System

Abstract

An object of the system according to the embodiment is to propose a specific makeup method or skin care method for the user to realize an ideal face.SOLUTION: A system according to an embodiment includes an ideal face generation unit, a gap analysis unit, a makeup proposal unit, a skin care proposal unit, and a makeup proposal unit. The ideal face generation unit generates an ideal face in accordance with a scene selected by the user. The gap analysis unit analyzes a gap between the ideal face generated by the ideal face generation unit and the current face of the user. The makeup proposal unit proposes a specific makeup method for filling the gap analyzed by the gap analyzer. The skin care proposal unit proposes a skin care method corresponding to an individual skin type. The makeup suggestion unit suggests makeup based on the skin condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to find specific makeup and skin care methods to achieve their ideal face.

[0005] The system according to the embodiment aims to suggest specific makeup and skin care methods for a user to achieve their ideal face. [Means for solving the problem]

[0006] The system according to the embodiment includes an ideal face generation unit, a gap analysis unit, a makeup suggestion unit, a skin care suggestion unit, and a cosmetics suggestion unit. The ideal face generation unit generates an ideal face to match a scene selected by a user. The gap analysis unit analyzes the gap between the ideal face generated by the ideal face generation unit and the user's current face. The makeup suggestion unit suggests specific makeup methods to fill the gap analyzed by the gap analysis unit. The skin care suggestion unit suggests skin care methods suited to individual skin types. The cosmetics suggestion unit suggests cosmetics based on skin conditions. [Effects of the Invention]

[0007] The system according to the embodiment can suggest specific makeup and skin care methods to help the user achieve their ideal face. [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) A makeup assist system according to an embodiment of the present invention generates an ideal face for a scene selected by a user and suggests makeup methods, skin care methods, and cosmetics to fill in the gap between the ideal and the actual face. In this way, the makeup assist system can resolve the user's facial concerns and achieve the user's ideal appearance.

[0029] A makeup assist system according to an embodiment includes an ideal face generation unit, a gap analysis unit, a makeup suggestion unit, a skin care suggestion unit, and a cosmetics suggestion unit. The ideal face generation unit generates an ideal face for a scene selected by a user. For example, an ideal face is generated for a scene such as a business meeting, a casual conversation with friends, or a formal event. The gap analysis unit analyzes the gap between the ideal face generated by the ideal face generation unit and the user's current face. For example, it analyzes differences between facial features and skin tone. The makeup suggestion unit suggests specific makeup methods to fill the gaps analyzed by the gap analysis unit. For example, it suggests specific makeup steps such as eye makeup to make the eyes look larger or how to choose a foundation to brighten the skin. The skin care suggestion unit suggests skin care methods tailored to individual skin types. For example, it suggests a skin care method that emphasizes moisturizing for a user with dry skin and a skin care method that controls oil content for a user with oily skin. The cosmetics suggestion unit suggests cosmetics based on the user's skin condition. For example, it can suggest mild cosmetics to users with sensitive skin, and cosmetics with whitening effects to users who are concerned about blemishes or dullness. In this way, the makeup assist system according to the embodiment can solve users' facial concerns and realize their ideal appearance. For example, it can generate an ideal face before a business meeting and teach makeup techniques to bring it closer to that ideal. Furthermore, by referring to the suggestions of the generation AI, more effective care can be achieved when choosing daily skin care and cosmetics.

[0030] The ideal face generation unit can analyze the user's past photos and videos and generate an ideal face that takes into account changes in the user's face over time. The ideal face generation unit, for example, collects the user's past photos and videos and analyzes facial features. For example, it compares a photo from 10 years ago with a current photo to identify changes in the face over time. The ideal face generation unit also generates an ideal face that takes into account changes in the face over time. For example, it generates an ideal face that takes into account increased wrinkles and sagging skin. This makes it possible to analyze the user's past photos and videos and generate an ideal face that takes into account changes in the face over time.

[0031] The ideal face generation unit can create a 3D model of the user's facial features and generate an ideal face from different angles. The ideal face generation unit, for example, creates a 3D model based on a photograph of the user's face and generates a face from different angles. For example, it displays the face from the front, side, and diagonal. The ideal face generation unit also uses the 3D model to generate an ideal face from different angles. For example, it uses 3D scanning technology to analyze the user's facial features in detail and generate an ideal face from different angles. This makes it possible to create a 3D model of the user's facial features and generate an ideal face from different angles.

[0032] The ideal face generation unit can propose a total coordination by taking into consideration the user's fashion style and hairstyle when generating an ideal face. The ideal face generation unit, for example, analyzes the user's fashion style and hairstyle and generates an ideal face based on the analysis. For example, it proposes a face that matches a casual style or a formal style. The ideal face generation unit also proposes a total coordination by taking into consideration the user's fashion style and hairstyle. For example, it proposes makeup and accessories that match the clothing and hairstyle. In this way, it can propose a total coordination by taking into consideration the user's fashion style and hairstyle when generating an ideal face.

[0033] The ideal face generation unit can incorporate aesthetic standards from different cultures and regions when generating an ideal face, and can accommodate a variety of beauty standards. For example, the ideal face generation unit analyzes aesthetic standards from different cultures and regions and generates an ideal face based on the analysis. For example, aesthetic standards from Asia, Europe, Africa, etc. are taken into consideration. The ideal face generation unit also incorporates aesthetic standards from different cultures and regions to generate an ideal face. For example, the ideal face generation unit adjusts the shape of the face, skin color, eye size, etc. based on the aesthetic standards of each region. This allows the ideal face to incorporate aesthetic standards from different cultures and regions and accommodate a variety of beauty standards when generating an ideal face.

[0034] The gap analysis unit analyzes the minute features of the user's face in detail, and can precisely identify the gap between the actual face and the ideal face. The gap analysis unit, for example, analyzes the minute features of the user's face in detail using high-resolution image analysis technology. For example, it identifies the position and shape of wrinkles and moles. The gap analysis unit also analyzes the minute features of the user's face in detail, and can precisely identify the gap between the actual face and the ideal face. For example, it analyzes the depth of wrinkles and the size of moles, and can precisely identify the gap between the actual face and the ideal face. This allows the minute features of the user's face to be analyzed in detail, and can precisely identify the gap between the actual face and the ideal face.

[0035] The gap analysis unit can analyze the user's facial movements and changes in facial expression to identify dynamic gaps. The gap analysis unit, for example, analyzes the user's facial movements and changes in facial expression using video analysis technology. For example, it analyzes smiling and surprised expressions. The gap analysis unit also analyzes the user's facial movements and changes in facial expression to identify dynamic gaps. For example, it identifies gaps due to changes in facial expression and gaps due to movements. This makes it possible to analyze the user's facial movements and changes in facial expression and identify dynamic gaps from the ideal face.

[0036] The gap analysis unit can analyze the results of the gap analysis in association with the user's health condition and lifestyle habits. The gap analysis unit, for example, analyzes the results of the gap analysis in association with the user's health condition and lifestyle habits. For example, it analyzes the impact of lack of sleep and stress on facial features. The gap analysis unit also analyzes the results of the gap analysis in association with the user's health condition and lifestyle habits. For example, it analyzes the impact of eating habits and exercise habits on facial features. This allows the results of the gap analysis to be analyzed in association with the user's health condition and lifestyle habits.

[0037] The gap analysis unit can analyze the results of the gap analysis in association with the skeletal and muscular structures of the user's face. The gap analysis unit, for example, analyzes the results of the gap analysis in association with the skeletal and muscular structures of the user's face. For example, it analyzes the shape of the skeleton and the degree of muscle development. The gap analysis unit also analyzes the results of the gap analysis in association with the skeletal and muscular structures of the user's face. For example, it analyzes the influence of the shape of the jaw and the position of the cheekbones on the facial features. This allows the results of the gap analysis to be analyzed in association with the skeletal and muscular structures of the user's face.

[0038] The makeup suggestion unit can suggest an individually customized makeup routine based on the user's facial features. The makeup suggestion unit, for example, analyzes the user's facial features and suggests an individually customized makeup routine. For example, it suggests a makeup method that matches the shape of the eyes or the height of the nose. The makeup suggestion unit also suggests an individually customized makeup routine based on the user's facial features. For example, it suggests a makeup routine that matches the contours of the face or the tone of the skin. In this way, it is possible to suggest an individually customized makeup routine based on the user's facial features.

[0039] The makeup suggestion unit can also take into account the user's fashion style and hairstyle when suggesting a makeup method. The makeup suggestion unit, for example, analyzes the user's fashion style and hairstyle and suggests a makeup method based on that. For example, it suggests makeup that matches a casual style or a formal style. The makeup suggestion unit also takes into account the user's fashion style and hairstyle when suggesting a makeup method. For example, it suggests makeup colors and styles that match the clothing and hairstyle. In this way, the user's fashion style and hairstyle can also be taken into account when suggesting a makeup method.

[0040] The makeup suggestion unit can incorporate makeup trends from different cultures and regions when proposing makeup techniques. For example, the makeup suggestion unit analyzes makeup trends from different cultures and regions and proposes makeup techniques based on the analysis. For example, makeup trends from Asia, Europe, Africa, etc. are taken into consideration. The makeup suggestion unit also incorporates makeup trends from different cultures and regions when proposing makeup techniques. For example, the makeup suggestion unit adjusts colors and styles based on the makeup trends of each region. This allows makeup techniques to incorporate makeup trends from different cultures and regions.

[0041] The skin care suggestion unit can analyze the user's skin type data in detail and suggest a skin care method that suits the seasons and environmental changes. The skin care suggestion unit, for example, analyzes the user's skin type data in detail and suggests a skin care method that suits the seasons and environmental changes. For example, it suggests measures to combat dryness in winter and measures to combat ultraviolet rays in summer. The skin care suggestion unit also analyzes the user's skin type data in detail and suggests a skin care method that suits the seasons and environmental changes. For example, it suggests a skin care method that suits changes in humidity and temperature. This makes it possible to analyze the user's skin type data in detail and suggest a skin care method that suits the seasons and environmental changes.

[0042] The skin care suggestion unit can analyze the user's diet and lifestyle habits and suggest a skin care method based on the results. The skin care suggestion unit, for example, analyzes the user's diet and lifestyle habits and suggests a skin care method based on the results. For example, it suggests skin care that takes nutritional balance and sleeping habits into consideration. The skin care suggestion unit also analyzes the user's diet and lifestyle habits and suggests a skin care method based on the results. For example, it suggests a skin care method based on the contents of meals and exercise habits. In this way, it is possible to analyze the user's diet and lifestyle habits and suggest a skin care method based on the results.

[0043] The skin care suggestion unit can also take into account the user's fashion style and hairstyle when providing a skin care lecture. The skin care suggestion unit, for example, analyzes the user's fashion style and hairstyle and suggests a skin care method based on the analysis. For example, it suggests skin care methods that match a casual style or a formal style. The skin care suggestion unit also provides a skin care lecture taking into account the user's fashion style and hairstyle. For example, it suggests a skin care method that matches the clothing and hairstyle. This allows the user's fashion style and hairstyle to be taken into account when providing a skin care lecture.

[0044] The skin care suggestion unit can incorporate skin care trends from different cultures and regions into the skin care lecture. For example, the skin care suggestion unit analyzes skin care trends from different cultures and regions and proposes skin care methods based on the analysis. For example, skin care trends from Asia, Europe, Africa, etc. are taken into consideration. The skin care suggestion unit also incorporates skin care trends from different cultures and regions into the skin care lecture. For example, ingredients and methods are adjusted based on the skin care trends of each region. This allows the skin care lecture to incorporate skin care trends from different cultures and regions.

[0045] The cosmetics suggestion unit can analyze the user's skin condition in detail and suggest cosmetics that suit the seasons and environmental changes. For example, the cosmetics suggestion unit can analyze the user's skin condition in detail and suggest cosmetics that suit the seasons and environmental changes. For example, it can suggest measures to combat dryness in winter and measures to combat UV rays in summer. The cosmetics suggestion unit can also analyze the user's skin condition in detail and suggest cosmetics that suit the seasons and environmental changes. For example, it can suggest cosmetics that suit changes in humidity and temperature. This makes it possible to analyze the user's skin condition in detail and suggest cosmetics that suit the seasons and environmental changes.

[0046] The cosmetics suggestion unit can analyze the user's allergy information and past usage history and suggest cosmetics based on that. For example, the cosmetics suggestion unit can analyze the user's allergy information and past usage history and suggest cosmetics based on that. For example, it can suggest cosmetics that take into account allergies to specific ingredients. The cosmetics suggestion unit can also analyze the user's allergy information and past usage history and suggest cosmetics based on that. For example, it can suggest the most suitable cosmetics taking into account the effects and reactions of cosmetics used in the past. This allows the cosmetics suggestion unit to analyze the user's allergy information and past usage history and suggest cosmetics based on that.

[0047] The cosmetics suggestion unit can also take into account the user's fashion style and hairstyle when recommending cosmetics. For example, the cosmetics suggestion unit analyzes the user's fashion style and hairstyle and suggests cosmetics based on that. For example, it suggests cosmetics that match casual styles and formal styles. The cosmetics suggestion unit also takes into account the user's fashion style and hairstyle when recommending cosmetics. For example, it suggests colors and types of cosmetics that match clothing and hairstyles. This allows the user's fashion style and hairstyle to be taken into account when recommending cosmetics.

[0048] The cosmetics suggestion unit can incorporate cosmetics trends from different cultures and regions into its cosmetics recommendations. For example, the cosmetics suggestion unit analyzes cosmetics trends from different cultures and regions and suggests cosmetics based on the results. For example, it takes into account cosmetics trends from Asia, Europe, Africa, etc. The cosmetics suggestion unit also incorporates cosmetics trends from different cultures and regions into its cosmetics recommendations. For example, it adjusts ingredients and colors based on the cosmetics trends of each region. This allows cosmetics recommendations to incorporate cosmetics trends from different cultures and regions.

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

[0050] The Makeup Assist system can also analyze the user's voice tone and speaking style, suggesting voice training methods to match their ideal face. For example, it can suggest ways to speak in a calm tone in a business meeting, a relaxed tone in a casual conversation with friends, and a confident tone in a formal event. This allows users to improve not only their face, but also their voice tone and speaking style.

[0051] The ideal face generator can also analyze the user's health data and generate an ideal face based on the user's health condition. For example, the ideal face can be generated taking into account the effects of lack of sleep and stress on the face. The ideal face generator can also analyze the user's diet and exercise habits and generate an ideal face based on these. This allows the generation of an ideal face that takes the user's health condition into account.

[0052] The ideal face generation unit can also analyze the user's genetic information and generate an ideal face that takes genetic characteristics into consideration. For example, it can analyze the facial characteristics of family members and generate an ideal face based on that. The ideal face generation unit can also predict future facial changes based on the user's genetic information and generate an ideal face based on that. This makes it possible to generate an ideal face that takes genetic characteristics into consideration.

[0053] The ideal face generation unit can also generate an ideal face based on the user's hobbies and lifestyle, taking into account the user's hobbies and lifestyle. For example, it can propose a healthy face to a user who likes the outdoors, and a unique face to a user who likes art. The ideal face generation unit also generates an ideal face taking into account the user's hobbies and lifestyle. This makes it possible to generate an ideal face that matches the user's hobbies and lifestyle.

[0054] When analyzing the user's facial features, the gap analysis unit can also take into account the user's facial bone structure and muscle structure to identify more precise gaps. For example, the gap analysis unit analyzes the shape of the bone structure and the level of muscle development. The gap analysis unit also takes into account the user's facial bone structure and muscle structure to identify gaps with the ideal face. This enables precise gap analysis that takes into account the user's facial bone structure and muscle structure.

[0055] When analyzing the user's facial features, the gap analysis unit can also consider the user's health condition and lifestyle habits to identify gaps based on the user's health condition. For example, it analyzes the effects of lack of sleep and stress on the face. The gap analysis unit also considers the user's health condition and lifestyle habits to identify gaps from the ideal face. This enables gap analysis that takes the user's health condition into account.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The ideal face generator generates an ideal face for a scene selected by the user, such as a business meeting, a casual conversation with friends, or a formal event. Step 2: The gap analysis unit analyzes the gap between the ideal face generated by the ideal face generation unit and the user's current face, for example, by analyzing differences in facial features and skin tone. Step 3: The makeup suggestion unit proposes specific makeup methods to fill the gaps analyzed by the gap analysis unit. For example, it suggests specific makeup steps, such as eye makeup to make the eyes look bigger or how to choose a foundation to make the skin look brighter. Step 4: The skin care suggestion unit suggests skin care methods suited to individual skin types. For example, it suggests moisturizing skin care methods for users with dry skin, and oil-control skin care methods for users with oily skin. Step 5: The cosmetics suggestion unit suggests cosmetics based on the user's skin condition. For example, it suggests mild cosmetics to users with sensitive skin, and cosmetics with whitening effects to users who are concerned about blemishes or dullness.

[0058] (Example 2) A makeup assist system according to an embodiment of the present invention generates an ideal face for a scene selected by a user and suggests makeup methods, skin care methods, and cosmetics to fill in the gap between the ideal and the actual face. In this way, the makeup assist system can resolve the user's facial concerns and achieve the user's ideal appearance.

[0059] A makeup assist system according to an embodiment includes an ideal face generation unit, a gap analysis unit, a makeup suggestion unit, a skin care suggestion unit, and a cosmetics suggestion unit. The ideal face generation unit generates an ideal face for a scene selected by a user. For example, an ideal face is generated for a scene such as a business meeting, a casual conversation with friends, or a formal event. The gap analysis unit analyzes the gap between the ideal face generated by the ideal face generation unit and the user's current face. For example, it analyzes differences between facial features and skin tone. The makeup suggestion unit suggests specific makeup methods to fill the gaps analyzed by the gap analysis unit. For example, it suggests specific makeup steps such as eye makeup to make the eyes look larger or how to choose a foundation to brighten the skin. The skin care suggestion unit suggests skin care methods tailored to individual skin types. For example, it suggests a skin care method that emphasizes moisturizing for a user with dry skin and a skin care method that controls oil content for a user with oily skin. The cosmetics suggestion unit suggests cosmetics based on the user's skin condition. For example, it can suggest mild cosmetics to users with sensitive skin, and cosmetics with whitening effects to users who are concerned about blemishes or dullness. In this way, the makeup assist system according to the embodiment can solve users' facial concerns and realize their ideal appearance. For example, it can generate an ideal face before a business meeting and teach makeup techniques to bring it closer to that ideal. Furthermore, by referring to the suggestions of the generation AI, more effective care can be achieved when choosing daily skin care and cosmetics.

[0060] The ideal face generation unit can analyze the user's past photos and videos and generate an ideal face that takes into account changes in the user's face over time. The ideal face generation unit, for example, collects the user's past photos and videos and analyzes facial features. For example, it compares a photo from 10 years ago with a current photo to identify changes in the face over time. The ideal face generation unit also generates an ideal face that takes into account changes in the face over time. For example, it generates an ideal face that takes into account increased wrinkles and sagging skin. This makes it possible to analyze the user's past photos and videos and generate an ideal face that takes into account changes in the face over time.

[0061] The ideal face generation unit can create a 3D model of the user's facial features and generate an ideal face from different angles. The ideal face generation unit, for example, creates a 3D model based on a photograph of the user's face and generates a face from different angles. For example, it displays the face from the front, side, and diagonal. The ideal face generation unit also uses the 3D model to generate an ideal face from different angles. For example, it uses 3D scanning technology to analyze the user's facial features in detail and generate an ideal face from different angles. This makes it possible to create a 3D model of the user's facial features and generate an ideal face from different angles.

[0062] The ideal face generation unit can use the emotion estimation function to generate an ideal face that reflects the facial expression that the user is most confident in. The ideal face generation unit, for example, analyzes a photo of the user's face and uses the emotion estimation function to identify the facial expression that the user is most confident in. For example, a smiling expression or a serious expression. The ideal face generation unit also uses the emotion estimation function to generate an ideal face that reflects the facial expression that the user is most confident in. For example, the emotion estimation algorithm is used to analyze the user's facial expression and generate an ideal face that reflects the facial expression that the user is most confident in. In this way, the emotion estimation function can be used to generate an ideal face that reflects the facial expression that the user is most confident in.

[0063] The ideal face generation unit can propose a total coordination by taking into consideration the user's fashion style and hairstyle when generating an ideal face. The ideal face generation unit, for example, analyzes the user's fashion style and hairstyle and generates an ideal face based on the analysis. For example, it proposes a face that matches a casual style or a formal style. The ideal face generation unit also proposes a total coordination by taking into consideration the user's fashion style and hairstyle. For example, it proposes makeup and accessories that match the clothing and hairstyle. In this way, it can propose a total coordination by taking into consideration the user's fashion style and hairstyle when generating an ideal face.

[0064] The ideal face generation unit can incorporate aesthetic standards from different cultures and regions when generating an ideal face, and can accommodate a variety of beauty standards. For example, the ideal face generation unit analyzes aesthetic standards from different cultures and regions and generates an ideal face based on the analysis. For example, aesthetic standards from Asia, Europe, Africa, etc. are taken into consideration. The ideal face generation unit also incorporates aesthetic standards from different cultures and regions to generate an ideal face. For example, the ideal face generation unit adjusts the shape of the face, skin color, eye size, etc. based on the aesthetic standards of each region. This allows the ideal face to incorporate aesthetic standards from different cultures and regions and accommodate a variety of beauty standards when generating an ideal face.

[0065] The ideal face generation unit can use the emotion estimation function to generate an ideal face based on the emotion felt by the user in a specific scene. For example, the ideal face generation unit uses the emotion estimation function to analyze the emotion felt by the user in a specific scene and generate an ideal face based on that. For example, it proposes a face suited to a scene such as a business meeting or a date. The ideal face generation unit also uses the emotion estimation function to generate an ideal face based on the emotion felt by the user in a specific scene. For example, it uses an emotion estimation algorithm to analyze the user's emotion and generates an ideal face based on that. In this way, the emotion estimation function can be used to generate an ideal face based on the emotion felt by the user in a specific scene.

[0066] The gap analysis unit analyzes the minute features of the user's face in detail, and can precisely identify the gap between the actual face and the ideal face. The gap analysis unit, for example, analyzes the minute features of the user's face in detail using high-resolution image analysis technology. For example, it identifies the position and shape of wrinkles and moles. The gap analysis unit also analyzes the minute features of the user's face in detail, and can precisely identify the gap between the actual face and the ideal face. For example, it analyzes the depth of wrinkles and the size of moles, and can precisely identify the gap between the actual face and the ideal face. This allows the minute features of the user's face to be analyzed in detail, and can precisely identify the gap between the actual face and the ideal face.

[0067] The gap analysis unit can analyze the user's facial movements and changes in facial expression to identify dynamic gaps. The gap analysis unit, for example, analyzes the user's facial movements and changes in facial expression using video analysis technology. For example, it analyzes smiling and surprised expressions. The gap analysis unit also analyzes the user's facial movements and changes in facial expression to identify dynamic gaps. For example, it identifies gaps due to changes in facial expression and gaps due to movements. This makes it possible to analyze the user's facial movements and changes in facial expression and identify dynamic gaps from the ideal face.

[0068] The gap analysis unit can use the emotion estimation function to analyze the complex emotions felt by the user and identify a gap based on the emotions. The gap analysis unit, for example, uses the emotion estimation function to analyze the complex emotions felt by the user. For example, it identifies negative emotions toward a specific facial feature. The gap analysis unit also uses the emotion estimation function to identify a gap based on the complex emotions felt by the user. For example, it uses an emotion estimation algorithm to analyze the user's emotions and identify a gap based on the emotions. In this way, it is possible to use the emotion estimation function to identify a gap based on the complex emotions felt by the user.

[0069] The gap analysis unit can analyze the results of the gap analysis in association with the user's health condition and lifestyle habits. The gap analysis unit, for example, analyzes the results of the gap analysis in association with the user's health condition and lifestyle habits. For example, it analyzes the impact of lack of sleep and stress on facial features. The gap analysis unit also analyzes the results of the gap analysis in association with the user's health condition and lifestyle habits. For example, it analyzes the impact of eating habits and exercise habits on facial features. This allows the results of the gap analysis to be analyzed in association with the user's health condition and lifestyle habits.

[0070] The gap analysis unit can analyze the results of the gap analysis in association with the skeletal and muscular structures of the user's face. The gap analysis unit, for example, analyzes the results of the gap analysis in association with the skeletal and muscular structures of the user's face. For example, it analyzes the shape of the skeleton and the degree of muscle development. The gap analysis unit also analyzes the results of the gap analysis in association with the skeletal and muscular structures of the user's face. For example, it analyzes the influence of the shape of the jaw and the position of the cheekbones on the facial features. This allows the results of the gap analysis to be analyzed in association with the skeletal and muscular structures of the user's face.

[0071] The makeup suggestion unit can suggest an individually customized makeup routine based on the user's facial features. The makeup suggestion unit, for example, analyzes the user's facial features and suggests an individually customized makeup routine. For example, it suggests a makeup method that matches the shape of the eyes or the height of the nose. The makeup suggestion unit also suggests an individually customized makeup routine based on the user's facial features. For example, it suggests a makeup routine that matches the contours of the face or the tone of the skin. In this way, it is possible to suggest an individually customized makeup routine based on the user's facial features.

[0072] The makeup suggestion unit can use the emotion estimation function to suggest a makeup method that will give the user the most confidence. The makeup suggestion unit, for example, uses the emotion estimation function to suggest a makeup method that will give the user the most confidence. For example, it suggests a makeup style that brings out positive emotions. The makeup suggestion unit also uses the emotion estimation function to suggest a makeup method that will give the user the most confidence. For example, it uses an emotion estimation algorithm to analyze the user's emotions and suggests a makeup method based on that. In this way, it is possible to use the emotion estimation function to suggest a makeup method that will give the user the most confidence.

[0073] The makeup suggestion unit can also take into account the user's fashion style and hairstyle when suggesting a makeup method. The makeup suggestion unit, for example, analyzes the user's fashion style and hairstyle and suggests a makeup method based on that. For example, it suggests makeup that matches a casual style or a formal style. The makeup suggestion unit also takes into account the user's fashion style and hairstyle when suggesting a makeup method. For example, it suggests makeup colors and styles that match the clothing and hairstyle. In this way, the user's fashion style and hairstyle can also be taken into account when suggesting a makeup method.

[0074] The makeup suggestion unit can incorporate makeup trends from different cultures and regions when proposing makeup techniques. For example, the makeup suggestion unit analyzes makeup trends from different cultures and regions and proposes makeup techniques based on the analysis. For example, makeup trends from Asia, Europe, Africa, etc. are taken into consideration. The makeup suggestion unit also incorporates makeup trends from different cultures and regions when proposing makeup techniques. For example, the makeup suggestion unit adjusts colors and styles based on the makeup trends of each region. This allows makeup techniques to incorporate makeup trends from different cultures and regions.

[0075] The makeup suggestion unit can use the emotion estimation function to suggest a makeup method based on the emotion the user feels in a specific scene. For example, the makeup suggestion unit uses the emotion estimation function to analyze the emotion the user feels in a specific scene and suggest a makeup method based on that. For example, the makeup suggestion unit suggests makeup suited to a scene such as a business meeting or a date. The makeup suggestion unit also uses the emotion estimation function to suggest a makeup method based on the emotion the user feels in a specific scene. For example, the emotion estimation algorithm is used to analyze the user's emotion and suggest a makeup method based on that. In this way, the emotion estimation function can be used to suggest a makeup method based on the emotion the user feels in a specific scene.

[0076] The skin care suggestion unit can analyze the user's skin type data in detail and suggest a skin care method that suits the seasons and environmental changes. The skin care suggestion unit, for example, analyzes the user's skin type data in detail and suggests a skin care method that suits the seasons and environmental changes. For example, it suggests measures to combat dryness in winter and measures to combat ultraviolet rays in summer. The skin care suggestion unit also analyzes the user's skin type data in detail and suggests a skin care method that suits the seasons and environmental changes. For example, it suggests a skin care method that suits changes in humidity and temperature. This makes it possible to analyze the user's skin type data in detail and suggest a skin care method that suits the seasons and environmental changes.

[0077] The skin care suggestion unit can analyze the user's diet and lifestyle habits and suggest a skin care method based on the results. The skin care suggestion unit, for example, analyzes the user's diet and lifestyle habits and suggests a skin care method based on the results. For example, it suggests skin care that takes nutritional balance and sleeping habits into consideration. The skin care suggestion unit also analyzes the user's diet and lifestyle habits and suggests a skin care method based on the results. For example, it suggests a skin care method based on the contents of meals and exercise habits. In this way, it is possible to analyze the user's diet and lifestyle habits and suggest a skin care method based on the results.

[0078] The skin care suggestion unit can use the emotion estimation function to suggest a skin care method that will be most relaxing for the user. The skin care suggestion unit, for example, uses the emotion estimation function to suggest a skin care method that will be most relaxing for the user. For example, it can suggest aromas or massages that have a relaxing effect. The skin care suggestion unit also uses the emotion estimation function to suggest a skin care method that will be most relaxing for the user. For example, it can use an emotion estimation algorithm to analyze the user's emotions and suggest a skin care method that will be most relaxing based on that. In this way, it is possible to use the emotion estimation function to suggest a skin care method that will be most relaxing for the user.

[0079] The skin care suggestion unit can also take into account the user's fashion style and hairstyle when providing a skin care lecture. The skin care suggestion unit, for example, analyzes the user's fashion style and hairstyle and suggests a skin care method based on the analysis. For example, it suggests skin care methods that match a casual style or a formal style. The skin care suggestion unit also provides a skin care lecture taking into account the user's fashion style and hairstyle. For example, it suggests a skin care method that matches the clothing and hairstyle. This allows the user's fashion style and hairstyle to be taken into account when providing a skin care lecture.

[0080] The skin care suggestion unit can incorporate skin care trends from different cultures and regions into the skin care lecture. For example, the skin care suggestion unit analyzes skin care trends from different cultures and regions and proposes skin care methods based on the analysis. For example, skin care trends from Asia, Europe, Africa, etc. are taken into consideration. The skin care suggestion unit also incorporates skin care trends from different cultures and regions into the skin care lecture. For example, ingredients and methods are adjusted based on the skin care trends of each region. This allows the skin care lecture to incorporate skin care trends from different cultures and regions.

[0081] The skin care suggestion unit can use the emotion estimation function to suggest a skin care method based on the emotion the user feels in a specific scene. For example, the skin care suggestion unit uses the emotion estimation function to analyze the emotion the user feels in a specific scene and suggest a skin care method based on that. For example, the skin care suggestion unit suggests skin care methods tailored to scenes such as a business meeting or a date. The skin care suggestion unit also uses the emotion estimation function to suggest a skin care method based on the emotion the user feels in a specific scene. For example, the emotion estimation algorithm is used to analyze the user's emotion and suggest a skin care method based on that. In this way, the emotion estimation function can be used to suggest a skin care method based on the emotion the user feels in a specific scene.

[0082] The cosmetics suggestion unit can analyze the user's skin condition in detail and suggest cosmetics that suit the seasons and environmental changes. For example, the cosmetics suggestion unit can analyze the user's skin condition in detail and suggest cosmetics that suit the seasons and environmental changes. For example, it can suggest measures to combat dryness in winter and measures to combat UV rays in summer. The cosmetics suggestion unit can also analyze the user's skin condition in detail and suggest cosmetics that suit the seasons and environmental changes. For example, it can suggest cosmetics that suit changes in humidity and temperature. This makes it possible to analyze the user's skin condition in detail and suggest cosmetics that suit the seasons and environmental changes.

[0083] The cosmetics suggestion unit can analyze the user's allergy information and past usage history and suggest cosmetics based on that. For example, the cosmetics suggestion unit can analyze the user's allergy information and past usage history and suggest cosmetics based on that. For example, it can suggest cosmetics that take into account allergies to specific ingredients. The cosmetics suggestion unit can also analyze the user's allergy information and past usage history and suggest cosmetics based on that. For example, it can suggest the most suitable cosmetics taking into account the effects and reactions of cosmetics used in the past. This allows the cosmetics suggestion unit to analyze the user's allergy information and past usage history and suggest cosmetics based on that.

[0084] The cosmetics suggestion unit can also take into account the user's fashion style and hairstyle when recommending cosmetics. For example, the cosmetics suggestion unit analyzes the user's fashion style and hairstyle and suggests cosmetics based on that. For example, it suggests cosmetics that match casual styles and formal styles. The cosmetics suggestion unit also takes into account the user's fashion style and hairstyle when recommending cosmetics. For example, it suggests colors and types of cosmetics that match clothing and hairstyles. This allows the user's fashion style and hairstyle to be taken into account when recommending cosmetics.

[0085] The cosmetics suggestion unit can incorporate cosmetics trends from different cultures and regions into its cosmetics recommendations. For example, the cosmetics suggestion unit analyzes cosmetics trends from different cultures and regions and suggests cosmetics based on the results. For example, it takes into account cosmetics trends from Asia, Europe, Africa, etc. The cosmetics suggestion unit also incorporates cosmetics trends from different cultures and regions into its cosmetics recommendations. For example, it adjusts ingredients and colors based on the cosmetics trends of each region. This allows cosmetics recommendations to incorporate cosmetics trends from different cultures and regions.

[0086] The cosmetics suggestion unit can use the emotion estimation function to suggest cosmetics based on the emotions felt by the user in a specific scene. For example, the cosmetics suggestion unit uses the emotion estimation function to analyze the emotions felt by the user in a specific scene and suggest cosmetics based on the emotions. For example, the cosmetics suggestion unit can suggest cosmetics suited to scenes such as a business meeting or a date. The cosmetics suggestion unit can also use the emotion estimation function to suggest cosmetics based on the emotions felt by the user in a specific scene. For example, the emotion estimation algorithm can be used to analyze the user's emotions and suggest cosmetics based on the emotions. In this way, the emotion estimation function can be used to suggest cosmetics based on the emotions felt by the user in a specific scene.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The Makeup Assist system can also analyze the user's voice tone and speaking style, suggesting voice training methods to match their ideal face. For example, it can suggest ways to speak in a calm tone in a business meeting, a relaxed tone in a casual conversation with friends, and a confident tone in a formal event. This allows users to improve not only their face, but also their voice tone and speaking style.

[0089] The ideal face generator can also analyze the user's health data and generate an ideal face based on the user's health condition. For example, the ideal face can be generated taking into account the effects of lack of sleep and stress on the face. The ideal face generator can also analyze the user's diet and exercise habits and generate an ideal face based on these. This allows the generation of an ideal face that takes the user's health condition into account.

[0090] The ideal face generation unit can also analyze the user's genetic information and generate an ideal face that takes genetic characteristics into consideration. For example, it can analyze the facial characteristics of family members and generate an ideal face based on that. The ideal face generation unit can also predict future facial changes based on the user's genetic information and generate an ideal face based on that. This makes it possible to generate an ideal face that takes genetic characteristics into consideration.

[0091] The ideal face generation unit can also estimate the user's emotions and generate an ideal face that reflects the user's most relaxing facial expression. For example, a relaxed facial expression or a calm facial expression. The ideal face generation unit also uses the emotion estimation function to generate an ideal face that reflects the user's most relaxing facial expression. In this way, the emotion estimation function can be used to generate an ideal face that reflects the user's most relaxing facial expression.

[0092] The ideal face generation unit can also generate an ideal face based on the user's hobbies and lifestyle, taking into account the user's hobbies and lifestyle. For example, it can propose a healthy face to a user who likes the outdoors, and a unique face to a user who likes art. The ideal face generation unit also generates an ideal face taking into account the user's hobbies and lifestyle. This makes it possible to generate an ideal face that matches the user's hobbies and lifestyle.

[0093] The ideal face generation unit can also estimate the user's emotions and generate an ideal face that reflects the expression that makes the user feel the happiest. For example, a smile or a joyful expression. The ideal face generation unit also uses the emotion estimation function to generate an ideal face that reflects the expression that makes the user feel the happiest. In this way, the emotion estimation function can be used to generate an ideal face that reflects the expression that makes the user feel the happiest.

[0094] The ideal face generation unit can also estimate the user's emotions and generate an ideal face that reflects the user's most comfortable expression. For example, a calm expression or a relaxed expression. The ideal face generation unit also uses the emotion estimation function to generate an ideal face that reflects the user's most comfortable expression. In this way, the emotion estimation function can be used to generate an ideal face that reflects the user's most comfortable expression.

[0095] When analyzing the user's facial features, the gap analysis unit can also take into account the user's facial bone structure and muscle structure to identify more precise gaps. For example, the gap analysis unit analyzes the shape of the bone structure and the level of muscle development. The gap analysis unit also takes into account the user's facial bone structure and muscle structure to identify gaps with the ideal face. This enables precise gap analysis that takes into account the user's facial bone structure and muscle structure.

[0096] When analyzing the user's facial features, the gap analysis unit can also consider the user's health condition and lifestyle habits to identify gaps based on the user's health condition. For example, it analyzes the effects of lack of sleep and stress on the face. The gap analysis unit also considers the user's health condition and lifestyle habits to identify gaps from the ideal face. This enables gap analysis that takes the user's health condition into account.

[0097] The gap analysis unit can also use the emotion estimation function to analyze the emotion of stress felt by the user and identify a gap based on the emotion. For example, the gap analysis unit can identify the impact of stress on the face. The gap analysis unit can also use the emotion estimation function to identify a gap based on the emotion of stress felt by the user. This allows the emotion estimation function to identify a gap based on the emotion of stress felt by the user.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The ideal face generator generates an ideal face for a scene selected by the user, such as a business meeting, a casual conversation with friends, or a formal event. Step 2: The gap analysis unit analyzes the gap between the ideal face generated by the ideal face generation unit and the user's current face, for example, by analyzing differences in facial features and skin tone. Step 3: The makeup suggestion unit proposes specific makeup methods to fill the gaps analyzed by the gap analysis unit. For example, it suggests specific makeup steps, such as eye makeup to make the eyes look bigger or how to choose a foundation to make the skin look brighter. Step 4: The skin care suggestion unit suggests skin care methods suited to individual skin types. For example, it suggests moisturizing skin care methods for users with dry skin, and oil-control skin care methods for users with oily skin. Step 5: The cosmetics suggestion unit suggests cosmetics based on the user's skin condition. For example, it suggests mild cosmetics to users with sensitive skin, and cosmetics with whitening effects to users who are concerned about blemishes or dullness.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0147] 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.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an ideal face generator that generates an ideal face according to a scene selected by a user; a gap analysis unit that analyzes a gap between the ideal face generated by the ideal face generation unit and the user's current face; a makeup suggestion unit that suggests a specific makeup method for filling the gap analyzed by the gap analysis unit; A skin care proposal department that proposes skin care methods suited to individual skin types, A cosmetics suggestion unit that suggests cosmetics based on the skin condition. A system characterized by:

2. The ideal face generator Generate the ideal face that reflects the facial expression that the user is most confident in.

2. The system of claim 1.

3. The gap analysis unit The minute features of the user's face are analyzed in detail, and the gap between the user's actual face and the ideal face is precisely identified.

2. The system of claim 1.

4. The makeup suggestion unit Suggesting a personalized makeup routine based on the user's facial features 2. The system of claim 1.

5. The skin care suggestion unit Analyzing the user's skin type data in detail and proposing the skin care method according to changes in the season and environment 2. The system of claim 1.

6. The cosmetic suggestion unit Analyze the user's skin condition in detail and suggest cosmetics that suit the season and environmental changes.

2. The system of claim 1.

7. The ideal face generator Generate the ideal face based on the emotion the user feels in a particular scene 2. The system of claim 1.

8. The gap analysis unit Analyzing the complex feelings felt by the user and identifying the gap based on the feelings.

2. The system of claim 1.

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