System

Abstract

An object of a system according to an embodiment is to propose and guide optimal makeup based on a face color, a hairstyle, and clothes of a user.SOLUTION: A system according to an embodiment includes a face color analysis unit, a hairstyle analysis unit, a clothing analysis unit, a makeup suggestion unit, and a makeup guidance unit. The face color analysis unit analyzes a face color of a user. The hairstyle analysis section analyzes a hairstyle of a user. The clothing analyzer analyzes the user's clothing. The makeup proposal unit proposes makeup on the basis of analysis results of the face color analysis unit, the hairstyle analysis unit, and the clothes analysis unit. The makeup guidance unit provides guidance on the makeup procedure proposed by the makeup proposal 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 technology has the problem of making it difficult to suggest optimal makeup based on a user's complexion, hairstyle, and clothing.

[0005] The system according to the embodiment aims to suggest and provide guidance on the most suitable makeup based on the user's complexion, hairstyle, and clothing. [Means for solving the problem]

[0006] The system according to the embodiment includes a complexion analysis unit, a hairstyle analysis unit, a clothing analysis unit, a makeup suggestion unit, and a makeup instruction unit. The complexion analysis unit analyzes the user's complexion. The hairstyle analysis unit analyzes the user's hairstyle. The clothing analysis unit analyzes the user's clothing. The makeup suggestion unit suggests makeup based on the analysis results of the complexion analysis unit, hairstyle analysis unit, and clothing analysis unit. The makeup instruction unit provides instructions on the makeup steps suggested by the makeup suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest and provide guidance on the most suitable makeup based on the user's complexion, hairstyle, and clothing. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​system according to the embodiment of the present invention is a system that proposes optimal makeup based on the user's complexion, hairstyle, and clothing, and provides specific instructions on how to apply the makeup. This allows the AI ​​system to propose makeup that matches the user's complexion, hairstyle, and clothing, and provide instructions on how to actually apply the makeup.

[0029] The AI ​​system according to the embodiment includes a complexion analysis unit, a hairstyle analysis unit, a clothing analysis unit, a makeup suggestion unit, and a makeup instruction unit. The complexion analysis unit analyzes the user's complexion. For example, the complexion analysis unit recognizes the user's complexion through a camera and analyzes the skin tone and complexion. The complexion analysis unit can also analyze the complexion based on RGB values, hue, and brightness. The complexion analysis unit can also monitor the user's complexion in real time and detect changes. The hairstyle analysis unit analyzes the user's hairstyle. For example, the hairstyle analysis unit recognizes the user's hairstyle through a camera and analyzes the length, shape, and color of the hair. The hairstyle analysis unit can also analyze the hairstyle based on the texture and volume of the hair. The hairstyle analysis unit can also monitor the user's hairstyle in real time and detect changes. The clothing analysis unit analyzes the user's clothing. For example, the clothing analysis unit recognizes the user's clothing through a camera and analyzes the color, style, and material of the clothing. The clothing analysis unit can also analyze clothing based on the texture and design of the clothing. The clothing analysis unit can also monitor the user's clothing in real time and detect changes. The makeup suggestion unit suggests makeup based on the analysis results of the complexion analysis unit, hairstyle analysis unit, and clothing analysis unit. For example, the makeup suggestion unit uses a generation AI to suggest makeup colors and styles that are optimal for the user's complexion, hairstyle, and clothing. The makeup suggestion unit can also use a generation AI to suggest makeup that reflects the user's preferences and trends. The makeup suggestion unit can also use a generation AI to suggest makeup that matches the user's emotional state. The makeup instruction unit provides instructions on the makeup steps suggested by the makeup suggestion unit. For example, the makeup instruction unit uses a generation AI to provide voice or text instructions on specific makeup steps. The makeup instruction unit can also use a generation AI to provide customized instruction based on the user's skill level. The makeup instruction unit can also use a generation AI to provide a relaxing environment that matches the user's emotional state. As a result, the AI ​​system according to the embodiment can suggest optimal makeup based on the user's complexion, hairstyle, and clothing, and provide specific instructions on the makeup steps. For example, users can easily achieve makeup that best suits them for special events or everyday outings.In addition, they provide detailed instructions on how to apply makeup, so even beginners can use the service with confidence.

[0030] The complexion analysis unit can incorporate an algorithm that takes into account the influence of seasons and weather and corrects for differences in sunlight and indoor lighting. The complexion analysis unit uses, for example, an algorithm that corrects for differences in sunlight and indoor lighting to take into account the influence of seasons and weather. For example, the complexion analysis unit corrects for differences in light on cloudy days and sunny days to accurately analyze complexion. The complexion analysis unit can also make corrections based on the intensity and color temperature of sunlight. The complexion analysis unit can also make corrections based on the position and intensity of indoor lighting. This allows for accurate complexion analysis by correcting for the influence of seasons and weather.

[0031] The complexion analysis unit can refer to the user's past makeup history and make suggestions that reflect their preferences and trends. The complexion analysis unit, for example, stores the user's past makeup history in a database and refers to it when analyzing their complexion. For example, suggestions are made based on makeup products and shades used in the past. The complexion analysis unit can also make suggestions based on past preference data and the latest fashion trend information to reflect the user's preferences and trends. The complexion analysis unit can also make individually customized makeup suggestions based on the user's makeup history. This makes it possible to make makeup suggestions that reflect the user's preferences and trends.

[0032] The complexion analysis unit can also suggest skin care products based on the results of the complexion analysis, thereby supporting skin preparation before makeup application. For example, the complexion analysis unit can suggest skin care products suitable for the user's skin condition based on the results of the complexion analysis. For example, it can suggest products with high moisturizing effects for dry skin. The complexion analysis unit can also suggest skin care products that address the user's skin problems based on the results of the complexion analysis. The complexion analysis unit can also suggest skin care products that suit the user's skin type based on the results of the complexion analysis. This makes it possible to suggest skin care products that support skin preparation before makeup application.

[0033] The facial color analysis unit can use the facial color analysis data to monitor the user's health condition and provide health advice as needed. The facial color analysis unit monitors the user's health condition based on, for example, the facial color analysis data. For example, a dull complexion suggests lack of sleep or stress. The facial color analysis unit can also monitor the user's health condition in real time and detect changes based on the facial color analysis data. The facial color analysis unit can also provide health advice according to the user's health condition based on the facial color analysis data. This makes it possible to monitor the user's health condition and provide health advice as needed.

[0034] The hairstyle analysis unit can also take into account hair texture and volume to make more detailed makeup suggestions. For example, the hairstyle analysis unit can incorporate an algorithm that takes hair texture and volume into account when analyzing hairstyles. For example, if the user has thin hair with little volume, the unit can suggest makeup that flatters the face. The hairstyle analysis unit can also suggest the most suitable makeup style for the user based on hair texture and volume. The hairstyle analysis unit can also monitor hair texture and volume in real time and detect changes. This makes it possible to make more detailed makeup suggestions that take hair texture and volume into account.

[0035] The hairstyle analysis unit can propose an optimal makeup style by taking into account the user's facial shape and bone structure. For example, the hairstyle analysis unit introduces an algorithm that takes into account the user's facial shape and bone structure when analyzing hairstyles. For example, it can propose sharp makeup to a user with a round face. The hairstyle analysis unit can also propose an optimal makeup style based on the user's facial shape and bone structure. The hairstyle analysis unit can also monitor the user's facial shape and bone structure in real time and detect changes. This makes it possible to propose an optimal makeup style that takes into account the user's facial shape and bone structure.

[0036] The hairstyle analysis unit can use the hairstyle analysis data to suggest accessories and hair accessories that suit the user. For example, the hairstyle analysis unit can suggest accessories that suit the user based on the hairstyle analysis data. For example, if the hair is up, earrings and a necklace can be suggested. The hairstyle analysis unit can also suggest hair accessories that suit the user based on the hairstyle analysis data. The hairstyle analysis unit can also suggest accessories that reflect the user's preferences and trends based on the hairstyle analysis data. This makes it possible to suggest accessories and hair accessories that suit the user based on the hairstyle analysis data.

[0037] The clothing analysis unit can also take into account materials and textures to make more detailed makeup suggestions. For example, the clothing analysis unit can implement an algorithm that takes materials and textures into account when analyzing clothing. For example, it can suggest matte makeup for shiny materials like silk. The clothing analysis unit can also suggest the most suitable makeup style for the user based on materials and textures. The clothing analysis unit can also monitor materials and textures in real time and detect changes. This makes it possible to make more detailed makeup suggestions that take materials and textures into account.

[0038] The clothing analysis unit can propose the optimal makeup style by taking into account the user's body type and style. For example, the clothing analysis unit introduces an algorithm that takes into account the user's body type and style when analyzing clothing. For example, it can propose balanced makeup to a tall user. The clothing analysis unit can also propose the optimal makeup style based on the user's body type and style. The clothing analysis unit can also monitor the user's body type and style in real time and detect changes. This makes it possible to propose the optimal makeup style by taking into account the user's body type and style.

[0039] The clothing analysis unit can also suggest fashion items and accessories based on the results of the clothing analysis. For example, the clothing analysis unit can suggest fashion items that are suitable for the user based on the results of the clothing analysis. For example, it can suggest sneakers and casual bags for casual clothing. The clothing analysis unit can also suggest accessories that suit the user based on the results of the clothing analysis. The clothing analysis unit can also suggest fashion items that reflect the user's preferences and trends based on the results of the clothing analysis. This makes it possible to suggest fashion items and accessories based on the results of the clothing analysis.

[0040] The clothing analysis unit can use the clothing analysis data to suggest perfumes and fragrances that suit the user. For example, the clothing analysis unit can suggest perfumes that suit the user based on the clothing analysis data. For example, a floral perfume can be suggested for elegant clothing. The clothing analysis unit can also suggest fragrances that suit the user based on the clothing analysis data. The clothing analysis unit can also suggest perfumes that reflect the user's preferences and trends based on the clothing analysis data. This makes it possible to suggest perfumes and fragrances that suit the user based on the clothing analysis data.

[0041] The makeup instructor can provide customized instruction according to the skill level of the user. For example, the makeup instructor can analyze the makeup skill level of the user and provide customized instruction accordingly. For example, the makeup instructor can provide detailed explanations of basic makeup procedures to beginners. The makeup instructor can also provide applied makeup techniques to intermediate makeup artists. The makeup instructor can also provide advanced makeup techniques to advanced makeup artists. This makes it possible to provide customized makeup instruction according to the skill level of the user.

[0042] The makeup instructor can refer to the user's past makeup history and provide instruction that reflects preferences and trends. For example, the makeup instructor can store the user's past makeup history in a database and refer to it when providing makeup instructions. For example, the instruction can be based on makeup products and shades used in the past. The makeup instructor can also provide instruction based on past preference data and the latest fashion trend information to reflect the user's preferences and trends. The makeup instructor can also provide individually customized makeup instruction based on the user's makeup history. This makes it possible to provide makeup instruction that reflects the user's preferences and trends.

[0043] The makeup instructor can propose makeup lessons and workshops that are suitable for the user based on the results of the makeup procedure instruction. The makeup instructor can, for example, propose makeup lessons that are suitable for the user based on the results of the makeup procedure instruction. For example, the makeup instructor can propose basic makeup lessons for beginners. The makeup instructor can also propose advanced makeup lessons for intermediate level users. The makeup instructor can also propose advanced makeup workshops for advanced users. This makes it possible to propose makeup lessons and workshops that are suitable for the user based on the results of the makeup procedure instruction.

[0044] The makeup instructor can use the makeup procedure instruction data to suggest makeup tools and brushes that suit the user. The makeup instructor can, for example, suggest makeup tools that suit the user based on the makeup procedure instruction data. For example, it can suggest easy-to-use makeup brushes to beginners. The makeup instructor can also suggest practical makeup tools to intermediate makeup artists. The makeup instructor can also suggest advanced makeup tools to advanced makeup artists. This makes it possible to suggest makeup tools and brushes that suit the user based on the makeup procedure instruction data.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The AI ​​system suggests the optimal makeup look based on the user's complexion, hairstyle, and clothing, and provides specific instructions. For example, when a user is attending a specific event, it can suggest makeup that matches the event's theme and dress code. It can also manage the inventory of makeup products that the user uses daily and automatically order the necessary products. It can also provide advice on optimizing the environment (lighting, mirror position, etc.) when applying makeup.

[0047] When analyzing the user's complexion, the complexion analysis unit can monitor the user's health condition and provide health advice as needed. For example, if the user's complexion is dull, advice suggesting lack of sleep or stress can be provided. The complexion analysis unit can also collect data on the user's diet and exercise habits and provide advice to support a healthy lifestyle. Furthermore, the complexion analysis unit can monitor the user's skin condition and suggest skin care products.

[0048] The complexion analysis unit can refer to the user's past makeup history and make suggestions that reflect their preferences and trends. For example, suggestions can be made based on makeup products and shades used in the past. The complexion analysis unit can also make suggestions based on past preference data and the latest fashion trend information to reflect the user's preferences and trends. The complexion analysis unit can also make individually customized makeup suggestions based on the user's makeup history. This makes it possible to make makeup suggestions that reflect the user's preferences and trends.

[0049] The complexion analysis unit can also suggest skin care products based on the results of the complexion analysis to support skin preparation before makeup. For example, it can suggest products with high moisturizing effects for dry skin. The complexion analysis unit can also suggest skin care products that address the user's skin problems based on the results of the complexion analysis. The complexion analysis unit can also suggest skin care products that suit the user's skin type based on the results of the complexion analysis. This makes it possible to suggest skin care products that support skin preparation before makeup.

[0050] The facial color analysis unit can use facial color analysis data to monitor the user's health condition and provide health advice as needed. For example, a dull complexion suggests lack of sleep or stress. The facial color analysis unit can also monitor the user's health condition in real time and detect changes based on the facial color analysis data. The facial color analysis unit can also provide health advice according to the user's health condition based on the facial color analysis data. This makes it possible to monitor the user's health condition and provide health advice as needed.

[0051] The hairstyle analysis unit can also take into account hair texture and volume to make more detailed makeup suggestions. For example, an algorithm can be introduced that takes hair texture and volume into account when analyzing hairstyles. For example, if a user has thin, voluminous hair, the unit can suggest makeup that flatters the face. The hairstyle analysis unit can also suggest the most suitable makeup style for the user based on hair texture and volume. The hairstyle analysis unit can also monitor hair texture and volume in real time and detect changes. This makes it possible to make more detailed makeup suggestions that take hair texture and volume into account.

[0052] The hairstyle analysis unit can propose the optimal makeup style by taking into account the user's facial shape and bone structure. For example, an algorithm can be introduced that takes into account the user's facial shape and bone structure when analyzing hairstyles. For example, a user with a round face can be suggested a sharp makeup style. The hairstyle analysis unit can also propose the optimal makeup style based on the user's facial shape and bone structure. The hairstyle analysis unit can also monitor the user's facial shape and bone structure in real time and detect changes. This makes it possible to propose the optimal makeup style by taking into account the user's facial shape and bone structure.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The complexion analysis unit analyzes the user's complexion. For example, the complexion analysis unit recognizes the user's complexion through a camera and analyzes their skin tone and color. The complexion analysis unit can also analyze complexion based on RGB values, hue, and brightness. Furthermore, the complexion analysis unit can monitor the user's complexion in real time and detect changes. Step 2: The hairstyle analysis unit analyzes the user's hairstyle. For example, the hairstyle analysis unit recognizes the user's hairstyle through a camera and analyzes the length, shape, and color of the hair. The hairstyle analysis unit can also analyze the hairstyle based on the texture and volume of the hair. Furthermore, the hairstyle analysis unit can monitor the user's hairstyle in real time and detect any changes. Step 3: The clothing analysis unit analyzes the user's clothing. For example, the clothing analysis unit recognizes the user's clothing through a camera and analyzes the color, style, and material of the clothing. The clothing analysis unit can also analyze clothing based on the texture and design of the clothing. Furthermore, the clothing analysis unit can monitor the user's clothing in real time and detect changes. Step 4: The makeup suggestion unit suggests makeup based on the analysis results of the complexion analysis unit, hairstyle analysis unit, and clothing analysis unit. For example, the makeup suggestion unit uses the generation AI to suggest makeup colors and styles that are best suited to the user's complexion, hairstyle, and clothing. The makeup suggestion unit can also use the generation AI to suggest makeup that reflects the user's preferences and trends. Furthermore, the makeup suggestion unit can also use the generation AI to suggest makeup that suits the user's emotional state. Step 5: The makeup instructor provides instructions on the makeup steps proposed by the makeup suggestion unit. For example, the makeup instructor uses the generation AI to provide specific makeup steps via voice or text. The makeup instructor can also use the generation AI to provide customized instructions according to the user's skill level. Furthermore, the makeup instructor can also use the generation AI to provide a relaxing environment according to the user's emotional state.

[0055] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that proposes optimal makeup based on the user's complexion, hairstyle, and clothing, and provides specific instructions on how to apply the makeup. This allows the AI ​​system to propose makeup that matches the user's complexion, hairstyle, and clothing, and provide instructions on how to actually apply the makeup.

[0056] The AI ​​system according to the embodiment includes a complexion analysis unit, a hairstyle analysis unit, a clothing analysis unit, a makeup suggestion unit, and a makeup instruction unit. The complexion analysis unit analyzes the user's complexion. For example, the complexion analysis unit recognizes the user's complexion through a camera and analyzes the skin tone and complexion. The complexion analysis unit can also analyze the complexion based on RGB values, hue, and brightness. The complexion analysis unit can also monitor the user's complexion in real time and detect changes. The hairstyle analysis unit analyzes the user's hairstyle. For example, the hairstyle analysis unit recognizes the user's hairstyle through a camera and analyzes the length, shape, and color of the hair. The hairstyle analysis unit can also analyze the hairstyle based on the texture and volume of the hair. The hairstyle analysis unit can also monitor the user's hairstyle in real time and detect changes. The clothing analysis unit analyzes the user's clothing. For example, the clothing analysis unit recognizes the user's clothing through a camera and analyzes the color, style, and material of the clothing. The clothing analysis unit can also analyze clothing based on the texture and design of the clothing. The clothing analysis unit can also monitor the user's clothing in real time and detect changes. The makeup suggestion unit suggests makeup based on the analysis results of the complexion analysis unit, hairstyle analysis unit, and clothing analysis unit. For example, the makeup suggestion unit uses a generation AI to suggest makeup colors and styles that are optimal for the user's complexion, hairstyle, and clothing. The makeup suggestion unit can also use a generation AI to suggest makeup that reflects the user's preferences and trends. The makeup suggestion unit can also use a generation AI to suggest makeup that matches the user's emotional state. The makeup instruction unit provides instructions on the makeup steps suggested by the makeup suggestion unit. For example, the makeup instruction unit uses a generation AI to provide voice or text instructions on specific makeup steps. The makeup instruction unit can also use a generation AI to provide customized instruction based on the user's skill level. The makeup instruction unit can also use a generation AI to provide a relaxing environment that matches the user's emotional state. As a result, the AI ​​system according to the embodiment can suggest optimal makeup based on the user's complexion, hairstyle, and clothing, and provide specific instructions on the makeup steps. For example, users can easily achieve makeup that best suits them for special events or everyday outings.In addition, they provide detailed instructions on how to apply makeup, so even beginners can use the service with confidence.

[0057] The complexion analysis unit can incorporate an algorithm that takes into account the influence of seasons and weather and corrects for differences in sunlight and indoor lighting. The complexion analysis unit uses, for example, an algorithm that corrects for differences in sunlight and indoor lighting to take into account the influence of seasons and weather. For example, the complexion analysis unit corrects for differences in light on cloudy days and sunny days to accurately analyze complexion. The complexion analysis unit can also make corrections based on the intensity and color temperature of sunlight. The complexion analysis unit can also make corrections based on the position and intensity of indoor lighting. This allows for accurate complexion analysis by correcting for the influence of seasons and weather.

[0058] The complexion analysis unit can refer to the user's past makeup history and make suggestions that reflect their preferences and trends. The complexion analysis unit, for example, stores the user's past makeup history in a database and refers to it when analyzing their complexion. For example, suggestions are made based on makeup products and shades used in the past. The complexion analysis unit can also make suggestions based on past preference data and the latest fashion trend information to reflect the user's preferences and trends. The complexion analysis unit can also make individually customized makeup suggestions based on the user's makeup history. This makes it possible to make makeup suggestions that reflect the user's preferences and trends.

[0059] The facial color analysis unit can use the emotion estimation function to analyze the user's emotional state and suggest makeup that matches the mood of the day. For example, the facial color analysis unit can use the emotion estimation function to analyze the user's facial expressions and voice to understand the user's emotional state of the day. For example, if the user is relaxed, the facial color analysis unit can suggest natural makeup. The facial color analysis unit can also use the emotion estimation function to analyze the user's pulse rate and electrodermal activity to understand the user's emotional state. The facial color analysis unit can also use the emotion estimation function to suggest makeup that matches the user's emotional state. This makes it possible to suggest makeup that matches the user's emotional state.

[0060] The complexion analysis unit can also suggest skin care products based on the results of the complexion analysis, thereby supporting skin preparation before makeup application. For example, the complexion analysis unit can suggest skin care products suitable for the user's skin condition based on the results of the complexion analysis. For example, it can suggest products with high moisturizing effects for dry skin. The complexion analysis unit can also suggest skin care products that address the user's skin problems based on the results of the complexion analysis. The complexion analysis unit can also suggest skin care products that suit the user's skin type based on the results of the complexion analysis. This makes it possible to suggest skin care products that support skin preparation before makeup application.

[0061] The facial color analysis unit can use the facial color analysis data to monitor the user's health condition and provide health advice as needed. The facial color analysis unit monitors the user's health condition based on, for example, the facial color analysis data. For example, a dull complexion suggests lack of sleep or stress. The facial color analysis unit can also monitor the user's health condition in real time and detect changes based on the facial color analysis data. The facial color analysis unit can also provide health advice according to the user's health condition based on the facial color analysis data. This makes it possible to monitor the user's health condition and provide health advice as needed.

[0062] The facial color analysis unit uses the emotion estimation function to monitor the emotions of the user when applying makeup in real time and can suggest relaxing music or aromas. The facial color analysis unit, for example, uses the emotion estimation function to monitor the emotions of the user when applying makeup in real time. For example, if the user is nervous, the facial color analysis unit can suggest relaxing music. The facial color analysis unit can also use the emotion estimation function to suggest aromas according to the user's emotional state. The facial color analysis unit can also use the emotion estimation function to provide a relaxing environment according to the user's emotional state. This makes it possible to monitor the emotions of the user when applying makeup in real time and suggest relaxing music or aromas.

[0063] The hairstyle analysis unit can also take into account hair texture and volume to make more detailed makeup suggestions. For example, the hairstyle analysis unit can incorporate an algorithm that takes hair texture and volume into account when analyzing hairstyles. For example, if the user has thin hair with little volume, the unit can suggest makeup that flatters the face. The hairstyle analysis unit can also suggest the most suitable makeup style for the user based on hair texture and volume. The hairstyle analysis unit can also monitor hair texture and volume in real time and detect changes. This makes it possible to make more detailed makeup suggestions that take hair texture and volume into account.

[0064] The hairstyle analysis unit can propose an optimal makeup style by taking into account the user's facial shape and bone structure. For example, the hairstyle analysis unit introduces an algorithm that takes into account the user's facial shape and bone structure when analyzing hairstyles. For example, it can propose sharp makeup to a user with a round face. The hairstyle analysis unit can also propose an optimal makeup style based on the user's facial shape and bone structure. The hairstyle analysis unit can also monitor the user's facial shape and bone structure in real time and detect changes. This makes it possible to propose an optimal makeup style that takes into account the user's facial shape and bone structure.

[0065] The hairstyle analysis unit uses the emotion estimation function to analyze the user's satisfaction with their hairstyle and can suggest makeup that will result in high satisfaction. The hairstyle analysis unit, for example, uses the emotion estimation function to analyze the user's satisfaction with their hairstyle. For example, if the user is satisfied with their hairstyle, the hairstyle analysis unit can suggest makeup that will complement that hairstyle. The hairstyle analysis unit can also use the emotion estimation function to monitor the user's satisfaction with their hairstyle in real time and detect changes. The hairstyle analysis unit can also use the emotion estimation function to suggest makeup that is based on the user's satisfaction with their hairstyle. This makes it possible to analyze the user's satisfaction with their hairstyle and suggest makeup that will result in high satisfaction.

[0066] The hairstyle analysis unit can use the hairstyle analysis data to suggest accessories and hair accessories that suit the user. For example, the hairstyle analysis unit can suggest accessories that suit the user based on the hairstyle analysis data. For example, if the hair is up, earrings and a necklace can be suggested. The hairstyle analysis unit can also suggest hair accessories that suit the user based on the hairstyle analysis data. The hairstyle analysis unit can also suggest accessories that reflect the user's preferences and trends based on the hairstyle analysis data. This makes it possible to suggest accessories and hair accessories that suit the user based on the hairstyle analysis data.

[0067] The hairstyle analysis unit can use the emotion estimation function to monitor the emotions of the user when changing their hairstyle in real time and make suggestions that will elicit positive emotions. The hairstyle analysis unit, for example, uses the emotion estimation function to monitor the emotions of the user when changing their hairstyle in real time. For example, if the user is feeling anxious, the hairstyle analysis unit can make suggestions that will give the user a sense of security. The hairstyle analysis unit can also use the emotion estimation function to provide a relaxing environment that corresponds to the user's emotional state. The hairstyle analysis unit can also use the emotion estimation function to provide positive feedback that corresponds to the user's emotional state. This makes it possible to monitor the emotions of the user when changing their hairstyle in real time and make suggestions that will elicit positive emotions.

[0068] The clothing analysis unit can also take into account materials and textures to make more detailed makeup suggestions. For example, the clothing analysis unit can implement an algorithm that takes materials and textures into account when analyzing clothing. For example, it can suggest matte makeup for shiny materials like silk. The clothing analysis unit can also suggest the most suitable makeup style for the user based on materials and textures. The clothing analysis unit can also monitor materials and textures in real time and detect changes. This makes it possible to make more detailed makeup suggestions that take materials and textures into account.

[0069] The clothing analysis unit can propose the optimal makeup style by taking into account the user's body type and style. For example, the clothing analysis unit introduces an algorithm that takes into account the user's body type and style when analyzing clothing. For example, it can propose balanced makeup to a tall user. The clothing analysis unit can also propose the optimal makeup style based on the user's body type and style. The clothing analysis unit can also monitor the user's body type and style in real time and detect changes. This makes it possible to propose the optimal makeup style by taking into account the user's body type and style.

[0070] The clothing analysis unit uses the emotion estimation function to analyze the user's level of satisfaction with their clothing and can suggest makeup that will result in high satisfaction. The clothing analysis unit, for example, uses the emotion estimation function to analyze the user's level of satisfaction with their clothing. For example, if the user is satisfied with their clothing, the clothing analysis unit can suggest makeup that will complement their clothing. The clothing analysis unit can also use the emotion estimation function to monitor the user's level of satisfaction with their clothing in real time and detect changes. The clothing analysis unit can also use the emotion estimation function to suggest makeup that is based on the user's level of satisfaction with their clothing. This makes it possible to analyze the user's level of satisfaction with their clothing and suggest makeup that will result in high satisfaction.

[0071] The clothing analysis unit can also suggest fashion items and accessories based on the results of the clothing analysis. For example, the clothing analysis unit can suggest fashion items that are suitable for the user based on the results of the clothing analysis. For example, it can suggest sneakers and casual bags for casual clothing. The clothing analysis unit can also suggest accessories that suit the user based on the results of the clothing analysis. The clothing analysis unit can also suggest fashion items that reflect the user's preferences and trends based on the results of the clothing analysis. This makes it possible to suggest fashion items and accessories based on the results of the clothing analysis.

[0072] The clothing analysis unit can use the clothing analysis data to suggest perfumes and fragrances that suit the user. For example, the clothing analysis unit can suggest perfumes that suit the user based on the clothing analysis data. For example, a floral perfume can be suggested for elegant clothing. The clothing analysis unit can also suggest fragrances that suit the user based on the clothing analysis data. The clothing analysis unit can also suggest perfumes that reflect the user's preferences and trends based on the clothing analysis data. This makes it possible to suggest perfumes and fragrances that suit the user based on the clothing analysis data.

[0073] The clothing analysis unit can use the emotion estimation function to monitor the emotions of the user when choosing clothes in real time and make suggestions that will elicit positive emotions. The clothing analysis unit, for example, uses the emotion estimation function to monitor the emotions of the user when choosing clothes in real time. For example, if the user is feeling anxious, the clothing analysis unit can make suggestions that will give the user a sense of security. The clothing analysis unit can also use the emotion estimation function to provide a relaxing environment that corresponds to the user's emotional state. The clothing analysis unit can also use the emotion estimation function to provide positive feedback that corresponds to the user's emotional state. This makes it possible to monitor the emotions of the user when choosing clothes in real time and make suggestions that will elicit positive emotions.

[0074] The makeup instructor can provide customized instruction according to the skill level of the user. For example, the makeup instructor can analyze the makeup skill level of the user and provide customized instruction accordingly. For example, the makeup instructor can provide detailed explanations of basic makeup procedures to beginners. The makeup instructor can also provide applied makeup techniques to intermediate makeup artists. The makeup instructor can also provide advanced makeup techniques to advanced makeup artists. This makes it possible to provide customized makeup instruction according to the skill level of the user.

[0075] The makeup instructor can refer to the user's past makeup history and provide instruction that reflects preferences and trends. For example, the makeup instructor can store the user's past makeup history in a database and refer to it when providing makeup instructions. For example, the instruction can be based on makeup products and shades used in the past. The makeup instructor can also provide instruction based on past preference data and the latest fashion trend information to reflect the user's preferences and trends. The makeup instructor can also provide individually customized makeup instruction based on the user's makeup history. This makes it possible to provide makeup instruction that reflects the user's preferences and trends.

[0076] The makeup instructor can use the emotion estimation function to analyze the user's emotional state while applying makeup and provide a relaxing environment. The makeup instructor can, for example, use the emotion estimation function to analyze the user's emotional state while applying makeup in real time. For example, if the user is nervous, the makeup instructor can provide relaxing music. The makeup instructor can also use the emotion estimation function to provide an aroma according to the user's emotional state. The makeup instructor can also use the emotion estimation function to provide a relaxing environment according to the user's emotional state. This makes it possible to analyze the user's emotional state while applying makeup and provide a relaxing environment.

[0077] The makeup instructor can propose makeup lessons and workshops that are suitable for the user based on the results of the makeup procedure instruction. The makeup instructor can, for example, propose makeup lessons that are suitable for the user based on the results of the makeup procedure instruction. For example, the makeup instructor can propose basic makeup lessons for beginners. The makeup instructor can also propose advanced makeup lessons for intermediate level users. The makeup instructor can also propose advanced makeup workshops for advanced users. This makes it possible to propose makeup lessons and workshops that are suitable for the user based on the results of the makeup procedure instruction.

[0078] The makeup instructor can use the makeup procedure instruction data to suggest makeup tools and brushes that suit the user. The makeup instructor can, for example, suggest makeup tools that suit the user based on the makeup procedure instruction data. For example, it can suggest easy-to-use makeup brushes to beginners. The makeup instructor can also suggest practical makeup tools to intermediate makeup artists. The makeup instructor can also suggest advanced makeup tools to advanced makeup artists. This makes it possible to suggest makeup tools and brushes that suit the user based on the makeup procedure instruction data.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The AI ​​system suggests the optimal makeup look based on the user's complexion, hairstyle, and clothing, and provides specific instructions. For example, when a user is attending a specific event, it can suggest makeup that matches the event's theme and dress code. It can also manage the inventory of makeup products that the user uses daily and automatically order the necessary products. It can also provide advice on optimizing the environment (lighting, mirror position, etc.) when applying makeup.

[0081] When analyzing the user's complexion, the complexion analysis unit can monitor the user's health condition and provide health advice as needed. For example, if the user's complexion is dull, advice suggesting lack of sleep or stress can be provided. The complexion analysis unit can also collect data on the user's diet and exercise habits and provide advice to support a healthy lifestyle. Furthermore, the complexion analysis unit can monitor the user's skin condition and suggest skin care products.

[0082] The complexion analysis unit can refer to the user's past makeup history and make suggestions that reflect their preferences and trends. For example, suggestions can be made based on makeup products and shades used in the past. The complexion analysis unit can also make suggestions based on past preference data and the latest fashion trend information to reflect the user's preferences and trends. The complexion analysis unit can also make individually customized makeup suggestions based on the user's makeup history. This makes it possible to make makeup suggestions that reflect the user's preferences and trends.

[0083] The facial color analysis unit can use the emotion estimation function to analyze the user's emotional state and suggest makeup that suits the mood of the day. For example, if the user is relaxed, it can suggest natural makeup. The facial color analysis unit can also use the emotion estimation function to analyze the user's pulse rate and electrodermal activity to understand the user's emotional state. The facial color analysis unit can also use the emotion estimation function to suggest makeup that suits the user's emotional state. This makes it possible to suggest makeup that suits the user's emotional state.

[0084] The complexion analysis unit can also suggest skin care products based on the results of the complexion analysis to support skin preparation before makeup. For example, it can suggest products with high moisturizing effects for dry skin. The complexion analysis unit can also suggest skin care products that address the user's skin problems based on the results of the complexion analysis. The complexion analysis unit can also suggest skin care products that suit the user's skin type based on the results of the complexion analysis. This makes it possible to suggest skin care products that support skin preparation before makeup.

[0085] The facial color analysis unit can use facial color analysis data to monitor the user's health condition and provide health advice as needed. For example, a dull complexion suggests lack of sleep or stress. The facial color analysis unit can also monitor the user's health condition in real time and detect changes based on the facial color analysis data. The facial color analysis unit can also provide health advice according to the user's health condition based on the facial color analysis data. This makes it possible to monitor the user's health condition and provide health advice as needed.

[0086] The facial color analysis unit can use the emotion estimation function to monitor the user's emotions in real time when applying makeup and suggest relaxing music or aromas. For example, if the user is nervous, the facial color analysis unit can suggest relaxing music. The facial color analysis unit can also use the emotion estimation function to suggest aromas according to the user's emotional state. The facial color analysis unit can also use the emotion estimation function to provide a relaxing environment according to the user's emotional state. This makes it possible to monitor the user's emotions in real time when applying makeup and suggest relaxing music or aromas.

[0087] The hairstyle analysis unit can also take into account hair texture and volume to make more detailed makeup suggestions. For example, an algorithm can be introduced that takes hair texture and volume into account when analyzing hairstyles. For example, if a user has thin, voluminous hair, the unit can suggest makeup that flatters the face. The hairstyle analysis unit can also suggest the most suitable makeup style for the user based on hair texture and volume. The hairstyle analysis unit can also monitor hair texture and volume in real time and detect changes. This makes it possible to make more detailed makeup suggestions that take hair texture and volume into account.

[0088] The hairstyle analysis unit can propose the optimal makeup style by taking into account the user's facial shape and bone structure. For example, an algorithm can be introduced that takes into account the user's facial shape and bone structure when analyzing hairstyles. For example, a user with a round face can be suggested a sharp makeup style. The hairstyle analysis unit can also propose the optimal makeup style based on the user's facial shape and bone structure. The hairstyle analysis unit can also monitor the user's facial shape and bone structure in real time and detect changes. This makes it possible to propose the optimal makeup style by taking into account the user's facial shape and bone structure.

[0089] The hairstyle analysis unit can use the emotion estimation function to analyze the user's satisfaction with their hairstyle and suggest makeup that will provide high satisfaction. For example, the emotion estimation function is used to analyze the user's satisfaction with their hairstyle. For example, if the user is satisfied with their hairstyle, makeup that will complement that hairstyle is suggested. The hairstyle analysis unit can also use the emotion estimation function to monitor the user's satisfaction with their hairstyle in real time and detect changes. The hairstyle analysis unit can also use the emotion estimation function to suggest makeup that is based on the user's satisfaction with their hairstyle. This makes it possible to analyze the user's satisfaction with their hairstyle and suggest makeup that will provide high satisfaction.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The complexion analysis unit analyzes the user's complexion. For example, the complexion analysis unit recognizes the user's complexion through a camera and analyzes their skin tone and color. The complexion analysis unit can also analyze complexion based on RGB values, hue, and brightness. Furthermore, the complexion analysis unit can monitor the user's complexion in real time and detect changes. Step 2: The hairstyle analysis unit analyzes the user's hairstyle. For example, the hairstyle analysis unit recognizes the user's hairstyle through a camera and analyzes the length, shape, and color of the hair. The hairstyle analysis unit can also analyze the hairstyle based on the texture and volume of the hair. Furthermore, the hairstyle analysis unit can monitor the user's hairstyle in real time and detect any changes. Step 3: The clothing analysis unit analyzes the user's clothing. For example, the clothing analysis unit recognizes the user's clothing through a camera and analyzes the color, style, and material of the clothing. The clothing analysis unit can also analyze clothing based on the texture and design of the clothing. Furthermore, the clothing analysis unit can monitor the user's clothing in real time and detect changes. Step 4: The makeup suggestion unit suggests makeup based on the analysis results of the complexion analysis unit, hairstyle analysis unit, and clothing analysis unit. For example, the makeup suggestion unit uses the generation AI to suggest makeup colors and styles that are best suited to the user's complexion, hairstyle, and clothing. The makeup suggestion unit can also use the generation AI to suggest makeup that reflects the user's preferences and trends. Furthermore, the makeup suggestion unit can also use the generation AI to suggest makeup that suits the user's emotional state. Step 5: The makeup instructor provides instructions on the makeup steps proposed by the makeup suggestion unit. For example, the makeup instructor uses the generation AI to provide specific makeup steps via voice or text. The makeup instructor can also use the generation AI to provide customized instructions according to the user's skill level. Furthermore, the makeup instructor can also use the generation AI to provide a relaxing environment according to the user's emotional state.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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. [Explanation of symbols]

[0159] 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 facial color analysis unit that analyzes the facial color of the user; a hairstyle analysis unit that analyzes a hairstyle of a user; a clothing analysis unit that analyzes the clothing of a user; a makeup suggestion unit that suggests makeup based on the analysis results of the complexion analysis unit, the hairstyle analysis unit, and the clothing analysis unit; a makeup instruction unit that instructs the makeup procedure proposed by the makeup suggestion unit. A system characterized by:

2. The complexion analysis unit Analyzes the user's emotional state and suggests makeup that matches the mood of the day 2. The system of claim 1.

3. The complexion analysis unit The system monitors the user's emotions in real time while applying makeup and suggests relaxing music and aromas.

2. The system of claim 1.

4. The clothing analysis unit Analyzes the user's satisfaction with their outfit and suggests makeup that will give them the highest satisfaction.

2. The system of claim 1.

5. The makeup instructors are: Analyzing the user's emotional state while applying makeup and providing a relaxing environment 2. The system of claim 1.

6. The hairstyle analysis unit We also take into consideration the texture and volume of your hair to provide more detailed makeup suggestions.

2. The system of claim 1.

7. The clothing analysis unit We offer more detailed makeup suggestions, taking into consideration materials and textures.

2. The system of claim 1.

8. The makeup instructors are: Provide customized instruction based on the user's skill level 2. The system of claim 1.

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