system

Abstract

An object of the system according to the embodiment is to allow even a physically handicapped person to easily create an original image and register and sell the original image as an NFT.SOLUTION: A system includes a reception unit, a generation unit, a registration unit, and a sales unit. The receiving unit receives a prompt from a user. The generation unit analyzes the prompt received by the reception unit and generates an original image. The registration unit registers the image generated by the generation unit as an NFT. The sales unit sells the NFT registered by the registration unit.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, the process for people with physical disabilities to create original images and register and sell them as NFTs was complicated and not easy to implement.

[0005] The system of this embodiment aims to enable even people with physical disabilities to easily create original images and register and sell them as NFTs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a registration unit, and a sales unit. The reception unit receives a prompt from a user. The generation unit analyzes the prompt received by the reception unit and generates an original image. The registration unit registers the image generated by the generation unit as an NFT. The sales unit sells the NFT registered by the registration unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even people with physical disabilities to easily create original images and register and sell them as NFTs. [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) An image generation system according to an embodiment of the present invention allows even people with physical disabilities to easily create and sell original image NFTs. In the image generation system, a user inputs a specific command prompt, and a generation AI analyzes the command prompt and generates an original image. The generated image is registered on the blockchain as an NFT, making it easy for users to sell. For example, in the image generation system, a user inputs a prompt such as "a landscape with blue skies and white clouds." This information is input to the generation AI. The image generation system then uses the generation AI to analyze the input prompt and generate an original image. The generation AI generates an image based on the prompt and registers the image as an NFT on the blockchain. For example, a beautiful landscape image is generated based on the prompt "a landscape with blue skies and white clouds." The generated image is registered on the blockchain as an NFT. This allows users to easily sell the generated images. For example, a user can list the generated image on an NFT marketplace, and a buyer can purchase the image. This allows even people with physical disabilities to create and sell original image NFTs without requiring specialized knowledge or skills. This makes it possible for people with physical disabilities to create and sell original image NFTs without the need for specialized knowledge or skills. Users simply input simple text, and the generation AI automatically generates an image and registers it as an NFT, allowing them to engage in creative activities without any hassle.

[0029] An image generation system according to an embodiment includes a reception unit, a generation unit, a registration unit, and a sales unit. The reception unit receives a prompt from a user. The prompt may be, for example, text, audio, or an image, but is not limited to these examples. The reception unit receives the prompt when the user simply inputs text. The reception unit can also receive audio input or image input. The generation unit uses a generation AI to analyze the prompt received by the reception unit and generate an original image. The generation unit uses, for example, a text generation AI (e.g., LLM) to analyze the prompt and generate an image. The generation unit can also generate an image based on the prompt using a multimodal generation AI. For example, the generation unit generates a landscape image or a portrait image based on the prompt. The registration unit registers the image generated by the generation unit as an NFT on a blockchain. The registration unit registers the generated image as an NFT using, for example, blockchain technology such as Ethereum or Binance Smart Chain. The registration unit can also register an NFT including metadata of the generated image on a blockchain. The sales unit sells NFTs registered by the registration unit. The sales unit, for example, handles sales procedures on an NFT marketplace (e.g., OpenSea, Rarible, etc.). The sales unit puts NFTs generated by users on the marketplace, allowing buyers to purchase the NFTs. In this way, the image generation system according to the embodiment allows users to easily create and sell original image NFTs. In this way, the image generation system allows users to easily create and sell original image NFTs.

[0030] The generation unit can generate an image based on the prompt. The generation unit analyzes the prompt using, for example, a text generation AI (e.g., LLM) and generates an image. For example, the generation unit generates a beautiful landscape image based on the prompt, "A landscape with blue sky and white clouds." The generation unit can also generate an image based on the prompt using a multimodal generation AI. For example, the generation unit can generate a portrait image based on the prompt. The generation unit can also generate an abstract painting based on the prompt using a generation AI. For example, the generation unit generates an abstract painting based on the prompt, "A combination of abstract shapes and colors." In this way, the generation unit generates an image based on the prompt, thereby generating an image that meets the user's intention.

[0031] The registration unit can register the generated image as an NFT on a blockchain. The registration unit registers the generated image as an NFT using blockchain technology such as Ethereum or Binance Smart Chain. For example, the registration unit registers an NFT including metadata of the generated image on a blockchain. When registering the generated image as an NFT, the registration unit can also include information to clarify the ownership of the image. For example, the registration unit registers an NFT including metadata such as owner information and creation date and time of the generated image. In this way, by registering the generated image as an NFT on a blockchain, the ownership of the image becomes clear.

[0032] The sales unit can handle sales procedures on NFT marketplaces. For example, the sales unit handles sales procedures on NFT marketplaces (e.g., OpenSea, Rarible, etc.). For example, the sales unit lists NFTs generated by users on the marketplace, allowing buyers to purchase the NFTs. The sales unit also has a function for setting the selling price of NFTs. For example, the sales unit allows users to input their desired selling price and list the NFT at that price. The sales unit also has a function for managing the sales history of NFTs. For example, the sales unit records the prices and buyer information of NFTs sold in the past, allowing users to refer to the records. This allows the sales unit to handle sales procedures on NFT marketplaces, allowing users to easily sell NFTs.

[0033] The reception unit can accept a prompt when the user simply inputs simple text. For example, the reception unit can accept a prompt such as "a scene of blue sky and white clouds" when the user simply inputs simple text. The reception unit can also accept voice input and image input. For example, when the user inputs a prompt by voice, the reception unit converts it into text using voice recognition technology and accepts it as a prompt. When the user inputs an image as a prompt, the reception unit can analyze the prompt using image analysis technology. This allows the reception unit to accept a prompt when the user simply inputs simple text, simplifying operation.

[0034] The reception unit can analyze the user's past prompt input history and select the optimal reception method. For example, the reception unit analyzes the user's past prompt input history and selects the optimal reception method. For example, the reception unit automatically displays prompts that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest prompts to be used in a specific time period based on the user's past input history. In this way, the optimal reception method can be provided by analyzing the user's past prompt input history.

[0035] The reception unit can filter prompts based on the user's current project or area of ​​interest when receiving the prompts. For example, the reception unit can filter prompts based on the user's current project or area of ​​interest when receiving the prompts. For example, the reception unit can preferentially display prompts related to the project the user is currently working on. The reception unit can also suggest related prompts based on the user's area of ​​interest. The reception unit can also analyze the user's past project history and filter and display related prompts. This makes it possible to provide highly relevant prompts by filtering prompts based on the user's current project or area of ​​interest.

[0036] The reception unit can select the optimal reception means depending on the user's input method when receiving a prompt. For example, the reception unit selects the optimal reception means depending on the user's input method (voice, text, image, etc.) when receiving a prompt. For example, if the user selects voice input, the reception unit can use voice recognition technology to receive the prompt. Also, if the user selects text input, the reception unit can provide a text box to receive the prompt. Also, if the user selects image input, the reception unit can use image analysis technology to receive the prompt. This improves user convenience by selecting the optimal reception means depending on the user's input method.

[0037] The reception unit can prioritize receiving highly relevant prompts by taking into account the user's geographical location information when receiving prompts. For example, the reception unit prioritizes receiving highly relevant prompts by taking into account the user's geographical location information when receiving prompts. For example, if the user is in a specific area, the reception unit can prioritize receiving prompts related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving prompts related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving prompts related to the user's home. In this way, highly relevant prompts can be provided by taking into account the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and receive related prompts when receiving a prompt. For example, the reception unit analyzes the user's social media activity and receives related prompts when receiving a prompt. For example, the reception unit can suggest related prompts based on content shared by the user on social media. The reception unit can also analyze the user's social media activity history and preferentially accept related prompts. The reception unit can also suggest related prompts based on the activities of the user's friends on social media. In this way, related prompts can be provided by analyzing the user's social media activity.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a prompt. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a prompt. For example, the reception unit adjusts the reception method based on feedback provided by the user in the past. The reception unit can also suggest an optimal reception method based on the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0040] The generation unit can adjust the level of detail of the generated image based on the importance of the prompt during generation. For example, the generation unit adjusts the level of detail of the generated image based on the importance of the prompt during generation. For example, the generation unit generates a detailed image for a prompt with high importance. The generation unit can also generate a simplified image for a prompt with low importance. The generation unit can also adjust the resolution of the image according to the importance of the prompt. In this way, an appropriate image can be generated by adjusting the level of detail of the image according to the importance of the prompt.

[0041] The generation unit can apply different generation algorithms depending on the category of the prompt during generation. For example, the generation unit applies different generation algorithms depending on the category of the prompt during generation. For example, the generation unit applies a generation algorithm dedicated to landscapes to a prompt for a landscape image. The generation unit can also apply a generation algorithm dedicated to portraits to a prompt for a portrait image. The generation unit can also apply a generation algorithm dedicated to abstract paintings to a prompt for an abstract painting. In this way, by applying an appropriate generation algorithm depending on the category of the prompt, it is possible to generate higher quality images.

[0042] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit improves the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit adjusts the generation algorithm based on images generated by the user in the past. The generation unit can also learn the user's preferred style from the user's past generation results and reflect this in the generation. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. In this way, the accuracy of generation is improved by referring to the user's past generation results.

[0043] The generation unit can determine the generation priority based on the submission time of the prompt at the time of generation. For example, the generation unit determines the generation priority based on the submission time of the prompt at the time of generation. For example, if a prompt is submitted early in the morning, the generation unit performs generation with priority. Also, if a prompt is submitted late at night, the generation unit can perform generation with normal priority. Also, the generation unit can adjust the generation schedule depending on the submission time of the prompt. In this way, efficient generation is possible by determining the generation priority based on the submission time of the prompt.

[0044] The generation unit can adjust the order of generation based on the relevance of the prompts during generation. For example, the generation unit adjusts the order of generation based on the relevance of the prompts during generation. For example, the generation unit generates prompts with high relevance with priority. The generation unit can also postpone prompts with low relevance. The generation unit can also adjust the order of generation according to the relevance of the prompts. In this way, by adjusting the order of generation according to the relevance of the prompts, it is possible to generate images with high relevance with priority.

[0045] The generation unit can adjust the use of technical terms in the generated image according to the user's level of expertise at the time of generation. For example, the generation unit adjusts the use of technical terms in the generated image according to the user's level of expertise at the time of generation. For example, the generation unit generates an image that uses a lot of technical terms for a user with high level of expertise. The generation unit can also generate an image that uses less technical terms for a user with low level of expertise. The generation unit can also adjust the use of technical terms in the generated image according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, an image suitable for the user can be generated.

[0046] The registration unit can improve the accuracy of registration by taking into account the interrelationships between the generated images during registration. For example, the registration unit improves the accuracy of registration by taking into account the interrelationships between the generated images during registration. For example, if the generated images are part of a series, the registration unit registers them by taking into account the interrelationships. Furthermore, if the generated images have a highly related theme, the registration unit can also register them by taking into account the interrelationships between the generated images. Furthermore, the registration unit can analyze the interrelationships between the generated images and improve the accuracy of registration. In this way, the accuracy of registration is improved by taking into account the interrelationships between the generated images.

[0047] The registration unit can perform registration while taking into consideration attribute information of the submitter of the generated image. For example, the registration unit performs registration while taking into consideration attribute information of the submitter of the generated image. For example, if the submitter is a professional artist, the registration unit may register the image with a special tag. Furthermore, if the submitter is a beginner, the registration unit may provide a simplified registration procedure. Furthermore, the registration unit may suggest an optimal registration method based on the submitter's attribute information. In this way, an appropriate registration method can be provided by taking into consideration the submitter's attribute information.

[0048] The registration unit can weight the registration based on the submission frequency of the generated image at the time of registration. For example, the registration unit weights the registration based on the submission frequency of the generated image at the time of registration. For example, the registration unit prioritizes registration for users with a high submission frequency. The registration unit can also register users with a low submission frequency with normal priority. The registration unit can also adjust the weighting of the registration based on the submission frequency. Thus, by weighting the registration based on the submission frequency, images that are frequently submitted can be registered with priority.

[0049] The registration unit can perform registration while taking into consideration the geographical distribution of the generated image. For example, when the registration unit performs registration while taking into consideration the geographical distribution of the generated image. For example, if the generated image relates to a specific region, the registration unit registers the image by attaching a tag related to the region. The registration unit can also analyze the geographical distribution of the generated image and propose an optimal registration method. Furthermore, if the generated image relates to multiple regions, the registration unit can register the image by attaching a tag related to each region. In this way, an appropriate registration method can be provided by taking into consideration the geographical distribution of the generated image.

[0050] The registration unit can improve the accuracy of registration by referring to literature related to the generated image at the time of registration. For example, the registration unit improves the accuracy of registration by referring to literature related to the generated image at the time of registration. For example, the registration unit refers to literature related to the generated image and registers it with appropriate tags. The registration unit can also analyze literature related to the theme of the generated image to improve the accuracy of registration. The registration unit can also suggest an optimal registration method based on literature related to the generated image. In this way, the accuracy of registration is improved by referring to literature related to the generated image.

[0051] The registration unit can perform registration taking into consideration the market value of the generated image at the time of registration. For example, the registration unit performs registration taking into consideration the market value of the generated image at the time of registration. For example, the registration unit prioritizes registration of images with high market value. The registration unit can also register images with low market value with normal priority. The registration unit can also analyze the market value of the generated image and propose an optimal registration method. In this way, an appropriate registration method can be provided by taking into consideration the market value of the generated image.

[0052] The sales department can select the optimal sales method by analyzing the user's past sales history at the time of sale. For example, the sales department can select the optimal sales method by analyzing the user's past sales history at the time of sale. For example, the sales department can propose the optimal sales method based on sales methods that have been successful for the user in the past. The sales department can also select the most effective sales method from the user's past sales history. The sales department can also analyze the user's past sales history to improve the success rate of sales. In this way, the optimal sales method can be provided by analyzing the user's past sales history.

[0053] The sales department can customize the sales method based on the user's current living situation at the time of sale. The sales department, for example, customizes the sales method based on the user's current living situation at the time of sale. For example, if the user is busy, the sales department can provide a simple sales procedure. Also, if the user has time, the sales department can provide detailed sales options. The sales department can also suggest the optimal sales method depending on the user's living situation. In this way, by customizing the sales method according to the user's current living situation, a sales method that is suitable for the user can be provided.

[0054] The sales department can improve the sales method by reflecting the user's feedback at the time of sale. For example, the sales department can adjust the sales method based on the feedback provided by the user. The sales department can also propose the optimal sales method based on the user's feedback. The sales department can also customize the sales interface by reflecting the user's feedback. In this way, the sales method can be improved by reflecting the user's feedback.

[0055] The sales department can select the optimal sales method by taking into account the user's geographical location information at the time of sale. For example, the sales department selects the optimal sales method by taking into account the user's geographical location information at the time of sale. For example, if the user is in a specific area, the sales department can suggest a sales method related to that area. Also, if the user is traveling, the sales department can suggest a sales method related to the user's travel destination. Also, if the user is at home, the sales department can suggest a sales method related to the user's home. In this way, the optimal sales method can be provided by taking into account the user's geographical location information.

[0056] The sales department can analyze the user's social media activity and suggest sales methods at the time of sale. For example, the sales department can analyze the user's social media activity and suggest sales methods at the time of sale. For example, the sales department can suggest relevant sales methods based on the content the user has shared on social media. The sales department can also analyze the user's social media activity history and suggest optimal sales methods. The sales department can also suggest relevant sales methods based on the activity of the user's friends on social media. In this way, the optimal sales methods can be provided by analyzing the user's social media activity.

[0057] The sales department can customize the sales method by reflecting the user's past feedback at the time of sale. For example, the sales department customizes the sales method by reflecting the user's past feedback at the time of sale. For example, the sales department adjusts the sales method based on the user's past feedback. The sales department can also propose the optimal sales method based on the user's past feedback. The sales department can also customize the sales interface by reflecting the user's past feedback. In this way, the optimal sales method can be provided by reflecting the user's past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can provide specific filtering conditions to the generation unit based on the user's input. For example, if the user inputs "natural scenery," the reception unit can instruct the generation unit to generate only images related to natural scenery. Furthermore, if the user inputs "abstract painting," the reception unit can instruct the generation unit to generate only images related to abstract painting. Furthermore, if the user inputs "portrait," the reception unit can instruct the generation unit to generate only images related to portraits. In this way, the reception unit can provide specific filtering conditions to the generation unit based on the user's input, thereby generating images that meet the user's intentions.

[0060] The registration unit can automatically tag images based on the metadata of the generated images. For example, if the generated image is a landscape image, the registration unit automatically assigns a "landscape" tag. Also, if the generated image is a portrait image, the registration unit can automatically assign a "portrait" tag. Furthermore, if the generated image is an abstract painting, the registration unit can automatically assign an "abstract painting" tag. In this way, the registration unit can improve the searchability of images by automatically tagging based on the metadata of the generated images.

[0061] The reception unit can provide specific style guidelines to the generation unit based on the user's input. For example, if the user inputs "vintage style," the reception unit can provide vintage style guidelines to the generation unit. Also, if the user inputs "modern," the reception unit can provide modern style guidelines to the generation unit. Furthermore, if the user inputs "minimalist," the reception unit can provide minimalist style guidelines to the generation unit. In this way, the reception unit can generate an image that meets the user's intentions by providing specific style guidelines to the generation unit based on the user's input.

[0062] The registration unit can evaluate the quality of the generated images and determine the priority of registration based on the evaluation result. For example, the registration unit can prioritize registration of high-quality images. The registration unit can also register low-quality images with normal priority. Furthermore, the registration unit can evaluate the quality of the generated images and adjust the priority of registration based on the evaluation result. In this way, the registration unit can provide an appropriate registration method by evaluating the quality of the generated images.

[0063] The reception unit can provide a specific color palette to the generation unit based on the user's input. For example, if the user inputs "warm colors," the reception unit can provide a warm color palette to the generation unit. Also, if the user inputs "cool colors," the reception unit can provide a cool color palette to the generation unit. Furthermore, if the user inputs "monochrome," the reception unit can provide a monochrome color palette to the generation unit. In this way, the reception unit can generate an image that meets the user's intentions by providing a specific color palette to the generation unit based on the user's input.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives a prompt from the user. The prompt can be text, audio, or an image. The user can receive a prompt by simply entering text, and audio or image input is also possible. Step 2: The generator uses a generation AI to analyze the prompt received by the reception unit and generate an original image. The generator uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate a landscape image or a portrait image based on the prompt. Step 3: The registration unit registers the image generated by the generation unit as an NFT on the blockchain. The registration unit uses blockchain technology such as Ethereum or Binance Smart Chain to register the NFT, which includes metadata for the generated image. Step 4: The Sales Department sells the NFTs registered by the Registration Department. The Sales Department handles the sales process on NFT marketplaces (e.g., OpenSea, Rarible, etc.), listing the NFTs generated by users and allowing buyers to purchase the NFTs.

[0066] (Example 2) An image generation system according to an embodiment of the present invention allows even people with physical disabilities to easily create and sell original image NFTs. In the image generation system, a user inputs a specific command prompt, and a generation AI analyzes the command prompt and generates an original image. The generated image is registered on the blockchain as an NFT, making it easy for users to sell. For example, in the image generation system, a user inputs a prompt such as "a landscape with blue skies and white clouds." This information is input to the generation AI. The image generation system then uses the generation AI to analyze the input prompt and generate an original image. The generation AI generates an image based on the prompt and registers the image as an NFT on the blockchain. For example, a beautiful landscape image is generated based on the prompt "a landscape with blue skies and white clouds." The generated image is registered on the blockchain as an NFT. This allows users to easily sell the generated images. For example, a user can list the generated image on an NFT marketplace, and a buyer can purchase the image. This allows even people with physical disabilities to create and sell original image NFTs without requiring specialized knowledge or skills. This makes it possible for people with physical disabilities to create and sell original image NFTs without the need for specialized knowledge or skills. Users simply input simple text, and the generation AI automatically generates an image and registers it as an NFT, allowing them to engage in creative activities without any hassle.

[0067] An image generation system according to an embodiment includes a reception unit, a generation unit, a registration unit, and a sales unit. The reception unit receives a prompt from a user. The prompt may be, for example, text, audio, or an image, but is not limited to these examples. The reception unit receives the prompt when the user simply inputs text. The reception unit can also receive audio input or image input. The generation unit uses a generation AI to analyze the prompt received by the reception unit and generate an original image. The generation unit uses, for example, a text generation AI (e.g., LLM) to analyze the prompt and generate an image. The generation unit can also generate an image based on the prompt using a multimodal generation AI. For example, the generation unit generates a landscape image or a portrait image based on the prompt. The registration unit registers the image generated by the generation unit as an NFT on a blockchain. The registration unit registers the generated image as an NFT using, for example, blockchain technology such as Ethereum or Binance Smart Chain. The registration unit can also register an NFT including metadata of the generated image on a blockchain. The sales unit sells NFTs registered by the registration unit. The sales unit, for example, handles sales procedures on an NFT marketplace (e.g., OpenSea, Rarible, etc.). The sales unit puts NFTs generated by users on the marketplace, allowing buyers to purchase the NFTs. In this way, the image generation system according to the embodiment allows users to easily create and sell original image NFTs. In this way, the image generation system allows users to easily create and sell original image NFTs.

[0068] The generation unit can generate an image based on the prompt. The generation unit analyzes the prompt using, for example, a text generation AI (e.g., LLM) and generates an image. For example, the generation unit generates a beautiful landscape image based on the prompt, "A landscape with blue sky and white clouds." The generation unit can also generate an image based on the prompt using a multimodal generation AI. For example, the generation unit can generate a portrait image based on the prompt. The generation unit can also generate an abstract painting based on the prompt using a generation AI. For example, the generation unit generates an abstract painting based on the prompt, "A combination of abstract shapes and colors." In this way, the generation unit generates an image based on the prompt, thereby generating an image that meets the user's intention.

[0069] The registration unit can register the generated image as an NFT on a blockchain. The registration unit registers the generated image as an NFT using blockchain technology such as Ethereum or Binance Smart Chain. For example, the registration unit registers an NFT including metadata of the generated image on a blockchain. When registering the generated image as an NFT, the registration unit can also include information to clarify the ownership of the image. For example, the registration unit registers an NFT including metadata such as owner information and creation date and time of the generated image. In this way, by registering the generated image as an NFT on a blockchain, the ownership of the image becomes clear.

[0070] The sales unit can handle sales procedures on NFT marketplaces. For example, the sales unit handles sales procedures on NFT marketplaces (e.g., OpenSea, Rarible, etc.). For example, the sales unit lists NFTs generated by users on the marketplace, allowing buyers to purchase the NFTs. The sales unit also has a function for setting the selling price of NFTs. For example, the sales unit allows users to input their desired selling price and list the NFT at that price. The sales unit also has a function for managing the sales history of NFTs. For example, the sales unit records the prices and buyer information of NFTs sold in the past, allowing users to refer to the records. This allows the sales unit to handle sales procedures on NFT marketplaces, allowing users to easily sell NFTs.

[0071] The reception unit can accept a prompt when the user simply inputs simple text. For example, the reception unit can accept a prompt such as "a scene of blue sky and white clouds" when the user simply inputs simple text. The reception unit can also accept voice input and image input. For example, when the user inputs a prompt by voice, the reception unit converts it into text using voice recognition technology and accepts it as a prompt. When the user inputs an image as a prompt, the reception unit can analyze the prompt using image analysis technology. This allows the reception unit to accept a prompt when the user simply inputs simple text, simplifying operation.

[0072] The reception unit can estimate the user's emotion and adjust the prompt reception method based on the estimated user emotion. For example, the reception unit can estimate the user's emotion and adjust the prompt reception method based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable prompt input quickly. This improves user convenience by adjusting the prompt reception method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can analyze the user's past prompt input history and select the optimal reception method. For example, the reception unit analyzes the user's past prompt input history and selects the optimal reception method. For example, the reception unit automatically displays prompts that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest prompts to be used in a specific time period based on the user's past input history. In this way, the optimal reception method can be provided by analyzing the user's past prompt input history.

[0074] The reception unit can filter prompts based on the user's current project or area of ​​interest when receiving the prompts. For example, the reception unit can filter prompts based on the user's current project or area of ​​interest when receiving the prompts. For example, the reception unit can preferentially display prompts related to the project the user is currently working on. The reception unit can also suggest related prompts based on the user's area of ​​interest. The reception unit can also analyze the user's past project history and filter and display related prompts. This makes it possible to provide highly relevant prompts by filtering prompts based on the user's current project or area of ​​interest.

[0075] The reception unit can select the optimal reception means depending on the user's input method when receiving a prompt. For example, the reception unit selects the optimal reception means depending on the user's input method (voice, text, image, etc.) when receiving a prompt. For example, if the user selects voice input, the reception unit can use voice recognition technology to receive the prompt. Also, if the user selects text input, the reception unit can provide a text box to receive the prompt. Also, if the user selects image input, the reception unit can use image analysis technology to receive the prompt. This improves user convenience by selecting the optimal reception means depending on the user's input method.

[0076] The reception unit can estimate the user's emotion and determine the priority of prompts to be received based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and determines the priority of prompts to be received based on the estimated user emotion. For example, when the user is stressed, the reception unit can prioritize receiving prompts with high importance. When the user is relaxed, the reception unit can also prioritize receiving prompts with normal priority. When the user is in a hurry, the reception unit can also prioritize receiving prompts with high urgency. In this way, by determining the priority of prompts according to the user's emotion, important prompts can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The reception unit can prioritize receiving highly relevant prompts by taking into account the user's geographical location information when receiving prompts. For example, the reception unit prioritizes receiving highly relevant prompts by taking into account the user's geographical location information when receiving prompts. For example, if the user is in a specific area, the reception unit can prioritize receiving prompts related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving prompts related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving prompts related to the user's home. In this way, highly relevant prompts can be provided by taking into account the user's geographical location information.

[0078] The reception unit can analyze the user's social media activity and receive related prompts when receiving a prompt. For example, the reception unit analyzes the user's social media activity and receives related prompts when receiving a prompt. For example, the reception unit can suggest related prompts based on content shared by the user on social media. The reception unit can also analyze the user's social media activity history and preferentially accept related prompts. The reception unit can also suggest related prompts based on the activities of the user's friends on social media. In this way, related prompts can be provided by analyzing the user's social media activity.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a prompt. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a prompt. For example, the reception unit adjusts the reception method based on feedback provided by the user in the past. The reception unit can also suggest an optimal reception method based on the user's past feedback. The reception unit can also customize the reception interface by reflecting the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0080] The generation unit can estimate the user's emotion and adjust the representation method of the generated image based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the representation method of the generated image based on the estimated user's emotion. For example, the generation unit generates an image with soft colors when the user is relaxed. The generation unit can also generate an image with vivid colors when the user is excited. The generation unit can also generate an image with subdued colors when the user is sad. In this way, by adjusting the representation method of the image according to the user's emotion, more appropriate images can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The generation unit can adjust the level of detail of the generated image based on the importance of the prompt during generation. For example, the generation unit adjusts the level of detail of the generated image based on the importance of the prompt during generation. For example, the generation unit generates a detailed image for a prompt with high importance. The generation unit can also generate a simplified image for a prompt with low importance. The generation unit can also adjust the resolution of the image according to the importance of the prompt. In this way, an appropriate image can be generated by adjusting the level of detail of the image according to the importance of the prompt.

[0082] The generation unit can apply different generation algorithms depending on the category of the prompt during generation. For example, the generation unit applies different generation algorithms depending on the category of the prompt during generation. For example, the generation unit applies a generation algorithm dedicated to landscapes to a prompt for a landscape image. The generation unit can also apply a generation algorithm dedicated to portraits to a prompt for a portrait image. The generation unit can also apply a generation algorithm dedicated to abstract paintings to a prompt for an abstract painting. In this way, by applying an appropriate generation algorithm depending on the category of the prompt, it is possible to generate higher quality images.

[0083] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit improves the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit adjusts the generation algorithm based on images generated by the user in the past. The generation unit can also learn the user's preferred style from the user's past generation results and reflect this in the generation. The generation unit can also analyze the user's past generation results and improve the accuracy of generation. In this way, the accuracy of generation is improved by referring to the user's past generation results.

[0084] The generation unit can estimate the user's emotion and adjust the length and size of the generated image based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the length and size of the generated image based on the estimated user emotion. For example, the generation unit generates a longer image when the user is relaxed. The generation unit can also generate a shorter image when the user is in a hurry. The generation unit can also generate a larger image when the user is excited. In this way, by adjusting the length and size of the image according to the user's emotion, more appropriate images can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The generation unit can determine the generation priority based on the submission time of the prompt at the time of generation. For example, the generation unit determines the generation priority based on the submission time of the prompt at the time of generation. For example, if a prompt is submitted early in the morning, the generation unit performs generation with priority. Also, if a prompt is submitted late at night, the generation unit can perform generation with normal priority. Also, the generation unit can adjust the generation schedule depending on the submission time of the prompt. In this way, efficient generation is possible by determining the generation priority based on the submission time of the prompt.

[0086] The generation unit can adjust the order of generation based on the relevance of the prompts during generation. For example, the generation unit adjusts the order of generation based on the relevance of the prompts during generation. For example, the generation unit generates prompts with high relevance with priority. The generation unit can also postpone prompts with low relevance. The generation unit can also adjust the order of generation according to the relevance of the prompts. In this way, by adjusting the order of generation according to the relevance of the prompts, it is possible to generate images with high relevance with priority.

[0087] The generation unit can adjust the use of technical terms in the generated image according to the user's level of expertise at the time of generation. For example, the generation unit adjusts the use of technical terms in the generated image according to the user's level of expertise at the time of generation. For example, the generation unit generates an image that uses a lot of technical terms for a user with high level of expertise. The generation unit can also generate an image that uses less technical terms for a user with low level of expertise. The generation unit can also adjust the use of technical terms in the generated image according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, an image suitable for the user can be generated.

[0088] The registration unit can estimate the user's emotions and determine the priority of NFTs to be registered based on the estimated user emotions. The registration unit, for example, estimates the user's emotions and determines the priority of NFTs to be registered based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can prioritize registering NFTs with high importance. Also, if the user is relaxed, the registration unit can register NFTs with normal priority. Also, if the user is in a hurry, the registration unit can prioritize registering NFTs with high urgency. In this way, by determining the registration priority of NFTs according to the user's emotions, important NFTs can be registered with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The registration unit can improve the accuracy of registration by taking into account the interrelationships between the generated images during registration. For example, the registration unit improves the accuracy of registration by taking into account the interrelationships between the generated images during registration. For example, if the generated images are part of a series, the registration unit registers them by taking into account the interrelationships. Furthermore, if the generated images have a highly related theme, the registration unit can also register them by taking into account the interrelationships between the generated images. Furthermore, the registration unit can analyze the interrelationships between the generated images and improve the accuracy of registration. In this way, the accuracy of registration is improved by taking into account the interrelationships between the generated images.

[0090] The registration unit can perform registration while taking into consideration attribute information of the submitter of the generated image. For example, the registration unit performs registration while taking into consideration attribute information of the submitter of the generated image. For example, if the submitter is a professional artist, the registration unit may register the image with a special tag. Furthermore, if the submitter is a beginner, the registration unit may provide a simplified registration procedure. Furthermore, the registration unit may suggest an optimal registration method based on the submitter's attribute information. In this way, an appropriate registration method can be provided by taking into consideration the submitter's attribute information.

[0091] The registration unit can weight the registration based on the submission frequency of the generated image at the time of registration. For example, the registration unit weights the registration based on the submission frequency of the generated image at the time of registration. For example, the registration unit prioritizes registration for users with a high submission frequency. The registration unit can also register users with a low submission frequency with normal priority. The registration unit can also adjust the weighting of the registration based on the submission frequency. Thus, by weighting the registration based on the submission frequency, images that are frequently submitted can be registered with priority.

[0092] The registration unit can estimate the user's emotions and adjust the display method of the NFT to be registered based on the estimated user's emotions. For example, the registration unit can estimate the user's emotions and adjust the display method of the NFT to be registered based on the estimated user's emotions. For example, if the user is relaxed, the registration unit can provide a display method including detailed information. If the user is in a hurry, the registration unit can also provide a display method including concise information. If the user is excited, the registration unit can also provide a visually stimulating display method. This allows the NFT display method to be adjusted according to the user's emotions, enabling a display that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The registration unit can perform registration while taking into consideration the geographical distribution of the generated image. For example, when the registration unit performs registration while taking into consideration the geographical distribution of the generated image. For example, if the generated image relates to a specific region, the registration unit registers the image by attaching a tag related to the region. The registration unit can also analyze the geographical distribution of the generated image and propose an optimal registration method. Furthermore, if the generated image relates to multiple regions, the registration unit can register the image by attaching a tag related to each region. In this way, an appropriate registration method can be provided by taking into consideration the geographical distribution of the generated image.

[0094] The registration unit can improve the accuracy of registration by referring to literature related to the generated image at the time of registration. For example, the registration unit improves the accuracy of registration by referring to literature related to the generated image at the time of registration. For example, the registration unit refers to literature related to the generated image and registers it with appropriate tags. The registration unit can also analyze literature related to the theme of the generated image to improve the accuracy of registration. The registration unit can also suggest an optimal registration method based on literature related to the generated image. In this way, the accuracy of registration is improved by referring to literature related to the generated image.

[0095] The registration unit can perform registration taking into consideration the market value of the generated image at the time of registration. For example, the registration unit performs registration taking into consideration the market value of the generated image at the time of registration. For example, the registration unit prioritizes registration of images with high market value. The registration unit can also register images with low market value with normal priority. The registration unit can also analyze the market value of the generated image and propose an optimal registration method. In this way, an appropriate registration method can be provided by taking into consideration the market value of the generated image.

[0096] The sales department can estimate the user's emotions and adjust the sales method based on the estimated user emotions. For example, the sales department can estimate the user's emotions and adjust the sales method based on the estimated user emotions. For example, if the user is relaxed, the sales department can provide detailed sales options. If the user is in a hurry, the sales department can provide concise sales options. If the user is excited, the sales department can provide visually stimulating sales options. In this way, by adjusting the sales method according to the user's emotions, a sales method suitable for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The sales department can select the optimal sales method by analyzing the user's past sales history at the time of sale. For example, the sales department can select the optimal sales method by analyzing the user's past sales history at the time of sale. For example, the sales department can propose the optimal sales method based on sales methods that have been successful for the user in the past. The sales department can also select the most effective sales method from the user's past sales history. The sales department can also analyze the user's past sales history to improve the success rate of sales. In this way, the optimal sales method can be provided by analyzing the user's past sales history.

[0098] The sales department can customize the sales method based on the user's current living situation at the time of sale. The sales department, for example, customizes the sales method based on the user's current living situation at the time of sale. For example, if the user is busy, the sales department can provide a simple sales procedure. Also, if the user has time, the sales department can provide detailed sales options. The sales department can also suggest the optimal sales method depending on the user's living situation. In this way, by customizing the sales method according to the user's current living situation, a sales method that is suitable for the user can be provided.

[0099] The sales department can improve the sales method by reflecting the user's feedback at the time of sale. For example, the sales department can adjust the sales method based on the feedback provided by the user. The sales department can also propose the optimal sales method based on the user's feedback. The sales department can also customize the sales interface by reflecting the user's feedback. In this way, the sales method can be improved by reflecting the user's feedback.

[0100] The sales department can estimate the user's emotions and determine sales priorities based on the estimated user emotions. For example, the sales department can estimate the user's emotions and determine sales priorities based on the estimated user emotions. For example, if the user is feeling stressed, the sales department can prioritize sales that are more important. Also, if the user is relaxed, the sales department can prioritize sales with normal priority. Also, if the user is in a hurry, the sales department can prioritize sales that are more urgent. In this way, by determining sales priorities according to the user's emotions, important sales can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The sales department can select the optimal sales method by taking into account the user's geographical location information at the time of sale. For example, the sales department selects the optimal sales method by taking into account the user's geographical location information at the time of sale. For example, if the user is in a specific area, the sales department can suggest a sales method related to that area. Also, if the user is traveling, the sales department can suggest a sales method related to the user's travel destination. Also, if the user is at home, the sales department can suggest a sales method related to the user's home. In this way, the optimal sales method can be provided by taking into account the user's geographical location information.

[0102] The sales department can analyze the user's social media activity and suggest sales methods at the time of sale. For example, the sales department can analyze the user's social media activity and suggest sales methods at the time of sale. For example, the sales department can suggest relevant sales methods based on the content the user has shared on social media. The sales department can also analyze the user's social media activity history and suggest optimal sales methods. The sales department can also suggest relevant sales methods based on the activity of the user's friends on social media. In this way, the optimal sales methods can be provided by analyzing the user's social media activity.

[0103] The sales department can customize the sales method by reflecting the user's past feedback at the time of sale. For example, the sales department customizes the sales method by reflecting the user's past feedback at the time of sale. For example, the sales department adjusts the sales method based on the user's past feedback. The sales department can also propose the optimal sales method based on the user's past feedback. The sales department can also customize the sales interface by reflecting the user's past feedback. In this way, the optimal sales method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, registration unit, and sales unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a prompt from a user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the prompt using a generation AI to generate an original image. For example, the registration unit is realized by the specific processing unit 290 of the data processing device 12 and registers the generated image as an NFT on the blockchain. For example, the sales unit is realized by the control unit 46A of the smart device 14 and performs sales procedures on the NFT marketplace. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, registration unit, and sales unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a prompt from a user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the prompt using a generation AI to generate an original image. For example, the registration unit is realized by the specific processing unit 290 of the data processing device 12 and registers the generated image as an NFT on the blockchain. For example, the sales unit is realized by the control unit 46A of the smart glasses 214 and performs sales procedures on the NFT marketplace. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, registration unit, and sales unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives prompts from the user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the prompt using a generation AI to generate an original image. For example, the registration unit is realized by the specific processing unit 290 of the data processing device 12 and registers the generated image as an NFT on the blockchain. For example, the sales unit is realized by the control unit 46A of the headset-type terminal 314 and performs sales procedures on the NFT marketplace. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, registration unit, and sales unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives prompts from a user. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the prompt using a generation AI to generate an original image. For example, the registration unit is realized by the specific processing unit 290 of the data processing device 12 and registers the generated image as an NFT on the blockchain. For example, the sales unit is realized by the control unit 46A of the robot 414 and performs sales procedures on the NFT marketplace.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can provide specific filtering conditions to the generation unit based on the user's input. For example, if the user inputs "natural scenery," the reception unit can instruct the generation unit to generate only images related to natural scenery. Furthermore, if the user inputs "abstract painting," the reception unit can instruct the generation unit to generate only images related to abstract painting. Furthermore, if the user inputs "portrait," the reception unit can instruct the generation unit to generate only images related to portraits. In this way, the reception unit can provide specific filtering conditions to the generation unit based on the user's input, thereby generating images that meet the user's intentions.

[0106] The generation unit can estimate the user's emotion and adjust the style of the generated image based on the estimated user's emotion. For example, if the user is happy, the generation unit can generate an image with a bright and colorful style. If the user is sad, the generation unit can also generate an image with a subdued color tone. Furthermore, if the user is excited, the generation unit can also generate an image with a dynamic style. In this way, the generation unit can generate a more appropriate image by adjusting the style of the image according to the user's emotion.

[0107] The registration unit can automatically tag images based on the metadata of the generated images. For example, if the generated image is a landscape image, the registration unit automatically assigns a "landscape" tag. Also, if the generated image is a portrait image, the registration unit can automatically assign a "portrait" tag. Furthermore, if the generated image is an abstract painting, the registration unit can automatically assign an "abstract painting" tag. In this way, the registration unit can improve the searchability of images by automatically tagging based on the metadata of the generated images.

[0108] The sales department can estimate the user's emotions and adjust the sales price based on the estimated user's emotions. For example, the sales department can set a normal sales price when the user is relaxed. The sales department can also set a discount price when the user is in a hurry. Furthermore, the sales department can set a premium price when the user is excited. This allows the sales department to make more effective sales by adjusting the sales price according to the user's emotions.

[0109] The reception unit can provide specific style guidelines to the generation unit based on the user's input. For example, if the user inputs "vintage style," the reception unit can provide vintage style guidelines to the generation unit. Also, if the user inputs "modern," the reception unit can provide modern style guidelines to the generation unit. Furthermore, if the user inputs "minimalist," the reception unit can provide minimalist style guidelines to the generation unit. In this way, the reception unit can generate an image that meets the user's intentions by providing specific style guidelines to the generation unit based on the user's input.

[0110] The generation unit can estimate the user's emotions and adjust the theme of the generated image based on the estimated user's emotions. For example, if the user is feeling happy, the generation unit can generate an image with a bright and cheerful theme. If the user is feeling depressed, the generation unit can also generate an image with a soothing and relaxing theme. Furthermore, if the user is excited, the generation unit can generate an image with an energetic theme. In this way, the generation unit can generate more appropriate images by adjusting the theme of the image according to the user's emotions.

[0111] The registration unit can evaluate the quality of the generated images and determine the priority of registration based on the evaluation result. For example, the registration unit can prioritize registration of high-quality images. The registration unit can also register low-quality images with normal priority. Furthermore, the registration unit can evaluate the quality of the generated images and adjust the priority of registration based on the evaluation result. In this way, the registration unit can provide an appropriate registration method by evaluating the quality of the generated images.

[0112] The sales department can estimate the user's emotions and adjust the sales strategy based on the estimated user's emotions. For example, the sales department can provide a detailed sales strategy if the user is relaxed. Alternatively, the sales department can provide a concise sales strategy if the user is in a hurry. Furthermore, the sales department can provide a visually stimulating sales strategy if the user is excited. This allows the sales department to make more effective sales by adjusting the sales strategy according to the user's emotions.

[0113] The reception unit can provide a specific color palette to the generation unit based on the user's input. For example, if the user inputs "warm colors," the reception unit can provide a warm color palette to the generation unit. Also, if the user inputs "cool colors," the reception unit can provide a cool color palette to the generation unit. Furthermore, if the user inputs "monochrome," the reception unit can provide a monochrome color palette to the generation unit. In this way, the reception unit can generate an image that meets the user's intentions by providing a specific color palette to the generation unit based on the user's input.

[0114] The generation unit can estimate the user's emotion and adjust the composition of the generated image based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate an image with a balanced composition. If the user is excited, the generation unit can also generate an image with a dynamic composition. Furthermore, if the user is sad, the generation unit can generate an image with a calm composition. In this way, the generation unit can generate a more appropriate image by adjusting the composition of the image according to the user's emotion.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives a prompt from the user. The prompt can be text, audio, or an image. The user can receive a prompt by simply entering text, and audio or image input is also possible. Step 2: The generator uses a generation AI to analyze the prompt received by the reception unit and generate an original image. The generator uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate a landscape image or a portrait image based on the prompt. Step 3: The registration unit registers the image generated by the generation unit as an NFT on the blockchain. The registration unit uses blockchain technology such as Ethereum or Binance Smart Chain to register the NFT, which includes metadata for the generated image. Step 4: The Sales Department sells the NFTs registered by the Registration Department. The Sales Department handles the sales process on NFT marketplaces (e.g., OpenSea, Rarible, etc.), listing the NFTs generated by users and allowing buyers to purchase the NFTs.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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, in order to avoid confusion and to 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a prompt from a user; a generation unit that analyzes the prompt received by the reception unit and generates an original image; A registration unit that registers the image generated by the generation unit as an NFT; A sales department that sells NFTs registered by the registration department; Equipped with A system characterized by:

2. The generation unit Generate an image based on a prompt 2. The system of claim 1.

3. The registration unit The generated image is registered on the blockchain as an NFT.

2. The system of claim 1.

4. The sales department Complete the sales process on the NFT marketplace 2. The system of claim 1.

5. The reception unit The user simply enters text to accept the prompt.

2. The system of claim 1.

6. The reception unit Inferring user emotions and adjusting prompt acceptance methods based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past prompt input history and select the optimal acceptance method 2. The system of claim 1.

8. The reception unit Filter prompts based on your current projects and interests 2. The system of claim 1.

Images