system

Abstract

The system according to the embodiment aims to enable anyone to easily create cosplay costumes. [Solution] A system according to an embodiment includes a photographing unit, an analysis unit, a capture unit, and a generation unit. The photographing unit captures images of a user's body from all directions. The analysis unit analyzes the images captured by the photographing unit to extract dimensions. The capture unit captures images of a costume. The generation unit analyzes the images captured by the capture unit to generate a pattern.

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, creating cosplay costumes requires time-consuming, accurate measurements and the creation of patterns, making it difficult for beginners.

[0005] The system according to the embodiment aims to enable anyone to easily create cosplay costumes. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, a capture unit, and a generation unit. The photographing unit captures images of the user's body from all directions. The analysis unit analyzes the images captured by the photographing unit to extract dimensions. The capture unit captures images of the costume. The generation unit analyzes the images captured by the capture unit to generate a pattern. [Effects of the Invention]

[0007] The system according to the embodiment can enable anyone to easily create cosplay costumes. [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) In an embodiment of the present invention, a costume generation system allows a user to photograph their own body from all angles and input the images into an AI to obtain accurate measurements. The system then generates patterns by importing costume images from animations or videos. This costume generation system allows a user to photograph their own body from all angles and input the images into an AI to extract accurate measurements. The AI ​​then analyzes the photographed images and extracts the dimensions of each body part. Furthermore, the user imports costume images from animations or videos, and the AI ​​analyzes those images to generate patterns. The generated patterns can be used as a guide for users to create handmade costumes. This system allows anyone to easily transform into a character. For example, a user can photograph their own body from all angles and input the images into an AI. For example, accurate measurements can be extracted by inputting images taken from the front, back, left, and right into the AI. The AI ​​then analyzes the photographed images and extracts the dimensions of each body part. For example, measurements such as shoulder width, chest circumference, waist, and hips are extracted, and patterns are generated based on those measurements. Furthermore, the user imports costume images from animations or videos. For example, by inputting an image of a favorite character's costume into AI, a pattern for that costume can be generated. The generated pattern can then be used as a guide for users to create their own handmade costumes. For example, the pattern can be printed and transferred to fabric, or it can be used as digital data. This system makes it easy for anyone to transform into a character. For example, children and the elderly can intuitively create costumes without complex operations. This allows the costume generation system to accurately measure the user's body and generate patterns based on images of costumes taken from anime or videos.

[0029] The costume generation system according to the embodiment includes a camera unit, an analyzer unit, an importer unit, and a generator unit. The camera unit captures images of a user's body from all directions. To capture images of the user's body from all directions, a 360-degree camera, for example, can be used. Alternatively, multiple cameras can be positioned to simultaneously capture images from different angles. For example, capturing images from the front, back, left, and right can capture a complete image of the user's body. The analyzer analyzes images captured by the camera unit to extract dimensions. The analyzer can extract dimensions such as shoulder width, chest circumference, waist circumference, and hip circumference using an image analysis algorithm. For example, the image analysis algorithm detects the user's body contours and measures the dimensions of each part. The analyzer can also extract more accurate dimensions using a machine learning model. For example, the machine learning model learns from a large amount of data and predicts dimensions based on the user's body shape. The importer imports images of costumes. For example, the importer can import costume images by users uploading images of costumes from anime or videos. For example, a user selects an image of a costume featuring a character they like and uploads it to the importer. The generation unit analyzes the image captured by the capture unit to generate a pattern. The generation unit can generate a pattern from an image of a costume using, for example, an image analysis algorithm. For example, the image analysis algorithm analyzes the shape and design of the costume and generates a pattern based on the analysis. The generation unit can also generate a more accurate pattern using a machine learning model. For example, the machine learning model learns from a large amount of costume data and generates a pattern tailored to the user's body shape. In this way, the costume generation system according to the embodiment can accurately measure the user's body and generate a pattern based on costume images captured from animation or video.

[0030] The costume creation system includes a providing unit that provides the generated pattern to the user. The providing unit provides the generated pattern to the user. The providing unit can provide the pattern in the form of a digital download, for example. For example, the user can download the generated pattern via the Internet and save it on their device. The providing unit can also provide the pattern in the form of a printed matter. For example, the user can print the generated pattern and transfer it to actual fabric. The providing unit can also provide a service of mailing the pattern. For example, if the user wishes, the generated pattern can be printed and mailed to the user. By providing the generated pattern to the user, the user can use it as a guide for creating a handmade costume.

[0031] The costume generation system includes a correction unit that performs corrections to improve the accuracy of the pattern. The correction unit performs corrections to improve the accuracy of the pattern. The correction unit can perform corrections using an algorithm, for example. For example, the correction unit fine-tunes the dimensions of the generated pattern to provide a more accurate pattern. The correction unit can also perform manual corrections. For example, the user can fine-tune the pattern to fit their own body shape. Furthermore, the correction unit can perform corrections using a machine learning model. For example, the machine learning model learns from past data and performs corrections to fit the user's body shape. This improves the accuracy of the pattern, allowing more accurate costumes to be created.

[0032] The analysis unit can extract the dimensions of shoulder width, chest circumference, waist, and hips. The analysis unit extracts, for example, the shoulder width dimension. The shoulder width dimension can be extracted by measuring the distance from one shoulder end to the other. The analysis unit can also extract the chest circumference dimension. The chest circumference dimension can be extracted by measuring the circumference of the widest part of the chest. The analysis unit can also extract the waist dimension. The waist dimension can be extracted by measuring the circumference of the narrowest part of the waist. The analysis unit can also extract the hip dimension. The hip dimension can be extracted by measuring the circumference of the widest part of the buttocks. This allows the dimensions of each part to be accurately extracted, making it possible to generate a more appropriate pattern.

[0033] The capture unit can capture an image of a specific character's costume. The capture unit, for example, captures an image of a character's costume that the user likes. The user can select an image of a specific character's costume from an anime or video and upload it to the capture unit. For example, the user can select an image of an anime character's costume and upload it to the capture unit to capture the image of that costume. The capture unit can also capture an image of a game character's costume. For example, the user can select an image of a game character's costume and upload it to the capture unit to capture the image of that costume. In this way, the user can create a costume for the specific character by capturing an image of the character's costume that the user likes.

[0034] The generation unit can analyze the captured image of the costume and generate a pattern. The generation unit analyzes the captured image of the costume using, for example, an image analysis algorithm. The image analysis algorithm can analyze the shape and design of the costume and generate a pattern based on the analysis. For example, the image analysis algorithm detects the contours of the costume and generates a pattern based on the contours. The generation unit can also analyze the captured image of the costume using a machine learning model. The machine learning model can learn from a large amount of costume data and generate a pattern that fits the user's body shape. For example, the machine learning model analyzes the design and pattern of the costume and generates a pattern based on the analysis. In this way, an accurate pattern can be generated by analyzing the captured image of the costume.

[0035] The camera unit can correct the user's posture and movements in real time during shooting and automatically select the optimal shooting angle. For example, if the user is leaning forward, the camera unit can provide audio guidance to correct their posture and take a photo with the correct posture. Furthermore, if the user is moving, the camera unit can detect the movement and release the shutter at the optimal timing. Furthermore, if the user's face is angled relative to the camera, the camera unit can correct the orientation of the face and take a photo from the front. By correcting the user's posture and movements, an image can be obtained at the optimal shooting angle. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without AI. For example, the camera unit can input the user's posture data into a generation AI and have the generation AI perform posture correction.

[0036] When taking a photograph, the photographing unit can take photographs under different lighting conditions and select the optimal image. For example, the photographing unit can take photographs under both natural light and artificial light and select the clearest image. The photographing unit can also take photographs from multiple lighting angles and select the image with the least shadows. Furthermore, the photographing unit can take photographs using lighting with different color temperatures and select the image with the most natural color tone. This allows the optimal image to be selected by taking photographs under different lighting conditions. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input image data taken under different lighting conditions into a generation AI and have the generation AI select the optimal image.

[0037] The photographing unit can suggest an optimal photographing environment based on the user's background information when photographing. For example, if the user photographs outdoors, the photographing unit can suggest a photographing environment that utilizes natural light. Furthermore, if the user photographs indoors, the photographing unit can also guide the user to set appropriate lighting and background. Furthermore, if the user wishes to photograph based on a specific theme, the photographing unit can also suggest backgrounds and props that match the theme. In this way, the optimal photographing environment can be suggested by taking the user's background information into consideration. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI. For example, the photographing unit can input the user's background information into a generation AI and have the generation AI suggest an optimal photographing environment.

[0038] The photographing unit can automatically adjust optimal photographing settings based on the color and material of the user's clothing when photographing. For example, if the user is wearing white clothing, the photographing unit adjusts the exposure so that the details of the clothing are visible. Furthermore, if the user is wearing clothing made of a shiny material, the photographing unit can also adjust the lighting settings to reduce reflections. Furthermore, if the user is wearing dark-colored clothing, the photographing unit can also adjust the contrast so that the shape of the clothing is clearly visible. This allows optimal photographing settings to be automatically adjusted by taking into account the color and material of the user's clothing. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's clothing data into a generation AI and have the generation AI adjust the photographing settings.

[0039] During analysis, the analysis unit can correct dimensions based on changes in the user's body shape and posture. For example, if the user is leaning forward, the analysis unit corrects the posture to extract an accurate shoulder width. Furthermore, if the user has one leg raised, the analysis unit can correct the waist dimension, taking into account the balance of the user's body shape. Furthermore, if the user has their arms spread, the analysis unit can correct the arm position to extract an accurate chest circumference. This allows for more accurate dimensions to be extracted by taking into account changes in the user's body shape and posture. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's body shape data into a generation AI and have the generation AI correct the dimensions.

[0040] The analysis unit can improve accuracy by combining different analysis algorithms during analysis. For example, the analysis unit can combine an image analysis algorithm and a machine learning algorithm to extract highly accurate dimensions. The analysis unit can also run multiple image analysis algorithms in parallel and select the most reliable result. Furthermore, the analysis unit can integrate analysis results and improve accuracy by utilizing the strengths of different algorithms. In this way, the accuracy of analysis can be improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the results of different analysis algorithms into a generation AI and have the generation AI select the optimal result.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. For example, the analysis unit can refer to the user's past dimensional data and compare it with the current analysis result to improve accuracy. The analysis unit can also refer to the user's past posture data and correct the current posture to improve analysis accuracy. Furthermore, the analysis unit can refer to the user's past body shape data and correct the current body shape to improve analysis accuracy. In this way, by referring to the user's past data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can track changes in the user's body shape and perform analysis based on long-term data. For example, the analysis unit can periodically record changes in the user's body shape and perform analysis based on long-term data. The analysis unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the analysis unit can predict and analyze future body shapes based on changes in the user's body shape. In this way, by tracking changes in the user's body shape, analysis can be performed based on long-term data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on changes in the user's body shape to the generation AI and cause the generation AI to perform analysis based on long-term data.

[0043] The capture unit can automatically optimize images of different resolutions or formats when capturing them. For example, the capture unit converts low-resolution images into high-resolution images to capture clear images. The capture unit can also convert images of different formats into a unified format and capture them in an easily analyzable format. Furthermore, the capture unit can automatically adjust the color tone and brightness of the image and capture it in an optimal state. This makes it possible to obtain clear images by optimizing images of different resolutions or formats. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input image data of different resolutions or formats to a generation AI and have the generation AI perform optimization.

[0044] The capture unit can remove noise from the image during capture and acquire a clear image. The capture unit, for example, filters noise from the image to acquire a clear image. The capture unit can also emphasize edges in the image to clarify details. Furthermore, the capture unit can correct blur in the image to acquire a clear image. This allows a clear image to be acquired by removing noise from the image. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input image data for noise removal to a generation AI and have the generation AI perform noise removal.

[0045] The capture unit can select the optimal capture method during capture by taking into account the user's device information. For example, if the user is using a smartphone, the capture unit can provide a capture method optimized for the smartphone. Furthermore, if the user is using a digital camera, the capture unit can also provide a method for capturing high-resolution images. Furthermore, if the user is using a tablet, the capture unit can also provide a capture method optimized for a large screen. This allows the optimal capture method to be selected by taking into account the user's device information. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input the user's device information into the generation AI and cause the generation AI to select the optimal capture method.

[0046] The capture unit can adjust the capture speed during capture, taking into account the user's internet connection status. For example, if the user has a high-speed internet connection, the capture unit captures images at a high speed. Furthermore, if the user has a low-speed internet connection, the capture unit can also optimize the capture speed by adjusting the image resolution. Furthermore, if the user is offline, the capture unit can also capture locally stored images. This allows for adjustment of the optimal capture speed by taking into account the user's internet connection status. Some or all of the above-described processing in the capture unit may be performed using, or without, AI. For example, the capture unit can input the user's internet connection status into the generation AI and cause the generation AI to adjust the capture speed.

[0047] The generation unit can improve the accuracy of the pattern by combining different generation algorithms during generation. The generation unit can, for example, combine an image analysis algorithm and a machine learning algorithm to generate a highly accurate pattern. The generation unit can also run multiple generation algorithms in parallel to select the most reliable pattern. Furthermore, the generation unit can integrate the generation results and improve accuracy by utilizing the strengths of different algorithms. In this way, the accuracy of the pattern can be improved by combining different generation algorithms. 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 results of different generation algorithms into a generation AI and have the generation AI select the optimal pattern.

[0048] The generation unit can correct the pattern during generation, taking into account changes in the user's body shape and posture. For example, if the user is leaning forward, the generation unit corrects the posture to generate an accurate pattern. Furthermore, if the user is raising one leg, the generation unit can correct the pattern by taking into account the balance of the user's body shape. Furthermore, if the user has their arms spread, the generation unit can correct the position of the arms to generate an accurate pattern. This makes it possible to generate a more accurate pattern by taking into account changes in the user's body shape and posture. 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 user's body shape data into the generation AI and cause the generation AI to perform pattern correction.

[0049] During generation, the generation unit can improve the accuracy of generation by referring to the user's past data. For example, the generation unit can refer to the user's past pattern data and compare it with the current generation result to improve accuracy. The generation unit can also refer to the user's past body shape data and generate a pattern by correcting the current body shape. Furthermore, the generation unit can refer to the user's past costume data and generate a pattern that is optimal for the current costume. In this way, by referring to the user's past data, the accuracy of generation can be improved. 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 user's past data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0050] The generation unit can track changes in the user's body shape during generation and generate a pattern based on long-term data. The generation unit, for example, periodically records changes in the user's body shape and generates a pattern based on the long-term data. The generation unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the generation unit can predict the user's future body shape based on changes in the user's body shape and generate a pattern. In this way, by tracking changes in the user's body shape, a pattern can be generated based on long-term 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 data on changes in the user's body shape into the generation AI and cause the generation AI to generate a pattern based on the long-term data.

[0051] The providing unit can select the optimal providing method by taking into account the user's device information when providing the image. For example, if the user is using a smartphone, the providing unit can provide a display method optimized for the smartphone. Furthermore, if the user is using a digital camera, the providing unit can also provide a method for providing high-resolution images. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. This makes it possible to select the optimal providing method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal providing method.

[0052] The providing unit can adjust the providing speed during providing, taking into account the user's internet connection status. For example, if the user has a high-speed internet connection, the providing unit provides the pattern at high speed. Also, if the user has a low-speed internet connection, the providing unit can optimize the providing speed by adjusting the resolution of the pattern. Furthermore, if the user is offline, the providing unit can provide a locally stored pattern. This makes it possible to adjust the optimal providing speed by taking into account the user's internet connection status. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's internet connection status into the generation AI and cause the generation AI to adjust the providing speed.

[0053] The correction unit can improve accuracy by combining different correction algorithms during correction. For example, the correction unit can combine an image analysis algorithm and a machine learning algorithm to perform highly accurate correction. The correction unit can also execute multiple correction algorithms in parallel and select the most reliable correction result. Furthermore, the correction unit can integrate the correction results and improve accuracy by utilizing the strengths of different algorithms. In this way, the accuracy of correction can be improved by combining different correction algorithms. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the results of different correction algorithms to a generation AI and have the generation AI select the optimal correction result.

[0054] During correction, the correction unit can improve the accuracy of the correction by referring to the user's past data. For example, the correction unit can refer to the user's past correction data and compare it with the current correction result to improve accuracy. The correction unit can also refer to the user's past body shape data and correct the current body shape to improve accuracy. Furthermore, the correction unit can refer to the user's past costume data and perform correction that is optimal for the current costume. In this way, by referring to the user's past data, the accuracy of the correction can be improved. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of the correction.

[0055] During correction, the correction unit can track changes in the user's body shape and perform correction based on long-term data. For example, the correction unit can periodically record changes in the user's body shape and perform correction based on long-term data. The correction unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the correction unit can predict and perform correction based on changes in the user's body shape. In this way, by tracking changes in the user's body shape, correction can be performed based on long-term data. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input data on changes in the user's body shape to the generation AI and cause the generation AI to perform correction based on the long-term data.

[0056] The correction unit can improve accuracy by combining different correction algorithms during correction. For example, the correction unit can combine an image analysis algorithm and a machine learning algorithm to perform highly accurate correction. The correction unit can also execute multiple correction algorithms in parallel and select the most reliable correction result. Furthermore, the correction unit can integrate the correction results and improve accuracy by utilizing the strengths of different algorithms. In this way, the accuracy of correction can be improved by combining different correction algorithms. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the results of different correction algorithms to a generation AI and have the generation AI select the optimal correction result.

[0057] During correction, the correction unit can improve the accuracy of the correction by referring to the user's past data. For example, the correction unit can refer to the user's past correction data and compare it with the current correction result to improve accuracy. The correction unit can also refer to the user's past body shape data and correct the current body shape to improve accuracy. Furthermore, the correction unit can refer to the user's past costume data and perform correction that is optimal for the current costume. In this way, by referring to the user's past data, the accuracy of the correction can be improved. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of the correction.

[0058] During correction, the correction unit can track changes in the user's body shape and perform correction based on long-term data. For example, the correction unit can periodically record changes in the user's body shape and perform correction based on long-term data. The correction unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the correction unit can predict and perform correction based on changes in the user's body shape. In this way, by tracking changes in the user's body shape, correction can be performed based on long-term data. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input data on changes in the user's body shape to the generation AI and cause the generation AI to perform correction based on the long-term data.

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

[0060] The costume generation system may further include a tracking unit that tracks changes in the user's body shape. The tracking unit may periodically record changes in the user's body shape and perform analysis based on long-term data. For example, the changes in the user's body shape may be graphed so that they can be visually confirmed. The tracking unit may also predict and analyze the user's future body shape based on changes in the user's body shape. In this way, by tracking changes in the user's body shape, analysis may be performed based on long-term data.

[0061] During analysis, the analysis unit can combine different analysis algorithms to improve accuracy. For example, it can combine an image analysis algorithm with a machine learning algorithm to extract highly accurate dimensions. It can also run multiple image analysis algorithms in parallel to select the most reliable results. Furthermore, it can integrate analysis results and utilize the strengths of different algorithms to improve accuracy. This allows the accuracy of analysis to be improved by combining different analysis algorithms.

[0062] The capture unit can automatically optimize images of different resolutions and formats when capturing them. For example, it can convert low-resolution images to high resolution and capture clear images. It can also convert images of different formats into a unified format and capture them in a format that is easy to analyze. It can also automatically adjust the color tone and brightness of the image and capture it in the optimal state. This makes it possible to obtain clear images by optimizing images of different resolutions and formats.

[0063] The analysis unit can improve the accuracy of the analysis by referring to the user's past data. For example, the analysis unit can refer to the user's past dimensional data and compare it with the current analysis result to improve accuracy. The analysis unit can also improve the accuracy of the analysis by referring to the user's past posture data and correcting the current posture. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past body shape data and correcting the current body shape. In this way, the analysis accuracy can be improved by referring to the user's past data.

[0064] The providing unit can select the optimal providing method by taking into consideration the device information of the user when providing the information. For example, if the user is using a smartphone, a display method optimized for the smartphone can be provided. Also, if the user is using a digital camera, a method for providing high-resolution images can be provided. Furthermore, if the user is using a tablet, a display method optimized for a large screen can be provided. In this way, the optimal providing method can be selected by taking into consideration the device information of the user.

[0065] During correction, the correction unit can track changes in the user's body shape and make corrections based on long-term data. For example, the correction unit can periodically record changes in the user's body shape and make corrections based on long-term data. The correction unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the correction unit can predict future body shape based on changes in the user's body shape and make corrections based on that. By tracking changes in the user's body shape, corrections can be made based on long-term data.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The camera captures the user's body from all directions. For example, a 360-degree camera can be used, or multiple cameras can be placed in the front, back, left, and right directions to capture the entire body. Step 2: The analysis unit analyzes the images captured by the camera unit to extract dimensions. Image analysis algorithms can be used to extract dimensions such as shoulder width, chest circumference, waist circumference, and hip circumference, and machine learning models can be used to predict more accurate dimensions. Step 3: The capture unit captures the costume image. The user can upload the costume image from the animation or video, and the capture unit can capture the image. Step 4: The generator analyzes the image captured by the capture unit to generate a pattern. Image analysis algorithms can be used to analyze the shape and design of the costume, and machine learning models can be used to generate more accurate patterns.

[0068] (Example 2) In an embodiment of the present invention, a costume generation system allows a user to photograph their own body from all angles and input the images into an AI to obtain accurate measurements. The system then generates patterns by importing costume images from animations or videos. This costume generation system allows a user to photograph their own body from all angles and input the images into an AI to extract accurate measurements. The AI ​​then analyzes the photographed images and extracts the dimensions of each body part. Furthermore, the user imports costume images from animations or videos, and the AI ​​analyzes those images to generate patterns. The generated patterns can be used as a guide for users to create handmade costumes. This system allows anyone to easily transform into a character. For example, a user can photograph their own body from all angles and input the images into an AI. For example, accurate measurements can be extracted by inputting images taken from the front, back, left, and right into the AI. The AI ​​then analyzes the photographed images and extracts the dimensions of each body part. For example, measurements such as shoulder width, chest circumference, waist, and hips are extracted, and patterns are generated based on those measurements. Furthermore, the user imports costume images from animations or videos. For example, by inputting an image of a favorite character's costume into AI, a pattern for that costume can be generated. The generated pattern can then be used as a guide for users to create their own handmade costumes. For example, the pattern can be printed and transferred to fabric, or it can be used as digital data. This system makes it easy for anyone to transform into a character. For example, children and the elderly can intuitively create costumes without complex operations. This allows the costume generation system to accurately measure the user's body and generate patterns based on images of costumes taken from anime or videos.

[0069] The costume generation system according to the embodiment includes a camera unit, an analyzer unit, an importer unit, and a generator unit. The camera unit captures images of a user's body from all directions. To capture images of the user's body from all directions, a 360-degree camera, for example, can be used. Alternatively, multiple cameras can be positioned to simultaneously capture images from different angles. For example, capturing images from the front, back, left, and right can capture a complete image of the user's body. The analyzer analyzes images captured by the camera unit to extract dimensions. The analyzer can extract dimensions such as shoulder width, chest circumference, waist circumference, and hip circumference using an image analysis algorithm. For example, the image analysis algorithm detects the user's body contours and measures the dimensions of each part. The analyzer can also extract more accurate dimensions using a machine learning model. For example, the machine learning model learns from a large amount of data and predicts dimensions based on the user's body shape. The importer imports images of costumes. For example, the importer can import costume images by users uploading images of costumes from anime or videos. For example, a user selects an image of a costume featuring a character they like and uploads it to the importer. The generation unit analyzes the image captured by the capture unit to generate a pattern. The generation unit can generate a pattern from an image of a costume using, for example, an image analysis algorithm. For example, the image analysis algorithm analyzes the shape and design of the costume and generates a pattern based on the analysis. The generation unit can also generate a more accurate pattern using a machine learning model. For example, the machine learning model learns from a large amount of costume data and generates a pattern tailored to the user's body shape. In this way, the costume generation system according to the embodiment can accurately measure the user's body and generate a pattern based on costume images captured from animation or video.

[0070] The costume creation system includes a providing unit that provides the generated pattern to the user. The providing unit provides the generated pattern to the user. The providing unit can provide the pattern in the form of a digital download, for example. For example, the user can download the generated pattern via the Internet and save it on their device. The providing unit can also provide the pattern in the form of a printed matter. For example, the user can print the generated pattern and transfer it to actual fabric. The providing unit can also provide a service of mailing the pattern. For example, if the user wishes, the generated pattern can be printed and mailed to the user. By providing the generated pattern to the user, the user can use it as a guide for creating a handmade costume.

[0071] The costume generation system includes a correction unit that performs corrections to improve the accuracy of the pattern. The correction unit performs corrections to improve the accuracy of the pattern. The correction unit can perform corrections using an algorithm, for example. For example, the correction unit fine-tunes the dimensions of the generated pattern to provide a more accurate pattern. The correction unit can also perform manual corrections. For example, the user can fine-tune the pattern to fit their own body shape. Furthermore, the correction unit can perform corrections using a machine learning model. For example, the machine learning model learns from past data and performs corrections to fit the user's body shape. This improves the accuracy of the pattern, allowing more accurate costumes to be created.

[0072] The analysis unit can extract the dimensions of shoulder width, chest circumference, waist, and hips. The analysis unit extracts, for example, the shoulder width dimension. The shoulder width dimension can be extracted by measuring the distance from one shoulder end to the other. The analysis unit can also extract the chest circumference dimension. The chest circumference dimension can be extracted by measuring the circumference of the widest part of the chest. The analysis unit can also extract the waist dimension. The waist dimension can be extracted by measuring the circumference of the narrowest part of the waist. The analysis unit can also extract the hip dimension. The hip dimension can be extracted by measuring the circumference of the widest part of the buttocks. This allows the dimensions of each part to be accurately extracted, making it possible to generate a more appropriate pattern.

[0073] The capture unit can capture an image of a specific character's costume. The capture unit, for example, captures an image of a character's costume that the user likes. The user can select an image of a specific character's costume from an anime or video and upload it to the capture unit. For example, the user can select an image of an anime character's costume and upload it to the capture unit to capture the image of that costume. The capture unit can also capture an image of a game character's costume. For example, the user can select an image of a game character's costume and upload it to the capture unit to capture the image of that costume. In this way, the user can create a costume for the specific character by capturing an image of the character's costume that the user likes.

[0074] The generation unit can analyze the captured image of the costume and generate a pattern. The generation unit analyzes the captured image of the costume using, for example, an image analysis algorithm. The image analysis algorithm can analyze the shape and design of the costume and generate a pattern based on the analysis. For example, the image analysis algorithm detects the contours of the costume and generates a pattern based on the contours. The generation unit can also analyze the captured image of the costume using a machine learning model. The machine learning model can learn from a large amount of costume data and generate a pattern that fits the user's body shape. For example, the machine learning model analyzes the design and pattern of the costume and generates a pattern based on the analysis. In this way, an accurate pattern can be generated by analyzing the captured image of the costume.

[0075] The image capture unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, if the user is relaxed, the image capture unit can start capturing images at a relaxed timing to capture natural facial expressions and postures. Furthermore, if the user is tense, the image capture unit can provide interactive guidance to relieve tension and start capturing images after the user has relaxed. Furthermore, if the user is in a hurry, the image capture unit can capture the necessary images in the shortest time possible to quickly complete the capture. This allows for more natural images to be obtained by adjusting the timing of capturing images according to 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 image capture unit can be performed using, for example, AI, or without AI. For example, the image capture unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0076] The camera unit can correct the user's posture and movements in real time during shooting and automatically select the optimal shooting angle. For example, if the user is leaning forward, the camera unit can provide audio guidance to correct their posture and take a photo with the correct posture. Furthermore, if the user is moving, the camera unit can detect the movement and release the shutter at the optimal timing. Furthermore, if the user's face is angled relative to the camera, the camera unit can correct the orientation of the face and take a photo from the front. By correcting the user's posture and movements, an image can be obtained at the optimal shooting angle. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without AI. For example, the camera unit can input the user's posture data into a generation AI and have the generation AI perform posture correction.

[0077] When taking a photograph, the photographing unit can take photographs under different lighting conditions and select the optimal image. For example, the photographing unit can take photographs under both natural light and artificial light and select the clearest image. The photographing unit can also take photographs from multiple lighting angles and select the image with the least shadows. Furthermore, the photographing unit can take photographs using lighting with different color temperatures and select the image with the most natural color tone. This allows the optimal image to be selected by taking photographs under different lighting conditions. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input image data taken under different lighting conditions into a generation AI and have the generation AI select the optimal image.

[0078] The camera unit can estimate the user's emotions and adjust the order of photographing based on the estimated user emotions. For example, if the user is relaxed, the camera unit can start by photographing the user's entire body and gradually move on to photographing details. Also, if the user is nervous, the camera unit can start by photographing the user's face and then photograph the entire body after the user has relaxed. Furthermore, if the user is in a hurry, the camera unit can prioritize photographing important parts and take complementary photographs later. This allows for more natural images to be obtained by adjusting the order of photographing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 camera unit can be performed using, for example, AI, or without AI. For example, the camera unit can input the user's emotion data into the generation AI and have the generation AI adjust the order of photographing.

[0079] The photographing unit can suggest an optimal photographing environment based on the user's background information when photographing. For example, if the user photographs outdoors, the photographing unit can suggest a photographing environment that utilizes natural light. Furthermore, if the user photographs indoors, the photographing unit can also guide the user to set appropriate lighting and background. Furthermore, if the user wishes to photograph based on a specific theme, the photographing unit can also suggest backgrounds and props that match the theme. In this way, the optimal photographing environment can be suggested by taking the user's background information into consideration. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI. For example, the photographing unit can input the user's background information into a generation AI and have the generation AI suggest an optimal photographing environment.

[0080] The photographing unit can automatically adjust optimal photographing settings based on the color and material of the user's clothing when photographing. For example, if the user is wearing white clothing, the photographing unit adjusts the exposure so that the details of the clothing are visible. Furthermore, if the user is wearing clothing made of a shiny material, the photographing unit can also adjust the lighting settings to reduce reflections. Furthermore, if the user is wearing dark-colored clothing, the photographing unit can also adjust the contrast so that the shape of the clothing is clearly visible. This allows optimal photographing settings to be automatically adjusted by taking into account the color and material of the user's clothing. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's clothing data into a generation AI and have the generation AI adjust the photographing settings.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis to extract highly accurate dimensions. Furthermore, when the user is nervous, the analysis unit can perform a simplified analysis to quickly extract dimensions. Furthermore, when the user is in a hurry, the analysis unit can perform the minimum necessary analysis to quickly provide results. This allows for more accurate dimensions to be extracted by adjusting the accuracy of the analysis according to 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 may 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis accuracy.

[0082] During analysis, the analysis unit can correct dimensions based on changes in the user's body shape and posture. For example, if the user is leaning forward, the analysis unit corrects the posture to extract an accurate shoulder width. Furthermore, if the user has one leg raised, the analysis unit can correct the waist dimension, taking into account the balance of the user's body shape. Furthermore, if the user has their arms spread, the analysis unit can correct the arm position to extract an accurate chest circumference. This allows for more accurate dimensions to be extracted by taking into account changes in the user's body shape and posture. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's body shape data into a generation AI and have the generation AI correct the dimensions.

[0083] The analysis unit can improve accuracy by combining different analysis algorithms during analysis. For example, the analysis unit can combine an image analysis algorithm and a machine learning algorithm to extract highly accurate dimensions. The analysis unit can also run multiple image analysis algorithms in parallel and select the most reliable result. Furthermore, the analysis unit can integrate analysis results and improve accuracy by utilizing the strengths of different algorithms. In this way, the accuracy of analysis can be improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the results of different analysis algorithms into a generation AI and have the generation AI select the optimal result.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results in an easy-to-understand format. Furthermore, if the user is nervous, the analysis unit can display concise analysis results to enable quick understanding. Furthermore, if the user is in a hurry, the analysis unit can display analysis results that focus on the main points to enable quick confirmation. By adjusting the display method of the analysis results according to the user's emotions, the results can be provided in a more understandable format. 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-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. For example, the analysis unit can refer to the user's past dimensional data and compare it with the current analysis result to improve accuracy. The analysis unit can also refer to the user's past posture data and correct the current posture to improve analysis accuracy. Furthermore, the analysis unit can refer to the user's past body shape data and correct the current body shape to improve analysis accuracy. In this way, by referring to the user's past data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] During analysis, the analysis unit can track changes in the user's body shape and perform analysis based on long-term data. For example, the analysis unit can periodically record changes in the user's body shape and perform analysis based on long-term data. The analysis unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the analysis unit can predict and analyze future body shapes based on changes in the user's body shape. In this way, by tracking changes in the user's body shape, analysis can be performed based on long-term data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on changes in the user's body shape to the generation AI and cause the generation AI to perform analysis based on long-term data.

[0087] The capture unit can estimate the user's emotions and adjust the timing of capture based on the estimated user emotions. For example, if the user is relaxed, the capture unit captures images of clothing at a natural timing. Furthermore, if the user is tense, the capture unit can provide an interactive guide to relieve tension and capture images after the user has relaxed. Furthermore, if the user is in a hurry, the capture unit can provide a simple procedure for quickly capturing images of clothing. This allows images to be captured at a more natural timing by adjusting the capture timing according to 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 may 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 capture unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the capture unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the capture timing.

[0088] The capture unit can automatically optimize images of different resolutions or formats when capturing them. For example, the capture unit converts low-resolution images into high-resolution images to capture clear images. The capture unit can also convert images of different formats into a unified format and capture them in an easily analyzable format. Furthermore, the capture unit can automatically adjust the color tone and brightness of the image and capture it in an optimal state. This makes it possible to obtain clear images by optimizing images of different resolutions or formats. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input image data of different resolutions or formats to a generation AI and have the generation AI perform optimization.

[0089] The capture unit can remove noise from the image during capture and acquire a clear image. The capture unit, for example, filters noise from the image to acquire a clear image. The capture unit can also emphasize edges in the image to clarify details. Furthermore, the capture unit can correct blur in the image to acquire a clear image. This allows a clear image to be acquired by removing noise from the image. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input image data for noise removal to a generation AI and have the generation AI perform noise removal.

[0090] The capture unit can estimate the user's emotions and determine the priority of images to be captured based on the estimated user's emotions. For example, if the user is relaxed, the capture unit can prioritize capturing detailed images. Furthermore, if the user is nervous, the capture unit can prioritize capturing simple images. Furthermore, if the user is in a hurry, the capture unit can prioritize capturing images of important parts. Thus, by determining the priority of images according to the user's emotions, important images can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 capture unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the capture unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of images.

[0091] The capture unit can select the optimal capture method during capture by taking into account the user's device information. For example, if the user is using a smartphone, the capture unit can provide a capture method optimized for the smartphone. Furthermore, if the user is using a digital camera, the capture unit can also provide a method for capturing high-resolution images. Furthermore, if the user is using a tablet, the capture unit can also provide a capture method optimized for a large screen. This allows the optimal capture method to be selected by taking into account the user's device information. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input the user's device information into the generation AI and cause the generation AI to select the optimal capture method.

[0092] The capture unit can adjust the capture speed during capture, taking into account the user's internet connection status. For example, if the user has a high-speed internet connection, the capture unit captures images at a high speed. Furthermore, if the user has a low-speed internet connection, the capture unit can also optimize the capture speed by adjusting the image resolution. Furthermore, if the user is offline, the capture unit can also capture locally stored images. This allows for adjustment of the optimal capture speed by taking into account the user's internet connection status. Some or all of the above-described processing in the capture unit may be performed using, or without, AI. For example, the capture unit can input the user's internet connection status into the generation AI and cause the generation AI to adjust the capture speed.

[0093] The generation unit can estimate the user's emotions and adjust the level of detail of the generated pattern based on the estimated user's emotions. For example, when the user is relaxed, the generation unit generates a detailed pattern. Furthermore, when the user is nervous, the generation unit can also generate a simple pattern with the minimum necessary level of detail. By adjusting the level of detail of the pattern according to the user's emotions, a more appropriate pattern can be generated. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, for example, 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-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the pattern.

[0094] The generation unit can improve the accuracy of the pattern by combining different generation algorithms during generation. The generation unit can, for example, combine an image analysis algorithm and a machine learning algorithm to generate a highly accurate pattern. The generation unit can also run multiple generation algorithms in parallel to select the most reliable pattern. Furthermore, the generation unit can integrate the generation results and improve accuracy by utilizing the strengths of different algorithms. In this way, the accuracy of the pattern can be improved by combining different generation algorithms. 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 results of different generation algorithms into a generation AI and have the generation AI select the optimal pattern.

[0095] The generation unit can correct the pattern during generation, taking into account changes in the user's body shape and posture. For example, if the user is leaning forward, the generation unit corrects the posture to generate an accurate pattern. Furthermore, if the user is raising one leg, the generation unit can correct the pattern by taking into account the balance of the user's body shape. Furthermore, if the user has their arms spread, the generation unit can correct the position of the arms to generate an accurate pattern. This makes it possible to generate a more accurate pattern by taking into account changes in the user's body shape and posture. 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 user's body shape data into the generation AI and cause the generation AI to perform pattern correction.

[0096] The generation unit can estimate the user's emotions and determine the priority of the patterns to be generated based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can prioritize generating detailed patterns. Furthermore, if the user is nervous, the generation unit can also prioritize generating simple patterns. Furthermore, if the user is in a hurry, the generation unit can prioritize generating patterns of important parts. Thus, by determining the priority of patterns according to the user's emotions, patterns of important parts can be generated preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the patterns.

[0097] During generation, the generation unit can improve the accuracy of generation by referring to the user's past data. For example, the generation unit can refer to the user's past pattern data and compare it with the current generation result to improve accuracy. The generation unit can also refer to the user's past body shape data and generate a pattern by correcting the current body shape. Furthermore, the generation unit can refer to the user's past costume data and generate a pattern that is optimal for the current costume. In this way, by referring to the user's past data, the accuracy of generation can be improved. 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 user's past data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0098] The generation unit can track changes in the user's body shape during generation and generate a pattern based on long-term data. The generation unit, for example, periodically records changes in the user's body shape and generates a pattern based on the long-term data. The generation unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the generation unit can predict the user's future body shape based on changes in the user's body shape and generate a pattern. In this way, by tracking changes in the user's body shape, a pattern can be generated based on long-term 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 data on changes in the user's body shape into the generation AI and cause the generation AI to generate a pattern based on the long-term data.

[0099] The providing unit can estimate the user's emotions and adjust the display method of the provided pattern based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can display a detailed pattern in an easy-to-understand format. Furthermore, if the user is nervous, the providing unit can display a concise pattern to enable quick understanding. Furthermore, if the user is in a hurry, the providing unit can display a pattern that focuses on the main points to enable quick confirmation. This allows the pattern to be provided in a more easily understandable format by adjusting the display method of the pattern according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the pattern.

[0100] The providing unit can select the optimal providing method by taking into account the user's device information when providing the image. For example, if the user is using a smartphone, the providing unit can provide a display method optimized for the smartphone. Furthermore, if the user is using a digital camera, the providing unit can also provide a method for providing high-resolution images. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. This makes it possible to select the optimal providing method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal providing method.

[0101] The providing unit can estimate the user's emotions and determine the priority of the patterns to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed patterns. Furthermore, when the user is nervous, the providing unit can prioritize providing simple patterns. Furthermore, when the user is in a hurry, the providing unit can prioritize providing patterns of important parts. In this way, by determining the priority of patterns according to the user's emotions, it is possible to provide patterns of important parts preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of the patterns.

[0102] The providing unit can adjust the providing speed during providing, taking into account the user's internet connection status. For example, if the user has a high-speed internet connection, the providing unit provides the pattern at high speed. Also, if the user has a low-speed internet connection, the providing unit can optimize the providing speed by adjusting the resolution of the pattern. Furthermore, if the user is offline, the providing unit can provide a locally stored pattern. This makes it possible to adjust the optimal providing speed by taking into account the user's internet connection status. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's internet connection status into the generation AI and cause the generation AI to adjust the providing speed.

[0103] The correction unit can estimate the user's emotions and adjust the accuracy of the correction based on the estimated user's emotions. For example, when the user is relaxed, the correction unit performs detailed corrections to provide a highly accurate pattern. Furthermore, when the user is nervous, the correction unit can perform simple corrections to quickly provide a pattern. Furthermore, when the user is in a hurry, the correction unit can perform minimal corrections to quickly provide a pattern. This allows for adjusting the accuracy of the corrections according to the user's emotions to provide a more accurate pattern. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 correction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the accuracy of the corrections.

[0104] The correction unit can improve accuracy by combining different correction algorithms during correction. For example, the correction unit can combine an image analysis algorithm and a machine learning algorithm to perform highly accurate correction. The correction unit can also execute multiple correction algorithms in parallel and select the most reliable correction result. Furthermore, the correction unit can integrate the correction results and improve accuracy by utilizing the strengths of different algorithms. In this way, the accuracy of correction can be improved by combining different correction algorithms. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the results of different correction algorithms to a generation AI and have the generation AI select the optimal correction result.

[0105] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user's emotions. For example, if the user is relaxed, the correction unit can prioritize detailed corrections. Furthermore, if the user is nervous, the correction unit can also prioritize simple corrections. Furthermore, if the user is in a hurry, the correction unit can prioritize corrections of important parts. Thus, by determining the priority of corrections according to the user's emotions, corrections of important parts can be prioritized. The emotion estimation is realized 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 correction unit can be performed using, for example, an AI, or without an AI. For example, the correction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of corrections.

[0106] During correction, the correction unit can improve the accuracy of the correction by referring to the user's past data. For example, the correction unit can refer to the user's past correction data and compare it with the current correction result to improve accuracy. The correction unit can also refer to the user's past body shape data and correct the current body shape to improve accuracy. Furthermore, the correction unit can refer to the user's past costume data and perform correction that is optimal for the current costume. In this way, by referring to the user's past data, the accuracy of the correction can be improved. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of the correction.

[0107] During correction, the correction unit can track changes in the user's body shape and perform correction based on long-term data. For example, the correction unit can periodically record changes in the user's body shape and perform correction based on long-term data. The correction unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the correction unit can predict and perform correction based on changes in the user's body shape. In this way, by tracking changes in the user's body shape, correction can be performed based on long-term data. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input data on changes in the user's body shape to the generation AI and cause the generation AI to perform correction based on the long-term data.

[0108] The correction unit can estimate the user's emotions and adjust the accuracy of the correction based on the estimated user's emotions. For example, when the user is relaxed, the correction unit performs detailed corrections to provide a highly accurate pattern. Furthermore, when the user is nervous, the correction unit can perform simple corrections to quickly provide a pattern. Furthermore, when the user is in a hurry, the correction unit can perform minimal corrections to quickly provide a pattern. This allows for adjusting the accuracy of the corrections according to the user's emotions to provide a more accurate pattern. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 correction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the accuracy of the corrections.

[0109] The correction unit can improve accuracy by combining different correction algorithms during correction. For example, the correction unit can combine an image analysis algorithm and a machine learning algorithm to perform highly accurate correction. The correction unit can also execute multiple correction algorithms in parallel and select the most reliable correction result. Furthermore, the correction unit can integrate the correction results and improve accuracy by utilizing the strengths of different algorithms. In this way, the accuracy of correction can be improved by combining different correction algorithms. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the results of different correction algorithms to a generation AI and have the generation AI select the optimal correction result.

[0110] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user's emotions. For example, if the user is relaxed, the correction unit can prioritize detailed corrections. Furthermore, if the user is nervous, the correction unit can also prioritize simple corrections. Furthermore, if the user is in a hurry, the correction unit can prioritize corrections of important parts. Thus, by determining the priority of corrections according to the user's emotions, corrections of important parts can be prioritized. The emotion estimation is realized 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 correction unit can be performed using, for example, an AI, or without an AI. For example, the correction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of corrections.

[0111] During correction, the correction unit can improve the accuracy of the correction by referring to the user's past data. For example, the correction unit can refer to the user's past correction data and compare it with the current correction result to improve accuracy. The correction unit can also refer to the user's past body shape data and correct the current body shape to improve accuracy. Furthermore, the correction unit can refer to the user's past costume data and perform correction that is optimal for the current costume. In this way, by referring to the user's past data, the accuracy of the correction can be improved. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input the user's past data into the generation AI and cause the generation AI to improve the accuracy of the correction.

[0112] During correction, the correction unit can track changes in the user's body shape and perform correction based on long-term data. For example, the correction unit can periodically record changes in the user's body shape and perform correction based on long-term data. The correction unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the correction unit can predict and perform correction based on changes in the user's body shape. In this way, by tracking changes in the user's body shape, correction can be performed based on long-term data. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input data on changes in the user's body shape to the generation AI and cause the generation AI to perform correction based on the long-term data. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, capture unit, generation unit, provision unit, and correction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit captures images of the user's body from all directions using the camera 42 of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the captured images and extracts dimensions. The capture unit, realized, for example, by the control unit 46A of the smart device 14, allows the user to upload images of costumes from anime or videos. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the captured images and generates patterns. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated patterns to the user. The correction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs corrections to improve the accuracy of the patterns. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, capture unit, generation unit, provision unit, and correction unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit uses the camera 42 of the smart glasses 214 to capture images of the user's body from all directions. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the captured images and extracts dimensions. The capture unit, realized, for example, by the control unit 46A of the smart glasses 214, allows the user to upload images of costumes from anime or videos. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the captured images and generates patterns. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the generated patterns to the user. The correction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs corrections to improve the accuracy of the patterns. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned photographing unit, analyzing unit, capturing unit, generating unit, providing unit, and correcting unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit captures images of the user's body from all directions using the camera 42 of the headset-type terminal 314. The analyzing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the captured images and extracts dimensions. The capturing unit, realized, for example, by the control unit 46A of the headset-type terminal 314, allows the user to upload images of costumes from anime or videos. The generating unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the captured images and generates patterns. The providing unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides the generated patterns to the user. The correcting unit, realized, for example, by the specific processing unit 290 of the data processing device 12, performs corrections to improve the accuracy of the patterns. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned photographing unit, analyzing unit, capturing unit, generating unit, providing unit, and correcting unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit captures images of the user's body from all directions using the camera 42 of the robot 414. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the captured images to extract dimensions. The capturing unit is realized, for example, by the control unit 46A of the robot 414 and allows the user to upload images of costumes from anime or videos. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the captured images to generate patterns. The providing unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated patterns to the user. The correcting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs corrections to improve the accuracy of the patterns.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] The costume generation system may further include a tracking unit that tracks changes in the user's body shape. The tracking unit may periodically record changes in the user's body shape and perform analysis based on long-term data. For example, the changes in the user's body shape may be graphed so that they can be visually confirmed. The tracking unit may also predict and analyze the user's future body shape based on changes in the user's body shape. In this way, by tracking changes in the user's body shape, analysis may be performed based on long-term data.

[0115] The providing unit can estimate the user's emotions and adjust the display method of the provided pattern based on the estimated user's emotions. For example, if the user is relaxed, a detailed pattern can be displayed and provided in an easy-to-understand format. If the user is nervous, a concise pattern can be displayed so that the user can understand it quickly. Furthermore, if the user is in a hurry, a pattern that focuses on the main points can be displayed so that the user can check it quickly. In this way, by adjusting the display method of the pattern according to the user's emotions, the pattern can be provided in a format that is easier to understand.

[0116] The correction unit can estimate the user's emotions and adjust the accuracy of the correction based on the estimated user's emotions. For example, if the user is relaxed, detailed corrections can be made to provide a highly accurate pattern. If the user is nervous, simple corrections can be made to quickly provide a pattern. Furthermore, if the user is in a hurry, minimal corrections can be made to quickly provide a pattern. In this way, by adjusting the accuracy of correction according to the user's emotions, a more accurate pattern can be provided.

[0117] During analysis, the analysis unit can combine different analysis algorithms to improve accuracy. For example, it can combine an image analysis algorithm with a machine learning algorithm to extract highly accurate dimensions. It can also run multiple image analysis algorithms in parallel to select the most reliable results. Furthermore, it can integrate analysis results and utilize the strengths of different algorithms to improve accuracy. This allows the accuracy of analysis to be improved by combining different analysis algorithms.

[0118] The capture unit can automatically optimize images of different resolutions and formats when capturing them. For example, it can convert low-resolution images to high resolution and capture clear images. It can also convert images of different formats into a unified format and capture them in a format that is easy to analyze. It can also automatically adjust the color tone and brightness of the image and capture it in the optimal state. This makes it possible to obtain clear images by optimizing images of different resolutions and formats.

[0119] The image capturing unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, if the user is relaxed, the image capturing unit can start capturing images at a relaxed timing to capture natural facial expressions and postures. If the user is tense, the image capturing unit can provide an interactive guide to relieve tension and start capturing images after the user has relaxed. Furthermore, if the user is in a hurry, the image capturing unit can capture the necessary images in the shortest time possible to quickly complete the capture. This allows more natural images to be obtained by adjusting the timing of capturing images according to the user's emotions.

[0120] The analysis unit can improve the accuracy of the analysis by referring to the user's past data. For example, the analysis unit can refer to the user's past dimensional data and compare it with the current analysis result to improve accuracy. The analysis unit can also improve the accuracy of the analysis by referring to the user's past posture data and correcting the current posture. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the user's past body shape data and correcting the current body shape. In this way, the analysis accuracy can be improved by referring to the user's past data.

[0121] The generation unit can estimate the user's emotions and adjust the level of detail of the generated pattern based on the estimated user's emotions. For example, if the user is relaxed, a detailed pattern can be generated. Also, if the user is nervous, a simple pattern can be generated. Furthermore, if the user is in a hurry, a pattern can be generated with the minimum necessary level of detail. In this way, by adjusting the level of detail of the pattern according to the user's emotions, a more appropriate pattern can be generated.

[0122] The providing unit can select the optimal providing method by taking into consideration the device information of the user when providing the information. For example, if the user is using a smartphone, a display method optimized for the smartphone can be provided. Also, if the user is using a digital camera, a method for providing high-resolution images can be provided. Furthermore, if the user is using a tablet, a display method optimized for a large screen can be provided. In this way, the optimal providing method can be selected by taking into consideration the device information of the user.

[0123] During correction, the correction unit can track changes in the user's body shape and make corrections based on long-term data. For example, the correction unit can periodically record changes in the user's body shape and make corrections based on long-term data. The correction unit can also graph changes in the user's body shape so that they can be visually confirmed. Furthermore, the correction unit can predict future body shape based on changes in the user's body shape and make corrections based on that. By tracking changes in the user's body shape, corrections can be made based on long-term data.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The camera captures the user's body from all directions. For example, a 360-degree camera can be used, or multiple cameras can be placed in the front, back, left, and right directions to capture the entire body. Step 2: The analysis unit analyzes the images captured by the camera unit to extract dimensions. Image analysis algorithms can be used to extract dimensions such as shoulder width, chest circumference, waist circumference, and hip circumference, and machine learning models can be used to predict more accurate dimensions. Step 3: The capture unit captures the costume image. The user can upload the costume image from the animation or video, and the capture unit can capture the image. Step 4: The generator analyzes the image captured by the capture unit to generate a pattern. Image analysis algorithms can be used to analyze the shape and design of the costume, and machine learning models can be used to generate more accurate patterns.

[0126] 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.

[0127] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Explanation of symbols]

[0198] 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. an imaging unit that captures images of the user's body from all directions; an analysis unit that analyzes the image captured by the imaging unit and extracts dimensions; an import unit that imports an image of a costume; a generation unit that analyzes the image captured by the capture unit and generates a paper pattern; Equipped with A system characterized by:

2. A providing unit is provided to provide the generated paper pattern to the user.

2. The system of claim 1.

3. Equipped with a correction unit that performs corrections to improve the accuracy of the paper pattern 2. The system of claim 1.

4. The analysis unit Extract shoulder, chest, waist, and hip measurements 2. The system of claim 1.

5. The capture unit is Importing images of specific character costumes 2. The system of claim 1.

6. The generation unit Analyze the captured image of the costume and generate a pattern 2. The system of claim 1.

7. The imaging unit is Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is When taking a photo, the system automatically corrects the user's posture and movements in real time to select the optimal shooting angle.

2. The system of claim 1.

9. The imaging unit is When shooting, take photos under different lighting conditions and select the best image.

2. The system of claim 1.

Images