system

Abstract

The system according to this embodiment aims to automatically generate and provide artworks and characters based on user instructions. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives instructions from the user. The generation unit generates artworks or characters based on the instructions received by the reception unit. The provision unit provides the artworks or characters generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of generating an art work or a character based on a user instruction is not automated and it is difficult to perform efficiently.

[0005] The system according to the embodiment aims to automatically generate and provide an art work or a character based on a user instruction.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives instructions from the user. The generation unit generates artworks or characters based on the instructions received by the reception unit. The provision unit provides the artworks or characters generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate and provide artworks and characters based on user instructions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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] [[ID=二十一]] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The virtual artist system according to an embodiment of the present invention is a mechanism in which a virtual artist generates original artworks and characters, and these works are used in the digital art market and the game industry. The virtual artist system uses generative AI to enable the virtual artist to generate artworks and characters. The generated artworks and characters are used in the digital art market and the game industry. This mechanism can solve the challenges faced by target audiences such as game developers, the entertainment industry, the digital art market, the advertising and marketing industry, virtual video streamers, virtual live streamers, and virtual live events. For example, the virtual artist system uses generative AI to enable the virtual artist to generate artworks and characters. For example, a game developer can use a virtual artist to create characters, backgrounds, and assets for use in their game. The characters and artwork created by the virtual artist shape the game's worldview and enrich the player's experience. Next, the generated artworks and characters are used in the digital art market and the game industry. For example, in the entertainment industry, virtual artists are utilized in the creation of character designs, background art, and effects for films, animations, and television programs. Their works contribute to the creation of visually appealing works. Furthermore, in the digital art market, works by virtual artists are exhibited and sold. Their art attracts the attention of collectors and art lovers, contributing to the growth of the digital art market. In the advertising and marketing industry, virtual artists are utilized in the creation of digital art and characters. Their works are used for branding and promotion of products and services. In addition, virtual video streamers and virtual live streamers create content using characters designed by virtual artists and interact with their fans. Their activities are attracting attention as a new form of online entertainment. Finally, virtual live events and concerts feature virtual artists performing and providing entertainment to viewers.Their participation will enable new forms of live entertainment. Thus, the existence and demand for virtual artists are expected to continue to expand along with the growth of the gaming industry, the entertainment industry, the digital art market, and virtual entertainment. This will allow virtual artist systems to generate and deliver artwork and characters based on user instructions.

[0029] The virtual artist system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives instructions from the user. Instructions from the user include, but are not limited to, text input, voice input, and image input. For example, the reception unit receives text input. The reception unit can also receive voice input. Furthermore, the reception unit can also receive image input. For example, the reception unit analyzes the text entered by the user and understands the content of the instructions. In the case of voice input, the reception unit uses speech recognition technology to convert the voice into text and understand the content of the instructions. In the case of image input, the reception unit uses image analysis technology to analyze the image and understand the content of the instructions. The generation unit uses a generational AI to generate artworks or characters based on the instructions received by the reception unit. The generational AI can use technologies such as GAN (Generative Adversarial Network) or VAE (Variational Autoencoder). For example, the generation unit generates artworks using GAN. The generation unit can also generate characters using VAE. Furthermore, the generation unit can also generate artworks and characters based on user instructions using a generation AI. For example, the generation unit can generate artworks based on a style or theme specified by the user. The supply unit provides the artworks and characters generated by the generation unit. For example, the supply unit provides the generated artworks and characters to the digital art market or the game industry. For example, the supply unit exhibits the generated artworks in an online gallery. The supply unit can also provide the generated characters to game development companies. Furthermore, the supply unit can exhibit and sell the generated artworks and characters. For example, the supply unit sells the generated artworks on an online marketplace. Thus, the virtual artist system according to this embodiment can generate and provide artworks and characters based on user instructions.

[0030] The reception unit receives instructions from the user. User instructions include, but are not limited to, text input, voice input, and image input. For example, the reception unit receives text input. Specifically, it analyzes the text entered by the user using natural language processing technology to understand the instruction. For example, if a user enters "I want a landscape painting," the reception unit analyzes this text and sends an instruction to the generation unit to generate a landscape painting. The reception unit can also receive voice input. In the case of voice input, the reception unit uses speech recognition technology to convert the voice into text and understand the instruction. For example, if a user gives a voice instruction saying "I want a character created," the voice is converted into text using speech recognition technology, the text is analyzed, and sent to the generation unit. Furthermore, the reception unit can also receive image input. In the case of image input, the reception unit uses image analysis technology to analyze the image and understand the instruction. For example, if a user uploads a reference image, the reception unit analyzes the features of that image and sends an appropriate instruction to the generation unit. In this way, the reception unit can handle a variety of input formats from users, accurately understand the instruction, and transmit it to the generation unit.

[0031] The generation unit uses generative AI to generate artworks and characters based on instructions received by the reception unit. The generative AI can utilize technologies such as GAN (Generative Adversarial Network) and VAE (Variational Autoencoder). Specifically, the generation unit generates artworks using GANs. A GAN consists of two networks: a generative network and a discriminative network. The generative network generates new data, and the discriminative network determines whether that data is real or fake. By repeating this process, the generative network can generate more realistic artworks. The generation unit can also generate characters using VAEs. A VAE encodes input data into latent variables and decodes new data from these latent variables to generate new characters. Furthermore, the generation unit can use generative AI to generate artworks and characters based on user instructions. For example, when generating artworks based on a style or theme specified by the user, the generative AI generates data to conform to the specified style or theme. This allows the generation unit to generate artworks and characters that meet diverse user requirements with high accuracy.

[0032] The provider provides artwork and characters generated by the generator. For example, the provider provides the generated artwork and characters to the digital art market and the game industry. Specifically, the provider exhibits the generated artwork in an online gallery. In the online gallery, users can view and evaluate the generated artwork. The provider can also provide generated characters to game development companies. Game development companies can use the provided characters in their games to enhance the game's appeal. Furthermore, the provider can exhibit and sell the generated artwork and characters. For example, the provider sells the generated artwork on an online marketplace. In the online marketplace, users can purchase the generated artwork and add it to their collections. This allows the provider to widely distribute the generated artwork and characters and meet the diverse needs of users. In addition, the provider can also manage the copyrights and license the generated artwork and characters. This protects the rights to the generated works and promotes their appropriate use.

[0033] The generation unit can generate artwork and characters based on user instructions. For example, the generation unit can generate artwork based on a style or theme specified by the user. For example, the generation unit can also generate characters based on a color scheme or design specified by the user. Furthermore, the generation unit can generate artwork based on images or text provided by the user. For example, the generation unit can analyze an image provided by the user and generate artwork that reflects its style. In this way, the generation unit can generate artwork and characters based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs user instructions into a generation AI, and the generation AI generates artwork or characters.

[0034] The provider can offer the generated artwork and characters to the digital art market and the gaming industry. For example, the provider can exhibit the generated artwork in an online gallery. For example, the provider can also sell the generated artwork on an online marketplace. The provider can also provide the generated characters to game development companies. For example, the provider can provide the generated characters for use in a game. In this way, the provider can offer the generated artwork and characters to the digital art market and the gaming industry. Some or all of the above processes in the provider may be performed using a generation AI, or not using a generation AI. For example, the provider inputs the generated artwork or characters into a generation AI, and the generation AI determines the method of provision.

[0035] The generation unit can generate artworks and characters using generative AI. For example, the generation unit can generate artworks using a GAN (Generative Adversarial Network). For example, the generation unit can generate abstract artworks using a GAN. The generation unit can also generate characters using a VAE (Variational Autoencoder). For example, the generation unit can generate realistic characters using a VAE. Furthermore, the generation unit can generate artworks and characters based on user instructions using generative AI. For example, the generation unit generates artworks based on a style or theme specified by the user. Thus, the generation unit can generate artworks and characters using generative AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit inputs user instructions into the generative AI, and the generative AI generates artworks or characters.

[0036] The provider can exhibit and sell the generated artwork and characters. For example, the provider can exhibit the generated artwork in an online gallery. For example, the provider can also sell the generated artwork on an online marketplace. The provider can also exhibit the generated characters at a physical exhibition. For example, the provider can exhibit and sell the generated characters at an exhibition. In this way, the provider can exhibit and sell the generated artwork and characters. Some or all of the above processes in the provider may be performed using a generation AI, or not using a generation AI. For example, the provider inputs the generated artwork or characters into a generation AI, and the generation AI determines the method of exhibition and sales.

[0037] The provider can use the generated artwork and characters within the game. For example, the provider provides the generated characters for use within the game. For example, the provider can use the generated characters as player characters within the game. The provider can also use the generated artwork as background art within the game. For example, the provider can use the generated artwork as a stage background within the game. In this way, the provider can use the generated artwork and characters within the game. Some or all of the above processing in the provider may be performed using a generation AI, for example, or without a generation AI. For example, the provider inputs the generated artwork and characters into a generation AI, and the generation AI determines how to use them within the game.

[0038] The reception desk can analyze the user's past instruction history and suggest the most suitable instructions. For example, the reception desk can suggest art styles that the user has previously preferred to create. For example, the reception desk can suggest new instructions based on art styles the user has previously created. The reception desk can also suggest characteristics of characters that the user has frequently used in the past. For example, the reception desk can suggest new characters based on characteristics of characters the user has previously used. The reception desk can also suggest new instructions based on the themes of works the user has previously created. For example, the reception desk can suggest instructions for new artwork based on the themes of works the user has previously created. In this way, the reception desk can analyze the user's past instruction history and suggest the most suitable instructions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's past instruction history into the AI, and the AI ​​suggests the most suitable instructions.

[0039] The reception unit can filter instructions based on the user's current projects and areas of interest. For example, the reception unit prioritizes instructions related to the user's current project. For instance, it might suggest art styles or characters related to the user's current project. The reception unit can also suggest relevant art styles or characters based on the user's areas of interest. For example, it might suggest instructions for new artwork based on the user's areas of interest. The reception unit can also filter instructions appropriately according to the progress of the user's project. For example, it might suggest instructions for necessary artwork or characters according to the progress of the user's project. This allows the reception unit to filter instructions based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit inputs the user's current projects and areas of interest into the AI, which then filters the instructions to the most appropriate content.

[0040] The reception desk can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest art styles or characters related to that region. For example, if the user is in a specific region, the reception desk can accept instructions for artwork that reflects the culture or scenery of that region. The reception desk can also accept instructions themed on local specialties or landmarks based on the user's location. For example, if the user is traveling, the reception desk can accept instructions for artwork or characters related to the travel destination. This allows the reception desk to prioritize receiving instructions that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk inputs the user's geographical location into the AI, and the AI ​​suggests the most appropriate instructions.

[0041] The reception desk can analyze the user's social media activity and accept relevant instructions when receiving instructions. For example, the reception desk can suggest instructions based on artwork the user has shared on social media. For example, the reception desk can suggest new instructions based on artwork the user has shared on social media. The reception desk can also suggest art styles preferred by the user's followers and friends. For example, the reception desk can suggest new instructions based on art styles preferred by the user's followers and friends. The reception desk can also accept instructions related to topics the user has shown interest in on social media. For example, the reception desk can suggest artwork related to topics the user has shown interest in on social media. In this way, the reception desk can accept relevant instructions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity into the AI, and the AI ​​can suggest the most appropriate instructions.

[0042] The generation unit can optimize its generation algorithm by referring to the user's past instruction history during generation. For example, the generation unit can adjust its generation algorithm based on the user's past preferred art style. For example, the generation unit can generate a new artwork based on the user's past preferred art style. The generation unit can also reflect the characteristics of characters the user has previously generated. For example, the generation unit can generate a new character based on the characteristics of characters the user has previously generated. The generation unit can also set optimal generation parameters from the user's past instruction history. For example, the generation unit sets optimal generation parameters based on the user's past instruction history and generates a new artwork. This allows the generation unit to optimize its generation algorithm by referring to the user's past instruction history. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit inputs the user's past instruction history into the generation AI, and the generation AI optimizes the generation algorithm.

[0043] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, the generation unit can generate artwork related to the user's ongoing projects. For example, the generation unit can generate new artwork based on the art style related to the user's ongoing projects. The generation unit can also customize character designs based on the user's areas of interest. For example, the generation unit can generate new characters based on the user's areas of interest. The generation unit can also generate art styles that match the theme of the user's projects. For example, the generation unit can generate background art that matches the theme of the user's projects. In this way, the generation unit can customize the generated content based on the user's current projects and areas of interest. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's current projects and areas of interest into the generation AI, and the generation AI customizes the generated content.

[0044] The generation unit can prioritize generating content that is highly relevant to the user, taking into account the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit can generate artwork that reflects the culture and scenery of that region. For example, if the user is in a specific region, the generation unit can generate new artwork based on the traditional art style of that region. The generation unit can also generate characters themed around local specialties and landmarks based on the user's location information. For example, if the user is traveling, the generation unit can generate characters related to their travel destination. The generation unit can also generate background art that reflects the scenery of a region based on the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate background art based on the scenery of that region. This allows the generation unit to prioritize generating content that is highly relevant to the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI proposes the most suitable content.

[0045] The generation unit can analyze the user's social media activity during generation and provide relevant generated content. For example, the generation unit can generate new artwork based on artwork shared by the user on social media. The generation unit can also reflect the art styles preferred by the user's followers and friends. The generation unit can also generate characters related to topics the user has shown interest in on social media. In this way, the generation unit can provide relevant generated content based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's social media activity into the generation AI, which then proposes the most suitable generated content.

[0046] The delivery unit can select the optimal delivery method by referring to the user's past purchase history at the time of delivery. For example, the delivery unit can suggest new works based on the art styles the user has purchased in the past. The delivery unit can also reflect the characteristics of characters the user has purchased in the past. For example, the delivery unit can suggest new characters based on the characteristics of characters the user has purchased in the past. The delivery unit can also select the optimal delivery method from the user's purchase history. For example, the delivery unit can select the optimal delivery method based on the user's purchase history and suggest new works or characters. In this way, the delivery unit can select the optimal delivery method based on the user's past purchase history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit inputs the user's past purchase history into AI, and the AI ​​suggests the optimal delivery method.

[0047] The service provider can customize the content offered based on the user's current projects and areas of interest at the time of delivery. For example, the service provider can provide artwork related to the user's ongoing projects. For example, the service provider can provide new artwork based on the art style related to the user's ongoing projects. The service provider can also customize character designs based on the user's areas of interest. For example, the service provider can provide new characters based on the user's areas of interest. The service provider can also provide art styles that match the theme of the user's project. For example, the service provider can provide background art that matches the theme of the user's project. In this way, the service provider can customize the content offered based on the user's current projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current projects and areas of interest into the AI, and the AI ​​can suggest the most suitable content to offer.

[0048] The service provider can prioritize providing highly relevant content by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide artwork that reflects the culture and scenery of that region. For example, if the user is in a specific region, the service provider can provide new artwork based on the traditional art style of that region. The service provider can also provide characters themed on local specialties or landmarks based on the user's location information. For example, if the user is traveling, the service provider can provide characters related to their travel destination. The service provider can also provide background art that reflects the scenery of a region based on the user's geographical location information. For example, if the user is in a specific region, the service provider can provide background art based on the scenery of that region. This allows the service provider to prioritize providing highly relevant content based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider inputs the user's geographical location information into AI, and the AI ​​proposes the most suitable content.

[0049] The service provider can analyze the user's social media activity and suggest relevant content at the time of delivery. For example, the service provider can provide new artwork based on artwork shared by the user on social media. The service provider can also reflect the art styles preferred by the user's followers and friends. The service provider can also provide characters related to topics the user has shown interest in on social media. This allows the service provider to suggest relevant content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's social media activity into AI, which then suggests the most suitable content.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can refer to the user's past instruction history when receiving user instructions and suggest the most appropriate instructions. For example, the reception desk can suggest new instructions based on the art style and character characteristics the user has previously created. It can also customize instructions by considering the user's preferred colors and designs. Furthermore, the reception desk can analyze the user's past instruction history and prioritize suggesting instructions that the user frequently uses. This allows the reception desk to leverage the user's past instruction history to suggest more appropriate instructions.

[0052] The service provider can customize the content they provide, such as generated artwork and characters, based on the user's current projects and areas of interest. For example, they can provide artwork related to the user's ongoing projects. They can also customize character designs based on the user's areas of interest. Furthermore, they can provide art styles that match the theme of the user's project. This allows the service provider to customize the content based on the user's current projects and areas of interest.

[0053] The generation unit can prioritize generating highly relevant content by considering the user's geographical location during the generation process. For example, if the user is in a specific region, it can generate artwork that reflects the culture and scenery of that region. It can also generate characters themed around local specialties or landmarks based on the user's location. Furthermore, if the user is traveling, it can generate characters related to their travel destination. In this way, the generation unit can prioritize generating highly relevant content based on the user's geographical location.

[0054] The reception desk can analyze the user's social media activity when receiving instructions and accept relevant instructions. For example, it can suggest instructions based on artwork the user has shared on social media. It can also suggest art styles preferred by the user's followers and friends. Furthermore, it can accept instructions related to topics the user has shown interest in on social media. This allows the reception desk to receive relevant instructions based on the user's social media activity.

[0055] The delivery unit can select the optimal delivery method by referring to the user's past purchase history at the time of delivery. For example, it can suggest new works based on the art style the user has purchased in the past. It can also reflect the characteristics of characters the user has purchased in the past. Furthermore, it can select the optimal delivery method from the user's purchase history. In this way, the delivery unit can select the optimal delivery method based on the user's past purchase history.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives instructions from the user. User instructions include text input, voice input, and image input. For example, the reception desk analyzes the text entered by the user to understand the instructions. In the case of voice input, the reception desk uses speech recognition technology to convert the voice into text and understand the instructions. In the case of image input, the reception desk uses image analysis technology to analyze the image and understand the instructions. Step 2: The generation unit uses a generation AI to generate artwork or characters based on instructions received by the reception unit. The generation AI can utilize technologies such as GAN (Generative Adversarial Network) or VAE (Variational Autoencoder). For example, the generation unit generates artwork based on a style or theme specified by the user. Step 3: The provider provides the artwork and characters generated by the generator. The provider provides the generated artwork and characters to the digital art market and the gaming industry. For example, the provider can exhibit the generated artwork in an online gallery or sell it on an online marketplace. They can also provide the generated characters to game development companies.

[0058] (Example of form 2) The virtual artist system according to an embodiment of the present invention is a mechanism in which a virtual artist generates original artworks and characters, and these works are used in the digital art market and the game industry. The virtual artist system uses generative AI to enable the virtual artist to generate artworks and characters. The generated artworks and characters are used in the digital art market and the game industry. This mechanism can solve the challenges faced by target audiences such as game developers, the entertainment industry, the digital art market, the advertising and marketing industry, virtual video streamers, virtual live streamers, and virtual live events. For example, the virtual artist system uses generative AI to enable the virtual artist to generate artworks and characters. For example, a game developer can use a virtual artist to create characters, backgrounds, and assets for use in their game. The characters and artwork created by the virtual artist shape the game's worldview and enrich the player's experience. Next, the generated artworks and characters are used in the digital art market and the game industry. For example, in the entertainment industry, virtual artists are utilized in the creation of character designs, background art, and effects for films, animations, and television programs. Their works contribute to the creation of visually appealing works. Furthermore, in the digital art market, works by virtual artists are exhibited and sold. Their art attracts the attention of collectors and art lovers, contributing to the growth of the digital art market. In the advertising and marketing industry, virtual artists are utilized in the creation of digital art and characters. Their works are used for branding and promotion of products and services. In addition, virtual video streamers and virtual live streamers create content using characters designed by virtual artists and interact with their fans. Their activities are attracting attention as a new form of online entertainment. Finally, virtual live events and concerts feature virtual artists performing and providing entertainment to viewers.Their participation will enable new forms of live entertainment. Thus, the existence and demand for virtual artists are expected to continue to expand along with the growth of the gaming industry, the entertainment industry, the digital art market, and virtual entertainment. This will allow virtual artist systems to generate and deliver artwork and characters based on user instructions.

[0059] The virtual artist system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives instructions from the user. Instructions from the user include, but are not limited to, text input, voice input, and image input. For example, the reception unit receives text input. The reception unit can also receive voice input. Furthermore, the reception unit can also receive image input. For example, the reception unit analyzes the text entered by the user and understands the content of the instructions. In the case of voice input, the reception unit uses speech recognition technology to convert the voice into text and understand the content of the instructions. In the case of image input, the reception unit uses image analysis technology to analyze the image and understand the content of the instructions. The generation unit uses a generational AI to generate artworks or characters based on the instructions received by the reception unit. The generational AI can use technologies such as GAN (Generative Adversarial Network) or VAE (Variational Autoencoder). For example, the generation unit generates artworks using GAN. The generation unit can also generate characters using VAE. Furthermore, the generation unit can also generate artworks and characters based on user instructions using a generation AI. For example, the generation unit can generate artworks based on a style or theme specified by the user. The supply unit provides the artworks and characters generated by the generation unit. For example, the supply unit provides the generated artworks and characters to the digital art market or the game industry. For example, the supply unit exhibits the generated artworks in an online gallery. The supply unit can also provide the generated characters to game development companies. Furthermore, the supply unit can exhibit and sell the generated artworks and characters. For example, the supply unit sells the generated artworks on an online marketplace. Thus, the virtual artist system according to this embodiment can generate and provide artworks and characters based on user instructions.

[0060] The reception unit receives instructions from the user. User instructions include, but are not limited to, text input, voice input, and image input. For example, the reception unit receives text input. Specifically, it analyzes the text entered by the user using natural language processing technology to understand the instruction. For example, if a user enters "I want a landscape painting," the reception unit analyzes this text and sends an instruction to the generation unit to generate a landscape painting. The reception unit can also receive voice input. In the case of voice input, the reception unit uses speech recognition technology to convert the voice into text and understand the instruction. For example, if a user gives a voice instruction saying "I want a character created," the voice is converted into text using speech recognition technology, the text is analyzed, and sent to the generation unit. Furthermore, the reception unit can also receive image input. In the case of image input, the reception unit uses image analysis technology to analyze the image and understand the instruction. For example, if a user uploads a reference image, the reception unit analyzes the features of that image and sends an appropriate instruction to the generation unit. In this way, the reception unit can handle a variety of input formats from users, accurately understand the instruction, and transmit it to the generation unit.

[0061] The generation unit uses generative AI to generate artworks and characters based on instructions received by the reception unit. The generative AI can utilize technologies such as GAN (Generative Adversarial Network) and VAE (Variational Autoencoder). Specifically, the generation unit generates artworks using GANs. A GAN consists of two networks: a generative network and a discriminative network. The generative network generates new data, and the discriminative network determines whether that data is real or fake. By repeating this process, the generative network can generate more realistic artworks. The generation unit can also generate characters using VAEs. A VAE encodes input data into latent variables and decodes new data from these latent variables to generate new characters. Furthermore, the generation unit can use generative AI to generate artworks and characters based on user instructions. For example, when generating artworks based on a style or theme specified by the user, the generative AI generates data to conform to the specified style or theme. This allows the generation unit to generate artworks and characters that meet diverse user requirements with high accuracy.

[0062] The provider provides artwork and characters generated by the generator. For example, the provider provides the generated artwork and characters to the digital art market and the game industry. Specifically, the provider exhibits the generated artwork in an online gallery. In the online gallery, users can view and evaluate the generated artwork. The provider can also provide generated characters to game development companies. Game development companies can use the provided characters in their games to enhance the game's appeal. Furthermore, the provider can exhibit and sell the generated artwork and characters. For example, the provider sells the generated artwork on an online marketplace. In the online marketplace, users can purchase the generated artwork and add it to their collections. This allows the provider to widely distribute the generated artwork and characters and meet the diverse needs of users. In addition, the provider can also manage the copyrights and license the generated artwork and characters. This protects the rights to the generated works and promotes their appropriate use.

[0063] The generation unit can generate artwork and characters based on user instructions. For example, the generation unit can generate artwork based on a style or theme specified by the user. For example, the generation unit can also generate characters based on a color scheme or design specified by the user. Furthermore, the generation unit can generate artwork based on images or text provided by the user. For example, the generation unit can analyze an image provided by the user and generate artwork that reflects its style. In this way, the generation unit can generate artwork and characters based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs user instructions into a generation AI, and the generation AI generates artwork or characters.

[0064] The provider can offer the generated artwork and characters to the digital art market and the gaming industry. For example, the provider can exhibit the generated artwork in an online gallery. For example, the provider can also sell the generated artwork on an online marketplace. The provider can also provide the generated characters to game development companies. For example, the provider can provide the generated characters for use in a game. In this way, the provider can offer the generated artwork and characters to the digital art market and the gaming industry. Some or all of the above processes in the provider may be performed using a generation AI, or not using a generation AI. For example, the provider inputs the generated artwork or characters into a generation AI, and the generation AI determines the method of provision.

[0065] The generation unit can generate artworks and characters using generative AI. For example, the generation unit can generate artworks using a GAN (Generative Adversarial Network). For example, the generation unit can generate abstract artworks using a GAN. The generation unit can also generate characters using a VAE (Variational Autoencoder). For example, the generation unit can generate realistic characters using a VAE. Furthermore, the generation unit can generate artworks and characters based on user instructions using generative AI. For example, the generation unit generates artworks based on a style or theme specified by the user. Thus, the generation unit can generate artworks and characters using generative AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit inputs user instructions into the generative AI, and the generative AI generates artworks or characters.

[0066] The provider can exhibit and sell the generated artwork and characters. For example, the provider can exhibit the generated artwork in an online gallery. For example, the provider can also sell the generated artwork on an online marketplace. The provider can also exhibit the generated characters at a physical exhibition. For example, the provider can exhibit and sell the generated characters at an exhibition. In this way, the provider can exhibit and sell the generated artwork and characters. Some or all of the above processes in the provider may be performed using a generation AI, or not using a generation AI. For example, the provider inputs the generated artwork or characters into a generation AI, and the generation AI determines the method of exhibition and sales.

[0067] The provider can use the generated artwork and characters within the game. For example, the provider provides the generated characters for use within the game. For example, the provider can use the generated characters as player characters within the game. The provider can also use the generated artwork as background art within the game. For example, the provider can use the generated artwork as a stage background within the game. In this way, the provider can use the generated artwork and characters within the game. Some or all of the above processing in the provider may be performed using a generation AI, for example, or without a generation AI. For example, the provider inputs the generated artwork and characters into a generation AI, and the generation AI determines how to use them within the game.

[0068] The reception desk can estimate the user's emotions and prioritize instructions based on those emotions. For example, if the user is excited, the reception desk will prioritize creative instructions. For instance, if the user is excited, the reception desk will suggest a new style of artwork. The reception desk can also prioritize detailed instructions if the user is relaxed. For example, if the user is relaxed, the reception desk will accept instructions regarding the details of a character. The reception desk can also prioritize simple instructions if the user is stressed. For example, if the user is stressed, the reception desk will accept instructions for a simple artwork. This allows the reception desk to prioritize instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs user emotion data into a generating AI, which then estimates the emotion.

[0069] The reception desk can analyze the user's past instruction history and suggest the most suitable instructions. For example, the reception desk can suggest art styles that the user has previously preferred to create. For example, the reception desk can suggest new instructions based on art styles the user has previously created. The reception desk can also suggest characteristics of characters that the user has frequently used in the past. For example, the reception desk can suggest new characters based on characteristics of characters the user has previously used. The reception desk can also suggest new instructions based on the themes of works the user has previously created. For example, the reception desk can suggest instructions for new artwork based on the themes of works the user has previously created. In this way, the reception desk can analyze the user's past instruction history and suggest the most suitable instructions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's past instruction history into the AI, and the AI ​​suggests the most suitable instructions.

[0070] The reception unit can filter instructions based on the user's current projects and areas of interest. For example, the reception unit prioritizes instructions related to the user's current project. For instance, it might suggest art styles or characters related to the user's current project. The reception unit can also suggest relevant art styles or characters based on the user's areas of interest. For example, it might suggest instructions for new artwork based on the user's areas of interest. The reception unit can also filter instructions appropriately according to the progress of the user's project. For example, it might suggest instructions for necessary artwork or characters according to the progress of the user's project. This allows the reception unit to filter instructions based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit inputs the user's current projects and areas of interest into the AI, which then filters the instructions to the most appropriate content.

[0071] The reception desk can estimate the user's emotions and adjust the level of detail in the instructions based on the estimated emotions. For example, if the user is relaxed, the reception desk will accept detailed instructions. For example, if the user is relaxed, the reception desk will accept instructions about the details of a character. The reception desk can also accept concise instructions if the user is in a hurry. For example, if the user is in a hurry, the reception desk will accept instructions for a simple artwork. The reception desk can also accept creative instructions if the user is excited. For example, if the user is excited, the reception desk will accept instructions for a new art style. This allows the reception desk to adjust the level of detail in the instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk inputs the user's emotion data into the generative AI, and the generative AI performs emotion estimation.

[0072] The reception desk can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest art styles or characters related to that region. For example, if the user is in a specific region, the reception desk can accept instructions for artwork that reflects the culture or scenery of that region. The reception desk can also accept instructions themed on local specialties or landmarks based on the user's location. For example, if the user is traveling, the reception desk can accept instructions for artwork or characters related to the travel destination. This allows the reception desk to prioritize receiving instructions that are highly relevant based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk inputs the user's geographical location into the AI, and the AI ​​suggests the most appropriate instructions.

[0073] The reception desk can analyze the user's social media activity and accept relevant instructions when receiving instructions. For example, the reception desk can suggest instructions based on artwork the user has shared on social media. For example, the reception desk can suggest new instructions based on artwork the user has shared on social media. The reception desk can also suggest art styles preferred by the user's followers and friends. For example, the reception desk can suggest new instructions based on art styles preferred by the user's followers and friends. The reception desk can also accept instructions related to topics the user has shown interest in on social media. For example, the reception desk can suggest artwork related to topics the user has shown interest in on social media. In this way, the reception desk can accept relevant instructions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity into the AI, and the AI ​​can suggest the most appropriate instructions.

[0074] The generation unit can estimate the user's emotions and adjust the style of the artwork and characters it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate artwork with soft colors. For example, if the user is relaxed, the generation unit can generate background art with soft colors. The generation unit can also generate characters with vibrant colors if the user is excited. For example, if the user is excited, the generation unit can generate characters with vibrant colors. The generation unit can also generate artwork with calm tones if the user is sad. For example, if the user is sad, the generation unit can generate background art with calm tones. In this way, the generation unit can adjust the style of the artwork and characters it generates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit inputs user emotion data into the generation AI, which then estimates the emotion.

[0075] The generation unit can optimize its generation algorithm by referring to the user's past instruction history during generation. For example, the generation unit can adjust its generation algorithm based on the user's past preferred art style. For example, the generation unit can generate a new artwork based on the user's past preferred art style. The generation unit can also reflect the characteristics of characters the user has previously generated. For example, the generation unit can generate a new character based on the characteristics of characters the user has previously generated. The generation unit can also set optimal generation parameters from the user's past instruction history. For example, the generation unit sets optimal generation parameters based on the user's past instruction history and generates a new artwork. This allows the generation unit to optimize its generation algorithm by referring to the user's past instruction history. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit inputs the user's past instruction history into the generation AI, and the generation AI optimizes the generation algorithm.

[0076] The generation unit can customize the generated content based on the user's current projects and areas of interest during the generation process. For example, the generation unit can generate artwork related to the user's ongoing projects. For example, the generation unit can generate new artwork based on the art style related to the user's ongoing projects. The generation unit can also customize character designs based on the user's areas of interest. For example, the generation unit can generate new characters based on the user's areas of interest. The generation unit can also generate art styles that match the theme of the user's projects. For example, the generation unit can generate background art that matches the theme of the user's projects. In this way, the generation unit can customize the generated content based on the user's current projects and areas of interest. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's current projects and areas of interest into the generation AI, and the generation AI customizes the generated content.

[0077] The generation unit can estimate the user's emotions and adjust the level of detail in the artwork and characters it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate detailed artwork. For example, if the user is relaxed, the generation unit can generate detailed background art. The generation unit can also generate simple characters if the user is in a hurry. For example, if the user is in a hurry, the generation unit can generate simple characters. The generation unit can also generate visually stimulating artwork if the user is excited. For example, if the user is excited, the generation unit can generate artwork with vibrant colors. In this way, the generation unit can adjust the level of detail in the artwork and characters it generates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit inputs user emotion data into the generation AI, which then estimates the emotion.

[0078] The generation unit can prioritize generating content that is highly relevant to the user, taking into account the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit can generate artwork that reflects the culture and scenery of that region. For example, if the user is in a specific region, the generation unit can generate new artwork based on the traditional art style of that region. The generation unit can also generate characters themed around local specialties and landmarks based on the user's location information. For example, if the user is traveling, the generation unit can generate characters related to their travel destination. The generation unit can also generate background art that reflects the scenery of a region based on the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate background art based on the scenery of that region. This allows the generation unit to prioritize generating content that is highly relevant to the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI proposes the most suitable content.

[0079] The generation unit can analyze the user's social media activity during generation and provide relevant generated content. For example, the generation unit can generate new artwork based on artwork shared by the user on social media. The generation unit can also reflect the art styles preferred by the user's followers and friends. The generation unit can also generate characters related to topics the user has shown interest in on social media. In this way, the generation unit can provide relevant generated content based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's social media activity into the generation AI, which then proposes the most suitable generated content.

[0080] The service provider can estimate the user's emotions and adjust how the artwork and characters are displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display the artwork against a soft-colored background. For example, if the user is relaxed, the service provider can display the characters against a soft-colored background. The service provider can also display the characters against a bright-colored background if the user is excited. For example, if the user is excited, the service provider can display the artwork against a bright-colored background. The service provider can also display the artwork against a calm-toned background if the user is sad. For example, if the user is sad, the service provider can display the characters against a calm-toned background. In this way, the service provider can adjust how the artwork and characters are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider inputs user emotion data into a generating AI, which then estimates the emotion.

[0081] The delivery unit can select the optimal delivery method by referring to the user's past purchase history at the time of delivery. For example, the delivery unit can suggest new works based on the art styles the user has purchased in the past. The delivery unit can also reflect the characteristics of characters the user has purchased in the past. For example, the delivery unit can suggest new characters based on the characteristics of characters the user has purchased in the past. The delivery unit can also select the optimal delivery method from the user's purchase history. For example, the delivery unit can select the optimal delivery method based on the user's purchase history and suggest new works or characters. In this way, the delivery unit can select the optimal delivery method based on the user's past purchase history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit inputs the user's past purchase history into AI, and the AI ​​suggests the optimal delivery method.

[0082] The service provider can customize the content offered based on the user's current projects and areas of interest at the time of delivery. For example, the service provider can provide artwork related to the user's ongoing projects. For example, the service provider can provide new artwork based on the art style related to the user's ongoing projects. The service provider can also customize character designs based on the user's areas of interest. For example, the service provider can provide new characters based on the user's areas of interest. The service provider can also provide art styles that match the theme of the user's project. For example, the service provider can provide background art that matches the theme of the user's project. In this way, the service provider can customize the content offered based on the user's current projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's current projects and areas of interest into the AI, and the AI ​​can suggest the most suitable content to offer.

[0083] The service provider can estimate the user's emotions and determine the priority of the artwork and characters to offer based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize detailed artwork. For example, if the user is relaxed, the service provider may prioritize detailed background art. The service provider may also prioritize simple characters if the user is in a hurry. For example, if the user is in a hurry, the service provider may prioritize simple characters. The service provider may also prioritize visually stimulating artwork if the user is excited. For example, if the user is excited, the service provider may prioritize artwork with vibrant colors. In this way, the service provider can determine the priority of the artwork and characters to offer based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider inputs user emotion data into a generating AI, which then estimates the emotion.

[0084] The service provider can prioritize providing highly relevant content by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide artwork that reflects the culture and scenery of that region. For example, if the user is in a specific region, the service provider can provide new artwork based on the traditional art style of that region. The service provider can also provide characters themed on local specialties or landmarks based on the user's location information. For example, if the user is traveling, the service provider can provide characters related to their travel destination. The service provider can also provide background art that reflects the scenery of a region based on the user's geographical location information. For example, if the user is in a specific region, the service provider can provide background art based on the scenery of that region. This allows the service provider to prioritize providing highly relevant content based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider inputs the user's geographical location information into AI, and the AI ​​proposes the most suitable content.

[0085] The service provider can analyze the user's social media activity and suggest relevant content at the time of delivery. For example, the service provider can provide new artwork based on artwork shared by the user on social media. The service provider can also reflect the art styles preferred by the user's followers and friends. The service provider can also provide characters related to topics the user has shown interest in on social media. This allows the service provider to suggest relevant content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's social media activity into AI, which then suggests the most suitable content.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can refer to the user's past instruction history when receiving user instructions and suggest the most appropriate instructions. For example, the reception desk can suggest new instructions based on the art style and character characteristics the user has previously created. It can also customize instructions by considering the user's preferred colors and designs. Furthermore, the reception desk can analyze the user's past instruction history and prioritize suggesting instructions that the user frequently uses. This allows the reception desk to leverage the user's past instruction history to suggest more appropriate instructions.

[0088] The generation unit can estimate the user's emotions and adjust the style of the artwork and characters it generates based on those emotions. For example, if the user is relaxed, the generation unit can generate artwork with soft colors. If the user is excited, it can generate characters with vibrant colors. Furthermore, if the user is sad, it can generate artwork with calm tones. In this way, the generation unit can adjust the style of the artwork and characters it generates based on the user's emotions.

[0089] The service provider can customize the content they provide, such as generated artwork and characters, based on the user's current projects and areas of interest. For example, they can provide artwork related to the user's ongoing projects. They can also customize character designs based on the user's areas of interest. Furthermore, they can provide art styles that match the theme of the user's project. This allows the service provider to customize the content based on the user's current projects and areas of interest.

[0090] The reception desk can estimate the user's emotions and prioritize instructions based on those emotions. For example, if the user is excited, it will prioritize creative instructions. If the user is relaxed, it may prioritize detailed instructions. Furthermore, if the user is stressed, it may prioritize simple instructions. In this way, the reception desk can prioritize instructions based on the user's emotions.

[0091] The generation unit can prioritize generating highly relevant content by considering the user's geographical location during the generation process. For example, if the user is in a specific region, it can generate artwork that reflects the culture and scenery of that region. It can also generate characters themed around local specialties or landmarks based on the user's location. Furthermore, if the user is traveling, it can generate characters related to their travel destination. In this way, the generation unit can prioritize generating highly relevant content based on the user's geographical location.

[0092] The service provider can estimate the user's emotions and adjust how artwork and characters are displayed based on those emotions. For example, if the user is relaxed, the artwork can be displayed against a soft-colored background. If the user is excited, the characters can be displayed against a bright-colored background. Furthermore, if the user is sad, the artwork can be displayed against a calm-toned background. In this way, the service provider can adjust how artwork and characters are displayed based on the user's emotions.

[0093] The reception desk can analyze the user's social media activity when receiving instructions and accept relevant instructions. For example, it can suggest instructions based on artwork the user has shared on social media. It can also suggest art styles preferred by the user's followers and friends. Furthermore, it can accept instructions related to topics the user has shown interest in on social media. This allows the reception desk to receive relevant instructions based on the user's social media activity.

[0094] The generation unit can estimate the user's emotions and adjust the level of detail in the generated artwork and characters based on those emotions. For example, if the user is relaxed, it can generate detailed artwork. If the user is in a hurry, it can generate simpler characters. Furthermore, if the user is excited, it can generate visually stimulating artwork. In this way, the generation unit can adjust the level of detail in the generated artwork and characters based on the user's emotions.

[0095] The delivery unit can select the optimal delivery method by referring to the user's past purchase history at the time of delivery. For example, it can suggest new works based on the art style the user has purchased in the past. It can also reflect the characteristics of characters the user has purchased in the past. Furthermore, it can select the optimal delivery method from the user's purchase history. In this way, the delivery unit can select the optimal delivery method based on the user's past purchase history.

[0096] The service provider can estimate the user's emotions and prioritize the artwork and characters offered based on those emotions. For example, if the user is relaxed, detailed artwork may be prioritized. If the user is in a hurry, concise characters may be prioritized. Furthermore, if the user is excited, visually stimulating artwork may be prioritized. In this way, the service provider can determine the priority of artwork and characters offered based on the user's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives instructions from the user. User instructions include text input, voice input, and image input. For example, the reception desk analyzes the text entered by the user to understand the instructions. In the case of voice input, the reception desk uses speech recognition technology to convert the voice into text and understand the instructions. In the case of image input, the reception desk uses image analysis technology to analyze the image and understand the instructions. Step 2: The generation unit uses a generation AI to generate artwork or characters based on instructions received by the reception unit. The generation AI can utilize technologies such as GAN (Generative Adversarial Network) or VAE (Variational Autoencoder). For example, the generation unit generates artwork based on a style or theme specified by the user. Step 3: The provider provides the artwork and characters generated by the generator. The provider provides the generated artwork and characters to the digital art market and the gaming industry. For example, the provider can exhibit the generated artwork in an online gallery or sell it on an online marketplace. They can also provide the generated characters to game development companies.

[0099] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0102] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives text input, voice input, and image input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates artworks and characters using a generation AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated artworks and characters to the digital art market and the game industry. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives text input, voice input, and image input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates artworks and characters using a generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated artworks and characters to the digital art market and the game industry. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives text input, voice input, and image input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates artworks and characters using a generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated artworks and characters to the digital art market and the game industry. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives text input, voice input, and image input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates artworks and characters using a generation AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated artworks and characters to the digital art market and the game industry. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception desk that receives instructions from users, A generation unit that generates artworks and characters based on instructions received by the aforementioned reception unit, The system includes a providing unit that provides artworks and characters generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generates artwork and characters based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide generated artwork and characters to the digital art market and the gaming industry. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Use generative AI to create artwork and characters. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Exhibit and sell the generated artwork and characters. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, The generated artwork and characters can be used in the game. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and prioritizes instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past instruction history and suggests the most suitable instructions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the level of detail in the instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving instructions, the system prioritizes accepting instructions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving instructions, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the style of the generated artwork and characters based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the generation algorithm is optimized by referring to the user's past instruction history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the generated content is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the level of detail in the generated artwork and characters based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the system prioritizes generating highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the system analyzes the user's social media activity and provides relevant generated content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how artwork and characters are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the artwork and characters to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing content, we prioritize offerings that are highly relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest relevant content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives instructions from users, A generation unit that generates artworks and characters based on instructions received by the aforementioned reception unit, The system includes a providing unit that provides artworks and characters generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generates artwork and characters based on user instructions. The system according to feature 1.

3. The aforementioned supply unit is, We provide generated artwork and characters to the digital art market and the gaming industry. The system according to feature 1.

4. The generating unit is Generating artwork and characters using generative AI. The system according to feature 1.

5. The aforementioned supply unit is, The generated artworks and characters will be exhibited and sold. The system according to feature 1.

6. The aforementioned supply unit is, The generated artwork and characters can be used in the game. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and prioritizes instructions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past instruction history and suggests the most suitable instructions. The system according to feature 1.

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