system

Abstract

The system according to this embodiment aims to easily generate artworks and characters desired by the user. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the user regarding the requirements for the artwork or character they wish to generate. The generation unit analyzes the requirements input by the reception unit and generates the artwork or character. The provision unit provides the artwork or character 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prior art, there is a problem that it is difficult to easily generate an art work or a character desired by a user.

[0005] The system according to the embodiment aims to easily generate an art work or a character desired by a user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs requirements for an art work or a character that a user wants to generate. The generation unit analyzes the requirements input by the reception unit and generates an art work or a character. The provision unit provides the art work or the character generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can easily generate artworks and characters desired by the user. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 art generation system according to an embodiment of the present invention is a system that uses GANs (Generative Opposite Networks) and other generative AI to enable virtual artists to generate their own artworks and characters. This art generation system is intended for use in the digital art market and the gaming industry. Specifically, it consists of the following steps: First, the user inputs the requirements for the artwork or character they want to generate. For example, they input requirements such as the character's appearance and personality, or the theme and style of the artwork. This information is input into the generative AI. Next, the generative AI analyzes the input requirements and generates the artwork or character. The generative AI uses technologies such as GANs to generate unique artworks and characters based on the user's requirements. For example, if the user inputs "fantasy-style character" as a requirement, the generative AI will generate a fantasy-style character. The generated artworks and characters are used in the digital art market and the gaming industry. For example, the generated characters may be used as game characters, or the generated artworks may be sold in the digital art market. This system allows users to easily generate their own artworks and characters, promoting their use in the digital art market and the gaming industry. This allows the art generation system to easily create original artwork and characters, which can then be offered to the digital art market and the gaming industry.

[0029] The art generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the user regarding the requirements for the artwork or character they wish to generate. The requirements entered by the user include, but are not limited to, the appearance and personality of the character, the theme and style of the artwork, etc. The reception unit can accept requirements in, for example, text format, image format, specific attributes, etc. The generation unit analyzes the requirements entered by the reception unit and generates the artwork or character. The generation unit analyzes the requirements using, for example, natural language processing, image analysis, machine learning algorithms, etc. The generation unit generates unique artwork or characters based on the user's requirements using technologies such as GAN (Generative Opposite Network). For example, if the user enters "fantasy-style character" as a requirement, the generation unit generates a fantasy-style character using GAN. The provision unit provides the artwork or character generated by the generation unit. The provision unit can provide the artwork or character in, for example, digital format, physical delivery, etc. Thus, the art generation system according to this embodiment allows users to easily generate and provide their own unique artwork or characters.

[0030] The generation unit can generate artwork or characters based on user input. For example, if a user inputs "fantasy-style character" as a requirement, the generation unit will generate a fantasy-style character. The generation unit can also generate artwork in different styles and themes based on user input. For example, if a user inputs "cyberpunk-style artwork" as a requirement, the generation unit can generate cyberpunk-style artwork. This allows for the generation of artwork and characters based on user requirements.

[0031] The generation unit can generate artwork or characters using GANs. For example, the generation unit generates artwork or characters using a GAN (Generative-Opposite Network). 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 the generated data is real or fake. For example, the generation unit can generate high-resolution artwork using DCGAN (Deep Convolutional GAN). The generation unit can also generate characters in various styles using StyleGAN. In this way, using GANs makes it possible to generate higher quality artwork and characters.

[0032] The provider can offer the generated artwork or characters to the digital art market or the gaming industry. For example, the provider can offer the generated artwork or characters to the digital art market or the gaming industry. The digital art market includes, for example, online platforms and specific marketplaces. The provider can sell the generated artwork on online platforms. Furthermore, the provider can use the generated characters in the gaming industry. For example, the generated characters may be used in console games, mobile games, PC games, etc. This allows the provider to offer the generated artwork or characters to the digital art market or the gaming industry.

[0033] The art generation system includes an evaluation unit that assesses the quality of the generated artwork and characters. The evaluation unit evaluates the quality based on criteria such as visual appeal and technical completeness. For example, the evaluation unit evaluates the color and composition of the generated artwork to assess its visual appeal. The evaluation unit can also evaluate the details and movement of the generated characters to assess their technical completeness. This allows for the evaluation of the quality of the generated artwork and characters.

[0034] The art generation system includes a sales department that sells the generated artwork and characters in the digital art market and the gaming industry. The sales department can sell the generated artwork and characters through methods such as online sales or auctions. For example, the sales department can sell the generated artwork on online platforms. The sales department can also sell the generated characters to game developers. This allows the generated artwork and characters to be sold in the digital art market and the gaming industry.

[0035] The reception desk can analyze the user's past input history and suggest appropriate requirements input methods. For example, the reception desk can automatically display requirements that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest requirements that the user will use during specific time periods based on their past input history. This allows the system to suggest the optimal requirements input method based on the user's past input history.

[0036] The input system can filter the input content based on the user's current projects or areas of interest when requirements are entered. For example, the system will prioritize displaying requirements related to the user's current ongoing projects. The system can also automatically suggest relevant requirements based on the user's areas of interest. Furthermore, the system can filter the input content based on areas the user has previously shown interest in. This allows the system to filter input content based on the user's current projects and areas of interest.

[0037] The reception desk can prioritize the input of highly relevant requirements based on the user's geographical location when they enter their requirements. For example, if the user is in a specific region, the reception desk will prioritize displaying requirements related to that region. It can also suggest requirements related to the user's travel destination if the user is traveling. Furthermore, if the reception desk is attending a specific event, it can prioritize the input of requirements related to that event. This allows the system to prioritize the input of highly relevant requirements based on the user's geographical location.

[0038] The reception desk can analyze the user's social media activity when they enter their requirements and suggest relevant requirements. For example, it can suggest requirements based on the interests and passions the user has shared on social media. It can also display requirements related to the artists and creators the user follows. Furthermore, it can suggest requirements based on the trends of the online communities the user participates in. This allows the reception desk to suggest relevant requirements based on the user's social media activity.

[0039] The generation unit can select an appropriate generation algorithm by referring to the user's past generation history during the generation process. For example, the generation unit can generate new works in a similar style based on the style of artwork previously generated by the user. It can also generate similar characters based on the characteristics of characters previously generated by the user. Furthermore, the generation unit can prioritize generating works in the style of the most highly-rated works from the user's past generation history. This allows for the selection of the optimal generation algorithm based on the user's past generation history.

[0040] 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 current ongoing projects. It can also generate relevant characters based on the user's areas of interest. Furthermore, the generation unit can customize the generated content based on areas the user has shown interest in in the past. This allows for the customization of generated content based on the user's current projects and areas of interest.

[0041] The generation unit can prioritize generating highly relevant content based on the user's geographical location during the generation process. For example, if the user is in a specific region, the generation unit can generate artwork inspired by the culture and scenery of that region. Furthermore, if the user is traveling, the generation unit can generate a character incorporating the characteristics of their travel destination. Additionally, if the user is participating in a specific event, the generation unit can generate artwork related to that event. This allows the generation unit to prioritize generating highly relevant content based on the user's geographical location.

[0042] The generation unit can analyze the user's social media activity during generation and suggest relevant generated content. For example, it can generate artwork based on the user's interests and passions shared on social media. It can also generate characters related to artists and creators the user follows. Furthermore, it can suggest generated content based on trends in the online communities the user participates in. This allows it to suggest relevant generated content based on the user's social media activity.

[0043] The service provider can select the appropriate delivery method by referring to the user's past delivery history at the time of delivery. For example, the service provider can provide new artwork in a similar style based on the style of artwork previously provided by the user. It can also provide similar characters based on the characteristics of characters previously provided by the user. Furthermore, the service provider can prioritize providing the style of the most highly-rated artwork from the user's past delivery history. This allows the service provider to select the optimal delivery method based on the user's past delivery history.

[0044] The service provider can customize the content offered based on the user's current projects and areas of interest. For example, the service provider can offer artwork related to a project the user is currently working on. It can also offer relevant characters based on the user's areas of interest. Furthermore, the service provider can customize the content based on areas the user has shown interest in in the past. This allows for the customization of content based on the user's current projects and areas of interest.

[0045] The service provider can prioritize providing highly relevant content based on the user's geographical location. For example, if a user is in a specific region, the service provider can offer artwork inspired by the culture and scenery of that region. If a user is traveling, the service provider can also offer characters that incorporate the characteristics of their travel destination. Furthermore, if a user is participating in a specific event, the service provider can offer artwork related to that event. This allows the service provider to prioritize providing highly relevant content based on the user's geographical location.

[0046] 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 offer artwork based on the user's interests and passions shared on social media. It can also offer characters related to artists and creators the user follows. Furthermore, the service provider can suggest content based on trends in the online communities the user participates in. This allows the service provider to suggest relevant content based on the user's social media activity.

[0047] The evaluation unit can select an appropriate evaluation method by referring to the user's past evaluation history during the evaluation process. For example, the evaluation unit can evaluate a new work based on the criteria of artworks that the user has previously given high ratings to. It can also adjust the evaluation criteria to avoid characteristics of characters that the user has previously given low ratings to. Furthermore, the evaluation unit can prioritize the criteria of the highest-rated works from the user's past evaluation history. This allows the evaluation unit to select the optimal evaluation method based on the user's past evaluation history.

[0048] The evaluation unit can customize the evaluation content based on the user's current projects and areas of interest during the evaluation process. For example, the evaluation unit can evaluate artwork related to the user's current project. It can also evaluate relevant characters based on the user's areas of interest. Furthermore, the evaluation unit can customize the evaluation content based on areas the user has shown interest in in the past. This allows for the customization of evaluation content based on the user's current projects and areas of interest.

[0049] The evaluation unit can prioritize evaluations based on the user's geographical location information. For example, if the user is in a specific region, the evaluation unit will prioritize evaluating artwork inspired by the culture and scenery of that region. If the user is traveling, the evaluation unit can also prioritize evaluating characters that incorporate the characteristics of their travel destination. Furthermore, if the user is participating in a specific event, the evaluation unit can prioritize evaluating artwork related to that event. This allows the evaluation unit to prioritize evaluations based on the user's geographical location information.

[0050] The evaluation unit can analyze the user's social media activity during the evaluation process and suggest relevant evaluation content. For example, the evaluation unit can evaluate artwork based on the user's interests and passions shared on social media. It can also evaluate characters related to artists and creators the user follows. Furthermore, the evaluation unit can suggest evaluation content based on trends in the online communities the user participates in. This allows the evaluation unit to suggest relevant evaluation content based on the user's social media activity.

[0051] The sales department can select the appropriate sales method by referring to the user's past purchase history at the time of sale. For example, the sales department can suggest new works in a similar style based on the style of artwork the user has previously purchased. It can also suggest similar characters based on the characteristics of characters the user has previously purchased. Furthermore, the sales department can prioritize suggesting the style of the most highly-rated works from the user's past purchase history. This allows for the selection of the optimal sales method based on the user's past purchase history.

[0052] The sales department can customize the sales offerings based on the user's current projects and areas of interest. For example, the sales department might suggest artwork related to a project the user is currently working on. It could also suggest relevant characters based on the user's areas of interest. Furthermore, the sales department can customize the sales offerings based on areas the user has shown interest in in the past. This allows for the customization of sales offerings based on the user's current projects and areas of interest.

[0053] The sales department can prioritize selling relevant products based on the user's geographical location. For example, if a user is in a specific region, the sales department can suggest artwork inspired by the culture and scenery of that region. If a user is traveling, the sales department can also suggest characters that incorporate the characteristics of their travel destination. Furthermore, if a user is attending a specific event, the sales department can suggest artwork related to that event. This allows the sales department to prioritize selling relevant products based on the user's geographical location.

[0054] The sales department can analyze users' social media activity and suggest relevant products at the time of sale. For example, the sales department can suggest artwork based on the interests and passions users have shared on social media. They can also suggest characters related to artists and creators users follow. Furthermore, the sales department can suggest products based on trends in the online communities users participate in. This allows for the suggestion of relevant products based on users' social media activity.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The art generation system can also include an inspiration-providing section. This section can present examples of relevant artwork and characters based on user-inputted requirements. For example, if a user inputs "fantasy-style character" as a requirement, the inspiration-providing section can display examples of fantasy-style characters previously generated. The inspiration-providing section can also suggest artwork related to the user's selected style or theme. Furthermore, it can provide new inspiration based on artwork and characters previously generated by the user. This allows users to input requirements with a more concrete image, which is expected to improve the quality of the generated artwork and characters.

[0057] The generation unit can adjust the background of artwork and characters by referencing the user's current weather information during generation. For example, if the user enters requirements for a rainy day, the generation unit can generate artwork with a rainy scene as the background. Similarly, if the user enters requirements for a sunny day, the generation unit can generate characters with a clear sky as the background. Furthermore, if the user enters requirements for a snowy day, the generation unit can generate artwork with a snowy landscape as the background. This allows the background of generated artwork and characters to be adjusted based on the user's current weather information.

[0058] The art generation system can also include a collaboration section. This section allows multiple users to collaboratively generate artwork or characters. For example, user A can input the character's appearance, and user B can input its personality. The collaboration section also allows users to share their generated artwork or characters with other users and receive feedback. Furthermore, the collaboration section can provide tools and functions for users to work on projects together. This enables users to collaborate with others to create more creative artwork and characters.

[0059] The art generation system can also include an education section. This section provides educational materials and tutorials for users to learn art generation techniques and knowledge. For example, it could provide video tutorials explaining the basic mechanisms and usage of GANs. It could also offer hints and tips for users generating artwork and characters. Furthermore, the education section could evaluate the artwork and characters generated by users and provide feedback on areas for improvement. This would allow users to improve their art generation skills and knowledge.

[0060] The generation unit can select themes for the artwork and characters to be generated by referring to the user's past generation history. For example, it can select a theme for a new artwork based on themes of artworks the user has previously generated. It can also generate characters with similar themes based on the characteristics of characters the user has previously generated. Furthermore, it can prioritize the generation of themes with the highest ratings from the user's past generation history. This allows for the selection of the most optimal theme based on the user's past generation history.

[0061] The art generation system can also include a storage section for artwork and characters. This storage section provides functions for securely saving user-generated artwork and characters. For example, the storage section can save generated artwork and characters to cloud storage. It can also download user-generated artwork and characters to a local device. Furthermore, the storage section can generate links for sharing user-generated artwork and characters with other users. This allows users to securely store their generated artwork and characters and access them as needed.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives the requirements for the artwork or character the user wants to create. These requirements may include, for example, the character's appearance and personality, or the artwork's theme and style. The reception desk can accept requirements in various formats, such as text, image, or specific attributes. Step 2: The generation unit analyzes the requirements entered by the reception unit and generates artwork or characters. The generation unit analyzes the requirements using technologies such as natural language processing, image analysis, and machine learning algorithms, and uses technologies such as GAN (Generative Opposite Network) to generate unique artwork or characters based on the user's requirements. Step 3: The provider provides the artwork or character generated by the generator. The provider can provide the artwork or character in various forms, such as digital format or physical delivery.

[0064] (Example of form 2) The art generation system according to an embodiment of the present invention is a system that uses GANs (Generative Opposite Networks) and other generative AI to enable virtual artists to generate their own artworks and characters. This art generation system is intended for use in the digital art market and the gaming industry. Specifically, it consists of the following steps: First, the user inputs the requirements for the artwork or character they want to generate. For example, they input requirements such as the character's appearance and personality, or the theme and style of the artwork. This information is input into the generative AI. Next, the generative AI analyzes the input requirements and generates the artwork or character. The generative AI uses technologies such as GANs to generate unique artworks and characters based on the user's requirements. For example, if the user inputs "fantasy-style character" as a requirement, the generative AI will generate a fantasy-style character. The generated artworks and characters are used in the digital art market and the gaming industry. For example, the generated characters may be used as game characters, or the generated artworks may be sold in the digital art market. This system allows users to easily generate their own artworks and characters, promoting their use in the digital art market and the gaming industry. This allows the art generation system to easily create original artwork and characters, which can then be offered to the digital art market and the gaming industry.

[0065] The art generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input from the user regarding the requirements for the artwork or character they wish to generate. The requirements entered by the user include, but are not limited to, the appearance and personality of the character, the theme and style of the artwork, etc. The reception unit can accept requirements in, for example, text format, image format, specific attributes, etc. The generation unit analyzes the requirements entered by the reception unit and generates the artwork or character. The generation unit analyzes the requirements using, for example, natural language processing, image analysis, machine learning algorithms, etc. The generation unit generates unique artwork or characters based on the user's requirements using technologies such as GAN (Generative Opposite Network). For example, if the user enters "fantasy-style character" as a requirement, the generation unit generates a fantasy-style character using GAN. The provision unit provides the artwork or character generated by the generation unit. The provision unit can provide the artwork or character in, for example, digital format, physical delivery, etc. Thus, the art generation system according to this embodiment allows users to easily generate and provide their own unique artwork or characters.

[0066] The generation unit can generate artwork or characters based on user input. For example, if a user inputs "fantasy-style character" as a requirement, the generation unit will generate a fantasy-style character. The generation unit can also generate artwork in different styles and themes based on user input. For example, if a user inputs "cyberpunk-style artwork" as a requirement, the generation unit can generate cyberpunk-style artwork. This allows for the generation of artwork and characters based on user requirements.

[0067] The generation unit can generate artwork or characters using GANs. For example, the generation unit generates artwork or characters using a GAN (Generative-Opposite Network). 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 the generated data is real or fake. For example, the generation unit can generate high-resolution artwork using DCGAN (Deep Convolutional GAN). The generation unit can also generate characters in various styles using StyleGAN. In this way, using GANs makes it possible to generate higher quality artwork and characters.

[0068] The provider can offer the generated artwork or characters to the digital art market or the gaming industry. For example, the provider can offer the generated artwork or characters to the digital art market or the gaming industry. The digital art market includes, for example, online platforms and specific marketplaces. The provider can sell the generated artwork on online platforms. Furthermore, the provider can use the generated characters in the gaming industry. For example, the generated characters may be used in console games, mobile games, PC games, etc. This allows the provider to offer the generated artwork or characters to the digital art market or the gaming industry.

[0069] The art generation system includes an evaluation unit that assesses the quality of the generated artwork and characters. The evaluation unit evaluates the quality based on criteria such as visual appeal and technical completeness. For example, the evaluation unit evaluates the color and composition of the generated artwork to assess its visual appeal. The evaluation unit can also evaluate the details and movement of the generated characters to assess their technical completeness. This allows for the evaluation of the quality of the generated artwork and characters.

[0070] The art generation system includes a sales department that sells the generated artwork and characters in the digital art market and the gaming industry. The sales department can sell the generated artwork and characters through methods such as online sales or auctions. For example, the sales department can sell the generated artwork on online platforms. The sales department can also sell the generated characters to game developers. This allows the generated artwork and characters to be sold in the digital art market and the gaming industry.

[0071] The reception desk can estimate the user's emotions and adjust the request input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick request entry. This allows the request input interface to be adjusted according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The reception desk can analyze the user's past input history and suggest appropriate requirements input methods. For example, the reception desk can automatically display requirements that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest requirements that the user will use during specific time periods based on their past input history. This allows the system to suggest the optimal requirements input method based on the user's past input history.

[0073] The input system can filter the input content based on the user's current projects or areas of interest when requirements are entered. For example, the system will prioritize displaying requirements related to the user's current ongoing projects. The system can also automatically suggest relevant requirements based on the user's areas of interest. Furthermore, the system can filter the input content based on areas the user has previously shown interest in. This allows the system to filter input content based on the user's current projects and areas of interest.

[0074] The reception desk can estimate the user's emotions and adjust the order in which requirements are entered based on the estimated emotions. For example, if the user is nervous, the reception desk may have them enter important requirements first and then details later. If the user is relaxed, the reception desk may have them enter detailed requirements first. Furthermore, if the user is in a hurry, the reception desk may have them enter the most important requirements first. This allows the order in which requirements are entered to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The reception desk can prioritize the input of highly relevant requirements based on the user's geographical location when they enter their requirements. For example, if the user is in a specific region, the reception desk will prioritize displaying requirements related to that region. It can also suggest requirements related to the user's travel destination if the user is traveling. Furthermore, if the reception desk is attending a specific event, it can prioritize the input of requirements related to that event. This allows the system to prioritize the input of highly relevant requirements based on the user's geographical location.

[0076] The reception desk can analyze the user's social media activity when they enter their requirements and suggest relevant requirements. For example, it can suggest requirements based on the interests and passions the user has shared on social media. It can also display requirements related to the artists and creators the user follows. Furthermore, it can suggest requirements based on the trends of the online communities the user participates in. This allows the reception desk to suggest relevant requirements based on the user's social media activity.

[0077] The generation unit can estimate the user's emotions and adjust the style of the artwork and characters it generates based on those estimated emotions. For example, if the user is relaxed, the generation unit can generate artwork with soft colors and a calm design. If the user is excited, the generation unit can also generate characters with vibrant colors and a dynamic design. Furthermore, if the user is sad, the generation unit can generate artwork with calm colors and a simple design. This allows the style of the artwork and characters generated to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0078] The generation unit can select an appropriate generation algorithm by referring to the user's past generation history during the generation process. For example, the generation unit can generate new works in a similar style based on the style of artwork previously generated by the user. It can also generate similar characters based on the characteristics of characters previously generated by the user. Furthermore, the generation unit can prioritize generating works in the style of the most highly-rated works from the user's past generation history. This allows for the selection of the optimal generation algorithm based on the user's past generation history.

[0079] 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 current ongoing projects. It can also generate relevant characters based on the user's areas of interest. Furthermore, the generation unit can customize the generated content based on areas the user has shown interest in in the past. This allows for the customization of generated content based on the user's current projects and areas of interest.

[0080] The generation unit can estimate the user's emotions and determine the priority of the artwork and characters to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize generating artwork with a calm design. If the user is excited, the generation unit can also prioritize generating characters with a dynamic design. Furthermore, if the user is sad, the generation unit can also prioritize generating artwork with a simple design. This allows the generation unit to determine the priority of the artwork and characters to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The generation unit can prioritize generating highly relevant content based on the user's geographical location during the generation process. For example, if the user is in a specific region, the generation unit can generate artwork inspired by the culture and scenery of that region. Furthermore, if the user is traveling, the generation unit can generate a character incorporating the characteristics of their travel destination. Additionally, if the user is participating in a specific event, the generation unit can generate artwork related to that event. This allows the generation unit to prioritize generating highly relevant content based on the user's geographical location.

[0082] The generation unit can analyze the user's social media activity during generation and suggest relevant generated content. For example, it can generate artwork based on the user's interests and passions shared on social media. It can also generate characters related to artists and creators the user follows. Furthermore, it can suggest generated content based on trends in the online communities the user participates in. This allows it to suggest relevant generated content based on the user's social media activity.

[0083] The service provider can estimate the user's emotions and adjust how the artwork and characters are displayed based on those estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. This allows the service provider to adjust how the artwork and characters are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The service provider can select the appropriate delivery method by referring to the user's past delivery history at the time of delivery. For example, the service provider can provide new artwork in a similar style based on the style of artwork previously provided by the user. It can also provide similar characters based on the characteristics of characters previously provided by the user. Furthermore, the service provider can prioritize providing the style of the most highly-rated artwork from the user's past delivery history. This allows the service provider to select the optimal delivery method based on the user's past delivery history.

[0085] The service provider can customize the content offered based on the user's current projects and areas of interest. For example, the service provider can offer artwork related to a project the user is currently working on. It can also offer relevant characters based on the user's areas of interest. Furthermore, the service provider can customize the content based on areas the user has shown interest in in the past. This allows for the customization of content based on the user's current projects and areas of interest.

[0086] The service provider can estimate the user's emotions and determine the priority of the artwork and characters offered based on those estimated emotions. For example, if the user is relaxed, the service provider will prioritize providing artwork with a calm design. If the user is excited, the service provider may also prioritize providing characters with a dynamic design. Furthermore, if the user is sad, the service provider may prioritize providing artwork with a simple design. This allows the service provider to determine the priority of the artwork and characters offered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The service provider can prioritize providing highly relevant content based on the user's geographical location. For example, if a user is in a specific region, the service provider can offer artwork inspired by the culture and scenery of that region. If a user is traveling, the service provider can also offer characters that incorporate the characteristics of their travel destination. Furthermore, if a user is participating in a specific event, the service provider can offer artwork related to that event. This allows the service provider to prioritize providing highly relevant content based on the user's geographical location.

[0088] 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 offer artwork based on the user's interests and passions shared on social media. It can also offer characters related to artists and creators the user follows. Furthermore, the service provider can suggest content based on trends in the online communities the user participates in. This allows the service provider to suggest relevant content based on the user's social media activity.

[0089] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can provide detailed evaluation criteria. If the user is stressed, it can also provide simpler evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can provide criteria that allow for quick evaluation. This allows the evaluation criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The evaluation unit can select an appropriate evaluation method by referring to the user's past evaluation history during the evaluation process. For example, the evaluation unit can evaluate a new work based on the criteria of artworks that the user has previously given high ratings to. It can also adjust the evaluation criteria to avoid characteristics of characters that the user has previously given low ratings to. Furthermore, the evaluation unit can prioritize the criteria of the highest-rated works from the user's past evaluation history. This allows the evaluation unit to select the optimal evaluation method based on the user's past evaluation history.

[0091] The evaluation unit can customize the evaluation content based on the user's current projects and areas of interest during the evaluation process. For example, the evaluation unit can evaluate artwork related to the user's current project. It can also evaluate relevant characters based on the user's areas of interest. Furthermore, the evaluation unit can customize the evaluation content based on areas the user has shown interest in in the past. This allows for the customization of evaluation content based on the user's current projects and areas of interest.

[0092] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. If the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a concise display method. This allows the display method of the evaluation results to be adjusted according to 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.

[0093] The evaluation unit can prioritize evaluations based on the user's geographical location information. For example, if the user is in a specific region, the evaluation unit will prioritize evaluating artwork inspired by the culture and scenery of that region. If the user is traveling, the evaluation unit can also prioritize evaluating characters that incorporate the characteristics of their travel destination. Furthermore, if the user is participating in a specific event, the evaluation unit can prioritize evaluating artwork related to that event. This allows the evaluation unit to prioritize evaluations based on the user's geographical location information.

[0094] The evaluation unit can analyze the user's social media activity during the evaluation process and suggest relevant evaluation content. For example, the evaluation unit can evaluate artwork based on the user's interests and passions shared on social media. It can also evaluate characters related to artists and creators the user follows. Furthermore, the evaluation unit can suggest evaluation content based on trends in the online communities the user participates in. This allows the evaluation unit to suggest relevant evaluation content based on the user's social media activity.

[0095] The sales department can estimate the user's emotions and adjust its sales strategy based on those emotions. For example, if the user is relaxed, the sales department can provide a detailed product description. If the user is stressed, the sales department can provide a simple and easy-to-understand product description. Furthermore, if the user is in a hurry, the sales department can provide a concise product description to enable quick purchase. This allows the sales department to adjust its sales strategy according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The sales department can select the appropriate sales method by referring to the user's past purchase history at the time of sale. For example, the sales department can suggest new works in a similar style based on the style of artwork the user has previously purchased. It can also suggest similar characters based on the characteristics of characters the user has previously purchased. Furthermore, the sales department can prioritize suggesting the style of the most highly-rated works from the user's past purchase history. This allows for the selection of the optimal sales method based on the user's past purchase history.

[0097] The sales department can customize the sales offerings based on the user's current projects and areas of interest. For example, the sales department might suggest artwork related to a project the user is currently working on. It could also suggest relevant characters based on the user's areas of interest. Furthermore, the sales department can customize the sales offerings based on areas the user has shown interest in in the past. This allows for the customization of sales offerings based on the user's current projects and areas of interest.

[0098] The sales department can estimate the user's emotions and prioritize the artwork and characters to sell based on those estimated emotions. For example, if the user is relaxed, the sales department will prioritize suggesting artwork with a calm design. If the user is excited, the sales department may prioritize suggesting characters with a dynamic design. Furthermore, if the user is sad, the sales department may prioritize suggesting artwork with a simple design. This allows the sales department to prioritize the artwork and characters to sell according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The sales department can prioritize selling relevant products based on the user's geographical location. For example, if a user is in a specific region, the sales department can suggest artwork inspired by the culture and scenery of that region. If a user is traveling, the sales department can also suggest characters that incorporate the characteristics of their travel destination. Furthermore, if a user is attending a specific event, the sales department can suggest artwork related to that event. This allows the sales department to prioritize selling relevant products based on the user's geographical location.

[0100] The sales department can analyze users' social media activity and suggest relevant products at the time of sale. For example, the sales department can suggest artwork based on the interests and passions users have shared on social media. They can also suggest characters related to artists and creators users follow. Furthermore, the sales department can suggest products based on trends in the online communities users participate in. This allows for the suggestion of relevant products based on users' social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, provision unit, evaluation unit, and sales unit, is implemented, for example, by 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, where the user inputs the requirements for the artwork or character they wish to generate. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the artwork or character is generated using technologies such as GANs. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, where the generated artwork or character is provided to the user. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the quality of the generated artwork or character is evaluated. The sales unit is implemented, for example, by the control unit 46A of the smart device 14, where the generated artwork or character is sold in the digital art market or the game industry. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, provision unit, evaluation unit, and sales unit, is implemented, for example, 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, where the user inputs the requirements for the artwork or character they want to generate. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the artwork or character is generated using technologies such as GANs. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the generated artwork or character is provided to the user. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the quality of the generated artwork or character is evaluated. The sales unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the generated artwork or character is sold in the digital art market or the gaming industry. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, provision unit, evaluation unit, and sales unit, is implemented, for example, by 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, where the user inputs the requirements for the artwork or character they wish to generate. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the artwork or character is generated using technologies such as GANs. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, where the generated artwork or character is provided to the user. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the quality of the generated artwork or character is evaluated. The sales unit is implemented, for example, by the control unit 46A of the headset terminal 314, where the generated artwork or character is sold in the digital art market or the game industry. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, provision unit, evaluation unit, and sales unit, is implemented by, for example, 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, where the user inputs the requirements for the artwork or character they wish to generate. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the artwork or character is generated using technologies such as GANs. The provision unit is implemented by, for example, the control unit 46A of the robot 414, where the generated artwork or character is provided to the user. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the quality of the generated artwork or character is evaluated. The sales unit is implemented by, for example, the control unit 46A of the robot 414, where the generated artwork or character is sold in the digital art market or the gaming industry.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The art generation system can also include an inspiration-providing section. This section can present examples of relevant artwork and characters based on user-inputted requirements. For example, if a user inputs "fantasy-style character" as a requirement, the inspiration-providing section can display examples of fantasy-style characters previously generated. The inspiration-providing section can also suggest artwork related to the user's selected style or theme. Furthermore, it can provide new inspiration based on artwork and characters previously generated by the user. This allows users to input requirements with a more concrete image, which is expected to improve the quality of the generated artwork and characters.

[0103] The generation unit can estimate the user's emotions and adjust the colors of the artwork and characters it generates based on those estimated emotions. For example, if the user is relaxed, the generation unit can generate artwork with soft colors. If the user is excited, the generation unit can generate characters with vibrant colors. Furthermore, if the user is sad, the generation unit can generate artwork with calm colors. This allows the colors of the artwork and characters generated to be adjusted according to the user's emotions.

[0104] The generation unit can adjust the background of artwork and characters by referencing the user's current weather information during generation. For example, if the user enters requirements for a rainy day, the generation unit can generate artwork with a rainy scene as the background. Similarly, if the user enters requirements for a sunny day, the generation unit can generate characters with a clear sky as the background. Furthermore, if the user enters requirements for a snowy day, the generation unit can generate artwork with a snowy landscape as the background. This allows the background of generated artwork and characters to be adjusted based on the user's current weather information.

[0105] The service provider can estimate the user's emotions and adjust the descriptions of artwork and characters based on those emotions. For example, if the user is relaxed, the service provider can provide a detailed description. If the user is stressed, the service provider can provide a concise description. Furthermore, if the user is in a hurry, the service provider can provide a to-the-point description. This allows the service provider to adjust the descriptions of artwork and characters according to the user's emotions.

[0106] The art generation system can also include a collaboration section. This section allows multiple users to collaboratively generate artwork or characters. For example, user A can input the character's appearance, and user B can input its personality. The collaboration section also allows users to share their generated artwork or characters with other users and receive feedback. Furthermore, the collaboration section can provide tools and functions for users to work on projects together. This enables users to collaborate with others to create more creative artwork and characters.

[0107] The art generation system can also include an education section. This section provides educational materials and tutorials for users to learn art generation techniques and knowledge. For example, it could provide video tutorials explaining the basic mechanisms and usage of GANs. It could also offer hints and tips for users generating artwork and characters. Furthermore, the education section could evaluate the artwork and characters generated by users and provide feedback on areas for improvement. This would allow users to improve their art generation skills and knowledge.

[0108] The reception desk can estimate the user's emotions and suggest a method for entering requirements based on those emotions. For example, if the user is relaxed, the reception desk can suggest detailed input options. If the user is stressed, the reception desk can suggest a simpler input method. Furthermore, if the user is in a hurry, the reception desk can suggest voice input. This allows the system to suggest the most suitable method for entering requirements according to the user's emotions.

[0109] The generation unit can select themes for the artwork and characters to be generated by referring to the user's past generation history. For example, it can select a theme for a new artwork based on themes of artworks the user has previously generated. It can also generate characters with similar themes based on the characteristics of characters the user has previously generated. Furthermore, it can prioritize the generation of themes with the highest ratings from the user's past generation history. This allows for the selection of the most optimal theme based on the user's past generation history.

[0110] The service provider can estimate the user's emotions and adjust the layout of the artwork and characters they provide based on those emotions. For example, if the user is relaxed, the service provider can provide a detailed layout. If the user is stressed, the service provider can provide a simple layout. Furthermore, if the user is in a hurry, the service provider can provide a concise layout. This allows the service provider to adjust the layout of the artwork and characters provided according to the user's emotions.

[0111] The art generation system can also include a storage section for artwork and characters. This storage section provides functions for securely saving user-generated artwork and characters. For example, the storage section can save generated artwork and characters to cloud storage. It can also download user-generated artwork and characters to a local device. Furthermore, the storage section can generate links for sharing user-generated artwork and characters with other users. This allows users to securely store their generated artwork and characters and access them as needed.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives the requirements for the artwork or character the user wants to create. These requirements may include, for example, the character's appearance and personality, or the artwork's theme and style. The reception desk can accept requirements in various formats, such as text, image, or specific attributes. Step 2: The generation unit analyzes the requirements entered by the reception unit and generates artwork or characters. The generation unit analyzes the requirements using technologies such as natural language processing, image analysis, and machine learning algorithms, and uses technologies such as GAN (Generative Opposite Network) to generate unique artwork or characters based on the user's requirements. Step 3: The provider provides the artwork or character generated by the generator. The provider can provide the artwork or character in various forms, such as digital format or physical delivery.

[0114] 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.

[0115] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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. 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.

[0132] 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.

[0133] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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. 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.

[0148] 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.

[0149] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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. 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.

[0165] 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.

[0166] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 area where the user inputs the requirements for the artwork or character they want to create, A generation unit analyzes the requirements entered by the reception unit and generates artwork or characters, The system comprises a providing unit that provides artwork or characters generated by the generating unit. A system characterized by the following features.

2. The generating unit is Generate artwork or characters based on user-entered requirements. The system according to feature 1.

3. The generating unit is Using GANs to generate artwork or characters The system according to feature 1.

4. The aforementioned supply unit is, Provide generated artwork or characters to the digital art market or the gaming industry. The system according to feature 1.

5. It includes an evaluation section that assesses the quality of the generated artwork and characters. The system according to feature 1.

6. It has a sales department that sells the generated artwork or characters in the digital art market or the gaming industry. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for requirements based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and suggest appropriate methods for entering requirements. The system according to feature 1.

9. The aforementioned reception unit is When entering requirements, the input is filtered based on the user's current project or area of ​​interest. The system according to feature 1.

Images