System

Abstract

An object of the system according to the embodiment is to efficiently perform a series of processes from generation to sales, manufacturing, and shipment of an artwork using generation AI.SOLUTION: A system according to an embodiment includes a generation and AI engine, a sales platform on the Web, and a 3D print outputting unit. The generative AI engine generates the artwork. The sales platform sells the artwork generated by the generative AI engine. The 3D print outputting section outputs the artwork ordered by the sales platform as a 3D print.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently carry out the entire process from creating artworks using generative AI to selling, manufacturing, and shipping.

[0005] The system of the embodiment aims to efficiently carry out a series of processes from the creation of artworks using generative AI to their sale, manufacturing, and shipping. [Means for solving the problem]

[0006] A system according to an embodiment includes a generative AI engine, an online sales platform, and a 3D print output unit. The generative AI engine generates artworks. The sales platform sells the artworks generated by the generative AI engine. The 3D print output unit 3D prints artworks ordered by the sales platform. [Effects of the Invention]

[0007] The system of the embodiment can efficiently carry out a series of processes from the creation of artworks using generative AI to their sale, manufacturing, and shipping. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The art work generation system according to an embodiment of the present invention is a system that uses generative AI to create art works and sell them on a web platform. This allows the art work generation system to build a series of environments from the creation of works using generative AI to sales, manufacturing, and shipping.

[0029] An artwork generation system according to an embodiment includes a generative AI engine, a sales platform, and a 3D printing output unit. The generative AI engine generates artworks. For example, the generative AI engine uses deep learning or GAN (generative artificial network) to generate new artworks based on user instructions. For example, when a user inputs a prompt such as "I want you to generate an abstract painting," the generative AI engine generates an abstract painting based on the instruction. The generative AI engine can also learn various art styles and techniques to generate artworks tailored to the user's preferences. The sales platform sells artworks generated by the generative AI engine. For example, the sales platform displays artworks and performs purchase procedures through a user interface. The sales platform also includes a payment system that allows users to make secure payments. The sales platform also includes an inventory management system that allows real-time management of artwork inventory. The 3D printing output unit 3D prints artworks ordered by the sales platform. For example, the 3D printing output unit selects the type of 3D printer to use and compatible materials, and outputs the artwork at high resolution. The 3D printing output unit can also adjust the size and color of the artwork according to the user's request. This allows the art generation system to streamline the entire process from the creation of artworks using generative AI to their sale, manufacturing, and shipping. For example, users can generate their own original artworks and easily sell them. 3D printing technology can also be used to create physical artworks from digital data.

[0030] The generative AI engine can learn a user's past artworks or preferred style and generate personalized artworks. For example, the generative AI engine stores artworks previously generated by the user in a database and generates new artworks based on that data. For example, it learns the user's preferred colors and style and generates new artworks based on that. In addition, to learn the user's preferred style, the generative AI engine collects data on artworks rated by the user and generates personalized artworks based on that information. For example, it incorporates the characteristics of artworks that the user has given high ratings. The generative AI engine also analyzes data on the user's past artworks and learns their characteristics to generate artworks tailored to the user's preferences. For example, it generates artworks that incorporate the user's favorite themes and motifs. This makes it possible to generate personalized artworks according to the user's preferences.

[0031] The generative AI engine can generate hybrid artworks that combine different art techniques. For example, the generative AI engine learns different art techniques and generates hybrid artworks that combine techniques selected by the user. For example, it generates artworks that combine the texture of oil painting with the transparency of watercolor. The generative AI engine also generates artworks in a new style by combining multiple art techniques specified by the user. For example, it generates artworks that combine the vividness of digital art with traditional painting techniques. The generative AI engine also develops algorithms for combining different art techniques and automatically selects the optimal combination of techniques to generate artworks. For example, it selects techniques based on the user's instructions and generates hybrid art. This makes it possible to generate artworks in a new style that combine different art techniques.

[0032] The generative AI engine can also be applied to other artistic fields, such as music or poetry, to generate multimedia artworks. For example, the generative AI engine can add a music generation function to generate music based on a theme specified by the user. For example, it can automatically generate background music to match an artwork. The generative AI engine can also add a function to generate poetry or literary works, generating poetry based on keywords or themes entered by the user. For example, it can generate poetry inspired by an artwork. The generative AI engine can also develop a function to generate multimedia artworks that combine music or poetry with artworks. For example, the generative AI can simultaneously generate music and poetry that are linked to the artwork. This makes it possible to generate multimedia artworks that combine other artistic fields, such as music and poetry.

[0033] Generative AI engines can be developed as versions specialized for educational use and used as teaching materials for art education. Generative AI engines can be specialized for educational use, adding functions for use as teaching materials for art education. For example, they can automatically generate art techniques that students should learn. Generative AI engines can also be developed for education, building systems in which students input their own work into the generative AI and receive feedback. For example, the generative AI can point out areas for improvement in the work. Generative AI engines can also be developed to fit art education curricula, generating artworks based on themes and techniques that students should learn. For example, they can generate works from a specific era or style. This makes it possible to develop generative AI engines that can be used as teaching materials for art education.

[0034] The sales platform may have a community function that allows users to share their artworks with other users and receive feedback. The sales platform may, for example, add a community function to allow users to share their artworks with other users. For example, it may provide a function for receiving comments and ratings on the artworks. The sales platform may also build a system that allows users to upload their artworks and receive feedback from other users. For example, it may provide a function for sharing improvements and impressions on the artworks. The sales platform may also use the community function to provide a forum where users can discuss artworks with each other. For example, it may set up a forum for exchanging opinions on the themes and techniques of the artworks. This makes it possible to improve the quality of artworks through interaction and feedback between users.

[0035] The sales platform can be equipped with a function for holding live art sessions using a generative AI engine and generating and selling artworks in real time. For example, the sales platform can add a live art session function and generate artworks in real time using a generative AI engine. For example, users can participate and watch the generation process. The sales platform can also build a system for selling artworks generated in live art sessions on the spot. For example, the generated artworks can be made available for immediate purchase after the session ends. The sales platform can also provide a function for receiving feedback from users during the live art session and for the generative AI to adjust the artworks based on that feedback. For example, the color or style of the artwork can be changed in response to a user request. This makes it possible to generate and sell artworks in real time through live art sessions.

[0036] Sales platforms can link with other online marketplaces to expand sales channels for artworks. Sales platforms can link with other online marketplaces to expand sales channels for artworks. For example, they can link with platforms such as Amazon and Etsy. Sales platforms can also link with other marketplaces via APIs to build systems that allow artworks on the platform to be sold simultaneously through multiple sales channels. For example, they can centralize inventory management of artworks. Sales platforms can also link with other online marketplaces to increase the exposure of artworks on the platform and expand sales opportunities. For example, they can promote artworks on partner platforms. This can expand sales channels for artworks and enable them to reach more users.

[0037] The sales platform may add an artwork rental service, providing a function that allows users to rent artworks for a certain period of time. For example, the sales platform may add an artwork rental service and build a system that allows users to rent artworks for a certain period of time. For example, artworks may be rented for a monthly fee. The sales platform may also provide an artwork rental service that allows users to try artworks before purchasing them. For example, the sales platform may provide a purchase option after the rental period ends. The sales platform may also provide users with an opportunity to experience various artworks through the rental service. For example, the sales platform may provide a subscription service that periodically exchanges artworks. This makes it possible to provide a rental service that allows users to rent artworks for a certain period of time.

[0038] The production process of collaborative works can be recorded in documentary format and published on the platform. The production process of collaborative works can be filmed in documentary format and published on the platform. For example, it could include behind-the-scenes footage and interviews with the artists. A system could also be created that allows users to track the production process of collaborative works in real time through documentary footage. For example, live streaming could be performed. The production process documentary could also be updated regularly to ensure users are always updated with new information. For example, a new episode could be published every week. This could attract users' interest by revealing the production process of collaborative works.

[0039] By providing limited edition or customized versions of collaborative works using a generative AI engine, users can obtain special works that are just their own. By providing limited edition versions of collaborative works using a generative AI engine, we can build a system that allows users to obtain special works. For example, limited edition works can be given serial numbers. Also, users can purchase works that have been customized using a generative AI engine. For example, users can customize them by choosing colors or styles. Furthermore, by providing limited edition or customized versions of collaborative works, users can obtain special works that are just their own. For example, the user's name or message can be incorporated into the work. This makes it possible for users to obtain special works of art that are just their own.

[0040] Collaborative works can be expanded to include collaborations with other well-known brands or companies to increase the diversity of artworks. Collaborative works can be created in collaboration with other well-known brands or companies to increase the diversity of artworks. For example, collaborations can be made with fashion brands and technology companies. Also, through collaborations with other brands and companies, artworks with different styles and themes can be provided. For example, collaborations can be made with sports brands to create artworks with a sports theme. Also, collaborative works can be created in collaboration with brands and companies in various fields to provide users with new experiences. For example, collaborations can be made with the food and beverage industry or the entertainment industry. This can increase the diversity of artworks and provide users with new experiences.

[0041] Collaborative works can be made available for users to experience in a virtual space using VR or AR technology. A system is built that allows users to experience collaborative works in a virtual space using VR technology. For example, a user can wear a VR headset and experience the work visually in 360 degrees. An application is also developed that allows users to experience collaborative works in real space using AR technology. For example, the work can be projected into real space using a smartphone. Furthermore, a system is provided that combines VR and AR technologies to allow users to interactively experience collaborative works in a virtual space. For example, users can touch and manipulate the work. This makes it possible for users to experience collaborative works in a virtual space.

[0042] The 3D printing output unit allows the generative AI engine to receive user feedback in real time and fine-tune the output content. The 3D printing output unit, for example, builds a system in which the generative AI engine receives user feedback in real time and fine-tunes the output content. For example, the user can request changes to color or shape. The 3D printing output unit also analyzes user feedback in real time and dynamically adjusts the 3D printing output content based on the results. For example, if the user requests a brighter color, the color is changed. The feedback function also provides a system that allows the user to make adjustments in real time during the 3D printing output process. For example, the user can fine-tune the shape or design. This makes it possible to fine-tune the 3D printing output content in real time based on user feedback.

[0043] The 3D printing output unit can use composite materials that combine different materials to enable a wider variety of textures and expressions. The 3D printing output unit, for example, uses composite materials that combine different materials to build a system that enables a wider variety of textures and expressions. For example, a hard material and a soft material can be combined for output. The use of composite materials also diversifies the textures and expressions of 3D printing outputs. For example, a transparent material can be combined for output. The use of composite materials that combine different materials also allows the user to achieve the textures and expressions they desire. For example, a metal and plastic can be combined for output. This allows for a wider variety of textures and expressions by using composite materials that combine different materials.

[0044] The 3D printing output unit can be combined with other manufacturing technologies to produce a wider variety of artworks. The 3D printing output unit can be combined with other manufacturing technologies (e.g., laser cutting or CNC machining) to build a system for producing a wider variety of artworks. For example, detailed patterns can be added to a 3D printed object by laser cutting. Also, CNC machining can be combined with 3D printing output to produce artworks with complex shapes and designs. For example, detailed engraving can be performed on a 3D printed base by CNC machining. Also, by combining 3D printing output with other manufacturing technologies, a wider variety of artworks desired by the user can be realized. For example, additional processing can be performed on the 3D printed work. This makes it possible to produce a wider variety of artworks by combining 3D printing output with other manufacturing technologies.

[0045] The 3D printing output unit can provide kits for home 3D printers so that users can easily print at home. The 3D printing output unit can, for example, provide kits optimized for home 3D printers that allow users to easily print at home. For example, it can provide a set of print data and necessary materials. It can also develop kits for home 3D printers so that users can print artwork at home. For example, it can provide simple operation guides and support services. It can also develop dedicated applications for home 3D printers so that users can perform 3D printing at home. For example, it can provide an app that allows users to easily download print data and make settings. This makes it possible to provide kits that allow users to easily perform 3D printing at home.

[0046] The generative AI engine can learn a user's past artworks or preferred style and generate personalized artworks. For example, the generative AI engine stores artworks previously generated by the user in a database and generates new artworks based on that data. For example, it learns the user's preferred colors and style and generates new artworks based on that. In addition, to learn the user's preferred style, the generative AI engine collects data on artworks rated by the user and generates personalized artworks based on that information. For example, it incorporates the characteristics of artworks that the user has given high ratings. The generative AI engine also analyzes data on the user's past artworks and learns their characteristics to generate artworks tailored to the user's preferences. For example, it generates artworks that incorporate the user's favorite themes and motifs. This makes it possible to learn the user's past artworks and preferred style and generate personalized artworks.

[0047] The generative AI engine can generate hybrid artworks that combine different art techniques. For example, the generative AI engine learns different art techniques and generates hybrid artworks that combine techniques selected by the user. For example, it generates artworks that combine the texture of oil painting with the transparency of watercolor. The generative AI engine also generates artworks in a new style by combining multiple art techniques specified by the user. For example, it generates artworks that combine the vividness of digital art with traditional painting techniques. The generative AI engine also develops algorithms for combining different art techniques and automatically selects the optimal combination of techniques to generate artworks. For example, it selects techniques based on the user's instructions and generates hybrid art. This makes it possible to generate hybrid artworks that combine different art techniques.

[0048] The generative AI engine can also be applied to other artistic fields, such as music or poetry, to generate multimedia artworks. For example, the generative AI engine can add a music generation function to generate music based on a theme specified by the user. For example, it can automatically generate background music to match an artwork. The generative AI engine can also add a function to generate poetry or literary works, generating poetry based on keywords or themes entered by the user. For example, it can generate poetry inspired by an artwork. The generative AI engine can also develop a function to generate multimedia artworks that combine music or poetry with artworks. For example, the generative AI can simultaneously generate music and poetry that are linked to the artwork. This makes it possible to generate multimedia artworks that combine other artistic fields, such as music and poetry.

[0049] Generative AI engines can be developed as versions specialized for educational use and used as teaching materials for art education. Generative AI engines can be specialized for educational use, adding functions for use as teaching materials for art education. For example, they can automatically generate art techniques that students should learn. Generative AI engines can also be developed for education, building systems in which students input their own work into the generative AI and receive feedback. For example, the generative AI can point out areas for improvement in the work. Generative AI engines can also be developed to fit art education curricula, generating artworks based on themes and techniques that students should learn. For example, they can generate works from a specific era or style. This makes it possible to develop generative AI engines that can be used as teaching materials for art education.

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

[0051] The generative AI engine can learn a user's past artwork or preferred style and generate personalized artwork. For example, it can store artwork created by the user in a database and generate new artwork based on that data. For example, it can learn the user's preferred colors and style and generate new artwork based on that. In addition, to learn the user's preferred style, the generative AI engine collects data on artwork rated by the user and generates personalized artwork based on that information. For example, it incorporates the characteristics of artwork that the user has given a high rating. The generative AI engine can also analyze data on the user's past artwork and learn its characteristics to generate artwork that matches the user's preferences. For example, it can generate artwork that incorporates the user's favorite themes and motifs. This makes it possible to generate personalized artwork that meets the user's preferences.

[0052] The generative AI engine can generate hybrid artworks that combine different art techniques. For example, it can learn different art techniques and generate hybrid artworks that combine techniques selected by the user. For example, it can generate artworks that combine the texture of oil painting with the transparency of watercolor. The generative AI engine can also generate artworks in a new style by combining multiple art techniques specified by the user. For example, it can generate artworks that combine the vividness of digital art with traditional painting techniques. The generative AI engine can also develop algorithms for combining different art techniques and automatically select the optimal combination of techniques to generate artworks. For example, it can select techniques based on the user's instructions and generate hybrid art. This makes it possible to generate artworks in a new style that combine different art techniques.

[0053] The generative AI engine can also be applied to other artistic fields, such as music or poetry, to generate multimedia artworks. For example, a music generation function can be added to generate music based on a theme specified by the user. For example, background music to accompany an artwork can be automatically generated. The generative AI engine can also add a function to generate poetry or literary works, generating poetry based on keywords or themes entered by the user. For example, generating poetry inspired by an artwork. The generative AI engine can also develop a function to generate multimedia artworks that combine music or poetry with artworks. For example, the generative AI can simultaneously generate music and poetry that are linked to the artwork. This makes it possible to generate multimedia artworks that combine other artistic fields, such as music and poetry.

[0054] Generative AI engines can be developed as versions specialized for educational use and used as teaching materials for art education. For example, by specializing it for educational use, functions can be added for use as teaching materials for art education. For example, it can automatically generate art techniques that students should learn. Generative AI engines can also be developed for education, and a system can be built in which students input their own work into the generative AI and receive feedback. For example, the generative AI can point out areas for improvement in the work. Generative AI engines can also be developed that are tailored to art education curricula, generating artworks based on themes and techniques that students should learn. For example, it can generate works from a specific era or style. This makes it possible to develop generative AI engines that can be used as teaching materials for art education.

[0055] The sales platform may be equipped with a community function that allows users to share their artworks with other users and receive feedback. For example, a community function may be added to allow users to share their artworks with other users. For example, a function may be provided to receive comments and ratings on the artworks. The sales platform may also build a system that allows users to upload their artworks and receive feedback from other users. For example, a function may be provided to share improvements and impressions on the artworks. The sales platform may also use the community function to provide a forum where users can discuss artworks with each other. For example, a forum may be set up for users to exchange opinions on the themes and techniques of the artworks. This makes it possible to improve the quality of artworks through interaction and feedback between users.

[0056] The sales platform can be equipped with a function for holding live art sessions using a generative AI engine and generating and selling artworks in real time. For example, a live art session function can be added, and artworks can be generated in real time using a generative AI engine. For example, users can participate and watch the generation process. The sales platform also builds a system for selling artworks generated in live art sessions on the spot. For example, the generated artworks can be made available for immediate purchase after the session ends. The sales platform also provides a function for receiving feedback from users during the live art session and for the generative AI to adjust the artworks based on that feedback. For example, the color or style of the artwork can be changed in response to a user request. This makes it possible to generate and sell artworks in real time through live art sessions.

[0057] Sales platforms can link with other online marketplaces to expand the sales channels for artworks. For example, they can link with other online marketplaces to expand the sales channels for artworks. For example, they can link with platforms such as Amazon and Etsy. Sales platforms can also link with other marketplaces via APIs to build systems that allow artworks on the platform to be sold simultaneously through multiple sales channels. For example, they can centralize inventory management of artworks. Sales platforms can also link with other online marketplaces to increase the exposure of artworks on the platform and expand sales opportunities. For example, they can promote artworks on partner platforms. This can expand the sales channels for artworks and enable them to reach more users.

[0058] The sales platform may add an artwork rental service, providing a function that allows users to rent artworks for a certain period of time. For example, an artwork rental service may be added, and a system may be constructed that allows users to rent artworks for a certain period of time. For example, artworks may be rented for a monthly fee. The sales platform may also provide an artwork rental service that allows users to try artworks before purchasing them. For example, it may provide a purchase option after the rental period ends. The sales platform may also provide users with the opportunity to experience various artworks through the rental service. For example, it may provide a subscription service that periodically exchanges artworks. This makes it possible to provide a rental service that allows users to rent artworks for a certain period of time.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The generative AI engine generates a work of art. For example, the generative AI engine uses deep learning and GAN (generative artificial network) to generate a new work of art based on the user's instructions. For example, if the user inputs a prompt such as "I want you to generate an abstract painting," the generative AI engine generates an abstract painting based on the user's instructions. The generative AI engine can also learn various art styles and techniques and generate works that meet the user's preferences. Step 2: The sales platform sells the artworks generated by the generative AI engine. For example, the sales platform displays the artworks through a user interface and performs the purchase procedure. The sales platform also has a payment system that allows users to make payments securely. Furthermore, the sales platform has an inventory management system that allows it to manage the inventory status of artworks in real time. Step 3: The 3D printing output unit 3D prints the artwork ordered by the sales platform. For example, the 3D printing output unit selects the type of 3D printer to use and the corresponding material, and outputs the artwork in high resolution. The 3D printing output unit can also adjust the size and color of the artwork according to the user's request.

[0061] (Example 2) The art work generation system according to an embodiment of the present invention is a system that uses generative AI to create art works and sell them on a web platform. This allows the art work generation system to build a series of environments from the creation of works using generative AI to sales, manufacturing, and shipping.

[0062] An artwork generation system according to an embodiment includes a generative AI engine, a sales platform, and a 3D printing output unit. The generative AI engine generates artworks. For example, the generative AI engine uses deep learning or GAN (generative artificial network) to generate new artworks based on user instructions. For example, when a user inputs a prompt such as "I want you to generate an abstract painting," the generative AI engine generates an abstract painting based on the instruction. The generative AI engine can also learn various art styles and techniques to generate artworks tailored to the user's preferences. The sales platform sells artworks generated by the generative AI engine. For example, the sales platform displays artworks and performs purchase procedures through a user interface. The sales platform also includes a payment system that allows users to make secure payments. The sales platform also includes an inventory management system that allows real-time management of artwork inventory. The 3D printing output unit 3D prints artworks ordered by the sales platform. For example, the 3D printing output unit selects the type of 3D printer to use and compatible materials, and outputs the artwork at high resolution. The 3D printing output unit can also adjust the size and color of the artwork according to the user's request. This allows the art generation system to streamline the entire process from the creation of artworks using generative AI to their sale, manufacturing, and shipping. For example, users can generate their own original artworks and easily sell them. 3D printing technology can also be used to create physical artworks from digital data.

[0063] The generative AI engine can automatically adjust the style or color of an artwork based on the user's emotions. For example, the generative AI engine can add an emotion estimation function and analyze emotions from text and images entered by the user. For example, if a user enters "I'm feeling happy," the generative AI generates artwork with bright colors and a positive style. The generative AI engine can also analyze the user's emotional state in real time and dynamically adjust the style and color of the artwork based on the results. For example, if the user is feeling sad, the generative AI generates artwork with calm colors and a tranquil atmosphere. The generative AI engine can also use the emotion estimation function to learn the user's past emotional data and generate personalized artwork. For example, it can generate a new artwork based on the style of artwork that the user previously felt joyful. This makes it possible to generate artwork that matches the user's emotions.

[0064] The generative AI engine can learn a user's past artworks or preferred style and generate personalized artworks. For example, the generative AI engine stores artworks previously generated by the user in a database and generates new artworks based on that data. For example, it learns the user's preferred colors and style and generates new artworks based on that. In addition, to learn the user's preferred style, the generative AI engine collects data on artworks rated by the user and generates personalized artworks based on that information. For example, it incorporates the characteristics of artworks that the user has given high ratings. The generative AI engine also analyzes data on the user's past artworks and learns their characteristics to generate artworks tailored to the user's preferences. For example, it generates artworks that incorporate the user's favorite themes and motifs. This makes it possible to generate personalized artworks according to the user's preferences.

[0065] The generative AI engine can generate hybrid artworks that combine different art techniques. For example, the generative AI engine learns different art techniques and generates hybrid artworks that combine techniques selected by the user. For example, it generates artworks that combine the texture of oil painting with the transparency of watercolor. The generative AI engine also generates artworks in a new style by combining multiple art techniques specified by the user. For example, it generates artworks that combine the vividness of digital art with traditional painting techniques. The generative AI engine also develops algorithms for combining different art techniques and automatically selects the optimal combination of techniques to generate artworks. For example, it selects techniques based on the user's instructions and generates hybrid art. This makes it possible to generate artworks in a new style that combine different art techniques.

[0066] The generative AI engine can also be applied to other artistic fields, such as music or poetry, to generate multimedia artworks. For example, the generative AI engine can add a music generation function to generate music based on a theme specified by the user. For example, it can automatically generate background music to match an artwork. The generative AI engine can also add a function to generate poetry or literary works, generating poetry based on keywords or themes entered by the user. For example, it can generate poetry inspired by an artwork. The generative AI engine can also develop a function to generate multimedia artworks that combine music or poetry with artworks. For example, the generative AI can simultaneously generate music and poetry that are linked to the artwork. This makes it possible to generate multimedia artworks that combine other artistic fields, such as music and poetry.

[0067] Generative AI engines can be developed as versions specialized for educational use and used as teaching materials for art education. Generative AI engines can be specialized for educational use, adding functions for use as teaching materials for art education. For example, they can automatically generate art techniques that students should learn. Generative AI engines can also be developed for education, building systems in which students input their own work into the generative AI and receive feedback. For example, the generative AI can point out areas for improvement in the work. Generative AI engines can also be developed to fit art education curricula, generating artworks based on themes and techniques that students should learn. For example, they can generate works from a specific era or style. This makes it possible to develop generative AI engines that can be used as teaching materials for art education.

[0068] The generative AI engine can add an emotion estimation function and suggest themes or motifs for artworks to be generated based on the user's emotions. For example, the generative AI engine can add an emotion estimation function and suggest themes or motifs for artworks based on the user's emotions. For example, if the user is feeling "happy," it can suggest artworks with a bright theme. The generative AI engine can also analyze the user's emotional state in real time and dynamically suggest themes or motifs for artworks based on the results. For example, if the user is feeling "excited," it can suggest artworks with an energetic theme. The generative AI engine can also use the emotion estimation function to learn the user's past emotional data and suggest personalized themes or motifs. For example, it can make new suggestions based on the themes of artworks that the user found "moving" in the past. This makes it possible to suggest themes and motifs based on the user's emotions.

[0069] The sales platform can introduce an emotion estimation function and display recommended artworks based on the user's emotions. For example, the sales platform adds an emotion estimation function and displays recommended artworks by analyzing the user's emotional state. For example, if the user is looking for "relaxation," calm artworks are displayed. The sales platform also builds a system that automatically selects recommended artworks based on the user's emotional data. For example, if the user is feeling "excited," dynamic artworks are displayed. The sales platform also uses the emotion estimation function to learn the user's past emotional data and display personalized recommended artworks. For example, artworks similar to artworks that the user previously felt "joy" are displayed. This makes it possible to display recommended artworks according to the user's emotions.

[0070] The sales platform may have a community function that allows users to share their artworks with other users and receive feedback. The sales platform may, for example, add a community function to allow users to share their artworks with other users. For example, it may provide a function for receiving comments and ratings on the artworks. The sales platform may also build a system that allows users to upload their artworks and receive feedback from other users. For example, it may provide a function for sharing improvements and impressions on the artworks. The sales platform may also use the community function to provide a forum where users can discuss artworks with each other. For example, it may set up a forum for exchanging opinions on the themes and techniques of the artworks. This makes it possible to improve the quality of artworks through interaction and feedback between users.

[0071] The sales platform can be equipped with a function for holding live art sessions using a generative AI engine and generating and selling artworks in real time. For example, the sales platform can add a live art session function and generate artworks in real time using a generative AI engine. For example, users can participate and watch the generation process. The sales platform can also build a system for selling artworks generated in live art sessions on the spot. For example, the generated artworks can be made available for immediate purchase after the session ends. The sales platform can also provide a function for receiving feedback from users during the live art session and for the generative AI to adjust the artworks based on that feedback. For example, the color or style of the artwork can be changed in response to a user request. This makes it possible to generate and sell artworks in real time through live art sessions.

[0072] Sales platforms can link with other online marketplaces to expand sales channels for artworks. Sales platforms can link with other online marketplaces to expand sales channels for artworks. For example, they can link with platforms such as Amazon and Etsy. Sales platforms can also link with other marketplaces via APIs to build systems that allow artworks on the platform to be sold simultaneously through multiple sales channels. For example, they can centralize inventory management of artworks. Sales platforms can also link with other online marketplaces to increase the exposure of artworks on the platform and expand sales opportunities. For example, they can promote artworks on partner platforms. This can expand sales channels for artworks and enable them to reach more users.

[0073] The sales platform may add an artwork rental service, providing a function that allows users to rent artworks for a certain period of time. For example, the sales platform may add an artwork rental service and build a system that allows users to rent artworks for a certain period of time. For example, artworks may be rented for a monthly fee. The sales platform may also provide an artwork rental service that allows users to try artworks before purchasing them. For example, the sales platform may provide a purchase option after the rental period ends. The sales platform may also provide users with an opportunity to experience various artworks through the rental service. For example, the sales platform may provide a subscription service that periodically exchanges artworks. This makes it possible to provide a rental service that allows users to rent artworks for a certain period of time.

[0074] The sales platform can add an emotion estimation function and dynamically adjust the price of artworks based on the user's emotions. For example, the sales platform adds the emotion estimation function and builds a system that analyzes the user's emotional state and dynamically adjusts the price of artworks. For example, if the user is feeling "excited," the price is raised. The sales platform also develops an algorithm that automatically adjusts the price of artworks based on the user's emotional data. For example, if the user is seeking "relaxation," the price is lowered. The sales platform also uses the emotion estimation function to learn the user's past emotional data and build a system that sets personalized prices. For example, the price is adjusted for artworks that the user has previously felt "joy." This makes it possible to dynamically adjust the price of artworks based on the user's emotions.

[0075] Collaborative works incorporate an emotion estimation function, allowing interactive art to be provided in which parts of the work change depending on the user's emotions. Collaborative works, for example, incorporate an emotion estimation function to provide interactive art in which the color or shape of the work changes depending on the user's emotions. For example, if the user feels "joy," the color changes to a brighter color. Collaborative works also build a system that analyzes the user's emotional state in real time and dynamically changes parts of the work based on the results. For example, if the user feels "surprise," the shape of the work changes. Collaborative works also use the emotion estimation function to learn the user's past emotional data and provide personalized interactive art. For example, the work can be modified by incorporating elements of works that the user found "moving" in the past. This makes it possible to provide interactive art that changes depending on the user's emotions.

[0076] The production process of collaborative works can be recorded in documentary format and published on the platform. The production process of collaborative works can be filmed in documentary format and published on the platform. For example, it could include behind-the-scenes footage and interviews with the artists. A system could also be created that allows users to track the production process of collaborative works in real time through documentary footage. For example, live streaming could be performed. The production process documentary could also be updated regularly to ensure users are always updated with new information. For example, a new episode could be published every week. This could attract users' interest by revealing the production process of collaborative works.

[0077] By providing limited edition or customized versions of collaborative works using a generative AI engine, users can obtain special works that are just their own. By providing limited edition versions of collaborative works using a generative AI engine, we can build a system that allows users to obtain special works. For example, limited edition works can be given serial numbers. Also, users can purchase works that have been customized using a generative AI engine. For example, users can customize them by choosing colors or styles. Furthermore, by providing limited edition or customized versions of collaborative works, users can obtain special works that are just their own. For example, the user's name or message can be incorporated into the work. This makes it possible for users to obtain special works of art that are just their own.

[0078] Collaborative works can be expanded to include collaborations with other well-known brands or companies to increase the diversity of artworks. Collaborative works can be created in collaboration with other well-known brands or companies to increase the diversity of artworks. For example, collaborations can be made with fashion brands and technology companies. Also, through collaborations with other brands and companies, artworks with different styles and themes can be provided. For example, collaborations can be made with sports brands to create artworks with a sports theme. Also, collaborative works can be created in collaboration with brands and companies in various fields to provide users with new experiences. For example, collaborations can be made with the food and beverage industry or the entertainment industry. This can increase the diversity of artworks and provide users with new experiences.

[0079] Collaborative works can be made available for users to experience in a virtual space using VR or AR technology. A system is built that allows users to experience collaborative works in a virtual space using VR technology. For example, a user can wear a VR headset and experience the work visually in 360 degrees. An application is also developed that allows users to experience collaborative works in real space using AR technology. For example, the work can be projected into real space using a smartphone. Furthermore, a system is provided that combines VR and AR technologies to allow users to interactively experience collaborative works in a virtual space. For example, users can touch and manipulate the work. This makes it possible for users to experience collaborative works in a virtual space.

[0080] By adding an emotion estimation function to collaborative works, it is possible to hold an interactive exhibition in which parts of the works change based on the user's emotions. By adding an emotion estimation function to collaborative works, it is possible to hold an interactive exhibition in which parts of the works change based on the user's emotions. For example, if the user is feeling "surprise," the color of the work changes. We also build a system that analyzes the user's emotional state in real time and dynamically changes the works in the exhibition based on the results. For example, if the user is feeling "moved," the shape of the work changes. We also use the emotion estimation function to learn the user's past emotional data and provide a personalized interactive exhibition. For example, an exhibition is created by incorporating elements of works that the user previously felt "joy." This makes it possible to hold an interactive exhibition that changes based on the user's emotions.

[0081] The 3D print output unit incorporates an emotion estimation function, allowing it to automatically adjust the output material or color based on the user's emotion. For example, the 3D print output unit may add an emotion estimation function to build a system that automatically adjusts the output material and color based on the user's emotion. For example, if the user is feeling "joy," a bright-colored material may be used. The system may also analyze the user's emotional state in real time and dynamically adjust the 3D print material and color based on the results. For example, if the user is seeking "relaxation," a soft-textured material may be used. The emotion estimation function may also be used to learn the user's past emotional data and provide personalized 3D print output. For example, the system may output using colors and materials that the user previously found "emotional." This makes it possible to automatically adjust the material and color of the 3D print output based on the user's emotion.

[0082] The 3D printing output unit allows the generative AI engine to receive user feedback in real time and fine-tune the output content. The 3D printing output unit, for example, builds a system in which the generative AI engine receives user feedback in real time and fine-tunes the output content. For example, the user can request changes to color or shape. The 3D printing output unit also analyzes user feedback in real time and dynamically adjusts the 3D printing output content based on the results. For example, if the user requests a brighter color, the color is changed. The feedback function also provides a system that allows the user to make adjustments in real time during the 3D printing output process. For example, the user can fine-tune the shape or design. This makes it possible to fine-tune the 3D printing output content in real time based on user feedback.

[0083] The 3D printing output unit can use composite materials that combine different materials to enable a wider variety of textures and expressions. The 3D printing output unit, for example, uses composite materials that combine different materials to build a system that enables a wider variety of textures and expressions. For example, a hard material and a soft material can be combined for output. The use of composite materials also diversifies the textures and expressions of 3D printing outputs. For example, a transparent material can be combined for output. The use of composite materials that combine different materials also allows the user to achieve the textures and expressions they desire. For example, a metal and plastic can be combined for output. This allows for a wider variety of textures and expressions by using composite materials that combine different materials.

[0084] The 3D printing output unit can be combined with other manufacturing technologies to produce a wider variety of artworks. The 3D printing output unit can be combined with other manufacturing technologies (e.g., laser cutting or CNC machining) to build a system for producing a wider variety of artworks. For example, detailed patterns can be added to a 3D printed object by laser cutting. Also, CNC machining can be combined with 3D printing output to produce artworks with complex shapes and designs. For example, detailed engraving can be performed on a 3D printed base by CNC machining. Also, by combining 3D printing output with other manufacturing technologies, a wider variety of artworks desired by the user can be realized. For example, additional processing can be performed on the 3D printed work. This makes it possible to produce a wider variety of artworks by combining 3D printing output with other manufacturing technologies.

[0085] The 3D printing output unit can provide kits for home 3D printers so that users can easily print at home. The 3D printing output unit can, for example, provide kits optimized for home 3D printers that allow users to easily print at home. For example, it can provide a set of print data and necessary materials. It can also develop kits for home 3D printers so that users can print artwork at home. For example, it can provide simple operation guides and support services. It can also develop dedicated applications for home 3D printers so that users can perform 3D printing at home. For example, it can provide an app that allows users to easily download print data and make settings. This makes it possible to provide kits that allow users to easily perform 3D printing at home.

[0086] The 3D print output unit can add an emotion estimation function to dynamically change the shape or design of the output work based on the user's emotions. The 3D print output unit can, for example, add an emotion estimation function to build a system that dynamically changes the shape or design of the output work based on the user's emotions. For example, if the user is feeling "surprised," the shape is changed. The unit can also analyze the user's emotional state in real time and dynamically adjust the shape or design of the 3D print based on the results. For example, if the user is seeking "relaxation," the shape is changed to a softer shape. The emotion estimation function can also be used to learn the user's past emotional data and provide personalized 3D print output. For example, the unit can output using shapes or designs that the user previously found "moving." This makes it possible to dynamically change the shape or design of the 3D print output based on the user's emotions.

[0087] The generative AI engine incorporates an emotion estimation function and can automatically adjust the style or color of an artwork based on the user's emotions. For example, the generative AI engine can add an emotion estimation function and analyze emotions from text and images entered by the user. For example, if a user inputs "I'm feeling happy," the generative AI generates artwork with bright colors and a positive style based on that instruction. The generative AI engine can also analyze the user's emotional state in real time and dynamically adjust the style and color of the artwork based on the results. For example, if the user is feeling sad, the generative AI will generate artwork with calm colors and a tranquil atmosphere. The emotion estimation function can also be used to learn the user's past emotional data and generate personalized artwork. For example, a new artwork can be generated based on the style of artwork that the user previously felt joyful. This makes it possible to automatically adjust the style and color of an artwork based on the user's emotions.

[0088] The generative AI engine can learn a user's past artworks or preferred style and generate personalized artworks. For example, the generative AI engine stores artworks previously generated by the user in a database and generates new artworks based on that data. For example, it learns the user's preferred colors and style and generates new artworks based on that. In addition, to learn the user's preferred style, the generative AI engine collects data on artworks rated by the user and generates personalized artworks based on that information. For example, it incorporates the characteristics of artworks that the user has given high ratings. The generative AI engine also analyzes data on the user's past artworks and learns their characteristics to generate artworks tailored to the user's preferences. For example, it generates artworks that incorporate the user's favorite themes and motifs. This makes it possible to learn the user's past artworks and preferred style and generate personalized artworks.

[0089] The generative AI engine can generate hybrid artworks that combine different art techniques. For example, the generative AI engine learns different art techniques and generates hybrid artworks that combine techniques selected by the user. For example, it generates artworks that combine the texture of oil painting with the transparency of watercolor. The generative AI engine also generates artworks in a new style by combining multiple art techniques specified by the user. For example, it generates artworks that combine the vividness of digital art with traditional painting techniques. The generative AI engine also develops algorithms for combining different art techniques and automatically selects the optimal combination of techniques to generate artworks. For example, it selects techniques based on the user's instructions and generates hybrid art. This makes it possible to generate hybrid artworks that combine different art techniques.

[0090] The generative AI engine can also be applied to other artistic fields, such as music or poetry, to generate multimedia artworks. For example, the generative AI engine can add a music generation function to generate music based on a theme specified by the user. For example, it can automatically generate background music to match an artwork. The generative AI engine can also add a function to generate poetry or literary works, generating poetry based on keywords or themes entered by the user. For example, it can generate poetry inspired by an artwork. The generative AI engine can also develop a function to generate multimedia artworks that combine music or poetry with artworks. For example, the generative AI can simultaneously generate music and poetry that are linked to the artwork. This makes it possible to generate multimedia artworks that combine other artistic fields, such as music and poetry.

[0091] Generative AI engines can be developed as versions specialized for educational use and used as teaching materials for art education. Generative AI engines can be specialized for educational use, adding functions for use as teaching materials for art education. For example, they can automatically generate art techniques that students should learn. Generative AI engines can also be developed for education, building systems in which students input their own work into the generative AI and receive feedback. For example, the generative AI can point out areas for improvement in the work. Generative AI engines can also be developed to fit art education curricula, generating artworks based on themes and techniques that students should learn. For example, they can generate works from a specific era or style. This makes it possible to develop generative AI engines that can be used as teaching materials for art education.

[0092] The generative AI engine can add an emotion estimation function and suggest themes or motifs for artworks to be generated based on the user's emotions. For example, the generative AI engine can add an emotion estimation function and suggest themes or motifs for artworks based on the user's emotions. For example, if the user is feeling "happy," it can suggest artworks with a bright theme. The generative AI engine can also analyze the user's emotional state in real time and dynamically suggest themes or motifs for artworks based on the results. For example, if the user is feeling "excited," it can suggest artworks with an energetic theme. The generative AI engine can also use the emotion estimation function to learn the user's past emotional data and suggest personalized themes or motifs. For example, it can make new suggestions based on the themes of artworks that the user found "moving" in the past. This makes it possible to suggest themes and motifs based on the user's emotions.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The generative AI engine can suggest themes or motifs for artworks based on the user's emotions. For example, if the user is feeling "happy," it will suggest artworks with a bright theme. The generative AI engine also analyzes the user's emotional state in real time and dynamically suggests themes and motifs for artworks based on the results. For example, if the user is feeling "excited," it will suggest artworks with an energetic theme. The generative AI engine also uses its emotion estimation function to learn the user's past emotional data and suggest personalized themes and motifs. For example, it can make new suggestions based on the themes of artworks that the user found "moving" in the past. This makes it possible to suggest themes and motifs based on the user's emotions.

[0095] The generative AI engine can learn a user's past artwork or preferred style and generate personalized artwork. For example, it can store artwork created by the user in a database and generate new artwork based on that data. For example, it can learn the user's preferred colors and style and generate new artwork based on that. In addition, to learn the user's preferred style, the generative AI engine collects data on artwork rated by the user and generates personalized artwork based on that information. For example, it incorporates the characteristics of artwork that the user has given a high rating. The generative AI engine can also analyze data on the user's past artwork and learn its characteristics to generate artwork that matches the user's preferences. For example, it can generate artwork that incorporates the user's favorite themes and motifs. This makes it possible to generate personalized artwork that meets the user's preferences.

[0096] The generative AI engine can generate hybrid artworks that combine different art techniques. For example, it can learn different art techniques and generate hybrid artworks that combine techniques selected by the user. For example, it can generate artworks that combine the texture of oil painting with the transparency of watercolor. The generative AI engine can also generate artworks in a new style by combining multiple art techniques specified by the user. For example, it can generate artworks that combine the vividness of digital art with traditional painting techniques. The generative AI engine can also develop algorithms for combining different art techniques and automatically select the optimal combination of techniques to generate artworks. For example, it can select techniques based on the user's instructions and generate hybrid art. This makes it possible to generate artworks in a new style that combine different art techniques.

[0097] The generative AI engine can also be applied to other artistic fields, such as music or poetry, to generate multimedia artworks. For example, a music generation function can be added to generate music based on a theme specified by the user. For example, background music to accompany an artwork can be automatically generated. The generative AI engine can also add a function to generate poetry or literary works, generating poetry based on keywords or themes entered by the user. For example, generating poetry inspired by an artwork. The generative AI engine can also develop a function to generate multimedia artworks that combine music or poetry with artworks. For example, the generative AI can simultaneously generate music and poetry that are linked to the artwork. This makes it possible to generate multimedia artworks that combine other artistic fields, such as music and poetry.

[0098] Generative AI engines can be developed as versions specialized for educational use and used as teaching materials for art education. For example, by specializing it for educational use, functions can be added for use as teaching materials for art education. For example, it can automatically generate art techniques that students should learn. Generative AI engines can also be developed for education, and a system can be built in which students input their own work into the generative AI and receive feedback. For example, the generative AI can point out areas for improvement in the work. Generative AI engines can also be developed that are tailored to art education curricula, generating artworks based on themes and techniques that students should learn. For example, it can generate works from a specific era or style. This makes it possible to develop generative AI engines that can be used as teaching materials for art education.

[0099] The sales platform can introduce an emotion estimation function to display recommended artworks based on the user's emotions. For example, by adding an emotion estimation function, the system analyzes the user's emotional state and displays recommended artworks. For example, if the user is looking for "relaxation," calm artworks are displayed. The sales platform also builds a system that automatically selects recommended artworks based on the user's emotional data. For example, if the user is feeling "excited," dynamic artworks are displayed. The sales platform also uses the emotion estimation function to learn the user's past emotional data and display personalized recommended artworks. For example, artworks similar to artworks that the user previously felt "joy" are displayed. This makes it possible to display recommended artworks according to the user's emotions.

[0100] The sales platform may be equipped with a community function that allows users to share their artworks with other users and receive feedback. For example, a community function may be added to allow users to share their artworks with other users. For example, a function may be provided to receive comments and ratings on the artworks. The sales platform may also build a system that allows users to upload their artworks and receive feedback from other users. For example, a function may be provided to share improvements and impressions on the artworks. The sales platform may also use the community function to provide a forum where users can discuss artworks with each other. For example, a forum may be set up for users to exchange opinions on the themes and techniques of the artworks. This makes it possible to improve the quality of artworks through interaction and feedback between users.

[0101] The sales platform can be equipped with a function for holding live art sessions using a generative AI engine and generating and selling artworks in real time. For example, a live art session function can be added, and artworks can be generated in real time using a generative AI engine. For example, users can participate and watch the generation process. The sales platform also builds a system for selling artworks generated in live art sessions on the spot. For example, the generated artworks can be made available for immediate purchase after the session ends. The sales platform also provides a function for receiving feedback from users during the live art session and for the generative AI to adjust the artworks based on that feedback. For example, the color or style of the artwork can be changed in response to a user request. This makes it possible to generate and sell artworks in real time through live art sessions.

[0102] Sales platforms can link with other online marketplaces to expand the sales channels for artworks. For example, they can link with other online marketplaces to expand the sales channels for artworks. For example, they can link with platforms such as Amazon and Etsy. Sales platforms can also link with other marketplaces via APIs to build systems that allow artworks on the platform to be sold simultaneously through multiple sales channels. For example, they can centralize inventory management of artworks. Sales platforms can also link with other online marketplaces to increase the exposure of artworks on the platform and expand sales opportunities. For example, they can promote artworks on partner platforms. This can expand the sales channels for artworks and enable them to reach more users.

[0103] The sales platform may add an artwork rental service, providing a function that allows users to rent artworks for a certain period of time. For example, an artwork rental service may be added, and a system may be constructed that allows users to rent artworks for a certain period of time. For example, artworks may be rented for a monthly fee. The sales platform may also provide an artwork rental service that allows users to try artworks before purchasing them. For example, it may provide a purchase option after the rental period ends. The sales platform may also provide users with the opportunity to experience various artworks through the rental service. For example, it may provide a subscription service that periodically exchanges artworks. This makes it possible to provide a rental service that allows users to rent artworks for a certain period of time.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The generative AI engine generates a work of art. For example, the generative AI engine uses deep learning and GAN (generative artificial network) to generate a new work of art based on the user's instructions. For example, if the user inputs a prompt such as "I want you to generate an abstract painting," the generative AI engine generates an abstract painting based on the user's instructions. The generative AI engine can also learn various art styles and techniques and generate works that meet the user's preferences. Step 2: The sales platform sells the artworks generated by the generative AI engine. For example, the sales platform displays the artworks through a user interface and performs the purchase procedure. The sales platform also has a payment system that allows users to make payments securely. Furthermore, the sales platform has an inventory management system that allows it to manage the inventory status of artworks in real time. Step 3: The 3D printing output unit 3D prints the artwork ordered by the sales platform. For example, the 3D printing output unit selects the type of 3D printer to use and the corresponding material, and outputs the artwork in high resolution. The 3D printing output unit can also adjust the size and color of the artwork according to the user's request.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generative AI engine and An online sales platform, a 3D print output unit; The generative AI engine: Generate artworks, The sales platform: Selling the artwork generated by the generative AI engine; The 3D print output unit is The artwork ordered through the sales platform is output by 3D printing. A system characterized by:

2. The generative AI engine: Automatically adjusting the style or color of the artwork based on the user's emotions 2. The system of claim 1.

3. The generative AI engine: Learns the user's past work or preferred style and generates personalized artwork 2. The system of claim 1.

4. The generative AI engine: Create hybrid artworks that combine different art techniques 2. The system of claim 1.

5. The generative AI engine: It can also be applied to other artistic fields such as music or poetry to create multimedia artworks.

2. The system of claim 1.

Images