System

Abstract

An object of a system according to an embodiment is to provide an authentication method that achieves both security and convenience.SOLUTION: A system according to an embodiment includes a user information learning unit, an authentication process unit, and a multifactor authentication unit. The user information learning unit learns specific information or a behavior pattern of the user. The authentication process unit performs authentication based on the information learned by the user information learning unit. The multi-factor authentication unit performs authentication by combining a plurality of factors.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional authentication methods have security and convenience concerns, leaving room for improvement.

[0005] The system according to the embodiment aims to provide an authentication method that is both secure and convenient. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information learning unit, an authentication process unit, and a multi-factor authentication unit. The user information learning unit learns specific information and behavioral patterns of a user. The authentication process unit performs authentication based on the information learned by the user information learning unit. The multi-factor authentication unit performs authentication using a combination of multiple factors. [Effects of the Invention]

[0007] The system according to the embodiment can provide an authentication method that is both secure and convenient. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The authentication system according to the embodiment of the present invention uses personal AI to learn specific information and behavioral patterns of users and performs authentication based on this information. This eliminates the concerns that exist in conventional authentication methods and provides a new authentication method suited to a future in which AI is used on a daily basis.

[0029] An authentication system according to an embodiment includes a user information learning unit, an authentication process unit, and a multi-factor authentication unit. The user information learning unit learns specific information and behavioral patterns of a user. For example, the user information learning unit learns the user's tone of voice and speaking style. The user information learning unit can also learn the user's typing speed and pattern. The user information learning unit can also learn the user's daily activities and schedule. The authentication process unit performs authentication based on the information learned by the user information learning unit. For example, the authentication process unit checks the user's voice and determines whether it matches pre-learned user characteristics. The authentication process unit can also check the user's typing pattern and determine whether it matches pre-learned user characteristics. The authentication process unit can also check the user's behavioral pattern and determine whether it matches pre-learned user characteristics. The multi-factor authentication unit performs authentication by combining multiple factors. For example, the multi-factor authentication unit performs authentication by combining the user's voice, facial recognition, typing pattern, and behavioral history. The multi-factor authentication unit can also perform authentication by combining the user's voice and typing pattern. In addition, the multi-factor authentication unit can perform authentication by combining the user's behavioral history and facial recognition. As a result, the authentication system according to the embodiment can resolve concerns that exist in conventional authentication methods and provide a new authentication method that is suited to the future where AI is used on a daily basis.

[0030] The user information learning unit can learn the user's voice tone and speaking style, typing speed and pattern, daily behavior and schedule. The user information learning unit, for example, analyzes and learns the user's voice tone and speaking style. For example, it analyzes the characteristics of the voice waveform, speaking speed, and pitch. The user information learning unit also analyzes and learns the typing speed and pattern. For example, it analyzes the interval between key presses and the frequency of use of specific keys. The user information learning unit also analyzes and learns the user's daily behavior and schedule. For example, it analyzes calendar app data and location information history. This allows for a detailed understanding of the user's characteristics and improves the accuracy of authentication.

[0031] The authentication process unit can check the user's voice and typing pattern and determine whether they match pre-learned user characteristics. For example, the authentication process unit checks the user's voice and determines whether they match pre-learned user characteristics. For example, it analyzes voice characteristics using voice recognition technology and calculates the degree of match. The authentication process unit also checks the typing pattern and determines whether they match pre-learned user characteristics. For example, it analyzes typing speed and patterns and calculates the degree of match. The authentication process unit also checks the user's behavioral pattern and determines whether they match pre-learned user characteristics. For example, it analyzes behavioral history and calculates the degree of match. This makes it possible to improve authentication accuracy based on user characteristics.

[0032] The multi-factor authentication unit can perform authentication by combining the user's voice, facial recognition, typing pattern, and behavioral history. The multi-factor authentication unit performs authentication by combining, for example, the user's voice and facial recognition. For example, it uses voice recognition technology and a facial recognition algorithm to calculate the degree of match between the two. The multi-factor authentication unit also performs authentication by combining the user's typing pattern and behavioral history. For example, it analyzes typing speed and pattern and behavioral history to calculate the degree of match between the two. The multi-factor authentication unit also performs authentication by combining the user's voice, facial recognition, typing pattern, and behavioral history. For example, it comprehensively evaluates the degree of match between each element to determine whether to authenticate. In this way, security can be strengthened by combining multiple elements.

[0033] Personal AI can collect a user's health data and improve authentication accuracy based on their health condition. For example, personal AI can collect a user's heart rate and sleep patterns and improve authentication accuracy based on their health condition. For example, it can use data from wearable devices. Personal AI can also build a system that evaluates a user's stress level and fatigue level based on the health data and adjusts authentication accuracy. For example, it can simplify the authentication process if the user is highly stressed. Personal AI can also monitor a user's health condition in real time and dynamically adjust authentication accuracy based on the health data. For example, it can relax authentication strictness if the user is sleep deprived. This allows authentication accuracy to be improved based on the user's health condition.

[0034] Personal AI can analyze a user's social media activity and incorporate online behavioral patterns into its learning. For example, personal AI can analyze a user's social media activity and incorporate online behavioral patterns into its learning. For example, it can analyze the content of posts and the frequency of comments. Personal AI can also identify a user's interests based on data collected from social media and reflect these in its learning content. For example, if there are many posts about a particular topic, it can provide learning content related to that topic. Personal AI can also create a user profile based on online behavioral patterns to improve authentication accuracy. For example, a user who is active during a specific time period can be provided with an authentication method specialized for that time period. In this way, by incorporating online behavioral patterns into learning, authentication accuracy can be improved.

[0035] Personal AI can collect data from home IoT devices and use home behavioral patterns for learning. For example, personal AI collects data from home IoT devices and uses home behavioral patterns for learning. For example, it analyzes usage data from smart speakers and smart lights. Personal AI can also identify users' lifestyles and behavioral patterns based on data obtained from home IoT devices and reflect this in its learning content. For example, it can learn patterns of home appliance usage during specific time periods. Personal AI can also collect data from IoT devices in real time and improve authentication accuracy based on home behavioral patterns. For example, it can adjust authentication methods according to the usage status of specific devices. This allows it to improve authentication accuracy by using home behavioral patterns for learning.

[0036] Personal AI can learn behavioral patterns in the workplace and provide authentication methods tailored to the workplace environment. For example, Personal AI can learn behavioral patterns in the workplace and provide authentication methods tailored to the workplace environment. For example, it can analyze PC usage at work and frequency of meeting participation. Personal AI can also identify a user's work style and work patterns based on workplace behavioral data and reflect this in the authentication method. For example, it can provide authentication methods tailored to specific work hours. Personal AI can also monitor behavioral patterns in the workplace in real time and dynamically adjust authentication methods tailored to the workplace. For example, it can adjust the strictness of authentication according to the security level of the workplace. This can improve authentication accuracy by providing authentication methods tailored to the workplace environment.

[0037] The authentication process unit can refer to a user's past authentication history during the authentication process and detect abnormal patterns. For example, the authentication process unit builds a system that refers to a user's past authentication history during the authentication process and detects abnormal patterns. For example, it detects authentication at times or places that are different from normal. The authentication process unit also learns the user's authentication patterns based on the past authentication history and detects abnormal patterns in real time. For example, it detects authentication from devices that are different from normal. The authentication process unit also analyzes authentication history data and develops an algorithm that identifies abnormal patterns. For example, it detects abnormalities in the frequency or timing of authentication. This makes it possible to detect abnormal patterns based on past authentication history and improve security.

[0038] The authentication process unit can analyze the user's environmental sounds during authentication and allow authentication only under specific environmental conditions. For example, the authentication process unit analyzes the user's environmental sounds during authentication and builds a system that allows authentication only under specific environmental conditions. For example, authentication is only allowed in a quiet environment. The authentication process unit also evaluates the user's authentication environment based on the environmental sound data and allows authentication only when specific conditions are met. For example, authentication is allowed when office environmental sounds are detected. The authentication process unit also monitors the user's environmental sounds in real time and develops an algorithm that allows authentication only under specific environmental conditions. For example, authentication is allowed when a specific voice pattern is detected. This allows authentication only under specific environmental conditions, thereby improving security.

[0039] The authentication process unit can analyze the user's walking pattern during the authentication process and perform authentication based on the walking style. For example, the authentication process unit can analyze the user's walking pattern during the authentication process and build a system that performs authentication based on the walking style. For example, the authentication process unit collects walking data using a smartphone sensor. The authentication process unit also identifies the user's walking style based on the walking pattern data and uses this for authentication. For example, authentication is performed based on a specific walking rhythm or speed. The authentication process unit can also monitor the user's walking pattern in real time and develop an algorithm that performs authentication based on the walking style. For example, the authentication process unit analyzes the sway of the user's walking and the length of their steps. This allows for authentication based on the walking style, thereby improving the accuracy of authentication.

[0040] The authentication processing unit can capture the user's hand movements during authentication and perform authentication based on the hand movement patterns. The authentication processing unit, for example, builds a system that captures the user's hand movements during authentication and performs authentication based on the hand movement patterns. For example, the authentication processing unit analyzes hand movements using a smartphone camera. The authentication processing unit also identifies the user's hand movement patterns based on the hand movement data and uses them for authentication. For example, authentication is performed based on specific gestures or tap patterns. The authentication processing unit also monitors the user's hand movements in real time and develops an algorithm that performs authentication based on the hand movement patterns. For example, the speed and direction of hand movements are analyzed. This allows authentication to be performed based on hand movement patterns, thereby improving the accuracy of authentication.

[0041] The multi-factor authentication unit can add a user's dietary patterns as an authentication factor and allow authentication only after a specific meal. For example, the multi-factor authentication unit builds a system that adds a user's dietary patterns as an authentication factor and allows authentication only after a specific meal. For example, it analyzes the timing and content of meals. The multi-factor authentication unit also monitors the user's dietary patterns in real time and develops an algorithm that allows authentication only after a specific meal. For example, it allows authentication after consuming specific nutrients. The multi-factor authentication unit also identifies the user's dietary patterns based on the dietary data and allows authentication only after a specific meal. For example, it allows authentication after breakfast or lunch. This allows authentication only after a specific meal, thereby improving the accuracy of authentication.

[0042] The multi-factor authentication unit can add a user's exercise pattern as an authentication factor and allow authentication only after specific exercise. For example, the multi-factor authentication unit builds a system that adds a user's exercise pattern as an authentication factor and allows authentication only after specific exercise. For example, it analyzes the type and intensity of exercise. The multi-factor authentication unit also monitors the user's exercise pattern in real time and develops an algorithm that allows authentication only after specific exercise. For example, it allows authentication after running or yoga. The multi-factor authentication unit also identifies the user's exercise pattern based on the exercise data and allows authentication only after specific exercise. For example, it allows authentication after training at the gym. This allows authentication only after specific exercise, thereby improving the accuracy of authentication.

[0043] The multi-factor authentication unit can add a user's music preferences as an authentication factor and allow authentication only when the user is listening to specific music. For example, the multi-factor authentication unit can build a system that adds a user's music preferences as an authentication factor and allows authentication only when the user is listening to specific music. For example, it can analyze data from a music streaming service. The multi-factor authentication unit can also monitor a user's music preferences in real time and develop an algorithm that allows authentication only when the user is listening to specific music. For example, it can allow authentication when the user is listening to music by a specific artist or genre. The multi-factor authentication unit can also identify a user's music preferences based on music data and allow authentication only when the user is listening to specific music. For example, it can allow authentication when the user is listening to relaxing music. This can improve the accuracy of authentication by allowing authentication only when the user is listening to specific music.

[0044] The multi-factor authentication unit can add a user's reading pattern as an authentication factor and allow authentication only when a specific book is being read. For example, the multi-factor authentication unit can build a system that adds a user's reading pattern as an authentication factor and allows authentication only when a specific book is being read. For example, it can analyze data from an e-book reader. The multi-factor authentication unit can also monitor a user's reading pattern in real time and develop an algorithm that allows authentication only when a specific book is being read. For example, it can allow authentication when a book of a specific genre or author is being read. The multi-factor authentication unit can also identify a user's reading pattern based on the reading data and allow authentication only when a specific book is being read. For example, it can allow authentication when the book is being read during a specific time period. This can improve the accuracy of authentication by allowing authentication only when a specific book is being read.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The authentication system can collect user health data and improve authentication accuracy based on the user's health condition. For example, the system can collect the user's heart rate and sleep patterns and improve authentication accuracy based on the user's health condition. Data from wearable devices can also be used. Furthermore, a system can be built that evaluates the user's stress level and fatigue level based on the health data and adjusts authentication accuracy accordingly. For example, the authentication process can be simplified if the user is under a high level of stress. Furthermore, the system can monitor the user's health condition in real time and dynamically adjust authentication accuracy based on the health data. For example, authentication strictness can be relaxed if the user is sleep deprived. This allows authentication accuracy to be improved based on the user's health condition.

[0047] Authentication systems can analyze a user's social media activity and incorporate online behavioral patterns into learning. For example, a system can analyze a user's social media activity and incorporate online behavioral patterns into learning. It can analyze the content of posts and the frequency of comments. It can also identify a user's interests and reflect them in the learning content based on data collected from social media. For example, if there are many posts about a particular topic, it can provide learning content related to that topic. Furthermore, it can create a user profile based on online behavioral patterns and improve the accuracy of authentication. For example, a user who is active during a particular time period can be provided with an authentication method specialized for that time period. In this way, incorporating online behavioral patterns into learning can improve the accuracy of authentication.

[0048] The authentication system can collect data from home IoT devices and use the home's behavioral patterns for learning. For example, data from home IoT devices can be collected and used for learning. Usage data from smart speakers and smart lights can be analyzed. Furthermore, based on data obtained from home IoT devices, it is possible to identify a user's daily rhythm and behavioral patterns and reflect them in the learning content. For example, it can learn patterns of home appliance usage during specific time periods. Furthermore, data from IoT devices can be collected in real time and the accuracy of authentication can be improved based on home behavioral patterns. For example, the authentication method can be adjusted according to the usage status of specific devices. This allows the accuracy of authentication to be improved by using home behavioral patterns for learning.

[0049] The authentication system can learn behavioral patterns in the workplace and provide authentication methods tailored to the workplace environment. For example, it can learn behavioral patterns in the workplace and provide authentication methods tailored to the workplace environment. It can analyze PC usage and meeting participation frequency in the workplace. It can also identify a user's work style and work patterns based on workplace behavioral data and reflect these in the authentication method. For example, it can provide authentication methods tailored to specific work hours. It can also monitor behavioral patterns in the workplace in real time and dynamically adjust authentication methods tailored to the workplace. For example, it can adjust the strictness of authentication according to the security level of the workplace. This can improve authentication accuracy by providing authentication methods tailored to the workplace environment.

[0050] An authentication system can detect abnormal patterns during the authentication process by referencing a user's past authentication history. For example, a system can be constructed that references a user's past authentication history during the authentication process to detect abnormal patterns. Authentication at times or locations that are different from normal can be detected. It is also possible to learn a user's authentication patterns based on past authentication history and detect abnormal patterns in real time. For example, authentication from devices that are different from normal can be detected. Furthermore, it is also possible to develop an algorithm that analyzes authentication history data and identifies abnormal patterns. For example, it is possible to detect abnormalities in the frequency or timing of authentication. This makes it possible to detect abnormal patterns based on past authentication history and improve security.

[0051] An authentication system can analyze a user's environmental sounds during authentication and allow authentication only under certain environmental conditions. For example, a system can be built that analyzes a user's environmental sounds during authentication and allows authentication only under certain environmental conditions. Authentication can be allowed only in quiet environments. It is also possible to evaluate a user's authentication environment based on environmental sound data and allow authentication only when certain conditions are met. For example, authentication can be allowed when office environmental sounds are detected. Furthermore, an algorithm can be developed that monitors a user's environmental sounds in real time and allows authentication only under certain environmental conditions. For example, authentication can be allowed when a specific voice pattern is detected. This improves security by allowing authentication only under certain environmental conditions.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The user information learning unit learns specific information and behavioral patterns of the user. For example, the user information learning unit learns the user's tone of voice, speaking style, typing speed and pattern, daily actions, and schedule. Step 2: The authentication process unit performs authentication based on the information learned by the user information learning unit. For example, the authentication process unit checks the user's voice, typing pattern, and behavioral pattern to determine whether they match the user's characteristics learned in advance. Step 3: The multi-factor authentication unit performs authentication by combining multiple factors. For example, the multi-factor authentication unit performs authentication by combining the user's voice, facial recognition, typing pattern, and behavioral history. Authentication can also be performed by combining the user's voice and typing pattern, or the user's behavioral history and facial recognition.

[0054] (Example 2) The authentication system according to the embodiment of the present invention uses personal AI to learn specific information and behavioral patterns of users and performs authentication based on this information. This eliminates the concerns that exist in conventional authentication methods and provides a new authentication method suited to a future in which AI is used on a daily basis.

[0055] An authentication system according to an embodiment includes a user information learning unit, an authentication process unit, and a multi-factor authentication unit. The user information learning unit learns specific information and behavioral patterns of a user. For example, the user information learning unit learns the user's tone of voice and speaking style. The user information learning unit can also learn the user's typing speed and pattern. The user information learning unit can also learn the user's daily activities and schedule. The authentication process unit performs authentication based on the information learned by the user information learning unit. For example, the authentication process unit checks the user's voice and determines whether it matches pre-learned user characteristics. The authentication process unit can also check the user's typing pattern and determine whether it matches pre-learned user characteristics. The authentication process unit can also check the user's behavioral pattern and determine whether it matches pre-learned user characteristics. The multi-factor authentication unit performs authentication by combining multiple factors. For example, the multi-factor authentication unit performs authentication by combining the user's voice, facial recognition, typing pattern, and behavioral history. The multi-factor authentication unit can also perform authentication by combining the user's voice and typing pattern. In addition, the multi-factor authentication unit can perform authentication by combining the user's behavioral history and facial recognition. As a result, the authentication system according to the embodiment can resolve concerns that exist in conventional authentication methods and provide a new authentication method that is suited to the future where AI is used on a daily basis.

[0056] The user information learning unit can learn the user's voice tone and speaking style, typing speed and pattern, daily behavior and schedule. The user information learning unit, for example, analyzes and learns the user's voice tone and speaking style. For example, it analyzes the characteristics of the voice waveform, speaking speed, and pitch. The user information learning unit also analyzes and learns the typing speed and pattern. For example, it analyzes the interval between key presses and the frequency of use of specific keys. The user information learning unit also analyzes and learns the user's daily behavior and schedule. For example, it analyzes calendar app data and location information history. This allows for a detailed understanding of the user's characteristics and improves the accuracy of authentication.

[0057] The authentication process unit can check the user's voice and typing pattern and determine whether they match pre-learned user characteristics. For example, the authentication process unit checks the user's voice and determines whether they match pre-learned user characteristics. For example, it analyzes voice characteristics using voice recognition technology and calculates the degree of match. The authentication process unit also checks the typing pattern and determines whether they match pre-learned user characteristics. For example, it analyzes typing speed and patterns and calculates the degree of match. The authentication process unit also checks the user's behavioral pattern and determines whether they match pre-learned user characteristics. For example, it analyzes behavioral history and calculates the degree of match. This makes it possible to improve authentication accuracy based on user characteristics.

[0058] The multi-factor authentication unit can perform authentication by combining the user's voice, facial recognition, typing pattern, and behavioral history. The multi-factor authentication unit performs authentication by combining, for example, the user's voice and facial recognition. For example, it uses voice recognition technology and a facial recognition algorithm to calculate the degree of match between the two. The multi-factor authentication unit also performs authentication by combining the user's typing pattern and behavioral history. For example, it analyzes typing speed and pattern and behavioral history to calculate the degree of match between the two. The multi-factor authentication unit also performs authentication by combining the user's voice, facial recognition, typing pattern, and behavioral history. For example, it comprehensively evaluates the degree of match between each element to determine whether to authenticate. In this way, security can be strengthened by combining multiple elements.

[0059] Personal AI can estimate a user's emotional state in real time and dynamically adjust learning content in response to emotional changes. For example, personal AI can analyze a user's facial expressions and vocal tone to estimate their emotional state in real time. For example, it can analyze the user's emotions using a camera or microphone and dynamically adjust learning content in response to emotional changes. Personal AI can also monitor a user's emotional state in real time and customize learning content in response to emotional changes. For example, if the user is feeling stressed, it can make suggestions for relaxation. Personal AI can also build a system that provides learning content according to the user's emotional state based on the emotion estimation data. For example, if the user has strong positive emotions, it can make the learning content more challenging. This allows learning content to be optimized according to the user's emotional state.

[0060] Personal AI can collect a user's health data and improve authentication accuracy based on their health condition. For example, personal AI can collect a user's heart rate and sleep patterns and improve authentication accuracy based on their health condition. For example, it can use data from wearable devices. Personal AI can also build a system that evaluates a user's stress level and fatigue level based on the health data and adjusts authentication accuracy. For example, it can simplify the authentication process if the user is highly stressed. Personal AI can also monitor a user's health condition in real time and dynamically adjust authentication accuracy based on the health data. For example, it can relax authentication strictness if the user is sleep deprived. This allows authentication accuracy to be improved based on the user's health condition.

[0061] Personal AI can analyze a user's social media activity and incorporate online behavioral patterns into its learning. For example, personal AI can analyze a user's social media activity and incorporate online behavioral patterns into its learning. For example, it can analyze the content of posts and the frequency of comments. Personal AI can also identify a user's interests based on data collected from social media and reflect these in its learning content. For example, if there are many posts about a particular topic, it can provide learning content related to that topic. Personal AI can also create a user profile based on online behavioral patterns to improve authentication accuracy. For example, a user who is active during a specific time period can be provided with an authentication method specialized for that time period. In this way, by incorporating online behavioral patterns into learning, authentication accuracy can be improved.

[0062] Personal AI can collect data from home IoT devices and use home behavioral patterns for learning. For example, personal AI collects data from home IoT devices and uses home behavioral patterns for learning. For example, it analyzes usage data from smart speakers and smart lights. Personal AI can also identify users' lifestyles and behavioral patterns based on data obtained from home IoT devices and reflect this in its learning content. For example, it can learn patterns of home appliance usage during specific time periods. Personal AI can also collect data from IoT devices in real time and improve authentication accuracy based on home behavioral patterns. For example, it can adjust authentication methods according to the usage status of specific devices. This allows it to improve authentication accuracy by using home behavioral patterns for learning.

[0063] Personal AI can learn behavioral patterns in the workplace and provide authentication methods tailored to the workplace environment. For example, Personal AI can learn behavioral patterns in the workplace and provide authentication methods tailored to the workplace environment. For example, it can analyze PC usage at work and frequency of meeting participation. Personal AI can also identify a user's work style and work patterns based on workplace behavioral data and reflect this in the authentication method. For example, it can provide authentication methods tailored to specific work hours. Personal AI can also monitor behavioral patterns in the workplace in real time and dynamically adjust authentication methods tailored to the workplace. For example, it can adjust the strictness of authentication according to the security level of the workplace. This can improve authentication accuracy by providing authentication methods tailored to the workplace environment.

[0064] The personal AI can use the emotion estimation function to make suggestions for relaxation when the user is feeling stressed. For example, the personal AI can use the emotion estimation function to make suggestions for relaxation when the user is feeling stressed. For example, it can suggest relaxing music or meditation. The personal AI can also monitor the user's emotional state in real time and suggest activities for relaxation when the user is feeling stressed. For example, it can suggest deep breathing or stretching. The personal AI can also use the emotion estimation data to configure the environment to help the user relax when they are feeling stressed. For example, it can adjust the brightness of smart lights. In this way, by making suggestions for relaxation when the user is feeling stressed, the psychological burden on the user can be reduced.

[0065] The authentication process unit can estimate the user's emotional state during authentication and allow authentication only if the emotion matches. The authentication process unit, for example, analyzes the user's facial expression and voice tone during authentication to estimate the emotional state. For example, a camera or microphone is used to analyze the user's emotion, and authentication is allowed if the emotion matches a pre-learned emotional state. The authentication process unit also monitors the user's emotional state in real time, and builds a system that allows authentication only if the emotion matches. For example, authentication is allowed only when the user is in a low-stress state. The authentication process unit also evaluates the emotional state at the time of authentication based on the emotion estimation data and allows authentication only if the emotion matches. For example, authentication is allowed if the emotion is strongly positive. This makes it possible to improve the accuracy of authentication based on the user's emotional state.

[0066] The authentication process unit can refer to a user's past authentication history during the authentication process and detect abnormal patterns. For example, the authentication process unit builds a system that refers to a user's past authentication history during the authentication process and detects abnormal patterns. For example, it detects authentication at times or places that are different from normal. The authentication process unit also learns the user's authentication patterns based on the past authentication history and detects abnormal patterns in real time. For example, it detects authentication from devices that are different from normal. The authentication process unit also analyzes authentication history data and develops an algorithm that identifies abnormal patterns. For example, it detects abnormalities in the frequency or timing of authentication. This makes it possible to detect abnormal patterns based on past authentication history and improve security.

[0067] The authentication process unit can analyze the user's environmental sounds during authentication and allow authentication only under specific environmental conditions. For example, the authentication process unit analyzes the user's environmental sounds during authentication and builds a system that allows authentication only under specific environmental conditions. For example, authentication is only allowed in a quiet environment. The authentication process unit also evaluates the user's authentication environment based on the environmental sound data and allows authentication only when specific conditions are met. For example, authentication is allowed when office environmental sounds are detected. The authentication process unit also monitors the user's environmental sounds in real time and develops an algorithm that allows authentication only under specific environmental conditions. For example, authentication is allowed when a specific voice pattern is detected. This allows authentication only under specific environmental conditions, thereby improving security.

[0068] The authentication process unit can analyze the user's walking pattern during the authentication process and perform authentication based on the walking style. For example, the authentication process unit can analyze the user's walking pattern during the authentication process and build a system that performs authentication based on the walking style. For example, the authentication process unit collects walking data using a smartphone sensor. The authentication process unit also identifies the user's walking style based on the walking pattern data and uses this for authentication. For example, authentication is performed based on a specific walking rhythm or speed. The authentication process unit can also monitor the user's walking pattern in real time and develop an algorithm that performs authentication based on the walking style. For example, the authentication process unit analyzes the sway of the user's walking and the length of their steps. This allows for authentication based on the walking style, thereby improving the accuracy of authentication.

[0069] The authentication processing unit can capture the user's hand movements during authentication and perform authentication based on the hand movement patterns. The authentication processing unit, for example, builds a system that captures the user's hand movements during authentication and performs authentication based on the hand movement patterns. For example, the authentication processing unit analyzes hand movements using a smartphone camera. The authentication processing unit also identifies the user's hand movement patterns based on the hand movement data and uses them for authentication. For example, authentication is performed based on specific gestures or tap patterns. The authentication processing unit also monitors the user's hand movements in real time and develops an algorithm that performs authentication based on the hand movement patterns. For example, the speed and direction of hand movements are analyzed. This allows authentication to be performed based on hand movement patterns, thereby improving the accuracy of authentication.

[0070] The authentication process unit can use the emotion estimation function to allow authentication only if the user is relaxed at the time of authentication. The authentication process unit, for example, uses the emotion estimation function to build a system that allows authentication only if the user is relaxed at the time of authentication. For example, it analyzes the user's facial expressions and voice tone. The authentication process unit also monitors the user's emotional state in real time and develops an algorithm that allows authentication only if the user is relaxed. For example, it allows authentication only when stress is low. The authentication process unit also evaluates the user's emotional state at the time of authentication based on the emotion estimation data and allows authentication only if the user is relaxed. For example, it allows authentication if the user has a strong positive emotion. This allows authentication to be performed only when the user is relaxed, thereby improving the accuracy of authentication.

[0071] The multi-factor authentication unit can add a user's dietary patterns as an authentication factor and allow authentication only after a specific meal. For example, the multi-factor authentication unit builds a system that adds a user's dietary patterns as an authentication factor and allows authentication only after a specific meal. For example, it analyzes the timing and content of meals. The multi-factor authentication unit also monitors the user's dietary patterns in real time and develops an algorithm that allows authentication only after a specific meal. For example, it allows authentication after consuming specific nutrients. The multi-factor authentication unit also identifies the user's dietary patterns based on the dietary data and allows authentication only after a specific meal. For example, it allows authentication after breakfast or lunch. This allows authentication only after a specific meal, thereby improving the accuracy of authentication.

[0072] The multi-factor authentication unit can add a user's exercise pattern as an authentication factor and allow authentication only after specific exercise. For example, the multi-factor authentication unit builds a system that adds a user's exercise pattern as an authentication factor and allows authentication only after specific exercise. For example, it analyzes the type and intensity of exercise. The multi-factor authentication unit also monitors the user's exercise pattern in real time and develops an algorithm that allows authentication only after specific exercise. For example, it allows authentication after running or yoga. The multi-factor authentication unit also identifies the user's exercise pattern based on the exercise data and allows authentication only after specific exercise. For example, it allows authentication after training at the gym. This allows authentication only after specific exercise, thereby improving the accuracy of authentication.

[0073] The multi-factor authentication unit can add a user's music preferences as an authentication factor and allow authentication only when the user is listening to specific music. For example, the multi-factor authentication unit can build a system that adds a user's music preferences as an authentication factor and allows authentication only when the user is listening to specific music. For example, it can analyze data from a music streaming service. The multi-factor authentication unit can also monitor a user's music preferences in real time and develop an algorithm that allows authentication only when the user is listening to specific music. For example, it can allow authentication when the user is listening to music by a specific artist or genre. The multi-factor authentication unit can also identify a user's music preferences based on music data and allow authentication only when the user is listening to specific music. For example, it can allow authentication when the user is listening to relaxing music. This can improve the accuracy of authentication by allowing authentication only when the user is listening to specific music.

[0074] The multi-factor authentication unit can add a user's reading pattern as an authentication factor and allow authentication only when a specific book is being read. For example, the multi-factor authentication unit can build a system that adds a user's reading pattern as an authentication factor and allows authentication only when a specific book is being read. For example, it can analyze data from an e-book reader. The multi-factor authentication unit can also monitor a user's reading pattern in real time and develop an algorithm that allows authentication only when a specific book is being read. For example, it can allow authentication when a book of a specific genre or author is being read. The multi-factor authentication unit can also identify a user's reading pattern based on the reading data and allow authentication only when a specific book is being read. For example, it can allow authentication when the book is being read during a specific time period. This can improve the accuracy of authentication by allowing authentication only when a specific book is being read.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The authentication system can collect user health data and improve authentication accuracy based on the user's health condition. For example, the system can collect the user's heart rate and sleep patterns and improve authentication accuracy based on the user's health condition. Data from wearable devices can also be used. Furthermore, a system can be built that evaluates the user's stress level and fatigue level based on the health data and adjusts authentication accuracy accordingly. For example, the authentication process can be simplified if the user is under a high level of stress. Furthermore, the system can monitor the user's health condition in real time and dynamically adjust authentication accuracy based on the health data. For example, authentication strictness can be relaxed if the user is sleep deprived. This allows authentication accuracy to be improved based on the user's health condition.

[0077] Authentication systems can analyze a user's social media activity and incorporate online behavioral patterns into learning. For example, a system can analyze a user's social media activity and incorporate online behavioral patterns into learning. It can analyze the content of posts and the frequency of comments. It can also identify a user's interests and reflect them in the learning content based on data collected from social media. For example, if there are many posts about a particular topic, it can provide learning content related to that topic. Furthermore, it can create a user profile based on online behavioral patterns and improve the accuracy of authentication. For example, a user who is active during a particular time period can be provided with an authentication method specialized for that time period. In this way, incorporating online behavioral patterns into learning can improve the accuracy of authentication.

[0078] The authentication system can collect data from home IoT devices and use the home's behavioral patterns for learning. For example, data from home IoT devices can be collected and used for learning. Usage data from smart speakers and smart lights can be analyzed. Furthermore, based on data obtained from home IoT devices, it is possible to identify a user's daily rhythm and behavioral patterns and reflect them in the learning content. For example, it can learn patterns of home appliance usage during specific time periods. Furthermore, data from IoT devices can be collected in real time and the accuracy of authentication can be improved based on home behavioral patterns. For example, the authentication method can be adjusted according to the usage status of specific devices. This allows the accuracy of authentication to be improved by using home behavioral patterns for learning.

[0079] The authentication system can learn behavioral patterns in the workplace and provide authentication methods tailored to the workplace environment. For example, it can learn behavioral patterns in the workplace and provide authentication methods tailored to the workplace environment. It can analyze PC usage and meeting participation frequency in the workplace. It can also identify a user's work style and work patterns based on workplace behavioral data and reflect these in the authentication method. For example, it can provide authentication methods tailored to specific work hours. It can also monitor behavioral patterns in the workplace in real time and dynamically adjust authentication methods tailored to the workplace. For example, it can adjust the strictness of authentication according to the security level of the workplace. This can improve authentication accuracy by providing authentication methods tailored to the workplace environment.

[0080] The authentication system can estimate a user's emotional state in real time and dynamically adjust the learning content in response to emotional changes. For example, it can analyze a user's facial expressions and vocal tone to estimate their emotional state in real time. It can also analyze the user's emotions using a camera or microphone and dynamically adjust the learning content in response to emotional changes. It can also monitor a user's emotional state in real time and customize the learning content in response to emotional changes. For example, if the user is feeling stressed, it can make suggestions for relaxation. Furthermore, it is possible to build a system that provides learning content tailored to the user's emotional state based on the emotion estimation data. For example, if the user's emotions are strong, the learning content can be made more challenging. This allows the learning content to be optimized according to the user's emotional state.

[0081] The authentication system can use the emotion estimation function to make suggestions for relaxation when the user is feeling stressed. For example, the emotion estimation function can be used to make suggestions for relaxation when the user is feeling stressed. The system can suggest relaxing music or meditation. The system can also monitor the user's emotional state in real time and suggest activities for relaxation when the user is feeling stressed. For example, suggestions for deep breathing or stretching can be made. Furthermore, based on the emotion estimation data, the system can also set environmental settings to help the user relax when they are feeling stressed. For example, the brightness of a smart light can be adjusted. In this way, by making suggestions for relaxation when the user is feeling stressed, the psychological burden on the user can be reduced.

[0082] An authentication system can estimate a user's emotional state during authentication and allow authentication only if the emotion matches. For example, the user's facial expression and voice tone can be analyzed during authentication to estimate the emotional state. A camera or microphone can be used to analyze the user's emotion, and authentication can be allowed only if the emotion matches a pre-learned emotional state. It is also possible to build a system that monitors a user's emotional state in real time and allows authentication only if the emotion matches. For example, authentication can be allowed only when the user is in a low-stress state. Furthermore, the emotional state at the time of authentication can be evaluated based on the emotion estimation data, and authentication can be allowed only if the emotion matches. For example, authentication can be allowed if the emotion is strongly positive. This can improve the accuracy of authentication based on the user's emotional state.

[0083] An authentication system can use emotion estimation to allow authentication only if the user is relaxed at the time of authentication. For example, using emotion estimation, a system can be built that allows authentication only if the user is relaxed at the time of authentication. The user's facial expressions and voice tone can be analyzed. It is also possible to develop an algorithm that monitors the user's emotional state in real time and allows authentication only if the user is relaxed. For example, authentication can be allowed only when stress levels are low. Furthermore, emotion estimation data can be used to evaluate the user's emotional state at the time of authentication and allow authentication only if the user is relaxed. For example, authentication can be allowed if the user has a strong positive emotion. This can improve authentication accuracy by allowing authentication only when the user is relaxed.

[0084] An authentication system can detect abnormal patterns during the authentication process by referencing a user's past authentication history. For example, a system can be constructed that references a user's past authentication history during the authentication process to detect abnormal patterns. Authentication at times or locations that are different from normal can be detected. It is also possible to learn a user's authentication patterns based on past authentication history and detect abnormal patterns in real time. For example, authentication from devices that are different from normal can be detected. Furthermore, it is also possible to develop an algorithm that analyzes authentication history data and identifies abnormal patterns. For example, it is possible to detect abnormalities in the frequency or timing of authentication. This makes it possible to detect abnormal patterns based on past authentication history and improve security.

[0085] An authentication system can analyze a user's environmental sounds during authentication and allow authentication only under certain environmental conditions. For example, a system can be built that analyzes a user's environmental sounds during authentication and allows authentication only under certain environmental conditions. Authentication can be allowed only in quiet environments. It is also possible to evaluate a user's authentication environment based on environmental sound data and allow authentication only when certain conditions are met. For example, authentication can be allowed when office environmental sounds are detected. Furthermore, an algorithm can be developed that monitors a user's environmental sounds in real time and allows authentication only under certain environmental conditions. For example, authentication can be allowed when a specific voice pattern is detected. This improves security by allowing authentication only under certain environmental conditions.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The user information learning unit learns specific information and behavioral patterns of the user. For example, the user information learning unit learns the user's tone of voice, speaking style, typing speed and pattern, daily actions, and schedule. Step 2: The authentication process unit performs authentication based on the information learned by the user information learning unit. For example, the authentication process unit checks the user's voice, typing pattern, and behavioral pattern to determine whether they match the user's characteristics learned in advance. Step 3: The multi-factor authentication unit performs authentication by combining multiple factors. For example, the multi-factor authentication unit performs authentication by combining the user's voice, facial recognition, typing pattern, and behavioral history. Authentication can also be performed by combining the user's voice and typing pattern, or the user's behavioral history and facial recognition.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0129] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0138] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0139] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0140] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0143] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0144] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0145] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0146] 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.

[0147] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0148] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0149] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0150] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0151] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0152] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0153] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information learning unit that learns specific information and behavioral patterns of a user; an authentication process unit that performs authentication based on the information learned by the user information learning unit; A multi-factor authentication unit that performs authentication by combining multiple factors. A system characterized by:

2. The user information learning unit Learn the user's tone of voice, speaking style, typing speed and patterns, daily activities and schedules 2. The system of claim 1.

3. The authentication process unit Checking the user's voice and typing patterns to determine whether they match pre-learned characteristics of the user 2. The system of claim 1.

4. The multi-factor authentication unit Authentication is performed by combining the user's voice, facial recognition, typing pattern, and behavioral history.

2. The system of claim 1.

5. Personal AI is The emotional state of the user is estimated in real time, and learning content is dynamically adjusted according to changes in the user's emotions.

2. The system of claim 1.

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