System

Abstract

A system in accordance with an embodiment is directed to individually evolving a created AI character based on user selections to form an identity.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, an implementation unit, an interaction unit, and a materialization unit. The reception unit receives a selection of an appearance, voice quality, and personality of a character from a user. The generation unit generates a AI character on the basis of the selection received by the reception unit. The implementing unit implements the AI character generated by the generating unit in the wearable device. The interaction unit performs interaction for the AI character and the user to interact with each other in the Metaverse space. The materializing unit materializes the AI character as a real robot.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately allow for the individual evolution and identity formation of generated AI characters, leaving room for improvement.

[0005] The system of the embodiment aims to individually evolve generated AI characters based on user choices and create an identity. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an implementation unit, an interaction unit, and a materialization unit. The reception unit receives selections from the user regarding the character's appearance, voice quality, and personality. The generation unit generates an AI character based on the selections received by the reception unit. The implementation unit implements the AI ​​character generated by the generation unit in a wearable device. The interaction unit enables the AI ​​character and the user to interact in the metaverse space. The materialization unit materializes the AI ​​character as a real robot. [Effects of the Invention]

[0007] The system according to the embodiment can individually evolve the generated AI character and form an identity based on the user's choices. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The generative AI characterization system according to an embodiment of the present invention anticipates the increasing importance of identity formation, including not only functionality but also appearance, voice quality, and personality, as generative AI evolves. The generative AI characterization system accepts user selections for a character's appearance, voice quality, and personality, generates a generative AI character, implements it on a wearable device, provides interaction in the metaverse, and embodies it as a real robot. For example, the generative AI characterization system utilizes Japanese anime materials to allow users to reflect the appearance, voice quality, and personality of their favorite anime characters in the generative AI. This allows users to have their own original generative AI character. Next, the generative AI characterization system displays the generated AI character on a wearable device such as a smartwatch or AR glasses, allowing for constant communication with the user. Furthermore, the generative AI characterization system provides a space where users and the generated AI character can interact in the metaverse. Users can access the metaverse and engage in virtual activities with their generated AI character. Finally, the generative AI characterization system can embody the generated AI character as an actual robot, allowing users to physically interact with the generated AI character. This allows the generated AI character system to provide new interaction experiences with users. For example, by reflecting the appearance, voice quality, and personality of a user's favorite anime character in the generated AI, the user can have their own original generated AI character. Also, by implementing the generated AI character on a wearable device, the user can interact with the generated AI character on a daily basis. Furthermore, by providing interaction in the metaverse space, the user can deepen their bond with the generated AI character. Finally, by embodying the generated AI character as a real robot, the user can physically interact with the generated AI character.

[0029] The AI ​​character generation system according to the embodiment includes a reception unit, a generation unit, an implementation unit, an interaction unit, and an embodiment unit. The reception unit receives a user's selection of a character's appearance, voice quality, and personality. For example, the user can select the appearance, voice quality, and personality of a favorite anime character through an app. The generation unit uses a generation AI to generate a generated AI character based on the selection received by the reception unit. The generation AI analyzes the user's selection and generates an optimal character using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The implementation unit implements the generated AI character generated by the generation unit in a wearable device. Examples of wearable devices include smartwatches and AR glasses. The implementation unit displays the generated AI character on these devices and allows constant communication with the user. The interaction unit provides a function for the generated AI character and the user to interact in the metaverse space. The user can access the metaverse space using, for example, a VR headset and participate in events and games with the generated AI character. The embodiment unit embodies the generated AI character as a real robot. The embodiment unit may, for example, use 3D printing technology to embody the generated AI character and use it as a household robot or a pet robot. As a result, the generated AI character creation system according to the embodiment generates the generated AI character based on a user's selection, and enables interaction in a wearable device or metaverse space, and embodying it as a real robot.

[0030] The reception unit can analyze the user's past selection history and suggest the most suitable character. For example, the reception unit can analyze the tendencies of characters selected by the user in the past and suggest characters with similar characteristics. The reception unit can also suggest characters played by the same voice actor based on the voice quality of a character selected by the user in the past. The reception unit can also suggest characters with the same personality based on the personality of a character selected by the user in the past. In this way, by suggesting the most suitable character based on the user's past selection history, it is possible to provide a character that suits the user's preferences. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past selection history data into a generation AI and cause the generation AI to suggest the most suitable character.

[0031] When selecting a character, the reception unit can filter based on the user's current interests and concerns. For example, the reception unit can prioritize presenting characters from anime that the user has recently watched. The reception unit can also suggest characters based on anime-related keywords that the user has recently searched for. The reception unit can also prioritize presenting characters from anime events that the user has recently participated in. This allows for filtering characters based on the user's current interests and concerns, thereby providing more relevant characters. Some or all of the above-described processing by the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's current interest and concern data into the generation AI and have the generation AI perform character filtering.

[0032] When selecting a character, the reception unit can provide an appropriate selection means according to the user's input method. For example, when the user describes the characteristics of a character by voice, the reception unit presents a character that matches the characteristics. The reception unit can also display a character when the user inputs the character's name in text. The reception unit can also present a character that resembles the image when the user uploads an image. This improves user convenience by providing an optimal selection means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's voice data into a generation AI and cause the generation AI to provide a character selection means.

[0033] When generating a character, the generation unit can adjust the level of detail of the generation based on the importance of elements selected by the user. For example, if the user places importance on appearance, the generation unit can increase the level of detail of the appearance. Furthermore, if the user places importance on voice quality, the generation unit can increase the level of detail of the voice quality. Furthermore, if the user places importance on personality, the generation unit can increase the level of detail of the personality. In this way, by adjusting the level of detail of the generation based on the importance of elements selected by the user, a character can be generated that meets the user's needs. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user selection data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0034] When generating a character, the generation unit can apply different generation algorithms depending on the category of the selected character. For example, if the character selected by the user is a fantasy character, the generation unit can apply a fantasy generation algorithm. Furthermore, if the character selected by the user is a science fiction character, the generation unit can also apply a science fiction generation algorithm. Furthermore, if the character selected by the user is a real character, the generation unit can also apply a real-life generation algorithm. In this way, by applying a generation algorithm according to the character category, a more appropriate character can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the selected character into the generation AI and cause the generation AI to apply the generation algorithm.

[0035] When generating a character, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of generation, for example, based on the characteristics of characters the user has generated in the past. The generation unit can also improve the accuracy of generation based on evaluations of characters the user has generated in the past. The generation unit can also improve the accuracy of generation based on the user's usage history of characters generated in the past. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0036] During implementation, the implementation unit can select the optimal implementation method by referring to the user's past device usage history. The implementation unit selects the optimal implementation method, for example, based on the settings of the device used by the user in the past. The implementation unit can also select the optimal implementation method based on the display method of the device used by the user in the past. The implementation unit can also select the optimal implementation method based on the operation method of the device used by the user in the past. This improves user convenience by selecting the optimal implementation method based on the user's past device usage history. Some or all of the above-described processing in the implementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the implementation unit can input the user's past device usage history data into the generation AI and cause the generation AI to select the optimal implementation method.

[0037] During implementation, the implementation unit can customize the implementation means based on the user's current device environment. For example, if the user is using a smartwatch, the implementation unit can provide an implementation means optimized for the device. Furthermore, if the user is using AR glasses, the implementation unit can also provide an implementation means optimized for the device. Furthermore, if the user is using a smartphone, the implementation unit can also provide an implementation means optimized for the device. This allows the implementation means to be customized based on the user's current device environment, thereby providing an optimal implementation method. Some or all of the above-described processing in the implementation unit may be performed, for example, using AI or without AI. For example, the implementation unit can input the user's current device environment data into the generation AI and cause the generation AI to customize the implementation means.

[0038] The implementation unit can improve the implementation method by reflecting user feedback during implementation. For example, the implementation unit can improve the display method based on user-provided feedback. The implementation unit can also improve the operation method based on user-provided feedback. The implementation unit can also improve the interface based on user-provided feedback. This allows the implementation method to be continuously improved by reflecting user feedback. Some or all of the above-described processing in the implementation unit may be performed using AI, for example, or may be performed without using AI. For example, the implementation unit can input user feedback data into a generation AI and have the generation AI execute improvements to the implementation method.

[0039] The interaction unit can select the optimal interaction method by referring to the user's past interaction history when interacting. For example, the interaction unit selects the optimal interaction method based on the history of events the user has previously participated in. The interaction unit can also select the optimal interaction method based on the history of characters the user has previously interacted with. The interaction unit can also select the optimal interaction method based on feedback the user has previously provided. In this way, by selecting the optimal interaction method based on the user's past interaction history, it is possible to provide an interaction experience that suits the user's preferences. Some or all of the above-described processing in the interaction unit may be performed using, for example, AI, or may be performed without using AI. For example, the interaction unit can input the user's past interaction history data into a generation AI and cause the generation AI to select the optimal interaction method.

[0040] The communication unit can customize the communication means based on the user's current interests and concerns during communication. For example, the communication unit customizes the communication means based on a theme that the user has recently been interested in. The communication unit can also customize the communication means based on an event that the user recently participated in. The communication unit can also customize the communication means based on an anime that the user recently watched. This allows for customizing the communication means based on the user's current interests and concerns, thereby providing a more relevant communication experience. Some or all of the above-described processing in the communication unit may be performed using, or without, AI, for example. For example, the communication unit can input the user's current interest and concern data into a generation AI and have the generation AI customize the communication means.

[0041] The communication unit can improve the communication method by reflecting user feedback during communication. The communication unit can improve the communication method, for example, based on feedback provided by the user. The communication unit can also improve the content of the event based on feedback provided by the user. The communication unit can also improve the means of communication based on feedback provided by the user. This allows the communication method to be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or can be performed without using AI. For example, the communication unit can input user feedback data into a generation AI and cause the generation AI to improve the communication method.

[0042] The embodiment unit can select the optimal embodiment method by referring to the user's past embodiment history at the time of embodiment. For example, the embodiment unit selects the optimal embodiment method based on the history of characters previously embodied by the user. The embodiment unit can also select the optimal embodiment method based on the user's evaluation of characters previously embodied by the user. The embodiment unit can also select the optimal embodiment method based on the user's usage history of characters previously embodied by the user. In this way, by selecting the optimal embodiment method based on the user's past embodiment history, it is possible to provide an embodiment method that suits the user's preferences. Some or all of the above-mentioned processing in the embodiment unit may be performed, for example, using AI or without AI. For example, the embodiment unit can input the user's past embodiment history data into the generation AI and cause the generation AI to select the optimal embodiment method.

[0043] The embodiment unit can customize the embodiment means based on the user's current living situation during embodiment. For example, if the user desires a home robot, the embodiment unit can provide an embodiment means optimized for that purpose. Furthermore, if the user desires a pet robot, the embodiment unit can provide an embodiment means optimized for that purpose. Furthermore, if the user desires a commercial robot, the embodiment unit can provide an embodiment means optimized for that purpose. This allows for customizing the embodiment means based on the user's current living situation, thereby providing an optimal embodiment method. Some or all of the above-described processing in the embodiment unit may be performed using, for example, AI, or may be performed without using AI. For example, the embodiment unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the embodiment means.

[0044] The embodiment unit can improve the embodiment method by reflecting user feedback during embodiment. The embodiment unit can improve the embodiment method, for example, based on feedback provided by the user. The embodiment unit can also improve the function of the robot based on feedback provided by the user. The embodiment unit can also improve the design of the robot based on feedback provided by the user. This allows the embodiment method to be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the embodiment unit can be performed using AI, for example, or without using AI. For example, the embodiment unit can input user feedback data into a generation AI and cause the generation AI to improve the embodiment method.

[0045] During embodiment, the embodiment unit can select the optimal embodiment method by taking into account the user's geographical location information. For example, if the user is in Japan, the embodiment unit can prioritize embodying Japanese anime characters. Also, if the user is in the United States, the embodiment unit can prioritize embodying anime characters that are popular in the United States. Also, if the user is in a specific region, the embodiment unit can prioritize embodying anime characters related to that region. In this way, the optimal embodiment method can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the embodiment unit can be performed using, or without, AI. For example, the embodiment unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal embodiment method.

[0046] During the embodiment, the embodiment unit can analyze the user's social media activity and suggest an embodiment means. For example, the embodiment unit can prioritize the embodiment of anime characters that the user follows on social media. The embodiment unit can also prioritize the embodiment of anime characters that the user has "liked" on social media. The embodiment unit can also prioritize the embodiment of anime characters that the user has shared on social media. In this way, by analyzing the user's social media activity, more relevant embodiment means can be provided. Some or all of the above-described processing in the embodiment unit can be performed, for example, using AI or without AI. For example, the embodiment unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest an embodiment means.

[0047] The embodiment unit can customize the embodiment method by reflecting the user's past feedback during embodiment. For example, the embodiment unit customizes the embodiment method based on the characteristics of characters that the user has previously rated highly. The embodiment unit can also customize the embodiment method by avoiding the characteristics of characters that the user has previously rated poorly. The embodiment unit can also customize the embodiment method based on feedback provided by the user in the past. In this way, the embodiment method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the embodiment unit may be performed using, for example, AI, or may be performed without using AI. For example, the embodiment unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the embodiment method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The implementation unit can also learn the user's device usage patterns and optimize the character display method based on those patterns. For example, if the user mainly uses the device at night, it can prioritize display in night mode. Also, if the user frequently uses the device while on the move, it can provide a display method with high visibility even while on the move. Furthermore, if the user frequently uses a specific application, it can provide a character display optimized for that application. This enables more convenient character display based on the user's device usage patterns.

[0050] The interaction unit can analyze the user's social network information and customize the interaction method in the metaverse space based on that information. For example, it can prioritize interactions with users who share common interests based on the user's friend list and follower information. It can also analyze the user's activity history on social networks and provide interaction methods related to events and groups the user has previously participated in. Furthermore, it can continuously improve the interaction method based on the user's feedback on social networks. This enables a more personalized interaction experience based on the user's social network information.

[0051] The embodiment unit can also monitor the user's living environment and adjust the functions of the embodied character based on the environment. For example, if the user has a pet, the embodiment unit can embody a character that can coexist with the pet. Also, if the user has small children, the embodiment unit can embody a character with child-friendly functions. Furthermore, if the user's living environment is small, the embodiment unit can embody a compact-sized character. This allows for more appropriate character embodiment based on the user's living environment.

[0052] The reception unit can also analyze the user's purchase history and present character options based on that history. For example, if the user has a history of purchasing goods for a specific anime, characters related to that anime can be presented preferentially. Also, if the user frequently purchases anime from a specific genre, characters related to that genre can be suggested. Furthermore, it is also possible to present highly rated characters preferentially based on the ratings of character goods purchased by the user in the past. This allows for more relevant character selection based on the user's purchase history.

[0053] The generation unit can also analyze the user's hobbies and preferences and customize the character's appearance and personality based on those preferences. For example, if the user likes sports, an athlete-type character can be generated. If the user likes music, a musician-type character can be generated. Furthermore, if the user likes a particular fashion style, a character that matches that style can be generated. This allows for more personalized character generation based on the user's hobbies and preferences.

[0054] The implementation unit can also monitor the remaining battery level of the user's device and adjust the character display method based on that remaining battery level. For example, if the battery level is low, it can prioritize display in power-saving mode. Alternatively, if the battery level is sufficient, it can provide a fully functional display. Furthermore, it can adjust the use of character animations and effects according to the remaining battery level. This enables more efficient character display based on the remaining battery level of the user's device.

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

[0056] Step 1: The reception unit receives the selection of the character's appearance, voice quality, and personality from the user. For example, the user can select the appearance, voice quality, and personality of their favorite anime character through the app. Step 2: The generation unit generates an AI character using a generation AI based on the selections received by the reception unit. The generation AI analyzes the user's selections and generates an optimal character using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The implementation unit implements the AI ​​character generated by the generation unit into a wearable device. Wearable devices include, for example, smartwatches and AR glasses. The implementation unit displays the generated AI character on these devices, allowing it to constantly communicate with the user. Step 4: The interaction unit provides a function for the generated AI character and the user to interact in the metaverse space. For example, the user can access the metaverse space using a VR headset and participate in events and games with the generated AI character. Step 5: The materialization unit materializes the generated AI character as a real robot. The materialization unit may embody the generated AI character using, for example, 3D printing technology, and use it as a household robot or a pet robot.

[0057] (Example 2) The generative AI characterization system according to an embodiment of the present invention anticipates the increasing importance of identity formation, including not only functionality but also appearance, voice quality, and personality, as generative AI evolves. The generative AI characterization system accepts user selections for a character's appearance, voice quality, and personality, generates a generative AI character, implements it on a wearable device, provides interaction in the metaverse, and embodies it as a real robot. For example, the generative AI characterization system utilizes Japanese anime materials to allow users to reflect the appearance, voice quality, and personality of their favorite anime characters in the generative AI. This allows users to have their own original generative AI character. Next, the generative AI characterization system displays the generated AI character on a wearable device such as a smartwatch or AR glasses, allowing for constant communication with the user. Furthermore, the generative AI characterization system provides a space where users and the generated AI character can interact in the metaverse. Users can access the metaverse and engage in virtual activities with their generated AI character. Finally, the generative AI characterization system can embody the generated AI character as an actual robot, allowing users to physically interact with the generated AI character. This allows the generated AI character system to provide new interaction experiences with users. For example, by reflecting the appearance, voice quality, and personality of a user's favorite anime character in the generated AI, the user can have their own original generated AI character. Also, by implementing the generated AI character on a wearable device, the user can interact with the generated AI character on a daily basis. Furthermore, by providing interaction in the metaverse space, the user can deepen their bond with the generated AI character. Finally, by embodying the generated AI character as a real robot, the user can physically interact with the generated AI character.

[0058] The AI ​​character generation system according to the embodiment includes a reception unit, a generation unit, an implementation unit, an interaction unit, and an embodiment unit. The reception unit receives a user's selection of a character's appearance, voice quality, and personality. For example, the user can select the appearance, voice quality, and personality of a favorite anime character through an app. The generation unit uses a generation AI to generate a generated AI character based on the selection received by the reception unit. The generation AI analyzes the user's selection and generates an optimal character using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The implementation unit implements the generated AI character generated by the generation unit in a wearable device. Examples of wearable devices include smartwatches and AR glasses. The implementation unit displays the generated AI character on these devices and allows constant communication with the user. The interaction unit provides a function for the generated AI character and the user to interact in the metaverse space. The user can access the metaverse space using, for example, a VR headset and participate in events and games with the generated AI character. The embodiment unit embodies the generated AI character as a real robot. The embodiment unit may, for example, use 3D printing technology to embody the generated AI character and use it as a household robot or a pet robot. As a result, the generated AI character creation system according to the embodiment generates the generated AI character based on a user's selection, and enables interaction in a wearable device or metaverse space, and embodying it as a real robot.

[0059] The reception unit estimates the user's emotions and presents character options based on the estimated user emotions. For example, if the user is relaxed, the reception unit may preferentially present characters with a calm personality. Furthermore, if the user is excited, the reception unit may preferentially present characters with a lively personality. Furthermore, if the user is stressed, the reception unit may preferentially present characters with a soothing personality. This allows for the presentation of character options according to the user's emotions, thereby providing a more appropriate character. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0060] The reception unit can analyze the user's past selection history and suggest the most suitable character. For example, the reception unit can analyze the tendencies of characters selected by the user in the past and suggest characters with similar characteristics. The reception unit can also suggest characters played by the same voice actor based on the voice quality of a character selected by the user in the past. The reception unit can also suggest characters with the same personality based on the personality of a character selected by the user in the past. In this way, by suggesting the most suitable character based on the user's past selection history, it is possible to provide a character that suits the user's preferences. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past selection history data into a generation AI and cause the generation AI to suggest the most suitable character.

[0061] When selecting a character, the reception unit can filter based on the user's current interests and concerns. For example, the reception unit can prioritize presenting characters from anime that the user has recently watched. The reception unit can also suggest characters based on anime-related keywords that the user has recently searched for. The reception unit can also prioritize presenting characters from anime events that the user has recently participated in. This allows for filtering characters based on the user's current interests and concerns, thereby providing more relevant characters. Some or all of the above-described processing by the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's current interest and concern data into the generation AI and have the generation AI perform character filtering.

[0062] When selecting a character, the reception unit can provide an appropriate selection means according to the user's input method. For example, when the user describes the characteristics of a character by voice, the reception unit presents a character that matches the characteristics. The reception unit can also display a character when the user inputs the character's name in text. The reception unit can also present a character that resembles the image when the user uploads an image. This improves user convenience by providing an optimal selection means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's voice data into a generation AI and cause the generation AI to provide a character selection means.

[0063] The generation unit can estimate the user's emotions and adjust the character generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a character with a calm personality. Furthermore, if the user is excited, the generation unit can generate a character with a lively personality. Furthermore, if the user is stressed, the generation unit can generate a soothing character. By adjusting the character generation method based on the user's emotions, a more appropriate character can be generated. The emotion estimation is realized using an emotion estimation function, for example, with 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 generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the character generation method.

[0064] When generating a character, the generation unit can adjust the level of detail of the generation based on the importance of elements selected by the user. For example, if the user places importance on appearance, the generation unit can increase the level of detail of the appearance. Furthermore, if the user places importance on voice quality, the generation unit can increase the level of detail of the voice quality. Furthermore, if the user places importance on personality, the generation unit can increase the level of detail of the personality. In this way, by adjusting the level of detail of the generation based on the importance of elements selected by the user, a character can be generated that meets the user's needs. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user selection data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0065] When generating a character, the generation unit can apply different generation algorithms depending on the category of the selected character. For example, if the character selected by the user is a fantasy character, the generation unit can apply a fantasy generation algorithm. Furthermore, if the character selected by the user is a science fiction character, the generation unit can also apply a science fiction generation algorithm. Furthermore, if the character selected by the user is a real character, the generation unit can also apply a real-life generation algorithm. In this way, by applying a generation algorithm according to the character category, a more appropriate character can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the selected character into the generation AI and cause the generation AI to apply the generation algorithm.

[0066] When generating a character, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of generation, for example, based on the characteristics of characters the user has generated in the past. The generation unit can also improve the accuracy of generation based on evaluations of characters the user has generated in the past. The generation unit can also improve the accuracy of generation based on the user's usage history of characters generated in the past. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0067] The implementation unit can estimate the user's emotions and adjust the implementation method of the wearable device based on the estimated user emotions. For example, the implementation unit can provide a calm display method when the user is relaxed. The implementation unit can also provide a visually stimulating display method when the user is excited. The implementation unit can also provide a simple, highly visible display method when the user is stressed. This allows for a more appropriate display method to be provided by adjusting the implementation method based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, 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 implementation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the implementation unit can input the user's emotion data into the generation AI and have the generation AI adjust the implementation method.

[0068] During implementation, the implementation unit can select the optimal implementation method by referring to the user's past device usage history. The implementation unit selects the optimal implementation method, for example, based on the settings of the device used by the user in the past. The implementation unit can also select the optimal implementation method based on the display method of the device used by the user in the past. The implementation unit can also select the optimal implementation method based on the operation method of the device used by the user in the past. This improves user convenience by selecting the optimal implementation method based on the user's past device usage history. Some or all of the above-described processing in the implementation unit may be performed using, for example, AI, or may be performed without using AI. For example, the implementation unit can input the user's past device usage history data into the generation AI and cause the generation AI to select the optimal implementation method.

[0069] During implementation, the implementation unit can customize the implementation means based on the user's current device environment. For example, if the user is using a smartwatch, the implementation unit can provide an implementation means optimized for the device. Furthermore, if the user is using AR glasses, the implementation unit can also provide an implementation means optimized for the device. Furthermore, if the user is using a smartphone, the implementation unit can also provide an implementation means optimized for the device. This allows the implementation means to be customized based on the user's current device environment, thereby providing an optimal implementation method. Some or all of the above-described processing in the implementation unit may be performed, for example, using AI or without AI. For example, the implementation unit can input the user's current device environment data into the generation AI and cause the generation AI to customize the implementation means.

[0070] The implementation unit can improve the implementation method by reflecting user feedback during implementation. For example, the implementation unit can improve the display method based on user-provided feedback. The implementation unit can also improve the operation method based on user-provided feedback. The implementation unit can also improve the interface based on user-provided feedback. This allows the implementation method to be continuously improved by reflecting user feedback. Some or all of the above-described processing in the implementation unit may be performed using AI, for example, or may be performed without using AI. For example, the implementation unit can input user feedback data into a generation AI and have the generation AI execute improvements to the implementation method.

[0071] The interaction unit can estimate the user's emotions and adjust the interaction method in the metaverse space based on the estimated user emotions. For example, the interaction unit can provide a calm interaction method when the user is relaxed. The interaction unit can also provide a lively interaction method when the user is excited. The interaction unit can also provide a soothing interaction method when the user is stressed. By adjusting the interaction method based on the user's emotions, a more appropriate interaction experience can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the interaction unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the interaction unit can input the user's emotion data into the generation AI and have the generation AI adjust the interaction method.

[0072] The interaction unit can select the optimal interaction method by referring to the user's past interaction history when interacting. For example, the interaction unit selects the optimal interaction method based on the history of events the user has previously participated in. The interaction unit can also select the optimal interaction method based on the history of characters the user has previously interacted with. The interaction unit can also select the optimal interaction method based on feedback the user has previously provided. In this way, by selecting the optimal interaction method based on the user's past interaction history, it is possible to provide an interaction experience that suits the user's preferences. Some or all of the above-described processing in the interaction unit may be performed using, for example, AI, or may be performed without using AI. For example, the interaction unit can input the user's past interaction history data into a generation AI and cause the generation AI to select the optimal interaction method.

[0073] The communication unit can customize the communication means based on the user's current interests and concerns during communication. For example, the communication unit customizes the communication means based on a theme that the user has recently been interested in. The communication unit can also customize the communication means based on an event that the user recently participated in. The communication unit can also customize the communication means based on an anime that the user recently watched. This allows for customizing the communication means based on the user's current interests and concerns, thereby providing a more relevant communication experience. Some or all of the above-described processing in the communication unit may be performed using, or without, AI, for example. For example, the communication unit can input the user's current interest and concern data into a generation AI and have the generation AI customize the communication means.

[0074] The communication unit can improve the communication method by reflecting user feedback during communication. The communication unit can improve the communication method, for example, based on feedback provided by the user. The communication unit can also improve the content of the event based on feedback provided by the user. The communication unit can also improve the means of communication based on feedback provided by the user. This allows the communication method to be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or can be performed without using AI. For example, the communication unit can input user feedback data into a generation AI and cause the generation AI to improve the communication method.

[0075] The embodiment unit can estimate the user's emotions and determine the priority of characters to be embodied based on the estimated user's emotions. For example, when the user is relaxed, the embodiment unit can prioritize embodying characters with a calm personality. Furthermore, when the user is excited, the embodiment unit can prioritize embodying characters with a lively personality. Furthermore, when the user is stressed, the embodiment unit can prioritize embodying characters with a soothing personality. Thus, by determining the priority of characters to be embodied based on the user's emotions, more appropriate characters can be embodied. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 embodiment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the embodiment unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of characters to be embodied.

[0076] The embodiment unit can select the optimal embodiment method by referring to the user's past embodiment history at the time of embodiment. For example, the embodiment unit selects the optimal embodiment method based on the history of characters previously embodied by the user. The embodiment unit can also select the optimal embodiment method based on the user's evaluation of characters previously embodied by the user. The embodiment unit can also select the optimal embodiment method based on the user's usage history of characters previously embodied by the user. In this way, by selecting the optimal embodiment method based on the user's past embodiment history, it is possible to provide an embodiment method that suits the user's preferences. Some or all of the above-mentioned processing in the embodiment unit may be performed, for example, using AI or without AI. For example, the embodiment unit can input the user's past embodiment history data into the generation AI and cause the generation AI to select the optimal embodiment method.

[0077] The embodiment unit can customize the embodiment means based on the user's current living situation during embodiment. For example, if the user desires a home robot, the embodiment unit can provide an embodiment means optimized for that purpose. Furthermore, if the user desires a pet robot, the embodiment unit can provide an embodiment means optimized for that purpose. Furthermore, if the user desires a commercial robot, the embodiment unit can provide an embodiment means optimized for that purpose. This allows for customizing the embodiment means based on the user's current living situation, thereby providing an optimal embodiment method. Some or all of the above-described processing in the embodiment unit may be performed using, for example, AI, or may be performed without using AI. For example, the embodiment unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the embodiment means.

[0078] The embodiment unit can improve the embodiment method by reflecting user feedback during embodiment. The embodiment unit can improve the embodiment method, for example, based on feedback provided by the user. The embodiment unit can also improve the function of the robot based on feedback provided by the user. The embodiment unit can also improve the design of the robot based on feedback provided by the user. This allows the embodiment method to be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the embodiment unit can be performed using AI, for example, or without using AI. For example, the embodiment unit can input user feedback data into a generation AI and cause the generation AI to improve the embodiment method.

[0079] The embodiment unit can estimate the user's emotion and adjust the display method of the embodied character based on the estimated user's emotion. For example, the embodiment unit can provide a calm display method when the user is relaxed. The embodiment unit can also provide a visually stimulating display method when the user is excited. The embodiment unit can also provide a simple, highly visible display method when the user is stressed. This allows for a more appropriate display method to be provided by adjusting the display method based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as 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 embodiment unit can be performed using, for example, an AI, or without an AI. For example, the embodiment unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0080] During embodiment, the embodiment unit can select the optimal embodiment method by taking into account the user's geographical location information. For example, if the user is in Japan, the embodiment unit can prioritize embodying Japanese anime characters. Also, if the user is in the United States, the embodiment unit can prioritize embodying anime characters that are popular in the United States. Also, if the user is in a specific region, the embodiment unit can prioritize embodying anime characters related to that region. In this way, the optimal embodiment method can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the embodiment unit can be performed using, or without, AI. For example, the embodiment unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal embodiment method.

[0081] During the embodiment, the embodiment unit can analyze the user's social media activity and suggest an embodiment means. For example, the embodiment unit can prioritize the embodiment of anime characters that the user follows on social media. The embodiment unit can also prioritize the embodiment of anime characters that the user has "liked" on social media. The embodiment unit can also prioritize the embodiment of anime characters that the user has shared on social media. In this way, by analyzing the user's social media activity, more relevant embodiment means can be provided. Some or all of the above-described processing in the embodiment unit can be performed, for example, using AI or without AI. For example, the embodiment unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest an embodiment means.

[0082] The embodiment unit can customize the embodiment method by reflecting the user's past feedback during embodiment. For example, the embodiment unit customizes the embodiment method based on the characteristics of characters that the user has previously rated highly. The embodiment unit can also customize the embodiment method by avoiding the characteristics of characters that the user has previously rated poorly. The embodiment unit can also customize the embodiment method based on feedback provided by the user in the past. In this way, the embodiment method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the embodiment unit may be performed using, for example, AI, or may be performed without using AI. For example, the embodiment unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the embodiment method. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, implementation unit, communication unit, and materialization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception 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. For example, to estimate a user's emotions, the camera 42 and microphone 38B of the smart device 14 are used to detect the user's facial expressions and voice, and the control unit 46A estimates the emotions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a character using a generation AI. The implementation unit is realized, for example, by the control unit 46A of the smart device 14 and displays the generated character on the wearable device. The communication unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides communication in the metaverse space. The materialization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and materializes the generated AI character as a real robot. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, implementation unit, communication unit, and materialization 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 reception 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. For example, to estimate a user's emotions, the camera 42 and microphone 238 of the smart glasses 214 are used to detect the user's facial expressions and voice, and the control unit 46A estimates the emotions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a character using a generation AI. The implementation unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays the generated character on the wearable device. The communication unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides communication in the metaverse space. The materialization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and materializes the generated AI character as a real robot. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, generation unit, implementation unit, communication unit, and materialization 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 reception 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. For example, to estimate a user's emotions, the camera 42 and microphone 238 of the headset-type terminal 314 are used to detect the user's facial expressions and voice, and the control unit 46A estimates the emotions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a character using a generation AI. The implementation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and displays the generated character on the wearable device. The communication unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides communication in the metaverse space. The materialization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and materializes the generated AI character as a real robot. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, implementation unit, communication unit, and materialization unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, to estimate a user's emotions, the camera 42 and microphone 238 of the robot 414 are used to detect the user's facial expressions and voice, and the control unit 46A estimates the emotions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a character using a generation AI. The implementation unit is realized, for example, by the control unit 46A of the robot 414 and displays the generated character on a wearable device. The communication unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides communication in the metaverse space. The materialization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and materializes the generated AI character as a real robot.

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

[0084] The reception unit can also acquire the user's biometric information and present character options based on that information. For example, it can monitor the user's heart rate and electrodermal activity, and present a calm character if the user is relaxed and a lively character if the user is excited. It can also analyze the user's sleep patterns and suggest a lively character in the morning and a calm character in the evening. It can also present active or relaxed characters as options based on the user's level of exercise. This enables more personalized character selection based on the user's biometric information.

[0085] The generation unit can also analyze the user's voice tone and adjust the character's personality based on that tone. For example, if the user's voice is calm, a character with a calm personality is generated, and if the voice is high-pitched, a character with a lively personality is generated. The generation unit can also analyze the speed and rhythm of the user's voice and generate a calm character if the tone is relaxed, or an energetic character if the tone is fast. Furthermore, it can analyze the emotion in the user's voice and generate a character that corresponds to an emotion such as joy or sadness. This makes it possible to generate a more appropriate character based on the user's voice tone.

[0086] The implementation unit can also learn the user's device usage patterns and optimize the character display method based on those patterns. For example, if the user mainly uses the device at night, it can prioritize display in night mode. Also, if the user frequently uses the device while on the move, it can provide a display method with high visibility even while on the move. Furthermore, if the user frequently uses a specific application, it can provide a character display optimized for that application. This enables more convenient character display based on the user's device usage patterns.

[0087] The interaction unit can analyze the user's social network information and customize the interaction method in the metaverse space based on that information. For example, it can prioritize interactions with users who share common interests based on the user's friend list and follower information. It can also analyze the user's activity history on social networks and provide interaction methods related to events and groups the user has previously participated in. Furthermore, it can continuously improve the interaction method based on the user's feedback on social networks. This enables a more personalized interaction experience based on the user's social network information.

[0088] The embodiment unit can also monitor the user's living environment and adjust the functions of the embodied character based on the environment. For example, if the user has a pet, the embodiment unit can embody a character that can coexist with the pet. Also, if the user has small children, the embodiment unit can embody a character with child-friendly functions. Furthermore, if the user's living environment is small, the embodiment unit can embody a compact-sized character. This allows for more appropriate character embodiment based on the user's living environment.

[0089] The reception unit can also analyze the user's purchase history and present character options based on that history. For example, if the user has a history of purchasing goods for a specific anime, characters related to that anime can be presented preferentially. Also, if the user frequently purchases anime from a specific genre, characters related to that genre can be suggested. Furthermore, it is also possible to present highly rated characters preferentially based on the ratings of character goods purchased by the user in the past. This allows for more relevant character selection based on the user's purchase history.

[0090] The generation unit can also analyze the user's hobbies and preferences and customize the character's appearance and personality based on those preferences. For example, if the user likes sports, an athlete-type character can be generated. If the user likes music, a musician-type character can be generated. Furthermore, if the user likes a particular fashion style, a character that matches that style can be generated. This allows for more personalized character generation based on the user's hobbies and preferences.

[0091] The implementation unit can also monitor the remaining battery level of the user's device and adjust the character display method based on that remaining battery level. For example, if the battery level is low, it can prioritize display in power-saving mode. Alternatively, if the battery level is sufficient, it can provide a fully functional display. Furthermore, it can adjust the use of character animations and effects according to the remaining battery level. This enables more efficient character display based on the remaining battery level of the user's device.

[0092] The interaction unit can also estimate the user's emotions and customize events in the metaverse space based on the estimated emotions. For example, if the user is relaxed, a calm event can be provided. If the user is excited, an active event can be provided. Furthermore, if the user is stressed, a soothing event can be provided. This allows for a more appropriate event experience based on the user's emotions.

[0093] The embodiment unit can also estimate the user's emotions and adjust the behavior of the embodied character based on the estimated emotions. For example, if the user is relaxed, the embodiment unit can embody a character that moves calmly. If the user is excited, the embodiment unit can embody a character that moves actively. Furthermore, if the user is stressed, the embodiment unit can embody a character that moves in a soothing manner. This allows for more appropriate character behavior based on the user's emotions.

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

[0095] Step 1: The reception unit receives the selection of the character's appearance, voice quality, and personality from the user. For example, the user can select the appearance, voice quality, and personality of their favorite anime character through the app. Step 2: The generation unit generates an AI character using a generation AI based on the selections received by the reception unit. The generation AI analyzes the user's selections and generates an optimal character using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The implementation unit implements the AI ​​character generated by the generation unit into a wearable device. Wearable devices include, for example, smartwatches and AR glasses. The implementation unit displays the generated AI character on these devices, allowing it to constantly communicate with the user. Step 4: The interaction unit provides a function for the generated AI character and the user to interact in the metaverse space. For example, the user can access the metaverse space using a VR headset and participate in events and games with the generated AI character. Step 5: The materialization unit materializes the generated AI character as a real robot. The materialization unit may embody the generated AI character using, for example, 3D printing technology, and use it as a household robot or a pet robot.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Explanation of symbols]

[0168] 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 reception unit that receives selections from a user of the character's appearance, voice quality, and personality; a generation unit that generates an AI character based on the selection accepted by the acceptance unit; an implementation unit that implements the AI ​​character generated by the generation unit in a wearable device; An interaction unit for allowing the AI ​​character and the user to interact in a metaverse space; and a realization unit that realizes the AI ​​character as a real robot. A system characterized by:

2. The reception unit Estimate the user's emotions and present character options based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past selection history and suggest characters that suit the user 2. The system of claim 1.

4. The reception unit Filter character selection based on the user's current interests 2. The system of claim 1.

5. The reception unit When selecting a character, provide an appropriate selection method depending on the user's input method.

2. The system of claim 1.

6. The generation unit Estimate the user's emotions and adjust the character generation method based on the estimated user emotions.

2. The system of claim 1.

7. The generation unit During character generation, adjust the level of detail of the generation based on the importance of factors selected by the user.

2. The system of claim 1.

8. The generation unit During character generation, different generation algorithms are applied depending on the character category selected.

2. The system of claim 1.

Images