System

Abstract

An object of the system according to the embodiment is to realize highly reliable identity authentication and privacy protection and to provide highly accurate affinity diagnosis and a high-quality virtual dating experience.SOLUTION: A system includes a personal authentication part, an image generation part, a congeniality diagnosis part, and a virtual dating part. The personal authentication unit performs personal authentication of a user by using an individual number card. The image generation unit generates an avatar based on a face photograph of the user authenticated by the identity authentication unit. The congeniality diagnosis unit analyzes profile information of the user on the basis of the avatar generated by the image generation unit and proposes a congeniality partner. The virtual dating unit provides dating within the metaverse with the partner proposed by the congeniality diagnosing unit.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] Existing technologies leave room for improvement in terms of the reliability of identity authentication, privacy protection, accuracy of compatibility diagnosis, and the quality of the virtual dating experience.

[0005] The system according to the embodiment aims to achieve highly reliable personal authentication and privacy protection, and to provide highly accurate compatibility diagnosis and a high-quality virtual dating experience. [Means for solving the problem]

[0006] The system according to the embodiment includes an identity authentication unit, an image generation unit, a compatibility diagnosis unit, and a virtual dating unit. The identity authentication unit authenticates the user using a My Number card. The image generation unit generates an avatar based on a facial photograph of the user authenticated by the identity authentication unit. The compatibility diagnosis unit analyzes the user's profile information based on the avatar generated by the image generation unit and suggests compatible partners. The virtual dating unit offers dates within the metaverse with partners suggested by the compatibility diagnosis unit. [Effects of the Invention]

[0007] The system according to the embodiment realizes highly reliable personal authentication and privacy protection, and can provide highly accurate compatibility diagnosis and a high-quality virtual dating experience. [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 matching app according to an embodiment of the present invention is a system that uses My Number cards to authenticate users, shares appearance information while protecting privacy using a generation AI, diagnoses compatibility, and provides dating within the metaverse. This makes the matching app highly reliable, protects privacy, and can diagnose compatibility between users and promote interaction in virtual space.

[0029] A matching app according to an embodiment includes an identity authentication unit, an image generation unit, a compatibility assessment unit, and a virtual dating unit. The identity authentication unit authenticates a user using a My Number card. For example, when a user registers for the app, the My Number card is scanned and the user's identity is verified based on the scanned information. This prevents fake profiles and fraudulent use. The image generation unit generates an avatar based on a facial photograph of the user authenticated by the identity authentication unit. For example, the generation AI uses the user's facial photograph as input to generate an avatar image that protects privacy. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate an avatar that reflects the user's characteristics while being processed to prevent personal identification. The compatibility assessment unit analyzes the user's profile information based on the avatar generated by the image generation unit and suggests compatible partners. For example, the generation AI analyzes information entered by the user and suggests optimal matches. The generation AI performs compatibility assessment based on prompts containing instructions from the user regarding what the user wants the generation AI to do. The virtual dating unit provides a date in the metaverse with a partner suggested by the compatibility assessment unit. For example, the user can visit a cafe or park in the metaverse and enjoy conversations and activities through avatars. This allows the matching app according to the embodiment to assess compatibility between users and promote interaction in a virtual space with high reliability and privacy protection.

[0030] The personal authentication unit can further increase reliability by referring to the user's past public records based on the information on the My Number card. For example, the personal authentication unit can refer to the user's past tax payment history based on the information on the My Number card to confirm the tax payment status. This evaluates the user's financial reliability. The personal authentication unit can also refer to the user's resident registration information based on the information on the My Number card to confirm the current address and past address history. This evaluates the reliability of the user's place of residence. The personal authentication unit can also refer to the user's public records (for example, pension enrollment history and health insurance enrollment history) based on the information on the My Number card to evaluate the user's social reliability. This can further increase the user's reliability.

[0031] The identity authentication unit can use facial recognition technology when scanning a My Number card to verify that the card owner matches the actual user. For example, when scanning a My Number card, the identity authentication unit uses facial recognition technology to photograph the user's face and verify that it matches the photo on the card. This prevents fraudulent use of the card. The identity authentication unit also uses facial recognition technology to analyze the user's facial features and compare them with the photo on the My Number card. For example, it evaluates the degree of match based on the position of the eyes, nose, and mouth. The identity authentication unit also uses facial recognition technology when scanning a My Number card to build a system that matches the user's face with the photo on the card in real time. This improves the accuracy of identity verification. This prevents fraudulent use of the card.

[0032] The identity authentication unit can also enable identity authentication using official IDs other than the My Number Card. For example, the identity authentication unit can scan official IDs other than the My Number Card (such as a driver's license or passport) and build a system to verify identity based on that information. This makes it possible to authenticate identity using multiple IDs. The identity authentication unit can also refer to the user's past official records based on the official ID information to evaluate trustworthiness. For example, it can check the traffic violation history based on the driver's license information. The identity authentication unit can also develop an identity authentication system that uses official IDs other than the My Number Card, allowing the user to select multiple IDs for identity verification. For example, it can evaluate international trustworthiness based on passport information. This makes it possible to authenticate identity using multiple IDs.

[0033] The identity authentication unit can provide double security by adding voiceprint authentication of the user when authenticating the user. For example, the identity authentication unit will build a system that records the user's voiceprint when scanning the My Number card and performs voiceprint authentication. This provides double security. The identity authentication unit also uses voiceprint authentication technology to analyze the characteristics of the user's voice and compare it with the information on the My Number card. For example, it will evaluate the degree of match based on the tone and rhythm of the voice. The identity authentication unit will also use voiceprint authentication when scanning the My Number card, developing a system that compares the user's voice with registered voiceprints in real time. This will improve the accuracy of identity verification. This will provide double security.

[0034] The image generation unit reflects the user's hobbies and preferences in the generated avatar, allowing for the creation of a more unique avatar. For example, the image generation unit builds a system in which a generation AI uses the user's hobbies and preferences as input to customize the avatar's appearance and clothing based on that data. For example, for a user who enjoys the outdoors, it generates an outdoor-style avatar. The image generation unit also uses the generation AI to analyze the user's hobbies and preferences and customize the avatar's design based on the results. For example, for a user whose hobby is watching movies, it generates an avatar that looks like a movie character. The image generation unit also develops a system in which the generation AI analyzes the user's input data and suggests unique designs to generate an avatar that reflects the user's hobbies and preferences. For example, for a user who enjoys music, it generates an avatar holding an instrument. This makes it possible to create a unique avatar that reflects the user's hobbies and preferences.

[0035] The image generation unit can create an avatar that reflects the user's growth process based on past photos, giving the impression of the passage of time. The image generation unit, for example, uses past photos of the user as input and builds a system in which the generation AI reflects the avatar's growth process based on that data. For example, the avatar is generated based on photos from childhood to the present. The image generation unit also uses the generation AI to analyze the user's past photos and reflect the avatar's growth process in real time based on the results. For example, the avatar's appearance is changed according to the user's age. The image generation unit also develops an algorithm that allows the generation AI to reflect the avatar's growth process based on the user's past photos. For example, the image generation unit analyzes past photos and changes the avatar's appearance and clothing over time. This makes it possible to create an avatar that reflects the user's growth process.

[0036] The image generation unit adds a voice synthesis function based on the user's voice to the generated avatar, enabling more realistic communication. For example, the image generation unit records the user's voice and builds a system in which a generation AI synthesizes the avatar's voice based on that data. For example, the avatar's voice is generated to reflect the characteristics of the user's voice. The image generation unit also uses voice synthesis technology to generate the avatar's voice in real time based on the user's voice. For example, the avatar reproduces what the user says in the same voice. The image generation unit also adds a voice synthesis function based on the user's voice, developing a system in which the generation AI changes the avatar's voice in real time. For example, the tone of the avatar's voice is changed depending on the user's emotional state. This enables realistic communication based on the user's voice.

[0037] The image generation unit adds a function to track the user's movements to the generated avatar, allowing the movements to be reflected in real time. For example, the image generation unit uses a sensor to track the user's movements, and the generation AI builds a system that uses that data to reflect the avatar's movements in real time. For example, when the user waves their hand, the avatar also waves their hand. The image generation unit also uses motion tracking technology to analyze the user's movements in real time, and the generation AI reproduces the avatar's movements based on that data. For example, when the user walks, the avatar also moves in the same way. The image generation unit also adds a function to track the user's movements, and a system is developed in which the generation AI changes the avatar's movements in real time. For example, the user's facial expressions and gestures can be reflected in the avatar. This allows the user's movements to be reflected in real time.

[0038] The compatibility diagnosis unit can analyze the user's past matching history and learn compatibility patterns with a high success rate. For example, the compatibility diagnosis unit constructs a system in which a generation AI analyzes the user's past matching history and learns compatibility patterns with a high success rate. For example, it suggests the most suitable partner based on past success cases. The compatibility diagnosis unit also analyzes the user's past matching history and develops an algorithm in which the generation AI learns compatibility patterns with a high success rate based on that data. For example, it performs matching based on patterns with a high success rate. The compatibility diagnosis unit also constructs a database in which the generation AI analyzes the user's past matching history and learns compatibility patterns with a high success rate. For example, it suggests the most suitable partner based on past matching data. This makes it possible to learn compatibility patterns with a high success rate.

[0039] The compatibility diagnosis unit can analyze a user's real-time behavioral data and dynamically evaluate compatibility. For example, the compatibility diagnosis unit constructs a system in which a generation AI analyzes a user's real-time behavioral data and dynamically evaluates compatibility. For example, compatibility is evaluated based on operation history within the app. The compatibility diagnosis unit also analyzes a user's real-time behavioral data and develops an algorithm in which the generation AI dynamically evaluates compatibility based on that data. For example, compatibility is evaluated based on operation history and browsing history. The compatibility diagnosis unit also constructs a database in which the generation AI analyzes a user's real-time behavioral data and dynamically evaluates compatibility. For example, the generation AI suggests the most suitable partner based on operation history within the app. This makes it possible to dynamically evaluate compatibility based on real-time behavioral data.

[0040] The compatibility diagnosis unit can perform compatibility diagnosis that takes into account not only the user's hobbies and preferences, but also their lifestyle habits and values. For example, the compatibility diagnosis unit constructs a system in which the generation AI analyzes the user's hobbies, preferences, lifestyle habits, and values ​​to perform a comprehensive compatibility diagnosis. For example, it evaluates compatibility based on eating habits and sleeping habits. The compatibility diagnosis unit also develops an algorithm in which the generation AI inputs the user's lifestyle habits and values ​​and performs a compatibility diagnosis based on that data. For example, it evaluates compatibility based on the degree of agreement of values. The compatibility diagnosis unit also constructs a database in which the generation AI analyzes the user's hobbies, preferences, lifestyle habits, and values ​​to perform a comprehensive compatibility diagnosis. For example, it suggests the most suitable partner based on data on lifestyle habits and values. This makes it possible to perform compatibility diagnosis that also takes lifestyle habits and values ​​into account.

[0041] The compatibility diagnosis unit can analyze a user's friendships and social network and perform compatibility diagnosis based on mutual acquaintances. For example, the compatibility diagnosis unit uses a generation AI to analyze a user's friendships and social network and build a system that performs compatibility diagnosis based on mutual acquaintances. For example, it matches users who have many mutual friends. The compatibility diagnosis unit also analyzes a user's social network and the generation AI develops an algorithm that performs compatibility diagnosis based on mutual acquaintances based on that data. For example, it evaluates compatibility based on the number of mutual friends. The compatibility diagnosis unit also uses a generation AI to analyze a user's friendships and social network and build a database for compatibility diagnosis based on mutual acquaintances. For example, it suggests the most suitable partner based on social network data. This makes it possible to perform compatibility diagnosis based on mutual acquaintances.

[0042] The virtual dating unit uses a generation AI to analyze a user's past dating history during a date in the metaverse and propose date plans with a high success rate. For example, the virtual dating unit builds a system in which, during a date in the metaverse, the generation AI analyzes a user's past dating history and proposes date plans with a high success rate based on that data. For example, it proposes date plans based on date plans that have been successful in the past. The virtual dating unit also analyzes a user's past dating history and develops an algorithm in which the generation AI uses that data to propose date plans with a high success rate. For example, it proposes plans based on the success rate of past date plans. The virtual dating unit also builds a database in which the generation AI analyzes a user's past dating history and proposes date plans with a high success rate. For example, it proposes the optimal plan based on data from past date plans. This makes it possible to propose date plans with a high success rate.

[0043] The virtual dating unit allows the generation AI to analyze the user's real-time behavioral data during a date in the metaverse and dynamically adjust the date plan. For example, the virtual dating unit builds a system in which the generation AI analyzes the user's real-time behavioral data during a date in the metaverse and dynamically adjusts the date plan based on that data. For example, the date plan is changed according to the user's behavior. The virtual dating unit also analyzes the user's real-time behavioral data and develops an algorithm in which the generation AI dynamically adjusts the date plan based on that data. For example, the date plan is adjusted based on the user's behavior patterns. The virtual dating unit also builds a database in which the generation AI analyzes the user's real-time behavioral data and dynamically adjusts the date plan. For example, the generation AI suggests the optimal date plan based on the user's behavior data. This makes it possible to dynamically adjust the date plan based on the real-time behavioral data.

[0044] The virtual dating department allows the generation AI to propose customized date plans based on the user's hobbies and preferences during a date in the metaverse. For example, the virtual dating department builds a system in which the generation AI analyzes the user's hobbies and preferences during a date in the metaverse and proposes customized date plans based on that data. For example, outdoor activities are proposed for a user who likes the outdoors. The virtual dating department also monitors the user's hobbies and preferences in real time and develops an algorithm in which the generation AI proposes customized date plans based on that data. For example, a movie date is proposed for a user who likes watching movies. The virtual dating department also builds a database in which the generation AI analyzes the user's hobbies and preferences and proposes customized date plans. For example, the optimal date plan is proposed based on data on the user's hobbies and preferences. This makes it possible to propose customized date plans based on the user's hobbies and preferences.

[0045] The virtual dating unit uses a generation AI to analyze a user's friendships during a date in the metaverse and suggest date plans based on mutual acquaintances. For example, the virtual dating unit builds a system in which a generation AI analyzes a user's friendships during a date in the metaverse and uses that data to suggest date plans based on mutual acquaintances. For example, it suggests date spots recommended by mutual friends. The virtual dating unit also monitors a user's friendships in real time and develops an algorithm in which the generation AI uses that data to suggest date plans based on mutual acquaintances. For example, it matches users who have many mutual friends. The virtual dating unit also builds a database in which the generation AI analyzes a user's friendships and suggests date plans based on mutual acquaintances. For example, it suggests optimal date plans based on social network data. This makes it possible to suggest date plans based on mutual acquaintances.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The personal authentication unit can also verify the identity of a user by using biometric authentication information of the user. For example, fingerprint authentication can be used to scan the user's fingerprint and verify the identity based on that information. The personal authentication unit can also use iris authentication to scan the user's iris and verify the identity based on that information. Furthermore, the personal authentication unit can use voice authentication to record the user's voice and verify the identity based on that voice data. This enables highly accurate identity verification using multiple biometric authentication methods.

[0048] The identity authentication unit can further increase trustworthiness by referencing the user's past online activity history. For example, it can analyze the user's past social media posting history to evaluate trustworthiness. The identity authentication unit can also evaluate the user's financial trustworthiness by referencing the user's past online shopping history. Furthermore, the identity authentication unit can also analyze the user's behavioral patterns by referencing the user's past online game play history. This enables a multifaceted trustworthiness evaluation based on the user's online activity history.

[0049] The personal authentication unit can also verify the user's identity using the user's health data. For example, it can verify the user's identity based on heart rate data obtained from the user's smartwatch. The personal authentication unit can also analyze the user's step count data and verify the user's identity based on that data. Furthermore, the personal authentication unit can also refer to the user's sleep data to evaluate the user's health condition. This enables multifaceted personal authentication using health data.

[0050] The personal authentication unit can further increase reliability by referencing the user's past travel history. For example, it can check the user's past travel history based on the user's passport information. The personal authentication unit can also evaluate the user's travel frequency by referencing the user's airline ticket purchase history. Furthermore, the personal authentication unit can also analyze the user's behavioral patterns at travel destinations by referencing the user's accommodation history. This makes it possible to evaluate reliability from multiple angles based on the user's travel history.

[0051] The image generation unit reflects the user's hobbies and preferences in the generated avatar, making it possible to create a more unique avatar. For example, we will build a system in which the generation AI uses the user's hobbies and preferences as input to customize the avatar's appearance and clothing based on that data. For example, for a user who likes the outdoors, it will generate an outdoor-style avatar. The image generation unit also uses the generation AI to analyze the user's hobbies and preferences and customize the avatar's design based on the results. For example, for a user who enjoys watching movies, it will generate an avatar that looks like a movie character. We will also develop a system in which the generation AI analyzes the user's input data and suggests unique designs to generate an avatar that reflects the user's hobbies and preferences. For example, for a user who likes music, it will generate an avatar holding an instrument. This makes it possible to create a unique avatar that reflects the user's hobbies and preferences.

[0052] The image generation unit can reflect the user's growth process based on past photos in the generated avatar, creating an avatar that gives the impression of the passage of time. For example, a system can be constructed in which a generation AI uses the user's past photos as input to reflect the avatar's growth process based on that data. For example, an avatar can be generated based on photos from childhood to the present. The image generation unit also uses the generation AI to analyze the user's past photos and, based on the results, reflect the avatar's growth process in real time. For example, the avatar's appearance can be changed according to the user's age. The image generation unit also develops an algorithm that uses the user's past photos to allow the generation AI to reflect the avatar's growth process. For example, the image generation unit can analyze past photos and change the avatar's appearance and clothing over time. This makes it possible to create an avatar that reflects the user's growth process.

[0053] The image generation unit can add a voice synthesis function based on the user's voice to the generated avatar, enabling more realistic communication. For example, a system can be built in which the user's voice is recorded and the generation AI synthesizes the avatar's voice based on that data. For example, the avatar's voice can be generated to reflect the characteristics of the user's voice. The image generation unit can also use voice synthesis technology to generate the avatar's voice in real time based on the user's voice. For example, the avatar can reproduce what the user says in the same voice. The image generation unit can also add a voice synthesis function based on the user's voice, developing a system in which the generation AI can change the avatar's voice in real time. For example, the tone of the avatar's voice can be changed depending on the user's emotional state. This makes it possible to achieve realistic communication based on the user's voice.

[0054] The image generation unit will add a function to track the user's movements to the generated avatar, allowing the movements to be reflected in real time. For example, a system will be built using a sensor that tracks the user's movements, allowing the generation AI to reflect the avatar's movements in real time based on that data. For example, when the user waves their hand, the avatar also waves their hand. The image generation unit will also use movement tracking technology to analyze the user's movements in real time, and the generation AI will reproduce the avatar's movements based on that data. For example, when the user walks, the avatar will also make the same movement. The image generation unit will also add a function to track the user's movements, allowing the generation AI to develop a system that changes the avatar's movements in real time. For example, the user's facial expressions and gestures will be reflected in the avatar. This will allow the user's movements to be reflected in real time.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The identity authentication unit authenticates the user using their My Number card. For example, when a user registers for the app, the My Number card is scanned and their identity is verified based on that information. This prevents fake profiles and fraudulent use. Step 2: The image generation unit generates an avatar based on the user's facial photo authenticated by the identity authentication unit. For example, the generation AI uses the user's facial photo as input to generate an avatar image that protects privacy. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate an avatar that reflects the user's features while being processed so that the individual cannot be identified. Step 3: The compatibility diagnosis unit analyzes the user's profile information based on the avatar generated by the image generation unit and suggests compatible partners. For example, the generation AI analyzes the information entered by the user and suggests optimal matching partners. The generation AI performs compatibility diagnosis based on prompts that include instructions on what the user wants the generation AI to do. Step 4: The virtual dating section offers a date in the metaverse with a partner suggested by the compatibility diagnosis section. For example, the user can visit a cafe or park in the metaverse and enjoy conversations and activities through avatars.

[0057] (Example 2) The matching app according to an embodiment of the present invention is a system that uses My Number cards to authenticate users, shares appearance information while protecting privacy using a generation AI, diagnoses compatibility, and provides dating within the metaverse. This makes the matching app highly reliable, protects privacy, and can diagnose compatibility between users and promote interaction in virtual space.

[0058] A matching app according to an embodiment includes an identity authentication unit, an image generation unit, a compatibility assessment unit, and a virtual dating unit. The identity authentication unit authenticates a user using a My Number card. For example, when a user registers for the app, the My Number card is scanned and the user's identity is verified based on the scanned information. This prevents fake profiles and fraudulent use. The image generation unit generates an avatar based on a facial photograph of the user authenticated by the identity authentication unit. For example, the generation AI uses the user's facial photograph as input to generate an avatar image that protects privacy. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate an avatar that reflects the user's characteristics while being processed to prevent personal identification. The compatibility assessment unit analyzes the user's profile information based on the avatar generated by the image generation unit and suggests compatible partners. For example, the generation AI analyzes information entered by the user and suggests optimal matches. The generation AI performs compatibility assessment based on prompts containing instructions from the user regarding what the user wants the generation AI to do. The virtual dating unit provides a date in the metaverse with a partner suggested by the compatibility assessment unit. For example, the user can visit a cafe or park in the metaverse and enjoy conversations and activities through avatars. This allows the matching app according to the embodiment to assess compatibility between users and promote interaction in a virtual space with high reliability and privacy protection.

[0059] The personal authentication unit can further increase reliability by referring to the user's past public records based on the information on the My Number card. For example, the personal authentication unit can refer to the user's past tax payment history based on the information on the My Number card to confirm the tax payment status. This evaluates the user's financial reliability. The personal authentication unit can also refer to the user's resident registration information based on the information on the My Number card to confirm the current address and past address history. This evaluates the reliability of the user's place of residence. The personal authentication unit can also refer to the user's public records (for example, pension enrollment history and health insurance enrollment history) based on the information on the My Number card to evaluate the user's social reliability. This can further increase the user's reliability.

[0060] The identity authentication unit can use facial recognition technology when scanning a My Number card to verify that the card owner matches the actual user. For example, when scanning a My Number card, the identity authentication unit uses facial recognition technology to photograph the user's face and verify that it matches the photo on the card. This prevents fraudulent use of the card. The identity authentication unit also uses facial recognition technology to analyze the user's facial features and compare them with the photo on the My Number card. For example, it evaluates the degree of match based on the position of the eyes, nose, and mouth. The identity authentication unit also uses facial recognition technology when scanning a My Number card to build a system that matches the user's face with the photo on the card in real time. This improves the accuracy of identity verification. This prevents fraudulent use of the card.

[0061] The identity authentication unit can estimate the user's emotional state based on the information on the My Number card and evaluate their psychological stability at the time of registration. For example, the identity authentication unit estimates the user's emotional state by referring to the user's past public records based on the information on the My Number card. For example, it evaluates psychological stability based on past tax payment history and resident registration information. The identity authentication unit also develops an algorithm to estimate the user's emotional state based on the information on the My Number card. For example, it analyzes past public records and quantifies psychological stability. The identity authentication unit also builds a system that estimates the user's emotional state in real time based on the information on the My Number card and evaluates their psychological stability at the time of registration. For example, it provides appropriate support based on the emotional score. This makes it possible to evaluate the user's psychological stability.

[0062] The identity authentication unit can also enable identity authentication using official IDs other than the My Number Card. For example, the identity authentication unit can scan official IDs other than the My Number Card (such as a driver's license or passport) and build a system to verify identity based on that information. This makes it possible to authenticate identity using multiple IDs. The identity authentication unit can also refer to the user's past official records based on the official ID information to evaluate trustworthiness. For example, it can check the traffic violation history based on the driver's license information. The identity authentication unit can also develop an identity authentication system that uses official IDs other than the My Number Card, allowing the user to select multiple IDs for identity verification. For example, it can evaluate international trustworthiness based on passport information. This makes it possible to authenticate identity using multiple IDs.

[0063] The identity authentication unit can provide double security by adding voiceprint authentication of the user when authenticating the user. For example, the identity authentication unit will build a system that records the user's voiceprint when scanning the My Number card and performs voiceprint authentication. This provides double security. The identity authentication unit also uses voiceprint authentication technology to analyze the characteristics of the user's voice and compare it with the information on the My Number card. For example, it will evaluate the degree of match based on the tone and rhythm of the voice. The identity authentication unit will also use voiceprint authentication when scanning the My Number card, developing a system that compares the user's voice with registered voiceprints in real time. This will improve the accuracy of identity verification. This will provide double security.

[0064] The identity authentication unit can use a user's emotion estimation function based on the information on the My Number card to evaluate the user's psychological stability at the time of registration and provide appropriate support. The identity authentication unit, for example, estimates the user's emotional state based on the information on the My Number card and builds a system to evaluate the user's psychological stability at the time of registration. For example, it provides appropriate support based on the emotion score. The identity authentication unit also uses the emotion estimation function to evaluate the user's psychological stability in real time and provide support as needed. For example, it provides information about psychological counseling. The identity authentication unit also estimates the user's emotional state based on the information on the My Number card and develops an algorithm to evaluate the user's psychological stability at the time of registration. For example, it analyzes past public records and quantifies psychological stability. This makes it possible to evaluate the user's psychological stability and provide appropriate support.

[0065] The image generation unit reflects the user's emotional state in the generated avatar, allowing it to change emotional expressions in real time. For example, the image generation unit will build a system in which the generation AI analyzes the user's emotional state and changes the avatar's facial expression in real time based on the results. For example, when the user smiles, the avatar will also smile. The image generation unit will also monitor the user's emotional state in real time, and the generation AI will dynamically adjust the avatar's emotional expression based on that data. For example, when the user is surprised, the avatar will also have a surprised expression. The image generation unit will also use an emotion estimation function to analyze the user's emotional state, and the generation AI will develop a system in which the avatar's facial expressions and movements will change in real time based on that data. For example, when the user is sad, the avatar will also have a sad expression. This allows the user's emotional state to be reflected in real time.

[0066] The image generation unit reflects the user's hobbies and preferences in the generated avatar, allowing for the creation of a more unique avatar. For example, the image generation unit builds a system in which a generation AI uses the user's hobbies and preferences as input to customize the avatar's appearance and clothing based on that data. For example, for a user who enjoys the outdoors, it generates an outdoor-style avatar. The image generation unit also uses the generation AI to analyze the user's hobbies and preferences and customize the avatar's design based on the results. For example, for a user whose hobby is watching movies, it generates an avatar that looks like a movie character. The image generation unit also develops a system in which the generation AI analyzes the user's input data and suggests unique designs to generate an avatar that reflects the user's hobbies and preferences. For example, for a user who enjoys music, it generates an avatar holding an instrument. This makes it possible to create a unique avatar that reflects the user's hobbies and preferences.

[0067] The image generation unit can create an avatar that reflects the user's growth process based on past photos, giving the impression of the passage of time. The image generation unit, for example, uses past photos of the user as input and builds a system in which the generation AI reflects the avatar's growth process based on that data. For example, the avatar is generated based on photos from childhood to the present. The image generation unit also uses the generation AI to analyze the user's past photos and reflect the avatar's growth process in real time based on the results. For example, the avatar's appearance is changed according to the user's age. The image generation unit also develops an algorithm that allows the generation AI to reflect the avatar's growth process based on the user's past photos. For example, the image generation unit analyzes past photos and changes the avatar's appearance and clothing over time. This makes it possible to create an avatar that reflects the user's growth process.

[0068] The image generation unit adds a voice synthesis function based on the user's voice to the generated avatar, enabling more realistic communication. For example, the image generation unit records the user's voice and builds a system in which a generation AI synthesizes the avatar's voice based on that data. For example, the avatar's voice is generated to reflect the characteristics of the user's voice. The image generation unit also uses voice synthesis technology to generate the avatar's voice in real time based on the user's voice. For example, the avatar reproduces what the user says in the same voice. The image generation unit also adds a voice synthesis function based on the user's voice, developing a system in which the generation AI changes the avatar's voice in real time. For example, the tone of the avatar's voice is changed depending on the user's emotional state. This enables realistic communication based on the user's voice.

[0069] The image generation unit adds a function to track the user's movements to the generated avatar, allowing the movements to be reflected in real time. For example, the image generation unit uses a sensor to track the user's movements, and the generation AI builds a system that uses that data to reflect the avatar's movements in real time. For example, when the user waves their hand, the avatar also waves their hand. The image generation unit also uses motion tracking technology to analyze the user's movements in real time, and the generation AI reproduces the avatar's movements based on that data. For example, when the user walks, the avatar also moves in the same way. The image generation unit also adds a function to track the user's movements, and a system is developed in which the generation AI changes the avatar's movements in real time. For example, the user's facial expressions and gestures can be reflected in the avatar. This allows the user's movements to be reflected in real time.

[0070] The image generation unit can change the emotional expression of the generated avatar in real time using a user emotion estimation function. For example, the image generation unit uses the emotion estimation function to analyze the user's emotional state in real time, and a system is constructed in which the generation AI changes the avatar's facial expression based on that data. For example, when the user smiles, the avatar also smiles. The image generation unit also monitors the user's emotional state in real time, and the generation AI dynamically adjusts the avatar's emotional expression based on that data. For example, when the user is surprised, the avatar also has a surprised expression. The image generation unit also uses the emotion estimation function to analyze the user's emotional state, and a system is developed in which the generation AI changes the avatar's facial expressions and movements in real time based on that data. For example, when the user is sad, the avatar also has a sad expression. This makes it possible to change the user's emotional expression in real time.

[0071] The compatibility diagnosis unit can analyze the user's emotional state and prioritize suggesting emotionally stable partners. For example, the compatibility diagnosis unit constructs a system in which a generation AI analyzes the user's emotional state and prioritizes suggesting emotionally stable partners. For example, it matches users with high emotional scores. The compatibility diagnosis unit also monitors the user's emotional state in real time, and the generation AI recommends emotionally stable partners based on that data. For example, it prioritizes suggesting users with little emotional fluctuation. The compatibility diagnosis unit also uses an emotion estimation function to analyze the user's emotional state, and the generation AI develops an algorithm that recommends emotionally stable partners based on that data. For example, it selects the optimal partner based on the emotional score. This makes it possible to prioritize suggesting emotionally stable partners.

[0072] The compatibility diagnosis unit can analyze the user's past matching history and learn compatibility patterns with a high success rate. For example, the compatibility diagnosis unit constructs a system in which a generation AI analyzes the user's past matching history and learns compatibility patterns with a high success rate. For example, it suggests the most suitable partner based on past success cases. The compatibility diagnosis unit also analyzes the user's past matching history and develops an algorithm in which the generation AI learns compatibility patterns with a high success rate based on that data. For example, it performs matching based on patterns with a high success rate. The compatibility diagnosis unit also constructs a database in which the generation AI analyzes the user's past matching history and learns compatibility patterns with a high success rate. For example, it suggests the most suitable partner based on past matching data. This makes it possible to learn compatibility patterns with a high success rate.

[0073] The compatibility diagnosis unit can analyze a user's real-time behavioral data and dynamically evaluate compatibility. For example, the compatibility diagnosis unit constructs a system in which a generation AI analyzes a user's real-time behavioral data and dynamically evaluates compatibility. For example, compatibility is evaluated based on operation history within the app. The compatibility diagnosis unit also analyzes a user's real-time behavioral data and develops an algorithm in which the generation AI dynamically evaluates compatibility based on that data. For example, compatibility is evaluated based on operation history and browsing history. The compatibility diagnosis unit also constructs a database in which the generation AI analyzes a user's real-time behavioral data and dynamically evaluates compatibility. For example, the generation AI suggests the most suitable partner based on operation history within the app. This makes it possible to dynamically evaluate compatibility based on real-time behavioral data.

[0074] The compatibility diagnosis unit can perform compatibility diagnosis that takes into account not only the user's hobbies and preferences, but also their lifestyle habits and values. For example, the compatibility diagnosis unit constructs a system in which the generation AI analyzes the user's hobbies, preferences, lifestyle habits, and values ​​to perform a comprehensive compatibility diagnosis. For example, it evaluates compatibility based on eating habits and sleeping habits. The compatibility diagnosis unit also develops an algorithm in which the generation AI inputs the user's lifestyle habits and values ​​and performs a compatibility diagnosis based on that data. For example, it evaluates compatibility based on the degree of agreement of values. The compatibility diagnosis unit also constructs a database in which the generation AI analyzes the user's hobbies, preferences, lifestyle habits, and values ​​to perform a comprehensive compatibility diagnosis. For example, it suggests the most suitable partner based on data on lifestyle habits and values. This makes it possible to perform compatibility diagnosis that also takes lifestyle habits and values ​​into account.

[0075] The compatibility diagnosis unit can analyze a user's friendships and social network and perform compatibility diagnosis based on mutual acquaintances. For example, the compatibility diagnosis unit uses a generation AI to analyze a user's friendships and social network and build a system that performs compatibility diagnosis based on mutual acquaintances. For example, it matches users who have many mutual friends. The compatibility diagnosis unit also analyzes a user's social network and the generation AI develops an algorithm that performs compatibility diagnosis based on mutual acquaintances based on that data. For example, it evaluates compatibility based on the number of mutual friends. The compatibility diagnosis unit also uses a generation AI to analyze a user's friendships and social network and build a database for compatibility diagnosis based on mutual acquaintances. For example, it suggests the most suitable partner based on social network data. This makes it possible to perform compatibility diagnosis based on mutual acquaintances.

[0076] The compatibility diagnosis unit can use the user's emotion estimation function to preferentially suggest emotionally stable partners. The compatibility diagnosis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time, and the generation AI builds a system that suggests emotionally stable partners based on that data. For example, it matches users with high emotion scores. The compatibility diagnosis unit also monitors the user's emotional state in real time, and the generation AI develops an algorithm that suggests emotionally stable partners based on that data. For example, it preferentially suggests users with little emotional fluctuation. The compatibility diagnosis unit also uses the emotion estimation function to analyze the user's emotional state, and builds a database for the generation AI to suggest emotionally stable partners based on that data. For example, it selects the optimal partner based on the emotion score. This makes it possible to preferentially suggest emotionally stable partners.

[0077] The virtual dating unit uses a generation AI to analyze the user's emotional state during a date in the metaverse and suggest appropriate conversations and activities. For example, the virtual dating unit builds a system in which the generation AI analyzes the user's emotional state in real time during a date in the metaverse and suggests appropriate conversations and activities based on that data. For example, when the user is relaxed, it suggests relaxing activities. The virtual dating unit also monitors the user's emotional state in real time and develops an algorithm in which the generation AI suggests appropriate conversations and activities based on that data. For example, when the user is excited, it suggests active activities. The virtual dating unit also uses an emotion estimation function to analyze the user's emotional state and builds a database in which the generation AI suggests appropriate conversations and activities based on that data. For example, it selects the optimal activity based on the emotion score. This makes it possible to suggest appropriate conversations and activities according to the user's emotional state.

[0078] The virtual dating unit uses a generation AI to analyze a user's past dating history during a date in the metaverse and propose date plans with a high success rate. For example, the virtual dating unit builds a system in which, during a date in the metaverse, the generation AI analyzes a user's past dating history and proposes date plans with a high success rate based on that data. For example, it proposes date plans based on date plans that have been successful in the past. The virtual dating unit also analyzes a user's past dating history and develops an algorithm in which the generation AI uses that data to propose date plans with a high success rate. For example, it proposes plans based on the success rate of past date plans. The virtual dating unit also builds a database in which the generation AI analyzes a user's past dating history and proposes date plans with a high success rate. For example, it proposes the optimal plan based on data from past date plans. This makes it possible to propose date plans with a high success rate.

[0079] The virtual dating unit allows the generation AI to analyze the user's real-time behavioral data during a date in the metaverse and dynamically adjust the date plan. For example, the virtual dating unit builds a system in which the generation AI analyzes the user's real-time behavioral data during a date in the metaverse and dynamically adjusts the date plan based on that data. For example, the date plan is changed according to the user's behavior. The virtual dating unit also analyzes the user's real-time behavioral data and develops an algorithm in which the generation AI dynamically adjusts the date plan based on that data. For example, the date plan is adjusted based on the user's behavior patterns. The virtual dating unit also builds a database in which the generation AI analyzes the user's real-time behavioral data and dynamically adjusts the date plan. For example, the generation AI suggests the optimal date plan based on the user's behavior data. This makes it possible to dynamically adjust the date plan based on the real-time behavioral data.

[0080] The virtual dating department allows the generation AI to propose customized date plans based on the user's hobbies and preferences during a date in the metaverse. For example, the virtual dating department builds a system in which the generation AI analyzes the user's hobbies and preferences during a date in the metaverse and proposes customized date plans based on that data. For example, outdoor activities are proposed for a user who likes the outdoors. The virtual dating department also monitors the user's hobbies and preferences in real time and develops an algorithm in which the generation AI proposes customized date plans based on that data. For example, a movie date is proposed for a user who likes watching movies. The virtual dating department also builds a database in which the generation AI analyzes the user's hobbies and preferences and proposes customized date plans. For example, the optimal date plan is proposed based on data on the user's hobbies and preferences. This makes it possible to propose customized date plans based on the user's hobbies and preferences.

[0081] The virtual dating unit uses a generation AI to analyze a user's friendships during a date in the metaverse and suggest date plans based on mutual acquaintances. For example, the virtual dating unit builds a system in which a generation AI analyzes a user's friendships during a date in the metaverse and uses that data to suggest date plans based on mutual acquaintances. For example, it suggests date spots recommended by mutual friends. The virtual dating unit also monitors a user's friendships in real time and develops an algorithm in which the generation AI uses that data to suggest date plans based on mutual acquaintances. For example, it matches users who have many mutual friends. The virtual dating unit also builds a database in which the generation AI analyzes a user's friendships and suggests date plans based on mutual acquaintances. For example, it suggests optimal date plans based on social network data. This makes it possible to suggest date plans based on mutual acquaintances.

[0082] The virtual dating unit allows the generation AI to use the user's emotion estimation function to suggest emotionally stable date plans during dates in the metaverse. For example, the virtual dating unit uses the emotion estimation function to analyze the user's emotional state in real time, and builds a system in which the generation AI suggests emotionally stable date plans based on that data. For example, it suggests relaxing date spots. The virtual dating unit also monitors the user's emotional state in real time, and develops an algorithm in which the generation AI suggests emotionally stable date plans based on that data. For example, it suggests date plans with minimal emotional fluctuations. The virtual dating unit also uses the emotion estimation function to analyze the user's emotional state, and builds a database in which the generation AI suggests emotionally stable date plans based on that data. For example, it selects the optimal date plan based on the emotion score. This makes it possible to suggest emotionally stable date plans.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The personal authentication unit can also verify the identity of a user by using biometric authentication information of the user. For example, fingerprint authentication can be used to scan the user's fingerprint and verify the identity based on that information. The personal authentication unit can also use iris authentication to scan the user's iris and verify the identity based on that information. Furthermore, the personal authentication unit can use voice authentication to record the user's voice and verify the identity based on that voice data. This enables highly accurate identity verification using multiple biometric authentication methods.

[0085] The identity authentication unit can further increase trustworthiness by referencing the user's past online activity history. For example, it can analyze the user's past social media posting history to evaluate trustworthiness. The identity authentication unit can also evaluate the user's financial trustworthiness by referencing the user's past online shopping history. Furthermore, the identity authentication unit can also analyze the user's behavioral patterns by referencing the user's past online game play history. This enables a multifaceted trustworthiness evaluation based on the user's online activity history.

[0086] The personal authentication unit can also verify the user's identity using the user's health data. For example, it can verify the user's identity based on heart rate data obtained from the user's smartwatch. The personal authentication unit can also analyze the user's step count data and verify the user's identity based on that data. Furthermore, the personal authentication unit can also refer to the user's sleep data to evaluate the user's health condition. This enables multifaceted personal authentication using health data.

[0087] The personal authentication unit can estimate the user's emotional state and evaluate their psychological stability at the time of registration. For example, it can analyze the user's past social media posts to estimate their emotional state. The personal authentication unit can also analyze the user's voice data to estimate their emotional state. Furthermore, the personal authentication unit can analyze the user's facial expression data to estimate their emotional state. This allows the system to evaluate the user's emotional state from multiple angles and provide appropriate support.

[0088] The personal authentication unit can further increase reliability by referencing the user's past travel history. For example, it can check the user's past travel history based on the user's passport information. The personal authentication unit can also evaluate the user's travel frequency by referencing the user's airline ticket purchase history. Furthermore, the personal authentication unit can also analyze the user's behavioral patterns at travel destinations by referencing the user's accommodation history. This makes it possible to evaluate reliability from multiple angles based on the user's travel history.

[0089] The image generation unit reflects the user's emotional state in the generated avatar, allowing it to change emotional expressions in real time. For example, when the user smiles, the avatar also smiles. The image generation unit also monitors the user's emotional state in real time, and the generation AI dynamically adjusts the avatar's emotional expression based on that data. For example, when the user is surprised, the avatar also has a surprised expression. The image generation unit also uses an emotion estimation function to analyze the user's emotional state, and the generation AI will develop a system that changes the avatar's facial expressions and movements in real time based on that data. For example, when the user is sad, the avatar also has a sad expression. This makes it possible to reflect the user's emotional state in real time.

[0090] The image generation unit reflects the user's hobbies and preferences in the generated avatar, making it possible to create a more unique avatar. For example, we will build a system in which the generation AI uses the user's hobbies and preferences as input to customize the avatar's appearance and clothing based on that data. For example, for a user who likes the outdoors, it will generate an outdoor-style avatar. The image generation unit also uses the generation AI to analyze the user's hobbies and preferences and customize the avatar's design based on the results. For example, for a user who enjoys watching movies, it will generate an avatar that looks like a movie character. We will also develop a system in which the generation AI analyzes the user's input data and suggests unique designs to generate an avatar that reflects the user's hobbies and preferences. For example, for a user who likes music, it will generate an avatar holding an instrument. This makes it possible to create a unique avatar that reflects the user's hobbies and preferences.

[0091] The image generation unit can reflect the user's growth process based on past photos in the generated avatar, creating an avatar that gives the impression of the passage of time. For example, a system can be constructed in which a generation AI uses the user's past photos as input to reflect the avatar's growth process based on that data. For example, an avatar can be generated based on photos from childhood to the present. The image generation unit also uses the generation AI to analyze the user's past photos and, based on the results, reflect the avatar's growth process in real time. For example, the avatar's appearance can be changed according to the user's age. The image generation unit also develops an algorithm that uses the user's past photos to allow the generation AI to reflect the avatar's growth process. For example, the image generation unit can analyze past photos and change the avatar's appearance and clothing over time. This makes it possible to create an avatar that reflects the user's growth process.

[0092] The image generation unit can add a voice synthesis function based on the user's voice to the generated avatar, enabling more realistic communication. For example, a system can be built in which the user's voice is recorded and the generation AI synthesizes the avatar's voice based on that data. For example, the avatar's voice can be generated to reflect the characteristics of the user's voice. The image generation unit can also use voice synthesis technology to generate the avatar's voice in real time based on the user's voice. For example, the avatar can reproduce what the user says in the same voice. The image generation unit can also add a voice synthesis function based on the user's voice, developing a system in which the generation AI can change the avatar's voice in real time. For example, the tone of the avatar's voice can be changed depending on the user's emotional state. This makes it possible to achieve realistic communication based on the user's voice.

[0093] The image generation unit will add a function to track the user's movements to the generated avatar, allowing the movements to be reflected in real time. For example, a system will be built using a sensor that tracks the user's movements, allowing the generation AI to reflect the avatar's movements in real time based on that data. For example, when the user waves their hand, the avatar also waves their hand. The image generation unit will also use movement tracking technology to analyze the user's movements in real time, and the generation AI will reproduce the avatar's movements based on that data. For example, when the user walks, the avatar will also make the same movement. The image generation unit will also add a function to track the user's movements, allowing the generation AI to develop a system that changes the avatar's movements in real time. For example, the user's facial expressions and gestures will be reflected in the avatar. This will allow the user's movements to be reflected in real time.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The identity authentication unit authenticates the user using their My Number card. For example, when a user registers for the app, the My Number card is scanned and their identity is verified based on that information. This prevents fake profiles and fraudulent use. Step 2: The image generation unit generates an avatar based on the user's facial photo authenticated by the identity authentication unit. For example, the generation AI uses the user's facial photo as input to generate an avatar image that protects privacy. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate an avatar that reflects the user's features while being processed so that the individual cannot be identified. Step 3: The compatibility diagnosis unit analyzes the user's profile information based on the avatar generated by the image generation unit and suggests compatible partners. For example, the generation AI analyzes the information entered by the user and suggests optimal matching partners. The generation AI performs compatibility diagnosis based on prompts that include instructions on what the user wants the generation AI to do. Step 4: The virtual dating section offers a date in the metaverse with a partner suggested by the compatibility diagnosis section. For example, the user can visit a cafe or park in the metaverse and enjoy conversations and activities through avatars.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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]

[0163] 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. an identity authentication unit that authenticates the identity of a user using a My Number card; an image generation unit that generates an avatar based on a facial photograph of the user authenticated by the personal authentication unit; a compatibility diagnosis unit that analyzes profile information of a user based on the avatar generated by the image generation unit and suggests a compatible partner; a virtual dating unit that provides a date in the metaverse with a partner suggested by the compatibility diagnosis unit; A system characterized by:

2. The personal authentication unit Based on the information on the My Number card, the user's past public records are referenced to further enhance reliability.

2. The system of claim 1.

3. The personal authentication unit When scanning the My Number card, facial recognition technology is used to verify whether the card holder matches the actual user.

2. The system of claim 1.

4. The personal authentication unit Based on the information on the My Number card, the emotional state of the user is estimated and the psychological stability at the time of registration is evaluated.

2. The system of claim 1.

5. The personal authentication unit It will also be possible to use official IDs other than the My Number card for identity authentication.

2. The system of claim 1.

6. The personal authentication unit Adds voice recognition to identity authentication, providing double security 2. The system of claim 1.

Images