System

Abstract

An object of the system according to the embodiment is to generate a post-makeup image based on the user's preference and counseling content, and to recommend appropriate makeup products and services.SOLUTION: A system includes a reception part, an analysis part, an acquisition part, a generation part, a provision part, and a recommendation part. The reception unit captures and uploads an image of a bare face of a user. The analysis unit analyzes the image uploaded by the reception unit. The acquisition part acquires user's preference and counseling contents. The generation unit generates the post-makeup image based on the information obtained by the analysis unit and the acquisition unit. The provision unit provides the user with the image generated by the generation unit. The recommendation unit recommends a makeup product, a brand, and a beauty salon based on the image provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately suggest makeup based on the user's preferences and counseling content, and there is room for improvement.

[0005] The system according to the embodiment aims to generate an image of a user after makeup application based on the user's preferences and counseling content, and to recommend appropriate makeup products and services. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an acquisition unit, a generation unit, a provision unit, and a recommendation unit. The reception unit takes an image of the user's bare face and uploads it. The analysis unit analyzes the image uploaded by the reception unit. The acquisition unit acquires the user's preferences and counseling details. The generation unit generates an image of the user with makeup applied based on the information obtained by the analysis unit and the acquisition unit. The provision unit provides the image generated by the generation unit to the user. The recommendation unit recommends makeup products, brands, and beauty salons based on the image provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an image of the user after makeup application based on the user's preferences and counseling content, and can recommend appropriate makeup products and services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A makeup counseling system according to an embodiment of the present invention captures and analyzes an image of a user's bare face, and generates and provides a makeup-applied image. The makeup counseling system captures and uploads an image of the user's bare face, which is then analyzed by a generation AI. A makeup-applied image is generated based on the user's preferences and the content of the counseling session. The generated image is provided to the user, allowing the user to view their makeup-applied appearance. The makeup counseling system also recommends makeup products, brands, and beauty salons and provides product advertisements. For example, the makeup counseling system allows a user to take an image of their bare face with a smartphone camera and upload it to an app. The user is encouraged to take the image from the front of their face. For example, taking the image under natural light can capture a more accurate skin tone. The makeup counseling system then analyzes the uploaded image using a generation AI. The generation AI recognizes the user's facial shape and skin tone and generates a makeup-applied image based on the user's preferences and the content of the counseling session. For example, if the user prefers natural makeup, the generation AI generates an image that applies a natural makeup style. The generated makeup-applied image is provided to the user. Users can check how they will look after applying makeup on the app. For example, they can compare different makeup styles by swiping left and right on the generated image. Furthermore, the makeup counseling system recommends makeup products, brands, and beauty salons. The generative AI suggests the most suitable makeup products and brands based on the user's image after applying makeup. For example, it can recommend foundation that matches the user's skin tone or lipstick that suits the user's preferences. The makeup counseling system also provides product advertisements. The generative AI displays relevant product advertisements based on the user's image after applying makeup. For example, it can provide the user with a link to purchase the makeup products used in the generated image.This allows the makeup counseling system to easily find the best makeup for the user. The makeup counseling system can take an image of the user's bare face, analyze it, and generate and provide an image of the user with makeup applied. For example, the user can find a makeup style that suits their face shape and skin tone. The app also allows users to easily find makeup products, brands, and beauty salons.

[0029] A makeup counseling system according to an embodiment includes a reception unit, an analysis unit, an acquisition unit, a generation unit, a provision unit, and a recommendation unit. The reception unit captures and uploads an image of a user's bare face. The image of the user's bare face may include, but is not limited to, the resolution, shooting angle, and lighting conditions. The reception unit, for example, allows the user to capture an image of the bare face using a smartphone camera and upload it to an app. The reception unit also recommends that the user take a photo from the front of their face. For example, taking a photo under natural light can obtain a more accurate skin tone. The analysis unit uses a generation AI to analyze the image uploaded by the reception unit. The analysis unit, for example, analyzes the user's face shape using a face recognition algorithm. The analysis unit can also analyze the user's skin tone using a skin tone analysis method. For example, the analysis unit extracts facial contours and feature points to analyze the face shape. The skin tone is analyzed using measurement methods for hue, lightness, and saturation. The acquisition unit acquires the user's preferences and counseling details. The acquisition unit acquires the user's preferences using, for example, a questionnaire. The acquisition unit can also acquire the counseling details through an interview. For example, the acquisition unit analyzes the user's past data to acquire the preferences and counseling details. The generation unit uses a generation AI to generate a post-makeup image based on the information obtained by the analysis unit and the acquisition unit. The generation unit generates the post-makeup image using, for example, an image generation algorithm. The generation unit can also generate the image using software to be used. For example, the generation unit generates the post-makeup image based on the user's face shape, skin tone, preferences, and counseling details. The provision unit provides the generated post-makeup image to the user. The provision unit provides the image, for example, through a user interface. The provision unit can also provide the image using a notification method. For example, the provision unit displays the generated image on an app so that the user can check their appearance after makeup. The recommendation unit recommends makeup products, brands, and beauty salons based on the image provided by the provision unit. The recommendation unit recommends makeup products using, for example, a recommendation algorithm.The recommendation unit can also recommend brands based on the user's past behavioral data. For example, the recommendation unit can recommend a foundation that matches the user's skin tone or a lipstick that matches the user's preferences. This allows the makeup counseling system according to the embodiment to capture and analyze an image of the user's bare face, and generate and provide an image of the user with makeup applied. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can provide related product advertisements based on the generated image.

[0030] The reception unit can take an image of the user's bare face and upload it. For example, the reception unit allows the user to take an image of their bare face using a smartphone camera and upload it to the app. The reception unit also recommends that the user take a photo of their face from the front. For example, taking a photo under natural light can capture more accurate skin tones. Thus, by taking an image of the user's bare face and uploading it, the analysis unit can obtain accurate information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input an image taken by the user to a generation AI and have the generation AI analyze the image.

[0031] The analysis unit can analyze the user's face shape and skin tone. The analysis unit can analyze the user's face shape using, for example, a face recognition algorithm. For example, the analysis unit can extract facial contours and feature points to analyze the face shape. The analysis unit can also analyze the user's skin tone using a skin tone analysis method. For example, the analysis unit can analyze the skin tone using a hue, brightness, and saturation measurement method. By analyzing the user's face shape and skin tone, the generation unit can generate a more accurate post-makeup image. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the user's face shape and skin tone into the generation AI and have the generation AI perform the analysis.

[0032] The acquisition unit can acquire the user's preferences and counseling content. The acquisition unit can acquire the user's preferences using, for example, a questionnaire. For example, the acquisition unit can ask the user about their preferences in makeup style and color. The acquisition unit can also acquire the counseling content through an interview. For example, the acquisition unit can analyze the user's past data to acquire the preferences and counseling content. By acquiring the user's preferences and counseling content, the generation unit can generate an image with makeup that is optimal for the user. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input data on the user's preferences and counseling content into the generation AI and cause the generation AI to perform the acquisition.

[0033] The generation unit can generate a post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. The generation unit generates the post-makeup image using, for example, an image generation algorithm. For example, the generation unit generates the post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. The generation unit can also generate images using software to be used. For example, the generation unit uses a generation AI to generate a post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. This allows the generation unit to suggest optimal makeup for the user by generating a post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. Some or all of the above-described processing in the generation unit can be performed using a generation AI (generative AI or LLM). For example, the generation unit can input data on the user's face shape, skin tone, preferences, and counseling content into the generation AI and cause the generation AI to generate a post-makeup image.

[0034] The providing unit can provide the generated post-makeup image to the user. The providing unit provides the image through, for example, a user interface. For example, the providing unit displays the generated image on an app, allowing the user to check their appearance after makeup. The providing unit can also provide the image using a notification method. For example, the providing unit notifies the user of the generated image, allowing the user to check the image. In this way, by providing the generated post-makeup image to the user, the user can check their appearance after makeup. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated image to a generation AI and cause the generation AI to execute an optimal method for providing the image to the user.

[0035] The recommendation unit can recommend makeup products, brands, and beauty salons. The recommendation unit recommends makeup products using, for example, a recommendation algorithm. For example, the recommendation unit recommends foundations that match the user's skin tone and lipsticks that match the user's preferences. The recommendation unit can also recommend brands based on the user's past behavioral data. For example, the recommendation unit can suggest optimal makeup products and brands based on an image of the user after makeup application. The recommendation unit can also recommend beauty salons. For example, the recommendation unit can suggest nearby beauty salons based on the user's location and preferences. This allows the user to find the most suitable makeup products and services by recommending makeup products, brands, and beauty salons. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can provide related product advertisements based on the generated image.

[0036] The recommendation unit can provide related product advertisements based on the generated image. The recommendation unit provides related product advertisements based on, for example, the generated image. For example, the recommendation unit provides a link for the user to purchase makeup products used in the generated image. The recommendation unit can also display related product advertisements based on the user's preferences and counseling content. For example, the recommendation unit displays advertisements for makeup products and brands that match the user's preferences. This makes it easier for the user to find products that interest them by providing related product advertisements based on the generated image. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause a generation AI to execute related product advertisements based on the generated image.

[0037] The reception unit can analyze the user's past image capture history and select the optimal capture method. The reception unit, for example, analyzes the user's past image capture history and selects the optimal capture method. For example, the reception unit automatically applies the user's previously preferred capture settings (lighting, background, etc.). The reception unit can also preferentially select the settings that were most highly rated in the images the user has previously taken. The reception unit can also suggest the optimal capture method for a specific time period or location based on the user's past capture history. In this way, by analyzing the user's past image capture history, the optimal capture method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past image capture history into a generation AI and cause the generation AI to select the optimal capture method.

[0038] The reception unit can perform filtering based on the user's current environment when capturing an image. For example, the reception unit performs filtering based on the user's current environment (lighting, background, etc.) when capturing an image. For example, the reception unit detects the user's current lighting conditions and automatically applies an optimal filter. The reception unit can also apply a filter that blurs the background if the user's background is cluttered. The reception unit can also detect the user's environmental sounds and recommend capturing an image in a quiet environment. This allows for filtering based on the user's current environment, enabling a more appropriate image to be captured. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's current environmental data into the generation AI and cause the generation AI to apply filtering.

[0039] The reception unit can select an appropriate imaging means according to the user's input method when capturing an image. For example, the reception unit selects an appropriate imaging means according to the user's input method (voice, text, gesture, etc.) when capturing an image. For example, when the user commands "take a picture" by voice, the reception unit performs imaging based on the voice input. Furthermore, when the user inputs "take a picture using natural light" by text, the reception unit can apply imaging settings using natural light. Furthermore, when the user gestures to wave their hand toward the camera, the reception unit can start imaging based on gesture input. This allows for the selection of an optimal imaging means according to the user's input method, thereby providing a system that is easy for users to use. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and cause the generation AI to select the optimal imaging means.

[0040] The reception unit can prioritize selecting a highly relevant shooting method in consideration of the user's geographical location information when capturing an image. For example, the reception unit prioritizes selecting a highly relevant shooting method in consideration of the user's geographical location information when capturing an image. For example, when the user is outdoors, the reception unit prioritizes selecting a shooting method that uses natural light. Furthermore, when the user is indoors, the reception unit can prioritize selecting a shooting method that is optimal for indoor lighting. Furthermore, when the user is in a specific tourist spot, the reception unit can prioritize selecting a shooting method that is optimal for that location. In this way, by considering the user's geographical location information, a more appropriate shooting method can be provided. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal shooting method.

[0041] The reception unit can analyze the user's social media activity when capturing an image and suggest a related shooting method. For example, the reception unit can analyze the user's social media activity when capturing an image and suggest a related shooting method. For example, the reception unit can automatically apply a filter that the user frequently uses on social media. The reception unit can also analyze the content of the user's social media posts and suggest a related shooting method. The reception unit can also suggest a related shooting method by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate shooting methods can be provided. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest a related shooting method.

[0042] The reception unit can customize the shooting method by reflecting the user's past feedback when capturing an image. The reception unit, for example, customizes the shooting method by reflecting the user's past feedback when capturing an image. For example, the reception unit automatically applies shooting settings that the user previously preferred. The reception unit can also suggest an optimal shooting method based on the user's past feedback. The reception unit can also customize the shooting method to avoid shooting settings that the user previously dissatisfied with. In this way, a more appropriate shooting method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the shooting method.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of facial shape and skin tone during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of facial shape and skin tone during analysis. For example, if the facial shape is distinctive, the analysis unit can perform a detailed analysis based on the shape. Furthermore, if the skin tone is uneven, the analysis unit can also perform an analysis taking into account differences in tone. Furthermore, if the facial shape or skin tone is standard, the analysis unit can perform a general analysis. By adjusting the level of detail of the analysis based on the importance of facial shape and skin tone, more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input facial shape and skin tone data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the face category during analysis. For example, the analysis unit applies different analysis algorithms depending on the face category during analysis. For example, the analysis unit can apply an analysis algorithm specialized for round faces to a user with a round face. The analysis unit can also apply an analysis algorithm specialized for square faces to a user with a square face. The analysis unit can also apply an analysis algorithm specialized for oval faces to a user with an oval face. In this way, by applying different analysis algorithms depending on the face category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input face category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also compare the user's past analysis results and select the most accurate analysis method. By referring to the user's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The analysis unit can determine the analysis priority based on the time when the images were taken during analysis. The analysis unit can, for example, determine the analysis priority based on the time when the images were taken during analysis. For example, the analysis unit prioritizes analyzing the most recent images. The analysis unit can also prioritize analyzing images taken during a specific event. The analysis unit can also prioritize analyzing images taken at a time specified by the user. In this way, by determining the analysis priority based on the time when the images were taken, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image shooting time data to the generation AI and have the generation AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on facial relevance during analysis. The analysis unit, for example, adjusts the order of analysis based on facial relevance during analysis. For example, the analysis unit prioritizes analysis of images with similar facial features. The analysis unit can also postpone analysis of images with different facial features. The analysis unit can also dynamically adjust the order of analysis based on facial relevance. In this way, adjusting the order of analysis based on facial relevance enables more efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input facial relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can use detailed technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for the provision of more easily understandable analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0049] The acquisition unit can select an appropriate acquisition method by analyzing the user's past preferences and counseling content at the time of acquisition. For example, the acquisition unit can select an appropriate acquisition method by analyzing the user's past preferences and counseling content at the time of acquisition. For example, the acquisition unit selects optimal counseling questions based on the user's past preferences. The acquisition unit can also analyze the user's past counseling content and select an optimal acquisition method. The acquisition unit can also customize the acquisition method by referring to the user's past preferences and counseling content. In this way, more appropriate information can be acquired by analyzing the user's past preferences and counseling content. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's past preferences and counseling content into a generation AI and cause the generation AI to select an acquisition method.

[0050] The acquisition unit can perform filtering based on the user's current living situation and areas of interest at the time of acquisition. The acquisition unit, for example, performs filtering based on the user's current living situation and areas of interest at the time of acquisition. For example, the acquisition unit asks relevant counseling questions taking into account the user's current living situation. The acquisition unit can also ask relevant counseling questions based on the user's areas of interest. The acquisition unit can also customize the acquisition method based on the user's living situation and areas of interest. This makes it possible to acquire more appropriate information by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's living situation and areas of interest to a generation AI and have the generation AI perform filtering.

[0051] The acquisition unit can select an appropriate acquisition means depending on the user's input method during acquisition. For example, the acquisition unit selects an appropriate acquisition means depending on the user's input method (voice, text, gesture, etc.) during acquisition. For example, when the user answers the counseling question by voice, the acquisition unit prioritizes voice input. Furthermore, when the user answers the counseling question by text, the acquisition unit can also prioritize text input. Furthermore, when the user answers the counseling question by gesture, the acquisition unit can also prioritize gesture input. This allows for selecting the optimal acquisition means depending on the user's input method, thereby providing a more user-friendly system. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to the generation AI and cause the generation AI to select the optimal acquisition means.

[0052] The acquisition unit can prioritize acquiring highly relevant content by taking into account the user's geographical location information during acquisition. For example, the acquisition unit prioritizes acquiring highly relevant content by taking into account the user's geographical location information during acquisition. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring counseling content related to that area. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring counseling content related to the travel destination. Furthermore, the acquisition unit can prioritize acquiring highly relevant counseling content based on the user's geographical location information. This makes it possible to provide more appropriate information by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant content.

[0053] The acquisition unit can analyze the user's social media activity at the time of acquisition and acquire related content. For example, the acquisition unit can analyze the user's social media activity at the time of acquisition and acquire related content. For example, the acquisition unit can acquire related counseling content based on hashtags frequently used by the user on social media. The acquisition unit can also analyze the content posted by the user on social media and acquire related counseling content. The acquisition unit can also acquire related counseling content by referring to the activities of the user's friends on social media. This makes it possible to provide more appropriate information by analyzing the user's social media activity. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related content.

[0054] The acquisition unit can customize the acquisition method by reflecting the user's past feedback at the time of acquisition. The acquisition unit, for example, customizes the acquisition method by reflecting the user's past feedback at the time of acquisition. For example, the acquisition unit prioritizes acquisition of counseling content that the user has previously preferred. The acquisition unit can also suggest an optimal acquisition method based on the user's past feedback. The acquisition unit can also customize the acquisition method to avoid counseling content that the user has previously dissatisfied with. This makes it possible to provide more appropriate information by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0055] The generation unit can adjust the level of detail of the generation based on the importance of facial shape and skin tone during generation. For example, the generation unit adjusts the level of detail of the generation based on the importance of facial shape and skin tone during generation. For example, if the facial shape is distinctive, the generation unit generates a detailed makeup style based on the shape. Furthermore, if the skin tone is uneven, the generation unit can generate a makeup style taking into account the difference in tone. Furthermore, if the facial shape and skin tone are standard, the generation unit can generate a general makeup style. In this way, by adjusting the level of detail of the generation based on the importance of facial shape and skin tone, a more accurate post-makeup image can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generative AI (generative AI or LLM). For example, the generation unit can input data on facial shape and skin tone into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0056] The generation unit can apply different generation algorithms depending on the makeup style category during generation. For example, the generation unit applies different generation algorithms depending on the makeup style category during generation. For example, the generation unit applies a generation algorithm specialized for natural makeup to a natural makeup style. The generation unit can also apply a generation algorithm specialized for dramatic makeup to a dramatic makeup style. The generation unit can also apply a generation algorithm specialized for casual makeup to a casual makeup style. In this way, by applying different generation algorithms depending on the makeup style category, it is possible to provide a more appropriate post-makeup image. Some or all of the above-mentioned processes in the generation unit are performed using a generation AI (generative AI or LLM). For example, the generation unit can input makeup style category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0057] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also extract specific patterns from the user's past generation results to improve the accuracy of generation. The generation unit can also compare the user's past generation results and select the most accurate generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0058] The generation unit can determine the generation priority based on the time when the images were taken at the time of generation. For example, the generation unit determines the generation priority based on the time when the images were taken at the time of generation. For example, the generation unit prioritizes generating the most recent images. The generation unit can also prioritize generating images taken at a specific event. The generation unit can also prioritize generating images taken at a time specified by the user. In this way, by determining the generation priority based on the time when the images were taken, it is possible to provide a more appropriate post-makeup image. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input image shooting time data into the generation AI and have the generation AI determine the generation priority.

[0059] The generation unit can adjust the order of generation based on the relevance of makeup styles during generation. The generation unit, for example, adjusts the order of generation based on the relevance of makeup styles during generation. For example, the generation unit prioritizes generating images with similar makeup styles. The generation unit can also postpone generating images with different makeup styles. The generation unit can also dynamically adjust the order of generation based on the relevance of makeup styles. In this way, by adjusting the order of generation based on the relevance of makeup styles, more efficient generation can be achieved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input makeup style relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0060] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. For example, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit uses detailed technical terminology. Also, if the user does not have technical expertise, the generation unit can explain the generated result in simple terms. The generation unit can also dynamically adjust the use of technical terminology in the generation according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, it is possible to provide a generated result that is easier to understand. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0061] The providing unit can select an appropriate display method by referring to the user's past provision history at the time of providing. For example, the providing unit selects an appropriate display method by referring to the user's past provision history at the time of providing. For example, the providing unit selects an optimal display method based on the user's past provision history. The providing unit can also extract a specific pattern from the user's past provision history and select an optimal display method. The providing unit can also compare the user's past provision history and select the most effective display method. In this way, by referring to the user's past provision history, a more appropriate display method can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision history data to a generation AI and cause the generation AI to select a display method.

[0062] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit customizes the display content according to the user's current task when providing the information. For example, when the user is actually applying makeup, the providing unit can display a step-by-step guide. Furthermore, when the user is selecting makeup products, the providing unit can display information about related products. Furthermore, when the user is comparing makeup styles, the providing unit can display images of different styles side by side. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into a generation AI and cause the generation AI to customize the display content.

[0063] The providing unit can improve the display method by reflecting user feedback when providing the display. For example, the providing unit improves the display method by reflecting user feedback when providing the display. For example, the providing unit preferentially applies a display method that the user has previously preferred. The providing unit can also suggest an optimal display method based on user feedback. The providing unit can also customize the display method to avoid display methods that the user has previously been dissatisfied with. In this way, a more appropriate display method can be provided by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the display method.

[0064] The providing unit can select an appropriate display method by taking into consideration the user's geographical location information when providing the images. For example, the providing unit selects an appropriate display method by taking into consideration the user's geographical location information when providing the images. For example, if the user is in a specific area, the providing unit can provide images of makeup styles related to that area. Furthermore, if the user is traveling, the providing unit can also provide images of makeup styles related to the user's travel destination. Furthermore, the providing unit can provide images of highly relevant makeup styles based on the user's geographical location information. In this way, a more appropriate display method can be provided by taking into consideration the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select a display method.

[0065] The providing unit can analyze the user's social media activity and suggest a relevant display method at the time of providing the data. For example, the providing unit can analyze the user's social media activity and suggest a relevant display method at the time of providing the data. For example, the providing unit can automatically apply filters that the user frequently uses on social media. The providing unit can also analyze the content of the user's social media posts and suggest a relevant display method. The providing unit can also suggest a relevant display method by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, a more appropriate display method can be provided. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a display method.

[0066] The providing unit can customize the display method by reflecting the user's past feedback when providing the display. The providing unit, for example, customizes the display method by reflecting the user's past feedback when providing the display. For example, the providing unit preferentially applies a display method that the user has previously preferred. The providing unit can also suggest an optimal display method based on the user's past feedback. The providing unit can also customize the display method to avoid a display method that the user has previously been dissatisfied with. In this way, a more appropriate display method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the display method.

[0067] The recommendation unit can select an appropriate recommendation method by referring to the user's past recommendation history when making a recommendation. For example, the recommendation unit selects an appropriate recommendation method by referring to the user's past recommendation history when making a recommendation. For example, the recommendation unit selects an optimal recommendation method based on the user's past recommendation history. The recommendation unit can also extract a specific pattern from the user's past recommendation history and select an optimal recommendation method. The recommendation unit can also compare the user's past recommendation history and select the most effective recommendation method. In this way, by referring to the user's past recommendation history, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation history data into a generation AI and cause the generation AI to select a recommendation method.

[0068] The recommendation unit can perform filtering based on the user's current living situation and areas of interest when making recommendations. The recommendation unit, for example, can perform filtering based on the user's current living situation and areas of interest when making recommendations. For example, the recommendation unit provides relevant recommendations taking into account the user's current living situation. The recommendation unit can also provide relevant recommendations based on the user's areas of interest. The recommendation unit can also customize the recommendation method based on the user's living situation and areas of interest. This makes it possible to provide more appropriate recommendations by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0069] The recommendation unit can select an appropriate recommendation means depending on the user's input method when making a recommendation. For example, the recommendation unit selects an appropriate recommendation means depending on the user's input method (voice, text, gesture, etc.) when making a recommendation. For example, when a user requests a recommendation by voice, the recommendation unit prioritizes voice input. Furthermore, when a user requests a recommendation by text, the recommendation unit can also prioritize text input. Furthermore, when a user requests a recommendation by gesture, the recommendation unit can also prioritize gesture input. This makes it possible to provide a system that is easier to use by selecting the optimal recommendation means depending on the user's input method. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's input data to a generation AI and cause the generation AI to select the optimal recommendation means.

[0070] The recommendation unit can prioritize highly relevant products when making recommendations, taking into account the user's geographical location information. For example, the recommendation unit prioritizes highly relevant products when making recommendations, taking into account the user's geographical location information. For example, if the user is in a specific area, the recommendation unit prioritizes recommending products related to that area. Furthermore, if the user is traveling, the recommendation unit can prioritize recommending products related to the travel destination. Furthermore, the recommendation unit can prioritize recommending highly relevant products based on the user's geographical location information. This makes it possible to provide more appropriate products by taking the user's geographical location information into account. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's geographical location information to a generation AI and cause the generation AI to determine the priority order of products.

[0071] The recommendation unit can analyze the user's social media activity and recommend related products when making a recommendation. For example, the recommendation unit can analyze the user's social media activity and recommend related products when making a recommendation. For example, the recommendation unit can recommend related products based on hashtags frequently used by the user on social media. The recommendation unit can also analyze the content of the user's social media posts and recommend related products. The recommendation unit can also recommend related products by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate products can be provided. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's social media activity data into a generation AI and cause the generation AI to recommend products.

[0072] The recommendation unit can customize the recommendation method by reflecting the user's past feedback when making a recommendation. The recommendation unit, for example, customizes the recommendation method by reflecting the user's past feedback when making a recommendation. For example, the recommendation unit prioritizes recommending products that the user has liked in the past. The recommendation unit can also propose an optimal recommendation method based on the user's past feedback. The recommendation unit can also customize the recommendation method to avoid products that the user has been dissatisfied with in the past. In this way, more appropriate recommendations can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the recommendation method.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The analysis unit can analyze the user's hairstyle and hair color in addition to the user's face shape and skin tone. For example, the analysis unit can recognize the user's hairstyle and suggest a makeup style that matches the hairstyle. The analysis unit can also analyze the user's hair color and recommend makeup products that match the hair color. Furthermore, the analysis unit can analyze the user's hair texture (straight hair, curly hair, etc.) and suggest a makeup style that matches the hair texture. This makes it possible to provide a more personalized makeup style based on the user's hairstyle, hair color, and hair texture.

[0075] The acquisition unit can acquire lifestyle information about the user in addition to the user's preferences and counseling content. For example, the acquisition unit can acquire information about the user's daily activities through a questionnaire. The acquisition unit can also acquire information about the user's occupation and hobbies and suggest a makeup style based on that information. Furthermore, the acquisition unit can acquire information about the user's health condition and allergies and recommend appropriate makeup products. This makes it possible to provide a makeup style that matches the user's lifestyle.

[0076] The generation unit can suggest makeup styles according to the season and weather when generating a makeup-applied image based on the user's face shape, skin tone, preferences, and counseling content. For example, the generation unit can suggest a light makeup style in summer and a moisturizing makeup style in winter. The generation unit can also suggest a style using water-resistant makeup products on rainy days. Furthermore, the generation unit can suggest makeup styles suited to specific events (e.g., weddings and parties). This makes it possible to provide optimal makeup styles according to the season, weather, and event.

[0077] When providing the generated post-makeup image to the user, the providing unit can select the optimal display method depending on the type of device the user is using. For example, the providing unit can provide a portrait image to a user using a smartphone, and a landscape image to a user using a tablet. The providing unit can also provide a high-resolution image to a device with a high-resolution display. Furthermore, the providing unit can adjust the layout of the image depending on the screen size of the user's device. This makes it possible to provide the post-makeup image in the optimal display method for the user's device.

[0078] When recommending makeup products, brands, or beauty salons, the recommendation unit can adjust the recommendation content by taking into account the user's purchase history and reviews. For example, the recommendation unit can analyze reviews of makeup products previously purchased by the user and prioritize highly rated products. The recommendation unit can also recommend new products of the same brand as products previously purchased by the user. Furthermore, the recommendation unit can recommend related products and services based on the user's purchase history. This makes it possible to provide more personalized recommendations based on the user's purchase history and reviews.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The reception unit takes and uploads an image of the user's natural face. The image of the user's natural face includes the resolution, shooting angle, lighting conditions, etc. For example, the user takes an image of their natural face with a smartphone camera and uploads it to the app. We also recommend that users take a photo from the front of their face under natural light to obtain a more accurate skin tone. Step 2: The analysis unit uses the generation AI to analyze the image uploaded by the reception unit. For example, it uses a facial recognition algorithm to analyze the user's face shape, and a skin tone analysis method to analyze the user's skin tone. It also extracts facial contours and feature points and analyzes them using methods to measure hue, brightness, and saturation. Step 3: The acquisition unit acquires the user's preferences and counseling content. For example, the acquisition unit acquires the user's preferences using a questionnaire and the counseling content through an interview. The acquisition unit also analyzes the user's past data to acquire the preferences and counseling content. Step 4: The generation unit uses a generation AI to generate a post-makeup image based on the information obtained by the analysis unit and acquisition unit. For example, an image generation algorithm is used to generate a post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. Step 5: The providing unit provides the generated post-makeup image to the user. For example, the image is provided through a user interface, and the generated image is displayed on the app so that the user can check their appearance after the make-up. Step 6: The recommendation unit recommends makeup products, brands, and beauty salons based on the images provided by the provision unit. For example, it recommends makeup products using a recommendation algorithm and brands based on the user's past behavior data. It also recommends foundations that match the user's skin tone and lipsticks that suit the user's preferences.

[0081] (Example 2) A makeup counseling system according to an embodiment of the present invention captures and analyzes an image of a user's bare face, and generates and provides a makeup-applied image. The makeup counseling system captures and uploads an image of the user's bare face, which is then analyzed by a generation AI. A makeup-applied image is generated based on the user's preferences and the content of the counseling session. The generated image is provided to the user, allowing the user to view their makeup-applied appearance. The makeup counseling system also recommends makeup products, brands, and beauty salons and provides product advertisements. For example, the makeup counseling system allows a user to take an image of their bare face with a smartphone camera and upload it to an app. The user is encouraged to take the image from the front of their face. For example, taking the image under natural light can capture a more accurate skin tone. The makeup counseling system then analyzes the uploaded image using a generation AI. The generation AI recognizes the user's facial shape and skin tone and generates a makeup-applied image based on the user's preferences and the content of the counseling session. For example, if the user prefers natural makeup, the generation AI generates an image that applies a natural makeup style. The generated makeup-applied image is provided to the user. Users can check how they will look after applying makeup on the app. For example, they can compare different makeup styles by swiping left and right on the generated image. Furthermore, the makeup counseling system recommends makeup products, brands, and beauty salons. The generative AI suggests the most suitable makeup products and brands based on the user's image after applying makeup. For example, it can recommend foundation that matches the user's skin tone or lipstick that suits the user's preferences. The makeup counseling system also provides product advertisements. The generative AI displays relevant product advertisements based on the user's image after applying makeup. For example, it can provide the user with a link to purchase the makeup products used in the generated image.This allows the makeup counseling system to easily find the best makeup for the user. The makeup counseling system can take an image of the user's bare face, analyze it, and generate and provide an image of the user with makeup applied. For example, the user can find a makeup style that suits their face shape and skin tone. The app also allows users to easily find makeup products, brands, and beauty salons.

[0082] A makeup counseling system according to an embodiment includes a reception unit, an analysis unit, an acquisition unit, a generation unit, a provision unit, and a recommendation unit. The reception unit captures and uploads an image of a user's bare face. The image of the user's bare face may include, but is not limited to, the resolution, shooting angle, and lighting conditions. The reception unit, for example, allows the user to capture an image of the bare face using a smartphone camera and upload it to an app. The reception unit also recommends that the user take a photo from the front of their face. For example, taking a photo under natural light can obtain a more accurate skin tone. The analysis unit uses a generation AI to analyze the image uploaded by the reception unit. The analysis unit, for example, analyzes the user's face shape using a face recognition algorithm. The analysis unit can also analyze the user's skin tone using a skin tone analysis method. For example, the analysis unit extracts facial contours and feature points to analyze the face shape. The skin tone is analyzed using measurement methods for hue, lightness, and saturation. The acquisition unit acquires the user's preferences and counseling details. The acquisition unit acquires the user's preferences using, for example, a questionnaire. The acquisition unit can also acquire the counseling details through an interview. For example, the acquisition unit analyzes the user's past data to acquire the preferences and counseling details. The generation unit uses a generation AI to generate a post-makeup image based on the information obtained by the analysis unit and the acquisition unit. The generation unit generates the post-makeup image using, for example, an image generation algorithm. The generation unit can also generate the image using software to be used. For example, the generation unit generates the post-makeup image based on the user's face shape, skin tone, preferences, and counseling details. The provision unit provides the generated post-makeup image to the user. The provision unit provides the image, for example, through a user interface. The provision unit can also provide the image using a notification method. For example, the provision unit displays the generated image on an app so that the user can check their appearance after makeup. The recommendation unit recommends makeup products, brands, and beauty salons based on the image provided by the provision unit. The recommendation unit recommends makeup products using, for example, a recommendation algorithm.The recommendation unit can also recommend brands based on the user's past behavioral data. For example, the recommendation unit can recommend a foundation that matches the user's skin tone or a lipstick that matches the user's preferences. This allows the makeup counseling system according to the embodiment to capture and analyze an image of the user's bare face, and generate and provide an image of the user with makeup applied. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can provide related product advertisements based on the generated image.

[0083] The reception unit can take an image of the user's bare face and upload it. For example, the reception unit allows the user to take an image of their bare face using a smartphone camera and upload it to the app. The reception unit also recommends that the user take a photo of their face from the front. For example, taking a photo under natural light can capture more accurate skin tones. Thus, by taking an image of the user's bare face and uploading it, the analysis unit can obtain accurate information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input an image taken by the user to a generation AI and have the generation AI analyze the image.

[0084] The analysis unit can analyze the user's face shape and skin tone. The analysis unit can analyze the user's face shape using, for example, a face recognition algorithm. For example, the analysis unit can extract facial contours and feature points to analyze the face shape. The analysis unit can also analyze the user's skin tone using a skin tone analysis method. For example, the analysis unit can analyze the skin tone using a hue, brightness, and saturation measurement method. By analyzing the user's face shape and skin tone, the generation unit can generate a more accurate post-makeup image. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the user's face shape and skin tone into the generation AI and have the generation AI perform the analysis.

[0085] The acquisition unit can acquire the user's preferences and counseling content. The acquisition unit can acquire the user's preferences using, for example, a questionnaire. For example, the acquisition unit can ask the user about their preferences in makeup style and color. The acquisition unit can also acquire the counseling content through an interview. For example, the acquisition unit can analyze the user's past data to acquire the preferences and counseling content. By acquiring the user's preferences and counseling content, the generation unit can generate an image with makeup that is optimal for the user. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input data on the user's preferences and counseling content into the generation AI and cause the generation AI to perform the acquisition.

[0086] The generation unit can generate a post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. The generation unit generates the post-makeup image using, for example, an image generation algorithm. For example, the generation unit generates the post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. The generation unit can also generate images using software to be used. For example, the generation unit uses a generation AI to generate a post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. This allows the generation unit to suggest optimal makeup for the user by generating a post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. Some or all of the above-described processing in the generation unit can be performed using a generation AI (generative AI or LLM). For example, the generation unit can input data on the user's face shape, skin tone, preferences, and counseling content into the generation AI and cause the generation AI to generate a post-makeup image.

[0087] The providing unit can provide the generated post-makeup image to the user. The providing unit provides the image through, for example, a user interface. For example, the providing unit displays the generated image on an app, allowing the user to check their appearance after makeup. The providing unit can also provide the image using a notification method. For example, the providing unit notifies the user of the generated image, allowing the user to check the image. In this way, by providing the generated post-makeup image to the user, the user can check their appearance after makeup. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated image to a generation AI and cause the generation AI to execute an optimal method for providing the image to the user.

[0088] The recommendation unit can recommend makeup products, brands, and beauty salons. The recommendation unit recommends makeup products using, for example, a recommendation algorithm. For example, the recommendation unit recommends foundations that match the user's skin tone and lipsticks that match the user's preferences. The recommendation unit can also recommend brands based on the user's past behavioral data. For example, the recommendation unit can suggest optimal makeup products and brands based on an image of the user after makeup application. The recommendation unit can also recommend beauty salons. For example, the recommendation unit can suggest nearby beauty salons based on the user's location and preferences. This allows the user to find the most suitable makeup products and services by recommending makeup products, brands, and beauty salons. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can provide related product advertisements based on the generated image.

[0089] The recommendation unit can provide related product advertisements based on the generated image. The recommendation unit provides related product advertisements based on, for example, the generated image. For example, the recommendation unit provides a link for the user to purchase makeup products used in the generated image. The recommendation unit can also display related product advertisements based on the user's preferences and counseling content. For example, the recommendation unit displays advertisements for makeup products and brands that match the user's preferences. This makes it easier for the user to find products that interest them by providing related product advertisements based on the generated image. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can cause a generation AI to execute related product advertisements based on the generated image.

[0090] The reception unit can estimate the user's emotions and adjust the timing of image capture based on the estimated user emotions. For example, the reception unit estimates the user's emotions and adjusts the timing of image capture based on the estimated user emotions. For example, if the user is relaxed, the reception unit counts down a few seconds before capturing an image to elicit a natural expression. If the user is nervous, the reception unit can display a guide to help the user relax and capture an image when the user feels calm. If the user is in a hurry, the reception unit can take an image immediately and provide the option to review the image later and retake the image. This allows images with more natural expressions to be captured by adjusting the timing of image capture based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the timing of image capture.

[0091] The reception unit can analyze the user's past image capture history and select the optimal capture method. The reception unit, for example, analyzes the user's past image capture history and selects the optimal capture method. For example, the reception unit automatically applies the user's previously preferred capture settings (lighting, background, etc.). The reception unit can also preferentially select the settings that were most highly rated in the images the user has previously taken. The reception unit can also suggest the optimal capture method for a specific time period or location based on the user's past capture history. In this way, by analyzing the user's past image capture history, the optimal capture method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past image capture history into a generation AI and cause the generation AI to select the optimal capture method.

[0092] The reception unit can perform filtering based on the user's current environment when capturing an image. For example, the reception unit performs filtering based on the user's current environment (lighting, background, etc.) when capturing an image. For example, the reception unit detects the user's current lighting conditions and automatically applies an optimal filter. The reception unit can also apply a filter that blurs the background if the user's background is cluttered. The reception unit can also detect the user's environmental sounds and recommend capturing an image in a quiet environment. This allows for filtering based on the user's current environment, enabling a more appropriate image to be captured. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's current environmental data into the generation AI and cause the generation AI to apply filtering.

[0093] The reception unit can select an appropriate imaging means according to the user's input method when capturing an image. For example, the reception unit selects an appropriate imaging means according to the user's input method (voice, text, gesture, etc.) when capturing an image. For example, when the user commands "take a picture" by voice, the reception unit performs imaging based on the voice input. Furthermore, when the user inputs "take a picture using natural light" by text, the reception unit can apply imaging settings using natural light. Furthermore, when the user gestures to wave their hand toward the camera, the reception unit can start imaging based on gesture input. This allows for the selection of an optimal imaging means according to the user's input method, thereby providing a system that is easy for users to use. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and cause the generation AI to select the optimal imaging means.

[0094] The reception unit can estimate the user's emotions and determine the priority of images to be captured based on the estimated user emotions. For example, the reception unit estimates the user's emotions and determines the priority of images to be captured based on the estimated user emotions. For example, when the user is excited, the reception unit captures multiple images and prioritizes selecting from among them the image with the most natural expression. Furthermore, when the user is relaxed, the reception unit can also prioritize selecting from among the captured images the image with the most relaxed expression. Furthermore, when the user is nervous, the reception unit can display a guide to help the user relieve tension and prioritize selecting the image with the most relaxed expression. Thus, by determining the priority of images to be captured based on the user's emotions, more appropriate images can be provided. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the images.

[0095] The reception unit can prioritize selecting a highly relevant shooting method in consideration of the user's geographical location information when capturing an image. For example, the reception unit prioritizes selecting a highly relevant shooting method in consideration of the user's geographical location information when capturing an image. For example, when the user is outdoors, the reception unit prioritizes selecting a shooting method that uses natural light. Furthermore, when the user is indoors, the reception unit can prioritize selecting a shooting method that is optimal for indoor lighting. Furthermore, when the user is in a specific tourist spot, the reception unit can prioritize selecting a shooting method that is optimal for that location. In this way, by considering the user's geographical location information, a more appropriate shooting method can be provided. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal shooting method.

[0096] The reception unit can analyze the user's social media activity when capturing an image and suggest a related shooting method. For example, the reception unit can analyze the user's social media activity when capturing an image and suggest a related shooting method. For example, the reception unit can automatically apply a filter that the user frequently uses on social media. The reception unit can also analyze the content of the user's social media posts and suggest a related shooting method. The reception unit can also suggest a related shooting method by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate shooting methods can be provided. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest a related shooting method.

[0097] The reception unit can customize the shooting method by reflecting the user's past feedback when capturing an image. The reception unit, for example, customizes the shooting method by reflecting the user's past feedback when capturing an image. For example, the reception unit automatically applies shooting settings that the user previously preferred. The reception unit can also suggest an optimal shooting method based on the user's past feedback. The reception unit can also customize the shooting method to avoid shooting settings that the user previously dissatisfied with. In this way, a more appropriate shooting method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the shooting method.

[0098] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise and concise analysis results when the user is nervous. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the analysis.

[0099] The analysis unit can adjust the level of detail of the analysis based on the importance of facial shape and skin tone during analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of facial shape and skin tone during analysis. For example, if the facial shape is distinctive, the analysis unit can perform a detailed analysis based on the shape. Furthermore, if the skin tone is uneven, the analysis unit can also perform an analysis taking into account differences in tone. Furthermore, if the facial shape or skin tone is standard, the analysis unit can perform a general analysis. By adjusting the level of detail of the analysis based on the importance of facial shape and skin tone, more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input facial shape and skin tone data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0100] The analysis unit can apply different analysis algorithms depending on the face category during analysis. For example, the analysis unit applies different analysis algorithms depending on the face category during analysis. For example, the analysis unit can apply an analysis algorithm specialized for round faces to a user with a round face. The analysis unit can also apply an analysis algorithm specialized for square faces to a user with a square face. The analysis unit can also apply an analysis algorithm specialized for oval faces to a user with an oval face. In this way, by applying different analysis algorithms depending on the face category, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input face category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0101] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also compare the user's past analysis results and select the most accurate analysis method. By referring to the user's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0102] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually appealing analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0103] The analysis unit can determine the analysis priority based on the time when the images were taken during analysis. The analysis unit can, for example, determine the analysis priority based on the time when the images were taken during analysis. For example, the analysis unit prioritizes analyzing the most recent images. The analysis unit can also prioritize analyzing images taken during a specific event. The analysis unit can also prioritize analyzing images taken at a time specified by the user. In this way, by determining the analysis priority based on the time when the images were taken, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image shooting time data to the generation AI and have the generation AI determine the analysis priority.

[0104] The analysis unit can adjust the order of analysis based on facial relevance during analysis. The analysis unit, for example, adjusts the order of analysis based on facial relevance during analysis. For example, the analysis unit prioritizes analysis of images with similar facial features. The analysis unit can also postpone analysis of images with different facial features. The analysis unit can also dynamically adjust the order of analysis based on facial relevance. In this way, adjusting the order of analysis based on facial relevance enables more efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input facial relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0105] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can use detailed technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for the provision of more easily understandable analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0106] The acquisition unit can estimate the user's emotions and adjust the method for acquiring preferences and counseling content based on the estimated user emotions. The acquisition unit, for example, estimates the user's emotions and adjusts the method for acquiring preferences and counseling content based on the estimated user emotions. For example, the acquisition unit can ask detailed counseling questions when the user is relaxed. The acquisition unit can also ask brief counseling questions when the user is nervous. The acquisition unit can also ask visually appealing counseling questions when the user is excited. This allows for adjusting the method for acquiring preferences and counseling content based on the user's emotions to acquire more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using an AI, or may be performed without an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the acquisition method.

[0107] The acquisition unit can select an appropriate acquisition method by analyzing the user's past preferences and counseling content at the time of acquisition. For example, the acquisition unit can select an appropriate acquisition method by analyzing the user's past preferences and counseling content at the time of acquisition. For example, the acquisition unit selects optimal counseling questions based on the user's past preferences. The acquisition unit can also analyze the user's past counseling content and select an optimal acquisition method. The acquisition unit can also customize the acquisition method by referring to the user's past preferences and counseling content. In this way, more appropriate information can be acquired by analyzing the user's past preferences and counseling content. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's past preferences and counseling content into a generation AI and cause the generation AI to select an acquisition method.

[0108] The acquisition unit can perform filtering based on the user's current living situation and areas of interest at the time of acquisition. The acquisition unit, for example, performs filtering based on the user's current living situation and areas of interest at the time of acquisition. For example, the acquisition unit asks relevant counseling questions taking into account the user's current living situation. The acquisition unit can also ask relevant counseling questions based on the user's areas of interest. The acquisition unit can also customize the acquisition method based on the user's living situation and areas of interest. This makes it possible to acquire more appropriate information by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's living situation and areas of interest to a generation AI and have the generation AI perform filtering.

[0109] The acquisition unit can select an appropriate acquisition means depending on the user's input method during acquisition. For example, the acquisition unit selects an appropriate acquisition means depending on the user's input method (voice, text, gesture, etc.) during acquisition. For example, when the user answers the counseling question by voice, the acquisition unit prioritizes voice input. Furthermore, when the user answers the counseling question by text, the acquisition unit can also prioritize text input. Furthermore, when the user answers the counseling question by gesture, the acquisition unit can also prioritize gesture input. This allows for selecting the optimal acquisition means depending on the user's input method, thereby providing a more user-friendly system. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to the generation AI and cause the generation AI to select the optimal acquisition means.

[0110] The acquisition unit can estimate the user's emotions and determine the priority of preferences and counseling content to be acquired based on the estimated user's emotions. The acquisition unit, for example, estimates the user's emotions and determines the priority of preferences and counseling content to be acquired based on the estimated user's emotions. For example, when the user is relaxed, the acquisition unit can prioritize acquiring detailed preferences and counseling content. Furthermore, when the user is nervous, the acquisition unit can prioritize acquiring concise preferences and counseling content. Furthermore, when the user is excited, the acquisition unit can prioritize acquiring visually appealing preferences and counseling content. This allows for more appropriate information to be provided by determining the priority of preferences and counseling content to be acquired based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI, or without an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0111] The acquisition unit can prioritize acquiring highly relevant content by taking into account the user's geographical location information during acquisition. For example, the acquisition unit prioritizes acquiring highly relevant content by taking into account the user's geographical location information during acquisition. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring counseling content related to that area. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring counseling content related to the travel destination. Furthermore, the acquisition unit can prioritize acquiring highly relevant counseling content based on the user's geographical location information. This makes it possible to provide more appropriate information by taking the user's geographical location information into account. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant content.

[0112] The acquisition unit can analyze the user's social media activity at the time of acquisition and acquire related content. For example, the acquisition unit can analyze the user's social media activity at the time of acquisition and acquire related content. For example, the acquisition unit can acquire related counseling content based on hashtags frequently used by the user on social media. The acquisition unit can also analyze the content posted by the user on social media and acquire related counseling content. The acquisition unit can also acquire related counseling content by referring to the activities of the user's friends on social media. This makes it possible to provide more appropriate information by analyzing the user's social media activity. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related content.

[0113] The acquisition unit can customize the acquisition method by reflecting the user's past feedback at the time of acquisition. The acquisition unit, for example, customizes the acquisition method by reflecting the user's past feedback at the time of acquisition. For example, the acquisition unit prioritizes acquisition of counseling content that the user has previously preferred. The acquisition unit can also suggest an optimal acquisition method based on the user's past feedback. The acquisition unit can also customize the acquisition method to avoid counseling content that the user has previously dissatisfied with. This makes it possible to provide more appropriate information by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0114] The generation unit can estimate the user's emotions and adjust the representation of the generated image based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the representation of the generated image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an image with a natural makeup style. If the user is excited, the generation unit can generate an image with a glamorous makeup style. If the user is nervous, the generation unit can generate an image with a subdued makeup style. This allows for adjusting the representation of the generated image based on the user's emotions to provide a more appropriate makeup-applied image. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., an LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using a generation AI (a generative AI or an LLM). For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the representation of the image.

[0115] The generation unit can adjust the level of detail of the generation based on the importance of facial shape and skin tone during generation. For example, the generation unit adjusts the level of detail of the generation based on the importance of facial shape and skin tone during generation. For example, if the facial shape is distinctive, the generation unit generates a detailed makeup style based on the shape. Furthermore, if the skin tone is uneven, the generation unit can generate a makeup style taking into account the difference in tone. Furthermore, if the facial shape and skin tone are standard, the generation unit can generate a general makeup style. In this way, by adjusting the level of detail of the generation based on the importance of facial shape and skin tone, a more accurate post-makeup image can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generative AI (generative AI or LLM). For example, the generation unit can input data on facial shape and skin tone into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0116] The generation unit can apply different generation algorithms depending on the makeup style category during generation. For example, the generation unit applies different generation algorithms depending on the makeup style category during generation. For example, the generation unit applies a generation algorithm specialized for natural makeup to a natural makeup style. The generation unit can also apply a generation algorithm specialized for dramatic makeup to a dramatic makeup style. The generation unit can also apply a generation algorithm specialized for casual makeup to a casual makeup style. In this way, by applying different generation algorithms depending on the makeup style category, it is possible to provide a more appropriate post-makeup image. Some or all of the above-mentioned processes in the generation unit are performed using a generation AI (generative AI or LLM). For example, the generation unit can input makeup style category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0117] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also extract specific patterns from the user's past generation results to improve the accuracy of generation. The generation unit can also compare the user's past generation results and select the most accurate generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0118] The generation unit can estimate the user's emotion and adjust the length of the generated image based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the generated image based on the estimated user emotion. For example, if the user is in a hurry, the generation unit can generate a short and concise makeup style image. If the user is relaxed, the generation unit can also generate a detailed makeup style image. If the user is excited, the generation unit can also generate a visually appealing makeup style image. This allows for adjusting the length of the generated image based on the user's emotion to provide a more appropriate makeup-applied image. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the image.

[0119] The generation unit can determine the generation priority based on the time when the images were taken at the time of generation. For example, the generation unit determines the generation priority based on the time when the images were taken at the time of generation. For example, the generation unit prioritizes generating the most recent images. The generation unit can also prioritize generating images taken at a specific event. The generation unit can also prioritize generating images taken at a time specified by the user. In this way, by determining the generation priority based on the time when the images were taken, it is possible to provide a more appropriate post-makeup image. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input image shooting time data into the generation AI and have the generation AI determine the generation priority.

[0120] The generation unit can adjust the order of generation based on the relevance of makeup styles during generation. The generation unit, for example, adjusts the order of generation based on the relevance of makeup styles during generation. For example, the generation unit prioritizes generating images with similar makeup styles. The generation unit can also postpone generating images with different makeup styles. The generation unit can also dynamically adjust the order of generation based on the relevance of makeup styles. In this way, by adjusting the order of generation based on the relevance of makeup styles, more efficient generation can be achieved. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input makeup style relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0121] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise during generation. For example, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit uses detailed technical terminology. Also, if the user does not have technical expertise, the generation unit can explain the generated result in simple terms. The generation unit can also dynamically adjust the use of technical terminology in the generation according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, it is possible to provide a generated result that is easier to understand. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI (generative AI or LLM). For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0122] The providing unit can estimate the user's emotions and adjust the display method of the image to be provided based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the display method of the image to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide an image of a detailed makeup style. If the user is nervous, the providing unit can provide an image of a concise and to-the-point makeup style. If the user is excited, the providing unit can provide an image of a visually appealing makeup style. This allows for adjusting the display method of the image to be provided based on the user's emotions to provide a more appropriate makeup-applied image. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method.

[0123] The providing unit can select an appropriate display method by referring to the user's past provision history at the time of providing. For example, the providing unit selects an appropriate display method by referring to the user's past provision history at the time of providing. For example, the providing unit selects an optimal display method based on the user's past provision history. The providing unit can also extract a specific pattern from the user's past provision history and select an optimal display method. The providing unit can also compare the user's past provision history and select the most effective display method. In this way, by referring to the user's past provision history, a more appropriate display method can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision history data to a generation AI and cause the generation AI to select a display method.

[0124] The providing unit can customize the display content according to the user's current task when providing the information. For example, the providing unit customizes the display content according to the user's current task when providing the information. For example, when the user is actually applying makeup, the providing unit can display a step-by-step guide. Furthermore, when the user is selecting makeup products, the providing unit can display information about related products. Furthermore, when the user is comparing makeup styles, the providing unit can display images of different styles side by side. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into a generation AI and cause the generation AI to customize the display content.

[0125] The providing unit can improve the display method by reflecting user feedback when providing the display. For example, the providing unit improves the display method by reflecting user feedback when providing the display. For example, the providing unit preferentially applies a display method that the user has previously preferred. The providing unit can also suggest an optimal display method based on user feedback. The providing unit can also customize the display method to avoid display methods that the user has previously been dissatisfied with. In this way, a more appropriate display method can be provided by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the display method.

[0126] The providing unit can estimate the user's emotions and determine the priority of images to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of images to be provided based on the estimated user emotions. For example, if the user is relaxed, the providing unit can prioritize providing images of detailed makeup styles. Furthermore, if the user is nervous, the providing unit can prioritize providing images of simple and to-the-point makeup styles. Furthermore, if the user is excited, the providing unit can prioritize providing images of visually appealing makeup styles. In this way, by determining the priority of images to be provided based on the user's emotions, more appropriate makeup-applied images can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of images.

[0127] The providing unit can select an appropriate display method by taking into consideration the user's geographical location information when providing the images. For example, the providing unit selects an appropriate display method by taking into consideration the user's geographical location information when providing the images. For example, if the user is in a specific area, the providing unit can provide images of makeup styles related to that area. Furthermore, if the user is traveling, the providing unit can also provide images of makeup styles related to the user's travel destination. Furthermore, the providing unit can provide images of highly relevant makeup styles based on the user's geographical location information. In this way, a more appropriate display method can be provided by taking into consideration the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select a display method.

[0128] The providing unit can analyze the user's social media activity and suggest a relevant display method at the time of providing the data. For example, the providing unit can analyze the user's social media activity and suggest a relevant display method at the time of providing the data. For example, the providing unit can automatically apply filters that the user frequently uses on social media. The providing unit can also analyze the content of the user's social media posts and suggest a relevant display method. The providing unit can also suggest a relevant display method by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, a more appropriate display method can be provided. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a display method.

[0129] The providing unit can customize the display method by reflecting the user's past feedback when providing the display. The providing unit, for example, customizes the display method by reflecting the user's past feedback when providing the display. For example, the providing unit preferentially applies a display method that the user has previously preferred. The providing unit can also suggest an optimal display method based on the user's past feedback. The providing unit can also customize the display method to avoid a display method that the user has previously been dissatisfied with. In this way, a more appropriate display method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the display method.

[0130] The recommendation unit can estimate the user's emotions and adjust the recommendation method based on the estimated user emotions. The recommendation unit, for example, estimates the user's emotions and adjusts the recommendation method based on the estimated user emotions. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. Furthermore, the recommendation unit can provide concise and to-the-point recommendations when the user is nervous. Furthermore, the recommendation unit can provide visually appealing recommendations when the user is excited. This allows for more appropriate recommendations to be provided by adjusting the recommendation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI or without AI. For example, the recommendation unit can input user emotional data into the generation AI and have the generation AI adjust the recommendation method.

[0131] The recommendation unit can select an appropriate recommendation method by referring to the user's past recommendation history when making a recommendation. For example, the recommendation unit selects an appropriate recommendation method by referring to the user's past recommendation history when making a recommendation. For example, the recommendation unit selects an optimal recommendation method based on the user's past recommendation history. The recommendation unit can also extract a specific pattern from the user's past recommendation history and select an optimal recommendation method. The recommendation unit can also compare the user's past recommendation history and select the most effective recommendation method. In this way, by referring to the user's past recommendation history, more appropriate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation history data into a generation AI and cause the generation AI to select a recommendation method.

[0132] The recommendation unit can perform filtering based on the user's current living situation and areas of interest when making recommendations. The recommendation unit, for example, can perform filtering based on the user's current living situation and areas of interest when making recommendations. For example, the recommendation unit provides relevant recommendations taking into account the user's current living situation. The recommendation unit can also provide relevant recommendations based on the user's areas of interest. The recommendation unit can also customize the recommendation method based on the user's living situation and areas of interest. This makes it possible to provide more appropriate recommendations by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's living situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0133] The recommendation unit can select an appropriate recommendation means depending on the user's input method when making a recommendation. For example, the recommendation unit selects an appropriate recommendation means depending on the user's input method (voice, text, gesture, etc.) when making a recommendation. For example, when a user requests a recommendation by voice, the recommendation unit prioritizes voice input. Furthermore, when a user requests a recommendation by text, the recommendation unit can also prioritize text input. Furthermore, when a user requests a recommendation by gesture, the recommendation unit can also prioritize gesture input. This makes it possible to provide a system that is easier to use by selecting the optimal recommendation means depending on the user's input method. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's input data to a generation AI and cause the generation AI to select the optimal recommendation means.

[0134] The recommendation unit can estimate a user's emotions and determine the priority of recommended products based on the estimated user emotions. The recommendation unit, for example, estimates a user's emotions and determines the priority of recommended products based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can prioritize detailed product recommendations. Also, if the user is nervous, the recommendation unit can prioritize concise and to-the-point product recommendations. Also, if the user is excited, the recommendation unit can prioritize visually appealing product recommendations. This allows for more appropriate products to be provided by determining the priority of recommended products based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input user emotional data into the generation AI and have the generation AI determine the priorities of products.

[0135] The recommendation unit can prioritize highly relevant products when making recommendations, taking into account the user's geographical location information. For example, the recommendation unit prioritizes highly relevant products when making recommendations, taking into account the user's geographical location information. For example, if the user is in a specific area, the recommendation unit prioritizes recommending products related to that area. Furthermore, if the user is traveling, the recommendation unit can prioritize recommending products related to the travel destination. Furthermore, the recommendation unit can prioritize recommending highly relevant products based on the user's geographical location information. This makes it possible to provide more appropriate products by taking the user's geographical location information into account. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's geographical location information to a generation AI and cause the generation AI to determine the priority order of products.

[0136] The recommendation unit can analyze the user's social media activity and recommend related products when making a recommendation. For example, the recommendation unit can analyze the user's social media activity and recommend related products when making a recommendation. For example, the recommendation unit can recommend related products based on hashtags frequently used by the user on social media. The recommendation unit can also analyze the content of the user's social media posts and recommend related products. The recommendation unit can also recommend related products by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate products can be provided. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's social media activity data into a generation AI and cause the generation AI to recommend products.

[0137] The recommendation unit can customize the recommendation method by reflecting the user's past feedback when making a recommendation. The recommendation unit, for example, customizes the recommendation method by reflecting the user's past feedback when making a recommendation. For example, the recommendation unit prioritizes recommending products that the user has liked in the past. The recommendation unit can also propose an optimal recommendation method based on the user's past feedback. The recommendation unit can also customize the recommendation method to avoid products that the user has been dissatisfied with in the past. In this way, more appropriate recommendations can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the recommendation method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, generation unit, provision unit, and recommendation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit takes an image of the user's bare face using the camera 42 of the smart device 14 and uploads the image to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded image using a generation AI. The acquisition unit, for example, acquires the user's preferences and counseling details via the control unit 46A of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a post-makeup image based on information obtained by the analysis unit and acquisition unit. The provision unit provides the generated post-makeup image to the user using the display 40A of the smart device 14. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends makeup products, brands, and beauty salons based on the image provided by the provision unit. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, acquisition unit, generation unit, provision unit, and recommendation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit takes an image of the user's bare face using the camera 42 of the smart glasses 214 and uploads it to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded image using a generation AI. The acquisition unit, for example, acquires the user's preferences and counseling details via the control unit 46A of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a post-makeup image based on information obtained by the analysis unit and acquisition unit. The provision unit provides the user with the post-makeup image generated using the display of the smart glasses 214. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends makeup products, brands, and beauty salons based on the image provided by the provision unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, acquisition unit, generation unit, provision unit, and recommendation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit takes an image of the user's bare face using the camera 42 of the headset-type terminal 314 and uploads the image to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded image using a generation AI. The acquisition unit, for example, acquires the user's preferences and counseling details via the control unit 46A of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a post-makeup image based on information obtained by the analysis unit and acquisition unit. The provision unit provides the generated post-makeup image to the user using the display 343 of the headset-type terminal 314. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends makeup products, brands, and beauty salons based on the image provided by the provision unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, acquisition unit, generation unit, provision unit, and recommendation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit takes an image of the user's bare face using the camera 42 of the robot 414 and uploads the image to the data processing device 12 via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded image using a generation AI. The acquisition unit, for example, acquires the user's preferences and counseling details via the control unit 46A of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a post-makeup image based on information obtained by the analysis unit and acquisition unit. The provision unit provides the user with the post-makeup image generated using the display of the robot 414. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends makeup products, brands, and beauty salons based on the image provided by the provision unit.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The analysis unit can analyze the user's hairstyle and hair color in addition to the user's face shape and skin tone. For example, the analysis unit can recognize the user's hairstyle and suggest a makeup style that matches the hairstyle. The analysis unit can also analyze the user's hair color and recommend makeup products that match the hair color. Furthermore, the analysis unit can analyze the user's hair texture (straight hair, curly hair, etc.) and suggest a makeup style that matches the hair texture. This makes it possible to provide a more personalized makeup style based on the user's hairstyle, hair color, and hair texture.

[0140] The acquisition unit can acquire lifestyle information about the user in addition to the user's preferences and counseling content. For example, the acquisition unit can acquire information about the user's daily activities through a questionnaire. The acquisition unit can also acquire information about the user's occupation and hobbies and suggest a makeup style based on that information. Furthermore, the acquisition unit can acquire information about the user's health condition and allergies and recommend appropriate makeup products. This makes it possible to provide a makeup style that matches the user's lifestyle.

[0141] The generation unit can suggest makeup styles according to the season and weather when generating a makeup-applied image based on the user's face shape, skin tone, preferences, and counseling content. For example, the generation unit can suggest a light makeup style in summer and a moisturizing makeup style in winter. The generation unit can also suggest a style using water-resistant makeup products on rainy days. Furthermore, the generation unit can suggest makeup styles suited to specific events (e.g., weddings and parties). This makes it possible to provide optimal makeup styles according to the season, weather, and event.

[0142] When providing the generated post-makeup image to the user, the providing unit can select the optimal display method depending on the type of device the user is using. For example, the providing unit can provide a portrait image to a user using a smartphone, and a landscape image to a user using a tablet. The providing unit can also provide a high-resolution image to a device with a high-resolution display. Furthermore, the providing unit can adjust the layout of the image depending on the screen size of the user's device. This makes it possible to provide the post-makeup image in the optimal display method for the user's device.

[0143] When recommending makeup products, brands, or beauty salons, the recommendation unit can adjust the recommendation content by taking into account the user's purchase history and reviews. For example, the recommendation unit can analyze reviews of makeup products previously purchased by the user and prioritize highly rated products. The recommendation unit can also recommend new products of the same brand as products previously purchased by the user. Furthermore, the recommendation unit can recommend related products and services based on the user's purchase history. This makes it possible to provide more personalized recommendations based on the user's purchase history and reviews.

[0144] The reception unit can estimate the user's emotions and provide guidance for taking an image based on the estimated user's emotions. For example, if the user is nervous, the reception unit can display a guide for deep breathing to relax. If the user is relaxed, the reception unit can also provide advice for bringing out a natural smile. Furthermore, if the user is excited, the reception unit can play music to calm the user. In this way, by providing guidance for taking an image based on the user's emotions, it is possible to take an image with a more natural facial expression.

[0145] The analysis unit can estimate the user's emotions and adjust the method of feedback of the analysis results based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Alternatively, the analysis unit can provide concise and to-the-point analysis results when the user is nervous. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. In this way, by adjusting the method of feedback of the analysis results based on the user's emotions, more appropriate analysis results can be provided.

[0146] The acquisition unit can estimate the user's emotions and adjust the order of counseling questions based on the estimated user's emotions. For example, if the user is relaxed, the acquisition unit can ask detailed questions first. If the user is nervous, the acquisition unit can also start with simple questions. Furthermore, if the user is excited, the acquisition unit can also ask visually appealing questions first. In this way, by adjusting the order of counseling questions based on the user's emotions, more appropriate information can be acquired.

[0147] The generation unit can estimate the user's emotions and adjust the style of the generated after-makeup image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an image with a natural makeup style. If the user is excited, the generation unit can also generate an image with a glamorous makeup style. Furthermore, if the user is nervous, the generation unit can also generate an image with a subdued makeup style. In this way, by adjusting the style of the after-makeup image to be generated based on the user's emotions, a more appropriate after-makeup image can be provided.

[0148] The providing unit can estimate the user's emotions and adjust the display order of the after-makeup images to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can first provide an image of a detailed makeup style. Also, if the user is nervous, the providing unit can first provide an image of a simple and to-the-point makeup style. Furthermore, if the user is excited, the providing unit can first provide an image of a visually appealing makeup style. In this way, by adjusting the display order of the after-makeup images to be provided based on the user's emotions, more appropriate after-makeup images can be provided.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The reception unit takes and uploads an image of the user's natural face. The image of the user's natural face includes the resolution, shooting angle, lighting conditions, etc. For example, the user takes an image of their natural face with a smartphone camera and uploads it to the app. We also recommend that users take a photo from the front of their face under natural light to obtain a more accurate skin tone. Step 2: The analysis unit uses the generation AI to analyze the image uploaded by the reception unit. For example, it uses a facial recognition algorithm to analyze the user's face shape, and a skin tone analysis method to analyze the user's skin tone. It also extracts facial contours and feature points and analyzes them using methods to measure hue, brightness, and saturation. Step 3: The acquisition unit acquires the user's preferences and counseling content. For example, the acquisition unit acquires the user's preferences using a questionnaire and the counseling content through an interview. The acquisition unit also analyzes the user's past data to acquire the preferences and counseling content. Step 4: The generation unit uses a generation AI to generate a post-makeup image based on the information obtained by the analysis unit and acquisition unit. For example, an image generation algorithm is used to generate a post-makeup image based on the user's face shape, skin tone, preferences, and counseling content. Step 5: The providing unit provides the generated post-makeup image to the user. For example, the image is provided through a user interface, and the generated image is displayed on the app so that the user can check their appearance after the make-up. Step 6: The recommendation unit recommends makeup products, brands, and beauty salons based on the images provided by the provision unit. For example, it recommends makeup products using a recommendation algorithm and brands based on the user's past behavior data. It also recommends foundations that match the user's skin tone and lipsticks that suit the user's preferences.

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0190] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0191] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0192] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0193] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0194] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0195] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0196] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0197] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0198] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0199] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0200] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0201] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0202] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0203] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0204] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0205] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0206] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0207] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0208] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0209] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0210] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0211] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0212] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0213] 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.

[0214] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0215] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0216] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0217] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0218] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0219] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0220] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0221] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0222] [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that takes an image of the user's real face and uploads it; an analysis unit that analyzes the image uploaded by the reception unit; an acquisition unit that acquires user preferences and counseling content; a generation unit that generates a make-up image based on the information obtained by the analysis unit and the acquisition unit; a providing unit that provides the image generated by the generating unit to a user; a recommendation unit that recommends makeup products, brands, and beauty salons based on the image provided by the provision unit. A system characterized by:

2. The reception unit Take a picture of the user's face and upload it 2. The system of claim 1.

3. The analysis unit Analyze the user's face shape and skin tone 2. The system of claim 1.

4. The acquisition unit Obtain user preferences and counseling details 2. The system of claim 1.

5. The generation unit Generates makeup images based on the user's face shape, skin tone, preferences, and consultation details 2. The system of claim 1.

6. The providing unit The generated makeup image is provided to the user.

2. The system of claim 1.

7. The recommendation unit Recommend makeup products, brands, and beauty salons 2. The system of claim 1.

8. The recommendation unit Serve relevant product ads based on the generated images 2. The system of claim 1.

Images