system

Abstract

An object of the system according to the embodiment is to give individuality and personality to a NFT3D model and cause a person to autonomously live in a Metaverse space.SOLUTION: A system according to an embodiment includes a personality forming unit, a Metaverse deployment unit, and a viewing and buying unit. The personality forming part imparts individuality and personality to the 3D model. The Metaverse placement unit places the NFT model to which the individuality and the personality are given by the personality forming unit in the Metaverse space. The viewing and selling unit views and sells the NFT model arranged by the Metaverse arrangement unit in real time.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] With conventional technology, NFT3D data was limited to its role as a simple digital asset, and had the problem of not being able to give it individuality or personality.

[0005] The system of the embodiment aims to give NFT 3D models individuality and personality, allowing them to live autonomously in the metaverse space. [Means for solving the problem]

[0006] The system according to the embodiment includes a personality development unit, a metaverse placement unit, and an appreciation / trading unit. The personality development unit assigns individuality and personality to 3D model data. The metaverse placement unit places the NFT model, to which the personality and personality has been assigned by the personality development unit, in the metaverse space. The appreciation / trading unit appreciates and trades the NFT model placed by the metaverse placement unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can give individuality and personality to NFT 3D models, allowing them to live autonomously in the metaverse space. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 system according to an embodiment of the present invention is a system that uses personality-building AI to give individuality and personality to 3D model data in NFT data, and allows the NFT model with that personality to live autonomously in the Metaverse space. This allows the system to view, buy, and sell life in the Metaverse space in real time 24 hours a day.

[0029] The system according to the embodiment includes a personality creation unit, a metaverse placement unit, and an appreciation / trading unit. The personality creation unit assigns individuality and personality to 3D model data. For example, the generation AI receives prompts containing instructions from a user as input information and generates individuality and personality for the 3D model. For example, by inputting a prompt such as, "Make this character curious and adventurous," the generation AI assigns an appropriate individuality and personality to the 3D model based on the instructions. The metaverse placement unit places the NFT model, to which the personality and personality have been assigned by the personality creation unit, in the metaverse space. For example, the generation AI generates the behavior and reactions of the NFT model in real time and simulates life in the metaverse space. For example, the generation AI controls how the NFT model converses with other characters and explores a virtual city. The appreciation / trading unit appreciates and trades the NFT model placed by the metaverse placement unit in real time. For example, a user can observe the life of the NFT model in the metaverse space and enjoy its behavior and reactions. In addition, NFT models can be bought and sold as digital assets, and users can sell their NFT models to other users. This allows the system according to the embodiment to allow NFT models to live autonomously and be viewed and traded in real time.

[0030] The personality creation unit learns the user's past behavioral history and preferences and, based on that, can bestow a personality and character that is optimal for the 3D model. For example, the generation AI analyzes the user's past behavioral history and extracts specific behavioral patterns and preferences. For example, it learns the characteristics and behavior of characters the user has previously selected and, based on that, bestows an appropriate personality and character on the 3D model. The personality creation unit also collects data on characters and avatars the user has previously created, and based on that data, the generation AI bestows a personality and character that is optimal for the 3D model. For example, it reflects the user's preferred personality and behavioral patterns. The personality creation unit also analyzes the user's social media posts and comments to understand the user's interests. Based on that information, the generation AI bestows a personality and character on the 3D model. For example, it reflects the hobbies and interests frequently mentioned by the user. This allows the model to be given a personality and character that is based on the user's preferences.

[0031] The personality development unit can dynamically adjust the personality and character based on the 3D model's appearance and movement patterns. For example, the generation AI analyzes the 3D model's appearance data and assigns a personality and character that matches its appearance. For example, a character with a brave warrior-like appearance is assigned a brave and leadership personality. The personality development unit also learns the 3D model's movement patterns and dynamically adjusts the personality and character based on those movements. For example, a character with quick movements is assigned a lively and energetic personality. The personality development unit also analyzes the 3D model's appearance and movement patterns in real time and dynamically adjusts the personality and character based on the results. For example, each time a character learns a new movement, a personality that matches that movement is added. This allows the personality and character to be dynamically adjusted according to the 3D model's appearance and movement.

[0032] The personality formation module can learn the characteristics of different cultures and regions and, based on that, give the 3D model diverse personalities and characters. For example, the generation AI learns the characteristics of different cultures and regions and, based on that information, gives the 3D model diverse personalities and characters. For example, it can give the 3D model a polite personality based on Japanese culture or a free-spirited personality based on American culture. The personality formation module can also learn the languages ​​and customs of different regions and, based on that information, give the 3D model diverse personalities and characters. For example, it can give the 3D model an elegant personality based on French culture or a spiritual personality based on Indian culture. The personality formation module can also learn the history and traditions of different cultures and regions and, based on that information, give the 3D model diverse personalities and characters. For example, it can give the 3D model a knowledgeable personality based on Chinese history or a community-oriented personality based on African traditions. This allows the generation AI to give the 3D model diverse personalities and characters based on the characteristics of different cultures and regions.

[0033] The personality formation unit can give 3D models characteristics and personalities that are suitable for specific occupations and roles. In this unit, the generation AI gives 3D models characteristics and personalities that are suitable for specific occupations. For example, a doctor character might be given a calm and caring personality. In the personality formation unit, the generation AI also gives 3D models characteristics and personalities that are suitable for specific roles. For example, a character with a leadership role might be given a decisive and leadership personality. In the personality formation unit, the generation AI also learns behavioral patterns according to occupations and roles, and gives 3D models characteristics and personalities based on those patterns. For example, a teacher character might be given an educational and patient personality. This makes it possible to give 3D models characteristics and personalities that are suitable for specific occupations and roles.

[0034] The metaverse placement unit can dynamically adjust the behavior and reactions of the NFT model in response to environmental changes within the metaverse space. For example, the generation AI dynamically adjusts the behavior and reactions of the NFT model in response to changes in the weather and time of day within the metaverse space. For example, when it is raining, the model will walk with an umbrella. The metaverse placement unit also dynamically adjusts the behavior of the NFT model in response to events and activities within the metaverse space. For example, when a festival is being held, the model will participate and enjoy the event. The metaverse placement unit also dynamically adjusts the behavior of the NFT model in response to interactions between the generation AI and other characters and objects within the metaverse space. For example, the model will respond appropriately when conversing with other characters. This allows the behavior and reactions of the NFT model to be dynamically adjusted in response to environmental changes within the metaverse space.

[0035] The metaverse placement unit can learn interactions with other NFT models and users and evolve the behavioral patterns of the NFT models based on that information. For example, the generation AI in the metaverse placement unit learns conversations and interactions with other NFT models and evolves the behavioral patterns based on the results. For example, it can make characters that frequently converse have friendly personalities. The metaverse placement unit also learns interactions with users and evolves the behavioral patterns of the NFT models based on that data. For example, it can prioritize actions that the user prefers. The metaverse placement unit also collects interaction data with other NFT models and users and evolves the behavioral patterns based on that data. For example, if a particular action is successful, it can repeat that action. This allows behavioral patterns to evolve based on interactions with other NFT models and users.

[0036] The metaverse placement unit can control the behavior of NFT models on a scenario-based basis in response to specific events and scenarios within the metaverse space. For example, the generation AI controls the behavior of NFT models on a scenario-based basis in response to specific events within the metaverse space. For example, a scenario for participating in a virtual concert can be set and behavior can be controlled based on that scenario. The metaverse placement unit also controls the behavior of NFT models in response to specific scenarios within the metaverse space. For example, an adventure scenario can be set and exploration and combat can be carried out based on that scenario. The metaverse placement unit also controls the behavior of NFT models in real time in response to specific events and scenarios in response to specific events and scenarios. For example, an emergency scenario can be set and evacuation behavior can be taken based on that scenario. This allows the behavior of NFT models to be controlled on a scenario-based basis in response to specific events and scenarios.

[0037] The metaverse placement unit can assign specific tasks and missions to the NFT model and generate actions to accomplish them. For example, the generation AI in the metaverse placement unit assigns a specific task to the NFT model and generates actions to accomplish the task. For example, the generation AI assigns a task to collect items and generates search actions for that purpose. The metaverse placement unit can also assign a specific mission to the NFT model and generate actions to accomplish that mission. For example, the generation AI can assign a mission to rescue other characters and generate actions for that purpose. The metaverse placement unit can also assign specific tasks and missions to the NFT model in real time and generate actions to accomplish that purpose. For example, the generation AI can assign a task to construct a building and generate actions for that purpose. This allows the generation AI to assign specific tasks and missions and generate actions to accomplish them.

[0038] The appreciation trading unit can add a function that records the behavioral history of an NFT model and allows users to replay past actions based on that data. For example, the appreciation trading unit adds a function where the generation AI records the behavioral history of an NFT model in real time and allows users to replay past actions based on that data. For example, replaying actions taken on a specific date and time. The appreciation trading unit also stores the behavioral history of an NFT model in a database and builds a system where users can replay past actions based on that data. For example, replaying specific events or activities. The appreciation trading unit also provides an interface where the generation AI analyzes the behavioral history of an NFT model and allows users to replay past actions based on that data. For example, the behavioral history can be displayed in timeline format and replayed. This allows the NFT model's past actions to be replayed.

[0039] The appreciation and trading unit can analyze the behavioral patterns of the NFT model and suggest optimal buying and selling times to users. For example, the generation AI analyzes the behavioral patterns of the NFT model and suggests optimal buying and selling times to users based on that data. For example, it recommends buying and selling during periods of high activity. The appreciation and trading unit also stores the behavioral patterns of the NFT model in a database and builds a system that suggests buying and selling times to users based on that data. For example, it recommends buying and selling around specific events. The appreciation and trading unit also provides an interface where the generation AI analyzes the behavioral patterns of the NFT model in real time and suggests buying and selling times to users based on that data. For example, it recommends buying and selling based on changes in behavioral patterns. This makes it possible to analyze the behavioral patterns of the NFT model and suggest optimal buying and selling times.

[0040] The appreciation trading unit can add a function that allows other users to observe the behavior of an NFT model by streaming it in real time. For example, the appreciation trading unit adds a function that allows the generation AI to stream the behavior of an NFT model in real time and allow other users to observe it. For example, specific events or activities can be live-streamed. The appreciation trading unit also builds a system that allows the generation AI to stream the behavior of an NFT model in real time and allow other users to observe that behavior. For example, it can live-stream life in the metaverse space. The appreciation trading unit also provides an interface that allows the generation AI to stream the behavior of an NFT model in real time and allow other users to observe that behavior. For example, a user can observe the behavior of their favorite NFT model in real time. This allows the behavior of an NFT model to be streamed in real time and observed by other users.

[0041] The appreciation trading unit can propose new investment opportunities to users based on the behavior of NFT models. For example, the generation AI analyzes the behavioral data of NFT models and proposes new investment opportunities to users based on that data. For example, it recommends NFT models with active behavior as investment targets. The appreciation trading unit also stores the behavioral data of NFT models in a database and builds a system that proposes new investment opportunities to users based on that data. For example, it recommends NFT models with specific behavioral patterns as investment targets. The appreciation trading unit also provides an interface where the generation AI analyzes the behavioral data of NFT models in real time and proposes new investment opportunities to users based on that data. For example, it proposes investment opportunities based on changes in behavioral data. This makes it possible to propose new investment opportunities based on the behavior of NFT models.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's vital signs and monitors the user's health condition in real time. For example, it collects data such as heart rate, blood pressure, and body temperature, and issues an alert if an abnormality is detected. The health management unit can also provide appropriate exercise and dietary advice based on the user's health data. For example, if it detects a lack of exercise, it can send a notification encouraging the user to exercise. The health management unit can also analyze the user's health data over the long term and predict health risks. For example, it can predict future health risks based on past data and suggest preventive measures. This makes it possible to monitor the user's health condition in real time and provide appropriate advice.

[0044] The system may further include a learning management unit that manages the user's learning progress. The learning management unit collects the user's learning data and monitors the user's learning progress in real time. For example, it collects data such as study time, study content, and test scores to grasp the learning progress. The learning management unit can also propose an appropriate study plan based on the user's learning data. For example, it can propose a plan that focuses on weak areas. The learning management unit can also analyze the user's learning data over the long term and provide advice to maximize learning effectiveness. For example, it can propose the optimal study method based on past data. This makes it possible to monitor the user's learning progress in real time and provide an appropriate study plan.

[0045] The system may further include a hobby management unit that manages the user's hobbies and interests. The hobby management unit collects data on the user's hobbies and interests and supports hobby activities. For example, it provides information on events and activities that the user is interested in. The hobby management unit can also suggest appropriate hobby activities based on the user's hobby data. For example, it can provide advice on finding a new hobby. The hobby management unit can also analyze the user's hobby data over the long term and provide advice on maximizing the effectiveness of hobby activities. For example, it can suggest optimal hobby activities based on past data. This supports the user's hobbies and interests and provides fulfilling hobby activities.

[0046] The system can further include a travel management unit that supports the user's travel plans. The travel management unit collects the user's travel data and supports the travel plan. For example, it collects data such as the places the user wants to visit, budget, and schedule, and proposes the optimal travel plan. The travel management unit can also propose appropriate travel destinations and activities based on the user's travel data. For example, it can suggest a resort when the user wants to relax. The travel management unit can also analyze the user's travel data over the long term and provide advice to maximize the effectiveness of the trip. For example, it can propose the optimal travel plan based on past data. This supports the user's travel plans and provides a fulfilling travel experience.

[0047] The system may further include a sleep management unit that manages the user's sleep state. The sleep management unit collects the user's sleep data and monitors the sleep state in real time. For example, it collects data such as sleep time, sleep quality, and the number of times the user turns over in sleep, and provides appropriate sleep advice. The sleep management unit can also suggest an appropriate sleep environment based on the user's sleep data. For example, it can suggest comfortable bedding, room temperature, lighting, etc. The sleep management unit can also analyze the user's sleep data over the long term and provide advice to improve sleep quality. For example, it can suggest optimal sleep habits based on past data. This makes it possible to monitor the user's sleep state in real time and provide appropriate advice.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The personality creation unit gives the 3D model data a personality and character. For example, the generation AI receives prompts containing instructions from the user as input information and generates a personality and character for the 3D model. For example, by inputting a prompt such as "Make this character curious and adventurous," the generation AI gives the 3D model an appropriate personality and character based on the instructions. Step 2: The Metaverse Placement Unit places the NFT model, given a personality and character by the Personality Creation Unit, into the Metaverse space. For example, the Generation AI generates the NFT model's actions and reactions in real time, simulating life in the Metaverse space. For example, the Generation AI controls how the NFT model converses with other characters and explores virtual towns. Step 3: The Viewing and Trading Unit views and trades the NFT models placed by the Metaverse Placement Unit in real time. For example, users can observe the life of the NFT models in the Metaverse space and enjoy their actions and reactions. NFT models can also be bought and sold as digital assets, and users can sell their NFT models to other users.

[0050] (Example 2) The system according to an embodiment of the present invention is a system that uses personality-building AI to give individuality and personality to 3D model data in NFT data, and allows the NFT model with that personality to live autonomously in the Metaverse space. This allows the system to view, buy, and sell life in the Metaverse space in real time 24 hours a day.

[0051] The system according to the embodiment includes a personality creation unit, a metaverse placement unit, and an appreciation / trading unit. The personality creation unit assigns individuality and personality to 3D model data. For example, the generation AI receives prompts containing instructions from a user as input information and generates individuality and personality for the 3D model. For example, by inputting a prompt such as, "Make this character curious and adventurous," the generation AI assigns an appropriate individuality and personality to the 3D model based on the instructions. The metaverse placement unit places the NFT model, to which the personality and personality have been assigned by the personality creation unit, in the metaverse space. For example, the generation AI generates the behavior and reactions of the NFT model in real time and simulates life in the metaverse space. For example, the generation AI controls how the NFT model converses with other characters and explores a virtual city. The appreciation / trading unit appreciates and trades the NFT model placed by the metaverse placement unit in real time. For example, a user can observe the life of the NFT model in the metaverse space and enjoy its behavior and reactions. In addition, NFT models can be bought and sold as digital assets, and users can sell their NFT models to other users. This allows the system according to the embodiment to allow NFT models to live autonomously and be viewed and traded in real time.

[0052] The personality creation unit learns the user's past behavioral history and preferences and, based on that, can bestow a personality and character that is optimal for the 3D model. For example, the generation AI analyzes the user's past behavioral history and extracts specific behavioral patterns and preferences. For example, it learns the characteristics and behavior of characters the user has previously selected and, based on that, bestows an appropriate personality and character on the 3D model. The personality creation unit also collects data on characters and avatars the user has previously created, and based on that data, the generation AI bestows a personality and character that is optimal for the 3D model. For example, it reflects the user's preferred personality and behavioral patterns. The personality creation unit also analyzes the user's social media posts and comments to understand the user's interests. Based on that information, the generation AI bestows a personality and character on the 3D model. For example, it reflects the hobbies and interests frequently mentioned by the user. This allows the model to be given a personality and character that is based on the user's preferences.

[0053] The personality development unit can dynamically adjust the personality and character based on the 3D model's appearance and movement patterns. For example, the generation AI analyzes the 3D model's appearance data and assigns a personality and character that matches its appearance. For example, a character with a brave warrior-like appearance is assigned a brave and leadership personality. The personality development unit also learns the 3D model's movement patterns and dynamically adjusts the personality and character based on those movements. For example, a character with quick movements is assigned a lively and energetic personality. The personality development unit also analyzes the 3D model's appearance and movement patterns in real time and dynamically adjusts the personality and character based on the results. For example, each time a character learns a new movement, a personality that matches that movement is added. This allows the personality and character to be dynamically adjusted according to the 3D model's appearance and movement.

[0054] The personality development unit can use the emotion estimation function to change the personality and character of the 3D model in real time according to the user's emotional state. For example, the personality development unit can use the emotion estimation function to analyze the user's emotional state in real time and change the personality and character of the 3D model based on the results. For example, when the user is happy, the 3D model will have a bright and cheerful personality. The personality development unit can also detect the user's emotional state using the emotion estimation function and change the behavior and reactions of the 3D model according to the emotion. For example, when the user is sad, the 3D model will take a comforting action. The personality development unit can also use the emotion estimation function to collect user emotional data and dynamically adjust the personality and character of the 3D model based on the data. For example, when the user is stressed, the 3D model will take a relaxing action. This allows the personality and character of the 3D model to change in real time according to the user's emotional state.

[0055] The personality formation module can learn the characteristics of different cultures and regions and, based on that, give the 3D model diverse personalities and characters. For example, the generation AI learns the characteristics of different cultures and regions and, based on that information, gives the 3D model diverse personalities and characters. For example, it can give the 3D model a polite personality based on Japanese culture or a free-spirited personality based on American culture. The personality formation module can also learn the languages ​​and customs of different regions and, based on that information, give the 3D model diverse personalities and characters. For example, it can give the 3D model an elegant personality based on French culture or a spiritual personality based on Indian culture. The personality formation module can also learn the history and traditions of different cultures and regions and, based on that information, give the 3D model diverse personalities and characters. For example, it can give the 3D model a knowledgeable personality based on Chinese history or a community-oriented personality based on African traditions. This allows the generation AI to give the 3D model diverse personalities and characters based on the characteristics of different cultures and regions.

[0056] The personality formation unit can give 3D models characteristics and personalities that are suitable for specific occupations and roles. In this unit, the generation AI gives 3D models characteristics and personalities that are suitable for specific occupations. For example, a doctor character might be given a calm and caring personality. In the personality formation unit, the generation AI also gives 3D models characteristics and personalities that are suitable for specific roles. For example, a character with a leadership role might be given a decisive and leadership personality. In the personality formation unit, the generation AI also learns behavioral patterns according to occupations and roles, and gives 3D models characteristics and personalities based on those patterns. For example, a teacher character might be given an educational and patient personality. This makes it possible to give 3D models characteristics and personalities that are suitable for specific occupations and roles.

[0057] The personality development unit can use the emotion estimation function to analyze the emotions the user has toward the 3D model and optimize the individuality and personality based on the results. For example, the personality development unit can use the emotion estimation function to analyze the emotions the user has toward the 3D model in real time and optimize the individuality and personality based on the results. For example, it can strengthen personality traits that the user has favorable feelings about. The personality development unit can also collect the user's emotional reactions using the emotion estimation function and adjust the individuality and personality of the 3D model based on the data. For example, it can remove personality traits that the user finds unpleasant and add personality traits that the user finds favorable. The personality development unit can also use the emotion estimation function to analyze the user's emotional data and optimize the individuality and personality of the 3D model based on the results. For example, when the user is having fun, the 3D model can be adjusted to have a cheerful personality. This allows the individuality and personality of the 3D model to be optimized based on the user's emotions.

[0058] The metaverse placement unit can dynamically adjust the behavior and reactions of the NFT model in response to environmental changes within the metaverse space. For example, the generation AI dynamically adjusts the behavior and reactions of the NFT model in response to changes in the weather and time of day within the metaverse space. For example, when it is raining, the model will walk with an umbrella. The metaverse placement unit also dynamically adjusts the behavior of the NFT model in response to events and activities within the metaverse space. For example, when a festival is being held, the model will participate and enjoy the event. The metaverse placement unit also dynamically adjusts the behavior of the NFT model in response to interactions between the generation AI and other characters and objects within the metaverse space. For example, the model will respond appropriately when conversing with other characters. This allows the behavior and reactions of the NFT model to be dynamically adjusted in response to environmental changes within the metaverse space.

[0059] The metaverse placement unit can learn interactions with other NFT models and users and evolve the behavioral patterns of the NFT models based on that information. For example, the generation AI in the metaverse placement unit learns conversations and interactions with other NFT models and evolves the behavioral patterns based on the results. For example, it can make characters that frequently converse have friendly personalities. The metaverse placement unit also learns interactions with users and evolves the behavioral patterns of the NFT models based on that data. For example, it can prioritize actions that the user prefers. The metaverse placement unit also collects interaction data with other NFT models and users and evolves the behavioral patterns based on that data. For example, if a particular action is successful, it can repeat that action. This allows behavioral patterns to evolve based on interactions with other NFT models and users.

[0060] The metaverse placement unit uses an emotion estimation function to enable the NFT model to show emotional reactions to other characters and the environment. For example, the metaverse placement unit adds a function using the emotion estimation function to enable the NFT model to show emotional reactions to conversations and interactions with other characters. For example, a friendly character may show an emotion of joy. The metaverse placement unit also adds a function to enable the NFT model to show emotional reactions to changes in the environment within the metaverse space. For example, a beautiful landscape may be displayed as an emotional expression. The metaverse placement unit also adds a function to generate emotional reactions to other characters and the environment in real time. For example, a dangerous situation may be displayed as an emotion. This allows the NFT model to show emotional reactions to other characters and the environment.

[0061] The metaverse placement unit can control the behavior of NFT models on a scenario-based basis in response to specific events and scenarios within the metaverse space. For example, the generation AI controls the behavior of NFT models on a scenario-based basis in response to specific events within the metaverse space. For example, a scenario for participating in a virtual concert can be set and behavior can be controlled based on that scenario. The metaverse placement unit also controls the behavior of NFT models in response to specific scenarios within the metaverse space. For example, an adventure scenario can be set and exploration and combat can be carried out based on that scenario. The metaverse placement unit also controls the behavior of NFT models in real time in response to specific events and scenarios in response to specific events and scenarios. For example, an emergency scenario can be set and evacuation behavior can be taken based on that scenario. This allows the behavior of NFT models to be controlled on a scenario-based basis in response to specific events and scenarios.

[0062] The metaverse placement unit can assign specific tasks and missions to the NFT model and generate actions to accomplish them. For example, the generation AI in the metaverse placement unit assigns a specific task to the NFT model and generates actions to accomplish the task. For example, the generation AI assigns a task to collect items and generates search actions for that purpose. The metaverse placement unit can also assign a specific mission to the NFT model and generate actions to accomplish that mission. For example, the generation AI can assign a mission to rescue other characters and generate actions for that purpose. The metaverse placement unit can also assign specific tasks and missions to the NFT model in real time and generate actions to accomplish that purpose. For example, the generation AI can assign a task to construct a building and generate actions for that purpose. This allows the generation AI to assign specific tasks and missions and generate actions to accomplish them.

[0063] The metaverse placement unit uses the emotion estimation function to enable the NFT model to change its behavior in response to the user's emotional state. For example, the metaverse placement unit uses the emotion estimation function to analyze the user's emotional state in real time and change the behavior of the NFT model based on the results. For example, when the user is happy, the NFT model also behaves in a happy manner. The metaverse placement unit also detects the user's emotional state using the emotion estimation function and changes the behavior of the NFT model in response to that emotion. For example, when the user is sad, the NFT model behaves in a comforting manner. The metaverse placement unit also uses the emotion estimation function to collect user emotional data and dynamically adjust the behavior of the NFT model based on that data. For example, when the user is stressed, the NFT model behaves in a relaxing manner. This allows the behavior of the NFT model to change in response to the user's emotional state.

[0064] The appreciation trading unit can add a function that records the behavioral history of an NFT model and allows users to replay past actions based on that data. For example, the appreciation trading unit adds a function where the generation AI records the behavioral history of an NFT model in real time and allows users to replay past actions based on that data. For example, replaying actions taken on a specific date and time. The appreciation trading unit also stores the behavioral history of an NFT model in a database and builds a system where users can replay past actions based on that data. For example, replaying specific events or activities. The appreciation trading unit also provides an interface where the generation AI analyzes the behavioral history of an NFT model and allows users to replay past actions based on that data. For example, the behavioral history can be displayed in timeline format and replayed. This allows the NFT model's past actions to be replayed.

[0065] The appreciation and trading unit can analyze the behavioral patterns of the NFT model and suggest optimal buying and selling times to users. For example, the generation AI analyzes the behavioral patterns of the NFT model and suggests optimal buying and selling times to users based on that data. For example, it recommends buying and selling during periods of high activity. The appreciation and trading unit also stores the behavioral patterns of the NFT model in a database and builds a system that suggests buying and selling times to users based on that data. For example, it recommends buying and selling around specific events. The appreciation and trading unit also provides an interface where the generation AI analyzes the behavioral patterns of the NFT model in real time and suggests buying and selling times to users based on that data. For example, it recommends buying and selling based on changes in behavioral patterns. This makes it possible to analyze the behavioral patterns of the NFT model and suggest optimal buying and selling times.

[0066] The appreciation and trading unit uses the emotion estimation function to analyze the emotional impact of the NFT model's behavior on the user and can provide buying and selling advice based on the results. The appreciation and trading unit, for example, uses the emotion estimation function to analyze the emotional impact of the NFT model's behavior on the user in real time and provides buying and selling advice based on the results. For example, it recommends buying and selling based on behavior that elicits positive emotions. The appreciation and trading unit also collects the user's emotional responses using the emotion estimation function and analyzes the emotional impact of the NFT model's behavior based on that data. For example, it recommends buying and selling when the user is enjoying themselves. The appreciation and trading unit also uses the emotion estimation function to analyze the emotional impact of the NFT model's behavior on the user and builds a system that provides buying and selling advice based on the results. For example, it recommends buying and selling when the emotion score is high. This allows the emotional impact of the NFT model's behavior on the user to be analyzed and buying and selling advice to be provided.

[0067] The appreciation trading unit can add a function that allows other users to observe the behavior of an NFT model by streaming it in real time. For example, the appreciation trading unit adds a function that allows the generation AI to stream the behavior of an NFT model in real time and allow other users to observe it. For example, specific events or activities can be live-streamed. The appreciation trading unit also builds a system that allows the generation AI to stream the behavior of an NFT model in real time and allow other users to observe that behavior. For example, it can live-stream life in the metaverse space. The appreciation trading unit also provides an interface that allows the generation AI to stream the behavior of an NFT model in real time and allow other users to observe that behavior. For example, a user can observe the behavior of their favorite NFT model in real time. This allows the behavior of an NFT model to be streamed in real time and observed by other users.

[0068] The appreciation trading unit can propose new investment opportunities to users based on the behavior of NFT models. For example, the generation AI analyzes the behavioral data of NFT models and proposes new investment opportunities to users based on that data. For example, it recommends NFT models with active behavior as investment targets. The appreciation trading unit also stores the behavioral data of NFT models in a database and builds a system that proposes new investment opportunities to users based on that data. For example, it recommends NFT models with specific behavioral patterns as investment targets. The appreciation trading unit also provides an interface where the generation AI analyzes the behavioral data of NFT models in real time and proposes new investment opportunities to users based on that data. For example, it proposes investment opportunities based on changes in behavioral data. This makes it possible to propose new investment opportunities based on the behavior of NFT models.

[0069] The appreciation and trading unit can use the emotion estimation function to analyze the emotional impact of the NFT model's behavior on other users and optimize a buying and selling strategy based on the results. For example, the appreciation and trading unit can use the emotion estimation function to analyze the emotional impact of the NFT model's behavior on other users in real time and optimize a buying and selling strategy based on the results. For example, it can create a buying and selling strategy based on behavior that elicits positive emotions. The appreciation and trading unit can also collect the emotional responses of other users using the emotion estimation function and analyze the emotional impact of the NFT model's behavior based on that data. For example, it can recommend buying and selling when the user is enjoying themselves. The appreciation and trading unit can also use the emotion estimation function to analyze the emotional impact of the NFT model's behavior on other users and build a system that optimizes a buying and selling strategy based on the results. For example, it can recommend buying and selling when the emotion score is high. This allows the emotional impact of the NFT model's behavior on other users to be analyzed and a buying and selling strategy to be optimized.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The system may further include a health management unit that monitors the user's health condition. The health management unit collects the user's vital signs and monitors the user's health condition in real time. For example, it collects data such as heart rate, blood pressure, and body temperature, and issues an alert if an abnormality is detected. The health management unit can also provide appropriate exercise and dietary advice based on the user's health data. For example, if it detects a lack of exercise, it can send a notification encouraging the user to exercise. The health management unit can also analyze the user's health data over the long term and predict health risks. For example, it can predict future health risks based on past data and suggest preventive measures. This makes it possible to monitor the user's health condition in real time and provide appropriate advice.

[0072] The system may further include a learning management unit that manages the user's learning progress. The learning management unit collects the user's learning data and monitors the user's learning progress in real time. For example, it collects data such as study time, study content, and test scores to grasp the learning progress. The learning management unit can also propose an appropriate study plan based on the user's learning data. For example, it can propose a plan that focuses on weak areas. The learning management unit can also analyze the user's learning data over the long term and provide advice to maximize learning effectiveness. For example, it can propose the optimal study method based on past data. This makes it possible to monitor the user's learning progress in real time and provide an appropriate study plan.

[0073] The system may further include a hobby management unit that manages the user's hobbies and interests. The hobby management unit collects data on the user's hobbies and interests and supports hobby activities. For example, it provides information on events and activities that the user is interested in. The hobby management unit can also suggest appropriate hobby activities based on the user's hobby data. For example, it can provide advice on finding a new hobby. The hobby management unit can also analyze the user's hobby data over the long term and provide advice on maximizing the effectiveness of hobby activities. For example, it can suggest optimal hobby activities based on past data. This supports the user's hobbies and interests and provides fulfilling hobby activities.

[0074] The system may further include a music recommendation unit that recommends music based on the user's emotional state. The music recommendation unit analyzes the user's emotional state in real time and recommends appropriate music based on the results. For example, when the user wants to relax, it recommends relaxing music. The music recommendation unit can also generate a playlist based on the user's emotional data according to the emotional state. For example, when the user wants to cheer up, it generates a playlist of uplifting music. The music recommendation unit can also analyze the user's emotional data over the long term and provide advice to maximize the effect of music according to the emotional state. For example, it can suggest optimal music based on past data. This makes it possible to recommend appropriate music based on the user's emotional state.

[0075] The system may further include an exercise suggestion unit that suggests an exercise program based on the user's emotional state. The exercise suggestion unit analyzes the user's emotional state in real time and suggests an appropriate exercise program based on the results. For example, when the user is feeling stressed, it suggests a relaxing exercise. The exercise suggestion unit can also generate an exercise program according to the user's emotional state based on the user's emotional data. For example, when the user is feeling energetic, it suggests a high-intensity exercise. The exercise suggestion unit can also analyze the user's emotional data over the long term and provide advice to maximize the effect of exercise according to the emotional state. For example, it can suggest an optimal exercise program based on past data. This makes it possible to suggest an appropriate exercise program based on the user's emotional state.

[0076] The system may further include a meal suggestion unit that suggests a meal plan based on the user's emotional state. The meal suggestion unit analyzes the user's emotional state in real time and suggests an appropriate meal plan based on the results. For example, when the user is tired, it suggests meals that will replenish energy. The meal suggestion unit can also generate a meal plan according to the user's emotional state based on the user's emotional data. For example, when the user wants to relax, it suggests meals that will help them relax. The meal suggestion unit can also analyze the user's emotional data over the long term and provide advice to maximize the effectiveness of meals according to the emotional state. For example, it can suggest an optimal meal plan based on past data. This makes it possible to suggest an appropriate meal plan based on the user's emotional state.

[0077] The system may further include a relaxation suggestion unit that suggests a relaxation method based on the user's emotional state. The relaxation suggestion unit analyzes the user's emotional state in real time and suggests an appropriate relaxation method based on the results. For example, when the user is feeling stressed, it suggests relaxation methods such as meditation or deep breathing. The relaxation suggestion unit can also generate relaxation methods according to the user's emotional state based on the user's emotional data. For example, when the user wants to relax, it can suggest aromatherapy or massage. The relaxation suggestion unit can also analyze the user's emotional data over the long term and provide advice to maximize the effects of relaxation according to the user's emotional state. For example, it can suggest an optimal relaxation method based on past data. This makes it possible to suggest an appropriate relaxation method based on the user's emotional state.

[0078] The system can further include a travel management unit that supports the user's travel plans. The travel management unit collects the user's travel data and supports the travel plan. For example, it collects data such as the places the user wants to visit, budget, and schedule, and proposes the optimal travel plan. The travel management unit can also propose appropriate travel destinations and activities based on the user's travel data. For example, it can suggest a resort when the user wants to relax. The travel management unit can also analyze the user's travel data over the long term and provide advice to maximize the effectiveness of the trip. For example, it can propose the optimal travel plan based on past data. This supports the user's travel plans and provides a fulfilling travel experience.

[0079] The system may further include a sleep management unit that manages the user's sleep state. The sleep management unit collects the user's sleep data and monitors the sleep state in real time. For example, it collects data such as sleep time, sleep quality, and the number of times the user turns over in sleep, and provides appropriate sleep advice. The sleep management unit can also suggest an appropriate sleep environment based on the user's sleep data. For example, it can suggest comfortable bedding, room temperature, lighting, etc. The sleep management unit can also analyze the user's sleep data over the long term and provide advice to improve sleep quality. For example, it can suggest optimal sleep habits based on past data. This makes it possible to monitor the user's sleep state in real time and provide appropriate advice.

[0080] The system may further include a reading suggestion unit that suggests a reading plan based on the user's emotional state. The reading suggestion unit analyzes the user's emotional state in real time and suggests an appropriate reading plan based on the results. For example, when the user wants to relax, it recommends relaxing books. The reading suggestion unit can also generate a reading plan based on the user's emotional data according to the emotional state. For example, when the user wants to cheer up, it generates a reading plan that includes books that will cheer up. The reading suggestion unit can also analyze the user's emotional data over the long term and provide advice to maximize the effectiveness of reading according to the user's emotional state. For example, it can suggest an optimal reading plan based on past data. This makes it possible to suggest an appropriate reading plan based on the user's emotional state.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The personality creation unit gives the 3D model data a personality and character. For example, the generation AI receives prompts containing instructions from the user as input information and generates a personality and character for the 3D model. For example, by inputting a prompt such as "Make this character curious and adventurous," the generation AI gives the 3D model an appropriate personality and character based on the instructions. Step 2: The Metaverse Placement Unit places the NFT model, given a personality and character by the Personality Creation Unit, into the Metaverse space. For example, the Generation AI generates the NFT model's actions and reactions in real time, simulating life in the Metaverse space. For example, the Generation AI controls how the NFT model converses with other characters and explores virtual towns. Step 3: The Viewing and Trading Unit views and trades the NFT models placed by the Metaverse Placement Unit in real time. For example, users can observe the life of the NFT models in the Metaverse space and enjoy their actions and reactions. NFT models can also be bought and sold as digital assets, and users can sell their NFT models to other users.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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, in order to avoid confusion and to 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a personality formation unit that imparts individuality and personality to 3D model data; a metaverse placement unit that places NFT models that have been given individuality and personality by the personality formation unit in a metaverse space; and an appreciation and trading unit that views and buys and sells NFT models placed by the metaverse placement unit in real time.

2. The system of claim 1 , wherein the personality development unit changes the personality and character of the 3D model in real time according to the emotional state of the user.

3. The system of claim 1, wherein the metaverse placement unit dynamically adjusts the behavior and reactions of the NFT model in response to environmental changes within the metaverse space.

4. The system described in claim 1, characterized in that the appreciation and sales unit adds a function that records the behavioral history of the NFT model and allows users to replay past behavior based on that data.

5. The system according to claim 1 , wherein the personality development unit analyzes the user's feelings toward the 3D model and optimizes the individuality and character based on the results of the analysis.

6. The system of claim 1, wherein the metaverse placement unit causes the NFT model to exhibit emotional responses to other characters and environments.

7. The system described in claim 1, characterized in that the appreciation and sales unit analyzes the emotional impact that the behavior of the NFT model has on the user and provides sales and purchase advice based on the results.

8. The system described in claim 1, characterized in that the appreciation and trading unit analyzes the emotional impact that the behavior of the NFT model has on other users and optimizes the trading strategy based on the results.

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