system

Abstract

The system according to the embodiment aims to construct a domestically produced large-scale image generation model by labeling images unique to Japan. [Solution] A system according to an embodiment includes a collection unit, a labeling unit, and a generation unit. The collection unit collects images including Japanese culture and scenery. The labeling unit performs labeling based on the images collected by the collection unit. The generation unit generates an AI model using the data labeled by the labeling unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, CAPTCHA systems relied on images from overseas, and there was a problem in that labeling using images unique to Japan was not sufficiently done.

[0005] The system according to the embodiment aims to construct a domestically produced large-scale image generation model by labeling images unique to Japan. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a labeling unit, and a generation unit. The collection unit collects images including Japanese culture and scenery. The labeling unit performs labeling based on the images collected by the collection unit. The generation unit generates an AI model using the data labeled by the labeling unit. [Effects of the Invention]

[0007] The system according to the embodiment performs labeling using images unique to Japan, and can build a domestically produced large-scale image generation model. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A CAPTCHA system according to an embodiment of the present invention collects images depicting Japanese culture and scenery, has Japanese users label them, and uses the data to generate an AI model. By using images unique to Japan, the CAPTCHA system aims to improve AI accuracy and security. For example, the CAPTCHA system collects images depicting Japanese culture and scenery and asks users to label them. The user clears the CAPTCHA by appropriately labeling the displayed images. This data is used to generate an AI model and develop AI that understands Japanese culture and scenery. This is expected to expand the use of AI both domestically and internationally. Potential applications include guidance systems at Japanese tourist destinations and cultural facilities, and product recommendation systems on online shopping sites. Furthermore, using image data unique to Japan improves AI accuracy and creates a system that is easier for users to use. Furthermore, this new CAPTCHA system also contributes to improved security. Conventional CAPTCHA systems rely on images from overseas, making them vulnerable to unauthorized access by users from overseas. However, using images unique to Japan prevents unauthorized access by non-Japanese users. This allows the CAPTCHA system to use images unique to Japan, improving the accuracy of AI and improving security. Potential applications include guidance systems at Japanese tourist destinations and cultural facilities, and product recommendation systems on online shopping sites. Using image data unique to Japan also improves the accuracy of AI and makes the system easier for users to use. Furthermore, this new CAPTCHA system also contributes to improved security. Conventional CAPTCHA systems rely on images from overseas, making it easy for overseas users to gain unauthorized access. However, using images unique to Japan makes it possible to prevent unauthorized access by non-Japanese users.

[0029] A CAPTCHA system according to an embodiment includes a collection unit, a labeling unit, and a generation unit. The collection unit collects images depicting Japanese culture and scenery. For example, the collection unit automatically collects images from the Internet. The collection unit can also accept image uploads from users. The collection unit can automatically classify images using AI and assign them to appropriate categories. For example, the collection unit crawls publicly available images on the Internet to collect images depicting Japanese culture and scenery. The collection unit can also receive images uploaded by users and automatically classify them using AI. The labeling unit performs labeling based on the images collected by the collection unit. For example, the labeling unit requests the user to attach appropriate labels to displayed images. The labeling unit can also automatically assign labels using AI. For example, the labeling unit requests the user to attach labels such as "cherry blossoms," "shrines," and "Mount Fuji" to displayed images. The labeling unit can also analyze the content of images using AI and automatically assign labels. The generation unit generates an AI model using the data labeled by the labeling unit. The generation unit builds an image generation model using, for example, deep learning technology. The generation unit can also improve the accuracy of the AI ​​using the collected image data. For example, the generation unit builds an image generation model using a generative adversarial network (GAN) using the labeled image data. The generation unit can also perform training to improve the accuracy of the AI ​​using the collected image data. As a result, the CAPTCHA system according to the embodiment can achieve improved accuracy and security of the AI ​​using images unique to Japan. For example, the generation unit can use the labeled image data to generate an AI model and develop an AI that understands culture and scenery unique to Japan.

[0030] The collection unit can collect images depicting Japanese culture and scenery. The collection unit, for example, automatically collects images from the Internet. The collection unit crawls public images on the Internet and collects images depicting Japanese culture and scenery. The collection unit can also accept image uploads from users. For example, the collection unit allows users to upload photos of Japanese festivals or images of traditional buildings. The collection unit can also automatically classify images using AI and assign them to appropriate categories. For example, the collection unit classifies collected images into categories such as "cherry blossoms," "shrines," and "Mount Fuji." This improves the accuracy of the AI ​​by collecting images depicting Japanese culture and scenery. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input image data collected from the Internet into a generation AI and have the generation AI classify the images.

[0031] The labeling unit allows a user to assign labels to displayed images. For example, the labeling unit requests the user to assign appropriate labels to displayed images. The labeling unit requests the user to assign labels such as "cherry blossoms," "shrine," or "Mount Fuji" to displayed images. The labeling unit can also automatically assign labels using AI. For example, the labeling unit can analyze the content of images using AI and automatically assign labels. This allows the user to assign appropriate labels, improving the accuracy of the AI. Some or all of the above-described processing in the labeling unit may be performed using AI, for example, or may be performed without using AI. For example, the labeling unit can input the labels assigned by the user to a generation AI and have the generation AI evaluate the accuracy of the labels.

[0032] The generation unit can generate an image generation model using the collected image data. The generation unit, for example, constructs the image generation model using deep learning technology. The generation unit constructs an image generation model using GAN (generative artificial network) using labeled image data. The generation unit can also perform training to improve the accuracy of the AI ​​using the collected image data. For example, the generation unit performs training to improve the accuracy of the AI ​​using the collected image data. This generates a large-scale image generation model, thereby improving the accuracy of the AI. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input labeled image data to the generation AI and cause the generation AI to construct an image generation model.

[0033] The generation unit can develop an AI that understands Japanese culture and scenery. The generation unit, for example, uses collected image data to develop an AI that understands Japanese culture and scenery. The generation unit uses the collected image data to perform training to improve the accuracy of the AI. For example, the generation unit uses the collected image data to perform training to develop an AI that understands Japanese culture and scenery. This develops an AI that understands culture and scenery unique to Japan, thereby improving the accuracy of the AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input collected image data into the generation AI and cause the generation AI to develop an AI that understands Japanese culture and scenery.

[0034] The generation unit can improve the performance of the AI ​​using the collected image data. For example, the generation unit uses the collected image data to perform training to improve the accuracy of the AI. For example, the generation unit uses the collected image data to perform training to improve the accuracy of the AI. As a result, the accuracy of the AI ​​is improved by using the collected image data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the collected image data to the generation AI and cause the generation AI to perform training to improve the accuracy of the AI.

[0035] When collecting images including culture and scenery unique to Japan, the collection unit can change the collection target based on the season or event. For example, the collection unit collects images of cherry blossoms in spring. The collection unit collects images of cherry blossoms in spring. The collection unit can also collect images of fireworks festivals in summer. For example, the collection unit collects images of fireworks festivals in summer. The collection unit can also collect images of snowy landscapes in winter. For example, the collection unit collects images of snowy landscapes in winter. In this way, by changing the collection target according to the season or event, a wider variety of images can be collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input image collection targets according to the season or event to the generation AI and cause the generation AI to change the collection target.

[0036] The collection unit can analyze the user's past image browsing history and prioritize collecting images of interest. For example, the collection unit prioritizes collecting cherry blossom images based on cherry blossom images viewed by the user in the past. The collection unit prioritizes collecting cherry blossom images based on cherry blossom images viewed by the user in the past. The collection unit can also prioritize collecting festival images based on festival images viewed by the user in the past. For example, the collection unit prioritizes collecting festival images based on festival images viewed by the user in the past. The collection unit can also prioritize collecting landscape images based on landscape images viewed by the user in the past. For example, the collection unit prioritizes collecting landscape images based on landscape images viewed by the user in the past. This allows for collecting more relevant images by collecting images based on the user's interests. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past image browsing history to the generation AI and cause the generation AI to prioritize collecting images of interest.

[0037] The collection unit can adjust the collection frequency for each region to collect images of different regions evenly. For example, the collection unit collects images of Hokkaido at a fixed frequency. The collection unit can also collect images of Okinawa at a fixed frequency. For example, the collection unit collects images of Okinawa at a fixed frequency. The collection unit can also collect images of urban areas and rural areas in a balanced manner. For example, the collection unit collects images of urban areas and rural areas in a balanced manner. In this way, by adjusting the collection frequency for each region, balanced image data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collection frequency for each region to the generation AI and cause the generation AI to adjust the collection frequency.

[0038] The collection unit can prioritize collecting highly relevant images based on the user's geographical location information. For example, if the user is in Tokyo, the collection unit prioritizes collecting images of Tokyo. If the user is in Tokyo, the collection unit prioritizes collecting images of Tokyo. Also, if the user is in Kyoto, the collection unit can prioritize collecting images of Kyoto. For example, if the user is in Kyoto, the collection unit prioritizes collecting images of Kyoto. Also, if the user is in Hokkaido, the collection unit can prioritize collecting images of Hokkaido. For example, if the user is in Hokkaido, the collection unit prioritizes collecting images of Hokkaido. In this way, by collecting images based on the user's geographical location information, more relevant images can be collected. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize collecting highly relevant images.

[0039] The collection unit can analyze images from social media and collect related images. For example, the collection unit collects images of tourist destinations that are popular on social media. The collection unit can also collect images of events that are trending on social media. For example, the collection unit collects images of events that are trending on social media. The collection unit can also collect images of scenery that are shared on social media. For example, the collection unit collects images of scenery that are shared on social media. In this way, by collecting images from social media, it is possible to collect images based on the latest trends. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data collected from social media to a generation AI and cause the generation AI to collect related images.

[0040] The collection unit can customize the collection method based on the user's past feedback. For example, the collection unit prioritizes collecting categories of images that the user has previously rated highly. The collection unit prioritizes collecting categories of images that the user has previously rated highly. The collection unit can also not collect categories of images that the user has previously rated poorly. For example, the collection unit does not collect categories of images that the user has previously rated poorly. The collection unit can also adjust the types of images to be collected based on the user's feedback. For example, the collection unit adjusts the types of images to be collected based on the user's feedback. In this way, by customizing the collection method based on the user's feedback, more appropriate images can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0041] The labeling unit can adjust the level of detail of the label based on the importance of the image. For example, the labeling unit assigns a detailed label to an important image. The labeling unit assigns a detailed label to an important image. The labeling unit can also assign a concise label to a general image. For example, the labeling unit assigns a concise label to a general image. The labeling unit can also assign a detailed label to an image that is of high interest to the user. For example, the labeling unit assigns a detailed label to an image that is of high interest to the user. In this way, by adjusting the level of detail of the label based on the importance of the image, more appropriate labels can be provided. Some or all of the above-mentioned processing in the labeling unit may be performed using AI, for example, or may be performed without using AI. For example, the labeling unit can input the importance of the image to the generation AI and cause the generation AI to adjust the level of detail of the label.

[0042] The labeling unit can apply different labeling algorithms based on the category of the image. For example, the labeling unit applies a labeling algorithm dedicated to landscapes to landscape images. The labeling unit can also apply a labeling algorithm dedicated to events to event images. For example, the labeling unit applies a labeling algorithm dedicated to events to event images. The labeling unit can also apply a labeling algorithm dedicated to culture to cultural images. For example, the labeling unit applies a labeling algorithm dedicated to culture to cultural images. In this way, by applying a labeling algorithm according to the category of the image, more appropriate labels can be provided. Some or all of the above-mentioned processing in the labeling unit may be performed using, for example, AI, or may be performed without using AI. For example, the labeling unit can input the category of the image to the generation AI and cause the generation AI to apply the labeling algorithm.

[0043] The labeling unit can improve the accuracy of labeling based on the user's past labeling results. For example, the labeling unit labels new images with reference to labels previously assigned by the user. The labeling unit labels new images with reference to labels previously assigned by the user. The labeling unit can also analyze the user's past labeling results and improve the labeling algorithm. For example, the labeling unit analyzes the user's past labeling results and improves the labeling algorithm. The labeling unit can also improve the accuracy of labeling based on the user's past labeling results. For example, the labeling unit improves the labeling accuracy based on the user's past labeling results. As a result, the accuracy of labeling is improved by referring to the user's past labeling results. Some or all of the above-described processing in the labeling unit may be performed using, for example, AI, or may be performed without using AI. For example, the labeling unit can input the user's past labeling results into the generation AI and cause the generation AI to improve the labeling accuracy.

[0044] The labeling unit can determine the priority of labels based on the time when the images were captured. For example, the labeling unit prioritizes labeling of recently captured images. The labeling unit prioritizes labeling of recently captured images. The labeling unit can also adjust the priority of labels according to the season. For example, the labeling unit adjusts the priority of labels according to the season. The labeling unit can also prioritize labeling of images captured around an event. For example, the labeling unit prioritizes labeling of images captured around an event. This allows for determining the priority of labels based on the time when the images were captured, thereby providing more appropriate labels. Some or all of the above-described processing in the labeling unit may be performed using AI, for example, or may be performed without using AI. For example, the labeling unit can input the time when the images were captured into the generation AI and cause the generation AI to determine the priority of labels.

[0045] The labeling unit can adjust the order of labels based on the relevance of the images. For example, the labeling unit prioritizes labeling highly relevant images. The labeling unit prioritizes labeling highly relevant images. The labeling unit can also postpone labeling low-relevance images. For example, the labeling unit postpones labeling low-relevance images. The labeling unit can also prioritize labeling images that are of high interest to the user. For example, the labeling unit prioritizes labeling images that are of high interest to the user. This makes it possible to provide more appropriate labels by adjusting the order of labels based on the relevance of the images. Some or all of the above-described processing in the labeling unit may be performed using AI, for example, or may be performed without using AI. For example, the labeling unit can input the relevance of the images to the generation AI and cause the generation AI to adjust the order of the labels.

[0046] The labeling unit can adjust the use of technical terms in the labels based on the user's level of expertise. For example, the labeling unit provides labels that use a lot of technical terms to users with high levels of expertise. The labeling unit can also provide labels in simple language to users with low levels of expertise. For example, the labeling unit provides labels in simple language to users with low levels of expertise. The labeling unit can also adjust the content of the labels according to the user's level of expertise. For example, the labeling unit adjusts the content of the labels according to the user's level of expertise. In this way, more appropriate labels can be provided by adjusting the content of the labels according to the user's level of expertise. Some or all of the above-described processing in the labeling unit may be performed using, for example, AI, or may be performed without using AI. For example, the labeling unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terms in the labels.

[0047] The generation unit can improve the accuracy of the AI ​​model based on the interrelationships of the collected image data. The generation unit, for example, analyzes the interrelationships of the collected image data and improves the accuracy of the AI ​​model. The generation unit can also adjust parameters of the AI ​​model by taking into account the interrelationships of the collected image data. For example, the generation unit adjusts parameters of the AI ​​model by taking into account the interrelationships of the collected image data. The generation unit can also improve the accuracy of the AI ​​model based on the interrelationships of the collected image data. For example, the generation unit improves the accuracy of the AI ​​model based on the interrelationships of the collected image data. As a result, the accuracy of the AI ​​model is improved by taking into account the interrelationships of the collected image data. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the interrelationships of the collected image data to the generation AI and cause the generation AI to improve the accuracy of the AI ​​model.

[0048] The generation unit can generate an AI model based on attribute information of the image submitter. For example, if the image submitter is an expert, the generation unit generates a specialized AI model. If the image submitter is an expert, the generation unit generates a specialized AI model. Furthermore, the generation unit can also generate a general AI model if the image submitter is a general user. For example, if the image submitter is a general user, the generation unit generates a general AI model. Furthermore, the generation unit can adjust parameters of the AI ​​model based on the attribute information of the image submitter. For example, the generation unit adjusts parameters of the AI ​​model based on the attribute information of the image submitter. This makes it possible to generate a more appropriate AI model by taking the attribute information of the image submitter into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input attribute information of the image submitter to the generation AI and cause the generation AI to generate an AI model.

[0049] The generation unit can weight the AI ​​model based on the frequency of image submission. For example, the generation unit assigns a higher weight to images that are submitted frequently. The generation unit assigns a higher weight to images that are submitted frequently. The generation unit can also assign a lower weight to images that are submitted infrequently. For example, the generation unit assigns a lower weight to images that are submitted infrequently. The generation unit can also adjust the weighting of the AI ​​model based on the submission frequency. For example, the generation unit adjusts the weighting of the AI ​​model based on the submission frequency. In this way, by weighting based on the frequency of image submission, a more appropriate AI model can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the frequency of image submission to the generation AI and cause the generation AI to weight the AI ​​model.

[0050] The generation unit can generate an AI model based on the geographic distribution of images. The generation unit, for example, analyzes the geographic distribution of images and adjusts parameters of the AI ​​model. The generation unit can also generate an AI model based on image data that is not geographically biased. For example, the generation unit generates an AI model based on image data that is not geographically biased. The generation unit can also improve the accuracy of the AI ​​model by taking the geographic distribution into account. For example, the generation unit improves the accuracy of the AI ​​model by taking the geographic distribution into account. In this way, a more appropriate AI model can be generated by taking the geographic distribution of images into account. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the geographic distribution of images to the generation AI and cause the generation AI to generate an AI model.

[0051] The generation unit can improve the accuracy of the AI ​​model based on literature related to the image. For example, the generation unit refers to literature related to the image and improves the accuracy of the AI ​​model. The generation unit refers to literature related to the image and improves the accuracy of the AI ​​model. The generation unit can also adjust parameters of the AI ​​model based on research papers related to the content of the image. For example, the generation unit adjusts parameters of the AI ​​model based on research papers related to the content of the image. The generation unit can also analyze literature related to the image and improve the accuracy of the AI ​​model. For example, the generation unit analyzes literature related to the image and improves the accuracy of the AI ​​model. As a result, the accuracy of the AI ​​model is improved by referring to literature related to the image. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input literature related to the image into the generation AI and cause the generation AI to improve the accuracy of the AI ​​model.

[0052] The generation unit can generate an AI model based on the market value of the image. For example, the generation unit assigns a high weight to an image with a high market value. The generation unit assigns a high weight to an image with a high market value. The generation unit can also assign a low weight to an image with a low market value. For example, the generation unit assigns a low weight to an image with a low market value. The generation unit can also adjust the parameters of the AI ​​model based on the market value of the image. For example, the generation unit adjusts the parameters of the AI ​​model based on the market value of the image. In this way, a more appropriate AI model can be generated by taking the market value of the image into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the market value of the image to the generation AI and cause the generation AI to generate an AI model.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can analyze the user's past search history and preferentially collect related images. For example, related images are collected based on keywords searched by the user in the past. The collection unit can also analyze the content of web pages viewed by the user in the past and collect related images. For example, related images are collected based on the content of web pages viewed by the user in the past. The collection unit can also analyze information on products purchased by the user in the past and collect related images. For example, related images are collected based on information on products purchased by the user in the past. In this way, by collecting images based on the user's past behavior, more relevant images can be collected.

[0055] The generation unit can use the collected image data to generate an AI model customized for users with different cultural backgrounds. For example, the collected image data is used to generate an AI model customized for users with a specific cultural background. The generation unit can also use the collected image data to generate an AI model customized for users who speak different languages. For example, the collected image data is used to generate an AI model customized for users who speak different languages. The generation unit can also use the collected image data to generate an AI model customized for users of a specific age group. For example, the collected image data is used to generate an AI model customized for users of a specific age group. In this way, by generating AI models customized according to different cultural backgrounds, languages, and age groups, it is possible to provide a more appropriate AI model.

[0056] The labeling unit can analyze the user's past labeling history and improve the accuracy of labeling. For example, the labeling unit labels new images with reference to labels previously assigned by the user. The labeling unit can also improve the labeling algorithm based on the user's past labeling history. For example, the labeling algorithm is improved based on the user's past labeling history. The labeling unit can also analyze the user's past labeling history and improve the accuracy of labeling. For example, the labeling accuracy is improved by analyzing the user's past labeling history. In this way, the accuracy of labeling is improved by referring to the user's past labeling history.

[0057] The generation unit can use the collected image data to generate an AI model based on a specific theme. For example, the collected image data is used to generate an AI model related to a specific season. The generation unit can also use the collected image data to generate an AI model related to a specific event. For example, the collected image data is used to generate an AI model related to a specific event. The generation unit can also use the collected image data to generate an AI model related to a specific region. For example, the collected image data is used to generate an AI model related to a specific region. In this way, by generating an AI model based on a specific theme, a more appropriate AI model can be provided.

[0058] The generation unit can use the collected image data to generate an AI model customized for a specific user group. For example, the collected image data can be used to generate an AI model customized for users of a specific age group. The generation unit can also use the collected image data to generate an AI model customized for users of a specific occupation. For example, the collected image data can be used to generate an AI model customized for users of a specific occupation. The generation unit can also use the collected image data to generate an AI model customized for users with a specific hobby. For example, the collected image data can be used to generate an AI model customized for users with a specific hobby. In this way, by generating an AI model customized for a specific user group, it is possible to provide a more appropriate AI model.

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

[0060] Step 1: The collection unit collects images that include Japanese culture and scenery. The collection unit has the function of automatically collecting images from the Internet and can also accept image uploads from users. The collection unit can also use AI to automatically classify images and assign them to appropriate categories. For example, it can crawl public images on the Internet and collect images that include Japanese culture and scenery. It can also receive images uploaded by users and automatically classify them using AI. Step 2: The labeling unit performs labeling based on the images collected by the collection unit. The labeling unit can ask the user to label the displayed image appropriately, or it can use AI to automatically label the image. For example, the user can ask the unit to label the displayed image with labels such as "cherry blossoms," "shrine," or "Mount Fuji." It can also use AI to analyze the content of the image and automatically label it. Step 3: The generation unit generates an AI model using the data labeled by the labeling unit. The generation unit can build an image generation model using deep learning technology and improve the accuracy of the AI ​​using the collected image data. For example, the generation unit can build an image generation model using a GAN (generative artificial network) using the labeled image data. The generation unit can also use the collected image data to train the AI ​​to improve its accuracy.

[0061] (Example 2) A CAPTCHA system according to an embodiment of the present invention collects images depicting Japanese culture and scenery, has Japanese users label them, and uses the data to generate an AI model. By using images unique to Japan, the CAPTCHA system aims to improve AI accuracy and security. For example, the CAPTCHA system collects images depicting Japanese culture and scenery and asks users to label them. The user clears the CAPTCHA by appropriately labeling the displayed images. This data is used to generate an AI model and develop AI that understands Japanese culture and scenery. This is expected to expand the use of AI both domestically and internationally. Potential applications include guidance systems at Japanese tourist destinations and cultural facilities, and product recommendation systems on online shopping sites. Furthermore, using image data unique to Japan improves AI accuracy and creates a system that is easier for users to use. Furthermore, this new CAPTCHA system also contributes to improved security. Conventional CAPTCHA systems rely on images from overseas, making them vulnerable to unauthorized access by users from overseas. However, using images unique to Japan prevents unauthorized access by non-Japanese users. This allows the CAPTCHA system to use images unique to Japan, improving the accuracy of AI and improving security. Potential applications include guidance systems at Japanese tourist destinations and cultural facilities, and product recommendation systems on online shopping sites. Using image data unique to Japan also improves the accuracy of AI and makes the system easier for users to use. Furthermore, this new CAPTCHA system also contributes to improved security. Conventional CAPTCHA systems rely on images from overseas, making it easy for overseas users to gain unauthorized access. However, using images unique to Japan makes it possible to prevent unauthorized access by non-Japanese users.

[0062] A CAPTCHA system according to an embodiment includes a collection unit, a labeling unit, and a generation unit. The collection unit collects images depicting Japanese culture and scenery. For example, the collection unit automatically collects images from the Internet. The collection unit can also accept image uploads from users. The collection unit can automatically classify images using AI and assign them to appropriate categories. For example, the collection unit crawls publicly available images on the Internet to collect images depicting Japanese culture and scenery. The collection unit can also receive images uploaded by users and automatically classify them using AI. The labeling unit performs labeling based on the images collected by the collection unit. For example, the labeling unit requests the user to attach appropriate labels to displayed images. The labeling unit can also automatically assign labels using AI. For example, the labeling unit requests the user to attach labels such as "cherry blossoms," "shrines," and "Mount Fuji" to displayed images. The labeling unit can also analyze the content of images using AI and automatically assign labels. The generation unit generates an AI model using the data labeled by the labeling unit. The generation unit builds an image generation model using, for example, deep learning technology. The generation unit can also improve the accuracy of the AI ​​using the collected image data. For example, the generation unit builds an image generation model using a generative adversarial network (GAN) using the labeled image data. The generation unit can also perform training to improve the accuracy of the AI ​​using the collected image data. As a result, the CAPTCHA system according to the embodiment can achieve improved accuracy and security of the AI ​​using images unique to Japan. For example, the generation unit can use the labeled image data to generate an AI model and develop an AI that understands culture and scenery unique to Japan.

[0063] The collection unit can collect images depicting Japanese culture and scenery. The collection unit, for example, automatically collects images from the Internet. The collection unit crawls public images on the Internet and collects images depicting Japanese culture and scenery. The collection unit can also accept image uploads from users. For example, the collection unit allows users to upload photos of Japanese festivals or images of traditional buildings. The collection unit can also automatically classify images using AI and assign them to appropriate categories. For example, the collection unit classifies collected images into categories such as "cherry blossoms," "shrines," and "Mount Fuji." This improves the accuracy of the AI ​​by collecting images depicting Japanese culture and scenery. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input image data collected from the Internet into a generation AI and have the generation AI classify the images.

[0064] The labeling unit allows a user to assign labels to displayed images. For example, the labeling unit requests the user to assign appropriate labels to displayed images. The labeling unit requests the user to assign labels such as "cherry blossoms," "shrine," or "Mount Fuji" to displayed images. The labeling unit can also automatically assign labels using AI. For example, the labeling unit can analyze the content of images using AI and automatically assign labels. This allows the user to assign appropriate labels, improving the accuracy of the AI. Some or all of the above-described processing in the labeling unit may be performed using AI, for example, or may be performed without using AI. For example, the labeling unit can input the labels assigned by the user to a generation AI and have the generation AI evaluate the accuracy of the labels.

[0065] The generation unit can generate an image generation model using the collected image data. The generation unit, for example, constructs the image generation model using deep learning technology. The generation unit constructs an image generation model using GAN (generative artificial network) using labeled image data. The generation unit can also perform training to improve the accuracy of the AI ​​using the collected image data. For example, the generation unit performs training to improve the accuracy of the AI ​​using the collected image data. This generates a large-scale image generation model, thereby improving the accuracy of the AI. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input labeled image data to the generation AI and cause the generation AI to construct an image generation model.

[0066] The generation unit can develop an AI that understands Japanese culture and scenery. The generation unit, for example, uses collected image data to develop an AI that understands Japanese culture and scenery. The generation unit uses the collected image data to perform training to improve the accuracy of the AI. For example, the generation unit uses the collected image data to perform training to develop an AI that understands Japanese culture and scenery. This develops an AI that understands culture and scenery unique to Japan, thereby improving the accuracy of the AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input collected image data into the generation AI and cause the generation AI to develop an AI that understands Japanese culture and scenery.

[0067] The generation unit can improve the performance of the AI ​​using the collected image data. For example, the generation unit uses the collected image data to perform training to improve the accuracy of the AI. For example, the generation unit uses the collected image data to perform training to improve the accuracy of the AI. As a result, the accuracy of the AI ​​is improved by using the collected image data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the collected image data to the generation AI and cause the generation AI to perform training to improve the accuracy of the AI.

[0068] The collection unit can analyze the user's emotions and adjust the timing of image collection based on the analyzed user's emotions. For example, when the user is relaxed, the collection unit periodically collects images. When the user is relaxed, the collection unit periodically collects images. The collection unit can also reduce the frequency of image collection when the user is stressed. For example, when the user is stressed, the collection unit reduces the frequency of image collection. The collection unit can also increase the frequency of image collection when the user is excited. For example, when the user is excited, the collection unit increases the frequency of image collection. This allows more appropriate images to be collected by adjusting the timing of image collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input user emotion data into the generation AI and cause the generation AI to adjust the timing of image collection.

[0069] When collecting images including culture and scenery unique to Japan, the collection unit can change the collection target based on the season or event. For example, the collection unit collects images of cherry blossoms in spring. The collection unit collects images of cherry blossoms in spring. The collection unit can also collect images of fireworks festivals in summer. For example, the collection unit collects images of fireworks festivals in summer. The collection unit can also collect images of snowy landscapes in winter. For example, the collection unit collects images of snowy landscapes in winter. In this way, by changing the collection target according to the season or event, a wider variety of images can be collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input image collection targets according to the season or event to the generation AI and cause the generation AI to change the collection target.

[0070] The collection unit can analyze the user's past image browsing history and prioritize collecting images of interest. For example, the collection unit prioritizes collecting cherry blossom images based on cherry blossom images viewed by the user in the past. The collection unit prioritizes collecting cherry blossom images based on cherry blossom images viewed by the user in the past. The collection unit can also prioritize collecting festival images based on festival images viewed by the user in the past. For example, the collection unit prioritizes collecting festival images based on festival images viewed by the user in the past. The collection unit can also prioritize collecting landscape images based on landscape images viewed by the user in the past. For example, the collection unit prioritizes collecting landscape images based on landscape images viewed by the user in the past. This allows for collecting more relevant images by collecting images based on the user's interests. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past image browsing history to the generation AI and cause the generation AI to prioritize collecting images of interest.

[0071] The collection unit can adjust the collection frequency for each region to collect images of different regions evenly. For example, the collection unit collects images of Hokkaido at a fixed frequency. The collection unit can also collect images of Okinawa at a fixed frequency. For example, the collection unit collects images of Okinawa at a fixed frequency. The collection unit can also collect images of urban areas and rural areas in a balanced manner. For example, the collection unit collects images of urban areas and rural areas in a balanced manner. In this way, by adjusting the collection frequency for each region, balanced image data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collection frequency for each region to the generation AI and cause the generation AI to adjust the collection frequency.

[0072] The collection unit can analyze the user's emotions and determine the priority of images to be collected based on the analyzed user's emotions. For example, if the user is relaxed, the collection unit prioritizes collecting landscape images. If the user is relaxed, the collection unit prioritizes collecting landscape images. Furthermore, if the user is excited, the collection unit can also prioritize collecting event images. For example, if the user is excited, the collection unit prioritizes collecting event images. Furthermore, if the user is stressed, the collection unit can also prioritize collecting nature images. For example, if the user is stressed, the collection unit prioritizes collecting nature images. This allows for more appropriate image collection by determining the priority of images based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of images.

[0073] The collection unit can prioritize collecting highly relevant images based on the user's geographical location information. For example, if the user is in Tokyo, the collection unit prioritizes collecting images of Tokyo. If the user is in Tokyo, the collection unit prioritizes collecting images of Tokyo. Also, if the user is in Kyoto, the collection unit can prioritize collecting images of Kyoto. For example, if the user is in Kyoto, the collection unit prioritizes collecting images of Kyoto. Also, if the user is in Hokkaido, the collection unit can prioritize collecting images of Hokkaido. For example, if the user is in Hokkaido, the collection unit prioritizes collecting images of Hokkaido. In this way, by collecting images based on the user's geographical location information, more relevant images can be collected. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize collecting highly relevant images.

[0074] The collection unit can analyze images from social media and collect related images. For example, the collection unit collects images of tourist destinations that are popular on social media. The collection unit can also collect images of events that are trending on social media. For example, the collection unit collects images of events that are trending on social media. The collection unit can also collect images of scenery that are shared on social media. For example, the collection unit collects images of scenery that are shared on social media. In this way, by collecting images from social media, it is possible to collect images based on the latest trends. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data collected from social media to a generation AI and cause the generation AI to collect related images.

[0075] The collection unit can customize the collection method based on the user's past feedback. For example, the collection unit prioritizes collecting categories of images that the user has previously rated highly. The collection unit prioritizes collecting categories of images that the user has previously rated highly. The collection unit can also not collect categories of images that the user has previously rated poorly. For example, the collection unit does not collect categories of images that the user has previously rated poorly. The collection unit can also adjust the types of images to be collected based on the user's feedback. For example, the collection unit adjusts the types of images to be collected based on the user's feedback. In this way, by customizing the collection method based on the user's feedback, more appropriate images can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0076] The labeling unit can analyze the user's emotions and adjust the labeling expression method based on the analyzed user's emotions. For example, the labeling unit provides a detailed label when the user is relaxed. The labeling unit can also provide a concise label when the user is stressed. For example, the labeling unit provides a concise label when the user is stressed. The labeling unit can also provide a visually appealing label when the user is excited. For example, the labeling unit provides a visually appealing label when the user is excited. This allows for adjusting the labeling expression method based on the user's emotions to provide a more appropriate label. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the labeling unit can be performed, for example, using AI or without AI. For example, the labeling unit can input the user's emotional data into the generation AI and have the generation AI adjust the labeling expression method.

[0077] The labeling unit can adjust the level of detail of the label based on the importance of the image. For example, the labeling unit assigns a detailed label to an important image. The labeling unit assigns a detailed label to an important image. The labeling unit can also assign a concise label to a general image. For example, the labeling unit assigns a concise label to a general image. The labeling unit can also assign a detailed label to an image that is of high interest to the user. For example, the labeling unit assigns a detailed label to an image that is of high interest to the user. In this way, by adjusting the level of detail of the label based on the importance of the image, more appropriate labels can be provided. Some or all of the above-mentioned processing in the labeling unit may be performed using AI, for example, or may be performed without using AI. For example, the labeling unit can input the importance of the image to the generation AI and cause the generation AI to adjust the level of detail of the label.

[0078] The labeling unit can apply different labeling algorithms based on the category of the image. For example, the labeling unit applies a labeling algorithm dedicated to landscapes to landscape images. The labeling unit can also apply a labeling algorithm dedicated to events to event images. For example, the labeling unit applies a labeling algorithm dedicated to events to event images. The labeling unit can also apply a labeling algorithm dedicated to culture to cultural images. For example, the labeling unit applies a labeling algorithm dedicated to culture to cultural images. In this way, by applying a labeling algorithm according to the category of the image, more appropriate labels can be provided. Some or all of the above-mentioned processing in the labeling unit may be performed using, for example, AI, or may be performed without using AI. For example, the labeling unit can input the category of the image to the generation AI and cause the generation AI to apply the labeling algorithm.

[0079] The labeling unit can improve the accuracy of labeling based on the user's past labeling results. For example, the labeling unit labels new images with reference to labels previously assigned by the user. The labeling unit labels new images with reference to labels previously assigned by the user. The labeling unit can also analyze the user's past labeling results and improve the labeling algorithm. For example, the labeling unit analyzes the user's past labeling results and improves the labeling algorithm. The labeling unit can also improve the accuracy of labeling based on the user's past labeling results. For example, the labeling unit improves the labeling accuracy based on the user's past labeling results. As a result, the accuracy of labeling is improved by referring to the user's past labeling results. Some or all of the above-described processing in the labeling unit may be performed using, for example, AI, or may be performed without using AI. For example, the labeling unit can input the user's past labeling results into the generation AI and cause the generation AI to improve the labeling accuracy.

[0080] The labeling unit can analyze the user's emotions and adjust the length of the labeling based on the analyzed user's emotions. For example, the labeling unit provides a longer label when the user is relaxed. The labeling unit can also provide a shorter label when the user is stressed. For example, the labeling unit provides a shorter label when the user is stressed. The labeling unit can also provide a visually appealing label when the user is excited. For example, the labeling unit provides a visually appealing label when the user is excited. This allows for adjusting the length of the labeling based on the user's emotions to provide a more appropriate label. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the labeling unit can be performed, for example, using AI or without AI. For example, the labeling unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the labeling.

[0081] The labeling unit can determine the priority of labels based on the time when the images were captured. For example, the labeling unit prioritizes labeling of recently captured images. The labeling unit prioritizes labeling of recently captured images. The labeling unit can also adjust the priority of labels according to the season. For example, the labeling unit adjusts the priority of labels according to the season. The labeling unit can also prioritize labeling of images captured around an event. For example, the labeling unit prioritizes labeling of images captured around an event. This allows for determining the priority of labels based on the time when the images were captured, thereby providing more appropriate labels. Some or all of the above-described processing in the labeling unit may be performed using AI, for example, or may be performed without using AI. For example, the labeling unit can input the time when the images were captured into the generation AI and cause the generation AI to determine the priority of labels.

[0082] The labeling unit can adjust the order of labels based on the relevance of the images. For example, the labeling unit prioritizes labeling highly relevant images. The labeling unit prioritizes labeling highly relevant images. The labeling unit can also postpone labeling low-relevance images. For example, the labeling unit postpones labeling low-relevance images. The labeling unit can also prioritize labeling images that are of high interest to the user. For example, the labeling unit prioritizes labeling images that are of high interest to the user. This makes it possible to provide more appropriate labels by adjusting the order of labels based on the relevance of the images. Some or all of the above-described processing in the labeling unit may be performed using AI, for example, or may be performed without using AI. For example, the labeling unit can input the relevance of the images to the generation AI and cause the generation AI to adjust the order of the labels.

[0083] The labeling unit can adjust the use of technical terms in the labels based on the user's level of expertise. For example, the labeling unit provides labels that use a lot of technical terms to users with high levels of expertise. The labeling unit can also provide labels in simple language to users with low levels of expertise. For example, the labeling unit provides labels in simple language to users with low levels of expertise. The labeling unit can also adjust the content of the labels according to the user's level of expertise. For example, the labeling unit adjusts the content of the labels according to the user's level of expertise. In this way, more appropriate labels can be provided by adjusting the content of the labels according to the user's level of expertise. Some or all of the above-described processing in the labeling unit may be performed using, for example, AI, or may be performed without using AI. For example, the labeling unit can input the user's level of expertise to the generation AI and cause the generation AI to use technical terms in the labels.

[0084] The generation unit can analyze the user's emotions and adjust the parameters of the AI ​​model to be generated based on the analyzed user's emotions. For example, if the user is relaxed, the generation unit generates a model in which the generation AI proceeds at a leisurely pace. For example, if the user is relaxed, the generation unit generates a model in which the generation AI proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a model in which the generation AI emphasizes the shortest route. For example, if the user is in a hurry, the generation unit generates a model in which the generation AI emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a model in which the generation AI adds visually stimulating effects. For example, if the user is excited, the generation unit generates a model in which the generation AI adds visually stimulating effects. In this way, by adjusting the parameters of the AI ​​model based on the user's emotions, a more appropriate model can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data to the generation AI and cause the generation AI to adjust the parameters of the AI ​​model.

[0085] The generation unit can improve the accuracy of the AI ​​model based on the interrelationships of the collected image data. The generation unit, for example, analyzes the interrelationships of the collected image data and improves the accuracy of the AI ​​model. The generation unit can also adjust parameters of the AI ​​model by taking into account the interrelationships of the collected image data. For example, the generation unit adjusts parameters of the AI ​​model by taking into account the interrelationships of the collected image data. The generation unit can also improve the accuracy of the AI ​​model based on the interrelationships of the collected image data. For example, the generation unit improves the accuracy of the AI ​​model based on the interrelationships of the collected image data. As a result, the accuracy of the AI ​​model is improved by taking into account the interrelationships of the collected image data. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the interrelationships of the collected image data to the generation AI and cause the generation AI to improve the accuracy of the AI ​​model.

[0086] The generation unit can generate an AI model based on attribute information of the image submitter. For example, if the image submitter is an expert, the generation unit generates a specialized AI model. If the image submitter is an expert, the generation unit generates a specialized AI model. Furthermore, the generation unit can also generate a general AI model if the image submitter is a general user. For example, if the image submitter is a general user, the generation unit generates a general AI model. Furthermore, the generation unit can adjust parameters of the AI ​​model based on the attribute information of the image submitter. For example, the generation unit adjusts parameters of the AI ​​model based on the attribute information of the image submitter. This makes it possible to generate a more appropriate AI model by taking the attribute information of the image submitter into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input attribute information of the image submitter to the generation AI and cause the generation AI to generate an AI model.

[0087] The generation unit can weight the AI ​​model based on the frequency of image submission. For example, the generation unit assigns a higher weight to images that are submitted frequently. The generation unit assigns a higher weight to images that are submitted frequently. The generation unit can also assign a lower weight to images that are submitted infrequently. For example, the generation unit assigns a lower weight to images that are submitted infrequently. The generation unit can also adjust the weighting of the AI ​​model based on the submission frequency. For example, the generation unit adjusts the weighting of the AI ​​model based on the submission frequency. In this way, by weighting based on the frequency of image submission, a more appropriate AI model can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the frequency of image submission to the generation AI and cause the generation AI to weight the AI ​​model.

[0088] The generation unit can analyze the user's emotions and adjust the display method of the generated AI model based on the analyzed user's emotions. For example, when the user is relaxed, the generation AI provides a relaxed display method. When the user is relaxed, the generation AI provides a relaxed display method. Furthermore, when the user is in a hurry, the generation unit can provide a concise display method. For example, when the user is in a hurry, the generation AI provides a concise display method. Furthermore, when the user is excited, the generation AI can provide a visually stimulating display method. For example, when the user is excited, the generation AI provides a visually stimulating display method. This enables a more appropriate display by adjusting the display method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the display method of the AI ​​model.

[0089] The generation unit can generate an AI model based on the geographic distribution of images. The generation unit, for example, analyzes the geographic distribution of images and adjusts parameters of the AI ​​model. The generation unit can also generate an AI model based on image data that is not geographically biased. For example, the generation unit generates an AI model based on image data that is not geographically biased. The generation unit can also improve the accuracy of the AI ​​model by taking the geographic distribution into account. For example, the generation unit improves the accuracy of the AI ​​model by taking the geographic distribution into account. In this way, a more appropriate AI model can be generated by taking the geographic distribution of images into account. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the geographic distribution of images to the generation AI and cause the generation AI to generate an AI model.

[0090] The generation unit can improve the accuracy of the AI ​​model based on literature related to the image. For example, the generation unit refers to literature related to the image and improves the accuracy of the AI ​​model. The generation unit refers to literature related to the image and improves the accuracy of the AI ​​model. The generation unit can also adjust parameters of the AI ​​model based on research papers related to the content of the image. For example, the generation unit adjusts parameters of the AI ​​model based on research papers related to the content of the image. The generation unit can also analyze literature related to the image and improve the accuracy of the AI ​​model. For example, the generation unit analyzes literature related to the image and improves the accuracy of the AI ​​model. As a result, the accuracy of the AI ​​model is improved by referring to literature related to the image. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input literature related to the image into the generation AI and cause the generation AI to improve the accuracy of the AI ​​model.

[0091] The generation unit can generate an AI model based on the market value of the image. For example, the generation unit assigns a high weight to an image with a high market value. The generation unit assigns a high weight to an image with a high market value. The generation unit can also assign a low weight to an image with a low market value. For example, the generation unit assigns a low weight to an image with a low market value. The generation unit can also adjust the parameters of the AI ​​model based on the market value of the image. For example, the generation unit adjusts the parameters of the AI ​​model based on the market value of the image. In this way, a more appropriate AI model can be generated by taking the market value of the image into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the market value of the image to the generation AI and cause the generation AI to generate an AI model. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, labeling unit, generation unit, and emotion analysis function, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects images depicting Japanese culture and scenery using the camera 42 and communication I / F 44 of the smart device 14, and the images are automatically classified by the specific processing unit 290 of the data processing device 12. The labeling unit uses the control unit 46A of the smart device 14 to request a user to label the images, and the generation unit generates an AI model using the data labeled by the specific processing unit 290 of the data processing device 12. The emotion analysis function analyzes the user's emotions using the microphone 38B and camera 42 of the smart device 14 and adjusts the timing of image collection by the collection unit. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, labeling unit, generation unit, and emotion analysis function, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects images containing Japanese culture and scenery using the camera 42 and communication I / F 44 of the smart glasses 214, and the images are automatically classified by the specific processing unit 290 of the data processing device 12. The labeling unit uses the control unit 46A of the smart glasses 214 to request the user to label the images, and the generation unit generates an AI model using the data labeled by the specific processing unit 290 of the data processing device 12. The emotion analysis function analyzes the user's emotions using the microphone 238 and camera 42 of the smart glasses 214 and adjusts the timing of image collection by the collection unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, labeling unit, generation unit, and emotion analysis function is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects images containing Japanese culture and scenery using the camera 42 and communication I / F 44 of the headset-type terminal 314, and the images are automatically classified by the specific processing unit 290 of the data processing device 12. The labeling unit uses the control unit 46A of the headset-type terminal 314 to request the user to label the images, and the generation unit generates an AI model using the data labeled by the specific processing unit 290 of the data processing device 12. The emotion analysis function analyzes the user's emotions using the microphone 238 and camera 42 of the headset-type terminal 314 and adjusts the timing of image collection by the collection unit. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, labeling unit, generation unit, and emotion analysis function described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects images containing Japanese culture and scenery using the camera 42 and communication I / F 44 of the robot 414, and the images are automatically classified by the specific processing unit 290 of the data processing device 12. The labeling unit uses the control unit 46A of the robot 414 to request a user to label the images, and the generation unit generates an AI model using the data labeled by the specific processing unit 290 of the data processing device 12. The emotion analysis function analyzes the user's emotions using the microphone 238 and camera 42 of the robot 414 and adjusts the timing of image collection by the collection unit.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The collection unit can analyze the user's past search history and preferentially collect related images. For example, related images are collected based on keywords searched by the user in the past. The collection unit can also analyze the content of web pages viewed by the user in the past and collect related images. For example, related images are collected based on the content of web pages viewed by the user in the past. The collection unit can also analyze information on products purchased by the user in the past and collect related images. For example, related images are collected based on information on products purchased by the user in the past. In this way, by collecting images based on the user's past behavior, more relevant images can be collected.

[0094] The labeling unit can analyze the user's emotions and adjust the difficulty of labeling based on the analyzed user's emotions. For example, if the user is relaxed, a label with a high level of difficulty can be provided. Also, if the user is stressed, a label with a low level of difficulty can be provided. For example, if the user is stressed, a simple label can be provided. Also, if the user is excited, a visually appealing label can be provided. For example, if the user is excited, a visually appealing label can be provided. In this way, by adjusting the difficulty of labeling based on the user's emotions, more appropriate labels can be provided.

[0095] The generation unit can use the collected image data to generate an AI model customized for users with different cultural backgrounds. For example, the collected image data is used to generate an AI model customized for users with a specific cultural background. The generation unit can also use the collected image data to generate an AI model customized for users who speak different languages. For example, the collected image data is used to generate an AI model customized for users who speak different languages. The generation unit can also use the collected image data to generate an AI model customized for users of a specific age group. For example, the collected image data is used to generate an AI model customized for users of a specific age group. In this way, by generating AI models customized according to different cultural backgrounds, languages, and age groups, it is possible to provide a more appropriate AI model.

[0096] The collection unit can analyze the user's emotions and determine the category of images to be collected based on the analyzed user's emotions. For example, if the user is relaxed, images of natural scenery can be preferentially collected. Also, if the user is feeling stressed, images that have a relaxing effect can be preferentially collected. For example, if the user is feeling stressed, images that have a relaxing effect can be preferentially collected. Also, if the user is excited, images of events and activities can be preferentially collected. For example, if the user is excited, images of events and activities can be preferentially collected. In this way, by determining the category of images to be collected based on the user's emotions, more appropriate images can be collected.

[0097] The labeling unit can analyze the user's past labeling history and improve the accuracy of labeling. For example, the labeling unit labels new images with reference to labels previously assigned by the user. The labeling unit can also improve the labeling algorithm based on the user's past labeling history. For example, the labeling algorithm is improved based on the user's past labeling history. The labeling unit can also analyze the user's past labeling history and improve the accuracy of labeling. For example, the labeling accuracy is improved by analyzing the user's past labeling history. In this way, the accuracy of labeling is improved by referring to the user's past labeling history.

[0098] The generation unit can use the collected image data to generate an AI model based on a specific theme. For example, the collected image data is used to generate an AI model related to a specific season. The generation unit can also use the collected image data to generate an AI model related to a specific event. For example, the collected image data is used to generate an AI model related to a specific event. The generation unit can also use the collected image data to generate an AI model related to a specific region. For example, the collected image data is used to generate an AI model related to a specific region. In this way, by generating an AI model based on a specific theme, a more appropriate AI model can be provided.

[0099] The collection unit can analyze the user's emotions and filter the images to be collected based on the analyzed user's emotions. For example, if the user is relaxed, images of calm scenery can be preferentially collected. Also, if the user is feeling stressed, images that have a relaxing effect can be preferentially collected. For example, if the user is feeling stressed, images that have a relaxing effect can be preferentially collected. Also, if the user is excited, images of active scenes can be preferentially collected. For example, if the user is excited, images of active scenes can be preferentially collected. In this way, by filtering the images to be collected based on the user's emotions, more appropriate images can be collected.

[0100] The labeling unit can analyze the user's emotions and adjust the labeling interface based on the analyzed user's emotions. For example, if the user is relaxed, a simple and intuitive interface can be provided. Also, if the user is stressed, an easy-to-use interface can be provided. For example, if the user is stressed, an easy-to-use interface can be provided. Also, if the user is excited, a visually appealing interface can be provided. For example, if the user is excited, a visually appealing interface can be provided. In this way, by adjusting the labeling interface based on the user's emotions, more appropriate labels can be provided.

[0101] The generation unit can use the collected image data to generate an AI model customized for a specific user group. For example, the collected image data can be used to generate an AI model customized for users of a specific age group. The generation unit can also use the collected image data to generate an AI model customized for users of a specific occupation. For example, the collected image data can be used to generate an AI model customized for users of a specific occupation. The generation unit can also use the collected image data to generate an AI model customized for users with a specific hobby. For example, the collected image data can be used to generate an AI model customized for users with a specific hobby. In this way, by generating an AI model customized for a specific user group, it is possible to provide a more appropriate AI model.

[0102] The collection unit can analyze the user's emotions and adjust the resolution of the images to be collected based on the analyzed user's emotions. For example, if the user is relaxed, high-resolution images can be collected preferentially. Also, if the user is feeling stressed, low-resolution images can be collected preferentially. For example, if the user is feeling stressed, low-resolution images can be collected preferentially. Also, if the user is excited, visually stimulating high-resolution images can be collected preferentially. For example, if the user is excited, visually stimulating high-resolution images can be collected preferentially. In this way, by adjusting the resolution of the images to be collected based on the user's emotions, more appropriate images can be collected.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects images that include Japanese culture and scenery. The collection unit has the function of automatically collecting images from the Internet and can also accept image uploads from users. The collection unit can also use AI to automatically classify images and assign them to appropriate categories. For example, it can crawl public images on the Internet and collect images that include Japanese culture and scenery. It can also receive images uploaded by users and automatically classify them using AI. Step 2: The labeling unit performs labeling based on the images collected by the collection unit. The labeling unit can ask the user to label the displayed image appropriately, or it can use AI to automatically label the image. For example, the user can ask the unit to label the displayed image with labels such as "cherry blossoms," "shrine," or "Mount Fuji." It can also use AI to analyze the content of the image and automatically label it. Step 3: The generation unit generates an AI model using the data labeled by the labeling unit. The generation unit can build an image generation model using deep learning technology and improve the accuracy of the AI ​​using the collected image data. For example, the generation unit can build an image generation model using a GAN (generative artificial network) using the labeled image data. The generation unit can also use the collected image data to train the AI ​​to improve its accuracy.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

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

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[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 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.

[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 (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).

[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] 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.

[0133] 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.

[0134] 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.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 collection department that collects images of Japanese culture and scenery; a labeling unit that performs labeling based on the images collected by the collecting unit; a generation unit that generates an AI model using the data labeled by the labeling unit. A system characterized by:

2. The collecting unit Collect images that include Japanese culture and scenery 2. The system of claim 1.

3. The labeling unit includes: The user labels the displayed image 2. The system of claim 1.

4. The generation unit Generate an image generation model using collected image data 2. The system of claim 1.

5. The generation unit Developing AI that understands Japanese culture and scenery 2. The system of claim 1.

6. The generation unit Using collected image data to improve AI performance 2. The system of claim 1.

7. The collecting unit Analyzing user emotions and adjusting the timing of image collection based on the analyzed user emotions 2. The system of claim 1.

8. The collecting unit When collecting images that include unique Japanese culture and scenery, change the collection target based on the season or event.

2. The system of claim 1.

Images