system

Abstract

The system according to this embodiment aims to provide an environment where anyone can easily create high-quality cosplay. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a presentation unit. The collection unit collects images of costumes and accessories owned by the user. The analysis unit analyzes the images collected by the collection unit and information about the character the user wants to cosplay. The presentation unit visually presents how to create the items necessary for cosplay based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there are problems that novice cosplayers do not know how to create their own costumes and accessories, and ready-made cosplay costumes are of low quality if they are inexpensive and unaffordable if they are expensive.

[0005] The system according to the embodiment aims to provide an environment in which anyone can easily perform high-quality cosplay.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a presentation unit. The collection unit collects images of costumes and accessories owned by the user. The analysis unit analyzes the images collected by the collection unit and information about the character the user wants to cosplay. The presentation unit visually presents how to create the items necessary for cosplay based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an environment where anyone can easily create high-quality cosplay. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The cosplay support system according to an embodiment of the present invention is a mechanism that utilizes a generative AI to enable cosplay beginners to easily enjoy high-quality cosplay. This cosplay support system uses a generative AI to learn images of costumes and accessories owned by the user, as well as information about the character the user wants to cosplay. Based on the learned data and commercially available products (costumes and accessories), the system visually presents how to create the necessary cosplay items. This allows even cosplay beginners to easily enjoy high-quality cosplay. For example, the user uploads images of their owned costumes and accessories and inputs information about the character they want to cosplay. The generative AI analyzes the images and character information provided by the user and identifies how to create the necessary items. Next, the generative AI visually presents how to create the necessary cosplay items based on commercially available products (costumes and accessories). This makes it easier for the user to understand visually, creating an environment where anyone can easily enjoy high-quality cosplay. Thus, the cosplay support system enables even cosplay beginners to easily enjoy high-quality cosplay.

[0029] The cosplay support system according to this embodiment comprises a collection unit, an analysis unit, and a presentation unit. The collection unit collects images of costumes and accessories owned by the user. The collection unit can collect images in formats such as JPEG or PNG. The collection unit can also collect images of the entire costume or accessories, or images of parts of them. The analysis unit analyzes the images collected by the collection unit and information about the character the user wants to cosplay. The analysis unit can analyze images using image recognition technology, for example. The analysis unit can also extract image features using a feature extraction algorithm. The presentation unit visually presents how to create the items necessary for cosplay based on the data analyzed by the analysis unit. The presentation unit can present the creation method using 2D images or 3D models, for example. The presentation unit can also visually present the creation method using animation. As a result, the cosplay support system collects, analyzes, and visually presents images of costumes and accessories owned by the user, allowing even cosplay beginners to easily enjoy high-quality cosplay. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit inputs images of costumes and accessories owned by the user into the AI, and has the AI ​​perform image collection. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the images collected by the collection unit and information about the character the user wants to cosplay into the generation AI, and has the generation AI perform the analysis. Some or all of the above processing in the presentation unit is performed using a generation AI. For example, the presentation unit inputs how to create the items necessary for cosplay into the generation AI based on the data analyzed by the analysis unit, and has the generation AI perform a method of visually presenting them.

[0030] The data collection unit collects images of clothing and accessories owned by the user. The unit can collect images in formats such as JPEG and PNG. It can also collect images of the entire garment or accessories, or images of specific parts. Specifically, it provides an interface for users to upload images taken with their smartphones or digital cameras, allowing them to easily import images into the system. Furthermore, the data collection unit automatically retrieves image metadata (e.g., date and time of shooting, resolution, file size, etc.) and stores it in a database. This allows for efficient use of data in subsequent analysis processes. The data collection unit also has a function to evaluate image quality using AI and filter out blurry or inappropriate images. For example, the AI ​​can evaluate image resolution, brightness, contrast, etc., and prompt the user to retake images that do not meet the criteria. This allows the data collection unit to provide high-quality image data for the analysis unit to perform highly accurate analysis.

[0031] The analysis unit analyzes the images collected by the collection unit and information about the character the user wants to cosplay. The analysis unit can analyze images using, for example, image recognition technology. It can also extract image features using feature extraction algorithms. Specifically, it uses a generative AI to extract features such as the shape, color, and texture of costumes and accessories from the collected images and compare them with information about the character the user wants to cosplay. The generative AI analyzes the collected images using, for example, an image recognition model using deep learning. First, the generative AI preprocesses the collected images by removing noise and normalizing the images. Next, the generative AI extracts image features using a convolutional neural network (CNN) and vectorizes these features. Furthermore, the generative AI compares these with information about the character the user wants to cosplay (for example, official artwork or screenshots) and calculates the similarity. This allows the analysis unit to evaluate how close the costumes and accessories owned by the user are to the character they want to cosplay. In addition, the analysis unit can use the generative AI to suggest necessary improvements or additional items for the costumes and accessories owned by the user. For example, the generation AI identifies differences between the user's costume and the character's costume and provides specific suggestions on how to correct them. This allows the analysis unit to provide concrete advice to help the user achieve higher-quality cosplay.

[0032] The presentation unit visually presents how to create items necessary for cosplay based on data analyzed by the analysis unit. The presentation unit can present creation methods using, for example, 2D images or 3D models. It can also visually present creation methods using animation. Specifically, it uses a generative AI to present information in a user-friendly format based on data provided by the analysis unit. For example, the generative AI can use images of the user's existing costumes and accessories to show how to improve them using 2D images or 3D models. Furthermore, the generative AI can visually explain the steps for creating costumes and accessories step-by-step using animation. For instance, the generative AI can animate how to sew a costume or assemble accessories, providing a reference for the user during actual work. The presentation unit can also provide a list of necessary materials and tools, along with information on where to purchase them. This allows users to easily acquire the necessary items and efficiently prepare for cosplay. Additionally, the presentation unit has a function to evaluate the quality of the costumes and accessories created by the user and provide feedback. For example, when a user uploads an image of a costume they have created, the generating AI evaluates its quality and provides suggestions for improvement and additional advice. This allows the presentation system to support users in continuously improving their skills and enjoying high-quality cosplay.

[0033] The presentation unit can generate videos using generative AI. For example, the presentation unit can visually present how to create items necessary for cosplay using a video generated by the generative AI. For example, the presentation unit can provide users with a step-by-step tutorial using a video generated by the generative AI. The presentation unit can also perform demonstrations for users using videos generated by the generative AI. In this way, the presentation unit makes it easier for users to visually understand by generating videos with the generative AI. The generative AI is implemented using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). Some or all of the above processing in the presentation unit is performed using the generative AI. For example, the presentation unit can input how to create items necessary for cosplay into the generative AI and have the generative AI execute the method of generating a video.

[0034] The analysis unit can compare the costumes and accessories owned by the user with those of the character the user wants to cosplay as, and identify how to create the necessary items. For example, the analysis unit can compare the costumes and accessories owned by the user with those of the character the user wants to cosplay as, using image similarity calculation. The analysis unit can also compare using feature matching scores. In this way, the analysis unit can identify how to create the necessary items by comparing the costumes and accessories owned by the user with those of the character the user wants to cosplay as, and some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input images of the costumes and accessories owned by the user and those of the character the user wants to cosplay as, and have the generative AI perform the comparison.

[0035] The analysis unit can analyze the clothing and accessories owned by the user and suggest customization methods. For example, the analysis unit can suggest how to change the color of the clothing and accessories owned by the user. It can also suggest how to add decorations. In this way, the analysis unit can analyze the clothing and accessories owned by the user and suggest customization methods, allowing the user to customize their items. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input images of the clothing and accessories owned by the user into the generation AI and have the generation AI execute suggestions for customization methods.

[0036] The presentation unit can suggest where to purchase the fabrics and accessories needed to create a specific character's costume and demonstrate how to combine them through videos. For example, the presentation unit can provide information on online shops and physical stores. It can also demonstrate how to combine fabrics and accessories using step-by-step tutorial videos. This allows users to easily create costumes by suggesting where to purchase the necessary fabrics and accessories and demonstrating how to combine them through videos. Some or all of the above processing in the presentation unit is performed using a generative AI. For example, the presentation unit can input information on where to purchase fabrics and accessories into the generative AI and have the AI ​​execute the process of generating videos.

[0037] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection method based on the camera angles and lighting conditions the user has used in the past. The data collection unit can also analyze the quality of images the user has collected in the past and automatically adjust the optimal data collection settings. Furthermore, the data collection unit can suggest the optimal data collection schedule based on the number and frequency of images the user has collected in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data collection history into AI and have the AI ​​select the optimal data collection method.

[0038] The data collection unit can filter images based on the user's current projects and areas of interest during the collection process. For example, the unit can collect only images related to the user's current cosplay project. The unit can also prioritize collecting images that are highly relevant based on the user's areas of interest. Furthermore, the unit can filter images based on characters or themes that the user has shown interest in in the past. This allows the unit to collect highly relevant images by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0039] The image collection unit can prioritize collecting highly relevant images by considering the user's geographical location information during the collection process. For example, if the user is participating in a specific event, the collection unit can prioritize collecting images related to that event. It can also prioritize collecting images related to a specific region if the user is in that region. Furthermore, if the user is traveling, the collection unit can prioritize collecting images related to their travel destination. In this way, the collection unit can prioritize collecting highly relevant images by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the user's geographical location information into an AI and have the AI ​​perform the collection of highly relevant images.

[0040] The data collection unit can analyze the user's social media activity and collect relevant images during the collection process. For example, the data collection unit can collect relevant images based on images shared by the user on social media. It can also analyze posts from accounts the user follows on social media and collect relevant images. Furthermore, it can collect relevant images based on posts the user "likes" on social media. In this way, the data collection unit can collect relevant images by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI and have the AI ​​perform the collection of relevant images.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the items during the analysis. For example, the analysis unit performs a detailed analysis for important items. It can also perform a simplified analysis for less important items. Furthermore, the analysis unit can perform a special analysis for items of particular interest to the user. In this way, the analysis unit can perform a detailed analysis for important items by adjusting the level of detail of the analysis based on the importance of the items. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input item importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the item category during analysis. For example, for clothing items, the analysis unit performs analysis on the fabric and sewing method. For small items, the analysis unit can also perform analysis on the material and assembly method. Furthermore, for accessory items, the analysis unit can perform analysis on the design and decoration method. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the item category. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input item category data into a generation AI and have the generation AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the submission date of items during the analysis process. For example, the analysis unit may prioritize analyzing items that have been submitted most recently. It can also prioritize analyzing items with approaching submission deadlines. Furthermore, it can prioritize analyzing items that users are particularly in a hurry to process. In this way, the analysis unit can prioritize the analysis of items with higher urgency by determining the priority of analysis based on the submission date of items. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input item submission date data into a generation AI and have the generation AI perform the determination of analysis priorities.

[0044] The analysis unit can adjust the order of analysis based on the relationships between items during the analysis process. For example, the analysis unit may prioritize analyzing items that are highly related to other items owned by the user. It can also prioritize analyzing items that are highly related to the costumes of characters the user wants to cosplay. Furthermore, it can prioritize analyzing items that are highly related to items the user has previously analyzed. In this way, the analysis unit can prioritize the analysis of highly related items by adjusting the order of analysis based on the relationships between items. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input item relationship data into a generation AI and have the generation AI perform the adjustment of the analysis order.

[0045] The presentation unit can adjust the level of detail in the presentation based on the importance of the item. For example, the presentation unit will provide a detailed presentation for important items. It can also provide a concise presentation for less important items. Furthermore, the presentation unit can provide a special presentation for items that the user is particularly interested in. In this way, the presentation unit can provide a detailed presentation for important items by adjusting the level of detail in the presentation based on the importance of the item. Some or all of the above processing in the presentation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation unit can input item importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in the presentation.

[0046] The presentation unit can apply different presentation algorithms depending on the item category during presentation. For example, for clothing items, the presentation unit can provide information on the fabric and sewing method. For small items, the presentation unit can also provide information on the materials and assembly method. Furthermore, for accessory items, the presentation unit can provide information on the design and decoration method. In this way, the presentation unit can provide more appropriate presentation results by applying different presentation algorithms depending on the item category. Some or all of the above processing in the presentation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation unit can input item category data into a generative AI and have the generative AI execute the application of the presentation algorithm.

[0047] The presentation unit can determine the priority of presentations based on the submission date of each item. For example, the presentation unit may prioritize presenting items that have been submitted most recently. It can also prioritize presenting items with approaching submission deadlines. Furthermore, it can prioritize presenting items that the user is particularly in a hurry for. In this way, the presentation unit can prioritize presenting items with higher urgency by determining the priority of presentations based on the submission date of each item. Some or all of the above processing in the presentation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the presentation unit can input item submission date data into a generation AI and have the generation AI perform the determination of presentation priorities.

[0048] The presentation unit can adjust the order of presentation based on the relevance of the items. For example, the presentation unit can prioritize presenting items that are highly relevant to other items owned by the user. It can also prioritize presenting items that are highly relevant to the costume of a character the user wants to cosplay as. Furthermore, it can prioritize presenting items that are highly relevant to items the user has previously presented. In this way, the presentation unit can prioritize presenting highly relevant items by adjusting the order of presentation based on the relevance of the items. Some or all of the above processing in the presentation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation unit can input item relevance data into a generative AI and have the generative AI perform the adjustment of the presentation order.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The cosplay support system can analyze a user's past cosplay history and suggest the optimal combination of cosplay items. For example, it can suggest new items with similar themes based on data of costumes and accessories the user has used in the past. It can also analyze the styles of successful cosplays the user has done in the past and suggest new styles based on that. Furthermore, it can suggest areas for improvement based on data of cosplays the user has failed at in the past. In this way, it can provide the optimal combination of cosplay items that leverages the user's past experience. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's past cosplay history data into the generation AI and have the generation AI suggest the optimal combination of items.

[0051] The cosplay support system can suggest the most suitable cosplay events and shops by taking into account the user's geographical location. For example, if the user is in a specific region, it can suggest cosplay events held in that region. If the user is traveling, it can also suggest cosplay events and shops in their travel destination. Furthermore, if the user is participating in a specific event, it can suggest shops and services related to that event. This allows the system to provide the most suitable cosplay events and shops based on the user's geographical location. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's geographical location information into the generation AI and have the generation AI generate suggestions for the most suitable events and shops.

[0052] The cosplay support system can analyze a user's social media activity and suggest the most suitable cosplay items and themes. For example, it can analyze posts from accounts the user follows on social media and suggest relevant cosplay items. It can also suggest themes the user is interested in based on posts the user has "liked" on social media. Furthermore, it can suggest cosplay items in a similar style based on images the user has shared on social media. This allows the system to provide the most suitable cosplay items and themes based on the user's social media activity. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit can input the user's social media activity data into the generative AI and have the generative AI suggest the most suitable items and themes.

[0053] The cosplay support system can analyze a user's past cosplay event participation history and propose an optimal event participation schedule. For example, it can suggest new events with similar themes based on data from events the user has previously attended. It can also analyze the user's past successful event participation schedules and propose new schedules based on that. Furthermore, it can suggest areas for improvement based on data from past unsuccessful event participations. This allows the system to provide an optimal event participation schedule that leverages the user's past experience. Some or all of the above processing in the proposal section is performed using a generation AI. For example, the proposal section can input the user's past event participation history data into the generation AI and have the generation AI propose an optimal schedule.

[0054] The cosplay support system can suggest optimal cosplay items and themes based on the user's current projects and areas of interest. For example, it can suggest items related to the cosplay project the user is currently working on. It can also suggest highly relevant themes based on the user's areas of interest. Furthermore, it can suggest new items and themes based on characters and themes the user has shown interest in in the past. This allows the system to provide optimal cosplay items and themes based on the user's current projects and areas of interest. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit can input data on the user's projects and areas of interest into the generative AI and have the generative AI suggest optimal items and themes.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection unit collects images of the user's clothing and accessories. The collection unit can collect images in formats such as JPEG or PNG. It can also collect images of the entire clothing or accessories, or images of parts of them. Furthermore, some or all of the processing in the collection unit may be performed using AI. For example, the collection unit can input images of the user's clothing and accessories into the AI ​​and have the AI ​​perform the image collection. Step 2: The analysis unit analyzes the images collected by the collection unit and the information of the character to be cosplayed. The analysis unit can analyze the images using, for example, image recognition technology. The analysis unit can also extract features from the images using a feature extraction algorithm. Furthermore, some or all of the processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the images collected by the collection unit and the information of the character to be cosplayed into the generative AI and have the generative AI perform the analysis. Step 3: The presentation unit visually presents how to create the items necessary for cosplay based on the data analyzed by the analysis unit. The presentation unit can present the creation method using, for example, 2D images or 3D models. It can also visually present the creation method using animation. Furthermore, some or all of the processing in the presentation unit is performed using a generative AI. For example, the presentation unit can input the creation method of the items necessary for cosplay into the generative AI based on the data analyzed by the analysis unit, and have the generative AI execute the method of visually presenting it.

[0057] (Example of form 2) The cosplay support system according to an embodiment of the present invention is a mechanism that utilizes a generative AI to enable cosplay beginners to easily enjoy high-quality cosplay. This cosplay support system uses a generative AI to learn images of costumes and accessories owned by the user, as well as information about the character the user wants to cosplay. Based on the learned data and commercially available products (costumes and accessories), the system visually presents how to create the necessary cosplay items. This allows even cosplay beginners to easily enjoy high-quality cosplay. For example, the user uploads images of their owned costumes and accessories and inputs information about the character they want to cosplay. The generative AI analyzes the images and character information provided by the user and identifies how to create the necessary items. Next, the generative AI visually presents how to create the necessary cosplay items based on commercially available products (costumes and accessories). This makes it easier for the user to understand visually, creating an environment where anyone can easily enjoy high-quality cosplay. Thus, the cosplay support system enables even cosplay beginners to easily enjoy high-quality cosplay.

[0058] The cosplay support system according to this embodiment comprises a collection unit, an analysis unit, and a presentation unit. The collection unit collects images of costumes and accessories owned by the user. The collection unit can collect images in formats such as JPEG or PNG. The collection unit can also collect images of the entire costume or accessories, or images of parts of them. The analysis unit analyzes the images collected by the collection unit and information about the character the user wants to cosplay. The analysis unit can analyze images using image recognition technology, for example. The analysis unit can also extract image features using a feature extraction algorithm. The presentation unit visually presents how to create the items necessary for cosplay based on the data analyzed by the analysis unit. The presentation unit can present the creation method using 2D images or 3D models, for example. The presentation unit can also visually present the creation method using animation. As a result, the cosplay support system collects, analyzes, and visually presents images of costumes and accessories owned by the user, allowing even cosplay beginners to easily enjoy high-quality cosplay. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit inputs images of costumes and accessories owned by the user into the AI, and has the AI ​​perform image collection. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the images collected by the collection unit and information about the character the user wants to cosplay into the generation AI, and has the generation AI perform the analysis. Some or all of the above processing in the presentation unit is performed using a generation AI. For example, the presentation unit inputs how to create the items necessary for cosplay into the generation AI based on the data analyzed by the analysis unit, and has the generation AI perform a method of visually presenting them.

[0059] The data collection unit collects images of clothing and accessories owned by the user. The unit can collect images in formats such as JPEG and PNG. It can also collect images of the entire garment or accessories, or images of specific parts. Specifically, it provides an interface for users to upload images taken with their smartphones or digital cameras, allowing them to easily import images into the system. Furthermore, the data collection unit automatically retrieves image metadata (e.g., date and time of shooting, resolution, file size, etc.) and stores it in a database. This allows for efficient use of data in subsequent analysis processes. The data collection unit also has a function to evaluate image quality using AI and filter out blurry or inappropriate images. For example, the AI ​​can evaluate image resolution, brightness, contrast, etc., and prompt the user to retake images that do not meet the criteria. This allows the data collection unit to provide high-quality image data for the analysis unit to perform highly accurate analysis.

[0060] The analysis unit analyzes the images collected by the collection unit and information about the character the user wants to cosplay. The analysis unit can analyze images using, for example, image recognition technology. It can also extract image features using feature extraction algorithms. Specifically, it uses a generative AI to extract features such as the shape, color, and texture of costumes and accessories from the collected images and compare them with information about the character the user wants to cosplay. The generative AI analyzes the collected images using, for example, an image recognition model using deep learning. First, the generative AI preprocesses the collected images by removing noise and normalizing the images. Next, the generative AI extracts image features using a convolutional neural network (CNN) and vectorizes these features. Furthermore, the generative AI compares these with information about the character the user wants to cosplay (for example, official artwork or screenshots) and calculates the similarity. This allows the analysis unit to evaluate how close the costumes and accessories owned by the user are to the character they want to cosplay. In addition, the analysis unit can use the generative AI to suggest necessary improvements or additional items for the costumes and accessories owned by the user. For example, the generation AI identifies differences between the user's costume and the character's costume and provides specific suggestions on how to correct them. This allows the analysis unit to provide concrete advice to help the user achieve higher-quality cosplay.

[0061] The presentation unit visually presents how to create items necessary for cosplay based on data analyzed by the analysis unit. The presentation unit can present creation methods using, for example, 2D images or 3D models. It can also visually present creation methods using animation. Specifically, it uses a generative AI to present information in a user-friendly format based on data provided by the analysis unit. For example, the generative AI can use images of the user's existing costumes and accessories to show how to improve them using 2D images or 3D models. Furthermore, the generative AI can visually explain the steps for creating costumes and accessories step-by-step using animation. For instance, the generative AI can animate how to sew a costume or assemble accessories, providing a reference for the user during actual work. The presentation unit can also provide a list of necessary materials and tools, along with information on where to purchase them. This allows users to easily acquire the necessary items and efficiently prepare for cosplay. Additionally, the presentation unit has a function to evaluate the quality of the costumes and accessories created by the user and provide feedback. For example, when a user uploads an image of a costume they have created, the generating AI evaluates its quality and provides suggestions for improvement and additional advice. This allows the presentation system to support users in continuously improving their skills and enjoying high-quality cosplay.

[0062] The presentation unit can generate videos using generative AI. For example, the presentation unit can visually present how to create items necessary for cosplay using a video generated by the generative AI. For example, the presentation unit can provide users with a step-by-step tutorial using a video generated by the generative AI. The presentation unit can also perform demonstrations for users using videos generated by the generative AI. In this way, the presentation unit makes it easier for users to visually understand by generating videos with the generative AI. The generative AI is implemented using technologies such as GAN (Generative Opposite Network) or VAE (Variational Autoencoder). Some or all of the above processing in the presentation unit is performed using the generative AI. For example, the presentation unit can input how to create items necessary for cosplay into the generative AI and have the generative AI execute the method of generating a video.

[0063] The analysis unit can compare the costumes and accessories owned by the user with those of the character the user wants to cosplay as, and identify how to create the necessary items. For example, the analysis unit can compare the costumes and accessories owned by the user with those of the character the user wants to cosplay as, using image similarity calculation. The analysis unit can also compare using feature matching scores. In this way, the analysis unit can identify how to create the necessary items by comparing the costumes and accessories owned by the user with those of the character the user wants to cosplay as, and some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input images of the costumes and accessories owned by the user and those of the character the user wants to cosplay as, and have the generative AI perform the comparison.

[0064] The analysis unit can analyze the clothing and accessories owned by the user and suggest customization methods. For example, the analysis unit can suggest how to change the color of the clothing and accessories owned by the user. It can also suggest how to add decorations. In this way, the analysis unit can analyze the clothing and accessories owned by the user and suggest customization methods, allowing the user to customize their items. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input images of the clothing and accessories owned by the user into the generation AI and have the generation AI execute suggestions for customization methods.

[0065] The presentation unit can suggest where to purchase the fabrics and accessories needed to create a specific character's costume and demonstrate how to combine them through videos. For example, the presentation unit can provide information on online shops and physical stores. It can also demonstrate how to combine fabrics and accessories using step-by-step tutorial videos. This allows users to easily create costumes by suggesting where to purchase the necessary fabrics and accessories and demonstrating how to combine them through videos. Some or all of the above processing in the presentation unit is performed using a generative AI. For example, the presentation unit can input information on where to purchase fabrics and accessories into the generative AI and have the AI ​​execute the process of generating videos.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of image collection based on the estimated emotions. For example, if the user is excited, the data collection unit can immediately collect images and begin analysis quickly. If the user is relaxed, the data collection unit can collect images when the user is ready. If the user is stressed, the data collection unit can delay the collection timing and wait until the user calms down. In this way, the data collection unit can collect images at a more appropriate time by adjusting the timing of image collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the collection timing.

[0067] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection method based on the camera angles and lighting conditions the user has used in the past. The data collection unit can also analyze the quality of images the user has collected in the past and automatically adjust the optimal data collection settings. Furthermore, the data collection unit can suggest the optimal data collection schedule based on the number and frequency of images the user has collected in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data collection history into AI and have the AI ​​select the optimal data collection method.

[0068] The data collection unit can filter images based on the user's current projects and areas of interest during the collection process. For example, the unit can collect only images related to the user's current cosplay project. The unit can also prioritize collecting images that are highly relevant based on the user's areas of interest. Furthermore, the unit can filter images based on characters or themes that the user has shown interest in in the past. This allows the unit to collect highly relevant images by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0069] The image collection unit can estimate the user's emotions and determine the priority of images to collect based on the estimated emotions. For example, if the user is excited, the collection unit will prioritize collecting important images. If the user is relaxed, the collection unit can also prioritize collecting detailed images. If the user is stressed, the collection unit can also prioritize collecting simple images. In this way, the collection unit can prioritize collecting more important images by determining the priority of images to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of images to collect.

[0070] The image collection unit can prioritize collecting highly relevant images by considering the user's geographical location information during the collection process. For example, if the user is participating in a specific event, the collection unit can prioritize collecting images related to that event. It can also prioritize collecting images related to a specific region if the user is in that region. Furthermore, if the user is traveling, the collection unit can prioritize collecting images related to their travel destination. In this way, the collection unit can prioritize collecting highly relevant images by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the user's geographical location information into an AI and have the AI ​​perform the collection of highly relevant images.

[0071] The data collection unit can analyze the user's social media activity and collect relevant images during the collection process. For example, the data collection unit can collect relevant images based on images shared by the user on social media. It can also analyze posts from accounts the user follows on social media and collect relevant images. Furthermore, it can collect relevant images based on posts the user "likes" on social media. In this way, the data collection unit can collect relevant images by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI and have the AI ​​perform the collection of relevant images.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the items during the analysis. For example, the analysis unit performs a detailed analysis for important items. It can also perform a simplified analysis for less important items. Furthermore, the analysis unit can perform a special analysis for items of particular interest to the user. In this way, the analysis unit can perform a detailed analysis for important items by adjusting the level of detail of the analysis based on the importance of the items. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input item importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the item category during analysis. For example, for clothing items, the analysis unit performs analysis on the fabric and sewing method. For small items, the analysis unit can also perform analysis on the material and assembly method. Furthermore, for accessory items, the analysis unit can perform analysis on the design and decoration method. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the item category. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input item category data into a generation AI and have the generation AI execute the application of the analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can perform a visually engaging analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the submission date of items during the analysis process. For example, the analysis unit may prioritize analyzing items that have been submitted most recently. It can also prioritize analyzing items with approaching submission deadlines. Furthermore, it can prioritize analyzing items that users are particularly in a hurry to process. In this way, the analysis unit can prioritize the analysis of items with higher urgency by determining the priority of analysis based on the submission date of items. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input item submission date data into a generation AI and have the generation AI perform the determination of analysis priorities.

[0077] The analysis unit can adjust the order of analysis based on the relationships between items during the analysis process. For example, the analysis unit may prioritize analyzing items that are highly related to other items owned by the user. It can also prioritize analyzing items that are highly related to the costumes of characters the user wants to cosplay. Furthermore, it can prioritize analyzing items that are highly related to items the user has previously analyzed. In this way, the analysis unit can prioritize the analysis of highly related items by adjusting the order of analysis based on the relationships between items. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input item relationship data into a generation AI and have the generation AI perform the adjustment of the analysis order.

[0078] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible presentation method. If the user is relaxed, the presentation unit can also provide a presentation method that includes detailed information. If the user is in a hurry, the presentation unit can provide a presentation method that gets straight to the point. In this way, the presentation unit can provide a more appropriate presentation method by adjusting the presentation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit is performed using generative AI. For example, the presentation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation method.

[0079] The presentation unit can adjust the level of detail in the presentation based on the importance of the item. For example, the presentation unit will provide a detailed presentation for important items. It can also provide a concise presentation for less important items. Furthermore, the presentation unit can provide a special presentation for items that the user is particularly interested in. In this way, the presentation unit can provide a detailed presentation for important items by adjusting the level of detail in the presentation based on the importance of the item. Some or all of the above processing in the presentation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation unit can input item importance data into a generative AI and have the generative AI perform the adjustment of the level of detail in the presentation.

[0080] The presentation unit can apply different presentation algorithms depending on the item category during presentation. For example, for clothing items, the presentation unit can provide information on the fabric and sewing method. For small items, the presentation unit can also provide information on the materials and assembly method. Furthermore, for accessory items, the presentation unit can provide information on the design and decoration method. In this way, the presentation unit can provide more appropriate presentation results by applying different presentation algorithms depending on the item category. Some or all of the above processing in the presentation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation unit can input item category data into a generative AI and have the generative AI execute the application of the presentation algorithm.

[0081] The presentation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated emotions. For example, if the user is in a hurry, the presentation unit will provide a short, concise presentation. If the user is relaxed, the presentation unit can provide a detailed presentation. If the user is excited, the presentation unit can provide a visually appealing presentation. In this way, the presentation unit can provide more appropriate presentation results by adjusting the length of the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit is performed using generative AI. For example, the presentation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the presentation.

[0082] The presentation unit can determine the priority of presentations based on the submission date of each item. For example, the presentation unit may prioritize presenting items that have been submitted most recently. It can also prioritize presenting items with approaching submission deadlines. Furthermore, it can prioritize presenting items that the user is particularly in a hurry for. In this way, the presentation unit can prioritize presenting items with higher urgency by determining the priority of presentations based on the submission date of each item. Some or all of the above processing in the presentation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the presentation unit can input item submission date data into a generation AI and have the generation AI perform the determination of presentation priorities.

[0083] The presentation unit can adjust the order of presentation based on the relevance of the items. For example, the presentation unit can prioritize presenting items that are highly relevant to other items owned by the user. It can also prioritize presenting items that are highly relevant to the costume of a character the user wants to cosplay as. Furthermore, it can prioritize presenting items that are highly relevant to items the user has previously presented. In this way, the presentation unit can prioritize presenting highly relevant items by adjusting the order of presentation based on the relevance of the items. Some or all of the above processing in the presentation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the presentation unit can input item relevance data into a generative AI and have the generative AI perform the adjustment of the presentation order.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The cosplay support system can estimate the user's emotions and suggest cosplay themes and characters based on those emotions. For example, if the user is excited, it can suggest action-oriented characters. If the user is relaxed, it can suggest calming characters. Furthermore, if the user is sad, it can suggest uplifting characters. This allows the system to provide the optimal cosplay theme according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit is performed using generative AI. For example, the suggestion unit can input the user's emotion data into the generative AI and have the generative AI suggest cosplay themes.

[0086] The cosplay support system can analyze a user's past cosplay history and suggest the optimal combination of cosplay items. For example, it can suggest new items with similar themes based on data of costumes and accessories the user has used in the past. It can also analyze the styles of successful cosplays the user has done in the past and suggest new styles based on that. Furthermore, it can suggest areas for improvement based on data of cosplays the user has failed at in the past. In this way, it can provide the optimal combination of cosplay items that leverages the user's past experience. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's past cosplay history data into the generation AI and have the generation AI suggest the optimal combination of items.

[0087] The cosplay support system can estimate the user's emotions and suggest the best timing for participating in a cosplay event based on those emotions. For example, if the user is excited, it can suggest an event they can participate in immediately. If the user is relaxed, it can suggest an event with ample preparation time. Furthermore, if the user is stressed, it can suggest an event that will help them relax. This allows the system to provide the optimal timing for event participation according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit is performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI suggest event participation timings.

[0088] The cosplay support system can suggest the most suitable cosplay events and shops by taking into account the user's geographical location. For example, if the user is in a specific region, it can suggest cosplay events held in that region. If the user is traveling, it can also suggest cosplay events and shops in their travel destination. Furthermore, if the user is participating in a specific event, it can suggest shops and services related to that event. This allows the system to provide the most suitable cosplay events and shops based on the user's geographical location. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit can input the user's geographical location information into the generation AI and have the generation AI generate suggestions for the most suitable events and shops.

[0089] The cosplay support system can estimate the user's emotions and suggest cosplay practice methods based on those emotions. For example, if the user is excited, it can suggest active practice methods. If the user is relaxed, it can suggest relaxing practice methods. Furthermore, if the user is stressed, it can suggest stress-reducing practice methods. This allows the system to provide the optimal cosplay practice method tailored to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit is performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI suggest practice methods.

[0090] The cosplay support system can analyze a user's social media activity and suggest the most suitable cosplay items and themes. For example, it can analyze posts from accounts the user follows on social media and suggest relevant cosplay items. It can also suggest themes the user is interested in based on posts the user has "liked" on social media. Furthermore, it can suggest cosplay items in a similar style based on images the user has shared on social media. This allows the system to provide the most suitable cosplay items and themes based on the user's social media activity. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit can input the user's social media activity data into the generative AI and have the generative AI suggest the most suitable items and themes.

[0091] The cosplay support system can estimate the user's emotions and suggest cosplay photography methods based on those emotions. For example, if the user is excited, it can suggest a dynamic photography method. If the user is relaxed, it can suggest a calmer photography method. Furthermore, if the user is stressed, it can suggest a simple and relaxing photography method. This allows the system to provide the optimal cosplay photography method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit is performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI suggest photography methods.

[0092] The cosplay support system can analyze a user's past cosplay event participation history and propose an optimal event participation schedule. For example, it can suggest new events with similar themes based on data from events the user has previously attended. It can also analyze the user's past successful event participation schedules and propose new schedules based on that. Furthermore, it can suggest areas for improvement based on data from past unsuccessful event participations. This allows the system to provide an optimal event participation schedule that leverages the user's past experience. Some or all of the above processing in the proposal section is performed using a generation AI. For example, the proposal section can input the user's past event participation history data into the generation AI and have the generation AI propose an optimal schedule.

[0093] The cosplay support system can estimate the user's emotions and suggest cosplay makeup methods based on those emotions. For example, if the user is excited, it can suggest a vibrant makeup style. If the user is relaxed, it can suggest a natural makeup style. Furthermore, if the user is stressed, it can suggest a simple and relaxing makeup style. This allows the system to provide the optimal cosplay makeup style according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit is performed using generative AI. For example, the suggestion unit can input the user's emotion data into the generative AI and have the generative AI suggest makeup methods.

[0094] The cosplay support system can suggest optimal cosplay items and themes based on the user's current projects and areas of interest. For example, it can suggest items related to the cosplay project the user is currently working on. It can also suggest highly relevant themes based on the user's areas of interest. Furthermore, it can suggest new items and themes based on characters and themes the user has shown interest in in the past. This allows the system to provide optimal cosplay items and themes based on the user's current projects and areas of interest. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit can input data on the user's projects and areas of interest into the generative AI and have the generative AI suggest optimal items and themes.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The collection unit collects images of the user's clothing and accessories. The collection unit can collect images in formats such as JPEG or PNG. It can also collect images of the entire clothing or accessories, or images of parts of them. Furthermore, some or all of the processing in the collection unit may be performed using AI. For example, the collection unit can input images of the user's clothing and accessories into the AI ​​and have the AI ​​perform the image collection. Step 2: The analysis unit analyzes the images collected by the collection unit and the information of the character to be cosplayed. The analysis unit can analyze the images using, for example, image recognition technology. The analysis unit can also extract features from the images using a feature extraction algorithm. Furthermore, some or all of the processing in the analysis unit is performed using a generative AI. For example, the analysis unit can input the images collected by the collection unit and the information of the character to be cosplayed into the generative AI and have the generative AI perform the analysis. Step 3: The presentation unit visually presents how to create the items necessary for cosplay based on the data analyzed by the analysis unit. The presentation unit can present the creation method using, for example, 2D images or 3D models. It can also visually present the creation method using animation. Furthermore, some or all of the processing in the presentation unit is performed using a generative AI. For example, the presentation unit can input the creation method of the items necessary for cosplay into the generative AI based on the data analyzed by the analysis unit, and have the generative AI execute the method of visually presenting it.

[0097] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0098] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 to collect images of the user's costumes and accessories. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 to analyze the collected images and information about the character the user wants to cosplay. The presentation unit uses the display 40A of the smart device 14 to visually present how to create the items necessary for cosplay based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] As shown in Figure 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.

[0103] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0107] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0108] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0109] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0110] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0111] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0115] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, and presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 to collect images of the user's costumes and accessories. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected images and information about the character the user wants to cosplay. The presentation unit uses the display of the smart glasses 214 to visually present how to create the items necessary for cosplay based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 of the headset terminal 314 to collect images of the user's costumes and accessories. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected images and information about the character the user wants to cosplay. The presentation unit uses the display 343 of the headset terminal 314 to visually present how to create the items necessary for cosplay based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0141] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, and presentation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to collect images of the user's costumes and accessories. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected images and information about the character the user wants to cosplay. The presentation unit uses, for example, the display of the robot 414 to visually present how to create the items necessary for cosplay based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0152] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0153] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0154] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0158] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0159] 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.

[0160] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0161] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0162] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0163] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0165] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0166] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0167] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0168] (Note 1) A collection unit that collects images of costumes and accessories owned by users, The aforementioned collection unit analyzes the images and information of the character the user wants to cosplay as, The system includes a presentation unit that visually presents how to create items necessary for cosplay based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned display unit is, Generate videos using AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system compares the user's existing costumes and accessories with those of the character they want to cosplay as, and identifies how to create the necessary items. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The system analyzes the user's owned clothing and accessories and suggests customization methods. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, This resource suggests where to purchase the fabrics and accessories needed to create a specific character's costume and provides videos demonstrating how to combine them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of image collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of images to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting images that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the item category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the items were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is, It estimates the user's emotions and adjusts the presentation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, When presenting items, adjust the level of detail based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting items, different presentation algorithms are applied depending on the item category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, It estimates the user's emotions and adjusts the length of the presentation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, When presenting items, we will prioritize them based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting items, adjust the presentation order based on their relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects images of costumes and accessories owned by users, The aforementioned collection unit analyzes the images and information of the character the user wants to cosplay as, The system includes a presentation unit that visually presents how to create items necessary for cosplay based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned display unit is, Generate videos using AI. The system according to feature 1.

3. The aforementioned analysis unit, The system compares the user's existing costumes and accessories with those of the character they want to cosplay as, and identifies how to create the necessary items. The system according to feature 1.

4. The aforementioned analysis unit, The system analyzes the user's owned clothing and accessories and suggests customization methods. The system according to feature 1.

5. The aforementioned display unit is, This resource suggests where to purchase the fabrics and accessories needed to create a specific character's costume and provides videos demonstrating how to combine them. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of image collection based on those emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of images to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting images that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

Images