System

Abstract

An object of a system according to an embodiment is to propose optimal makeup to a user and explain a procedure thereof.SOLUTION: A system according to an embodiment includes a face analysis unit, a makeup suggestion unit, a makeup simulation unit, and a makeup procedure explanation unit. The face analysis unit analyzes features of the face of the user. The makeup suggestion unit suggests optimal makeup based on the feature analyzed by the face analysis unit. The makeup simulator applies the makeup proposed by the makeup proposal unit to the face of the user. The makeup procedure explanation unit explains a makeup procedure applied by the makeup simulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find makeup that suited you and understand the steps to apply it.

[0005] The system according to the embodiment aims to suggest the most suitable makeup to the user and explain the procedure for applying it. [Means for solving the problem]

[0006] The system according to the embodiment includes a face analysis unit, a makeup suggestion unit, a makeup simulation unit, and a makeup procedure explanation unit. The face analysis unit analyzes the facial features of a user. The makeup suggestion unit suggests optimal makeup based on the features analyzed by the face analysis unit. The makeup simulation unit applies the makeup suggested by the makeup suggestion unit to the user's face. The makeup procedure explanation unit explains the makeup procedure applied by the makeup simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable makeup to the user and explain the steps to apply it. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The makeup suggestion system according to an embodiment of the present invention analyzes the user's facial features, uses a generative AI to suggest optimal makeup, and uses AR technology to simulate and explain the steps for applying the makeup. This allows the user to easily find makeup that suits them and learn the steps for applying the makeup.

[0029] A makeup suggestion system according to an embodiment includes a face analysis unit, a makeup suggestion unit, a makeup simulation unit, and a makeup procedure explanation unit. The face analysis unit analyzes the user's facial features. For example, the face analysis unit analyzes the user's face shape, skin color, eye shape, etc. The face analysis unit also receives an image of the user's face as input and extracts facial features. For example, the face analysis unit detects facial contours and extracts feature points using image processing technology. The face analysis unit can also classify facial features using a machine learning algorithm. The makeup suggestion unit suggests optimal makeup based on the features analyzed by the face analysis unit. For example, the makeup suggestion unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to suggest makeup that suits the user's face. The makeup suggestion unit can also suggest makeup based on the user's preferences and trends. For example, if a user inputs, "I want to try natural makeup," the makeup suggestion unit suggests that makeup. The makeup simulation unit applies the makeup suggested by the makeup suggestion unit to the user's face. For example, the makeup simulation unit uses AR technology to display makeup on the user's face in real time. The makeup simulation unit can also generate a 3D model of the user's face and simulate how the makeup will look from different angles. For example, the makeup simulation unit simulates how the makeup will look based on the 3D model of the user's face. The makeup procedure explanation unit explains the makeup procedure applied by the makeup simulation unit. For example, the makeup procedure explanation unit explains the makeup procedure step-by-step using a generative AI. The makeup procedure explanation unit can also provide customized makeup procedures, ranging from beginner to advanced, depending on the user's skill level. For example, the makeup procedure explanation unit suggests basic procedures for beginners and advanced techniques for advanced users. This allows the makeup suggestion system according to the embodiment to easily find makeup that suits them and learn the makeup procedure.

[0030] The face analysis unit learns the user's past makeup history and can suggest optimal makeup based on past successes and failures. For example, the face analysis unit uses a generative AI to store the user's past makeup history in a database and analyze successes and failures. For example, it can suggest makeup that suits the user based on photos and ratings of makeup they have tried in the past. The face analysis unit also learns the makeup history the user has tried in the past and extracts successful makeup patterns. For example, if a particular makeup style was highly rated, it will prioritize suggestions for that style. The face analysis unit also analyzes the causes of makeup failures based on the user's past makeup history and makes suggestions to avoid the same mistakes. For example, if a particular color or style does not suit the user, it will make suggestions to avoid it. In this way, by learning from the user's past makeup history, it can suggest the optimal makeup for the user.

[0031] The facial analysis unit can analyze subtle changes in a user's facial expression and suggest makeup that matches the expression. For example, the generation AI in the facial analysis unit analyzes subtle changes in a user's facial expression in real time and suggests makeup that matches the expression. For example, it can suggest lip colors that suit a smiling face. The facial analysis unit also collects data on a user's facial expression and builds a system that suggests the best makeup for a specific expression. For example, it can suggest eye makeup that suits a serious expression. The facial analysis unit also uses the generation AI to analyze changes in a user's facial expression and adjust the makeup according to the expression. For example, it can suggest makeup that emphasizes the eyes when the user looks surprised. This allows for makeup suggestions that match the user's facial expression, resulting in more natural and attractive makeup.

[0032] The facial analysis unit can analyze the user's hairstyle and accessories and suggest makeup based on that. For example, the facial analysis unit's generative AI can analyze the user's hairstyle and accessories and suggest makeup that suits them. For example, it can suggest eye makeup that goes well with an upstyle hairstyle. The facial analysis unit can also collect data on the user's hairstyle and accessories and build a system that suggests optimal makeup based on that data. For example, it can suggest lip colors that go well with large earrings. The facial analysis unit can also use the generative AI to analyze the user's hairstyle and accessories in real time and adjust the makeup to suit them. For example, it can suggest makeup that goes well with a hat. This makes it possible to create a total outfit by suggesting makeup that matches the hairstyle and accessories.

[0033] The facial analysis unit can suggest makeup that suits the season and weather. For example, the generation AI in the facial analysis unit collects seasonal and weather data and suggests makeup that suits it. For example, it suggests light makeup that is suitable for hot summer days. The facial analysis unit also builds a system that suggests optimal makeup based on the season and weather. For example, it suggests moisturizing makeup that is suitable for dry winter days. The facial analysis unit also uses the generation AI to analyze weather data in real time and suggest makeup that suits the weather of the day. For example, it suggests waterproof makeup that is suitable for rainy days. This makes it possible to achieve more appropriate makeup by suggesting makeup that suits the season and weather.

[0034] The makeup simulation unit generates a 3D model of the user's face and can simulate how the makeup will look from different angles. For example, the generation AI generates a 3D model of the user's face and simulates how the makeup will look from different angles. For example, it displays how it will look from the side or at an angle. The makeup simulation unit also builds a system that simulates how the makeup will look based on the 3D model of the user's face. For example, it displays how it will look from above or below. The makeup simulation unit also generates a 3D model of the user's face in real time using the generation AI and adjusts how the makeup will look from different angles. For example, it displays how it will look while moving. This allows the user to check their makeup from multiple angles by simulating how it will look from different angles.

[0035] The makeup simulation unit can analyze the user's facial features in detail and show subtle differences in makeup in real time. For example, the makeup simulation unit uses a generation AI to analyze the user's facial features in detail and display subtle differences in makeup in real time. For example, it displays subtle differences in eyeshadow color. The makeup simulation unit also builds a system that displays subtle differences in makeup in real time based on the user's facial features. For example, it displays subtle differences in lip color. The makeup simulation unit also uses a generation AI to analyze the user's facial features in real time and adjust subtle differences in makeup. For example, it displays subtle differences in blush color. This allows subtle differences in makeup to be displayed in real time, allowing the user to check the details.

[0036] The makeup simulation unit can simulate how makeup looks under different lighting conditions. For example, the generation AI in the makeup simulation unit simulates how makeup looks under different lighting conditions. For example, it displays how it looks under natural light and fluorescent light. The makeup simulation unit also builds a system that simulates how makeup looks under different lighting conditions based on a 3D model of the user's face. For example, it displays how it looks under evening light and night light. The generation AI in the makeup simulation unit also adjusts how makeup looks under different lighting conditions in real time. For example, it displays how it looks under party lights. This allows users to check their makeup in a variety of environments by simulating how makeup looks under different lighting conditions.

[0037] The makeup simulation unit can adjust the appearance of makeup in real time according to the user's facial movements. For example, the generation AI in the makeup simulation unit adjusts the appearance of makeup in real time according to the user's facial movements. For example, it displays how the makeup will look when smiling or with a surprised expression. The makeup simulation unit also builds a system that adjusts the appearance of makeup in real time based on the user's facial movements. For example, it displays how the makeup will look when talking or eating. The makeup simulation unit also analyzes the user's facial movements in real time using the generation AI to adjust the appearance of the makeup. For example, it displays how the makeup will look while exercising or dancing. This allows the appearance of makeup to be adjusted according to the user's facial movements, achieving a more natural look.

[0038] The makeup procedure explanation unit can analyze the user's skill level and provide customized makeup procedures ranging from beginner to advanced. For example, the generation AI analyzes the user's skill level and provides customized makeup procedures ranging from beginner to advanced. For example, it suggests basic procedures for beginners and advanced techniques for advanced users. The makeup procedure explanation unit also builds a system that provides optimal makeup procedures based on the user's makeup skills. For example, it determines the skill level based on past makeup history and evaluations. The generation AI also analyzes the user's skill level in real time and provides customized makeup procedures. For example, it suggests the next step depending on the makeup progress. This allows the system to accommodate a wide range of users, from beginners to advanced users, by providing makeup procedures according to the user's skill level.

[0039] The makeup procedure explanation unit can suggest the most suitable makeup tools and cosmetics according to the user's facial features. For example, the makeup procedure explanation unit uses a generation AI to analyze the user's facial features and suggest the most suitable makeup tools and cosmetics accordingly. For example, it can suggest a foundation that suits a specific skin type. The makeup procedure explanation unit also builds a system that suggests the most suitable makeup tools and cosmetics based on the user's facial features. For example, it can suggest an eyeliner that suits the shape of the eyes. The makeup procedure explanation unit also uses a generation AI to analyze the user's facial features in real time and suggest the most suitable makeup tools and cosmetics. For example, it can suggest a lipstick that suits the shape of the lips. This allows for more effective makeup by suggesting makeup tools and cosmetics that suit the user's facial features.

[0040] The makeup procedure explanation unit can monitor the user's makeup progress in real time and suggest the next step at the appropriate time. For example, the generation AI of the makeup procedure explanation unit monitors the user's makeup progress in real time and suggests the next step at the appropriate time. For example, it suggests eyeliner after eyeshadow has been applied. The makeup procedure explanation unit also builds a system that suggests the next step based on the user's makeup progress. For example, it suggests blush after foundation has been applied. The makeup procedure explanation unit also analyzes the user's makeup progress in real time and suggests the next step at the appropriate time. For example, it suggests lip gloss after lipstick has been applied. In this way, the next step can be suggested according to the makeup progress, allowing the user to smoothly proceed with their makeup.

[0041] The makeup procedure explanation unit can provide feedback in real time according to the progress of the user's makeup. For example, the generation AI in the makeup procedure explanation unit provides feedback in real time according to the progress of the user's makeup. For example, it evaluates whether the way eyeshadow is applied is appropriate. The makeup procedure explanation unit also builds a system that provides feedback in real time based on the progress of the user's makeup. For example, it evaluates whether the way foundation is applied is even. The makeup procedure explanation unit also analyzes the progress of the user's makeup in real time and provides feedback. For example, it evaluates whether the way lipstick is applied is appropriate. In this way, feedback is provided in real time according to the progress of the makeup, allowing the user to apply appropriate makeup.

[0042] A smart mirror can analyze the subtle movements of a user's face and adjust the appearance of makeup in real time according to the movements. For example, a smart mirror can analyze the subtle movements of a user's face in real time and adjust the appearance of makeup according to the movements. For example, it can display how the makeup will look when the user is smiling or looking surprised. A smart mirror can also build a system that adjusts the appearance of makeup in real time based on the user's facial movements. For example, it can display how the makeup will look when the user is talking or eating. A smart mirror can also analyze the user's facial movements in real time and adjust the appearance of makeup. For example, it can display how the makeup will look when the user is exercising or dancing. This allows the appearance of makeup to be adjusted according to the user's facial movements, resulting in a more natural-looking makeup look.

[0043] A smart mirror can generate a 3D model of a user's face and simulate how makeup will look from different angles. For example, a smart mirror can generate a 3D model of a user's face and simulate how makeup will look from different angles. For example, it can display how the makeup will look from the side or at an angle. A smart mirror can also build a system that simulates how makeup will look based on the 3D model of the user's face. For example, it can display how the makeup will look from above or below. A smart mirror can also generate a 3D model of the user's face in real time and adjust how the makeup will look from different angles. For example, it can display how the makeup will look while moving. This allows the user to check their makeup from multiple angles by simulating how the makeup will look from different angles.

[0044] Smart mirrors can simulate how makeup looks under different lighting conditions. For example, smart mirrors simulate how makeup looks under different lighting conditions. For example, they display how it looks under natural light and fluorescent light. Smart mirrors also build a system that simulates how makeup looks under different lighting conditions based on a 3D model of the user's face. For example, they display how it looks under evening light and night light. Smart mirrors also adjust how makeup looks under different lighting conditions in real time. For example, they display how it looks under party lights. This allows users to check their makeup in a variety of environments by simulating how it looks under different lighting conditions.

[0045] A smart mirror can adjust the appearance of makeup in real time according to the user's facial movements. For example, a smart mirror adjusts the appearance of makeup in real time according to the user's facial movements. For example, it displays how the makeup will look when the user is smiling or expressing surprise. A smart mirror also builds a system that adjusts the appearance of makeup in real time based on the user's facial movements. For example, it displays how the makeup will look when the user is talking or eating. A smart mirror also analyzes the user's facial movements in real time and adjusts the appearance of the makeup. For example, it displays how the makeup will look while the user is exercising or dancing. This allows the appearance of makeup to be adjusted according to the user's facial movements, resulting in a more natural makeup look.

[0046] The makeup procedure explanation unit can analyze the user's skill level and provide customized makeup procedures ranging from beginner to advanced. For example, the generation AI analyzes the user's skill level and provides customized makeup procedures ranging from beginner to advanced. For example, it suggests basic procedures for beginners and advanced techniques for advanced users. The makeup procedure explanation unit also builds a system that provides optimal makeup procedures based on the user's makeup skills. For example, it determines the skill level based on past makeup history and evaluations. The generation AI also analyzes the user's skill level in real time and provides customized makeup procedures. For example, it suggests the next step depending on the makeup progress. This allows the system to accommodate a wide range of users, from beginners to advanced users, by providing makeup procedures according to the user's skill level.

[0047] The makeup procedure explanation unit can suggest the most suitable makeup tools and cosmetics according to the user's facial features. For example, the makeup procedure explanation unit uses a generation AI to analyze the user's facial features and suggest the most suitable makeup tools and cosmetics accordingly. For example, it can suggest a foundation that suits a specific skin type. The makeup procedure explanation unit also builds a system that suggests the most suitable makeup tools and cosmetics based on the user's facial features. For example, it can suggest an eyeliner that suits the shape of the eyes. The makeup procedure explanation unit also uses a generation AI to analyze the user's facial features in real time and suggest the most suitable makeup tools and cosmetics. For example, it can suggest a lipstick that suits the shape of the lips. This allows for more effective makeup by suggesting makeup tools and cosmetics that suit the user's facial features.

[0048] The makeup procedure explanation unit can monitor the user's makeup progress in real time and suggest the next step at the appropriate time. For example, the generation AI of the makeup procedure explanation unit monitors the user's makeup progress in real time and suggests the next step at the appropriate time. For example, it suggests eyeliner after eyeshadow has been applied. The makeup procedure explanation unit also builds a system that suggests the next step based on the user's makeup progress. For example, it suggests blush after foundation has been applied. The makeup procedure explanation unit also analyzes the user's makeup progress in real time and suggests the next step at the appropriate time. For example, it suggests lip gloss after lipstick has been applied. In this way, the next step can be suggested according to the makeup progress, allowing the user to smoothly proceed with their makeup.

[0049] The makeup procedure explanation unit can provide feedback in real time according to the progress of the user's makeup. For example, the generation AI in the makeup procedure explanation unit provides feedback in real time according to the progress of the user's makeup. For example, it evaluates whether the way eyeshadow is applied is appropriate. The makeup procedure explanation unit also builds a system that provides feedback in real time based on the progress of the user's makeup. For example, it evaluates whether the way foundation is applied is even. The makeup procedure explanation unit also analyzes the progress of the user's makeup in real time and provides feedback. For example, it evaluates whether the way lipstick is applied is appropriate. In this way, feedback is provided in real time according to the progress of the makeup, allowing the user to apply appropriate makeup.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The makeup suggestion system can also collect lifestyle data about the user and suggest makeup based on that data. For example, if the user plays sports on a daily basis, it can suggest sweat-resistant makeup. If the user frequently goes out, it can also suggest makeup that lasts for a long time. Furthermore, if the user is often active at night, it can also suggest makeup that looks good under night lighting. In this way, makeup that suits the user's lifestyle can be suggested, resulting in more practical and satisfying makeup.

[0052] The makeup suggestion system can also monitor the user's health condition and suggest makeup based on that. For example, it can detect the dryness of the user's skin and suggest makeup with moisturizing effects. It can also detect the user's lack of sleep and suggest makeup that will cover up tired faces. It can also take into account the user's allergy information and suggest makeup that will not cause allergic reactions. This allows for safer and more effective makeup by suggesting makeup that is appropriate for the user's health condition.

[0053] The makeup suggestion system can also analyze the user's fashion style and suggest makeup based on that. For example, if the user prefers casual fashion, it can suggest natural makeup. If the user is attending a formal occasion, it can suggest elegant makeup. Furthermore, if the user is wearing clothes of a specific color or design, it can suggest makeup that matches the clothes. This makes it possible to create a total outfit by suggesting makeup that suits the user's fashion style.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The face analysis unit analyzes the user's facial features. For example, the face analysis unit analyzes the user's face shape, skin color, eye shape, etc. The face analysis unit also receives an image of the user's face as input and extracts facial features. For example, the face analysis unit uses image processing technology to detect the contours of the face and extract feature points. The face analysis unit can also classify facial features using machine learning algorithms. Step 2: The makeup suggestion unit suggests optimal makeup based on the features analyzed by the face analysis unit. For example, the makeup suggestion unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to suggest makeup that suits the user's face. The makeup suggestion unit can also suggest makeup based on the user's preferences and trends. For example, if the user inputs, "I want to try natural makeup," the makeup suggestion unit will suggest that makeup. Step 3: The makeup simulation unit applies the makeup suggested by the makeup suggestion unit to the user's face. For example, the makeup simulation unit uses AR technology to display the makeup on the user's face in real time. The makeup simulation unit can also generate a 3D model of the user's face and simulate how the makeup will look from different angles. For example, the makeup simulation unit simulates how the makeup will look based on the 3D model of the user's face. Step 4: The makeup procedure explanation unit explains the makeup procedure applied by the makeup simulation unit. For example, the makeup procedure explanation unit uses generative AI to explain the makeup procedure step by step. The makeup procedure explanation unit can also provide customized makeup procedures ranging from beginner to advanced depending on the user's skill level. For example, the makeup procedure explanation unit suggests basic procedures for beginners and advanced techniques for advanced users.

[0056] (Example 2) The makeup suggestion system according to an embodiment of the present invention analyzes the user's facial features, uses a generative AI to suggest optimal makeup, and uses AR technology to simulate and explain the steps for applying the makeup. This allows the user to easily find makeup that suits them and learn the steps for applying the makeup.

[0057] A makeup suggestion system according to an embodiment includes a face analysis unit, a makeup suggestion unit, a makeup simulation unit, and a makeup procedure explanation unit. The face analysis unit analyzes the user's facial features. For example, the face analysis unit analyzes the user's face shape, skin color, eye shape, etc. The face analysis unit also receives an image of the user's face as input and extracts facial features. For example, the face analysis unit detects facial contours and extracts feature points using image processing technology. The face analysis unit can also classify facial features using a machine learning algorithm. The makeup suggestion unit suggests optimal makeup based on the features analyzed by the face analysis unit. For example, the makeup suggestion unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to suggest makeup that suits the user's face. The makeup suggestion unit can also suggest makeup based on the user's preferences and trends. For example, if a user inputs, "I want to try natural makeup," the makeup suggestion unit suggests that makeup. The makeup simulation unit applies the makeup suggested by the makeup suggestion unit to the user's face. For example, the makeup simulation unit uses AR technology to display makeup on the user's face in real time. The makeup simulation unit can also generate a 3D model of the user's face and simulate how the makeup will look from different angles. For example, the makeup simulation unit simulates how the makeup will look based on the 3D model of the user's face. The makeup procedure explanation unit explains the makeup procedure applied by the makeup simulation unit. For example, the makeup procedure explanation unit explains the makeup procedure step-by-step using a generative AI. The makeup procedure explanation unit can also provide customized makeup procedures, ranging from beginner to advanced, depending on the user's skill level. For example, the makeup procedure explanation unit suggests basic procedures for beginners and advanced techniques for advanced users. This allows the makeup suggestion system according to the embodiment to easily find makeup that suits them and learn the makeup procedure.

[0058] The face analysis unit learns the user's past makeup history and can suggest optimal makeup based on past successes and failures. For example, the face analysis unit uses a generative AI to store the user's past makeup history in a database and analyze successes and failures. For example, it can suggest makeup that suits the user based on photos and ratings of makeup they have tried in the past. The face analysis unit also learns the makeup history the user has tried in the past and extracts successful makeup patterns. For example, if a particular makeup style was highly rated, it will prioritize suggestions for that style. The face analysis unit also analyzes the causes of makeup failures based on the user's past makeup history and makes suggestions to avoid the same mistakes. For example, if a particular color or style does not suit the user, it will make suggestions to avoid it. In this way, by learning from the user's past makeup history, it can suggest the optimal makeup for the user.

[0059] The facial analysis unit can analyze subtle changes in a user's facial expression and suggest makeup that matches the expression. For example, the generation AI in the facial analysis unit analyzes subtle changes in a user's facial expression in real time and suggests makeup that matches the expression. For example, it can suggest lip colors that suit a smiling face. The facial analysis unit also collects data on a user's facial expression and builds a system that suggests the best makeup for a specific expression. For example, it can suggest eye makeup that suits a serious expression. The facial analysis unit also uses the generation AI to analyze changes in a user's facial expression and adjust the makeup according to the expression. For example, it can suggest makeup that emphasizes the eyes when the user looks surprised. This allows for makeup suggestions that match the user's facial expression, resulting in more natural and attractive makeup.

[0060] The face analysis unit can use the emotion estimation function to analyze the user's current emotional state and suggest makeup based on that emotion. For example, the face analysis unit can use the emotion estimation function to analyze the user's current emotional state and suggest makeup that matches that emotion. For example, natural makeup can be suggested when the user is relaxed. The face analysis unit also collects the user's emotional data and builds a system that suggests makeup that is optimal for a specific emotional state. For example, glamorous makeup can be suggested when the user is excited. The face analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust makeup to match that emotion. For example, bright-colored makeup can be suggested when the user is sad. This allows makeup to be suggested according to the user's emotions, resulting in more satisfying makeup.

[0061] The facial analysis unit can analyze the user's hairstyle and accessories and suggest makeup based on that. For example, the facial analysis unit's generative AI can analyze the user's hairstyle and accessories and suggest makeup that suits them. For example, it can suggest eye makeup that goes well with an upstyle hairstyle. The facial analysis unit can also collect data on the user's hairstyle and accessories and build a system that suggests optimal makeup based on that data. For example, it can suggest lip colors that go well with large earrings. The facial analysis unit can also use the generative AI to analyze the user's hairstyle and accessories in real time and adjust the makeup to suit them. For example, it can suggest makeup that goes well with a hat. This makes it possible to create a total outfit by suggesting makeup that matches the hairstyle and accessories.

[0062] The facial analysis unit can suggest makeup that suits the season and weather. For example, the generation AI in the facial analysis unit collects seasonal and weather data and suggests makeup that suits it. For example, it suggests light makeup that is suitable for hot summer days. The facial analysis unit also builds a system that suggests optimal makeup based on the season and weather. For example, it suggests moisturizing makeup that is suitable for dry winter days. The facial analysis unit also uses the generation AI to analyze weather data in real time and suggest makeup that suits the weather of the day. For example, it suggests waterproof makeup that is suitable for rainy days. This makes it possible to achieve more appropriate makeup by suggesting makeup that suits the season and weather.

[0063] The face analysis unit can use the emotion estimation function to analyze the emotions of the user when trying out makeup in real time and suggest makeup based on positive emotions. For example, the face analysis unit can use the emotion estimation function to analyze the emotions of the user when trying out makeup in real time and suggest makeup that elicits positive emotions. For example, it can suggest makeup that makes the user smile more. The face analysis unit also collects emotional data from users and builds a system that analyzes their emotional reactions when trying out makeup. For example, it can suggest makeup that elicits relaxed emotions. The face analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust makeup that elicits positive emotions. For example, it can suggest makeup with energizing colors. In this way, makeup that matches the user's emotions can be suggested, resulting in more satisfying makeup.

[0064] The makeup simulation unit generates a 3D model of the user's face and can simulate how the makeup will look from different angles. For example, the generation AI generates a 3D model of the user's face and simulates how the makeup will look from different angles. For example, it displays how it will look from the side or at an angle. The makeup simulation unit also builds a system that simulates how the makeup will look based on the 3D model of the user's face. For example, it displays how it will look from above or below. The makeup simulation unit also generates a 3D model of the user's face in real time using the generation AI and adjusts how the makeup will look from different angles. For example, it displays how it will look while moving. This allows the user to check their makeup from multiple angles by simulating how it will look from different angles.

[0065] The makeup simulation unit can analyze the user's facial features in detail and show subtle differences in makeup in real time. For example, the makeup simulation unit uses a generation AI to analyze the user's facial features in detail and display subtle differences in makeup in real time. For example, it displays subtle differences in eyeshadow color. The makeup simulation unit also builds a system that displays subtle differences in makeup in real time based on the user's facial features. For example, it displays subtle differences in lip color. The makeup simulation unit also uses a generation AI to analyze the user's facial features in real time and adjust subtle differences in makeup. For example, it displays subtle differences in blush color. This allows subtle differences in makeup to be displayed in real time, allowing the user to check the details.

[0066] The makeup simulation unit can use the emotion estimation function to analyze the emotional response of the user when trying out makeup and suggest makeup based on the most positive response. For example, the makeup simulation unit can use the emotion estimation function to analyze the emotional response of the user when trying out makeup and suggest makeup that elicits the most positive response. For example, it can suggest makeup that will make the user smile more. The makeup simulation unit also collects emotional data from users and builds a system that analyzes the emotional response when trying out makeup. For example, it can suggest makeup that will elicit a relaxed emotion. The makeup simulation unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust makeup that will elicit positive emotions. For example, it can suggest makeup with energizing colors. In this way, optimal makeup can be suggested based on the user's emotional response, thereby achieving more satisfying makeup.

[0067] The makeup simulation unit can simulate how makeup looks under different lighting conditions. For example, the generation AI in the makeup simulation unit simulates how makeup looks under different lighting conditions. For example, it displays how it looks under natural light and fluorescent light. The makeup simulation unit also builds a system that simulates how makeup looks under different lighting conditions based on a 3D model of the user's face. For example, it displays how it looks under evening light and night light. The generation AI in the makeup simulation unit also adjusts how makeup looks under different lighting conditions in real time. For example, it displays how it looks under party lights. This allows users to check their makeup in a variety of environments by simulating how makeup looks under different lighting conditions.

[0068] The makeup simulation unit can adjust the appearance of makeup in real time according to the user's facial movements. For example, the generation AI in the makeup simulation unit adjusts the appearance of makeup in real time according to the user's facial movements. For example, it displays how the makeup will look when smiling or with a surprised expression. The makeup simulation unit also builds a system that adjusts the appearance of makeup in real time based on the user's facial movements. For example, it displays how the makeup will look when talking or eating. The makeup simulation unit also analyzes the user's facial movements in real time using the generation AI to adjust the appearance of the makeup. For example, it displays how the makeup will look while exercising or dancing. This allows the appearance of makeup to be adjusted according to the user's facial movements, achieving a more natural look.

[0069] The makeup simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out makeup in real time and suggest the optimal makeup. For example, the makeup simulation unit can use the emotion estimation function to analyze the emotions of the user when trying out makeup in real time and suggest the optimal makeup. For example, it can suggest makeup that makes the user smile more. The makeup simulation unit also collects emotional data from users and builds a system that analyzes their emotional reactions when trying out makeup. For example, it can suggest makeup that brings out a relaxed emotion. The makeup simulation unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust the optimal makeup. For example, it can suggest makeup with energizing colors. In this way, makeup that matches the user's emotions can be suggested, thereby achieving more satisfying makeup.

[0070] The makeup procedure explanation unit can analyze the user's skill level and provide customized makeup procedures ranging from beginner to advanced. For example, the generation AI analyzes the user's skill level and provides customized makeup procedures ranging from beginner to advanced. For example, it suggests basic procedures for beginners and advanced techniques for advanced users. The makeup procedure explanation unit also builds a system that provides optimal makeup procedures based on the user's makeup skills. For example, it determines the skill level based on past makeup history and evaluations. The generation AI also analyzes the user's skill level in real time and provides customized makeup procedures. For example, it suggests the next step depending on the makeup progress. This allows the system to accommodate a wide range of users, from beginners to advanced users, by providing makeup procedures according to the user's skill level.

[0071] The makeup procedure explanation unit can suggest the most suitable makeup tools and cosmetics according to the user's facial features. For example, the makeup procedure explanation unit uses a generation AI to analyze the user's facial features and suggest the most suitable makeup tools and cosmetics accordingly. For example, it can suggest a foundation that suits a specific skin type. The makeup procedure explanation unit also builds a system that suggests the most suitable makeup tools and cosmetics based on the user's facial features. For example, it can suggest an eyeliner that suits the shape of the eyes. The makeup procedure explanation unit also uses a generation AI to analyze the user's facial features in real time and suggest the most suitable makeup tools and cosmetics. For example, it can suggest a lipstick that suits the shape of the lips. This allows for more effective makeup by suggesting makeup tools and cosmetics that suit the user's facial features.

[0072] The makeup procedure explanation unit can use the emotion estimation function to analyze the emotional response of the user when learning makeup procedures and suggest procedures based on positive emotions. For example, the makeup procedure explanation unit can use the emotion estimation function to analyze the emotional response of the user when learning makeup procedures and suggest procedures that elicit positive emotions. For example, it can suggest procedures that elicit a sense of enjoyment. The makeup procedure explanation unit can also collect user emotion data and build a system that analyzes the user's emotional response when learning makeup procedures. For example, it can suggest procedures that elicit a sense of relaxation. The makeup procedure explanation unit can also use the emotion estimation function to analyze the user's emotional state in real time and adjust procedures that elicit positive emotions. For example, it can suggest procedures that make the user feel energized. This makes learning makeup more enjoyable by suggesting optimal makeup procedures based on the user's emotional responses.

[0073] The makeup procedure explanation unit can monitor the user's makeup progress in real time and suggest the next step at the appropriate time. For example, the generation AI of the makeup procedure explanation unit monitors the user's makeup progress in real time and suggests the next step at the appropriate time. For example, it suggests eyeliner after eyeshadow has been applied. The makeup procedure explanation unit also builds a system that suggests the next step based on the user's makeup progress. For example, it suggests blush after foundation has been applied. The makeup procedure explanation unit also analyzes the user's makeup progress in real time and suggests the next step at the appropriate time. For example, it suggests lip gloss after lipstick has been applied. In this way, the next step can be suggested according to the makeup progress, allowing the user to smoothly proceed with their makeup.

[0074] The makeup procedure explanation unit can provide feedback in real time according to the progress of the user's makeup. For example, the generation AI in the makeup procedure explanation unit provides feedback in real time according to the progress of the user's makeup. For example, it evaluates whether the way eyeshadow is applied is appropriate. The makeup procedure explanation unit also builds a system that provides feedback in real time based on the progress of the user's makeup. For example, it evaluates whether the way foundation is applied is even. The makeup procedure explanation unit also analyzes the progress of the user's makeup in real time and provides feedback. For example, it evaluates whether the way lipstick is applied is appropriate. In this way, feedback is provided in real time according to the progress of the makeup, allowing the user to apply appropriate makeup.

[0075] The makeup procedure explanation unit can use the emotion estimation function to analyze the user's emotions in real time when learning makeup procedures and suggest the optimal procedure. The makeup procedure explanation unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when learning makeup procedures and suggest the optimal procedure. For example, it can suggest a procedure that brings out a sense of enjoyment. The makeup procedure explanation unit also collects user emotion data and builds a system that analyzes the user's emotional response when learning makeup procedures. For example, it can suggest a procedure that brings out a relaxed emotion. The makeup procedure explanation unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust the optimal procedure. For example, it can suggest a procedure that makes the user feel energized. In this way, by suggesting makeup procedures that correspond to the user's emotions, learning makeup can be more enjoyable.

[0076] A smart mirror can analyze the subtle movements of a user's face and adjust the appearance of makeup in real time according to the movements. For example, a smart mirror can analyze the subtle movements of a user's face in real time and adjust the appearance of makeup according to the movements. For example, it can display how the makeup will look when the user is smiling or looking surprised. A smart mirror can also build a system that adjusts the appearance of makeup in real time based on the user's facial movements. For example, it can display how the makeup will look when the user is talking or eating. A smart mirror can also analyze the user's facial movements in real time and adjust the appearance of makeup. For example, it can display how the makeup will look when the user is exercising or dancing. This allows the appearance of makeup to be adjusted according to the user's facial movements, resulting in a more natural-looking makeup look.

[0077] A smart mirror can generate a 3D model of a user's face and simulate how makeup will look from different angles. For example, a smart mirror can generate a 3D model of a user's face and simulate how makeup will look from different angles. For example, it can display how the makeup will look from the side or at an angle. A smart mirror can also build a system that simulates how makeup will look based on the 3D model of the user's face. For example, it can display how the makeup will look from above or below. A smart mirror can also generate a 3D model of the user's face in real time and adjust how the makeup will look from different angles. For example, it can display how the makeup will look while moving. This allows the user to check their makeup from multiple angles by simulating how the makeup will look from different angles.

[0078] A smart mirror can use its emotion estimation function to analyze a user's emotional response when using the mirror and display content based on positive emotions. For example, a smart mirror can use its emotion estimation function to analyze a user's emotional response when using the mirror and display content that elicits positive emotions. For example, it can display makeup that increases smiles. The smart mirror can also build a system that collects users' emotional data and analyzes their emotional responses when using the mirror. For example, it can display content that elicits relaxed emotions. The smart mirror can also use its emotion estimation function to analyze a user's emotional state in real time and adjust the display to elicit positive emotions. For example, it can display makeup that inspires positive emotions. This allows the smart mirror to provide a more positive experience by displaying content that matches the user's emotions.

[0079] Smart mirrors can simulate how makeup looks under different lighting conditions. For example, smart mirrors simulate how makeup looks under different lighting conditions. For example, they display how it looks under natural light and fluorescent light. Smart mirrors also build a system that simulates how makeup looks under different lighting conditions based on a 3D model of the user's face. For example, they display how it looks under evening light and night light. Smart mirrors also adjust how makeup looks under different lighting conditions in real time. For example, they display how it looks under party lights. This allows users to check their makeup in a variety of environments by simulating how it looks under different lighting conditions.

[0080] A smart mirror can adjust the appearance of makeup in real time according to the user's facial movements. For example, a smart mirror adjusts the appearance of makeup in real time according to the user's facial movements. For example, it displays how the makeup will look when the user is smiling or expressing surprise. A smart mirror also builds a system that adjusts the appearance of makeup in real time based on the user's facial movements. For example, it displays how the makeup will look when the user is talking or eating. A smart mirror also analyzes the user's facial movements in real time and adjusts the appearance of the makeup. For example, it displays how the makeup will look while the user is exercising or dancing. This allows the appearance of makeup to be adjusted according to the user's facial movements, resulting in a more natural makeup look.

[0081] A smart mirror can use its emotion estimation function to analyze a user's emotions in real time when using the mirror and suggest the most suitable makeup. For example, a smart mirror can use its emotion estimation function to analyze a user's emotions in real time when using the mirror and suggest the most suitable makeup. For example, it can suggest makeup that will make the user smile more. The smart mirror can also collect users' emotional data and build a system that analyzes their emotional reactions when using the mirror. For example, it can suggest makeup that will bring out a relaxed feeling. The smart mirror can also use its emotion estimation function to analyze a user's emotional state in real time and adjust the most suitable makeup. For example, it can suggest makeup with energizing colors. This allows for makeup suggestions that match the user's emotions, resulting in more satisfying makeup.

[0082] The makeup procedure explanation unit can analyze the user's skill level and provide customized makeup procedures ranging from beginner to advanced. For example, the generation AI analyzes the user's skill level and provides customized makeup procedures ranging from beginner to advanced. For example, it suggests basic procedures for beginners and advanced techniques for advanced users. The makeup procedure explanation unit also builds a system that provides optimal makeup procedures based on the user's makeup skills. For example, it determines the skill level based on past makeup history and evaluations. The generation AI also analyzes the user's skill level in real time and provides customized makeup procedures. For example, it suggests the next step depending on the makeup progress. This allows the system to accommodate a wide range of users, from beginners to advanced users, by providing makeup procedures according to the user's skill level.

[0083] The makeup procedure explanation unit can suggest the most suitable makeup tools and cosmetics according to the user's facial features. For example, the makeup procedure explanation unit uses a generation AI to analyze the user's facial features and suggest the most suitable makeup tools and cosmetics accordingly. For example, it can suggest a foundation that suits a specific skin type. The makeup procedure explanation unit also builds a system that suggests the most suitable makeup tools and cosmetics based on the user's facial features. For example, it can suggest an eyeliner that suits the shape of the eyes. The makeup procedure explanation unit also uses a generation AI to analyze the user's facial features in real time and suggest the most suitable makeup tools and cosmetics. For example, it can suggest a lipstick that suits the shape of the lips. This allows for more effective makeup by suggesting makeup tools and cosmetics that suit the user's facial features.

[0084] The makeup procedure explanation unit can use the emotion estimation function to analyze the emotional response of the user when learning makeup procedures and suggest procedures based on positive emotions. For example, the makeup procedure explanation unit can use the emotion estimation function to analyze the emotional response of the user when learning makeup procedures and suggest procedures that elicit positive emotions. For example, it can suggest procedures that elicit a sense of enjoyment. The makeup procedure explanation unit can also collect user emotion data and build a system that analyzes the user's emotional response when learning makeup procedures. For example, it can suggest procedures that elicit a sense of relaxation. The makeup procedure explanation unit can also use the emotion estimation function to analyze the user's emotional state in real time and adjust procedures that elicit positive emotions. For example, it can suggest procedures that make the user feel energized. This makes learning makeup more enjoyable by suggesting optimal makeup procedures based on the user's emotional responses.

[0085] The makeup procedure explanation unit can monitor the user's makeup progress in real time and suggest the next step at the appropriate time. For example, the generation AI of the makeup procedure explanation unit monitors the user's makeup progress in real time and suggests the next step at the appropriate time. For example, it suggests eyeliner after eyeshadow has been applied. The makeup procedure explanation unit also builds a system that suggests the next step based on the user's makeup progress. For example, it suggests blush after foundation has been applied. The makeup procedure explanation unit also analyzes the user's makeup progress in real time and suggests the next step at the appropriate time. For example, it suggests lip gloss after lipstick has been applied. In this way, the next step can be suggested according to the makeup progress, allowing the user to smoothly proceed with their makeup.

[0086] The makeup procedure explanation unit can provide feedback in real time according to the progress of the user's makeup. For example, the generation AI in the makeup procedure explanation unit provides feedback in real time according to the progress of the user's makeup. For example, it evaluates whether the way eyeshadow is applied is appropriate. The makeup procedure explanation unit also builds a system that provides feedback in real time based on the progress of the user's makeup. For example, it evaluates whether the way foundation is applied is even. The makeup procedure explanation unit also analyzes the progress of the user's makeup in real time and provides feedback. For example, it evaluates whether the way lipstick is applied is appropriate. In this way, feedback is provided in real time according to the progress of the makeup, allowing the user to apply appropriate makeup.

[0087] The makeup procedure explanation unit can use the emotion estimation function to analyze the user's emotions in real time when learning makeup procedures and suggest the optimal procedure. The makeup procedure explanation unit, for example, uses the emotion estimation function to analyze the user's emotions in real time when learning makeup procedures and suggest the optimal procedure. For example, it can suggest a procedure that brings out a sense of enjoyment. The makeup procedure explanation unit also collects user emotion data and builds a system that analyzes the user's emotional response when learning makeup procedures. For example, it can suggest a procedure that brings out a relaxed emotion. The makeup procedure explanation unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust the optimal procedure. For example, it can suggest a procedure that makes the user feel energized. In this way, by suggesting makeup procedures that correspond to the user's emotions, learning makeup can be more enjoyable.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The makeup suggestion system can also collect lifestyle data about the user and suggest makeup based on that data. For example, if the user plays sports on a daily basis, it can suggest sweat-resistant makeup. If the user frequently goes out, it can also suggest makeup that lasts for a long time. Furthermore, if the user is often active at night, it can also suggest makeup that looks good under night lighting. In this way, makeup that suits the user's lifestyle can be suggested, resulting in more practical and satisfying makeup.

[0090] The makeup suggestion system can also monitor the user's health condition and suggest makeup based on that. For example, it can detect the dryness of the user's skin and suggest makeup with moisturizing effects. It can also detect the user's lack of sleep and suggest makeup that will cover up tired faces. It can also take into account the user's allergy information and suggest makeup that will not cause allergic reactions. This allows for safer and more effective makeup by suggesting makeup that is appropriate for the user's health condition.

[0091] The makeup suggestion system can also analyze the user's fashion style and suggest makeup based on that. For example, if the user prefers casual fashion, it can suggest natural makeup. If the user is attending a formal occasion, it can suggest elegant makeup. Furthermore, if the user is wearing clothes of a specific color or design, it can suggest makeup that matches the clothes. This makes it possible to create a total outfit by suggesting makeup that suits the user's fashion style.

[0092] The makeup suggestion system can also use a user emotion estimation function to suggest makeup based on the user's emotions. For example, if the user is feeling stressed, makeup with a relaxing effect can be suggested. If the user wants to feel more confident, makeup that will bring out that confidence can be suggested. Furthermore, if the user is feeling happy, makeup that will further enhance that mood can be suggested. In this way, makeup that is suggested according to the user's emotions can be achieved, resulting in more satisfying makeup.

[0093] The makeup suggestion system can also use the user's emotion estimation function to analyze the user's emotions in real time when trying out makeup, and suggest makeup based on positive emotions. For example, it can suggest makeup that will make the user smile more when trying out makeup. It can also suggest makeup that will bring out a relaxed feeling in the user. It can also suggest makeup in colors that will energize the user. This makes it possible to suggest makeup that matches the user's emotions, resulting in more satisfying makeup.

[0094] The makeup suggestion system can also use the user's emotion estimation function to analyze the user's emotional state and suggest makeup based on that emotion. For example, it can suggest natural makeup when the user is relaxed, or suggest vibrant makeup when the user is excited. It can also suggest bright makeup when the user is sad. This allows for makeup suggestions that match the user's emotions, resulting in more satisfying makeup.

[0095] The makeup suggestion system can also use the user's emotion estimation function to analyze the user's emotional reactions when learning makeup steps, and suggest steps based on positive emotions. For example, it can suggest steps that the user finds enjoyable. It can also suggest steps that elicit a relaxed feeling in the user. It can also suggest steps that make the user feel energized. In this way, learning makeup can be made more enjoyable by suggesting optimal makeup steps based on the user's emotional reactions.

[0096] The makeup suggestion system can also use the user's emotion estimation function to analyze the user's emotional response when trying out makeup and suggest makeup based on the most positive response. For example, it can suggest makeup that will make the user smile more. It can also suggest makeup that will bring out a relaxed feeling in the user. It can also suggest makeup in colors that will energize the user. This allows the system to suggest optimal makeup based on the user's emotional response, resulting in more satisfying makeup.

[0097] The makeup suggestion system can also use the user's emotion estimation function to analyze the user's emotions in real time when trying out makeup and suggest the most suitable makeup. For example, it can suggest makeup that will make the user smile more. It can also suggest makeup that will bring out a relaxed feeling in the user. It can also suggest makeup in colors that will energize the user. In this way, makeup suggestions that match the user's emotions can be made to achieve a more satisfying makeup look.

[0098] The makeup suggestion system can also use the user's emotion estimation function to analyze the user's emotions in real time as they learn makeup steps and suggest optimal steps. For example, it can suggest steps that the user finds enjoyable. It can also suggest steps that bring out a relaxed feeling in the user. It can also suggest steps that make the user feel energized. In this way, by suggesting makeup steps that correspond to the user's emotions, it can make learning makeup more enjoyable.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The face analysis unit analyzes the user's facial features. For example, the face analysis unit analyzes the user's face shape, skin color, eye shape, etc. The face analysis unit also receives an image of the user's face as input and extracts facial features. For example, the face analysis unit uses image processing technology to detect the contours of the face and extract feature points. The face analysis unit can also classify facial features using machine learning algorithms. Step 2: The makeup suggestion unit suggests optimal makeup based on the features analyzed by the face analysis unit. For example, the makeup suggestion unit uses a generative AI (e.g., a text generation AI or a multimodal generation AI) to suggest makeup that suits the user's face. The makeup suggestion unit can also suggest makeup based on the user's preferences and trends. For example, if the user inputs, "I want to try natural makeup," the makeup suggestion unit will suggest that makeup. Step 3: The makeup simulation unit applies the makeup suggested by the makeup suggestion unit to the user's face. For example, the makeup simulation unit uses AR technology to display the makeup on the user's face in real time. The makeup simulation unit can also generate a 3D model of the user's face and simulate how the makeup will look from different angles. For example, the makeup simulation unit simulates how the makeup will look based on the 3D model of the user's face. Step 4: The makeup procedure explanation unit explains the makeup procedure applied by the makeup simulation unit. For example, the makeup procedure explanation unit uses generative AI to explain the makeup procedure step by step. The makeup procedure explanation unit can also provide customized makeup procedures ranging from beginner to advanced depending on the user's skill level. For example, the makeup procedure explanation unit suggests basic procedures for beginners and advanced techniques for advanced users.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a face analysis unit that analyzes the facial features of a user; a makeup suggestion unit that suggests optimal makeup based on the features analyzed by the face analysis unit; a makeup simulation unit that applies the makeup suggested by the makeup suggestion unit to a user's face; a makeup procedure explanation unit that explains the makeup procedure applied by the makeup simulation unit. A system characterized by:

2. The face analysis unit Learns the user's makeup history and suggests the best makeup based on past successes and failures 2. The system of claim 1.

3. The face analysis unit Analyzes subtle changes in the user's facial expression and suggests makeup that matches the expression 2. The system of claim 1.

4. The face analysis unit Analyzes the user's current emotional state and suggests makeup based on that emotion 2. The system of claim 1.

5. The face analysis unit Analyzes the user's hairstyle and accessories and suggests makeup based on them 2. The system of claim 1.

6. The face analysis unit Providing makeup that suits the season and weather 2. The system of claim 1.

Images