system

Abstract

The system of the embodiment aims to apply personality formation AI to NFT-registered 3D model materials to create dynamic value. [Solution] A system according to an embodiment includes a personality development unit, a behavior control unit, and a market provision unit. The personality development unit applies personality development AI to NFT-registered 3D model materials. The behavior control unit autonomously controls the behavior of the 3D model based on the personality and behavior patterns generated by the personality development unit. The market provision unit provides users with 3D models that behave autonomously via the behavior control unit.

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, NFT-registered 3D model materials were treated as static assets, making it difficult to create dynamic value.

[0005] The system of the embodiment aims to apply personality formation AI to NFT-registered 3D model materials to create dynamic value. [Means for solving the problem]

[0006] The system according to the embodiment includes a personality development unit, a behavior control unit, and a market provision unit. The personality development unit applies personality development AI to NFT-registered 3D model materials. The behavior control unit autonomously controls the behavior of the 3D model based on the personality and behavior patterns generated by the personality development unit. The market provision unit provides users with 3D models that behave autonomously via the behavior control unit. [Effects of the Invention]

[0007] The system according to the embodiment applies personality development AI to NFT-registered 3D model materials, creating dynamic value. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides a marketplace for NFT-registered 3D model materials that are equipped with personality-building AI and allow them to behave autonomously and freely within the metaverse. This system allows NFT-registered 3D model materials to behave autonomously. For example, specific characters can interact with users and take specific actions. Next, a marketplace for these 3D models is provided within the metaverse, allowing them to behave autonomously and freely. In this marketplace, users can purchase or rent 3D models. Furthermore, 3D models can interact with other users and participate in events within the metaverse. This allows NFT assets to become living models, generating more liquid value. This allows the system to increase the value of NFT assets. For example, the popularity of a specific character can increase its value. Furthermore, the autonomous behavior of 3D models attracts user interest and stimulates activity within the metaverse.

[0029] The system according to the embodiment includes a personality development unit, a behavior control unit, and a market providing unit. The personality development unit applies personality development AI to NFT-registered 3D model materials. The personality development AI generates individual personalities and behavioral patterns for the 3D models using, for example, machine learning algorithms or neural networks. For example, the personality development unit can generate a dialogue model for a specific character to interact with a user. The personality development unit can also generate characters with specific behavioral patterns. The behavior control unit autonomously controls the behavior of the 3D models based on the personality and behavioral patterns generated by the personality development unit. For example, the behavior control unit controls the behavior of the 3D models interacting with other users in the metaverse. The behavior control unit can also control the behavior of the 3D models participating in specific events. The market providing unit provides users with 3D models that behave autonomously via the behavior control unit. For example, the market providing unit provides a platform for users to purchase 3D models. The market providing unit can also provide a function for users to rent 3D models. As a result, the system according to the embodiment can increase the value of NFT assets by providing a market in which NFT-registered 3D model materials are endowed with personality-forming AI and allowed to act autonomously and openly within the metaverse.

[0030] The behavior control unit includes a behavior recording unit that records the behavior of the 3D model. The behavior recording unit records the behavior of the 3D model. For example, the behavior recording unit stores the history of the behavior of the 3D model in a database. The behavior recording unit can also keep detailed records of when the 3D model interacts with other users. Furthermore, the behavior recording unit can also record the behavior of the 3D model when it participates in a specific event. In this way, by recording the behavior of the 3D model, the behavior history can be managed and analyzed. Some or all of the above-mentioned processing in the behavior recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior recording unit can input behavior data of the 3D model to a generation AI and cause the generation AI to record the behavior.

[0031] The market providing unit includes a rating collection unit that collects user ratings. The rating collection unit collects user ratings. For example, the rating collection unit stores ratings provided by users on 3D models in a database. The rating collection unit can also collect feedback provided by users on actions of the 3D models. Furthermore, the rating collection unit can collect user ratings regarding the quality and popularity of the 3D models. In this way, by collecting user ratings, the quality and popularity of the 3D models can be evaluated and improved. Some or all of the above-described processing in the rating collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the rating collection unit can input user rating data into a generation AI and cause the generation AI to collect ratings.

[0032] The personality development unit can generate individual personalities and behavioral patterns for the 3D model. The personality development unit can generate individual personalities and behavioral patterns for the 3D model using, for example, a machine learning algorithm or a neural network. For example, the personality development unit can generate a dialogue model for a specific character to interact with a user. The personality development unit can also generate characters with specific behavioral patterns. By generating individual personalities and behavioral patterns for the 3D model, it is possible to provide more realistic and attractive characters. Some or all of the above-described processing in the personality development unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the personality development unit can input data of the 3D model into the generation AI and cause the generation AI to generate individual personalities and behavioral patterns.

[0033] The behavior control unit can control the behavior of the 3D model when interacting with other users in the metaverse. For example, the behavior control unit controls the behavior of the 3D model when interacting with other users in the metaverse. For example, the behavior control unit controls the behavior of the 3D model when interacting with other users. The behavior control unit can also control the behavior of the 3D model when collaborating with other users to accomplish a task. Furthermore, the behavior control unit can also control the behavior of the 3D model when competing with other users. In this way, by controlling the behavior of the 3D model when interacting with other users in the metaverse, a more interactive experience can be provided. Some or all of the above-mentioned processing in the behavior control unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior control unit can input behavioral data of the 3D model to a generation AI and cause the generation AI to control the interaction behavior with other users.

[0034] The market providing unit can provide a function for purchasing or renting 3D models. For example, the market providing unit provides a platform for users to purchase 3D models. For example, the market providing unit can also provide a function for users to rent 3D models. For example, the market providing unit manages transactions when users purchase 3D models. The market providing unit can also manage contracts when users rent 3D models. By providing the function for purchasing or renting 3D models, users can freely use 3D models. Some or all of the above-described processing in the market providing unit may be performed using, or without, AI. For example, the market providing unit can input user transaction data into a generation AI and have the generation AI manage the purchase or rental.

[0035] The personality development unit can analyze the past behavioral history of the 3D model and generate an optimal personality. For example, the personality development unit retrieves and analyzes the past behavioral history of the 3D model from a database. For example, if the 3D model has interacted with many users in the past, the personality development unit can generate a sociable personality. Furthermore, if the 3D model has frequently performed a specific task in the past, the personality development unit can also generate a personality specialized for that task. Furthermore, if the 3D model has received high ratings from users in the past, the personality development unit can adjust the personality based on those ratings. This allows for the generation of a more appropriate personality by analyzing the past behavioral history of the 3D model. Some or all of the above-described processing in the personality development unit can be performed using, or without, a generation AI. For example, the personality development unit can input the behavioral history data of the 3D model into the generation AI and cause the generation AI to generate an optimal personality.

[0036] The personality development unit can apply different personality generation algorithms depending on the intended use of the 3D model. For example, the personality development unit applies an algorithm that generates a knowledgeable and teachable personality to a 3D model intended for educational purposes. For example, the personality development unit generates an educational dialogue model for a 3D model intended for educational purposes. The personality development unit can also apply an algorithm that generates a humorous and fun personality to a 3D model intended for entertainment purposes. For example, the personality development unit generates an entertaining behavior pattern for a 3D model intended for entertainment purposes. Furthermore, the personality development unit can apply an algorithm that generates a professional and reliable personality to a 3D model intended for business purposes. For example, the personality development unit generates a business-like dialogue model for a 3D model intended for business purposes. This allows for more effective use by generating an appropriate personality depending on the intended use of the 3D model. Some or all of the above-described processing in the personality development unit may be performed using, or without, a generation AI. For example, the personality development department can input data on the intended use of the 3D model into the generation AI and have the generation AI apply an appropriate personality generation algorithm.

[0037] The personality development unit can customize individual personalities based on the appearance and attributes of the 3D model. The personality development unit, for example, acquires appearance data and attribute data of the 3D model and customizes individual personalities. For example, if the 3D model has a cute appearance, the personality development unit can customize an approachable and friendly personality. Furthermore, if the 3D model's attribute is a warrior, the personality development unit can customize an individual personality with bravery and leadership skills. Furthermore, if the 3D model has a cool and stylish appearance, the personality development unit can customize an individual personality with coolness and intelligence. This allows for customizing individual personalities based on the appearance and attributes of the 3D model, thereby providing a more attractive character. Some or all of the above-described processing in the personality development unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personality development unit can input appearance data and attribute data of the 3D model into the generation AI and cause the generation AI to customize the individual personality.

[0038] When generating a personality for a 3D model, the personality development unit can generate an optimal personality by taking into account the user's geographical location information. The personality development unit, for example, acquires the user's geographical location information and generates an optimal personality. For example, if the user is in an urban area, the personality development unit can generate an urbane and sophisticated personality. Also, if the user is in the countryside, the personality development unit can generate a friendly and nature-loving personality. Furthermore, if the user is overseas, the personality development unit can generate a personality that matches the culture and customs of that region. This allows for generating an optimal personality by taking into account the user's geographical location information, thereby providing a more appropriate character. Some or all of the above-described processing in the personality development unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personality development unit can input the user's geographical location information into the generation AI and cause the generation AI to generate an optimal personality.

[0039] The personality creation unit can analyze the user's social media activity and generate a relevant personality when generating a personality for the 3D model. The personality creation unit, for example, analyzes the user's social media activity and generates a relevant personality. For example, if the user interacts with many friends on social media, the personality creation unit can generate a sociable personality. Furthermore, if the user frequently posts about a particular hobby on social media, the personality creation unit can generate a personality related to that hobby. Furthermore, if the user frequently posts about travel on social media, the personality creation unit can generate an adventurous personality. This allows for the generation of a more relevant personality by analyzing the user's social media activity. Some or all of the above-described processing in the personality creation unit may be performed using, or without, a generation AI. For example, the personality creation unit can input the user's social media data into the generation AI and cause the generation AI to generate a relevant personality.

[0040] The personality development unit can customize the personality by reflecting the user's past feedback when generating the personality of the 3D model. The personality development unit, for example, retrieves the user's past feedback from a database and customizes the personality. For example, if the user has previously given positive feedback about the 3D model, the personality development unit can strengthen the personality based on that feedback. Furthermore, if the user has previously given negative feedback about the 3D model, the personality development unit can also improve the personality based on that feedback. Furthermore, if the user has previously made specific requests about the 3D model, the personality development unit can customize the personality according to those requests. This allows for a more appropriate personality to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the personality development unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personality development unit can input the user's feedback data into the generation AI and have the generation AI customize the personality.

[0041] When controlling the behavior of a 3D model, the behavior control unit can optimize the behavior by taking into account interrelationships with other 3D models. For example, the behavior control unit controls the behavior of a 3D model to cooperate with other 3D models to accomplish a task. The behavior control unit can also control the behavior of a 3D model to compete with other 3D models. Furthermore, the behavior control unit can also control the behavior of a 3D model to share information with other 3D models. This allows for more cooperative behavior by taking into account interrelationships with other 3D models. Some or all of the above-described processing in the behavior control unit may be performed using, or without, AI. For example, the behavior control unit can input interrelationship data of the 3D models into a generation AI and cause the generation AI to optimize the behavior.

[0042] When controlling the behavior of the 3D model, the behavior control unit can customize the behavior by taking into account the user's attribute information. The behavior control unit, for example, acquires the user's attribute information and customizes the behavior. For example, if the user is young, the behavior control unit can cause the 3D model to behave in a youthful and active manner. Furthermore, if the user is elderly, the behavior control unit can cause the 3D model to behave in a calm and polite manner. Furthermore, if the user is a businessman, the behavior control unit can cause the 3D model to behave in a professional manner. This allows more appropriate behavior to be taken by taking into account the user's attribute information. Some or all of the above-described processing in the behavior control unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior control unit can input the user's attribute information into the generation AI and cause the generation AI to customize the behavior.

[0043] When controlling the behavior of a 3D model, the behavior control unit can optimize the behavior pattern by referring to past behavior data. For example, the behavior control unit obtains the 3D model's past behavior data from a database and optimizes the behavior pattern. For example, the behavior control unit reproduces behavior patterns that the 3D model has been successful in the past. The behavior control unit can also avoid behavior patterns that the 3D model has failed in the past. Furthermore, the behavior control unit can generate new behavior patterns for the 3D model based on the past behavior data. In this way, by referring to the past behavior data, more appropriate behavior patterns can be generated. Some or all of the above-described processing in the behavior control unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the behavior control unit can input the 3D model's past behavior data into the generation AI and cause the generation AI to optimize the behavior pattern.

[0044] When controlling the behavior of the 3D model, the behavior control unit can optimize the behavior by taking into account the geographical distribution. For example, the behavior control unit optimizes the behavior of the 3D model in urban areas. For example, the behavior control unit applies an algorithm for optimizing the behavior of the 3D model in urban areas. The behavior control unit can also optimize the behavior of the 3D model in rural areas. Furthermore, the behavior control unit can optimize the behavior of the 3D model in specific regions. This allows for more appropriate behavior by taking the geographical distribution into account. Some or all of the above-described processing in the behavior control unit may be performed using, or without, the generation AI. For example, the behavior control unit can input geographical distribution data of the 3D model into the generation AI and cause the generation AI to optimize the behavior.

[0045] When controlling the behavior of the 3D model, the behavior control unit can optimize the behavior pattern by referring to related literature. For example, the behavior control unit optimizes the behavior pattern of the 3D model based on the latest research results. For example, the behavior control unit applies an algorithm for optimizing the behavior pattern of the 3D model based on the latest research results. The behavior control unit can also optimize the behavior pattern of the 3D model based on past success stories. Furthermore, the behavior control unit can optimize the behavior pattern of the 3D model based on the opinions of experts. This makes it possible to generate more appropriate behavior patterns by referring to related literature. Some or all of the above-described processing in the behavior control unit may be performed using, or without, a generation AI. For example, the behavior control unit can input related literature data into the generation AI and cause the generation AI to optimize the behavior pattern.

[0046] When controlling the behavior of a 3D model, the behavior control unit can optimize the behavior by taking market value into consideration. For example, the behavior control unit prioritizes actions that result in a high market value for the 3D model. For example, the behavior control unit applies an algorithm for prioritizing actions that result in a high market value for the 3D model. The behavior control unit can also take actions that increase the market value of the 3D model. Furthermore, the behavior control unit can also take actions that maintain the market value of the 3D model. This allows actions with higher value to be taken by taking market value into consideration. Some or all of the above-described processing in the behavior control unit may be performed using, or without, the generation AI. For example, the behavior control unit can input market value data into the generation AI and cause the generation AI to optimize the behavior.

[0047] When providing a marketplace, the market providing unit can suggest optimal products by referring to the user's past purchase history. The market providing unit, for example, retrieves the user's past purchase history from a database and suggests optimal products. For example, the market providing unit suggests products related to products the user has previously purchased. The market providing unit can also suggest products related to products that the user has previously given high ratings to. Furthermore, the market providing unit can analyze trends in products the user has previously purchased and suggest optimal products. In this way, by referring to the user's past purchase history, more relevant products can be suggested. Some or all of the above-described processing in the market providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the market providing unit can input the user's purchase history data into the generation AI and cause the generation AI to suggest optimal products.

[0048] When providing a market, the market providing unit can customize the display content of the market taking into account the user's attribute information. The market providing unit, for example, acquires the user's attribute information and customizes the display content of the market. For example, if the user is a young person, the market providing unit can display products aimed at young people. Furthermore, if the user is an elderly person, the market providing unit can also display products aimed at elderly people. Furthermore, if the user is a businessman, the market providing unit can also display business-related products. In this way, more appropriate products can be displayed by taking the user's attribute information into consideration. Some or all of the above-described processing in the market providing unit may be performed using, or without, a generation AI. For example, the market providing unit can input the user's attribute information into the generation AI and cause the generation AI to customize the display content of the market.

[0049] The market providing unit can improve the market display method by reflecting user feedback when providing a market. The market providing unit, for example, obtains user feedback from a database and improves the market display method. For example, if a user has previously provided positive feedback on market display, the market providing unit can strengthen the display method based on that feedback. Also, if a user has previously provided negative feedback on market display, the market providing unit can improve the display method based on that feedback. Furthermore, if a user has previously provided specific requests, the market providing unit can customize the display method in accordance with those requests. In this way, by reflecting user feedback, a more user-friendly market can be provided. Some or all of the above-described processing in the market providing unit may be performed using, or without, a generation AI. For example, the market providing unit can input user feedback data into the generation AI and cause the generation AI to improve the market display method.

[0050] The market providing unit can propose optimal products taking geographical distribution into consideration when providing a market. The market providing unit, for example, acquires geographical distribution data of users and proposes optimal products. For example, if the user is in an urban area, the market providing unit can propose urban products. Also, if the user is in a rural area, the market providing unit can propose nature-related products. Furthermore, if the user is overseas, the market providing unit can propose products tailored to the culture and customs of that region. This allows for more appropriate product proposals by taking geographical distribution into consideration. Some or all of the above-described processing in the market providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the market providing unit can input the user's geographical distribution data into the generation AI and cause the generation AI to propose optimal products.

[0051] The market providing unit can analyze social media activity and suggest related products when providing a market. For example, the market providing unit analyzes a user's social media activity and suggests related products. For example, if a user interacts with many friends on social media, the market providing unit can suggest sociable products. Also, if a user posts frequently about a particular hobby on social media, the market providing unit can suggest products related to that hobby. Furthermore, if a user frequently posts about travel on social media, the market providing unit can suggest travel-related products. In this way, more relevant products can be suggested by analyzing social media activity. Some or all of the above-described processing in the market providing unit may be performed using, or without, a generation AI. For example, the market providing unit may input the user's social media data into the generation AI and cause the generation AI to suggest related products.

[0052] When providing a market, the market providing unit can customize the market display method by reflecting past feedback. The market providing unit, for example, obtains past user feedback from a database and customizes the market display method. For example, if a user has previously provided positive feedback on market display, the market providing unit can strengthen the display method based on that feedback. Also, if a user has previously provided negative feedback on market display, the market providing unit can improve the display method based on that feedback. Furthermore, if a user has previously made specific requests, the market providing unit can customize the display method in accordance with those requests. In this way, by reflecting past feedback, a more user-friendly market can be provided. Some or all of the above-described processing in the market providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the market providing unit can input user feedback data into the generation AI and have the generation AI customize the market display method.

[0053] When recording behavior, the behavior recording unit can select the optimal recording method by referring to the past behavior data of the 3D model. The behavior recording unit, for example, retrieves the past behavior data of the 3D model from a database and selects the optimal recording method. For example, the behavior recording unit records in detail the behaviors that the 3D model successfully performed in the past. The behavior recording unit can also briefly record the behaviors that the 3D model failed in the past. Furthermore, the behavior recording unit can generate new behavior patterns for the 3D model based on the past behavior data. This allows for more appropriate behavior recording by referring to the past behavior data. Some or all of the above-described processing in the behavior recording unit may be performed using, or without, a generation AI. For example, the behavior recording unit can input the past behavior data of the 3D model into the generation AI and have the generation AI select the optimal recording method.

[0054] The behavior recording unit can customize the recorded content by taking into account the attribute information of the 3D model when recording the behavior. The behavior recording unit, for example, acquires the attribute information of the 3D model and customizes the recorded content. For example, if the 3D model is a warrior, the behavior recording unit can record the combat behavior in detail. Furthermore, if the 3D model is a merchant, the behavior recording unit can also record the trading behavior in detail. Furthermore, if the 3D model is a scholar, the behavior recording unit can also record the research behavior in detail. This allows for more appropriate behavior recording by taking into account the attribute information of the 3D model. Some or all of the above-described processing in the behavior recording unit may be performed using, or without, the generation AI. For example, the behavior recording unit can input the attribute information of the 3D model into the generation AI and have the generation AI customize the recorded content.

[0055] The behavior recording unit can select the optimal recording method by taking geographical distribution into consideration when recording behavior. The behavior recording unit, for example, acquires geographical distribution data of the 3D model and selects the optimal recording method. For example, the behavior recording unit records in detail the behavior of the 3D model in urban areas. The behavior recording unit can also record in detail the behavior of the 3D model in rural areas. Furthermore, the behavior recording unit can also record in detail the behavior of the 3D model in a specific region. This allows for more appropriate behavior recording by taking geographical distribution into consideration. Some or all of the above-described processing in the behavior recording unit may be performed using, or without, a generation AI. For example, the behavior recording unit can input geographical distribution data of the 3D model into the generation AI and have the generation AI select the optimal recording method.

[0056] The behavior recording unit can analyze social media activities and record related behaviors when recording behaviors. The behavior recording unit, for example, analyzes the user's social media activities and records related behaviors. For example, if the user interacts with many friends on social media, the behavior recording unit records the interaction behaviors. In addition, if the user posts frequently about a particular hobby on social media, the behavior recording unit can also record behaviors related to the hobby. Furthermore, if the user frequently posts about travel on social media, the behavior recording unit can also record the travel behaviors. In this way, by analyzing social media activities, more relevant behaviors can be recorded. Some or all of the above-described processing in the behavior recording unit may be performed using, or without, a generation AI. For example, the behavior recording unit may input the user's social media data into the generation AI and cause the generation AI to record related behaviors.

[0057] When collecting ratings, the rating collection unit can select the optimal collection method by referring to past rating data. The rating collection unit, for example, obtains the user's past rating data from a database and selects the optimal collection method. For example, if the user has previously provided a detailed rating, the rating collection unit collects the detailed rating. Also, if the user has previously provided a brief rating, the rating collection unit can collect a brief rating. Furthermore, the rating collection unit can suggest a new rating method to the user based on the past rating data. In this way, more appropriate ratings can be collected by referring to the past rating data. Some or all of the above-described processing in the rating collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the rating collection unit can input the user's past rating data into the generation AI and have the generation AI select the optimal collection method.

[0058] The evaluation collection unit can customize the evaluation content by taking into account the user's attribute information when collecting evaluations. The evaluation collection unit, for example, acquires the user's attribute information and customizes the evaluation content. For example, if the user is young, the evaluation collection unit can provide evaluation items for young people. Furthermore, if the user is elderly, the evaluation collection unit can also provide evaluation items for elderly people. Furthermore, if the user is a businessman, the evaluation collection unit can also provide business-related evaluation items. This makes it possible to collect more appropriate evaluations by taking into account the user's attribute information. Some or all of the above-mentioned processing in the evaluation collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the evaluation collection unit can input the user's attribute information into the generation AI and cause the generation AI to customize the evaluation content.

[0059] When collecting ratings, the rating collection unit can select the optimal collection method taking into account geographical distribution. The rating collection unit, for example, acquires geographical distribution data of users and selects the optimal collection method. For example, if the user is in an urban area, the rating collection unit can provide urban rating items. Also, if the user is in a rural area, the rating collection unit can provide nature-related rating items. Furthermore, if the user is overseas, the rating collection unit can provide rating items tailored to the culture and customs of that region. This allows for more appropriate ratings to be collected by taking geographical distribution into consideration. Some or all of the above-described processing in the rating collection unit may be performed using, or without, a generation AI. For example, the rating collection unit can input the user's geographical distribution data into the generation AI and have the generation AI select the optimal collection method.

[0060] The rating collection unit can analyze social media activities to collect related ratings when collecting ratings. The rating collection unit, for example, analyzes the user's social media activities and collects related ratings. For example, if the user interacts with many friends on social media, the rating collection unit collects ratings related to the interactions. In addition, if the user posts frequently about a particular hobby on social media, the rating collection unit can collect ratings related to the hobby. Furthermore, if the user frequently posts about travel on social media, the rating collection unit can collect ratings related to the travel. This makes it possible to collect more relevant ratings by analyzing social media activities. Some or all of the above-described processing in the rating collection unit may be performed using, or without, a generation AI. For example, the rating collection unit may input the user's social media data into the generation AI and cause the generation AI to collect related ratings.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The personality development unit can impart specific skills to the 3D model. For example, the personality development unit can impart cooking skills to the 3D model, enabling it to cook in the metaverse. The personality development unit can also impart musical skills to the 3D model, enabling it to play musical instruments in the metaverse. Furthermore, the personality development unit can also impart sports skills to the 3D model, enabling it to enjoy sports in the metaverse. In this way, by imparting specific skills to the 3D model, it is possible to provide a more diverse experience.

[0063] The behavior control unit may include a cooperative control unit for coordinating the behavior of the 3D model with other 3D models. For example, the cooperative control unit controls the behavior of the 3D model in cooperation with other 3D models to accomplish a task. The cooperative control unit may also control the behavior of the 3D model in competition with other 3D models. Furthermore, the cooperative control unit may also control the behavior of the 3D model in sharing information with other 3D models. This makes it possible to provide a more interactive experience by taking into account cooperation with other 3D models.

[0064] The market providing unit can analyze the user's purchase history and suggest individual recommended products. For example, the market providing unit can suggest related products based on products the user has purchased in the past. The market providing unit can also suggest similar products based on products that the user has given high ratings to in the past. Furthermore, the market providing unit can analyze the user's purchase history and suggest products based on trends. In this way, by utilizing the user's purchase history, more personalized product suggestions can be made.

[0065] The personality development module can customize individual personalities based on the appearance and attributes of the 3D model. For example, if the 3D model has a cute appearance, the personality development module can customize a friendly and approachable personality. Also, if the 3D model's attribute is a warrior, the personality development module can customize a brave and leadership personality. Furthermore, if the 3D model has a cool and stylish appearance, the personality development module can customize a cool and intelligent personality. This makes it possible to provide more attractive characters by customizing individual personalities based on the appearance and attributes of the 3D model.

[0066] The behavior control unit may include a behavior recording unit that records the behavior of the 3D model. For example, the behavior recording unit may store a history of the behavior of the 3D model in a database. The behavior recording unit may also maintain detailed records of the 3D model's interactions with other users. Furthermore, the behavior recording unit may record the behavior of the 3D model when it participates in a specific event. In this way, by recording the behavior of the 3D model, the behavior history can be managed and analyzed.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The personality development department applies personality development AI to the NFT-registered 3D model material. The personality development AI uses machine learning algorithms and neural networks to generate individual personalities and behavioral patterns for the 3D model. For example, it generates a dialogue model for a specific character to interact with the user, or a character with a specific behavioral pattern. Step 2: The behavior control unit autonomously controls the behavior of the 3D model based on the personality and behavioral patterns generated by the personality formation unit. For example, it controls the 3D model's behavior to interact with other users in the metaverse and participate in specific events. Step 3: The market provider provides users with 3D models that behave autonomously via the behavior control unit. For example, it provides a platform for users to purchase 3D models and a function for renting 3D models.

[0069] (Example 2) A system according to an embodiment of the present invention provides a marketplace for NFT-registered 3D model materials that are equipped with personality-building AI and allow them to behave autonomously and freely within the metaverse. This system allows NFT-registered 3D model materials to behave autonomously. For example, specific characters can interact with users and take specific actions. Next, a marketplace for these 3D models is provided within the metaverse, allowing them to behave autonomously and freely. In this marketplace, users can purchase or rent 3D models. Furthermore, 3D models can interact with other users and participate in events within the metaverse. This allows NFT assets to become living models, generating more liquid value. This allows the system to increase the value of NFT assets. For example, the popularity of a specific character can increase its value. Furthermore, the autonomous behavior of 3D models attracts user interest and stimulates activity within the metaverse.

[0070] The system according to the embodiment includes a personality development unit, a behavior control unit, and a market providing unit. The personality development unit applies personality development AI to NFT-registered 3D model materials. The personality development AI generates individual personalities and behavioral patterns for the 3D models using, for example, machine learning algorithms or neural networks. For example, the personality development unit can generate a dialogue model for a specific character to interact with a user. The personality development unit can also generate characters with specific behavioral patterns. The behavior control unit autonomously controls the behavior of the 3D models based on the personality and behavioral patterns generated by the personality development unit. For example, the behavior control unit controls the behavior of the 3D models interacting with other users in the metaverse. The behavior control unit can also control the behavior of the 3D models participating in specific events. The market providing unit provides users with 3D models that behave autonomously via the behavior control unit. For example, the market providing unit provides a platform for users to purchase 3D models. The market providing unit can also provide a function for users to rent 3D models. As a result, the system according to the embodiment can increase the value of NFT assets by providing a market in which NFT-registered 3D model materials are endowed with personality-forming AI and allowed to act autonomously and openly within the metaverse.

[0071] The behavior control unit includes a behavior recording unit that records the behavior of the 3D model. The behavior recording unit records the behavior of the 3D model. For example, the behavior recording unit stores the history of the behavior of the 3D model in a database. The behavior recording unit can also keep detailed records of when the 3D model interacts with other users. Furthermore, the behavior recording unit can also record the behavior of the 3D model when it participates in a specific event. In this way, by recording the behavior of the 3D model, the behavior history can be managed and analyzed. Some or all of the above-mentioned processing in the behavior recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior recording unit can input behavior data of the 3D model to a generation AI and cause the generation AI to record the behavior.

[0072] The market providing unit includes a rating collection unit that collects user ratings. The rating collection unit collects user ratings. For example, the rating collection unit stores ratings provided by users on 3D models in a database. The rating collection unit can also collect feedback provided by users on actions of the 3D models. Furthermore, the rating collection unit can collect user ratings regarding the quality and popularity of the 3D models. In this way, by collecting user ratings, the quality and popularity of the 3D models can be evaluated and improved. Some or all of the above-described processing in the rating collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the rating collection unit can input user rating data into a generation AI and cause the generation AI to collect ratings.

[0073] The personality development unit can generate individual personalities and behavioral patterns for the 3D model. The personality development unit can generate individual personalities and behavioral patterns for the 3D model using, for example, a machine learning algorithm or a neural network. For example, the personality development unit can generate a dialogue model for a specific character to interact with a user. The personality development unit can also generate characters with specific behavioral patterns. By generating individual personalities and behavioral patterns for the 3D model, it is possible to provide more realistic and attractive characters. Some or all of the above-described processing in the personality development unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the personality development unit can input data of the 3D model into the generation AI and cause the generation AI to generate individual personalities and behavioral patterns.

[0074] The behavior control unit can control the behavior of the 3D model when interacting with other users in the metaverse. For example, the behavior control unit controls the behavior of the 3D model when interacting with other users in the metaverse. For example, the behavior control unit controls the behavior of the 3D model when interacting with other users. The behavior control unit can also control the behavior of the 3D model when collaborating with other users to accomplish a task. Furthermore, the behavior control unit can also control the behavior of the 3D model when competing with other users. In this way, by controlling the behavior of the 3D model when interacting with other users in the metaverse, a more interactive experience can be provided. Some or all of the above-mentioned processing in the behavior control unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior control unit can input behavioral data of the 3D model to a generation AI and cause the generation AI to control the interaction behavior with other users.

[0075] The market providing unit can provide a function for purchasing or renting 3D models. For example, the market providing unit provides a platform for users to purchase 3D models. For example, the market providing unit can also provide a function for users to rent 3D models. For example, the market providing unit manages transactions when users purchase 3D models. The market providing unit can also manage contracts when users rent 3D models. By providing the function for purchasing or renting 3D models, users can freely use 3D models. Some or all of the above-described processing in the market providing unit may be performed using, or without, AI. For example, the market providing unit can input user transaction data into a generation AI and have the generation AI manage the purchase or rental.

[0076] The personality development unit can estimate the user's emotions and adjust the personality of the 3D model based on the estimated user emotions. The personality development unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the personality development unit analyzes the user's facial expression data to estimate emotions. The personality development unit can also analyze the user's voice data to estimate emotions. Furthermore, the personality development unit can analyze the user's text data to estimate emotions. Next, the personality development unit adjusts the personality of the 3D model based on the estimated user emotions. For example, if the user is happy, the personality of the 3D model can be adjusted to be cheerful and sociable. If the user is sad, the personality of the 3D model can be adjusted to be gentle and comforting. Furthermore, if the user is angry, the personality of the 3D model can be adjusted to be calm and collected. This allows for a more personalized experience by adjusting the personality of the 3D model based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the personality development unit may be performed using AI, or may be performed without using AI. For example, the personality development unit may input the user's emotional data into the generation AI and cause the generation AI to adjust the personality of the 3D model.

[0077] The personality development unit can analyze the past behavioral history of the 3D model and generate an optimal personality. For example, the personality development unit retrieves and analyzes the past behavioral history of the 3D model from a database. For example, if the 3D model has interacted with many users in the past, the personality development unit can generate a sociable personality. Furthermore, if the 3D model has frequently performed a specific task in the past, the personality development unit can also generate a personality specialized for that task. Furthermore, if the 3D model has received high ratings from users in the past, the personality development unit can adjust the personality based on those ratings. This allows for the generation of a more appropriate personality by analyzing the past behavioral history of the 3D model. Some or all of the above-described processing in the personality development unit can be performed using, or without, a generation AI. For example, the personality development unit can input the behavioral history data of the 3D model into the generation AI and cause the generation AI to generate an optimal personality.

[0078] The personality development unit can apply different personality generation algorithms depending on the intended use of the 3D model. For example, the personality development unit applies an algorithm that generates a knowledgeable and teachable personality to a 3D model intended for educational purposes. For example, the personality development unit generates an educational dialogue model for a 3D model intended for educational purposes. The personality development unit can also apply an algorithm that generates a humorous and fun personality to a 3D model intended for entertainment purposes. For example, the personality development unit generates an entertaining behavior pattern for a 3D model intended for entertainment purposes. Furthermore, the personality development unit can apply an algorithm that generates a professional and reliable personality to a 3D model intended for business purposes. For example, the personality development unit generates a business-like dialogue model for a 3D model intended for business purposes. This allows for more effective use by generating an appropriate personality depending on the intended use of the 3D model. Some or all of the above-described processing in the personality development unit may be performed using, or without, a generation AI. For example, the personality development department can input data on the intended use of the 3D model into the generation AI and have the generation AI apply an appropriate personality generation algorithm.

[0079] The personality development unit can customize individual personalities based on the appearance and attributes of the 3D model. The personality development unit, for example, acquires appearance data and attribute data of the 3D model and customizes individual personalities. For example, if the 3D model has a cute appearance, the personality development unit can customize an approachable and friendly personality. Furthermore, if the 3D model's attribute is a warrior, the personality development unit can customize an individual personality with bravery and leadership skills. Furthermore, if the 3D model has a cool and stylish appearance, the personality development unit can customize an individual personality with coolness and intelligence. This allows for customizing individual personalities based on the appearance and attributes of the 3D model, thereby providing a more attractive character. Some or all of the above-described processing in the personality development unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personality development unit can input appearance data and attribute data of the 3D model into the generation AI and cause the generation AI to customize the individual personality.

[0080] The personality development unit can estimate the user's emotions and adjust the behavioral patterns of the 3D model based on the estimated user emotions. The personality development unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the personality development unit analyzes the user's facial expression data to estimate emotions. The personality development unit can also analyze the user's voice data to estimate emotions. Furthermore, the personality development unit can analyze the user's text data to estimate emotions. Next, the personality development unit adjusts the behavioral patterns of the 3D model based on the estimated user emotions. For example, if the user is relaxed, the behavioral patterns of the 3D model can be adjusted to be calm and relaxed. If the user is excited, the behavioral patterns of the 3D model can be adjusted to be active and energetic. Furthermore, if the user is tired, the behavioral patterns of the 3D model can be adjusted to be quiet and calm. This allows for a more personalized experience by adjusting the behavioral patterns of the 3D model based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the personality development unit may be performed using AI, or may be performed without using AI. For example, the personality development unit may input the user's emotional data into the generation AI and cause the generation AI to adjust the behavioral patterns of the 3D model.

[0081] When generating a personality for a 3D model, the personality development unit can generate an optimal personality by taking into account the user's geographical location information. The personality development unit, for example, acquires the user's geographical location information and generates an optimal personality. For example, if the user is in an urban area, the personality development unit can generate an urbane and sophisticated personality. Also, if the user is in the countryside, the personality development unit can generate a friendly and nature-loving personality. Furthermore, if the user is overseas, the personality development unit can generate a personality that matches the culture and customs of that region. This allows for generating an optimal personality by taking into account the user's geographical location information, thereby providing a more appropriate character. Some or all of the above-described processing in the personality development unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personality development unit can input the user's geographical location information into the generation AI and cause the generation AI to generate an optimal personality.

[0082] The personality creation unit can analyze the user's social media activity and generate a relevant personality when generating a personality for the 3D model. The personality creation unit, for example, analyzes the user's social media activity and generates a relevant personality. For example, if the user interacts with many friends on social media, the personality creation unit can generate a sociable personality. Furthermore, if the user frequently posts about a particular hobby on social media, the personality creation unit can generate a personality related to that hobby. Furthermore, if the user frequently posts about travel on social media, the personality creation unit can generate an adventurous personality. This allows for the generation of a more relevant personality by analyzing the user's social media activity. Some or all of the above-described processing in the personality creation unit may be performed using, or without, a generation AI. For example, the personality creation unit can input the user's social media data into the generation AI and cause the generation AI to generate a relevant personality.

[0083] The personality development unit can customize the personality by reflecting the user's past feedback when generating the personality of the 3D model. The personality development unit, for example, retrieves the user's past feedback from a database and customizes the personality. For example, if the user has previously given positive feedback about the 3D model, the personality development unit can strengthen the personality based on that feedback. Furthermore, if the user has previously given negative feedback about the 3D model, the personality development unit can also improve the personality based on that feedback. Furthermore, if the user has previously made specific requests about the 3D model, the personality development unit can customize the personality according to those requests. This allows for a more appropriate personality to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the personality development unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personality development unit can input the user's feedback data into the generation AI and have the generation AI customize the personality.

[0084] The behavior control unit can estimate the user's emotions and adjust the behavior of the 3D model based on the estimated user emotions. The behavior control unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the behavior control unit analyzes the user's facial expression data to estimate emotions. The behavior control unit can also analyze the user's voice data to estimate emotions. Furthermore, the behavior control unit can analyze the user's text data to estimate emotions. Next, the behavior control unit adjusts the behavior of the 3D model based on the estimated user emotions. For example, if the user is happy, the 3D model can behave in a proactive manner to interact with the user. If the user is sad, the 3D model can behave in a comforting manner. Furthermore, if the user is angry, the 3D model can behave in a calm and collected manner. This allows for a more personalized experience by adjusting the behavior of the 3D model based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the behavior control unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior control unit may input user emotion data to the generation AI and cause the generation AI to adjust the behavior of the 3D model.

[0085] When controlling the behavior of a 3D model, the behavior control unit can optimize the behavior by taking into account interrelationships with other 3D models. For example, the behavior control unit controls the behavior of a 3D model to cooperate with other 3D models to accomplish a task. The behavior control unit can also control the behavior of a 3D model to compete with other 3D models. Furthermore, the behavior control unit can also control the behavior of a 3D model to share information with other 3D models. This allows for more cooperative behavior by taking into account interrelationships with other 3D models. Some or all of the above-described processing in the behavior control unit may be performed using, or without, AI. For example, the behavior control unit can input interrelationship data of the 3D models into a generation AI and cause the generation AI to optimize the behavior.

[0086] When controlling the behavior of the 3D model, the behavior control unit can customize the behavior by taking into account the user's attribute information. The behavior control unit, for example, acquires the user's attribute information and customizes the behavior. For example, if the user is young, the behavior control unit can cause the 3D model to behave in a youthful and active manner. Furthermore, if the user is elderly, the behavior control unit can cause the 3D model to behave in a calm and polite manner. Furthermore, if the user is a businessman, the behavior control unit can cause the 3D model to behave in a professional manner. This allows more appropriate behavior to be taken by taking into account the user's attribute information. Some or all of the above-described processing in the behavior control unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior control unit can input the user's attribute information into the generation AI and cause the generation AI to customize the behavior.

[0087] When controlling the behavior of a 3D model, the behavior control unit can optimize the behavior pattern by referring to past behavior data. For example, the behavior control unit obtains the 3D model's past behavior data from a database and optimizes the behavior pattern. For example, the behavior control unit reproduces behavior patterns that the 3D model has been successful in the past. The behavior control unit can also avoid behavior patterns that the 3D model has failed in the past. Furthermore, the behavior control unit can generate new behavior patterns for the 3D model based on the past behavior data. In this way, by referring to the past behavior data, more appropriate behavior patterns can be generated. Some or all of the above-described processing in the behavior control unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the behavior control unit can input the 3D model's past behavior data into the generation AI and cause the generation AI to optimize the behavior pattern.

[0088] The behavior control unit can estimate the user's emotions and prioritize the 3D model's actions based on the estimated user emotions. The behavior control unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the behavior control unit analyzes the user's facial expression data to estimate emotions. The behavior control unit can also analyze the user's voice data to estimate emotions. Furthermore, the behavior control unit can analyze the user's text data to estimate emotions. Next, the behavior control unit prioritizes the 3D model's actions based on the estimated user emotions. For example, if the user is happy, the 3D model can prioritize interacting with the user. If the user is sad, the 3D model can prioritize comforting actions. Furthermore, if the user is angry, the 3D model can prioritize calm actions. This allows the 3D model to take more appropriate actions by prioritizing actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the behavior control unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior control unit may input user emotion data to the generation AI and have the generation AI determine the priority of actions.

[0089] When controlling the behavior of the 3D model, the behavior control unit can optimize the behavior by taking into account the geographical distribution. For example, the behavior control unit optimizes the behavior of the 3D model in urban areas. For example, the behavior control unit applies an algorithm for optimizing the behavior of the 3D model in urban areas. The behavior control unit can also optimize the behavior of the 3D model in rural areas. Furthermore, the behavior control unit can optimize the behavior of the 3D model in specific regions. This allows for more appropriate behavior by taking the geographical distribution into account. Some or all of the above-described processing in the behavior control unit may be performed using, or without, the generation AI. For example, the behavior control unit can input geographical distribution data of the 3D model into the generation AI and cause the generation AI to optimize the behavior.

[0090] When controlling the behavior of the 3D model, the behavior control unit can optimize the behavior pattern by referring to related literature. For example, the behavior control unit optimizes the behavior pattern of the 3D model based on the latest research results. For example, the behavior control unit applies an algorithm for optimizing the behavior pattern of the 3D model based on the latest research results. The behavior control unit can also optimize the behavior pattern of the 3D model based on past success stories. Furthermore, the behavior control unit can optimize the behavior pattern of the 3D model based on the opinions of experts. This makes it possible to generate more appropriate behavior patterns by referring to related literature. Some or all of the above-described processing in the behavior control unit may be performed using, or without, a generation AI. For example, the behavior control unit can input related literature data into the generation AI and cause the generation AI to optimize the behavior pattern.

[0091] When controlling the behavior of a 3D model, the behavior control unit can optimize the behavior by taking market value into consideration. For example, the behavior control unit prioritizes actions that result in a high market value for the 3D model. For example, the behavior control unit applies an algorithm for prioritizing actions that result in a high market value for the 3D model. The behavior control unit can also take actions that increase the market value of the 3D model. Furthermore, the behavior control unit can also take actions that maintain the market value of the 3D model. This allows actions with higher value to be taken by taking market value into consideration. Some or all of the above-described processing in the behavior control unit may be performed using, or without, the generation AI. For example, the behavior control unit can input market value data into the generation AI and cause the generation AI to optimize the behavior.

[0092] The market providing unit can estimate a user's emotions and adjust the market display method based on the estimated user emotions. The market providing unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the market providing unit analyzes the user's facial expression data to estimate the user's emotions. The market providing unit can also analyze the user's voice data to estimate the user's emotions. Furthermore, the market providing unit can analyze the user's text data to estimate the user's emotions. Next, the market providing unit adjusts the market display method based on the estimated user emotions. For example, if the user is relaxed, the market display can be provided in subdued colors. If the user is excited, the market display can be provided in bright and lively colors. Furthermore, if the user is tired, the market display can be provided in a simple and highly visible color. This allows for a more personalized market experience by adjusting the market display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the market providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the market providing unit may input user emotion data into the generation AI and have the generation AI adjust the market display method.

[0093] When providing a marketplace, the market providing unit can suggest optimal products by referring to the user's past purchase history. The market providing unit, for example, retrieves the user's past purchase history from a database and suggests optimal products. For example, the market providing unit suggests products related to products the user has previously purchased. The market providing unit can also suggest products related to products that the user has previously given high ratings to. Furthermore, the market providing unit can analyze trends in products the user has previously purchased and suggest optimal products. In this way, by referring to the user's past purchase history, more relevant products can be suggested. Some or all of the above-described processing in the market providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the market providing unit can input the user's purchase history data into the generation AI and cause the generation AI to suggest optimal products.

[0094] When providing a market, the market providing unit can customize the display content of the market taking into account the user's attribute information. The market providing unit, for example, acquires the user's attribute information and customizes the display content of the market. For example, if the user is a young person, the market providing unit can display products aimed at young people. Furthermore, if the user is an elderly person, the market providing unit can also display products aimed at elderly people. Furthermore, if the user is a businessman, the market providing unit can also display business-related products. In this way, more appropriate products can be displayed by taking the user's attribute information into consideration. Some or all of the above-described processing in the market providing unit may be performed using, or without, a generation AI. For example, the market providing unit can input the user's attribute information into the generation AI and cause the generation AI to customize the display content of the market.

[0095] The market providing unit can improve the market display method by reflecting user feedback when providing a market. The market providing unit, for example, obtains user feedback from a database and improves the market display method. For example, if a user has previously provided positive feedback on market display, the market providing unit can strengthen the display method based on that feedback. Also, if a user has previously provided negative feedback on market display, the market providing unit can improve the display method based on that feedback. Furthermore, if a user has previously provided specific requests, the market providing unit can customize the display method in accordance with those requests. In this way, by reflecting user feedback, a more user-friendly market can be provided. Some or all of the above-described processing in the market providing unit may be performed using, or without, a generation AI. For example, the market providing unit can input user feedback data into the generation AI and cause the generation AI to improve the market display method.

[0096] The market providing unit can estimate a user's emotions and determine market priorities based on the estimated user emotions. The market providing unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the market providing unit analyzes the user's facial expression data to estimate emotions. The market providing unit can also analyze the user's voice data to estimate emotions. Furthermore, the market providing unit can analyze the user's text data to estimate emotions. Next, the market providing unit determines market priorities based on the estimated user emotions. For example, if the user is relaxed, the market can be displayed at a leisurely pace. If the user is excited, lively products can be prioritized. Furthermore, if the user is tired, simple and highly visible products can be prioritized. Thus, by determining market priorities based on the user's emotions, more appropriate products can be displayed. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the market provider may be performed using, for example, AI, or may be performed without using AI. For example, the market provider may input user emotion data into the generation AI and have the generation AI determine the priority of the market.

[0097] The market providing unit can propose optimal products taking geographical distribution into consideration when providing a market. The market providing unit, for example, acquires geographical distribution data of users and proposes optimal products. For example, if the user is in an urban area, the market providing unit can propose urban products. Also, if the user is in a rural area, the market providing unit can propose nature-related products. Furthermore, if the user is overseas, the market providing unit can propose products tailored to the culture and customs of that region. This allows for more appropriate product proposals by taking geographical distribution into consideration. Some or all of the above-described processing in the market providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the market providing unit can input the user's geographical distribution data into the generation AI and cause the generation AI to propose optimal products.

[0098] The market providing unit can analyze social media activity and suggest related products when providing a market. For example, the market providing unit analyzes a user's social media activity and suggests related products. For example, if a user interacts with many friends on social media, the market providing unit can suggest sociable products. Also, if a user posts frequently about a particular hobby on social media, the market providing unit can suggest products related to that hobby. Furthermore, if a user frequently posts about travel on social media, the market providing unit can suggest travel-related products. In this way, more relevant products can be suggested by analyzing social media activity. Some or all of the above-described processing in the market providing unit may be performed using, or without, a generation AI. For example, the market providing unit may input the user's social media data into the generation AI and cause the generation AI to suggest related products.

[0099] When providing a market, the market providing unit can customize the market display method by reflecting past feedback. The market providing unit, for example, obtains past user feedback from a database and customizes the market display method. For example, if a user has previously provided positive feedback on market display, the market providing unit can strengthen the display method based on that feedback. Also, if a user has previously provided negative feedback on market display, the market providing unit can improve the display method based on that feedback. Furthermore, if a user has previously made specific requests, the market providing unit can customize the display method in accordance with those requests. In this way, by reflecting past feedback, a more user-friendly market can be provided. Some or all of the above-described processing in the market providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the market providing unit can input user feedback data into the generation AI and have the generation AI customize the market display method.

[0100] The behavior recording unit can estimate the user's emotions and adjust the behavior recording method based on the estimated user emotions. The behavior recording unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the behavior recording unit analyzes the user's facial expression data to estimate the emotions. The behavior recording unit can also analyze the user's voice data to estimate the emotions. Furthermore, the behavior recording unit can analyze the user's text data to estimate the emotions. Next, the behavior recording unit adjusts the behavior recording method based on the estimated user emotions. For example, if the user is relaxed, detailed behavior recording can be performed. Also, if the user is excited, only important actions can be recorded. Furthermore, if the user is tired, brief behavior recording can be performed. In this way, by adjusting the behavior recording method based on the user's emotions, more appropriate behavior recording can be performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the behavior recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior recording unit may input user emotion data to the generation AI and cause the generation AI to adjust the behavior recording method.

[0101] When recording behavior, the behavior recording unit can select the optimal recording method by referring to the past behavior data of the 3D model. The behavior recording unit, for example, retrieves the past behavior data of the 3D model from a database and selects the optimal recording method. For example, the behavior recording unit records in detail the behaviors that the 3D model successfully performed in the past. The behavior recording unit can also briefly record the behaviors that the 3D model failed in the past. Furthermore, the behavior recording unit can generate new behavior patterns for the 3D model based on the past behavior data. This allows for more appropriate behavior recording by referring to the past behavior data. Some or all of the above-described processing in the behavior recording unit may be performed using, or without, a generation AI. For example, the behavior recording unit can input the past behavior data of the 3D model into the generation AI and have the generation AI select the optimal recording method.

[0102] The behavior recording unit can customize the recorded content by taking into account the attribute information of the 3D model when recording the behavior. The behavior recording unit, for example, acquires the attribute information of the 3D model and customizes the recorded content. For example, if the 3D model is a warrior, the behavior recording unit can record the combat behavior in detail. Furthermore, if the 3D model is a merchant, the behavior recording unit can also record the trading behavior in detail. Furthermore, if the 3D model is a scholar, the behavior recording unit can also record the research behavior in detail. This allows for more appropriate behavior recording by taking into account the attribute information of the 3D model. Some or all of the above-described processing in the behavior recording unit may be performed using, or without, the generation AI. For example, the behavior recording unit can input the attribute information of the 3D model into the generation AI and have the generation AI customize the recorded content.

[0103] The behavior recording unit can estimate the user's emotions and determine the priority of behavior records based on the estimated user emotions. The behavior recording unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the behavior recording unit analyzes the user's facial expression data to estimate the emotions. The behavior recording unit can also analyze the user's voice data to estimate the emotions. Furthermore, the behavior recording unit can analyze the user's text data to estimate the emotions. Next, the behavior recording unit determines the priority of behavior records based on the estimated user emotions. For example, if the user is relaxed, detailed behavior records can be prioritized. Also, if the user is excited, only important actions can be prioritized and recorded. Furthermore, if the user is tired, concise behavior records can be prioritized. Thus, by determining the priority of behavior records based on the user's emotions, more appropriate behavior records can be recorded. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the behavior recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the behavior recording unit may input user emotion data to the generation AI and have the generation AI determine the priority of behavior records.

[0104] The behavior recording unit can select the optimal recording method by taking geographical distribution into consideration when recording behavior. The behavior recording unit, for example, acquires geographical distribution data of the 3D model and selects the optimal recording method. For example, the behavior recording unit records in detail the behavior of the 3D model in urban areas. The behavior recording unit can also record in detail the behavior of the 3D model in rural areas. Furthermore, the behavior recording unit can also record in detail the behavior of the 3D model in a specific region. This allows for more appropriate behavior recording by taking geographical distribution into consideration. Some or all of the above-described processing in the behavior recording unit may be performed using, or without, a generation AI. For example, the behavior recording unit can input geographical distribution data of the 3D model into the generation AI and have the generation AI select the optimal recording method.

[0105] The behavior recording unit can analyze social media activities and record related behaviors when recording behaviors. The behavior recording unit, for example, analyzes the user's social media activities and records related behaviors. For example, if the user interacts with many friends on social media, the behavior recording unit records the interaction behaviors. In addition, if the user posts frequently about a particular hobby on social media, the behavior recording unit can also record behaviors related to the hobby. Furthermore, if the user frequently posts about travel on social media, the behavior recording unit can also record the travel behaviors. In this way, by analyzing social media activities, more relevant behaviors can be recorded. Some or all of the above-described processing in the behavior recording unit may be performed using, or without, a generation AI. For example, the behavior recording unit may input the user's social media data into the generation AI and cause the generation AI to record related behaviors.

[0106] The rating collection unit can estimate the user's emotion and adjust the rating collection method based on the estimated user's emotion. The rating collection unit estimates the user's emotion using, for example, an emotion recognition algorithm. For example, the rating collection unit analyzes the user's facial expression data to estimate the emotion. The rating collection unit can also analyze the user's voice data to estimate the emotion. The rating collection unit can also analyze the user's text data to estimate the emotion. Next, the rating collection unit adjusts the rating collection method based on the estimated user's emotion. For example, if the user is relaxed, detailed ratings can be collected. If the user is excited, brief ratings can be collected. If the user is tired, simple ratings can be collected. In this way, by adjusting the rating collection method based on the user's emotion, more appropriate ratings can be collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation collection unit may input user emotion data to the generation AI and cause the generation AI to adjust the evaluation collection method.

[0107] When collecting ratings, the rating collection unit can select the optimal collection method by referring to past rating data. The rating collection unit, for example, obtains the user's past rating data from a database and selects the optimal collection method. For example, if the user has previously provided a detailed rating, the rating collection unit collects the detailed rating. Also, if the user has previously provided a brief rating, the rating collection unit can collect a brief rating. Furthermore, the rating collection unit can suggest a new rating method to the user based on the past rating data. In this way, more appropriate ratings can be collected by referring to the past rating data. Some or all of the above-described processing in the rating collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the rating collection unit can input the user's past rating data into the generation AI and have the generation AI select the optimal collection method.

[0108] The evaluation collection unit can customize the evaluation content by taking into account the user's attribute information when collecting evaluations. The evaluation collection unit, for example, acquires the user's attribute information and customizes the evaluation content. For example, if the user is young, the evaluation collection unit can provide evaluation items for young people. Furthermore, if the user is elderly, the evaluation collection unit can also provide evaluation items for elderly people. Furthermore, if the user is a businessman, the evaluation collection unit can also provide business-related evaluation items. This makes it possible to collect more appropriate evaluations by taking into account the user's attribute information. Some or all of the above-mentioned processing in the evaluation collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the evaluation collection unit can input the user's attribute information into the generation AI and cause the generation AI to customize the evaluation content.

[0109] The rating collection unit can estimate the user's emotions and determine the priority of rating collection based on the estimated user's emotions. The rating collection unit estimates the user's emotions using, for example, an emotion recognition algorithm. For example, the rating collection unit analyzes the user's facial expression data to estimate the emotions. The rating collection unit can also analyze the user's voice data to estimate the emotions. The rating collection unit can also analyze the user's text data to estimate the emotions. Next, the rating collection unit determines the priority of rating collection based on the estimated user's emotions. For example, if the user is relaxed, detailed ratings can be collected with priority. If the user is excited, brief ratings can be collected with priority. If the user is tired, simple ratings can be collected with priority. This allows for more appropriate rating collection by determining the priority of rating collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation collection unit may input user emotion data to the generation AI and have the generation AI determine the priority of evaluation collection.

[0110] When collecting ratings, the rating collection unit can select the optimal collection method taking into account geographical distribution. The rating collection unit, for example, acquires geographical distribution data of users and selects the optimal collection method. For example, if the user is in an urban area, the rating collection unit can provide urban rating items. Also, if the user is in a rural area, the rating collection unit can provide nature-related rating items. Furthermore, if the user is overseas, the rating collection unit can provide rating items tailored to the culture and customs of that region. This allows for more appropriate ratings to be collected by taking geographical distribution into consideration. Some or all of the above-described processing in the rating collection unit may be performed using, or without, a generation AI. For example, the rating collection unit can input the user's geographical distribution data into the generation AI and have the generation AI select the optimal collection method.

[0111] The rating collection unit can analyze social media activities to collect related ratings when collecting ratings. The rating collection unit, for example, analyzes the user's social media activities and collects related ratings. For example, if the user interacts with many friends on social media, the rating collection unit collects ratings related to the interactions. In addition, if the user posts frequently about a particular hobby on social media, the rating collection unit can collect ratings related to the hobby. Furthermore, if the user frequently posts about travel on social media, the rating collection unit can collect ratings related to the travel. This makes it possible to collect more relevant ratings by analyzing social media activities. Some or all of the above-described processing in the rating collection unit may be performed using, or without, a generation AI. For example, the rating collection unit may input the user's social media data into the generation AI and cause the generation AI to collect related ratings. === Hard Collateral 1-1 === Each of the multiple elements including the personality development unit, behavior control unit, market providing unit, and behavior recording unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the personality development unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The behavior control unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The behavior recording unit is realized by the specific processing unit 290 of the data processing device 12. The market providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the personality development unit, behavior control unit, market providing unit, and behavior recording unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the personality development unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The behavior control unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The behavior recording unit is realized by the specific processing unit 290 of the data processing device 12. The market providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the personality development unit, behavior control unit, market providing unit, and behavior recording unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the personality development unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The behavior control unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The behavior recording unit is realized by the specific processing unit 290 of the data processing device 12. The market providing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the personality development unit, behavior control unit, market providing unit, and behavior recording unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the personality development unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The behavior control unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The behavior recording unit is realized by the specific processing unit 290 of the data processing device 12. The market providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The personality development unit can impart specific skills to the 3D model. For example, the personality development unit can impart cooking skills to the 3D model, enabling it to cook in the metaverse. The personality development unit can also impart musical skills to the 3D model, enabling it to play musical instruments in the metaverse. Furthermore, the personality development unit can also impart sports skills to the 3D model, enabling it to enjoy sports in the metaverse. In this way, by imparting specific skills to the 3D model, it is possible to provide a more diverse experience.

[0114] The behavior control unit may include a cooperative control unit for coordinating the behavior of the 3D model with other 3D models. For example, the cooperative control unit controls the behavior of the 3D model in cooperation with other 3D models to accomplish a task. The cooperative control unit may also control the behavior of the 3D model in competition with other 3D models. Furthermore, the cooperative control unit may also control the behavior of the 3D model in sharing information with other 3D models. This makes it possible to provide a more interactive experience by taking into account cooperation with other 3D models.

[0115] The market providing unit can analyze the user's purchase history and suggest individual recommended products. For example, the market providing unit can suggest related products based on products the user has purchased in the past. The market providing unit can also suggest similar products based on products that the user has given high ratings to in the past. Furthermore, the market providing unit can analyze the user's purchase history and suggest products based on trends. In this way, by utilizing the user's purchase history, more personalized product suggestions can be made.

[0116] The personality development module can customize individual personalities based on the appearance and attributes of the 3D model. For example, if the 3D model has a cute appearance, the personality development module can customize a friendly and approachable personality. Also, if the 3D model's attribute is a warrior, the personality development module can customize a brave and leadership personality. Furthermore, if the 3D model has a cool and stylish appearance, the personality development module can customize a cool and intelligent personality. This makes it possible to provide more attractive characters by customizing individual personalities based on the appearance and attributes of the 3D model.

[0117] The behavior control unit may include a behavior recording unit that records the behavior of the 3D model. For example, the behavior recording unit may store a history of the behavior of the 3D model in a database. The behavior recording unit may also maintain detailed records of the 3D model's interactions with other users. Furthermore, the behavior recording unit may record the behavior of the 3D model when it participates in a specific event. In this way, by recording the behavior of the 3D model, the behavior history can be managed and analyzed.

[0118] The personality development unit can estimate the user's emotions and adjust the personality of the 3D model based on the estimated user emotions. For example, the personality development unit can analyze the user's facial expression data to estimate emotions. The personality development unit can also analyze the user's voice data to estimate emotions. Furthermore, the personality development unit can analyze the user's text data to estimate emotions. Next, the personality development unit adjusts the personality of the 3D model based on the estimated user emotions. For example, if the user is happy, the personality of the 3D model can be adjusted to be cheerful and sociable. If the user is sad, the personality of the 3D model can be adjusted to be gentle and comforting. Furthermore, if the user is angry, the personality of the 3D model can be adjusted to be calm and collected. This makes it possible to provide a more personalized experience by adjusting the personality of the 3D model based on the user's emotions.

[0119] The behavior control unit can estimate the user's emotions and adjust the behavior of the 3D model based on the estimated user emotions. For example, the behavior control unit can analyze the user's facial expression data to estimate emotions. The behavior control unit can also analyze the user's voice data to estimate emotions. Furthermore, the behavior control unit can analyze the user's text data to estimate emotions. Next, the behavior control unit adjusts the behavior of the 3D model based on the estimated user emotions. For example, if the user is happy, the 3D model can behave in a proactive manner to interact with the user. If the user is sad, the 3D model can behave in a comforting manner. Furthermore, if the user is angry, the 3D model can behave in a calm and collected manner. This allows the user to enjoy a more personalized experience by adjusting the behavior of the 3D model based on the user's emotions.

[0120] The market providing unit can estimate a user's emotions and adjust the market display method based on the estimated user emotions. For example, the market providing unit can analyze a user's facial expression data to estimate emotions. The market providing unit can also analyze a user's voice data to estimate emotions. Furthermore, the market providing unit can analyze a user's text data to estimate emotions. Next, the market providing unit adjusts the market display method based on the estimated user emotions. For example, if the user is relaxed, a market display in subdued colors can be provided. If the user is excited, a bright and lively market display can be provided. Furthermore, if the user is tired, a simple and highly visible market display can be provided. In this way, by adjusting the market display method based on the user's emotions, a more personalized market experience can be provided.

[0121] The behavior recording unit can estimate the user's emotions and adjust the behavior recording method based on the estimated user's emotions. For example, the behavior recording unit analyzes the user's facial expression data to estimate the emotions. The behavior recording unit can also analyze the user's voice data to estimate the emotions. Furthermore, the behavior recording unit can analyze the user's text data to estimate the emotions. Next, the behavior recording unit adjusts the behavior recording method based on the estimated user's emotions. For example, if the user is relaxed, detailed behavior recording can be performed. Also, if the user is excited, only important actions can be recorded. Furthermore, if the user is tired, brief behavior recording can be performed. In this way, by adjusting the behavior recording method based on the user's emotions, more appropriate behavior recording can be performed.

[0122] The evaluation collection unit can estimate the user's emotions and adjust the evaluation collection method based on the estimated user's emotions. For example, the evaluation collection unit analyzes the user's facial expression data to estimate the emotions. The evaluation collection unit can also analyze the user's voice data to estimate the emotions. Furthermore, the evaluation collection unit can analyze the user's text data to estimate the emotions. Next, the evaluation collection unit adjusts the evaluation collection method based on the estimated user's emotions. For example, if the user is relaxed, detailed evaluations can be collected. If the user is excited, brief evaluations can be collected. Furthermore, if the user is tired, simple evaluations can be collected. In this way, by adjusting the evaluation collection method based on the user's emotions, more appropriate evaluations can be collected.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The personality development department applies personality development AI to the NFT-registered 3D model material. The personality development AI uses machine learning algorithms and neural networks to generate individual personalities and behavioral patterns for the 3D model. For example, it generates a dialogue model for a specific character to interact with the user, or a character with a specific behavioral pattern. Step 2: The behavior control unit autonomously controls the behavior of the 3D model based on the personality and behavioral patterns generated by the personality formation unit. For example, it controls the 3D model's behavior to interact with other users in the metaverse and participate in specific events. Step 3: The market provider provides users with 3D models that behave autonomously via the behavior control unit. For example, it provides a platform for users to purchase 3D models and a function for renting 3D models.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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 personality development department that applies personality development AI to NFT-registered 3D model materials, a behavior control unit that autonomously controls the behavior of the 3D model based on the personality and behavior patterns generated by the personality formation unit; a market providing unit that provides users with 3D models that behave autonomously using the behavior control unit; Equipped with A system characterized by:

2. The behavior control unit Equipped with a behavior recording unit that records the behavior of the 3D model 2. The system of claim 1.

3. The market providing unit: Equipped with a rating collection unit that collects user ratings 2. The system of claim 1.

4. The personality development department Generate individual personalities and behavioral patterns for 3D models 2. The system of claim 1.

5. The behavior control unit 3D models control how users interact with other users in the metaverse 2. The system of claim 1.

6. The market providing unit: Offering the ability to purchase or rent 3D models 2. The system of claim 1.

7. The personality development department Estimate the user's emotions and adjust the personality of the 3D model based on the estimated user emotions.

2. The system of claim 1.

8. The personality development department Analyzing the past behavioral history of a 3D model to generate an optimal personality 2. The system of claim 1.

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