System

Abstract

An object of a system according to an embodiment is to easily generate an image of a hybrid animal having a feature desired by a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a feature of a hybrid animal desired to be generated by a user. The analysis unit analyzes the feature input by the reception unit. The generation unit generates an image of a new hybrid animal based on the feature analyzed by the analysis unit. The providing unit provides the image generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to easily generate images of hybrid animals that have the characteristics desired by the user.

[0005] The system according to the embodiment aims to enable a user to easily generate images of hybrid animals having desired characteristics. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs the characteristics of the hybrid animal that the user wants to generate. The analysis unit analyzes the characteristics input by the reception unit. The generation unit generates an image of the new hybrid animal based on the characteristics analyzed by the analysis unit. The provision unit provides the image generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily generate images of hybrid animals with desired characteristics. [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) A system according to an embodiment of the present invention is a simple image generation system specialized for hybrid animals. This system allows a user to input the characteristics of the hybrid animal they wish to generate, and a generation AI analyzes those characteristics to generate an image of the new hybrid animal and provides it to the user. For example, if a user inputs specific characteristics, such as "an animal with a lion's head and eagle's wings," the generation AI analyzes those characteristics and generates an image of the new hybrid animal. The generated image is provided to the user, who can download or share it. This allows the system to easily generate and provide images of new hybrid animals. For example, the system can be used as educational materials for learning about animal characteristics. It can also be used in the entertainment industry, for example, to design new characters.

[0029] An image generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives input of the characteristics of a hybrid animal a user wishes to generate. The characteristics input by the user include, but are not limited to, the animal's shape, color, and pattern. For example, the receiving unit may receive an input such as "an animal with a lion's head and eagle's wings." The analysis unit uses a generation AI to analyze the characteristics input by the receiving unit. The generation AI understands and combines the animal's characteristics using technologies such as deep learning models and GANs (generative artificial network). The generation unit uses the generation AI to generate a new hybrid animal image based on the characteristics analyzed by the analysis unit. The generation AI uses, for example, an algorithm that combines the animal's characteristics to generate a new image. The providing unit provides the image generated by the generation unit to a user. For example, the providing unit provides the generated image in a downloadable format. The providing unit can also provide the generated image so that users can share it. This allows the image generation system according to an embodiment to easily generate and provide new hybrid animal images.

[0030] The receiving unit allows the user to input specific animal characteristics. Examples of specific animal characteristics include, but are not limited to, the type of animal, body part, and color. For example, the receiving unit allows the user to input "an animal with a lion's head and eagle's wings." The receiving unit can also save the characteristics input by the user and reuse them later. This allows the user to input specific animal characteristics, thereby improving the accuracy of the generated image.

[0031] The analysis unit uses generative AI to understand the characteristics of animals and combine them. The generative AI uses technologies such as deep learning models and GAN (generative artificial network) to understand and combine the characteristics of animals. For example, the generative AI analyzes the characteristics of animals, such as their shape, color, and patterns, and combines them to generate new hybrid animal images. The generative AI can also learn the characteristics of animals and generate more natural combinations. This allows the generative AI to understand and combine the characteristics of animals to generate more natural hybrid animal images.

[0032] The generation unit can generate new hybrid animal images using generative AI. The generative AI generates new hybrid animal images using technologies such as deep learning models and GAN (generative artificial network). For example, the generative AI uses an algorithm that combines animal features to generate new images. The generative AI can also learn animal features and generate more natural combinations. This allows the generative AI to generate new hybrid animal images, thereby providing images that meet user requests.

[0033] The providing unit can provide the generated image to the user so that it can be downloaded. The providing unit can provide the generated image to the user so that it can be downloaded. Specific methods for providing the image so that it can be downloaded include, but are not limited to, a file format or a method for providing a download link. For example, the providing unit can provide the generated image in a JPEG format or a PNG format. The providing unit can also generate a download link and provide it to the user. This allows the user to download the generated image.

[0034] The providing unit can provide the generated image so that the user can share it. The providing unit can provide the generated image so that the user can share it. Specific methods for providing the image so that it can be shared include, but are not limited to, sharing on a social networking site or sending it by email, for example. For example, the providing unit can generate a link for sharing the generated image on a social networking site and provide it to the user. The providing unit can also provide a function for sending the generated image by email. This allows the user to share the generated image with other users.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates the characteristics of animals that the user has frequently input in the past. 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 the characteristics of animals to be used in a specific time period based on the user's past input history. This improves the efficiency of input work by suggesting the optimal input method based on the user's past input history.

[0036] The reception unit can add a function to automatically complete the characteristics of an animal entered by the user. For example, if the user enters "lion's head," the system can automatically suggest other characteristics such as "eagle's wings." Also, if the user enters "eagle's wings," the reception unit can automatically suggest other characteristics such as "lion's head." Also, if the user enters "lion's head and eagle's wings," the reception unit can automatically suggest other characteristics such as "tiger's body." This simplifies the input process by automatically completing the characteristics of animals entered by the user.

[0037] The reception unit can enable a user to input the characteristics of an animal using voice input. For example, the reception unit can automatically set the characteristics of an animal when the user simply inputs "a lion's head and an eagle's wings" by voice. The reception unit can also automatically set the characteristics of an animal when the user simply inputs "a tiger's body and a dolphin's tail" by voice. The reception unit can also automatically set the characteristics of an animal when the user simply inputs "an elephant's ears and a kangaroo's feet" by voice. This allows a user to easily input the characteristics of an animal using voice input.

[0038] The reception unit can suggest characteristics of animals specific to a region based on the user's geographical location information. For example, if the user is in Africa, the reception unit can suggest characteristics of animals specific to the region, such as lions and elephants. If the user is in the Amazon, the reception unit can also suggest characteristics of animals specific to the region, such as jaguars and capybaras. If the user is in Australia, the reception unit can also suggest characteristics of animals specific to the region, such as kangaroos and koalas. In this way, the characteristics of animals specific to a region can be suggested by taking the user's geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity and suggest characteristics of related animals. For example, if the user posts a photo of a lion on social media, the reception unit can suggest characteristics of a lion. Also, if the user posts a photo of an eagle on social media, the reception unit can suggest characteristics of an eagle. Also, if the user posts a photo of a tiger on social media, the reception unit can suggest characteristics of a tiger. In this way, by analyzing the user's social media activity, it is possible to suggest characteristics of related animals.

[0040] The reception unit can customize the input interface by reflecting the user's past feedback. For example, the reception unit adjusts the design of the interface based on the user's past feedback. The reception unit can also adjust the input method based on the user's past feedback. The reception unit can also adjust the priority of input content based on the user's past feedback. In this way, the input interface can be customized by reflecting the user's past feedback.

[0041] When analyzing the characteristics of an animal, the analysis unit can improve the accuracy based on past analysis results. For example, the analysis unit improves the accuracy of the current analysis result by referring to the characteristics of a lion that were analyzed in the past. The analysis unit can also improve the accuracy of the current analysis result by referring to the characteristics of an eagle that were analyzed in the past. The analysis unit can also improve the accuracy of the current analysis result by referring to the characteristics of a tiger that were analyzed in the past. In this way, the accuracy of the current analysis result is improved by referring to the past analysis results.

[0042] The analysis unit can improve accuracy by combining different analysis algorithms when analyzing the characteristics of animals. For example, the analysis unit combines an image analysis algorithm and a text analysis algorithm to analyze the characteristics of animals. The analysis unit can also combine a machine learning algorithm and a deep learning algorithm to analyze the characteristics of animals. The analysis unit can also combine a clustering algorithm and a classification algorithm to analyze the characteristics of animals. In this way, the accuracy of the analysis can be improved by combining different analysis algorithms.

[0043] When analyzing the characteristics of an animal, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide detailed analysis results. If the user is a beginner, the analysis unit can also provide concise analysis results. If the user is an intermediate user, the analysis unit can also provide analysis results with an appropriate level of detail. This allows the user to deepen their understanding by providing analysis results according to the user's level of expertise.

[0044] When analyzing the characteristics of an animal, the analysis unit can improve the accuracy of the analysis based on related literature data. For example, the analysis unit analyzes the characteristics of the animal by referring to literature data on zoology. The analysis unit can also analyze the characteristics of the animal by referring to literature data on ecology. The analysis unit can also analyze the characteristics of the animal by referring to literature data on genetics. In this way, by referring to the related literature data, the accuracy of the analysis is improved.

[0045] When analyzing the characteristics of animals, the analysis unit can perform the analysis based on the user's geographical location information. For example, if the user is in Africa, the analysis unit analyzes the characteristics of animals specific to Africa. Furthermore, if the user is in the Amazon, the analysis unit can analyze the characteristics of animals specific to the Amazon. Furthermore, if the user is in Australia, the analysis unit can analyze the characteristics of animals specific to Australia. In this way, by taking the user's geographical location information into consideration, it is possible to analyze the characteristics of animals specific to a region.

[0046] When analyzing the characteristics of animals, the analysis unit can improve the accuracy of the analysis by analyzing the user's social media activities. For example, if a user posts a photo of a lion on social media, the analysis unit analyzes the characteristics of the lion. Furthermore, if a user posts a photo of an eagle on social media, the analysis unit can analyze the characteristics of the eagle. Furthermore, if a user posts a photo of a tiger on social media, the analysis unit can analyze the characteristics of the tiger. In this way, by analyzing the user's social media activities, the accuracy of the analysis is improved.

[0047] The generation unit can optimize the resolution of the generated image according to the user's device. For example, if the user is using a smartphone, the generation unit generates an image with a resolution optimal for the smartphone. Furthermore, if the user is using a tablet, the generation unit can also generate an image with a resolution optimal for the tablet. Furthermore, if the user is using a desktop, the generation unit can also generate an image with a resolution optimal for the desktop. This improves display quality by providing an image with a resolution optimal for the user's device.

[0048] The generation unit can provide a function of adding a background specified by the user to the image to be generated. For example, the generation unit generates an image by adding a landscape background specified by the user. The generation unit can also generate an image by adding a city background specified by the user. The generation unit can also generate an image by adding a natural background specified by the user. In this way, a customized image can be generated by adding a background specified by the user.

[0049] The generation unit can provide a function of reflecting a hue specified by the user in the image to be generated. For example, the generation unit generates an image by reflecting a warm color hue specified by the user. The generation unit can also generate an image by reflecting a cool color hue specified by the user. The generation unit can also generate an image by reflecting a monochrome color hue specified by the user. This allows a customized image to be generated by reflecting the color hue specified by the user.

[0050] The generation unit can add region-specific elements to the generated image by taking into account the user's geographical location information. For example, if the user is in Africa, the generation unit can add African scenery and animals to the background. If the user is in the Amazon, the generation unit can also add Amazon scenery and animals to the background. If the user is in Australia, the generation unit can also add Australian scenery and animals to the background. In this way, an image including region-specific elements can be generated by taking into account the user's geographical location information.

[0051] The generation unit can analyze the user's social media activity and add related elements to the image to be generated. For example, if the user posts a photo of a lion on social media, the generation unit can add a lion element to the image. Also, if the user posts a photo of an eagle on social media, the generation unit can add an eagle element to the image. Also, if the user posts a photo of a tiger on social media, the generation unit can add a tiger element to the image. In this way, by analyzing the user's social media activity, it is possible to generate images including related elements.

[0052] The generation unit can adjust the generation algorithm for the image to be generated by reflecting the user's past feedback. For example, the generation unit adjusts the generation algorithm based on images in a style that the user previously preferred. The generation unit can also adjust the generation algorithm based on images in a style that the user previously avoided. The generation unit can also adjust the parameters of the generation algorithm based on the user's past feedback. In this way, by reflecting the user's past feedback, it is possible to generate images that meet the user's preferences.

[0053] The providing unit can provide the generated image in a format optimal for the user's device. For example, if the user is using a smartphone, the providing unit can provide the image in a format optimal for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide the image in a format optimal for the tablet. Furthermore, if the user is using a desktop, the providing unit can also provide the image in a format optimal for the desktop. This improves display quality by providing the image in a format optimal for the user's device.

[0054] The providing unit can provide a tool that allows the user to easily edit the generated image. For example, the providing unit can provide a tool that allows the user to adjust the color tone of the image. The providing unit can also provide a tool that allows the user to change the background of the image. The providing unit can also provide a tool that allows the user to change the size of the image. This allows the user to easily edit the generated image, making customization easier.

[0055] The providing unit can enable the user to share the generated image on a specific platform. For example, the providing unit can provide a function that allows the user to share the image on social media. The providing unit can also provide a function that allows the user to share the image by email. The providing unit can also provide a function that allows the user to share the image on cloud storage. This allows the user to easily share the generated image.

[0056] The providing unit can provide the generated image based on the user's geographical location information. For example, if the user is in Africa, the providing unit can provide an image with a background of scenery or animals unique to Africa. If the user is in the Amazon, the providing unit can also provide an image with a background of scenery or animals unique to the Amazon. If the user is in Australia, the providing unit can also provide an image with a background of scenery or animals unique to Australia. In this way, by taking the user's geographical location information into consideration, it is possible to provide an image including region-specific elements.

[0057] The providing unit can analyze the user's social media activity and share the generated image on a related platform. For example, if the user posts a photo of a lion on social media, the providing unit can share an image including a lion element. Also, if the user posts a photo of an eagle on social media, the providing unit can share an image including an eagle element. Also, if the user posts a photo of a tiger on social media, the providing unit can share an image including a tiger element. In this way, by analyzing the user's social media activity, the image can be shared on a related platform.

[0058] The providing unit can customize the method of providing generated images by reflecting the user's past feedback. For example, the providing unit can preferentially provide images in a style that the user has previously preferred. The providing unit can also avoid providing images in a style that the user has previously avoided. The providing unit can also adjust the method of providing images based on the user's past feedback. In this way, the method of providing images can be customized by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can also provide biological information about related animals based on the characteristics of the animals input by the user. For example, if the user inputs "an animal with a lion's head and an eagle's wings," the reception unit displays biological information about lions and eagles. If the user inputs "a tiger's body and a dolphin's tail," the reception unit can provide biological information about tigers and dolphins. Furthermore, if the user inputs "elephant's ears and kangaroo's feet," the reception unit can display biological information about elephants and kangaroos. This allows the user to obtain background information about the generated hybrid animal.

[0061] The analysis unit can also simulate the behavior of the hybrid animal to be generated based on the animal characteristics entered by the user. For example, it can simulate how an animal with a lion's head and eagle's wings will fly. It can also simulate how an animal with a tiger's body and a dolphin's tail will swim. It can also simulate how an animal with elephant's ears and kangaroo's feet will hop. This allows the user to visually understand the behavior of the generated hybrid animal.

[0062] The generation unit can add animation effects specified by the user to the image of the generated hybrid animal. For example, it can add an animation of the animal specified by the user walking. It can also add an animation of the animal specified by the user flying. It can also add an animation of the animal specified by the user swimming. This allows the user to enjoy the movements of the generated hybrid animal.

[0063] The providing unit can provide the generated image in a format that allows the user to 3D print it. For example, the providing unit can provide an image of the generated hybrid animal in STL format. The providing unit can also provide a function that allows the user to customize settings for 3D printing. Furthermore, the providing unit can also provide a function that allows the user to directly send the generated image to a 3D printing service. This allows the user to obtain a three-dimensional model of the generated hybrid animal.

[0064] The providing unit can provide the generated image in a format that allows the user to view it in a virtual reality (VR) environment. For example, the providing unit can provide an image of the generated hybrid animal in a VR-compatible file format. The providing unit can also provide a function that allows the user to view the generated image using a VR headset. Furthermore, the providing unit can also provide a function that allows the user to interact with the generated hybrid animal in the VR environment. This allows the user to experience the generated hybrid animal more realistically.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The receiving unit receives input of the characteristics of the hybrid animal the user wishes to create. The characteristics input by the user include, but are not limited to, the animal's shape, color, pattern, etc. For example, the user can input "an animal with a lion's head and eagle's wings" into the receiving unit. Step 2: The analysis unit uses the generation AI to analyze the features input by the reception unit. The generation AI uses technologies such as deep learning models and GAN (generative artificial network) to understand the animal's features and combine them. Step 3: The generator uses a generation AI to generate an image of a new hybrid animal based on the features analyzed by the analyzer. The generation AI uses, for example, an algorithm that combines the features of the animals to generate a new image. Step 4: The providing unit provides the image generated by the generating unit to the user. For example, the providing unit provides the generated image so that it can be downloaded. The providing unit can also provide the generated image so that it can be shared by the user.

[0067] (Example 2) A system according to an embodiment of the present invention is a simple image generation system specialized for hybrid animals. This system allows a user to input the characteristics of the hybrid animal they wish to generate, and a generation AI analyzes those characteristics to generate an image of the new hybrid animal and provides it to the user. For example, if a user inputs specific characteristics, such as "an animal with a lion's head and eagle's wings," the generation AI analyzes those characteristics and generates an image of the new hybrid animal. The generated image is provided to the user, who can download or share it. This allows the system to easily generate and provide images of new hybrid animals. For example, the system can be used as educational materials for learning about animal characteristics. It can also be used in the entertainment industry, for example, to design new characters.

[0068] An image generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives input of the characteristics of a hybrid animal a user wishes to generate. The characteristics input by the user include, but are not limited to, the animal's shape, color, and pattern. For example, the receiving unit may receive an input such as "an animal with a lion's head and eagle's wings." The analysis unit uses a generation AI to analyze the characteristics input by the receiving unit. The generation AI understands and combines the animal's characteristics using technologies such as deep learning models and GANs (generative artificial network). The generation unit uses the generation AI to generate a new hybrid animal image based on the characteristics analyzed by the analysis unit. The generation AI uses, for example, an algorithm that combines the animal's characteristics to generate a new image. The providing unit provides the image generated by the generation unit to a user. For example, the providing unit provides the generated image in a downloadable format. The providing unit can also provide the generated image so that users can share it. This allows the image generation system according to an embodiment to easily generate and provide new hybrid animal images.

[0069] The receiving unit allows the user to input specific animal characteristics. Examples of specific animal characteristics include, but are not limited to, the type of animal, body part, and color. For example, the receiving unit allows the user to input "an animal with a lion's head and eagle's wings." The receiving unit can also save the characteristics input by the user and reuse them later. This allows the user to input specific animal characteristics, thereby improving the accuracy of the generated image.

[0070] The analysis unit uses generative AI to understand the characteristics of animals and combine them. The generative AI uses technologies such as deep learning models and GAN (generative artificial network) to understand and combine the characteristics of animals. For example, the generative AI analyzes the characteristics of animals, such as their shape, color, and patterns, and combines them to generate new hybrid animal images. The generative AI can also learn the characteristics of animals and generate more natural combinations. This allows the generative AI to understand and combine the characteristics of animals to generate more natural hybrid animal images.

[0071] The generation unit can generate new hybrid animal images using generative AI. The generative AI generates new hybrid animal images using technologies such as deep learning models and GAN (generative artificial network). For example, the generative AI uses an algorithm that combines animal features to generate new images. The generative AI can also learn animal features and generate more natural combinations. This allows the generative AI to generate new hybrid animal images, thereby providing images that meet user requests.

[0072] The providing unit can provide the generated image to the user so that it can be downloaded. The providing unit can provide the generated image to the user so that it can be downloaded. Specific methods for providing the image so that it can be downloaded include, but are not limited to, a file format or a method for providing a download link. For example, the providing unit can provide the generated image in a JPEG format or a PNG format. The providing unit can also generate a download link and provide it to the user. This allows the user to download the generated image.

[0073] The providing unit can provide the generated image so that the user can share it. The providing unit can provide the generated image so that the user can share it. Specific methods for providing the image so that it can be shared include, but are not limited to, sharing on a social networking site or sending it by email, for example. For example, the providing unit can generate a link for sharing the generated image on a social networking site and provide it to the user. The providing unit can also provide a function for sending the generated image by email. This allows the user to share the generated image with other users.

[0074] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This makes the user's input work more comfortable by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates the characteristics of animals that the user has frequently input in the past. 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 the characteristics of animals to be used in a specific time period based on the user's past input history. This improves the efficiency of input work by suggesting the optimal input method based on the user's past input history.

[0076] The reception unit can add a function to automatically complete the characteristics of an animal entered by the user. For example, if the user enters "lion's head," the system can automatically suggest other characteristics such as "eagle's wings." Also, if the user enters "eagle's wings," the reception unit can automatically suggest other characteristics such as "lion's head." Also, if the user enters "lion's head and eagle's wings," the reception unit can automatically suggest other characteristics such as "tiger's body." This simplifies the input process by automatically completing the characteristics of animals entered by the user.

[0077] The reception unit can enable a user to input the characteristics of an animal using voice input. For example, the reception unit can automatically set the characteristics of an animal when the user simply inputs "a lion's head and an eagle's wings" by voice. The reception unit can also automatically set the characteristics of an animal when the user simply inputs "a tiger's body and a dolphin's tail" by voice. The reception unit can also automatically set the characteristics of an animal when the user simply inputs "an elephant's ears and a kangaroo's feet" by voice. This allows a user to easily input the characteristics of an animal using voice input.

[0078] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize providing the simplest input method. Also, if the user is relaxed, the reception unit can prioritize providing detailed input options. Also, if the user is in a hurry, the reception unit can prioritize providing voice input. This prioritizes input content according to the user's emotions, thereby streamlining input work. Emotion estimation is achieved 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.

[0079] The reception unit can suggest characteristics of animals specific to a region based on the user's geographical location information. For example, if the user is in Africa, the reception unit can suggest characteristics of animals specific to the region, such as lions and elephants. If the user is in the Amazon, the reception unit can also suggest characteristics of animals specific to the region, such as jaguars and capybaras. If the user is in Australia, the reception unit can also suggest characteristics of animals specific to the region, such as kangaroos and koalas. In this way, the characteristics of animals specific to a region can be suggested by taking the user's geographical location information into consideration.

[0080] The reception unit can analyze the user's social media activity and suggest characteristics of related animals. For example, if the user posts a photo of a lion on social media, the reception unit can suggest characteristics of a lion. Also, if the user posts a photo of an eagle on social media, the reception unit can suggest characteristics of an eagle. Also, if the user posts a photo of a tiger on social media, the reception unit can suggest characteristics of a tiger. In this way, by analyzing the user's social media activity, it is possible to suggest characteristics of related animals.

[0081] The reception unit can customize the input interface by reflecting the user's past feedback. For example, the reception unit adjusts the design of the interface based on the user's past feedback. The reception unit can also adjust the input method based on the user's past feedback. The reception unit can also adjust the priority of input content based on the user's past feedback. In this way, the input interface can be customized by reflecting the user's past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit uses an algorithm that performs a detailed analysis. If the user is in a hurry, the analysis unit can also use an algorithm that performs a quick analysis. If the user is excited, the analysis unit can also use an algorithm that provides visually stimulating analysis results. This improves the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved 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.

[0083] When analyzing the characteristics of an animal, the analysis unit can improve the accuracy based on past analysis results. For example, the analysis unit improves the accuracy of the current analysis result by referring to the characteristics of a lion that were analyzed in the past. The analysis unit can also improve the accuracy of the current analysis result by referring to the characteristics of an eagle that were analyzed in the past. The analysis unit can also improve the accuracy of the current analysis result by referring to the characteristics of a tiger that were analyzed in the past. In this way, the accuracy of the current analysis result is improved by referring to the past analysis results.

[0084] The analysis unit can improve accuracy by combining different analysis algorithms when analyzing the characteristics of animals. For example, the analysis unit combines an image analysis algorithm and a text analysis algorithm to analyze the characteristics of animals. The analysis unit can also combine a machine learning algorithm and a deep learning algorithm to analyze the characteristics of animals. The analysis unit can also combine a clustering algorithm and a classification algorithm to analyze the characteristics of animals. In this way, the accuracy of the analysis can be improved by combining different analysis algorithms.

[0085] When analyzing the characteristics of an animal, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide detailed analysis results. If the user is a beginner, the analysis unit can also provide concise analysis results. If the user is an intermediate user, the analysis unit can also provide analysis results with an appropriate level of detail. This allows the user to deepen their understanding by providing analysis results according to the user's level of expertise.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This provides a display method that corresponds to the user's emotions, thereby deepening understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When analyzing the characteristics of an animal, the analysis unit can improve the accuracy of the analysis based on related literature data. For example, the analysis unit analyzes the characteristics of the animal by referring to literature data on zoology. The analysis unit can also analyze the characteristics of the animal by referring to literature data on ecology. The analysis unit can also analyze the characteristics of the animal by referring to literature data on genetics. In this way, by referring to the related literature data, the accuracy of the analysis is improved.

[0088] When analyzing the characteristics of animals, the analysis unit can perform the analysis based on the user's geographical location information. For example, if the user is in Africa, the analysis unit analyzes the characteristics of animals specific to Africa. Furthermore, if the user is in the Amazon, the analysis unit can analyze the characteristics of animals specific to the Amazon. Furthermore, if the user is in Australia, the analysis unit can analyze the characteristics of animals specific to Australia. In this way, by taking the user's geographical location information into consideration, it is possible to analyze the characteristics of animals specific to a region.

[0089] When analyzing the characteristics of animals, the analysis unit can improve the accuracy of the analysis by analyzing the user's social media activities. For example, if a user posts a photo of a lion on social media, the analysis unit analyzes the characteristics of the lion. Furthermore, if a user posts a photo of an eagle on social media, the analysis unit can analyze the characteristics of the eagle. Furthermore, if a user posts a photo of a tiger on social media, the analysis unit can analyze the characteristics of the tiger. In this way, by analyzing the user's social media activities, the accuracy of the analysis is improved.

[0090] The generation unit can estimate the user's emotion and adjust the style of the generated image based on the estimated user emotion. For example, if the user is relaxed, the generation unit can generate an image with soft colors. If the user is excited, the generation unit can also generate an image with vivid colors. If the user is calm, the generation unit can also generate an image with a simple and sophisticated style. This improves user satisfaction by providing an image style that corresponds to the user's emotion. 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.

[0091] The generation unit can optimize the resolution of the generated image according to the user's device. For example, if the user is using a smartphone, the generation unit generates an image with a resolution optimal for the smartphone. Furthermore, if the user is using a tablet, the generation unit can also generate an image with a resolution optimal for the tablet. Furthermore, if the user is using a desktop, the generation unit can also generate an image with a resolution optimal for the desktop. This improves display quality by providing an image with a resolution optimal for the user's device.

[0092] The generation unit can provide a function of adding a background specified by the user to the image to be generated. For example, the generation unit generates an image by adding a landscape background specified by the user. The generation unit can also generate an image by adding a city background specified by the user. The generation unit can also generate an image by adding a natural background specified by the user. In this way, a customized image can be generated by adding a background specified by the user.

[0093] The generation unit can provide a function of reflecting a hue specified by the user in the image to be generated. For example, the generation unit generates an image by reflecting a warm color hue specified by the user. The generation unit can also generate an image by reflecting a cool color hue specified by the user. The generation unit can also generate an image by reflecting a monochrome color hue specified by the user. This allows a customized image to be generated by reflecting the color hue specified by the user.

[0094] The generation unit can estimate the user's emotions and determine the priority of images to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can prioritize generating images that have a relaxing effect. Furthermore, if the user is having fun, the generation unit can also prioritize generating images that are highly entertaining. Furthermore, if the user is in a hurry, the generation unit can also prioritize generating images that can be generated quickly. In this way, by determining the priority of images according to the user's emotions, it is possible to provide images that meet the user's needs. 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.

[0095] The generation unit can add region-specific elements to the generated image by taking into account the user's geographical location information. For example, if the user is in Africa, the generation unit can add African scenery and animals to the background. If the user is in the Amazon, the generation unit can also add Amazon scenery and animals to the background. If the user is in Australia, the generation unit can also add Australian scenery and animals to the background. In this way, an image including region-specific elements can be generated by taking into account the user's geographical location information.

[0096] The generation unit can analyze the user's social media activity and add related elements to the image to be generated. For example, if the user posts a photo of a lion on social media, the generation unit can add a lion element to the image. Also, if the user posts a photo of an eagle on social media, the generation unit can add an eagle element to the image. Also, if the user posts a photo of a tiger on social media, the generation unit can add a tiger element to the image. In this way, by analyzing the user's social media activity, it is possible to generate images including related elements.

[0097] The generation unit can adjust the generation algorithm for the image to be generated by reflecting the user's past feedback. For example, the generation unit adjusts the generation algorithm based on images in a style that the user previously preferred. The generation unit can also adjust the generation algorithm based on images in a style that the user previously avoided. The generation unit can also adjust the parameters of the generation algorithm based on the user's past feedback. In this way, by reflecting the user's past feedback, it is possible to generate images that meet the user's preferences.

[0098] The providing unit can estimate the user's emotions and adjust the display method of the images to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by providing a display method according to the user's emotions, the display of images is optimized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] The providing unit can provide the generated image in a format optimal for the user's device. For example, if the user is using a smartphone, the providing unit can provide the image in a format optimal for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide the image in a format optimal for the tablet. Furthermore, if the user is using a desktop, the providing unit can also provide the image in a format optimal for the desktop. This improves display quality by providing the image in a format optimal for the user's device.

[0100] The providing unit can provide a tool that allows the user to easily edit the generated image. For example, the providing unit can provide a tool that allows the user to adjust the color tone of the image. The providing unit can also provide a tool that allows the user to change the background of the image. The providing unit can also provide a tool that allows the user to change the size of the image. This allows the user to easily edit the generated image, making customization easier.

[0101] The providing unit can enable the user to share the generated image on a specific platform. For example, the providing unit can provide a function that allows the user to share the image on social media. The providing unit can also provide a function that allows the user to share the image by email. The providing unit can also provide a function that allows the user to share the image on cloud storage. This allows the user to easily share the generated image.

[0102] The providing unit can estimate the user's emotions and determine the priority of images to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing images that have a relaxing effect. Furthermore, if the user is enjoying themselves, the providing unit can prioritize providing images that are highly entertaining. Furthermore, if the user is in a hurry, the providing unit can prioritize providing images that can be provided quickly. In this way, by determining the priority of images according to the user's emotions, it is possible to provide images that meet the user's needs. 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 such examples.

[0103] The providing unit can provide the generated image based on the user's geographical location information. For example, if the user is in Africa, the providing unit can provide an image with a background of scenery or animals unique to Africa. If the user is in the Amazon, the providing unit can also provide an image with a background of scenery or animals unique to the Amazon. If the user is in Australia, the providing unit can also provide an image with a background of scenery or animals unique to Australia. In this way, by taking the user's geographical location information into consideration, it is possible to provide an image including region-specific elements.

[0104] The providing unit can analyze the user's social media activity and share the generated image on a related platform. For example, if the user posts a photo of a lion on social media, the providing unit can share an image including a lion element. Also, if the user posts a photo of an eagle on social media, the providing unit can share an image including an eagle element. Also, if the user posts a photo of a tiger on social media, the providing unit can share an image including a tiger element. In this way, by analyzing the user's social media activity, the image can be shared on a related platform.

[0105] The providing unit can customize the method of providing generated images by reflecting the user's past feedback. For example, the providing unit can preferentially provide images in a style that the user has previously preferred. The providing unit can also avoid providing images in a style that the user has previously avoided. The providing unit can also adjust the method of providing images based on the user's past feedback. In this way, the method of providing images can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 the user inputs the characteristics of the hybrid animal they want to create. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input characteristics using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an image of the new hybrid animal based on the analyzed characteristics. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the generated image to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 the user inputs the characteristics of the hybrid animal they want to generate by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input characteristics using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an image of the new hybrid animal based on the analyzed characteristics. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated image to the user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 the user inputs the characteristics of the hybrid animal they want to create by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input characteristics using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an image of the new hybrid animal based on the analyzed characteristics. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and provides the generated image to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 the user inputs the characteristics of the hybrid animal they want to create by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input characteristics using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an image of the new hybrid animal based on the analyzed characteristics. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated image to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also provide biological information about related animals based on the characteristics of the animals input by the user. For example, if the user inputs "an animal with a lion's head and an eagle's wings," the reception unit displays biological information about lions and eagles. If the user inputs "a tiger's body and a dolphin's tail," the reception unit can provide biological information about tigers and dolphins. Furthermore, if the user inputs "elephant's ears and kangaroo's feet," the reception unit can display biological information about elephants and kangaroos. This allows the user to obtain background information about the generated hybrid animal.

[0108] The analysis unit can also simulate the behavior of the hybrid animal to be generated based on the animal characteristics entered by the user. For example, it can simulate how an animal with a lion's head and eagle's wings will fly. It can also simulate how an animal with a tiger's body and a dolphin's tail will swim. It can also simulate how an animal with elephant's ears and kangaroo's feet will hop. This allows the user to visually understand the behavior of the generated hybrid animal.

[0109] The generation unit can add animation effects specified by the user to the image of the generated hybrid animal. For example, it can add an animation of the animal specified by the user walking. It can also add an animation of the animal specified by the user flying. It can also add an animation of the animal specified by the user swimming. This allows the user to enjoy the movements of the generated hybrid animal.

[0110] The providing unit can provide the generated image in a format that allows the user to 3D print it. For example, the providing unit can provide an image of the generated hybrid animal in STL format. The providing unit can also provide a function that allows the user to customize settings for 3D printing. Furthermore, the providing unit can also provide a function that allows the user to directly send the generated image to a 3D printing service. This allows the user to obtain a three-dimensional model of the generated hybrid animal.

[0111] The providing unit can provide the generated image in a format that allows the user to view it in a virtual reality (VR) environment. For example, the providing unit can provide an image of the generated hybrid animal in a VR-compatible file format. The providing unit can also provide a function that allows the user to view the generated image using a VR headset. Furthermore, the providing unit can also provide a function that allows the user to interact with the generated hybrid animal in the VR environment. This allows the user to experience the generated hybrid animal more realistically.

[0112] The reception unit can estimate the user's emotions and suggest characteristics of the hybrid animal to be generated based on the estimated user's emotions. For example, if the user is excited, the reception unit can suggest characteristics of an exotic animal. If the user is relaxed, the reception unit can suggest characteristics of a calm animal. Furthermore, if the user is sad, the reception unit can suggest characteristics of an animal with a soothing effect. In this way, by suggesting characteristics of a hybrid animal according to the user's emotions, user satisfaction is improved.

[0113] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by providing a display method that corresponds to the user's emotions, the analysis results can be more easily understood.

[0114] The generation unit can estimate the user's emotion and adjust the style of the image to be generated based on the estimated user's emotion. For example, if the user is relaxed, an image with soft colors can be generated. If the user is excited, an image with vivid colors can be generated. Furthermore, if the user is calm, an image with a simple and sophisticated style can be generated. This improves user satisfaction by providing an image style that corresponds to the user's emotion.

[0115] The providing unit can estimate the user's emotions and adjust the display method of the images to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the display method according to the user's emotions is provided, thereby optimizing the display of images.

[0116] The providing unit can estimate the user's emotions and determine the priority of images to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, images with a relaxing effect can be provided preferentially. Also, if the user is having fun, images with a high entertainment value can be provided preferentially. Furthermore, if the user is in a hurry, images that can be provided quickly can be provided preferentially. In this way, by determining the priority of images according to the user's emotions, it is possible to provide images that meet the user's needs.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The receiving unit receives input of the characteristics of the hybrid animal the user wishes to create. The characteristics input by the user include, but are not limited to, the animal's shape, color, pattern, etc. For example, the user can input "an animal with a lion's head and eagle's wings" into the receiving unit. Step 2: The analysis unit uses the generation AI to analyze the features input by the reception unit. The generation AI uses technologies such as deep learning models and GAN (generative artificial network) to understand the animal's features and combine them. Step 3: The generator uses a generation AI to generate an image of a new hybrid animal based on the features analyzed by the analyzer. The generation AI uses, for example, an algorithm that combines the features of the animals to generate a new image. Step 4: The providing unit provides the image generated by the generating unit to the user. For example, the providing unit provides the generated image so that it can be downloaded. The providing unit can also provide the generated image so that it can be shared by the user.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

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

[0142] The 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.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 for inputting characteristics of the hybrid animal that a user wants to generate; an analysis unit that analyzes the characteristics input by the reception unit; a generation unit that generates an image of a new hybrid animal based on the characteristics analyzed by the analysis unit; a providing unit that provides the image generated by the generating unit to a user; Equipped with A system characterized by:

2. The reception unit The user inputs the characteristics of a specific animal.

2. The system of claim 1.

3. The analysis unit Generative AI understands animal characteristics and combines them 2. The system of claim 1.

4. The generation unit Generate images of new hybrid animals using generative AI 2. The system of claim 1.

5. The providing unit The generated image is provided to the user for download.

2. The system of claim 1.

6. The providing unit Provide generated images for users to share 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

Images