System

Abstract

An object of a system according to an embodiment is to protect the right of a creator while determining whether an existing image can be commercially used and generating a commercially usable image.SOLUTION: A system includes a commercial use propriety determination unit, an image generation unit, an image capturing unit, and a right protection unit. The commercial availability determination unit determines commercial availability of an existing image. The image generation part generates a new commercially available image by combining the existing images whose commercial use is permitted by the commercial use propriety determination part. The image capturing unit captures an image posted on the Internet. The rights protector protects the rights and interests of the creator.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 technology has the drawback of being complex in the process of determining whether an existing image can be used commercially and generating commercially usable images, and of not adequately protecting the rights of creators.

[0005] The system according to the embodiment aims to determine whether an existing image can be used commercially, generate commercially usable images, and protect the rights of creators. [Means for solving the problem]

[0006] The system according to the embodiment includes a commercial use permission determination unit, an image generation unit, an image capture unit, and a rights protection unit. The commercial use permission determination unit determines whether an existing image can be used commercially. The image generation unit generates a new commercially usable image by combining existing images whose commercial use has been permitted by the commercial use permission determination unit. The image capture unit captures images posted on the Internet. The rights protection unit protects the rights and interests of creators. [Effects of the Invention]

[0007] The system according to the embodiment can determine whether an existing image can be used commercially, and can generate commercially usable images while protecting the rights of creators. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The AI ​​service according to an embodiment of the present invention is a system that determines whether an existing image can be used commercially and further combines the existing images to generate a new image that can be used commercially. This enables corporations and individual businesses to reduce the risks involved in their marketing activities and efficiently generate images that can be used commercially.

[0029] An AI service according to an embodiment includes a commercial use permission determination unit, an image generation unit, an image capture unit, and a rights protection unit. The commercial use permission determination unit determines whether an existing image can be used commercially. For example, the generation AI analyzes the copyright, license information, and terms of use of the image to determine whether it can be used commercially. The image generation unit generates a new commercially usable image by combining existing images that are permitted for commercial use. For example, the generation AI appropriately combines images that are permitted for commercial use to generate a new image. The image capture unit captures images posted on the Internet. For example, the generation AI captures the latest images on the Internet and uses them as training data. The rights protection unit protects the rights and interests of creators. For example, the generation AI provides incentives for allowing uploaded images to be used for training. This allows the AI ​​service according to an embodiment to reduce risks in marketing activities for corporations and individual businesses and efficiently generate commercially usable images. For example, it can avoid the risk of accidentally using images that are not permitted for commercial use and generate high-quality images based on the latest data. Furthermore, protecting the rights and interests of creators promotes the use of images on the Internet.

[0030] The commercial use eligibility determination unit can analyze not only image metadata but also text and logos within the image to assess the risk of commercial use. For example, the commercial use eligibility determination unit not only analyzes image metadata using generative AI, but also automatically detects text and logos contained within the image and analyzes their content. For example, if text within an image contains a specific brand name or trademark, it assesses the risk of its commercial use. This allows for a more accurate assessment of the risk of commercial use by analyzing text and logos within the image.

[0031] The commercial use permission determination unit can refer to a database of past precedents and reinforce its determination based on precedents of similar cases. For example, the commercial use permission determination unit uses the generation AI to refer to a database of past precedents and search for precedents related to similar commercial use cases. For example, it determines whether the current image can be used commercially based on precedents from when a specific image was used commercially in the past. This improves the accuracy of the determination by assessing the risk of commercial use based on past precedents.

[0032] The commercial use eligibility determination unit can evaluate the commercial usability of the entire multimedia content, including audio and video data, in its analysis. For example, the commercial use eligibility determination unit evaluates the commercial usability of the entire multimedia content by having the generative AI analyze not only images but also audio and video data. For example, it analyzes the audio and video within a video to evaluate the risk of commercial use. This makes it possible to evaluate the risk of commercial use, including audio and video data.

[0033] The commercial use eligibility determination unit can make a decision from a global perspective, taking into account the legal regulations of different languages ​​and cultural spheres. For example, the commercial use eligibility determination unit builds a system in which the generation AI takes into account the legal regulations of different languages ​​and cultural spheres and determines whether or not a commercial use is possible from a global perspective. For example, it refers to the copyright laws and trademark laws of each country and evaluates the risk of commercial use. This makes it possible to evaluate the risk of commercial use by taking into account the legal regulations of different languages ​​and cultural spheres.

[0034] When combining images, the image generation unit can automatically adjust the color tone and style of the images to generate a commercially usable image with a unified look. For example, when the generation AI combines images, the image generation unit automatically adjusts the color tone and style of each image to generate a commercially usable image with a unified look. For example, images with different color tones are adjusted to the same color tone. In this way, by adjusting the color tone and style of the images, a commercially usable image with a unified look can be generated.

[0035] When combining images, the image generation unit understands the context of the images and can generate commercially usable images that tell a story. For example, when the generative AI combines images, the image generation unit understands the context of each image and generates commercially usable images that tell a story. For example, it combines images that have related themes or scenes. In this way, by understanding the context of the images, it is possible to generate commercially usable images that tell a story.

[0036] The image generation unit generates 3D models and animations when combining images, making it possible to provide more interactive, commercially available content. For example, when the generation AI combines images, the image generation unit generates 3D models to provide more interactive, commercially available content. For example, a 3D model of a product may be generated, allowing users to freely rotate and view it. This allows the generation of 3D models and animations to provide more interactive, commercially available content.

[0037] The image generation unit can prepare templates specialized for different industries and uses and generate customizable commercial images. For example, the image generation unit constructs a system in which a generation AI prepares templates specialized for different industries and uses and generates customizable commercial images. For example, it provides templates for the medical industry and templates for the education industry. This makes it possible to generate customizable commercial images by preparing templates specialized for different industries and uses.

[0038] When importing images from the internet, the image import unit evaluates the reliability and origin of the image, allowing only highly reliable data to be used for training. For example, the image import unit builds a system that evaluates the reliability and origin of an image when the generation AI imports an image from the internet. For example, it analyzes the image metadata and source URL, and uses only highly reliable data for training. This improves the accuracy of the generation AI by using only highly reliable data for training.

[0039] The image capture unit can analyze image trends and popularity when capturing images from the internet and learn based on the latest trends. For example, the image capture unit builds a system that analyzes image trends and popularity when the generation AI captures images from the internet. For example, it analyzes data from social media and news sites and learns based on the latest trends. This improves the accuracy of the generation AI by learning based on the latest trends.

[0040] When importing images from the internet, the image import unit imports images from different languages ​​and cultural spheres, enabling learning from a global perspective. The image import unit, for example, constructs a system in which the generation AI imports images from different languages ​​and cultural spheres and learns from a global perspective. For example, it imports images from each country and performs learning that corresponds to different cultures and languages. In this way, by importing images from different languages ​​and cultural spheres, learning from a global perspective is possible.

[0041] The Rights Protection Department can track the usage history of images and automatically calculate compensation to creators in order to protect the rights and interests of creators. For example, the Rights Protection Department will build a system in which the Generative AI tracks the usage history of images and automatically calculates compensation to creators in order to protect the rights and interests of creators. For example, compensation can be calculated based on the number of times and period an image has been used. This protects the rights and interests of creators and automatically calculates compensation, thereby increasing creator motivation.

[0042] The Rights Protection Department can use blockchain technology to manage image copyright information and ensure transparency. For example, the Rights Protection Department will build a system that uses blockchain technology to manage image copyright information and ensure transparency so that generative AI can protect the rights and interests of creators. For example, image copyright information can be recorded on the blockchain to prevent tampering. In this way, the use of blockchain technology can ensure transparency of copyright information and protect the rights of creators.

[0043] The Rights Protection Department will provide similar incentives to audio and video creators, enabling rights protection for the entire multimedia content. For example, the Rights Protection Department will build a system that provides similar incentives to audio and video creators so that the generative AI can protect the rights and interests of creators. For example, it will track the usage history of audio and video and automatically calculate compensation. This will enable rights protection for the entire multimedia content by providing incentives to audio and video creators.

[0044] The Rights Protection Department can share copyright information across different platforms to achieve global rights protection. For example, the Rights Protection Department can build a system for sharing copyright information across different platforms so that generative AI can protect the rights and interests of creators. For example, it can integrate the copyright information of each platform to achieve global rights protection. This allows copyright information to be shared across different platforms, thereby achieving global rights protection.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The commercial use determination unit can analyze not only image metadata but also text and logos within the image to assess the risk of commercial use. For example, the generative AI not only analyzes image metadata, but also automatically detects text and logos contained within the image and analyzes their content. For example, if the text within an image contains a specific brand name or trademark, it assesses the risk of its commercial use. This allows for a more accurate assessment of the risk of commercial use by analyzing the text and logos within the image.

[0047] The commercial use permission determination unit can refer to a database of past precedents and reinforce its judgment based on precedents from similar cases. For example, the generation AI can refer to a database of past precedents and search for precedents related to similar commercial use cases. For example, it can determine whether a current image can be used commercially based on precedents from when a specific image was used commercially in the past. This improves the accuracy of the judgment by assessing the risk of commercial use based on past precedents.

[0048] The commercial use eligibility determination unit can also include audio and video data in its analysis to assess whether the entire multimedia content is suitable for commercial use. For example, if the generative AI analyzes not only images but also audio and video data, it can assess whether the entire multimedia content is suitable for commercial use. For example, it can analyze the audio and video within a video to assess the risk of commercial use. This makes it possible to assess the risk of commercial use, including audio and video data.

[0049] The commercial use eligibility determination unit can make a decision from a global perspective, taking into account the legal regulations of different languages ​​and cultural spheres. For example, we will build a system in which the generative AI takes into account the legal regulations of different languages ​​and cultural spheres and determines whether commercial use is permitted from a global perspective. For example, it will refer to the copyright and trademark laws of each country and evaluate the risk of commercial use. This makes it possible to evaluate the risk of commercial use by taking into account the legal regulations of different languages ​​and cultural spheres.

[0050] When combining images, the image generation unit automatically adjusts the color tone and style of the images to generate a unified, commercially available image. For example, when the generation AI combines images, it automatically adjusts the color tone and style of each image to generate a unified, commercially available image. For example, it adjusts images with different color tones to the same color tone. In this way, by adjusting the color tone and style of the images, it is possible to generate a unified, commercially available image.

[0051] When combining images, the image generation unit understands the context of the images and can generate commercially usable images that tell a story. For example, when the generation AI combines images, it understands the context of each image and generates commercially usable images that tell a story. For example, it combines images that have related themes or scenes. In this way, by understanding the context of the images, it can generate commercially usable images that tell a story.

[0052] The image generation unit generates 3D models and animations when combining images, making it possible to provide more interactive and commercially usable content. For example, when the generation AI combines images, it generates 3D models to provide more interactive and commercially usable content. For example, it generates a 3D model of a product, allowing users to freely rotate and view it. This allows the generation of 3D models and animations to provide more interactive and commercially usable content.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The commercial use eligibility determination unit determines whether an existing image can be used commercially. For example, the generation AI analyzes the image's copyright, license information, and terms of use to determine whether it can be used commercially. Step 2: The image generation unit generates a new commercially available image by combining existing images that are licensed for commercial use. For example, the generation AI appropriately combines images that are licensed for commercial use to generate a new image. Step 3: The image capture unit captures images posted on the Internet. For example, the generation AI captures the latest images on the Internet and uses them as learning data. Step 4: The Rights Protection Department protects the rights and interests of creators. For example, the Generative AI provides incentives for users to allow uploaded images to be used for training.

[0055] (Example 2) The AI ​​service according to an embodiment of the present invention is a system that determines whether an existing image can be used commercially and further combines the existing images to generate a new image that can be used commercially. This enables corporations and individual businesses to reduce the risks involved in their marketing activities and efficiently generate images that can be used commercially.

[0056] An AI service according to an embodiment includes a commercial use permission determination unit, an image generation unit, an image capture unit, and a rights protection unit. The commercial use permission determination unit determines whether an existing image can be used commercially. For example, the generation AI analyzes the copyright, license information, and terms of use of the image to determine whether it can be used commercially. The image generation unit generates a new commercially usable image by combining existing images that are permitted for commercial use. For example, the generation AI appropriately combines images that are permitted for commercial use to generate a new image. The image capture unit captures images posted on the Internet. For example, the generation AI captures the latest images on the Internet and uses them as training data. The rights protection unit protects the rights and interests of creators. For example, the generation AI provides incentives for allowing uploaded images to be used for training. This allows the AI ​​service according to an embodiment to reduce risks in marketing activities for corporations and individual businesses and efficiently generate commercially usable images. For example, it can avoid the risk of accidentally using images that are not permitted for commercial use and generate high-quality images based on the latest data. Furthermore, protecting the rights and interests of creators promotes the use of images on the Internet.

[0057] The commercial use eligibility determination unit can analyze not only image metadata but also text and logos within the image to assess the risk of commercial use. For example, the commercial use eligibility determination unit not only analyzes image metadata using generative AI, but also automatically detects text and logos contained within the image and analyzes their content. For example, if text within an image contains a specific brand name or trademark, it assesses the risk of its commercial use. This allows for a more accurate assessment of the risk of commercial use by analyzing text and logos within the image.

[0058] The commercial use permission determination unit can refer to a database of past precedents and reinforce its determination based on precedents of similar cases. For example, the commercial use permission determination unit uses the generation AI to refer to a database of past precedents and search for precedents related to similar commercial use cases. For example, it determines whether the current image can be used commercially based on precedents from when a specific image was used commercially in the past. This improves the accuracy of the determination by assessing the risk of commercial use based on past precedents.

[0059] The commercial use eligibility determination unit can use the emotion estimation function to analyze the user's emotional response and select images that elicit positive emotions. For example, when the generation AI determines whether an image can be used commercially, the commercial use eligibility determination unit analyzes the user's emotional response in real time and prioritizes the selection of images that elicit positive emotions. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This allows the selection of images that elicit positive emotions, taking into account the user's emotional response.

[0060] The commercial use eligibility determination unit can evaluate the commercial usability of the entire multimedia content, including audio and video data, in its analysis. For example, the commercial use eligibility determination unit evaluates the commercial usability of the entire multimedia content by having the generative AI analyze not only images but also audio and video data. For example, it analyzes the audio and video within a video to evaluate the risk of commercial use. This makes it possible to evaluate the risk of commercial use, including audio and video data.

[0061] The commercial use eligibility determination unit can make a decision from a global perspective, taking into account the legal regulations of different languages ​​and cultural spheres. For example, the commercial use eligibility determination unit builds a system in which the generation AI takes into account the legal regulations of different languages ​​and cultural spheres and determines whether or not a commercial use is possible from a global perspective. For example, it refers to the copyright laws and trademark laws of each country and evaluates the risk of commercial use. This makes it possible to evaluate the risk of commercial use by taking into account the legal regulations of different languages ​​and cultural spheres.

[0062] When combining images, the image generation unit can automatically adjust the color tone and style of the images to generate a commercially usable image with a unified look. For example, when the generation AI combines images, the image generation unit automatically adjusts the color tone and style of each image to generate a commercially usable image with a unified look. For example, images with different color tones are adjusted to the same color tone. In this way, by adjusting the color tone and style of the images, a commercially usable image with a unified look can be generated.

[0063] When combining images, the image generation unit understands the context of the images and can generate commercially usable images that tell a story. For example, when the generative AI combines images, the image generation unit understands the context of each image and generates commercially usable images that tell a story. For example, it combines images that have related themes or scenes. In this way, by understanding the context of the images, it is possible to generate commercially usable images that tell a story.

[0064] The image generation unit can use the emotion estimation function to analyze the user's emotional response and generate images that elicit positive emotions. For example, the image generation unit constructs a system in which a generation AI uses the emotion estimation function to analyze the user's emotional response and generate images that elicit positive emotions. For example, images that elicit positive emotions are automatically generated based on the user's emotional data. This makes it possible to generate images that elicit positive emotions by taking the user's emotional response into consideration.

[0065] The image generation unit generates 3D models and animations when combining images, making it possible to provide more interactive, commercially available content. For example, when the generation AI combines images, the image generation unit generates 3D models to provide more interactive, commercially available content. For example, a 3D model of a product may be generated, allowing users to freely rotate and view it. This allows the generation of 3D models and animations to provide more interactive, commercially available content.

[0066] The image generation unit can prepare templates specialized for different industries and uses and generate customizable commercial images. For example, the image generation unit constructs a system in which a generation AI prepares templates specialized for different industries and uses and generates customizable commercial images. For example, it provides templates for the medical industry and templates for the education industry. This makes it possible to generate customizable commercial images by preparing templates specialized for different industries and uses.

[0067] The image generation unit can use the emotion estimation function to prioritize generating combinations that evoke the most positive emotions in the user. For example, the image generation unit uses the emotion estimation function to build a system in which the generation AI uses the emotion estimation function to prioritize generating combinations of images that evoke the most positive emotions in the user. For example, based on the user's emotion data, images that elicit positive emotions are automatically combined. This makes it possible to generate combinations of images that evoke the most positive emotions in the user.

[0068] When importing images from the internet, the image import unit evaluates the reliability and origin of the image, allowing only highly reliable data to be used for training. For example, the image import unit builds a system that evaluates the reliability and origin of an image when the generation AI imports an image from the internet. For example, it analyzes the image metadata and source URL, and uses only highly reliable data for training. This improves the accuracy of the generation AI by using only highly reliable data for training.

[0069] The image capture unit can analyze image trends and popularity when capturing images from the internet and learn based on the latest trends. For example, the image capture unit builds a system that analyzes image trends and popularity when the generation AI captures images from the internet. For example, it analyzes data from social media and news sites and learns based on the latest trends. This improves the accuracy of the generation AI by learning based on the latest trends.

[0070] The image capture unit can use the emotion estimation function to analyze the user's emotional response and prioritize learning images that elicit positive emotions. For example, the image capture unit can build a system in which the generation AI uses the emotion estimation function to analyze the user's emotional response and prioritize learning images that elicit positive emotions. For example, based on the user's emotional data, images that elicit positive emotions can be automatically captured. This improves the accuracy of the generation AI by prioritizing learning images that elicit positive emotions.

[0071] When importing images from the internet, the image import unit imports images from different languages ​​and cultural spheres, enabling learning from a global perspective. The image import unit, for example, constructs a system in which the generation AI imports images from different languages ​​and cultural spheres and learns from a global perspective. For example, it imports images from each country and performs learning that corresponds to different cultures and languages. In this way, by importing images from different languages ​​and cultural spheres, learning from a global perspective is possible.

[0072] The image capture unit can use the emotion estimation function to prioritize learning of images that evoke the most positive emotions in the user. For example, the image capture unit constructs a system in which the generation AI uses the emotion estimation function to analyze the user's emotional response and prioritize learning of images that evoke positive emotions. For example, based on the user's emotion data, images that evoke positive emotions are automatically captured. This improves the accuracy of the generation AI by prioritized learning of images that evoke the most positive emotions in the user.

[0073] The Rights Protection Department can track the usage history of images and automatically calculate compensation to creators in order to protect the rights and interests of creators. For example, the Rights Protection Department will build a system in which the Generative AI tracks the usage history of images and automatically calculates compensation to creators in order to protect the rights and interests of creators. For example, compensation can be calculated based on the number of times and period an image has been used. This protects the rights and interests of creators and automatically calculates compensation, thereby increasing creator motivation.

[0074] The Rights Protection Department can use blockchain technology to manage image copyright information and ensure transparency. For example, the Rights Protection Department will build a system that uses blockchain technology to manage image copyright information and ensure transparency so that generative AI can protect the rights and interests of creators. For example, image copyright information can be recorded on the blockchain to prevent tampering. In this way, the use of blockchain technology can ensure transparency of copyright information and protect the rights of creators.

[0075] The rights protection unit can use the emotion estimation function to analyze the user's emotional response and provide incentives that elicit positive emotions. For example, the rights protection unit builds a system in which a generative AI uses the emotion estimation function to analyze the user's emotional response and provide incentives that elicit positive emotions. For example, based on the user's emotional data, rewards and benefits that elicit positive emotions are provided. This makes it possible to protect the rights and interests of creators by providing incentives that elicit positive emotions, taking into account the user's emotional response.

[0076] The Rights Protection Department will provide similar incentives to audio and video creators, enabling rights protection for the entire multimedia content. For example, the Rights Protection Department will build a system that provides similar incentives to audio and video creators so that the generative AI can protect the rights and interests of creators. For example, it will track the usage history of audio and video and automatically calculate compensation. This will enable rights protection for the entire multimedia content by providing incentives to audio and video creators.

[0077] The Rights Protection Department can share copyright information across different platforms to achieve global rights protection. For example, the Rights Protection Department can build a system for sharing copyright information across different platforms so that generative AI can protect the rights and interests of creators. For example, it can integrate the copyright information of each platform to achieve global rights protection. This allows copyright information to be shared across different platforms, thereby achieving global rights protection.

[0078] The rights protection unit can use the emotion estimation function to provide incentives that will evoke the most positive emotions in users. For example, the rights protection unit constructs a system in which a generative AI uses the emotion estimation function to analyze a user's emotional reactions and provide incentives that will evoke positive emotions. For example, based on the user's emotional data, the rights protection unit can provide rewards or benefits that will evoke positive emotions. This can protect the rights and interests of creators by providing incentives that will evoke the most positive emotions in users.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The commercial use determination unit can analyze not only image metadata but also text and logos within the image to assess the risk of commercial use. For example, the generative AI not only analyzes image metadata, but also automatically detects text and logos contained within the image and analyzes their content. For example, if the text within an image contains a specific brand name or trademark, it assesses the risk of its commercial use. This allows for a more accurate assessment of the risk of commercial use by analyzing the text and logos within the image.

[0081] The commercial use permission determination unit can refer to a database of past precedents and reinforce its judgment based on precedents from similar cases. For example, the generation AI can refer to a database of past precedents and search for precedents related to similar commercial use cases. For example, it can determine whether a current image can be used commercially based on precedents from when a specific image was used commercially in the past. This improves the accuracy of the judgment by assessing the risk of commercial use based on past precedents.

[0082] The commercial use eligibility determination unit can use the emotion estimation function to analyze the user's emotional response and select images that elicit positive emotions. For example, when the generation AI determines whether an image can be used commercially, it analyzes the user's emotional response in real time and prioritizes the selection of images that elicit positive emotions. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This allows it to select images that elicit positive emotions, taking into account the user's emotional response.

[0083] The commercial use eligibility determination unit can also include audio and video data in its analysis to assess whether the entire multimedia content is suitable for commercial use. For example, if the generative AI analyzes not only images but also audio and video data, it can assess whether the entire multimedia content is suitable for commercial use. For example, it can analyze the audio and video within a video to assess the risk of commercial use. This makes it possible to assess the risk of commercial use, including audio and video data.

[0084] The commercial use eligibility determination unit can make a decision from a global perspective, taking into account the legal regulations of different languages ​​and cultural spheres. For example, we will build a system in which the generative AI takes into account the legal regulations of different languages ​​and cultural spheres and determines whether commercial use is permitted from a global perspective. For example, it will refer to the copyright and trademark laws of each country and evaluate the risk of commercial use. This makes it possible to evaluate the risk of commercial use by taking into account the legal regulations of different languages ​​and cultural spheres.

[0085] When combining images, the image generation unit automatically adjusts the color tone and style of the images to generate a unified, commercially available image. For example, when the generation AI combines images, it automatically adjusts the color tone and style of each image to generate a unified, commercially available image. For example, it adjusts images with different color tones to the same color tone. In this way, by adjusting the color tone and style of the images, it is possible to generate a unified, commercially available image.

[0086] When combining images, the image generation unit understands the context of the images and can generate commercially usable images that tell a story. For example, when the generation AI combines images, it understands the context of each image and generates commercially usable images that tell a story. For example, it combines images that have related themes or scenes. In this way, by understanding the context of the images, it can generate commercially usable images that tell a story.

[0087] The image generation unit can use the emotion estimation function to analyze the user's emotional response and generate images that elicit positive emotions. For example, a system can be constructed in which the generation AI uses the emotion estimation function to analyze the user's emotional response and generate images that elicit positive emotions. For example, images that elicit positive emotions can be automatically generated based on the user's emotional data. This makes it possible to generate images that elicit positive emotions, taking into account the user's emotional response.

[0088] The image generation unit generates 3D models and animations when combining images, making it possible to provide more interactive and commercially usable content. For example, when the generation AI combines images, it generates 3D models to provide more interactive and commercially usable content. For example, it generates a 3D model of a product, allowing users to freely rotate and view it. This allows the generation of 3D models and animations to provide more interactive and commercially usable content.

[0089] The image generation unit can use the emotion estimation function to prioritize generating combinations that evoke the most positive emotions in the user. For example, a system can be constructed in which the generation AI uses the emotion estimation function to prioritize generating combinations of images that evoke the most positive emotions in the user. For example, based on the user's emotion data, images that evoke positive emotions can be automatically combined. This makes it possible to generate combinations of images that evoke the most positive emotions in the user.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The commercial use eligibility determination unit determines whether an existing image can be used commercially. For example, the generation AI analyzes the image's copyright, license information, and terms of use to determine whether it can be used commercially. Step 2: The image generation unit generates a new commercially available image by combining existing images that are licensed for commercial use. For example, the generation AI appropriately combines images that are licensed for commercial use to generate a new image. Step 3: The image capture unit captures images posted on the Internet. For example, the generation AI captures the latest images on the Internet and uses them as learning data. Step 4: The Rights Protection Department protects the rights and interests of creators. For example, the Generative AI provides incentives for users to allow uploaded images to be used for training.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a commercial use permission determination unit that determines whether an existing image is commercially usable; an image generation unit that generates a new commercially available image by combining existing images whose commercial use has been permitted by the commercial use permission determination unit; an image capturing unit that captures images posted on the Internet; and a rights protection department that protects the rights and interests of creators. A system characterized by:

2. The commercial use permission determination unit Analyzes the image metadata as well as text and logos within the image to assess the risk of commercial use 2. The system of claim 1.

3. The commercial use permission determination unit The analysis also includes audio and video data, and evaluates the commercial applicability of the entire multimedia content.

2. The system of claim 1.

4. The image generation unit When combining the images, the color tone and style of the images are automatically adjusted to generate a uniform, commercially available image.

2. The system of claim 1.

5. The image capture unit When importing images from the internet, the reliability and origin of the images are evaluated, and only the highly reliable data is used for learning.

2. The system of claim 1.

6. The Rights Protection Department: Tracking the usage history of the image and automatically calculating compensation for the creator in order to protect the rights and interests of the creator.

2. The system of claim 1.

7. The commercial use permission determination unit Analyze users' emotional responses and select images that elicit positive emotions 2. The system of claim 1.

8. The image generation unit Analyzing the user's emotional response and generating images that evoke positive emotions 2. The system of claim 1.

Images