system

Abstract

The system according to this embodiment aims to make it easy for even cosplay beginners to find a makeup method that suits the character. [Solution] The system according to the embodiment comprises an image receiving unit, an information receiving unit, a learning unit, and a presentation unit. The image receiving unit receives images of makeup tools owned by the user. The information receiving unit receives information of the character the user wants to cosplay. The learning unit learns the information received by the image receiving unit and the information receiving unit. The presentation unit presents the optimal makeup plan based on the data learned by the learning 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 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for cosplay beginners to find a makeup method suitable for the character they wanted to play.

[0005] The system according to the embodiment aims to enable even cosplay beginners to easily find a makeup method suitable for the character.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an image receiving unit, an information receiving unit, a learning unit, and a presentation unit. The image receiving unit receives images of makeup tools owned by the user. The information receiving unit receives information of the character the user wants to cosplay as. The learning unit learns the information received by the image receiving unit and the information receiving unit. The presentation unit presents the optimal makeup plan based on the data learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment allows even cosplay beginners to easily find a makeup method that suits the character. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 makeup support system according to an embodiment of the present invention is a system that utilizes generative AI to enable even beginners to easily create high-quality cosplay makeup. This cosplay makeup support system allows users to upload images of their makeup tools and input information about the character they wish to cosplay. The generative AI learns this information and, based on the user's makeup tools and commercially available products, presents an optimal makeup plan. For example, if a user wants to cosplay a specific character, the generative AI analyzes the character's characteristics and provides specific instructions on how to use the user's makeup tools. This allows even beginners to easily create high-quality cosplay makeup. Furthermore, the generative AI also suggests commercially available products for makeup tools the user does not own. This allows users to easily acquire the necessary makeup tools. This system creates an environment where anyone can easily enjoy high-quality cosplay. With advancements in generative AI, there is a technological foundation for visually generating the entire makeup process, and given the increasing desire for visual validation due to the spread of social media, this service is considered highly likely to succeed. Thus, the cosplay makeup support system can provide an environment where anyone can easily enjoy high-quality cosplay.

[0029] The cosplay makeup support system according to this embodiment comprises an image receiving unit, an information receiving unit, a learning unit, and a presentation unit. The image receiving unit receives images of makeup tools owned by the user. For example, the user can take pictures of their makeup tools with a smartphone or digital camera and upload them to the system. The image receiving unit can also scan images of the user's makeup tools and import them into the system as digital data. Furthermore, the image receiving unit can directly obtain images of the user's makeup tools from cloud storage. The information receiving unit receives information about the character the user wants to cosplay. For example, the user can input the name and image of the character they want to cosplay. The information receiving unit can also input the characteristics and settings of the character the user wants to cosplay in text format. Furthermore, the information receiving unit can automatically obtain information about the character the user wants to cosplay from the internet. The learning unit learns the information received by the image receiving unit and the information receiving unit. For example, the learning unit uses a generative AI to analyze images of the user's makeup tools and information about the character they want to cosplay and derive the optimal makeup plan. The learning unit uses a generating AI to learn the characteristics of the user's makeup tools and the character they are cosplaying as, and then generates an optimal makeup plan. The presentation unit presents the optimal makeup plan based on the data learned by the learning unit. For example, the presentation unit can instruct the user specifically on which makeup tools to use and how to use them. The presentation unit can also visually show the user the steps and tips for applying makeup using the generating AI. Furthermore, the presentation unit can also suggest necessary makeup tools to the user using the generating AI. As a result, the cosplay makeup support system according to this embodiment can easily enable even beginners to create high-quality cosplay makeup by presenting an optimal makeup plan based on the user's makeup tools and information about the character they wish to cosplay as.

[0030] The image reception unit accepts images of makeup tools owned by users. Specifically, users can take pictures of their makeup tools using their smartphones or digital cameras and upload them to the system. This allows users to easily provide information about their makeup tools to the system. The image reception unit can also capture images of makeup tools as digital data using a scanner. This makes it easy to import information about makeup tools from printed catalogs and packaging into the system. Furthermore, the image reception unit also has a function to directly retrieve images of makeup tools from cloud storage. By linking images saved by users in cloud storage to the system, images can be imported without any hassle. In this way, the image reception unit can collect information about users' makeup tools and import it into the system in a variety of ways.

[0031] The information reception section accepts information about characters that users wish to cosplay. Specifically, users can input the name and image of the character they want to cosplay. This allows the system to understand the specific visual appearance of the character the user is aiming for. The information reception section also allows users to input the characteristics and settings of the character they wish to cosplay in text format. For example, by describing the character's hairstyle, eye color, and makeup characteristics in detail, the system can derive a more accurate makeup plan. Furthermore, the information reception section has a function to automatically retrieve character information from the internet. By simply having the user input the character's name, the system automatically collects related images and information, saving the user time and effort. In this way, the information reception section can collect information about the character that the user wants to cosplay from multiple perspectives and provide it to the system.

[0032] The learning unit learns from the information received by the image reception unit and the information reception unit. Specifically, it uses a generative AI to analyze images of the makeup tools owned by the user and information about the character they want to cosplay as, and derives the optimal makeup plan. The generative AI learns the characteristics of the user's makeup tools and the characteristics of the character, and generates the optimal makeup plan. For example, the generative AI uses image recognition technology to analyze the type, color, and texture of the makeup tools and identifies the tools suitable for the character's makeup. It also analyzes the character's image and characteristics to determine which makeup tools should be used for which parts of the face. Furthermore, the generative AI can learn from past data and makeup examples from other users to provide a more accurate makeup plan. As a result, the learning unit can derive the optimal makeup plan based on the user's makeup tools and information about the character they want to cosplay as.

[0033] The presentation unit presents the optimal makeup plan based on data learned by the learning unit. Specifically, the generating AI instructs the user on which makeup tools to use and how to use them. For example, the generating AI selects appropriate items from the user's makeup tools and explains how to use each in detail. It can also visually demonstrate makeup procedures and tips. For example, it can use videos or animations to show the makeup procedure step by step, making it easy for the user to understand. Furthermore, the presentation unit can also suggest necessary makeup tools. If the user does not own a particular makeup tool, the generating AI will suggest it and provide purchase links or information on alternatives. In this way, the presentation unit can present the optimal makeup plan concretely and visually, based on the user's existing makeup tools and information about the character they want to cosplay.

[0034] The suggestion unit can also suggest commercially available products for makeup tools that the user does not own. For example, the suggestion unit can identify makeup tools that the user does not own and suggest commercially available alternatives. The suggestion unit's generating AI can detect the user's lack of makeup tools and list the necessary items. The suggestion unit's generating AI can also suggest products to the user based on specific brands, product categories, and price ranges. For example, the suggestion unit can detect an eyeshadow palette that the user does not own and suggest an eyeshadow palette from a specific brand. It can also detect a foundation that the user does not own and suggest the best foundation based on price range. Furthermore, it can detect a lipstick that the user does not own and suggest a lipstick based on product category. This allows the user to easily acquire the necessary makeup tools. Some or all of the above processing in the suggestion unit may be performed using the generating AI or not. For example, the suggestion unit can input information about makeup tools that the user does not own into the generating AI, and the generating AI can suggest the best product. This allows the user to easily acquire the necessary makeup tools.

[0035] The display unit can provide specific instructions on how to use the makeup tools the user owns. For example, the display unit can provide specific instructions on how to use the eyeshadow palette the user owns. The display unit can use a generating AI to specifically show the user how to choose and apply eyeshadow colors. The display unit can also use a generating AI to visually show the user how to create eyeshadow gradients and how to blend them. For example, the display unit can provide specific instructions on how to use the foundation the user owns. The display unit can use a generating AI to specifically show the user how to apply and blend the foundation. The display unit can also use a generating AI to visually show the user how to choose a foundation color and how to use it according to their skin tone. Furthermore, the display unit can provide specific instructions on how to use the lipstick the user owns. The display unit can use a generating AI to specifically show the user how to apply lipstick and how to choose a color. The display unit can also use a generating AI to visually show the user how to create lipstick gradients and how to use lip liner. This allows even beginners to easily create high-quality cosplay makeup by providing users with specific instructions on how to effectively use their makeup tools. Some or all of the above-described processing in the presentation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the presentation unit can input information about the makeup tools owned by the user into the generating AI, which can then provide specific instructions on how to use them. This allows even beginners to easily create high-quality cosplay makeup by providing users with specific instructions on how to effectively use their makeup tools.

[0036] The image reception unit can analyze the user's past makeup tool usage history and select the optimal image acquisition method. For example, the image reception unit can automatically acquire images of makeup tools previously used by the user, eliminating the need for re-uploading. The image reception unit's AI analyzes the user's past makeup tool usage history and selects the optimal image acquisition method. The image reception unit's AI analyzes the user's makeup tool usage frequency and patterns and can suggest the optimal image acquisition method. For example, the image reception unit analyzes the frequency of use of makeup tools previously used by the user and prioritizes acquiring images of frequently used tools. Furthermore, the image reception unit can analyze combinations of makeup tools previously used by the user and suggest the optimal image acquisition method. The image reception unit's AI analyzes the user's makeup tool usage history and selects the optimal image acquisition method. This allows the system to select the optimal image acquisition method by analyzing the user's past makeup tool usage history, saving the user time and effort. Some or all of the processing described above in the image reception unit may be performed using AI or not. For example, the image reception unit can input the user's past makeup tool usage history into the AI, which can then select the optimal image acquisition method. This allows for the selection of the optimal image acquisition method by analyzing the user's past makeup tool usage history, thereby saving the user time and effort.

[0037] The image reception unit can filter images based on the user's current makeup skill level. For example, if the user's makeup skill level is beginner, the image reception unit will prioritize receiving images of basic makeup tools. The image reception unit can use AI to analyze the user's makeup skill level and select the most suitable image. The image reception unit can use AI to evaluate the user's makeup skill level and filter for appropriate images. For example, if the user's makeup skill level is intermediate, the image reception unit will prioritize receiving images of advanced makeup tools. The image reception unit can use AI to analyze the user's makeup skill level and select the most suitable image. Furthermore, if the user's makeup skill level is advanced, the image reception unit will prioritize receiving images of professional makeup tools. The image reception unit can use AI to analyze the user's makeup skill level and select the most suitable image. In this way, by filtering images according to the user's makeup skill level, the image reception unit can receive images of appropriate makeup tools. Some or all of the above processing in the image reception unit may be performed using AI or not. For example, the image reception unit inputs the user's makeup skill level into the AI, which can then filter for the most suitable images. This allows the system to receive images of appropriate makeup tools by filtering them according to the user's makeup skill level.

[0038] The image reception unit can prioritize receiving images that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the image reception unit will prioritize receiving images of popular makeup products in that region. The image reception unit can use AI to analyze the user's geographical location information and select the most suitable images. The image reception unit can use AI to evaluate the user's geographical location information and filter for appropriate images. For example, if the user is traveling, the image reception unit will prioritize receiving images of makeup products to be used at their travel destination. The image reception unit can use AI to analyze the user's geographical location information and select the most suitable images. Furthermore, if the user is at home, the image reception unit will prioritize receiving images of makeup products they use daily. The image reception unit can use AI to analyze the user's geographical location information and select the most suitable images. This allows the image reception unit to prioritize receiving images that are highly relevant, by taking into account the user's geographical location information. Some or all of the above processing in the image reception unit may be performed using AI or not. For example, the image reception unit inputs the user's geographical location information into the AI, which can then filter for the most relevant images. This allows the system to prioritize receiving images that are highly relevant by considering the user's geographical location.

[0039] The image reception unit can analyze the user's social media activity and receive relevant images upon receiving an image. For example, the image reception unit may prioritize receiving images of makeup tools shared by the user on social media. The image reception unit can use AI to analyze the user's social media activity and select the most suitable image. The image reception unit can use AI to evaluate the user's social media activity and filter for appropriate images. For example, the image reception unit may prioritize receiving images of makeup artists followed by the user on social media. The image reception unit can use AI to analyze the user's social media activity and select the most suitable image. Furthermore, the image reception unit may prioritize receiving images of makeup tools that the user has "liked" on social media. The image reception unit can use AI to analyze the user's social media activity and select the most suitable image. This allows the image reception unit to prioritize receiving relevant images by analyzing the user's social media activity. Some or all of the above processing in the image reception unit may be performed using AI or not. For example, the image reception unit can input the user's social media activity into AI, which can then filter for the most suitable image. This allows for prioritizing the reception of relevant images by analyzing users' social media activity.

[0040] The information reception unit can analyze the user's past cosplay history and select the optimal method of information acquisition. For example, the information reception unit can automatically acquire information about characters the user has cosplayed in the past, saving the user the trouble of re-entering the information. The information reception unit can use AI to analyze the user's past cosplay history and select the optimal method of information acquisition. The information reception unit can use AI to analyze the user's cosplay history and propose the optimal method of information acquisition. For example, the information reception unit can analyze the popularity of characters the user has cosplayed in the past and prioritize acquiring information on popular characters. The information reception unit can use AI to analyze the user's cosplay history and select the optimal method of information acquisition. Furthermore, the information reception unit can analyze the characteristics of characters the user has cosplayed in the past and propose the optimal method of information acquisition. The information reception unit can use AI to analyze the user's cosplay history and select the optimal method of information acquisition. This allows the system to select the optimal method of information acquisition by analyzing the user's past cosplay history, saving the user time and effort. Some or all of the above-described processes in the information reception unit may be performed using AI or without AI. For example, the information reception unit can input the user's past cosplay history into the AI, which can then select the most suitable method for obtaining the information. This allows the AI ​​to analyze the user's past cosplay history to select the optimal method for obtaining the information, thus saving the user time and effort.

[0041] The information receiving unit can filter information based on the user's current interests and preferences when receiving it. For example, the information receiving unit prioritizes receiving information about characters the user is currently interested in. The information receiving unit's AI can analyze the user's interests and preferences and select the most suitable information. The information receiving unit's AI can evaluate the user's interests and preferences and filter the information appropriately. For example, the information receiving unit prioritizes receiving information about makeup techniques the user is currently interested in. The information receiving unit's AI can analyze the user's interests and preferences and select the most suitable information. Furthermore, the information receiving unit prioritizes receiving information about cosplay events the user is currently interested in. The information receiving unit's AI can analyze the user's interests and preferences and select the most suitable information. In this way, by filtering information according to the user's interests and preferences, the information receiving unit can receive appropriate information. Some or all of the above processing in the information receiving unit may be performed using AI or not. For example, the information receiving unit can input the user's interests and preferences into the AI, which can then filter the information appropriately. This allows information to be filtered according to the user's interests and preferences, enabling them to receive appropriate information.

[0042] The information receiving unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, if the user is in a specific region, the information receiving unit will prioritize receiving information about popular cosplay characters in that region. The information receiving unit can use AI to analyze the user's geographical location and select the most relevant information. The information receiving unit can use AI to evaluate the user's geographical location and filter the information to be appropriate. For example, if the user is traveling, the information receiving unit will prioritize receiving information about popular cosplay characters in their travel destination. The information receiving unit can use AI to analyze the user's geographical location and select the most relevant information. Furthermore, if the user is at home, the information receiving unit will prioritize receiving information about characters they regularly cosplay. The information receiving unit can use AI to analyze the user's geographical location and select the most relevant information. This allows the information receiving unit to prioritize receiving highly relevant information by considering the user's geographical location. Some or all of the above processing in the information receiving unit may be performed using AI or not. For example, the information reception unit inputs the user's geographical location information into the AI, which can then filter the most relevant information. This allows the system to prioritize receiving highly relevant information by considering the user's geographical location.

[0043] The information receiving unit can analyze the user's social media activity and receive relevant information when information is received. For example, the information receiving unit can prioritize receiving information about cosplay characters shared by the user on social media. The information receiving unit can use AI to analyze the user's social media activity and select the most relevant information. The information receiving unit can use AI to evaluate the user's social media activity and filter appropriate information. For example, the information receiving unit can prioritize receiving information about cosplayers followed by the user on social media. The information receiving unit can use AI to analyze the user's social media activity and select the most relevant information. Furthermore, the information receiving unit can prioritize receiving information about cosplay characters liked by the user on social media. The information receiving unit can use AI to analyze the user's social media activity and select the most relevant information. In this way, by analyzing the user's social media activity, relevant information can be prioritized. Some or all of the above processing in the information receiving unit may be performed using AI or not. For example, the information receiving unit can input the user's social media activity into AI, which can then filter the most relevant information. This allows for the prioritization of relevant information by analyzing users' social media activity.

[0044] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can have the generative AI analyze past learning data and select the optimal learning algorithm. The learning unit can have the generative AI evaluate past learning data and select an appropriate learning algorithm. For example, the learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. The learning unit can have the generative AI analyze past learning data and select the optimal parameters. Furthermore, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. The learning unit can have the generative AI analyze past learning data and select the optimal learning algorithm. This allows the learning algorithm to be optimized and its accuracy improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using the generative AI or not. For example, the learning unit can input past learning data into the generative AI, and the generative AI can select the optimal learning algorithm. This allows the learning algorithm to be optimized and its accuracy improved by referring to past learning data.

[0045] The learning unit can weight the training data based on the frequency of use of the user's makeup tools during training. For example, the learning unit can train by weighting the data of makeup tools that the user frequently uses. The learning unit can have the generative AI analyze the frequency of use of the user's makeup tools and perform optimal weighting. The learning unit can have the generative AI evaluate the frequency of use of the user's makeup tools and perform appropriate weighting. For example, the learning unit can train by reducing the weight of data for makeup tools that the user rarely uses. The learning unit can have the generative AI analyze the frequency of use of the user's makeup tools and perform optimal weighting. Furthermore, the learning unit adjusts the weighting of the training data based on the frequency of use of the user's makeup tools. The learning unit can have the generative AI analyze the frequency of use of the user's makeup tools and perform optimal weighting. In this way, by weighting the training data based on the frequency of use of the user's makeup tools, it is possible to train by weighting the data of frequently used makeup tools. Some or all of the above processing in the learning unit may be performed using the generative AI or not. For example, the learning unit inputs the frequency of use of a user's makeup tools into the generating AI, which can then perform optimal weighting. This allows the learning model to learn by weighting the training data based on the frequency of use of the user's makeup tools, thereby giving more weight to the data of frequently used makeup tools.

[0046] The learning unit can weight the training data based on the popularity of the user's cosplay characters during training. For example, the learning unit can train by weighting the data of popular cosplay characters. The learning unit can have the generative AI analyze the popularity of the user's cosplay characters and perform optimal weighting. The learning unit can have the generative AI evaluate the popularity of the user's cosplay characters and perform appropriate weighting. For example, the learning unit can train by reducing the weight of the data of unpopular cosplay characters. The learning unit can have the generative AI analyze the popularity of the user's cosplay characters and perform optimal weighting. Furthermore, the learning unit adjusts the weighting of the training data based on the popularity of the cosplay characters. The learning unit can have the generative AI analyze the popularity of the user's cosplay characters and perform optimal weighting. As a result, by weighting the training data based on the popularity of the cosplay characters, it is possible to train by weighting the data of popular characters. Some or all of the above processing in the learning unit may be performed using the generative AI or not. For example, the learning unit inputs the popularity ranking of the user's cosplay character into the generating AI, which can then perform optimal weighting. This allows the learning model to learn by weighting the training data based on the popularity ranking of the cosplay character, thereby giving more weight to the data of popular characters.

[0047] The learning unit can analyze the user's social media activity during training and incorporate relevant data into the learning process. For example, the learning unit can incorporate data on cosplay characters shared by the user on social media into the learning process. The learning unit can use a generative AI to analyze the user's social media activity and select the optimal data. The learning unit can use a generative AI to evaluate the user's social media activity and incorporate appropriate data into the learning process. For example, the learning unit can incorporate data on cosplayers followed by the user on social media into the learning process. The learning unit can use a generative AI to analyze the user's social media activity and select the optimal data. Furthermore, the learning unit can incorporate data on cosplay characters that the user "liked" on social media into the learning process. The learning unit can use a generative AI to analyze the user's social media activity and select the optimal data. This allows relevant data to be incorporated into the learning process by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using a generative AI or not. For example, the learning unit can input the user's social media activity into a generative AI, which can then select the optimal data. This allows relevant data to be incorporated into the learning process by analyzing the user's social media activity.

[0048] The presentation unit can, at the time of presentation, refer to the user's past successful makeup examples to present the optimal makeup strategy. For example, the presentation unit can refer to an image of a successful makeup look the user has achieved in the past and present a similar makeup strategy. The presentation unit can use a generating AI to analyze the user's past successful makeup examples and select the optimal makeup strategy. The presentation unit can use a generating AI to evaluate the user's past successful makeup examples and present an appropriate makeup strategy. For example, the presentation unit can refer to a combination of makeup tools the user has used in the past and present the optimal makeup strategy. The presentation unit can use a generating AI to analyze the user's past successful makeup examples and select the optimal makeup strategy. Furthermore, the presentation unit can refer to the steps of a successful makeup look the user has achieved in the past and present a similar step. The presentation unit can use a generating AI to analyze the user's past successful makeup examples and select the optimal makeup strategy. In this way, the optimal makeup strategy can be presented by referring to the user's past successful makeup examples. Some or all of the above processing in the presentation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the presentation unit inputs the user's past successful makeup examples into a generating AI, which can then select the optimal makeup strategy. This allows the system to present the optimal makeup strategy by referencing the user's past successful makeup examples.

[0049] The presentation unit can customize makeup strategies based on the user's current makeup skill level at the time of presentation. For example, if the user's makeup skill level is beginner, the presentation unit will present a basic makeup strategy. The presentation unit's generating AI can analyze the user's makeup skill level and select the optimal makeup strategy. The presentation unit's generating AI can evaluate the user's makeup skill level and present an appropriate makeup strategy. For example, if the user's makeup skill level is intermediate, the presentation unit will present an advanced makeup strategy. The presentation unit's generating AI can analyze the user's makeup skill level and select the optimal makeup strategy. Furthermore, if the user's makeup skill level is advanced, the presentation unit will present a professional makeup strategy. The presentation unit's generating AI can analyze the user's makeup skill level and select the optimal makeup strategy. In this way, by customizing the makeup strategy according to the user's makeup skill level, an appropriate makeup strategy can be presented. Some or all of the above-described processes in the presentation unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the presentation unit can input the user's makeup skill level into the generating AI, which can then select the optimal makeup strategy. This allows for the presentation of an appropriate makeup strategy by customizing it according to the user's makeup skill level.

[0050] The presentation unit can present the optimal makeup plan considering the user's geographical location information. For example, if the user is in a specific region, the presentation unit can present a makeup plan popular in that region. The presentation unit's generating AI can analyze the user's geographical location information and select the optimal makeup plan. The presentation unit's generating AI can evaluate the user's geographical location information and present an appropriate makeup plan. For example, if the user is traveling, the presentation unit can present a makeup plan to use at their travel destination. The presentation unit's generating AI can analyze the user's geographical location information and select the optimal makeup plan. Furthermore, if the user is at home, the presentation unit can present a makeup plan they use on a daily basis. The presentation unit's generating AI can analyze the user's geographical location information and select the optimal makeup plan. In this way, the optimal makeup plan can be presented by considering the user's geographical location information. Some or all of the above processing in the presentation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the presentation unit can input the user's geographical location information into the generating AI, and the generating AI can select the optimal makeup plan. This allows us to suggest the optimal makeup plan by taking the user's geographical location into consideration.

[0051] The presentation unit can analyze the user's social media activity and present relevant makeup strategies at the time of presentation. For example, the presentation unit can prioritize presenting makeup strategies shared by the user on social media. The presentation unit can use a generative AI to analyze the user's social media activity and select the optimal makeup strategy. The presentation unit can use a generative AI to evaluate the user's social media activity and present appropriate makeup strategies. For example, the presentation unit can prioritize presenting strategies from makeup artists followed by the user on social media. The presentation unit can use a generative AI to analyze the user's social media activity and select the optimal makeup strategy. Furthermore, the presentation unit can prioritize presenting makeup strategies that the user has "liked" on social media. The presentation unit can use a generative AI to analyze the user's social media activity and select the optimal makeup strategy. In this way, by analyzing the user's social media activity, relevant makeup strategies can be presented preferentially. Some or all of the above processing in the presentation unit may be performed using a generative AI or not. For example, the presentation unit can input the user's social media activity into a generative AI, which can then select the optimal makeup strategy. This allows us to analyze users' social media activity and prioritize presenting relevant makeup advice.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The cosplay makeup support system can suggest makeup products while considering the user's skin type and allergy information. For example, if the user has sensitive skin, the AI ​​will prioritize suggesting hypoallergenic makeup products. Furthermore, if the user is allergic to a specific ingredient, the system can suggest makeup products that do not contain that ingredient. In addition, depending on the user's skin type, it can suggest highly moisturizing foundations for dry skin and matte-finish foundations for oily skin. This allows users to choose makeup products that are appropriate for their skin type and allergies.

[0054] The cosplay makeup support system can monitor the user's makeup progress in real time and provide advice as needed. For example, when a user applies eyeshadow, the generating AI evaluates the application in real time and provides specific advice if corrections are needed. Similarly, when a user applies lipstick, the generating AI monitors the application and suggests correction methods if it is not applied evenly. Furthermore, when a user applies foundation, the generating AI evaluates the application and provides specific instructions on how to correct any unevenness. This allows users to check the progress of their makeup in real time and make corrections as needed.

[0055] The cosplay makeup support system can suggest makeup products based on the user's makeup preferences and style. For example, if the user prefers natural makeup, the generating AI will suggest makeup products that provide a natural finish. If the user prefers glamorous makeup, the generating AI can suggest makeup products that provide a more dramatic finish. Furthermore, if the user prefers a specific brand, the system can prioritize suggesting makeup products from that brand. This allows users to choose makeup products that match their preferences and style.

[0056] The cosplay makeup support system can evaluate the quality of the user's makeup and suggest corrections as needed. For example, after a user applies eyeshadow, the generating AI evaluates the result and suggests corrections if the gradation is insufficient. Similarly, after a user applies foundation, the generating AI evaluates the result and provides specific instructions on how to correct any unevenness. Furthermore, after a user applies lipstick, the generating AI evaluates the result and suggests corrections if the application is not even. This allows users to check the quality of their makeup and make corrections as necessary.

[0057] A cosplay makeup support system can record the user's makeup progress and allow them to refer to it later. For example, it can record the steps a user takes when applying eyeshadow and review them later. It can also record the steps a user takes when applying foundation and review them later. Furthermore, it can record the steps a user takes when applying lipstick and review them later. This allows the user to record their makeup progress and refer to it later.

[0058] The cosplay makeup support system allows users to share their makeup progress with other users. For example, users can share their eyeshadow application steps with others and receive advice. They can also share their foundation application steps and receive feedback. Furthermore, they can share their lipstick application steps with others and exchange opinions. This allows users to share their makeup progress with other users and receive advice and feedback.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The image reception unit accepts images of makeup tools owned by the user. For example, users can take pictures of their makeup tools with their smartphone or digital camera and upload them to the system. They can also scan the images and import them into the system as digital data. Furthermore, it is possible to retrieve images directly from cloud storage. Step 2: The information reception section receives information about the character the user wants to cosplay. For example, the user can enter the name and image of the character they want to cosplay. They can also enter the character's characteristics and background in text format. Furthermore, it is possible to automatically retrieve information from the internet. Step 3: The learning unit learns from the information received by the image reception unit and the information reception unit. For example, using a generative AI, it analyzes images of the makeup tools owned by the user and information about the character the user wants to cosplay as, and derives the optimal makeup plan. The generative AI learns the characteristics of the user's makeup tools and the characteristics of the character, and generates the optimal makeup plan. Step 4: The presentation unit presents the optimal makeup strategy based on the data learned by the learning unit. For example, the generating AI can give specific instructions to the user on which makeup tools to use and how to use them. It can also visually demonstrate makeup steps and tips. Furthermore, it can suggest necessary makeup tools.

[0061] (Example of form 2) The cosplay makeup support system according to an embodiment of the present invention is a system that utilizes generative AI to enable even beginners to easily create high-quality cosplay makeup. This cosplay makeup support system allows users to upload images of their makeup tools and input information about the character they wish to cosplay. The generative AI learns this information and, based on the user's makeup tools and commercially available products, presents an optimal makeup plan. For example, if a user wants to cosplay a specific character, the generative AI analyzes the character's characteristics and provides specific instructions on how to use the user's makeup tools. This allows even beginners to easily create high-quality cosplay makeup. Furthermore, the generative AI also suggests commercially available products for makeup tools the user does not own. This allows users to easily acquire the necessary makeup tools. This system creates an environment where anyone can easily enjoy high-quality cosplay. With advancements in generative AI, there is a technological foundation for visually generating the entire makeup process, and given the increasing desire for visual validation due to the spread of social media, this service is considered highly likely to succeed. Thus, the cosplay makeup support system can provide an environment where anyone can easily enjoy high-quality cosplay.

[0062] The cosplay makeup support system according to this embodiment comprises an image receiving unit, an information receiving unit, a learning unit, and a presentation unit. The image receiving unit receives images of makeup tools owned by the user. For example, the user can take pictures of their makeup tools with a smartphone or digital camera and upload them to the system. The image receiving unit can also scan images of the user's makeup tools and import them into the system as digital data. Furthermore, the image receiving unit can directly obtain images of the user's makeup tools from cloud storage. The information receiving unit receives information about the character the user wants to cosplay. For example, the user can input the name and image of the character they want to cosplay. The information receiving unit can also input the characteristics and settings of the character the user wants to cosplay in text format. Furthermore, the information receiving unit can automatically obtain information about the character the user wants to cosplay from the internet. The learning unit learns the information received by the image receiving unit and the information receiving unit. For example, the learning unit uses a generative AI to analyze images of the user's makeup tools and information about the character they want to cosplay and derive the optimal makeup plan. The learning unit uses a generating AI to learn the characteristics of the user's makeup tools and the character they are cosplaying as, and then generates an optimal makeup plan. The presentation unit presents the optimal makeup plan based on the data learned by the learning unit. For example, the presentation unit can instruct the user specifically on which makeup tools to use and how to use them. The presentation unit can also visually show the user the steps and tips for applying makeup using the generating AI. Furthermore, the presentation unit can also suggest necessary makeup tools to the user using the generating AI. As a result, the cosplay makeup support system according to this embodiment can easily enable even beginners to create high-quality cosplay makeup by presenting an optimal makeup plan based on the user's makeup tools and information about the character they wish to cosplay as.

[0063] The image reception unit accepts images of makeup tools owned by users. Specifically, users can take pictures of their makeup tools using their smartphones or digital cameras and upload them to the system. This allows users to easily provide information about their makeup tools to the system. The image reception unit can also capture images of makeup tools as digital data using a scanner. This makes it easy to import information about makeup tools from printed catalogs and packaging into the system. Furthermore, the image reception unit also has a function to directly retrieve images of makeup tools from cloud storage. By linking images saved by users in cloud storage to the system, images can be imported without any hassle. In this way, the image reception unit can collect information about users' makeup tools and import it into the system in a variety of ways.

[0064] The information reception section accepts information about characters that users wish to cosplay. Specifically, users can input the name and image of the character they want to cosplay. This allows the system to understand the specific visual appearance of the character the user is aiming for. The information reception section also allows users to input the characteristics and settings of the character they wish to cosplay in text format. For example, by describing the character's hairstyle, eye color, and makeup characteristics in detail, the system can derive a more accurate makeup plan. Furthermore, the information reception section has a function to automatically retrieve character information from the internet. By simply having the user input the character's name, the system automatically collects related images and information, saving the user time and effort. In this way, the information reception section can collect information about the character that the user wants to cosplay from multiple perspectives and provide it to the system.

[0065] The learning unit learns from the information received by the image reception unit and the information reception unit. Specifically, it uses a generative AI to analyze images of the makeup tools owned by the user and information about the character they want to cosplay as, and derives the optimal makeup plan. The generative AI learns the characteristics of the user's makeup tools and the characteristics of the character, and generates the optimal makeup plan. For example, the generative AI uses image recognition technology to analyze the type, color, and texture of the makeup tools and identifies the tools suitable for the character's makeup. It also analyzes the character's image and characteristics to determine which makeup tools should be used for which parts of the face. Furthermore, the generative AI can learn from past data and makeup examples from other users to provide a more accurate makeup plan. As a result, the learning unit can derive the optimal makeup plan based on the user's makeup tools and information about the character they want to cosplay as.

[0066] The presentation unit presents the optimal makeup plan based on data learned by the learning unit. Specifically, the generating AI instructs the user on which makeup tools to use and how to use them. For example, the generating AI selects appropriate items from the user's makeup tools and explains how to use each in detail. It can also visually demonstrate makeup procedures and tips. For example, it can use videos or animations to show the makeup procedure step by step, making it easy for the user to understand. Furthermore, the presentation unit can also suggest necessary makeup tools. If the user does not own a particular makeup tool, the generating AI will suggest it and provide purchase links or information on alternatives. In this way, the presentation unit can present the optimal makeup plan concretely and visually, based on the user's existing makeup tools and information about the character they want to cosplay.

[0067] The suggestion unit can also suggest commercially available products for makeup tools that the user does not own. For example, the suggestion unit can identify makeup tools that the user does not own and suggest commercially available alternatives. The suggestion unit's generating AI can detect the user's lack of makeup tools and list the necessary items. The suggestion unit's generating AI can also suggest products to the user based on specific brands, product categories, and price ranges. For example, the suggestion unit can detect an eyeshadow palette that the user does not own and suggest an eyeshadow palette from a specific brand. It can also detect a foundation that the user does not own and suggest the best foundation based on price range. Furthermore, it can detect a lipstick that the user does not own and suggest a lipstick based on product category. This allows the user to easily acquire the necessary makeup tools. Some or all of the above processing in the suggestion unit may be performed using the generating AI or not. For example, the suggestion unit can input information about makeup tools that the user does not own into the generating AI, and the generating AI can suggest the best product. This allows the user to easily acquire the necessary makeup tools.

[0068] The display unit can provide specific instructions on how to use the makeup tools the user owns. For example, the display unit can provide specific instructions on how to use the eyeshadow palette the user owns. The display unit can use a generating AI to specifically show the user how to choose and apply eyeshadow colors. The display unit can also use a generating AI to visually show the user how to create eyeshadow gradients and how to blend them. For example, the display unit can provide specific instructions on how to use the foundation the user owns. The display unit can use a generating AI to specifically show the user how to apply and blend the foundation. The display unit can also use a generating AI to visually show the user how to choose a foundation color and how to use it according to their skin tone. Furthermore, the display unit can provide specific instructions on how to use the lipstick the user owns. The display unit can use a generating AI to specifically show the user how to apply lipstick and how to choose a color. The display unit can also use a generating AI to visually show the user how to create lipstick gradients and how to use lip liner. This allows even beginners to easily create high-quality cosplay makeup by providing users with specific instructions on how to effectively use their makeup tools. Some or all of the above-described processing in the presentation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the presentation unit can input information about the makeup tools owned by the user into the generating AI, which can then provide specific instructions on how to use them. This allows even beginners to easily create high-quality cosplay makeup by providing users with specific instructions on how to effectively use their makeup tools.

[0069] The image reception unit can estimate the user's emotions and adjust the timing of image reception based on the estimated emotions. For example, if the user is relaxed, the image reception unit can delay the image reception timing, allowing the user to upload the image calmly. The image reception unit's generating AI can analyze the user's facial expressions and voice to determine if they are relaxed. The image reception unit's generating AI monitors the user's emotions in real time and can receive images at the appropriate time. For example, if the user is in a hurry, the image reception unit can speed up the image reception timing, allowing them to upload the image quickly. The image reception unit's generating AI analyzes the user's actions and behavior to determine if they are in a hurry. The image reception unit's generating AI monitors the user's emotions in real time and can receive images at the appropriate time. Furthermore, if the user is feeling anxious, the image reception unit can adjust the timing of image reception, allowing the user to upload the image with peace of mind. The image reception unit's generating AI analyzes the user's facial expressions and voice to determine if they are feeling anxious. The image reception unit's generating AI monitors the user's emotions in real time and can receive images at the appropriate time. This allows the timing of image submission to be adjusted according to the user's emotions, enabling the user to upload images in a relaxed state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 image submission unit may be performed using the generative AI or not. For example, the image submission unit can input the user's facial expression data into the generative AI, which can estimate the emotion and submit the image at an appropriate time. This allows the timing of image submission to be adjusted according to the user's emotions, enabling the user to upload images in a relaxed state.

[0070] The image reception unit can analyze the user's past makeup tool usage history and select the optimal image acquisition method. For example, the image reception unit can automatically acquire images of makeup tools previously used by the user, eliminating the need for re-uploading. The image reception unit's AI analyzes the user's past makeup tool usage history and selects the optimal image acquisition method. The image reception unit's AI analyzes the user's makeup tool usage frequency and patterns and can suggest the optimal image acquisition method. For example, the image reception unit analyzes the frequency of use of makeup tools previously used by the user and prioritizes acquiring images of frequently used tools. Furthermore, the image reception unit can analyze combinations of makeup tools previously used by the user and suggest the optimal image acquisition method. The image reception unit's AI analyzes the user's makeup tool usage history and selects the optimal image acquisition method. This allows the system to select the optimal image acquisition method by analyzing the user's past makeup tool usage history, saving the user time and effort. Some or all of the processing described above in the image reception unit may be performed using AI or not. For example, the image reception unit can input the user's past makeup tool usage history into the AI, which can then select the optimal image acquisition method. This allows for the selection of the optimal image acquisition method by analyzing the user's past makeup tool usage history, thereby saving the user time and effort.

[0071] The image reception unit can filter images based on the user's current makeup skill level. For example, if the user's makeup skill level is beginner, the image reception unit will prioritize receiving images of basic makeup tools. The image reception unit can use AI to analyze the user's makeup skill level and select the most suitable image. The image reception unit can use AI to evaluate the user's makeup skill level and filter for appropriate images. For example, if the user's makeup skill level is intermediate, the image reception unit will prioritize receiving images of advanced makeup tools. The image reception unit can use AI to analyze the user's makeup skill level and select the most suitable image. Furthermore, if the user's makeup skill level is advanced, the image reception unit will prioritize receiving images of professional makeup tools. The image reception unit can use AI to analyze the user's makeup skill level and select the most suitable image. In this way, by filtering images according to the user's makeup skill level, the image reception unit can receive images of appropriate makeup tools. Some or all of the above processing in the image reception unit may be performed using AI or not. For example, the image reception unit inputs the user's makeup skill level into the AI, which can then filter for the most suitable images. This allows the system to receive images of appropriate makeup tools by filtering them according to the user's makeup skill level.

[0072] The image reception unit can estimate the user's emotions and determine the priority of images to receive based on the estimated emotions. For example, if the user is excited, the image reception unit will prioritize receiving images of the latest makeup products. The image reception unit's generating AI can analyze the user's facial expressions and voice to determine if they are excited. The image reception unit's generating AI can monitor the user's emotions in real time and prioritize receiving appropriate images. For example, if the user is relaxed, the image reception unit will prioritize receiving images of makeup products they have used in the past. The image reception unit's generating AI can analyze the user's movements and behavior to determine if they are relaxed. The image reception unit's generating AI can monitor the user's emotions in real time and prioritize receiving appropriate images. Furthermore, if the user is feeling anxious, the image reception unit will prioritize receiving images of basic makeup products. The image reception unit's generating AI can analyze the user's facial expressions and voice to determine if they are feeling anxious. The image reception unit's generating AI can monitor the user's emotions in real time and prioritize receiving appropriate images. This allows the system to prioritize images according to the user's emotions, so that when the user is excited, images of the latest makeup products can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 image reception unit may be performed using the generative AI or not. For example, the image reception unit can input the user's facial expression data into the generative AI, which will estimate the emotion and prioritize the reception of appropriate images. This allows the system to prioritize images according to the user's emotions, so that when the user is excited, images of the latest makeup products can be prioritized.

[0073] The image reception unit can prioritize receiving images that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the image reception unit will prioritize receiving images of popular makeup products in that region. The image reception unit can use AI to analyze the user's geographical location information and select the most suitable images. The image reception unit can use AI to evaluate the user's geographical location information and filter for appropriate images. For example, if the user is traveling, the image reception unit will prioritize receiving images of makeup products to be used at their travel destination. The image reception unit can use AI to analyze the user's geographical location information and select the most suitable images. Furthermore, if the user is at home, the image reception unit will prioritize receiving images of makeup products they use daily. The image reception unit can use AI to analyze the user's geographical location information and select the most suitable images. This allows the image reception unit to prioritize receiving images that are highly relevant, by taking into account the user's geographical location information. Some or all of the above processing in the image reception unit may be performed using AI or not. For example, the image reception unit inputs the user's geographical location information into the AI, which can then filter for the most relevant images. This allows the system to prioritize receiving images that are highly relevant by considering the user's geographical location.

[0074] The image reception unit can analyze the user's social media activity and receive relevant images upon receiving an image. For example, the image reception unit may prioritize receiving images of makeup tools shared by the user on social media. The image reception unit can use AI to analyze the user's social media activity and select the most suitable image. The image reception unit can use AI to evaluate the user's social media activity and filter for appropriate images. For example, the image reception unit may prioritize receiving images of makeup artists followed by the user on social media. The image reception unit can use AI to analyze the user's social media activity and select the most suitable image. Furthermore, the image reception unit may prioritize receiving images of makeup tools that the user has "liked" on social media. The image reception unit can use AI to analyze the user's social media activity and select the most suitable image. This allows the image reception unit to prioritize receiving relevant images by analyzing the user's social media activity. Some or all of the above processing in the image reception unit may be performed using AI or not. For example, the image reception unit can input the user's social media activity into AI, which can then filter for the most suitable image. This allows for prioritizing the reception of relevant images by analyzing users' social media activity.

[0075] The information reception unit can estimate the user's emotions and adjust the timing of information reception based on the estimated emotions. For example, if the user is relaxed, the information reception unit can delay the timing of information reception, allowing the user to calmly input the information. The information reception unit's generating AI can analyze the user's facial expressions and voice to determine if they are relaxed. The information reception unit's generating AI can monitor the user's emotions in real time and receive information at the appropriate time. For example, if the user is in a hurry, the information reception unit can speed up the timing of information reception, allowing them to input the information quickly. The information reception unit's generating AI can analyze the user's actions and behavior to determine if they are in a hurry. The information reception unit's generating AI can monitor the user's emotions in real time and receive information at the appropriate time. Furthermore, if the user is feeling anxious, the information reception unit can adjust the timing of information reception, allowing the user to input the information with peace of mind. The information reception unit's generating AI can analyze the user's facial expressions and voice to determine if they are feeling anxious. The information reception unit's generating AI can monitor the user's emotions in real time and receive information at the appropriate time. This allows the timing of information reception to be adjusted according to the user's emotions, enabling the user to input information in a relaxed state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 information reception unit may be performed using the generative AI or not. For example, the information reception unit can input the user's facial expression data into the generative AI, the generative AI can estimate the emotion, and the information can be received at an appropriate time. This allows the timing of information reception to be adjusted according to the user's emotions, enabling the user to input information in a relaxed state.

[0076] The information reception unit can analyze the user's past cosplay history and select the optimal method of information acquisition. For example, the information reception unit can automatically acquire information about characters the user has cosplayed in the past, saving the user the trouble of re-entering the information. The information reception unit can use AI to analyze the user's past cosplay history and select the optimal method of information acquisition. The information reception unit can use AI to analyze the user's cosplay history and propose the optimal method of information acquisition. For example, the information reception unit can analyze the popularity of characters the user has cosplayed in the past and prioritize acquiring information on popular characters. The information reception unit can use AI to analyze the user's cosplay history and select the optimal method of information acquisition. Furthermore, the information reception unit can analyze the characteristics of characters the user has cosplayed in the past and propose the optimal method of information acquisition. The information reception unit can use AI to analyze the user's cosplay history and select the optimal method of information acquisition. This allows the system to select the optimal method of information acquisition by analyzing the user's past cosplay history, saving the user time and effort. Some or all of the above-described processes in the information reception unit may be performed using AI or without AI. For example, the information reception unit can input the user's past cosplay history into the AI, which can then select the most suitable method for obtaining the information. This allows the AI ​​to analyze the user's past cosplay history to select the optimal method for obtaining the information, thus saving the user time and effort.

[0077] The information receiving unit can filter information based on the user's current interests and preferences when receiving it. For example, the information receiving unit prioritizes receiving information about characters the user is currently interested in. The information receiving unit's AI can analyze the user's interests and preferences and select the most suitable information. The information receiving unit's AI can evaluate the user's interests and preferences and filter the information appropriately. For example, the information receiving unit prioritizes receiving information about makeup techniques the user is currently interested in. The information receiving unit's AI can analyze the user's interests and preferences and select the most suitable information. Furthermore, the information receiving unit prioritizes receiving information about cosplay events the user is currently interested in. The information receiving unit's AI can analyze the user's interests and preferences and select the most suitable information. In this way, by filtering information according to the user's interests and preferences, the information receiving unit can receive appropriate information. Some or all of the above processing in the information receiving unit may be performed using AI or not. For example, the information receiving unit can input the user's interests and preferences into the AI, which can then filter the information appropriately. This allows information to be filtered according to the user's interests and preferences, enabling them to receive appropriate information.

[0078] The information receiving unit can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is excited, the information receiving unit will prioritize receiving the latest cosplay information. The information receiving unit's generating AI can analyze the user's facial expressions and voice to determine if they are excited. The information receiving unit's generating AI can monitor the user's emotions in real time and prioritize receiving appropriate information. For example, if the user is relaxed, the information receiving unit will prioritize receiving past cosplay information. The information receiving unit's generating AI can analyze the user's movements and behavior to determine if they are relaxed. The information receiving unit's generating AI can monitor the user's emotions in real time and prioritize receiving appropriate information. Furthermore, if the user is feeling anxious, the information receiving unit will prioritize receiving basic cosplay information. The information receiving unit's generating AI can analyze the user's facial expressions and voice to determine if they are feeling anxious. The information receiving unit's generating AI can monitor the user's emotions in real time and prioritize receiving appropriate information. This allows the system to prioritize information based on the user's emotions, enabling it to prioritize receiving the latest cosplay information when the user is excited. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 information receiving unit may be performed using the generative AI or not. For example, the information receiving unit can input the user's facial expression data into the generative AI, which can estimate the emotion and prioritize receiving appropriate information. This allows the system to prioritize information based on the user's emotions, enabling it to prioritize receiving the latest cosplay information when the user is excited.

[0079] The information receiving unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, if the user is in a specific region, the information receiving unit will prioritize receiving information about popular cosplay characters in that region. The information receiving unit can use AI to analyze the user's geographical location and select the most relevant information. The information receiving unit can use AI to evaluate the user's geographical location and filter the information to be appropriate. For example, if the user is traveling, the information receiving unit will prioritize receiving information about popular cosplay characters in their travel destination. The information receiving unit can use AI to analyze the user's geographical location and select the most relevant information. Furthermore, if the user is at home, the information receiving unit will prioritize receiving information about characters they regularly cosplay. The information receiving unit can use AI to analyze the user's geographical location and select the most relevant information. This allows the information receiving unit to prioritize receiving highly relevant information by considering the user's geographical location. Some or all of the above processing in the information receiving unit may be performed using AI or not. For example, the information reception unit inputs the user's geographical location information into the AI, which can then filter the most relevant information. This allows the system to prioritize receiving highly relevant information by considering the user's geographical location.

[0080] The information receiving unit can analyze the user's social media activity and receive relevant information when information is received. For example, the information receiving unit can prioritize receiving information about cosplay characters shared by the user on social media. The information receiving unit can use AI to analyze the user's social media activity and select the most relevant information. The information receiving unit can use AI to evaluate the user's social media activity and filter appropriate information. For example, the information receiving unit can prioritize receiving information about cosplayers followed by the user on social media. The information receiving unit can use AI to analyze the user's social media activity and select the most relevant information. Furthermore, the information receiving unit can prioritize receiving information about cosplay characters liked by the user on social media. The information receiving unit can use AI to analyze the user's social media activity and select the most relevant information. In this way, by analyzing the user's social media activity, relevant information can be prioritized. Some or all of the above processing in the information receiving unit may be performed using AI or not. For example, the information receiving unit can input the user's social media activity into AI, which can then filter the most relevant information. This allows for the prioritization of relevant information by analyzing users' social media activity.

[0081] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select detailed training data. The learning unit can determine whether the user is relaxed by having the generative AI analyze the user's facial expressions and voice. The learning unit can select appropriate training data by having the generative AI monitor the user's emotions in real time. For example, if the user is in a hurry, the learning unit will select training data that gets straight to the point. The learning unit can determine whether the user is in a hurry by having the generative AI analyze the user's movements and actions. The learning unit can select appropriate training data by having the generative AI monitor the user's emotions in real time. Furthermore, if the user is excited, the learning unit will select visually stimulating training data. The learning unit can determine whether the user is excited by having the generative AI analyze the user's facial expressions and voice. The learning unit can select appropriate training data by having the generative AI monitor the user's emotions in real time. As a result, by selecting training data according to the user's emotions, detailed training data can be selected when the user is relaxed. Emotion estimation is achieved using an emotion estimation function, such as 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 above-described processing in the learning unit may be performed using or without the generative AI. For example, the learning unit can input user facial expression data into the generative AI, which can estimate emotions and select appropriate training data. By selecting training data according to the user's emotions, detailed training data can be selected when the user is relaxed.

[0082] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can have the generative AI analyze past learning data and select the optimal learning algorithm. The learning unit can have the generative AI evaluate past learning data and select an appropriate learning algorithm. For example, the learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. The learning unit can have the generative AI analyze past learning data and select the optimal parameters. Furthermore, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. The learning unit can have the generative AI analyze past learning data and select the optimal learning algorithm. This allows the learning algorithm to be optimized and its accuracy improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using the generative AI or not. For example, the learning unit can input past learning data into the generative AI, and the generative AI can select the optimal learning algorithm. This allows the learning algorithm to be optimized and its accuracy improved by referring to past learning data.

[0083] The learning unit can weight the training data based on the frequency of use of the user's makeup tools during training. For example, the learning unit can train by weighting the data of makeup tools that the user frequently uses. The learning unit can have the generative AI analyze the frequency of use of the user's makeup tools and perform optimal weighting. The learning unit can have the generative AI evaluate the frequency of use of the user's makeup tools and perform appropriate weighting. For example, the learning unit can train by reducing the weight of data for makeup tools that the user rarely uses. The learning unit can have the generative AI analyze the frequency of use of the user's makeup tools and perform optimal weighting. Furthermore, the learning unit adjusts the weighting of the training data based on the frequency of use of the user's makeup tools. The learning unit can have the generative AI analyze the frequency of use of the user's makeup tools and perform optimal weighting. In this way, by weighting the training data based on the frequency of use of the user's makeup tools, it is possible to train by weighting the data of frequently used makeup tools. Some or all of the above processing in the learning unit may be performed using the generative AI or not. For example, the learning unit inputs the frequency of use of a user's makeup tools into the generating AI, which can then perform optimal weighting. This allows the learning model to learn by weighting the training data based on the frequency of use of the user's makeup tools, thereby giving more weight to the data of frequently used makeup tools.

[0084] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit increases the learning frequency. The learning unit can determine whether the user is relaxed by having the generative AI analyze the user's facial expressions and voice. The learning unit can set an appropriate learning frequency by having the generative AI monitor the user's emotions in real time. For example, if the user is in a hurry, the learning unit decreases the learning frequency. The learning unit can determine whether the user is in a hurry by having the generative AI analyze the user's movements and actions. The learning unit can set an appropriate learning frequency by having the generative AI monitor the user's emotions in real time. Furthermore, if the user is excited, the learning unit adjusts the learning frequency. The learning unit can determine whether the user is excited by having the generative AI analyze the user's facial expressions and voice. The learning unit can set an appropriate learning frequency by having the generative AI monitor the user's emotions in real time. This allows the learning frequency to be increased when the user is relaxed by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 above-described processing in the learning unit may be performed using the generative AI or not. For example, the learning unit can input user facial expression data into the generative AI, which can estimate emotions and set an appropriate learning frequency. This allows the learning frequency to be adjusted according to the user's emotions, increasing the learning frequency when the user is relaxed.

[0085] The learning unit can weight the training data based on the popularity of the user's cosplay characters during training. For example, the learning unit can train by weighting the data of popular cosplay characters. The learning unit can have the generative AI analyze the popularity of the user's cosplay characters and perform optimal weighting. The learning unit can have the generative AI evaluate the popularity of the user's cosplay characters and perform appropriate weighting. For example, the learning unit can train by reducing the weight of the data of unpopular cosplay characters. The learning unit can have the generative AI analyze the popularity of the user's cosplay characters and perform optimal weighting. Furthermore, the learning unit adjusts the weighting of the training data based on the popularity of the cosplay characters. The learning unit can have the generative AI analyze the popularity of the user's cosplay characters and perform optimal weighting. As a result, by weighting the training data based on the popularity of the cosplay characters, it is possible to train by weighting the data of popular characters. Some or all of the above processing in the learning unit may be performed using the generative AI or not. For example, the learning unit inputs the popularity ranking of the user's cosplay character into the generating AI, which can then perform optimal weighting. This allows the learning model to learn by weighting the training data based on the popularity ranking of the cosplay character, thereby giving more weight to the data of popular characters.

[0086] The learning unit can analyze the user's social media activity during training and incorporate relevant data into the learning process. For example, the learning unit can incorporate data on cosplay characters shared by the user on social media into the learning process. The learning unit can use a generative AI to analyze the user's social media activity and select the optimal data. The learning unit can use a generative AI to evaluate the user's social media activity and incorporate appropriate data into the learning process. For example, the learning unit can incorporate data on cosplayers followed by the user on social media into the learning process. The learning unit can use a generative AI to analyze the user's social media activity and select the optimal data. Furthermore, the learning unit can incorporate data on cosplay characters that the user "liked" on social media into the learning process. The learning unit can use a generative AI to analyze the user's social media activity and select the optimal data. This allows relevant data to be incorporated into the learning process by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using a generative AI or not. For example, the learning unit can input the user's social media activity into a generative AI, which can then select the optimal data. This allows relevant data to be incorporated into the learning process by analyzing the user's social media activity.

[0087] The presentation unit can estimate the user's emotions and adjust the way makeup advice is presented based on the estimated emotions. For example, if the user is relaxed, the presentation unit will present detailed makeup advice. The presentation unit's generating AI can analyze the user's facial expressions and voice to determine if they are relaxed. The presentation unit's generating AI can monitor the user's emotions in real time and present appropriate makeup advice. For example, if the user is in a hurry, the presentation unit will present concise makeup advice that gets straight to the point. The presentation unit's generating AI can analyze the user's movements and actions to determine if they are in a hurry. The presentation unit's generating AI can monitor the user's emotions in real time and present appropriate makeup advice. Furthermore, if the user is feeling anxious, the presentation unit will present reassuring makeup advice. The presentation unit's generating AI can analyze the user's facial expressions and voice to determine if they are anxious. The presentation unit's generating AI can monitor the user's emotions in real time and present appropriate makeup advice. This allows the presentation unit to adjust the way makeup advice is presented according to the user's emotions, enabling it to present detailed makeup advice when the user is relaxed. Emotion estimation is achieved using an emotion estimation function, such as 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 above processing in the presentation unit may be performed using or without a generative AI. For example, the presentation unit can input user facial expression data into a generative AI, which can estimate emotions and present appropriate makeup advice. By adjusting the method of presenting makeup advice according to the user's emotions, detailed makeup advice can be presented when the user is relaxed.

[0088] The presentation unit can, at the time of presentation, refer to the user's past successful makeup examples to present the optimal makeup strategy. For example, the presentation unit can refer to an image of a successful makeup look the user has achieved in the past and present a similar makeup strategy. The presentation unit can use a generating AI to analyze the user's past successful makeup examples and select the optimal makeup strategy. The presentation unit can use a generating AI to evaluate the user's past successful makeup examples and present an appropriate makeup strategy. For example, the presentation unit can refer to a combination of makeup tools the user has used in the past and present the optimal makeup strategy. The presentation unit can use a generating AI to analyze the user's past successful makeup examples and select the optimal makeup strategy. Furthermore, the presentation unit can refer to the steps of a successful makeup look the user has achieved in the past and present a similar step. The presentation unit can use a generating AI to analyze the user's past successful makeup examples and select the optimal makeup strategy. In this way, the optimal makeup strategy can be presented by referring to the user's past successful makeup examples. Some or all of the above processing in the presentation unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the presentation unit inputs the user's past successful makeup examples into a generating AI, which can then select the optimal makeup strategy. This allows the system to present the optimal makeup strategy by referencing the user's past successful makeup examples.

[0089] The presentation unit can customize makeup strategies based on the user's current makeup skill level at the time of presentation. For example, if the user's makeup skill level is beginner, the presentation unit will present a basic makeup strategy. The presentation unit's generating AI can analyze the user's makeup skill level and select the optimal makeup strategy. The presentation unit's generating AI can evaluate the user's makeup skill level and present an appropriate makeup strategy. For example, if the user's makeup skill level is intermediate, the presentation unit will present an advanced makeup strategy. The presentation unit's generating AI can analyze the user's makeup skill level and select the optimal makeup strategy. Furthermore, if the user's makeup skill level is advanced, the presentation unit will present a professional makeup strategy. The presentation unit's generating AI can analyze the user's makeup skill level and select the optimal makeup strategy. In this way, by customizing the makeup strategy according to the user's makeup skill level, an appropriate makeup strategy can be presented. Some or all of the above-described processes in the presentation unit may be performed using the generating AI, or they may be performed without the generating AI. For example, the presentation unit can input the user's makeup skill level into the generating AI, which can then select the optimal makeup strategy. This allows for the presentation of an appropriate makeup strategy by customizing it according to the user's makeup skill level.

[0090] The presentation unit can estimate the user's emotions and prioritize makeup styles based on those emotions. For example, if the user is excited, the presentation unit will prioritize presenting the latest makeup style. The presentation unit's generating AI can analyze the user's facial expressions and voice to determine if they are excited. The presentation unit's generating AI can monitor the user's emotions in real time and prioritize presenting appropriate makeup styles. For example, if the user is relaxed, the presentation unit will prioritize presenting past makeup styles. The presentation unit's generating AI can analyze the user's movements and behavior to determine if they are relaxed. The presentation unit's generating AI can monitor the user's emotions in real time and prioritize presenting appropriate makeup styles. Furthermore, if the user is feeling anxious, the presentation unit will prioritize presenting basic makeup styles. The presentation unit's generating AI can analyze the user's facial expressions and voice to determine if they are feeling anxious. The presentation unit's generating AI can monitor the user's emotions in real time and prioritize presenting appropriate makeup styles. This allows the system to prioritize makeup styles based on the user's emotions, so that the latest makeup style is presented preferentially when the user is excited. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 may be performed using the generative AI or not. For example, the presentation unit can input the user's facial expression data into the generative AI, which will estimate the emotion and preferentially present an appropriate makeup style. This allows the system to prioritize makeup styles based on the user's emotions, so that the latest makeup style is presented preferentially when the user is excited.

[0091] The presentation unit can present the optimal makeup plan considering the user's geographical location information. For example, if the user is in a specific region, the presentation unit can present a makeup plan popular in that region. The presentation unit's generating AI can analyze the user's geographical location information and select the optimal makeup plan. The presentation unit's generating AI can evaluate the user's geographical location information and present an appropriate makeup plan. For example, if the user is traveling, the presentation unit can present a makeup plan to use at their travel destination. The presentation unit's generating AI can analyze the user's geographical location information and select the optimal makeup plan. Furthermore, if the user is at home, the presentation unit can present a makeup plan they use on a daily basis. The presentation unit's generating AI can analyze the user's geographical location information and select the optimal makeup plan. In this way, the optimal makeup plan can be presented by considering the user's geographical location information. Some or all of the above processing in the presentation unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the presentation unit can input the user's geographical location information into the generating AI, and the generating AI can select the optimal makeup plan. This allows us to suggest the optimal makeup plan by taking the user's geographical location into consideration.

[0092] The presentation unit can analyze the user's social media activity and present relevant makeup strategies at the time of presentation. For example, the presentation unit can prioritize presenting makeup strategies shared by the user on social media. The presentation unit can use a generative AI to analyze the user's social media activity and select the optimal makeup strategy. The presentation unit can use a generative AI to evaluate the user's social media activity and present appropriate makeup strategies. For example, the presentation unit can prioritize presenting strategies from makeup artists followed by the user on social media. The presentation unit can use a generative AI to analyze the user's social media activity and select the optimal makeup strategy. Furthermore, the presentation unit can prioritize presenting makeup strategies that the user has "liked" on social media. The presentation unit can use a generative AI to analyze the user's social media activity and select the optimal makeup strategy. In this way, by analyzing the user's social media activity, relevant makeup strategies can be presented preferentially. Some or all of the above processing in the presentation unit may be performed using a generative AI or not. For example, the presentation unit can input the user's social media activity into a generative AI, which can then select the optimal makeup strategy. This allows us to analyze users' social media activity and prioritize presenting relevant makeup advice.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The cosplay makeup support system can suggest makeup products while considering the user's skin type and allergy information. For example, if the user has sensitive skin, the AI ​​will prioritize suggesting hypoallergenic makeup products. Furthermore, if the user is allergic to a specific ingredient, the system can suggest makeup products that do not contain that ingredient. In addition, depending on the user's skin type, it can suggest highly moisturizing foundations for dry skin and matte-finish foundations for oily skin. This allows users to choose makeup products that are appropriate for their skin type and allergies.

[0095] The cosplay makeup support system can monitor the user's makeup progress in real time and provide advice as needed. For example, when a user applies eyeshadow, the generating AI evaluates the application in real time and provides specific advice if corrections are needed. Similarly, when a user applies lipstick, the generating AI monitors the application and suggests correction methods if it is not applied evenly. Furthermore, when a user applies foundation, the generating AI evaluates the application and provides specific instructions on how to correct any unevenness. This allows users to check the progress of their makeup in real time and make corrections as needed.

[0096] The cosplay makeup support system can estimate the user's emotions and customize makeup advice based on those emotions. For example, if the user is nervous, the generating AI can suggest makeup techniques to help them relax. If the user is excited, the generating AI can suggest a makeup style that reflects that energy. Furthermore, if the user is feeling down, the generating AI can suggest a cheerful makeup style to lift their spirits. This allows the system to provide makeup advice tailored to the user's emotions.

[0097] The cosplay makeup support system can suggest makeup products based on the user's makeup preferences and style. For example, if the user prefers natural makeup, the generating AI will suggest makeup products that provide a natural finish. If the user prefers glamorous makeup, the generating AI can suggest makeup products that provide a more dramatic finish. Furthermore, if the user prefers a specific brand, the system can prioritize suggesting makeup products from that brand. This allows users to choose makeup products that match their preferences and style.

[0098] The cosplay makeup support system can estimate the user's emotions and adjust the makeup process based on those emotions. For example, if the user is relaxed, the generating AI will instruct the system to proceed slowly with the makeup. If the user is in a hurry, the generating AI can instruct the system to proceed quickly. Furthermore, if the user is feeling anxious, the generating AI can temporarily stop the makeup process and wait until the user calms down. This allows the system to adjust the makeup process according to the user's emotions.

[0099] The cosplay makeup support system can evaluate the quality of the user's makeup and suggest corrections as needed. For example, after a user applies eyeshadow, the generating AI evaluates the result and suggests corrections if the gradation is insufficient. Similarly, after a user applies foundation, the generating AI evaluates the result and provides specific instructions on how to correct any unevenness. Furthermore, after a user applies lipstick, the generating AI evaluates the result and suggests corrections if the application is not even. This allows users to check the quality of their makeup and make corrections as necessary.

[0100] The cosplay makeup support system can estimate the user's emotions and provide makeup advice based on those emotions. For example, if the user is relaxed, the generating AI can provide detailed makeup advice. If the user is in a hurry, the generating AI can provide concise makeup advice. Furthermore, if the user is feeling anxious, the generating AI can provide reassuring makeup advice. In this way, it can provide makeup advice that is tailored to the user's emotions.

[0101] A cosplay makeup support system can record the user's makeup progress and allow them to refer to it later. For example, it can record the steps a user takes when applying eyeshadow and review them later. It can also record the steps a user takes when applying foundation and review them later. Furthermore, it can record the steps a user takes when applying lipstick and review them later. This allows the user to record their makeup progress and refer to it later.

[0102] The cosplay makeup support system can estimate the user's emotions and record the progress of the makeup application based on those emotions. For example, if the user is relaxed, the generating AI will record detailed progress. If the user is in a hurry, the generating AI can record concise progress. Furthermore, if the user is feeling anxious, the generating AI can record reassuring progress. This allows the system to record makeup progress in a way that aligns with the user's emotions.

[0103] The cosplay makeup support system allows users to share their makeup progress with other users. For example, users can share their eyeshadow application steps with others and receive advice. They can also share their foundation application steps and receive feedback. Furthermore, they can share their lipstick application steps with others and exchange opinions. This allows users to share their makeup progress with other users and receive advice and feedback.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The image reception unit accepts images of makeup tools owned by the user. For example, users can take pictures of their makeup tools with their smartphone or digital camera and upload them to the system. They can also scan the images and import them into the system as digital data. Furthermore, it is possible to retrieve images directly from cloud storage. Step 2: The information reception section receives information about the character the user wants to cosplay. For example, the user can enter the name and image of the character they want to cosplay. They can also enter the character's characteristics and background in text format. Furthermore, it is possible to automatically retrieve information from the internet. Step 3: The learning unit learns from the information received by the image reception unit and the information reception unit. For example, using a generative AI, it analyzes images of the makeup tools owned by the user and information about the character the user wants to cosplay as, and derives the optimal makeup plan. The generative AI learns the characteristics of the user's makeup tools and the characteristics of the character, and generates the optimal makeup plan. Step 4: The presentation unit presents the optimal makeup strategy based on the data learned by the learning unit. For example, the generating AI can give specific instructions to the user on which makeup tools to use and how to use them. It can also visually demonstrate makeup steps and tips. Furthermore, it can suggest necessary makeup tools.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the image receiving unit, information receiving unit, learning unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the image receiving unit uses the camera 42 or scanner of the smart device 14 to acquire images of the user's makeup tools and transmits them to the data processing unit 12 via the control unit 46A. The information receiving unit uses the touch panel 38A or microphone 38B of the smart device 14 to input information about the character the user wants to cosplay as and transmits it to the data processing unit 12 via the control unit 46A. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the user's makeup tools and character information to derive the optimal makeup plan. The presentation unit uses the display 40A or speaker 40B of the smart device 14 to visually and audibly present the makeup plan to the user. 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.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the image receiving unit, information receiving unit, learning unit, and presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the image receiving unit uses the camera 42 of the smart glasses 214 to acquire images of the user's makeup tools and transmits them to the data processing unit 12 via the control unit 46A. The information receiving unit uses the microphone 238 of the smart glasses 214 to input information about the character the user wants to cosplay as and transmits it to the data processing unit 12 via the control unit 46A. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generating AI to analyze the user's makeup tools and character information to derive the optimal makeup plan. The presentation unit uses the display and speaker 240 of the smart glasses 214 to visually and audibly present the makeup plan to the user. 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.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the image receiving unit, information receiving unit, learning unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the image receiving unit uses the camera 42 of the headset terminal 314 to acquire images of the user's makeup tools and transmits them to the data processing unit 12 via the control unit 46A. The information receiving unit uses the microphone 238 of the headset terminal 314 to input information about the character the user wants to cosplay as and transmits it to the data processing unit 12 via the control unit 46A. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses a generation AI to analyze the user's makeup tools and character information and derive the optimal makeup plan. The presentation unit uses the display 343 and speaker 240 of the headset terminal 314 to visually and audibly present the makeup plan to the user. 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.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the image receiving unit, information receiving unit, learning unit, and presentation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the image receiving unit uses the camera 42 of the robot 414 to acquire images of the user's makeup tools and transmits them to the data processing unit 12 via the control unit 46A. The information receiving unit uses the microphone 238 of the robot 414 to input information about the character the user wants to cosplay as and transmits it to the data processing unit 12 via the control unit 46A. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses a generating AI to analyze the user's makeup tools and character information and derive the optimal makeup plan. The presentation unit uses the display and speaker 240 of the robot 414 to visually and audibly present the makeup plan to the user. 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) An image reception section that accepts images of makeup tools owned by the user, An information reception desk that accepts information about characters people want to cosplay, A learning unit that learns the information received by the image receiving unit and the information receiving unit, A presentation unit presents the optimal makeup strategy based on the data learned by the learning unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned display unit is, Even for makeup tools that the user doesn't own, we suggest commercially available products. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is, Provide specific instructions on how to use the makeup tools the user owns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned image receiving unit is The system estimates the user's emotions and adjusts the timing of image submission based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned image receiving unit is The system analyzes the user's past makeup tool usage history and selects the optimal image acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned image receiving unit is When images are submitted, filtering is performed based on the user's current makeup skill level. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned image receiving unit is The system estimates the user's emotions and determines the priority of images to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned image receiving unit is When receiving images, the system prioritizes accepting 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 9) The aforementioned image receiving unit is When receiving an image, the system analyzes the user's social media activity and accepts relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information receiving unit is It estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned information receiving unit is Analyze the user's past cosplay history and select the optimal method for obtaining information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information receiving unit is When receiving information, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned information receiving unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned information receiving unit is When receiving information, the system prioritizes receiving highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned information receiving unit is When information is received, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During training, the training data is weighted based on the frequency of users' use of makeup tools. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During training, the training data is weighted based on the popularity of the user's cosplay character. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During training, the system analyzes users' social media activity and incorporates relevant data into the learning process. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, The system estimates the user's emotions and adjusts the way makeup recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting the information, the system will refer to the user's past successful makeup examples to suggest the most suitable makeup plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When presenting the product, the makeup plan is customized based on the user's current makeup skill level. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, The system estimates the user's emotions and prioritizes makeup strategies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting the product, the system takes the user's geographical location into consideration to suggest the most suitable makeup plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When presenting the information, the system analyzes the user's social media activity and suggests relevant makeup tips. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 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. An image reception section that accepts images of makeup tools owned by the user, An information reception desk that accepts information about characters people want to cosplay, A learning unit that learns the information received by the image receiving unit and the information receiving unit, A presentation unit presents the optimal makeup strategy based on the data learned by the learning unit, Equipped with A system characterized by the following features.

2. The aforementioned display unit is, Even for makeup tools that the user doesn't own, we suggest commercially available products. The system according to feature 1.

3. The aforementioned display unit is, Provide specific instructions on how to use the makeup tools the user owns. The system according to feature 1.

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

5. The aforementioned image receiving unit is The system analyzes the user's past makeup tool usage history and selects the optimal image acquisition method. The system according to feature 1.

6. The aforementioned image receiving unit is When images are submitted, filtering is performed based on the user's current makeup skill level. The system according to feature 1.

7. The aforementioned image receiving unit is The system estimates the user's emotions and determines the priority of images to accept based on those estimated emotions. The system according to feature 1.

8. The aforementioned image receiving unit is When receiving images, the system prioritizes accepting images that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned image receiving unit is When receiving an image, the system analyzes the user's social media activity and accepts relevant images. The system according to feature 1.

10. The aforementioned information receiving unit is It estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The system according to feature 1.

Images