System

Abstract

An object of the system according to the embodiment is to integrate an image generating AI and an image recognizing technique to enhance the general-purpose property of the AI.SOLUTION: A system according to an embodiment includes a generative AI, an image recognizer, a dataset generator, and an image recognizer model integrator. The generation AI generates an image. The image recognition technology recognizes the generated image. The data set generation unit generates a new data set. The image recognition model integration unit integrates the image recognition models.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 technologies do not adequately integrate image generation and recognition technologies to enhance the versatility of AI, and there is room for improvement.

[0005] The system according to the embodiment aims to integrate image generation AI and image recognition technology to enhance the versatility of AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a generative AI, an image recognition technology, a dataset generation unit, and an image recognition model integration unit. The generative AI generates an image. The image recognition technology recognizes the generated image. The dataset generation unit generates a new dataset. The image recognition model integration unit integrates image recognition models. [Effects of the Invention]

[0007] The system according to the embodiment integrates image generation AI and image recognition technology, thereby enhancing the versatility of AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An artificial general intelligence (AGI) system according to an embodiment of the present invention is a system that integrates image generation AI and image recognition technology to enhance the versatility of AI. As a result, the artificial general intelligence (AGI) system generates new datasets and integrates image recognition models, giving the AI ​​the ability to identify new objects.

[0029] An artificial general intelligence (AGI) system according to an embodiment includes a generative AI, image recognition technology, a dataset generation unit, and an image recognition model integration unit. The generative AI receives as input a prompt containing instructions from a user and generates a new image based on the prompt. For example, when a user inputs the prompt "Generate an image of a cat," the generative AI generates an image of a cat. The generative AI can also generate, for example, an image dataset of dogs. The generative AI can also generate, for example, a new image dataset of lesions to be used in the medical field. The image recognition technology analyzes the generated images and identifies their contents. For example, the image recognition technology can analyze a generated image of a cat and identify that it is a cat. The image recognition technology can also analyze a generated image of a dog and identify that it is a dog. The image recognition technology can also analyze a generated image of a lesion and identify that it is a lesion. The dataset generation unit generates a new dataset. For example, the dataset generation unit uses the image dataset of dogs generated by the generative AI to generate a new image dataset of dogs. The dataset generation unit can also use the image dataset of lesions generated by the generative AI to generate a new image dataset of lesions. The dataset generation unit can also generate a new cat image dataset using, for example, a cat image dataset generated by the generation AI. The image recognition model integration unit integrates image recognition models. For example, the image recognition model integration unit can train an image recognition model for identifying dogs using a dog image dataset generated by the generation AI and integrate it with other image recognition models. The image recognition model integration unit can also train an image recognition model for identifying lesions using, for example, a lesion image dataset generated by the generation AI and integrate it with other image recognition models. The image recognition model integration unit can also train an image recognition model for identifying cats using, for example, a cat image dataset generated by the generation AI and integrate it with other image recognition models. This enables the artificial general intelligence (AGI) system according to the embodiment to increase the versatility of AI and have the ability to identify new objects.For example, in the medical field, AI will have the ability to identify new pathologies, improving diagnostic accuracy. In agriculture, AI will have the ability to identify new crop diseases, improving crop management. In manufacturing, AI will have the ability to identify new defects, improving product quality control.

[0030] The generative AI can generate new images based on prompts from the user. For example, if the user inputs the prompt "Generate an image of a cat," the generative AI will generate an image of a cat. Also, if the user inputs the prompt "Generate an image of a dog," the generative AI can generate an image of a dog. Also, if the user inputs the prompt "Generate an image of a landscape," the generative AI can generate an image of a landscape. This allows new images to be generated based on the user's instructions.

[0031] Image recognition technology can analyze a generated image and identify its content. For example, image recognition technology can analyze a generated image of a cat and identify that it is a cat. Image recognition technology can also analyze a generated image of a dog and identify that it is a dog. Image recognition technology can also analyze a generated image of a landscape and identify that it is a landscape. This makes it possible to identify the content of the generated image.

[0032] The dataset generation unit can generate a new dataset based on a prompt from a user. For example, the dataset generation unit can generate a new dog image dataset using a dog image dataset generated by the generation AI. The dataset generation unit can also generate a new lesion image dataset using a lesion image dataset generated by the generation AI. The dataset generation unit can also generate a new cat image dataset using a cat image dataset generated by the generation AI. This allows new datasets to be generated based on user instructions.

[0033] The image recognition model integrating unit can train an image recognition model using the generated dataset and integrate it with other image recognition models. For example, the image recognition model integrating unit can train an image recognition model for identifying dogs using an image dataset of dogs generated by the generation AI and integrate it with other image recognition models. The image recognition model integrating unit can also train an image recognition model for identifying lesions using an image dataset of lesions generated by the generation AI and integrate it with other image recognition models. The image recognition model integrating unit can also train an image recognition model for identifying cats using an image dataset of cats generated by the generation AI and integrate it with other image recognition models. This allows an image recognition model to be trained using the generated dataset and integrated with other models.

[0034] Generative AI can generate images from different perspectives based on user prompts, and image recognition technology can integrate them to build a 3D model. For example, generative AI can generate images of the same object from different perspectives based on user prompts. For example, when generating an image of a cat, it can simultaneously generate front, side, and back images, and image recognition technology can integrate them to build a 3D model. Generative AI can also randomly change the angle and distance of the viewpoint when generating images from different perspectives. For example, when generating an image of a car, it can generate images from different angles and distances, and image recognition technology can integrate them to build a 3D model. Generative AI can also analyze images from multiple perspectives in real time and instantly build a 3D model. For example, when generating an image of a building, it can analyze images from different perspectives in real time to build a 3D model. This allows images from different perspectives to be integrated to build a 3D model.

[0035] The generative AI generates variations for different times of day or seasons based on prompts from the user, and image recognition technology can identify them. For example, the generative AI generates images of the same object at different times of day (day, night, evening, etc.) based on user prompts. For example, when generating a landscape image, day and night variations are generated, and image recognition technology can identify them. The generative AI also generates variations for different seasons (spring, summer, autumn, winter). For example, when generating an image of a tree, spring cherry blossoms, summer greenery, autumn leaves, and winter snowscapes can be generated, and image recognition technology can identify them. The generative AI also generates variations for different times of day or seasons in real time, and image recognition technology can instantly identify them. For example, when generating a cityscape, day and night variations and spring and winter variations can be generated and identified in real time. This makes it possible to identify variations for different times of day or seasons.

[0036] Generative AI and image recognition technology can analyze generated images in real time and provide instant feedback. Generative AI and image recognition technology, for example, build a system that analyzes generated images in real time and provides instant feedback. For example, image recognition technology can instantly display analysis results for images generated by a user. Generative AI and image recognition technology can also provide real-time feedback for generated images. For example, image recognition technology can instantly display identification results for images generated by a user. Generative AI can also develop a system that analyzes images to be generated in real time and provides feedback. For example, image recognition technology can instantly feed back analysis results for images generated by a user. This makes it possible to analyze generated images in real time and provide instant feedback.

[0037] Generative AI customizes images based on the characteristics of different cultures and regions, and image recognition technology can identify them. For example, generative AI customizes the images it generates based on the characteristics of different cultures and regions. For example, when generating a Japanese landscape, it can incorporate characteristics such as cherry blossoms and shrines. Generative AI also customizes images based on the characteristics of different regions. For example, when generating an African landscape, it can incorporate characteristics of the savanna and wild animals. Generative AI also customizes images based on the characteristics of different cultures. For example, when generating a European landscape, it can incorporate characteristics of old castles and cobblestone cityscapes. This makes it possible to customize and identify images based on the characteristics of different cultures and regions.

[0038] The generation AI adds detailed metadata to the images it generates, and image recognition technology can use that metadata to perform identification. For example, the generation AI adds detailed metadata (e.g., object type, position, color, etc.) to the images it generates, and image recognition technology uses that metadata to perform identification. For example, the type of cat and location information can be added to an image of a cat. The generation AI can also automatically generate metadata for the images it generates, and image recognition technology uses that metadata to perform identification. For example, location information of mountains and rivers can be added to an image of a landscape. The generation AI can also add user-specified metadata to the images it generates, and image recognition technology uses that metadata to perform identification. For example, the type of object and location information specified by the user can be added. This allows for the use of detailed metadata to improve identification accuracy.

[0039] The generative AI generates images in different resolutions and formats, and image recognition technology can distinguish between them. For example, the generative AI can generate images of the same object in different resolutions (e.g., high resolution, low resolution) based on user prompts, and image recognition technology can distinguish between them. For example, a generative AI can generate images of a cat in high resolution and low resolution. The generative AI can also generate variations in different formats (e.g., JPEG, PNG, GIF), and image recognition technology can distinguish between them. For example, a landscape image can be generated in JPEG, PNG, and GIF. The generative AI can also generate variations in different resolutions and formats in real time, and image recognition technology can instantly distinguish between them. For example, a building image can be generated in high resolution and low resolution, or in JPEG and PNG. This allows it to distinguish between images generated in different resolutions and formats.

[0040] Generative AI generates images optimized for different devices and platforms, and image recognition technology can identify them. For example, generative AI optimizes and provides images for different devices, such as smartphones, tablets, and PCs. For example, it generates images with the resolution adjusted for smartphones. Generative AI also optimizes and provides images for different platforms, such as social media, websites, and apps. For example, it can generate images with the format adjusted for social media. Generative AI also optimizes and provides images for different devices and platforms in real time. For example, it can generate images to match the device or platform specified by the user. This makes it possible to identify images optimized for different devices and platforms.

[0041] Generative AI customizes images for different uses, and image recognition technology can identify them. For example, generative AI customizes the images it generates for different uses (e.g., education, entertainment, medical). For example, it generates images suitable for educational materials. Generative AI can also generate images customized for entertainment uses, for example, generating images suitable for game or movie scenes. Generative AI can also generate images customized for medical uses, for example, generating images useful for medical diagnosis and treatment. This makes it possible to customize and identify images for different uses.

[0042] The generative AI generates variations that simulate different environmental conditions, and the dataset generator can include them in the dataset. For example, the generative AI generates images of the same object under different weather conditions (e.g., sunny, rainy, and snowy) based on user prompts and includes them in the dataset. For example, an image of a car can be generated in sunny and rainy day variations. The generative AI can also generate variations under different lighting conditions (e.g., daytime, nighttime, indoors, and outdoors) and include them in the dataset. For example, an image of a building can be generated in daytime and nighttime, and indoors and outdoors variations. The generative AI can also simulate different environmental conditions in real time and include them in the dataset. For example, an image of a landscape can be generated in sunny, rainy, snowy, daytime, and nighttime variations. This allows variations that simulate different environmental conditions to be included in the dataset.

[0043] The generative AI generates images from different viewpoints and angles, and the dataset generator can include them in the dataset. For example, the generative AI generates images of the same object from different viewpoints (e.g., front, side, and back) based on user prompts and includes them in the dataset. For example, an image of a cat can be generated from the front, side, and back. The generative AI can also generate variations from different angles (e.g., from above, below, and at an oblique angle) and include them in the dataset. For example, an image of a car can be generated from above, below, and at an oblique angle. The generative AI can also simulate different viewpoints and angles in real time and include them in the dataset. For example, an image of a building can be generated from the front, side, back, top, and bottom variations. This allows images from different viewpoints and angles to be included in the dataset.

[0044] The generative AI can customize datasets for different industries and applications, and the dataset generation unit can generate them. For example, the generative AI can customize the dataset it generates for medical applications. For example, it can generate an image dataset that is useful for medical diagnosis and treatment. The generative AI can also generate datasets customized for educational applications. For example, it can generate an image dataset that is suitable for educational teaching materials. The generative AI can also generate datasets customized for entertainment applications. For example, it can generate an image dataset that is suitable for game or movie scenes. This makes it possible to customize and generate datasets for different industries and applications.

[0045] The generative AI generates multilingual datasets that correspond to different languages ​​and cultures, and the dataset generation unit can generate them. The generative AI, for example, customizes the dataset to be generated to correspond to different languages. For example, it generates multilingual datasets such as English, Japanese, and Chinese. The generative AI also generates datasets customized to correspond to different cultures, for example. For example, it can generate a dataset based on Japanese culture or a dataset based on American culture. The generative AI also generates datasets customized to correspond to different languages ​​and cultures in real time, for example. For example, it can generate a dataset to match a language or culture specified by a user. This makes it possible to generate multilingual datasets that correspond to different languages ​​and cultures.

[0046] The generative AI generates variations that simulate different scenarios and contexts, and the dataset generator can include them in the dataset. For example, the generative AI generates images of the same object in different scenarios (e.g., everyday life, emergency situations) based on user prompts and includes them in the dataset. For example, images of a car are generated in everyday life and emergency situation scenarios. The generative AI can also generate variations in different contexts (e.g., urban, rural, seaside) and include them in the dataset. For example, images of a building can be generated in urban, rural, and seaside contexts. The generative AI can also simulate different scenarios and contexts in real time and include them in the dataset. For example, images of a landscape can be generated in everyday life, emergency situations, urban, rural, and seaside variations. In this way, variations that simulate different scenarios and contexts can be included in the dataset.

[0047] The generative AI can generate datasets in different resolutions and formats, and the dataset generator can include them in the dataset. For example, the generative AI can generate images of the same object at different resolutions (e.g., high resolution, low resolution) based on a user prompt and include them in the dataset. For example, an image of a cat can be generated in high resolution and low resolution. The generative AI can also generate variations in different formats (e.g., JPEG, PNG, GIF) and include them in the dataset. For example, an image of a landscape can be generated in JPEG, PNG, and GIF. The generative AI can also generate variations in different resolutions and formats in real time and include them in the dataset. For example, an image of a building can be generated in high resolution and low resolution, or in JPEG and PNG. This allows datasets generated in different resolutions and formats to be included.

[0048] The generation AI generates datasets optimized for different devices and platforms, and the dataset generation unit can generate them. For example, the generation AI optimizes the dataset it generates for different devices, such as smartphones, tablets, and PCs, and provides it. For example, it generates a dataset with the resolution adjusted for smartphones. The generation AI also optimizes the dataset it generates for different platforms, such as social media, websites, and apps, and provides it. For example, it can generate a dataset with the format adjusted for social media. The generation AI also optimizes the dataset it generates for different devices and platforms in real time and provides it. For example, it can generate a dataset to suit a device or platform specified by a user. This makes it possible to generate datasets optimized for different devices and platforms.

[0049] The generative AI can customize datasets for different uses, and the dataset generation unit can generate them. The generative AI, for example, customizes the dataset to be generated for educational purposes. For example, it generates an image dataset suitable for educational teaching materials. The generative AI can also generate a dataset customized for entertainment purposes. For example, it can generate an image dataset suitable for game or movie scenes. The generative AI can also generate a dataset customized for medical purposes. For example, it can generate an image dataset useful for medical diagnosis and treatment. This makes it possible to customize and generate datasets for different uses.

[0050] The image recognition model integrating unit can train an image recognition model using the generated dataset and integrate it with other image recognition models. For example, the image recognition model integrating unit can train an image recognition model for identifying dogs using an image dataset of dogs generated by the generation AI and integrate it with other image recognition models. The image recognition model integrating unit can also train an image recognition model for identifying lesions using an image dataset of lesions generated by the generation AI and integrate it with other image recognition models. The image recognition model integrating unit can also train an image recognition model for identifying cats using an image dataset of cats generated by the generation AI and integrate it with other image recognition models. This allows an image recognition model to be trained using the generated dataset and integrated with other models.

[0051] The image recognition model integrator can train an image recognition model using the generated dataset and customize it for different applications. The image recognition model integrator can train an image recognition model using, for example, a dataset generated by the generative AI and customize the model for different applications. For example, it can build an image recognition model specialized for medical applications. The image recognition model integrator can also train an image recognition model using, for example, a dataset generated by the generative AI and optimize the model for different devices or platforms. For example, it can build an image recognition model optimized for smartphones. The image recognition model integrator can also train an image recognition model using, for example, a dataset generated by the generative AI and customize the model for different industries or applications. For example, it can build an image recognition model specialized for educational applications. This allows the image recognition model to be trained using the generated dataset and customized for different applications.

[0052] The image recognition model integration unit can train an image recognition model using the generated dataset and customize it to support different languages ​​and cultures. The image recognition model integration unit can train an image recognition model using, for example, a dataset generated by the generative AI and customize the model to support different languages ​​and cultures. For example, it can build an image recognition model that supports multiple languages. The image recognition model integration unit can also train an image recognition model using, for example, a dataset generated by the generative AI and customize the model to support different environmental conditions (e.g., weather, lighting). For example, it can build an image recognition model that supports different weather conditions. The image recognition model integration unit can train an image recognition model using, for example, a dataset generated by the generative AI and customize the model to support different viewpoints and angles. For example, it can build a model that can identify images from different viewpoints and angles. This allows the image recognition model to be trained using the generated dataset and customized to support different languages ​​and cultures.

[0053] The image recognition model integrator can train an image recognition model using the generated dataset and customize it to respond to different environmental conditions. For example, the image recognition model integrator can train an image recognition model using a dataset generated by the generative AI and customize the model for different applications. For example, it can build an image recognition model specialized for medical applications. The image recognition model integrator can also train an image recognition model using a dataset generated by the generative AI and optimize the model for different devices or platforms. For example, it can build an image recognition model optimized for smartphones. The image recognition model integrator can also train an image recognition model using a dataset generated by the generative AI and customize the model for different industries or applications. For example, it can build an image recognition model specialized for educational applications. This allows the image recognition model to be trained using the generated dataset and customized to respond to different environmental conditions.

[0054] The image recognition model integration unit can train an image recognition model using the generated dataset and customize it to support different viewpoints and angles. The image recognition model integration unit can train an image recognition model using, for example, a dataset generated by the generation AI and customize the model to support different languages ​​and cultures. For example, it can build an image recognition model that supports multiple languages. The image recognition model integration unit can also train an image recognition model using, for example, a dataset generated by the generation AI and customize the model to support different environmental conditions (e.g., weather, lighting). For example, it can build an image recognition model that supports different weather conditions. The image recognition model integration unit can train an image recognition model using, for example, a dataset generated by the generation AI and customize the model to support different viewpoints and angles. For example, it can build a model that can identify images from different viewpoints and angles. This allows the image recognition model to be trained using the generated dataset and customized to support different viewpoints and angles.

[0055] The image recognition model integrator can train an image recognition model using the generated dataset and customize it for different applications. The image recognition model integrator can train an image recognition model using, for example, a dataset generated by the generative AI and customize the model for different applications. For example, it can build an image recognition model specialized for medical applications. The image recognition model integrator can also train an image recognition model using, for example, a dataset generated by the generative AI and optimize the model for different devices or platforms. For example, it can build an image recognition model optimized for smartphones. The image recognition model integrator can also train an image recognition model using, for example, a dataset generated by the generative AI and customize the model for different industries or applications. For example, it can build an image recognition model specialized for educational applications. This allows the image recognition model to be trained using the generated dataset and customized for different applications.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The generative AI can generate images in different art styles based on user prompts. For example, if a user inputs the prompt "Generate a landscape in the impressionist style," the generative AI can generate a landscape image in the impressionist style. Alternatively, if a user inputs the prompt "Generate a portrait in the pop art style," the generative AI can generate a portrait image in the pop art style. Furthermore, if a user inputs the prompt "Generate a cityscape in the abstract painting style," the generative AI can generate a cityscape image in the abstract painting style. This allows images in different art styles to be generated based on user instructions.

[0058] The generative AI can generate images in different era styles based on user prompts. For example, if a user inputs the prompt "Generate a portrait in the Renaissance era style," the generative AI can generate a portrait image in the Renaissance era style. Alternatively, if a user inputs the prompt "Generate a landscape in the Victorian era style," the generative AI can generate a landscape image in the Victorian era style. Furthermore, if a user inputs the prompt "Generate a cityscape in a futuristic style," the generative AI can generate a cityscape image in a futuristic style. This allows the generation of images in different era styles based on user instructions.

[0059] The generative AI can generate images using different techniques based on user prompts. For example, if a user inputs the prompt "Generate an image of a flower using watercolor techniques," the generative AI will generate an image of a flower using watercolor techniques. Alternatively, if a user inputs the prompt "Generate a landscape using oil painting techniques," the generative AI can generate an image of a landscape using oil painting techniques. Furthermore, if a user inputs the prompt "Generate a portrait using pencil drawing techniques," the generative AI can generate a portrait image using pencil drawing techniques. This allows images to be generated using different techniques based on user instructions.

[0060] The generative AI can generate images that incorporate elements of different cultures based on user prompts. For example, if a user inputs the prompt "Generate a landscape that incorporates traditional Japanese elements," the generative AI will generate an image of a landscape that incorporates traditional Japanese elements. Alternatively, if a user inputs the prompt "Generate a portrait that incorporates African cultural elements," the generative AI can generate an image of a portrait that incorporates African cultural elements. Furthermore, if a user inputs the prompt "Generate an image of a building that incorporates historical European elements," the generative AI can generate an image of a building that incorporates historical European elements. This allows the generation of images that incorporate elements of different cultures based on user instructions.

[0061] The generation AI can generate different seasons and weather variations based on user prompts. For example, if a user inputs the prompt "Generate a spring landscape," the generation AI will generate an image of a spring landscape. Alternatively, if a user inputs the prompt "Generate a cityscape on a rainy day," the generation AI can generate an image of a cityscape on a rainy day. Furthermore, if a user inputs the prompt "Generate a winter snowy landscape," the generation AI can generate an image of a winter snowy landscape. This allows the generation of different seasons and weather variations based on user instructions.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The generative AI receives a prompt containing instructions from the user as input and generates a new image based on the prompt. For example, if the user inputs the prompt "Generate an image of a cat," the generative AI will generate an image of a cat. The generative AI can also generate an image dataset of dogs or an image dataset of new pathologies for use in the medical field. Step 2: Image recognition technology analyzes the generated image and identifies its content. For example, image recognition technology can analyze a generated image of a cat and identify it as a cat. It can also analyze a generated image of a dog or an image of a lesion and identify it as a dog or a lesion, respectively. Step 3: The dataset generation unit generates a new dataset. For example, a new dog image dataset is generated using the dog image dataset generated by the generation AI. Also, a new lesion image dataset or cat image dataset can be generated using the lesion image dataset or cat image dataset generated by the generation AI. Step 4: The image recognition model integration unit integrates the image recognition models. For example, an image recognition model that identifies dogs can be trained using an image dataset of dogs generated by the generation AI, and integrated with other image recognition models. Alternatively, an image dataset of lesions and an image dataset of cats generated by the generation AI can be used to train image recognition models that identify lesions and cats, respectively, and integrated with other image recognition models.

[0064] (Example 2) An artificial general intelligence (AGI) system according to an embodiment of the present invention is a system that integrates image generation AI and image recognition technology to enhance the versatility of AI. As a result, the artificial general intelligence (AGI) system generates new datasets and integrates image recognition models, giving the AI ​​the ability to identify new objects.

[0065] An artificial general intelligence (AGI) system according to an embodiment includes a generative AI, image recognition technology, a dataset generation unit, and an image recognition model integration unit. The generative AI receives as input a prompt containing instructions from a user and generates a new image based on the prompt. For example, when a user inputs the prompt "Generate an image of a cat," the generative AI generates an image of a cat. The generative AI can also generate, for example, an image dataset of dogs. The generative AI can also generate, for example, a new image dataset of lesions to be used in the medical field. The image recognition technology analyzes the generated images and identifies their contents. For example, the image recognition technology can analyze a generated image of a cat and identify that it is a cat. The image recognition technology can also analyze a generated image of a dog and identify that it is a dog. The image recognition technology can also analyze a generated image of a lesion and identify that it is a lesion. The dataset generation unit generates a new dataset. For example, the dataset generation unit uses the image dataset of dogs generated by the generative AI to generate a new image dataset of dogs. The dataset generation unit can also use the image dataset of lesions generated by the generative AI to generate a new image dataset of lesions. The dataset generation unit can also generate a new cat image dataset using, for example, a cat image dataset generated by the generation AI. The image recognition model integration unit integrates image recognition models. For example, the image recognition model integration unit can train an image recognition model for identifying dogs using a dog image dataset generated by the generation AI and integrate it with other image recognition models. The image recognition model integration unit can also train an image recognition model for identifying lesions using, for example, a lesion image dataset generated by the generation AI and integrate it with other image recognition models. The image recognition model integration unit can also train an image recognition model for identifying cats using, for example, a cat image dataset generated by the generation AI and integrate it with other image recognition models. This enables the artificial general intelligence (AGI) system according to the embodiment to increase the versatility of AI and have the ability to identify new objects.For example, in the medical field, AI will have the ability to identify new pathologies, improving diagnostic accuracy. In agriculture, AI will have the ability to identify new crop diseases, improving crop management. In manufacturing, AI will have the ability to identify new defects, improving product quality control.

[0066] The generative AI can generate new images based on prompts from the user. For example, if the user inputs the prompt "Generate an image of a cat," the generative AI will generate an image of a cat. Also, if the user inputs the prompt "Generate an image of a dog," the generative AI can generate an image of a dog. Also, if the user inputs the prompt "Generate an image of a landscape," the generative AI can generate an image of a landscape. This allows new images to be generated based on the user's instructions.

[0067] Image recognition technology can analyze a generated image and identify its content. For example, image recognition technology can analyze a generated image of a cat and identify that it is a cat. Image recognition technology can also analyze a generated image of a dog and identify that it is a dog. Image recognition technology can also analyze a generated image of a landscape and identify that it is a landscape. This makes it possible to identify the content of the generated image.

[0068] The dataset generation unit can generate a new dataset based on a prompt from a user. For example, the dataset generation unit can generate a new dog image dataset using a dog image dataset generated by the generation AI. The dataset generation unit can also generate a new lesion image dataset using a lesion image dataset generated by the generation AI. The dataset generation unit can also generate a new cat image dataset using a cat image dataset generated by the generation AI. This allows new datasets to be generated based on user instructions.

[0069] The image recognition model integrating unit can train an image recognition model using the generated dataset and integrate it with other image recognition models. For example, the image recognition model integrating unit can train an image recognition model for identifying dogs using an image dataset of dogs generated by the generation AI and integrate it with other image recognition models. The image recognition model integrating unit can also train an image recognition model for identifying lesions using an image dataset of lesions generated by the generation AI and integrate it with other image recognition models. The image recognition model integrating unit can also train an image recognition model for identifying cats using an image dataset of cats generated by the generation AI and integrate it with other image recognition models. This allows an image recognition model to be trained using the generated dataset and integrated with other models.

[0070] Generative AI can generate images from different perspectives based on user prompts, and image recognition technology can integrate them to build a 3D model. For example, generative AI can generate images of the same object from different perspectives based on user prompts. For example, when generating an image of a cat, it can simultaneously generate front, side, and back images, and image recognition technology can integrate them to build a 3D model. Generative AI can also randomly change the angle and distance of the viewpoint when generating images from different perspectives. For example, when generating an image of a car, it can generate images from different angles and distances, and image recognition technology can integrate them to build a 3D model. Generative AI can also analyze images from multiple perspectives in real time and instantly build a 3D model. For example, when generating an image of a building, it can analyze images from different perspectives in real time to build a 3D model. This allows images from different perspectives to be integrated to build a 3D model.

[0071] The generative AI generates variations for different times of day or seasons based on prompts from the user, and image recognition technology can identify them. For example, the generative AI generates images of the same object at different times of day (day, night, evening, etc.) based on user prompts. For example, when generating a landscape image, day and night variations are generated, and image recognition technology can identify them. The generative AI also generates variations for different seasons (spring, summer, autumn, winter). For example, when generating an image of a tree, spring cherry blossoms, summer greenery, autumn leaves, and winter snowscapes can be generated, and image recognition technology can identify them. The generative AI also generates variations for different times of day or seasons in real time, and image recognition technology can instantly identify them. For example, when generating a cityscape, day and night variations and spring and winter variations can be generated and identified in real time. This makes it possible to identify variations for different times of day or seasons.

[0072] The generative AI can analyze the user's emotional response and generate images that elicit positive emotions. For example, the generative AI can analyze the user's emotional response to the image to be generated in real time and generate images that elicit positive emotions. For example, it can generate images that make the user feel happy. The generative AI can also use an emotion estimation function to calculate the user's emotional score for the generated image and prioritize the generation of images with a high positive emotion score. For example, it can generate images that make the user feel surprised. The generative AI can also optimize the image generation algorithm to elicit positive emotions based on the user's emotional response. For example, it can generate images that make the user feel happy. This makes it possible to generate images that elicit positive emotions in the user.

[0073] Generative AI and image recognition technology can analyze generated images in real time and provide instant feedback. Generative AI and image recognition technology, for example, build a system that analyzes generated images in real time and provides instant feedback. For example, image recognition technology can instantly display analysis results for images generated by a user. Generative AI and image recognition technology can also provide real-time feedback for generated images. For example, image recognition technology can instantly display identification results for images generated by a user. Generative AI can also develop a system that analyzes images to be generated in real time and provides feedback. For example, image recognition technology can instantly feed back analysis results for images generated by a user. This makes it possible to analyze generated images in real time and provide instant feedback.

[0074] Generative AI customizes images based on the characteristics of different cultures and regions, and image recognition technology can identify them. For example, generative AI customizes the images it generates based on the characteristics of different cultures and regions. For example, when generating a Japanese landscape, it can incorporate characteristics such as cherry blossoms and shrines. Generative AI also customizes images based on the characteristics of different regions. For example, when generating an African landscape, it can incorporate characteristics of the savanna and wild animals. Generative AI also customizes images based on the characteristics of different cultures. For example, when generating a European landscape, it can incorporate characteristics of old castles and cobblestone cityscapes. This makes it possible to customize and identify images based on the characteristics of different cultures and regions.

[0075] The generation AI can monitor the user's emotions in real time and generate images according to the emotions. The generation AI can, for example, use an emotion estimation function to monitor the user's emotions toward the generated image in real time and generate images according to the emotions. For example, it can generate an image that makes the user feel happy. The generation AI can also, for example, analyze the user's emotional response in real time and generate images according to the emotions. For example, it can generate an image that makes the user feel surprised. The generation AI can also, for example, use the emotion estimation function to calculate the user's emotion score for the generated image and generate images according to the emotions. For example, it can generate an image that makes the user feel happy. In this way, it is possible to generate images according to the user's emotions.

[0076] The generation AI adds detailed metadata to the images it generates, and image recognition technology can use that metadata to perform identification. For example, the generation AI adds detailed metadata (e.g., object type, position, color, etc.) to the images it generates, and image recognition technology uses that metadata to perform identification. For example, the type of cat and location information can be added to an image of a cat. The generation AI can also automatically generate metadata for the images it generates, and image recognition technology uses that metadata to perform identification. For example, location information of mountains and rivers can be added to an image of a landscape. The generation AI can also add user-specified metadata to the images it generates, and image recognition technology uses that metadata to perform identification. For example, the type of object and location information specified by the user can be added. This allows for the use of detailed metadata to improve identification accuracy.

[0077] The generative AI generates images in different resolutions and formats, and image recognition technology can distinguish between them. For example, the generative AI can generate images of the same object in different resolutions (e.g., high resolution, low resolution) based on user prompts, and image recognition technology can distinguish between them. For example, a generative AI can generate images of a cat in high resolution and low resolution. The generative AI can also generate variations in different formats (e.g., JPEG, PNG, GIF), and image recognition technology can distinguish between them. For example, a landscape image can be generated in JPEG, PNG, and GIF. The generative AI can also generate variations in different resolutions and formats in real time, and image recognition technology can instantly distinguish between them. For example, a building image can be generated in high resolution and low resolution, or in JPEG and PNG. This allows it to distinguish between images generated in different resolutions and formats.

[0078] The generative AI can analyze the user's emotions and optimize image generation based on emotions. The generative AI can, for example, use an emotion estimation function to analyze the user's emotions toward the generated image in real time and optimize image generation based on emotions. For example, it can generate an image that makes the user feel happy. The generative AI can also optimize an emotion-based image generation algorithm based on the user's emotional response. For example, it can generate an image that makes the user feel surprised. The generative AI can also, for example, use the emotion estimation function to calculate the user's emotion score for the generated image and optimize image generation based on emotions. For example, it can generate an image that makes the user feel happy. This makes it possible to optimize image generation based on the user's emotions.

[0079] Generative AI generates images optimized for different devices and platforms, and image recognition technology can identify them. For example, generative AI optimizes and provides images for different devices, such as smartphones, tablets, and PCs. For example, it generates images with the resolution adjusted for smartphones. Generative AI also optimizes and provides images for different platforms, such as social media, websites, and apps. For example, it can generate images with the format adjusted for social media. Generative AI also optimizes and provides images for different devices and platforms in real time. For example, it can generate images to match the device or platform specified by the user. This makes it possible to identify images optimized for different devices and platforms.

[0080] Generative AI customizes images for different uses, and image recognition technology can identify them. For example, generative AI customizes the images it generates for different uses (e.g., education, entertainment, medical). For example, it generates images suitable for educational materials. Generative AI can also generate images customized for entertainment uses, for example, generating images suitable for game or movie scenes. Generative AI can also generate images customized for medical uses, for example, generating images useful for medical diagnosis and treatment. This makes it possible to customize and identify images for different uses.

[0081] The generation AI can provide feedback on the user's emotions in real time and adjust the generation process. For example, the generation AI can use an emotion estimation function to provide feedback on the user's emotions regarding the generated image in real time and adjust the generation process. For example, the generation process can be adjusted to generate an image that makes the user feel happy. The generation AI can also analyze the user's emotional response in real time and optimize the generation process based on the emotions. For example, the generation AI can also adjust the generation process to generate an image that makes the user feel surprised. The generation AI can also calculate the user's emotion score regarding the generated image using the emotion estimation function and adjust the generation process based on the emotions. For example, the generation process can be adjusted to generate an image that makes the user feel happy. This makes it possible to adjust the generation process based on the user's emotions.

[0082] The generative AI generates variations that simulate different environmental conditions, and the dataset generator can include them in the dataset. For example, the generative AI generates images of the same object under different weather conditions (e.g., sunny, rainy, and snowy) based on user prompts and includes them in the dataset. For example, an image of a car can be generated in sunny and rainy day variations. The generative AI can also generate variations under different lighting conditions (e.g., daytime, nighttime, indoors, and outdoors) and include them in the dataset. For example, an image of a building can be generated in daytime and nighttime, and indoors and outdoors variations. The generative AI can also simulate different environmental conditions in real time and include them in the dataset. For example, an image of a landscape can be generated in sunny, rainy, snowy, daytime, and nighttime variations. This allows variations that simulate different environmental conditions to be included in the dataset.

[0083] The generative AI generates images from different viewpoints and angles, and the dataset generator can include them in the dataset. For example, the generative AI generates images of the same object from different viewpoints (e.g., front, side, and back) based on user prompts and includes them in the dataset. For example, an image of a cat can be generated from the front, side, and back. The generative AI can also generate variations from different angles (e.g., from above, below, and at an oblique angle) and include them in the dataset. For example, an image of a car can be generated from above, below, and at an oblique angle. The generative AI can also simulate different viewpoints and angles in real time and include them in the dataset. For example, an image of a building can be generated from the front, side, back, top, and bottom variations. This allows images from different viewpoints and angles to be included in the dataset.

[0084] The generative AI can analyze a user's emotions and generate a dataset that elicits positive emotions. The generative AI can, for example, use an emotion estimation function to analyze the user's emotions toward the generated dataset in real time and generate a dataset that elicits positive emotions. For example, it generates a dataset that makes the user feel happy. The generative AI can also, for example, optimize a dataset generation algorithm for eliciting positive emotions based on the user's emotional response. For example, it can generate a dataset that makes the user feel surprised. The generative AI can also, for example, use an emotion estimation function to calculate the user's emotion score for the generated dataset and generate a dataset that elicits positive emotions. For example, it can generate a dataset that makes the user feel happy. In this way, a dataset that elicits positive emotions can be generated.

[0085] The generative AI can customize datasets for different industries and applications, and the dataset generation unit can generate them. For example, the generative AI can customize the dataset it generates for medical applications. For example, it can generate an image dataset that is useful for medical diagnosis and treatment. The generative AI can also generate datasets customized for educational applications. For example, it can generate an image dataset that is suitable for educational teaching materials. The generative AI can also generate datasets customized for entertainment applications. For example, it can generate an image dataset that is suitable for game or movie scenes. This makes it possible to customize and generate datasets for different industries and applications.

[0086] The generative AI generates multilingual datasets that correspond to different languages ​​and cultures, and the dataset generation unit can generate them. The generative AI, for example, customizes the dataset to be generated to correspond to different languages. For example, it generates multilingual datasets such as English, Japanese, and Chinese. The generative AI also generates datasets customized to correspond to different cultures, for example. For example, it can generate a dataset based on Japanese culture or a dataset based on American culture. The generative AI also generates datasets customized to correspond to different languages ​​and cultures in real time, for example. For example, it can generate a dataset to match a language or culture specified by a user. This makes it possible to generate multilingual datasets that correspond to different languages ​​and cultures.

[0087] The generation AI can monitor the user's emotions in real time and generate a dataset according to the emotions. The generation AI, for example, uses an emotion estimation function to monitor the user's emotions regarding the generated dataset in real time and generate a dataset according to the emotions. For example, it generates a dataset that makes the user feel happy. The generation AI can also, for example, analyze the user's emotional reactions in real time and optimize dataset generation based on the emotions. For example, it can generate a dataset that makes the user feel surprised. The generation AI can also, for example, use the emotion estimation function to calculate the user's emotion score regarding the generated dataset and generate a dataset according to the emotions. For example, it can generate a dataset that makes the user feel happy. In this way, it is possible to generate a dataset according to the user's emotions.

[0088] The generative AI generates variations that simulate different scenarios and contexts, and the dataset generator can include them in the dataset. For example, the generative AI generates images of the same object in different scenarios (e.g., everyday life, emergency situations) based on user prompts and includes them in the dataset. For example, images of a car are generated in everyday life and emergency situation scenarios. The generative AI can also generate variations in different contexts (e.g., urban, rural, seaside) and include them in the dataset. For example, images of a building can be generated in urban, rural, and seaside contexts. The generative AI can also simulate different scenarios and contexts in real time and include them in the dataset. For example, images of a landscape can be generated in everyday life, emergency situations, urban, rural, and seaside variations. In this way, variations that simulate different scenarios and contexts can be included in the dataset.

[0089] The generative AI can generate datasets in different resolutions and formats, and the dataset generator can include them in the dataset. For example, the generative AI can generate images of the same object at different resolutions (e.g., high resolution, low resolution) based on a user prompt and include them in the dataset. For example, an image of a cat can be generated in high resolution and low resolution. The generative AI can also generate variations in different formats (e.g., JPEG, PNG, GIF) and include them in the dataset. For example, an image of a landscape can be generated in JPEG, PNG, and GIF. The generative AI can also generate variations in different resolutions and formats in real time and include them in the dataset. For example, an image of a building can be generated in high resolution and low resolution, or in JPEG and PNG. This allows datasets generated in different resolutions and formats to be included.

[0090] The generation AI can analyze user emotions and optimize dataset generation based on emotions. The generation AI can, for example, use an emotion estimation function to analyze user emotions regarding a generated dataset in real time and optimize dataset generation based on emotions. For example, a dataset that makes the user feel happy can be generated. The generation AI can also optimize an emotion-based dataset generation algorithm based on the user's emotional response, for example. For example, a dataset that makes the user feel surprised can be generated. The generation AI can also, for example, use an emotion estimation function to calculate a user's emotion score regarding a generated dataset and optimize dataset generation based on emotions. For example, a dataset that makes the user feel happy can be generated. This makes it possible to optimize dataset generation based on the user's emotions.

[0091] The generation AI generates datasets optimized for different devices and platforms, and the dataset generation unit can generate them. For example, the generation AI optimizes the dataset it generates for different devices, such as smartphones, tablets, and PCs, and provides it. For example, it generates a dataset with the resolution adjusted for smartphones. The generation AI also optimizes the dataset it generates for different platforms, such as social media, websites, and apps, and provides it. For example, it can generate a dataset with the format adjusted for social media. The generation AI also optimizes the dataset it generates for different devices and platforms in real time and provides it. For example, it can generate a dataset to suit a device or platform specified by a user. This makes it possible to generate datasets optimized for different devices and platforms.

[0092] The generative AI can customize datasets for different uses, and the dataset generation unit can generate them. The generative AI, for example, customizes the dataset to be generated for educational purposes. For example, it generates an image dataset suitable for educational teaching materials. The generative AI can also generate a dataset customized for entertainment purposes. For example, it can generate an image dataset suitable for game or movie scenes. The generative AI can also generate a dataset customized for medical purposes. For example, it can generate an image dataset useful for medical diagnosis and treatment. This makes it possible to customize and generate datasets for different uses.

[0093] The generation AI can provide feedback on the user's emotions in real time and adjust the dataset generation process. The generation AI can, for example, use an emotion estimation function to provide feedback on the user's emotions regarding the generated dataset in real time and adjust the generation process. For example, the generation AI can adjust the generation process to generate a dataset that makes the user feel happy. The generation AI can also, for example, analyze the user's emotional reactions in real time and optimize the generation process based on the emotions. For example, the generation AI can also adjust the generation process to generate a dataset that makes the user feel surprised. The generation AI can also, for example, use the emotion estimation function to calculate the user's emotion score regarding the generated dataset and adjust the generation process based on the emotions. For example, the generation AI can adjust the generation process to generate a dataset that makes the user feel happy. This makes it possible to adjust the dataset generation process based on the user's emotions.

[0094] The image recognition model integrating unit can train an image recognition model using the generated dataset and integrate it with other image recognition models. For example, the image recognition model integrating unit can train an image recognition model for identifying dogs using an image dataset of dogs generated by the generation AI and integrate it with other image recognition models. The image recognition model integrating unit can also train an image recognition model for identifying lesions using an image dataset of lesions generated by the generation AI and integrate it with other image recognition models. The image recognition model integrating unit can also train an image recognition model for identifying cats using an image dataset of cats generated by the generation AI and integrate it with other image recognition models. This allows an image recognition model to be trained using the generated dataset and integrated with other models.

[0095] The image recognition model integrator can train an image recognition model using the generated dataset and customize it for different applications. The image recognition model integrator can train an image recognition model using, for example, a dataset generated by the generative AI and customize the model for different applications. For example, it can build an image recognition model specialized for medical applications. The image recognition model integrator can also train an image recognition model using, for example, a dataset generated by the generative AI and optimize the model for different devices or platforms. For example, it can build an image recognition model optimized for smartphones. The image recognition model integrator can also train an image recognition model using, for example, a dataset generated by the generative AI and customize the model for different industries or applications. For example, it can build an image recognition model specialized for educational applications. This allows the image recognition model to be trained using the generated dataset and customized for different applications.

[0096] The image recognition model integration unit can train an image recognition model using the generated dataset and customize it to support different languages ​​and cultures. The image recognition model integration unit can train an image recognition model using, for example, a dataset generated by the generative AI and customize the model to support different languages ​​and cultures. For example, it can build an image recognition model that supports multiple languages. The image recognition model integration unit can also train an image recognition model using, for example, a dataset generated by the generative AI and customize the model to support different environmental conditions (e.g., weather, lighting). For example, it can build an image recognition model that supports different weather conditions. The image recognition model integration unit can train an image recognition model using, for example, a dataset generated by the generative AI and customize the model to support different viewpoints and angles. For example, it can build a model that can identify images from different viewpoints and angles. This allows the image recognition model to be trained using the generated dataset and customized to support different languages ​​and cultures.

[0097] The image recognition model integrator can train an image recognition model using the generated dataset and customize it to respond to different environmental conditions. For example, the image recognition model integrator can train an image recognition model using a dataset generated by the generative AI and customize the model for different applications. For example, it can build an image recognition model specialized for medical applications. The image recognition model integrator can also train an image recognition model using a dataset generated by the generative AI and optimize the model for different devices or platforms. For example, it can build an image recognition model optimized for smartphones. The image recognition model integrator can also train an image recognition model using a dataset generated by the generative AI and customize the model for different industries or applications. For example, it can build an image recognition model specialized for educational applications. This allows the image recognition model to be trained using the generated dataset and customized to respond to different environmental conditions.

[0098] The image recognition model integration unit can train an image recognition model using the generated dataset and customize it to support different viewpoints and angles. The image recognition model integration unit can train an image recognition model using, for example, a dataset generated by the generation AI and customize the model to support different languages ​​and cultures. For example, it can build an image recognition model that supports multiple languages. The image recognition model integration unit can also train an image recognition model using, for example, a dataset generated by the generation AI and customize the model to support different environmental conditions (e.g., weather, lighting). For example, it can build an image recognition model that supports different weather conditions. The image recognition model integration unit can train an image recognition model using, for example, a dataset generated by the generation AI and customize the model to support different viewpoints and angles. For example, it can build a model that can identify images from different viewpoints and angles. This allows the image recognition model to be trained using the generated dataset and customized to support different viewpoints and angles.

[0099] The image recognition model integrator can train an image recognition model using the generated dataset and customize it for different applications. The image recognition model integrator can train an image recognition model using, for example, a dataset generated by the generative AI and customize the model for different applications. For example, it can build an image recognition model specialized for medical applications. The image recognition model integrator can also train an image recognition model using, for example, a dataset generated by the generative AI and optimize the model for different devices or platforms. For example, it can build an image recognition model optimized for smartphones. The image recognition model integrator can also train an image recognition model using, for example, a dataset generated by the generative AI and customize the model for different industries or applications. For example, it can build an image recognition model specialized for educational applications. This allows the image recognition model to be trained using the generated dataset and customized for different applications.

[0100] The generation AI can provide feedback on the user's emotions in real time and adjust the generation process. For example, the generation AI can use an emotion estimation function to provide feedback on the user's emotions regarding the generated image in real time and adjust the generation process. For example, the generation process can be adjusted to generate an image that makes the user feel happy. The generation AI can also analyze the user's emotional response in real time and optimize the generation process based on the emotions. For example, the generation AI can also adjust the generation process to generate an image that makes the user feel surprised. The generation AI can also calculate the user's emotion score regarding the generated image using the emotion estimation function and adjust the generation process based on the emotions. For example, the generation process can be adjusted to generate an image that makes the user feel happy. This makes it possible to adjust the generation process based on the user's emotions.

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

[0102] The generative AI can estimate the user's emotions and change the style of the images it generates based on the estimated emotions. For example, if the user feels like relaxing, the generative AI can generate images of calm landscapes and soft colors. If the user is feeling energetic, the generative AI can also generate images with vivid colors and movement. Furthermore, if the user is looking to be moved, the generative AI can generate magnificent landscapes and moving scenes. This allows it to generate images in a style that matches the user's emotions.

[0103] The generative AI can estimate the user's emotions and change the content of the images it generates based on the estimated emotions. For example, if the user is sad, the generative AI can generate comforting images. Also, if the user is excited, the generative AI can generate images that further increase that excitement. Furthermore, if the user is seeking a sense of security, the generative AI can generate images that provide a sense of security. This makes it possible to generate images with content that corresponds to the user's emotions.

[0104] The generative AI can estimate the user's emotions and change the composition of the generated image based on the estimated emotions. For example, if the user is seeking calmness, the generative AI will generate an image with a simple and balanced composition. Alternatively, if the user is seeking stimulation, the generative AI can generate an image with a dynamic and complex composition. Furthermore, if the user wants to increase their concentration, the generative AI can generate an image with a clear focus and a composition that guides the user's gaze. In this way, it is possible to generate images with a composition that matches the user's emotions.

[0105] The generative AI can estimate the user's emotions and change the colors of the images it generates based on the estimated emotions. For example, if the user wants to relax, the generative AI can generate images in calming colors such as blue and green. If the user wants to feel energized, the generative AI can also generate images in bright colors such as red and yellow. Furthermore, if the user wants to increase concentration, the generative AI can generate images in simple colors such as black and white. This makes it possible to generate images with colors that correspond to the user's emotions.

[0106] The generative AI can estimate the user's emotions and change the theme of the images it generates based on the estimated emotions. For example, if the user is seeking relaxation, the generative AI can generate images with a soothing theme, such as nature or animals. If the user is seeking adventure, the generative AI can also generate images with an adventure or travel theme. Furthermore, if the user is seeking learning, the generative AI can generate images with an education or knowledge theme. This allows it to generate images with a theme that matches the user's emotions.

[0107] The generative AI can generate images in different art styles based on user prompts. For example, if a user inputs the prompt "Generate a landscape in the impressionist style," the generative AI can generate a landscape image in the impressionist style. Alternatively, if a user inputs the prompt "Generate a portrait in the pop art style," the generative AI can generate a portrait image in the pop art style. Furthermore, if a user inputs the prompt "Generate a cityscape in the abstract painting style," the generative AI can generate a cityscape image in the abstract painting style. This allows images in different art styles to be generated based on user instructions.

[0108] The generative AI can generate images in different era styles based on user prompts. For example, if a user inputs the prompt "Generate a portrait in the Renaissance era style," the generative AI can generate a portrait image in the Renaissance era style. Alternatively, if a user inputs the prompt "Generate a landscape in the Victorian era style," the generative AI can generate a landscape image in the Victorian era style. Furthermore, if a user inputs the prompt "Generate a cityscape in a futuristic style," the generative AI can generate a cityscape image in a futuristic style. This allows the generation of images in different era styles based on user instructions.

[0109] The generative AI can generate images using different techniques based on user prompts. For example, if a user inputs the prompt "Generate an image of a flower using watercolor techniques," the generative AI will generate an image of a flower using watercolor techniques. Alternatively, if a user inputs the prompt "Generate a landscape using oil painting techniques," the generative AI can generate an image of a landscape using oil painting techniques. Furthermore, if a user inputs the prompt "Generate a portrait using pencil drawing techniques," the generative AI can generate a portrait image using pencil drawing techniques. This allows images to be generated using different techniques based on user instructions.

[0110] The generative AI can generate images that incorporate elements of different cultures based on user prompts. For example, if a user inputs the prompt "Generate a landscape that incorporates traditional Japanese elements," the generative AI will generate an image of a landscape that incorporates traditional Japanese elements. Alternatively, if a user inputs the prompt "Generate a portrait that incorporates African cultural elements," the generative AI can generate an image of a portrait that incorporates African cultural elements. Furthermore, if a user inputs the prompt "Generate an image of a building that incorporates historical European elements," the generative AI can generate an image of a building that incorporates historical European elements. This allows the generation of images that incorporate elements of different cultures based on user instructions.

[0111] The generation AI can generate different seasons and weather variations based on user prompts. For example, if a user inputs the prompt "Generate a spring landscape," the generation AI will generate an image of a spring landscape. Alternatively, if a user inputs the prompt "Generate a cityscape on a rainy day," the generation AI can generate an image of a cityscape on a rainy day. Furthermore, if a user inputs the prompt "Generate a winter snowy landscape," the generation AI can generate an image of a winter snowy landscape. This allows the generation of different seasons and weather variations based on user instructions.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The generative AI receives a prompt containing instructions from the user as input and generates a new image based on the prompt. For example, if the user inputs the prompt "Generate an image of a cat," the generative AI will generate an image of a cat. The generative AI can also generate an image dataset of dogs or an image dataset of new pathologies for use in the medical field. Step 2: Image recognition technology analyzes the generated image and identifies its content. For example, image recognition technology can analyze a generated image of a cat and identify it as a cat. It can also analyze a generated image of a dog or an image of a lesion and identify it as a dog or a lesion, respectively. Step 3: The dataset generation unit generates a new dataset. For example, a new dog image dataset is generated using the dog image dataset generated by the generation AI. Also, a new lesion image dataset or cat image dataset can be generated using the lesion image dataset or cat image dataset generated by the generation AI. Step 4: The image recognition model integration unit integrates the image recognition models. For example, an image recognition model that identifies dogs can be trained using an image dataset of dogs generated by the generation AI, and integrated with other image recognition models. Alternatively, an image dataset of lesions and an image dataset of cats generated by the generation AI can be used to train image recognition models that identify lesions and cats, respectively, and integrated with other image recognition models.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0115] 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.

[0116] 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.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0181] 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. Generative AI and Image recognition technology and a dataset generator for generating a new dataset; and an image recognition model integration unit that integrates image recognition models.

2. The generated AI is Generate a new image based on a prompt from the user 2. The system of claim 1.

3. The image recognition technology is Analyze the generated image and identify its contents 2. The system of claim 1.

4. The dataset generation unit Generate a new dataset based on a prompt from the user 2. The system of claim 1.

5. The image recognition model integration unit The generated dataset is used to train the image recognition model and integrate it with other image recognition models.

2. The system of claim 1.

6. The generated AI is Generate images from different viewpoints based on prompts from the user; The image recognition technology is Integrate them to build the 3D model 2. The system of claim 1.

7. The generated AI is Generate variations for different times of day and seasons based on user prompts, The image recognition technology is Identify them 2. The system of claim 1.

8. The generated AI is Analyzing users' emotional responses and generating images that elicit positive emotions 2. The system of claim 1.

Images