforementioned emotional state,

[0833] Output means for presenting the aforementioned response to the user,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein the user's biometric information is a facial image and voice information.

[0837] (Claim 3)

[0838] The system according to claim 1, wherein the dialogue means generates a response using a generation AI model.

[0839] "Application example 2 when combining with an emotional engine"

[0840] (Claim 1)

[0841] A device for obtaining the user's biometric authentication information,

[0842] An information processing device for analyzing the aforementioned biometric authentication information and performing user authentication,

[0843] An emotion analysis device for determining the user's emotional state based on the results of the aforementioned authentication,

[0844] A response control means for personalizing the user experience based on the aforementioned emotional state,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the user's biometric authentication information is image data.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the user's biometric authentication information is acoustic data. [Explanation of symbols]

[0850] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for acquiring user biometric authentication data, An information processing device for analyzing the aforementioned biometric authentication data and performing user authentication, A management mechanism for controlling user access based on the results of the aforementioned authentication, A means of managing access to a specific area via a smart device using biometric authentication, A system that includes this.

2. The system according to claim 1, wherein the user's biometric authentication data is facial image data and is acquired by the smart device.

3. The system according to claim 1, wherein the user's biometric authentication data is voice data and is analyzed by the management mechanism.

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