system

Abstract

The system according to the embodiment aims to analyze the facial features of the user and propose optimal makeup. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit inputs a facial image of a user. The analysis unit analyzes the facial image input by the reception unit and extracts facial features. The suggestion unit suggests makeup based on the facial features extracted by the analysis unit. The provision unit provides the makeup suggested by the suggestion unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately analyze facial features to suggest optimal makeup to users, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the facial features of the user and propose optimal makeup. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a provision unit. The reception unit inputs a facial image of a user. The analysis unit analyzes the facial image input by the reception unit and extracts facial features. The suggestion unit suggests makeup based on the facial features extracted by the analysis unit. The provision unit provides the makeup suggested by the suggestion unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the facial features of the user and suggest the most suitable makeup. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A makeup advice system according to an embodiment of the present invention automatically reads a user's facial image, analyzes the user's facial features, and proposes and provides optimal makeup. The makeup advice system inputs the user's facial image, analyzes the facial features, and proposes optimal makeup to the user. For example, the makeup advice system allows the user to input their own facial image. For example, the user may take a facial image using a smartphone or camera and upload it to the system. This information is then input into the system. The makeup advice system then analyzes the input facial image. The system extracts facial features and analyzes the user's face type. For example, the system analyzes features such as the face shape, eye position, nose shape, and mouth shape. This allows the system to identify the user's face type. The makeup advice system then provides detailed advice on makeup that suits the user based on the user's face type. For example, the system suggests optimal foundation color and application method, eye shadow color and application method, lip color and application method, etc. based on the user's face shape. This allows the user to easily apply makeup that suits them. The makeup advice system then provides the proposed makeup to the user. For example, the system may refer to a database of past makeup to propose makeup that best suits the user's face type. This allows the user to find the makeup that best suits them. This allows the makeup advice system to easily apply makeup that suits the user's face. This allows the makeup advice system to automatically analyze the user's facial image and suggest and provide the most suitable makeup. For example, even a beginner at makeup can apply professional makeup by following the system's advice. In addition, because the system suggests makeup based on the user's face type, it can provide personalized makeup advice tailored to each individual user.

[0029] A makeup advice system according to an embodiment includes a receiving unit, an analysis unit, a suggestion unit, and a providing unit. The receiving unit inputs a facial image of a user. The facial image of the user may be in, for example, JPEG format, PNG format, or a range of resolutions, but is not limited to these examples. The receiving unit, for example, allows the user to take a facial image using a smartphone or camera and upload it to the system. The receiving unit can also input a facial image of the user taken from the front to the system. The analysis unit uses a generation AI to analyze the facial image input by the receiving unit and extract facial features. The facial features include, for example, face shape, eye position, nose shape, mouth shape, etc., but are not limited to these examples. For example, the analysis unit analyzes the face shape and identifies a face type such as round, oval, or square. The analysis unit can also analyze the eye position and identify the distance between the eyes and their height. The analysis unit can also analyze the nose shape and identify the width and height of the nose. The suggestion unit uses the generation AI to suggest optimal makeup based on the facial features extracted by the analysis unit. Examples of makeup include, but are not limited to, foundation, eye shadow, and lipstick. For example, the suggestion unit suggests an optimal foundation color and application method based on the user's facial shape. The suggestion unit can also suggest an optimal eye shadow color and application method based on the user's facial shape. The suggestion unit can also suggest an optimal lipstick color and application method based on the user's facial shape. The provision unit provides the user with the makeup suggested by the suggestion unit. Providing the makeup includes, but is not limited to, online provision and provision in a physical store. For example, the provision unit can provide the suggested makeup to the user online. The provision unit can also provide the suggested makeup in a physical store. In this way, the makeup advice system according to the embodiment can automatically analyze a user's facial image and suggest and provide optimal makeup. For example, even a makeup beginner can perform professional makeup by following the system's advice. Furthermore, because the system suggests makeup based on the user's facial type, it can provide personalized makeup advice tailored to each individual user.

[0030] The analysis unit can extract features such as facial shape, eye position, nose shape, and mouth shape. For example, the analysis unit analyzes facial shape to identify a face type such as round, oval, or square. For example, the analysis unit analyzes facial shape to identify a face type such as round, oval, or square. The analysis unit can also analyze eye position to identify the distance between the eyes and the height of the eyes. For example, the analysis unit analyzes eye position to identify the distance between the eyes and the height of the eyes. The analysis unit can also analyze nose shape to identify the width and height of the nose. For example, the analysis unit analyzes nose shape to identify the width and height of the nose. The analysis unit can also analyze mouth shape to identify the width and height of the mouth. For example, the analysis unit analyzes mouth shape to identify the width and height of the mouth. This allows for the extraction of detailed facial features, enabling more accurate makeup suggestions. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a facial image into the generation AI to extract features such as facial shape, eye position, nose shape, and mouth shape, and have the generation AI extract the features.

[0031] The suggestion unit can suggest a foundation color and application method, an eyeshadow color and application method, and a lip color and application method based on the shape of the user's face. The suggestion unit, for example, suggests an optimal foundation color based on the shape of the user's face. For example, the suggestion unit can suggest an optimal foundation color based on the shape of the user's face. The suggestion unit can also suggest an optimal foundation application method based on the shape of the user's face. For example, the suggestion unit can suggest an optimal eyeshadow color based on the shape of the user's face. The suggestion unit can also suggest an optimal eyeshadow color based on the shape of the user's face. For example, the suggestion unit can suggest an optimal eyeshadow application method based on the shape of the user's face. For example, the suggestion unit can suggest an optimal eyeshadow application method based on the shape of the user's face. The suggestion unit can also suggest an optimal lipstick color based on the shape of the user's face. For example, the suggestion unit can suggest an optimal lipstick color based on the shape of the user's face. The suggestion unit can also suggest an optimal lipstick application method based on the shape of the user's face. For example, the suggestion unit suggests an optimal way to apply lipstick based on the shape of the user's face. This makes it possible to suggest optimal makeup based on the shape of the user's face. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input facial feature data into a generation AI and have the generation AI make suggestions in order to suggest optimal foundation colors and application methods, eyeshadow colors and application methods, and lipstick colors and application methods based on the shape of the user's face.

[0032] The suggestion unit can refer to a past makeup database and suggest makeup that is suitable for the user's face type. The suggestion unit can, for example, refer to a past makeup database and suggest makeup that is most suitable for the user's face type. For example, the suggestion unit can refer to a past makeup database and suggest makeup that is most suitable for the user's face type. The suggestion unit can also refer to a past makeup database and suggest makeup that is most suitable for the user's face type. For example, the suggestion unit can refer to a past makeup database and suggest makeup that is most suitable for the user's face type. The suggestion unit can also refer to a past makeup database and suggest makeup that is most suitable for the user's face type. For example, the suggestion unit can refer to a past makeup database and suggest makeup that is most suitable for the user's face type. In this way, by referring to the past makeup database, it is possible to suggest makeup that is optimal for the user. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the past makeup database into a generation AI, which can then suggest makeup that is most suitable for the user's face type.

[0033] The providing unit can provide the suggested makeup to the user. The providing unit, for example, provides the suggested makeup to the user online. For example, the providing unit provides the suggested makeup to the user online. The providing unit can also provide the suggested makeup in a physical store. For example, the providing unit provides the suggested makeup in a physical store. The providing unit can also customize a method of providing the suggested makeup online to the user. For example, the providing unit customizes a method of providing the suggested makeup online to the user online. The providing unit can also customize a method of providing the suggested makeup in a physical store to the user. For example, the providing unit customizes a method of providing the suggested makeup in a physical store to the user in the physical store. This allows the user to easily apply makeup by providing the suggested makeup to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the suggested makeup to a generation AI and have the generation AI provide it to the user.

[0034] The reception unit can analyze the user's past facial image input history and select an input method. The reception unit, for example, automatically sets a camera angle that the user used in the past. For example, the reception unit automatically sets a camera angle that the user used in the past. The reception unit can also automatically apply a filter that the user used preferentially in the past. For example, the reception unit automatically applies a filter that the user used preferentially in the past. The reception unit can also suggest an optimal input timing by referring to time periods in which the user previously inputted data. For example, the reception unit suggests an optimal input timing by referring to time periods in which the user previously inputted data. In this way, the optimal input method can be selected by analyzing the past facial image input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past facial image input history to a generation AI, causing the generation AI to select an optimal input method.

[0035] The reception unit can perform filtering based on the user's current environment when inputting a facial image. The reception unit automatically adjusts optimal brightness based on the user's current lighting conditions, for example. For example, the reception unit automatically adjusts optimal brightness based on the user's current lighting conditions. The reception unit can also apply a filter that blurs the background when the user's background is cluttered. For example, the reception unit can apply a filter that blurs the background when the user's background is cluttered. The reception unit can also perform noise cancellation when the user's environmental sound is loud. For example, the reception unit performs noise cancellation when the user's environmental sound is loud. This allows filtering based on the user's current environment to input a more appropriate facial image. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's current environmental data to a generation AI and have the generation AI perform filtering.

[0036] The reception unit can select an input means according to the user's input method when inputting a facial image. For example, if the user selects voice input, the reception unit provides voice guidance to support the input of the facial image. For example, if the user selects voice input, the reception unit provides voice guidance to support the input of the facial image. Furthermore, if the user selects text input, the reception unit can display simple instructions to prompt the user to input a facial image. For example, if the user selects text input, the reception unit displays simple instructions to prompt the user to input a facial image. Furthermore, the reception unit can automatically adjust optimal camera settings if the user selects image input. For example, if the user selects image input, the reception unit automatically adjusts optimal camera settings. This allows the user to input a more appropriate facial image by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data to a generation AI, causing the generation AI to select the optimal input means.

[0037] When inputting a facial image, the reception unit can prioritize inputting highly relevant images by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes suggesting makeup styles related to that area. For example, when the user is in a specific area, the reception unit prioritizes suggesting makeup styles related to that area. Furthermore, when the user is traveling, the reception unit can also suggest makeup styles that match the culture of the travel destination. For example, when the user is traveling, the reception unit can suggest makeup styles that match the culture of the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize suggesting everyday makeup styles. For example, when the user is at home, the reception unit prioritizes suggesting everyday makeup styles. In this way, highly relevant images can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI, causing the generation AI to prioritize inputting highly relevant images.

[0038] When inputting a facial image, the reception unit can analyze the user's social media activity and input related images. The reception unit, for example, inputs the facial image with reference to images shared by the user on social media. For example, the reception unit inputs the facial image with reference to images shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related makeup styles. For example, the reception unit can analyze the user's social media activity and suggest related makeup styles. The reception unit can also input the facial image with reference to the activity of the user's friends on social media. For example, the reception unit inputs the facial image with reference to the activity of the user's friends on social media. In this way, related images can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to a generation AI, which can input related images.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting a facial image. The reception unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially provide an input method that the user previously preferred. For example, the reception unit preferentially provides an input method that the user previously preferred. The reception unit can also analyze the user's past feedback and improve the input method. For example, the reception unit analyzes the user's past feedback and improves the input method. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data to a generation AI and have the generation AI customize the input method.

[0040] When analyzing facial features, the analysis unit can extract detailed features such as face shape, eye position, nose shape, and mouth shape. The analysis unit, for example, analyzes face shape to identify face types such as round, oval, and square. For example, the analysis unit analyzes face shape to identify face types such as round, oval, and square. The analysis unit can also analyze eye position to identify eye spacing and height. For example, the analysis unit analyzes eye position to identify eye spacing and height. The analysis unit can also analyze nose shape to identify nose width and height. For example, the analysis unit analyzes nose shape to identify nose width and height. The analysis unit can also analyze mouth shape to identify mouth width and height. For example, the analysis unit analyzes mouth shape to identify mouth width and height. This extracts detailed facial features, improving the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a facial image into the generation AI to extract detailed features such as the shape of the face, the position of the eyes, the shape of the nose, and the shape of the mouth, and have the generation AI extract the features.

[0041] When analyzing facial features, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial image data. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial image data and comparing it with the current facial image. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial image data and comparing it with the current facial image. The analysis unit can also analyze facial changes based on the user's past facial image data. For example, the analysis unit analyzes facial changes based on the user's past facial image data. The analysis unit can also refer to the user's past facial image data to more accurately extract facial features. For example, the analysis unit can refer to the user's past facial image data to more accurately extract facial features. By referring to the past facial image data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past facial image data into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0042] When analyzing facial features, the analysis unit can integrate images taken under different lighting conditions and angles to perform the analysis. For example, the analysis unit can integrate facial images taken under different lighting conditions to improve the accuracy of the analysis. For example, the analysis unit can integrate facial images taken under different lighting conditions to improve the accuracy of the analysis. The analysis unit can also integrate facial images taken from different angles to more accurately analyze facial features. For example, the analysis unit can integrate facial images taken from different angles to more accurately analyze facial features. The analysis unit can also analyze facial features by correcting for differences in lighting conditions and angles. For example, the analysis unit analyzes facial features by correcting for differences in lighting conditions and angles. In this way, the accuracy of the analysis is improved by integrating images taken under different lighting conditions and angles. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data taken under different lighting conditions and angles to a generation AI, which can then integrate and analyze the data.

[0043] When analyzing facial features, the analysis unit can take into account the user's geographical location information. For example, when the user is in a specific region, the analysis unit can perform the analysis taking into account the makeup style of that region. For example, when the user is in a specific region, the analysis unit can perform the analysis taking into account the makeup style of that region. Furthermore, when the user is traveling, the analysis unit can perform an analysis tailored to the culture of the destination. For example, when the user is traveling, the analysis unit can perform an analysis tailored to the culture of the destination. Furthermore, when the user is at home, the analysis unit can perform an analysis taking into account the user's everyday makeup style. For example, when the user is at home, the analysis unit can perform an analysis taking into account the user's everyday makeup style. This allows for more appropriate analysis by taking into account the user's geographical location information. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's geographical location information to a generation AI and have the generation AI perform the analysis.

[0044] When analyzing facial features, the analysis unit can improve the accuracy of the analysis by referring to related literature and databases. For example, the analysis unit refers to related literature and performs the analysis while taking into account the latest makeup trends. For example, the analysis unit refers to related literature and performs the analysis while taking into account the latest makeup trends. The analysis unit can also refer to a database and improve the accuracy of the analysis based on past makeup data. For example, the analysis unit can refer to a database and improve the accuracy of the analysis based on past makeup data. The analysis unit can also analyze facial features more accurately by referring to related research data. For example, the analysis unit can analyze facial features more accurately by referring to related research data. As a result, the accuracy of the analysis is improved by referring to related literature and databases. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature and databases into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0045] When analyzing facial features, the analysis unit can perform the analysis while taking into account the user's lifestyle and habits. The analysis unit, for example, takes into account the user's lifestyle and suggests an everyday makeup style. For example, the analysis unit takes into account the user's lifestyle and suggests an everyday makeup style. The analysis unit can also take into account the user's lifestyle and suggest a makeup style suitable for a special event. For example, the analysis unit takes into account the user's lifestyle and suggests a makeup style suitable for a special event. The analysis unit can also take into account the user's occupation and suggest a makeup style suitable for the workplace. For example, the analysis unit takes into account the user's occupation and suggests a makeup style suitable for the workplace. This allows for more appropriate analysis by taking into account the user's lifestyle and habits. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's lifestyle and habits data into a generation AI and have the generation AI perform the analysis.

[0046] When suggesting makeup, the suggestion unit can suggest a foundation color and application method based on the user's facial shape. The suggestion unit, for example, suggests an optimal foundation color based on the user's facial shape. For example, the suggestion unit suggests an optimal foundation color based on the user's facial shape. The suggestion unit can also suggest an optimal foundation application method based on the user's facial shape. For example, the suggestion unit suggests an optimal foundation application method based on the user's facial shape. The suggestion unit can also suggest an amount of foundation to use based on the user's facial shape. For example, the suggestion unit suggests an amount of foundation to use based on the user's facial shape. This allows for more appropriate makeup application by suggesting an optimal foundation color and application method based on the user's facial shape. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's facial shape data into a generation AI, causing the generation AI to suggest an optimal foundation color and application method.

[0047] When suggesting makeup, the suggestion unit can suggest an eyeshadow color and application method based on the shape and position of the user's eyes. The suggestion unit, for example, suggests an optimal eyeshadow color based on the shape of the user's eyes. For example, the suggestion unit suggests an optimal eyeshadow color based on the shape of the user's eyes. The suggestion unit can also suggest an optimal eyeshadow application method based on the position of the user's eyes. For example, the suggestion unit suggests an optimal eyeshadow application method based on the position of the user's eyes. The suggestion unit can also suggest an amount of eyeshadow to use based on the shape and position of the user's eyes. For example, the suggestion unit suggests an amount of eyeshadow to use based on the shape and position of the user's eyes. This allows for more appropriate makeup by suggesting an optimal eyeshadow color and application method based on the shape and position of the user's eyes. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the shape and position of the user's eyes into a generation AI, causing the generation AI to suggest an optimal eyeshadow color and application method.

[0048] When suggesting makeup, the suggestion unit can suggest a lip color and application method based on the shape of the user's lips. The suggestion unit, for example, suggests an optimal lip color based on the shape of the user's lips. For example, the suggestion unit can suggest an optimal lip color based on the shape of the user's lips. The suggestion unit can also suggest an optimal lip application method based on the shape of the user's lips. For example, the suggestion unit can suggest an optimal lip application method based on the shape of the user's lips. The suggestion unit can also suggest an amount of lip product to use based on the shape of the user's lips. For example, the suggestion unit can suggest an amount of lip product to use based on the shape of the user's lips. This allows for more appropriate makeup by suggesting an optimal lip color and application method based on the shape of the user's lips. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data of the user's lip shape into a generation AI, which can then suggest an optimal lip color and application method.

[0049] When suggesting makeup, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past makeup history. For example, the suggestion unit can suggest optimal makeup by referring to the user's past makeup history. For example, the suggestion unit can suggest optimal makeup by referring to the user's past makeup history. The suggestion unit can also customize the makeup suggestion based on the user's past makeup history. For example, the suggestion unit customizes the makeup suggestion based on the user's past makeup history. The suggestion unit can also analyze the user's past makeup history and improve the makeup suggestion. For example, the suggestion unit analyzes the user's past makeup history and improves the makeup suggestion. In this way, the accuracy of the suggestion is improved by referring to the user's past makeup history. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past makeup history data into a generation AI, which can improve the accuracy of the suggestion.

[0050] When suggesting makeup, the suggestion unit can make suggestions taking into account the user's lifestyle and habits. The suggestion unit, for example, takes into account the user's lifestyle and suggests an everyday makeup style. For example, the suggestion unit takes into account the user's lifestyle and suggests an everyday makeup style. The suggestion unit can also take into account the user's lifestyle and suggest a makeup style suitable for a special event. For example, the suggestion unit takes into account the user's lifestyle and suggests a makeup style suitable for a special event. The suggestion unit can also take into account the user's occupation and suggest a makeup style suitable for the workplace. For example, the suggestion unit takes into account the user's occupation and suggests a makeup style suitable for the workplace. This allows for more appropriate suggestions to be made by taking into account the user's lifestyle and habits. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's lifestyle and habits data into a generation AI and have the generation AI make suggestions.

[0051] When suggesting makeup, the suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, the suggestion unit uses simple terms to make suggestions to makeup beginners. For example, the suggestion unit uses simple terms to make suggestions to makeup beginners. The suggestion unit can also use detailed technical terms to make suggestions to users with experience in makeup. For example, the suggestion unit uses detailed technical terms to make suggestions to users with experience in makeup. The suggestion unit can also customize the content of the suggestions depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the suggestions depending on the user's level of expertise, more appropriate suggestions can be made. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI, causing the generation AI to adjust the use of technical terms in the suggestions.

[0052] When providing makeup, the providing unit can select the optimal makeup providing method by referring to the user's past makeup history. The providing unit, for example, refers to the user's past makeup history and selects the optimal makeup providing method. For example, the providing unit refers to the user's past makeup history and selects the optimal makeup providing method. The providing unit can also customize the makeup providing method based on the user's past makeup history. For example, the providing unit customizes the makeup providing method based on the user's past makeup history. The providing unit can also analyze the user's past makeup history and improve the makeup providing method. For example, the providing unit analyzes the user's past makeup history and improves the makeup providing method. In this way, the optimal makeup providing method can be selected by referring to the user's past makeup history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past makeup history data into the generation AI, causing the generation AI to select the optimal makeup providing method.

[0053] When providing makeup, the providing unit can customize the provided content based on the user's current environment. The providing unit provides optimal makeup based on, for example, the user's current lighting conditions. For example, the providing unit provides optimal makeup based on the user's current lighting conditions. Furthermore, if the user's background is cluttered, the providing unit can provide makeup by applying a filter that blurs the background. For example, if the user's background is cluttered, the providing unit can provide makeup by applying a filter that blurs the background. Furthermore, if the user's environmental noise is loud, the providing unit can provide makeup by performing noise cancellation. For example, if the user's environmental noise is loud, the providing unit can provide makeup by performing noise cancellation. This allows the provided content to be customized based on the user's current environment, thereby providing more appropriate content. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the user's current environmental data into a generation AI, causing the generation AI to customize the provided content.

[0054] The providing unit can improve the providing method by reflecting user feedback when providing makeup. For example, if a user provides feedback on the provided makeup, the providing unit improves the providing method based on that feedback. For example, if a user provides feedback on the provided makeup, the providing unit improves the providing method based on that feedback. The providing unit can also analyze the user's feedback and customize the makeup providing method. For example, the providing unit analyzes the user's feedback and customizes the makeup providing method. The providing unit can also update the makeup to be provided based on the user's feedback. For example, the providing unit updates the makeup to be provided based on the user's feedback. In this way, the providing method can be improved by reflecting the user's feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generating AI, causing the generating AI to improve the providing method.

[0055] When providing makeup, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, when the user is in a specific region, the providing unit selects the providing method by taking into account the makeup style of the region. For example, when the user is in a specific region, the providing unit selects the providing method by taking into account the makeup style of the region. Furthermore, when the user is traveling, the providing unit can provide makeup that matches the culture of the destination. For example, when the user is traveling, the providing unit provides makeup that matches the culture of the destination. Furthermore, when the user is at home, the providing unit can select the providing method by taking into account the user's everyday makeup style. For example, when the user is at home, the providing unit selects the providing method by taking into account the user's everyday makeup style. In this way, the optimal providing method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI, causing the generation AI to select the optimal providing method.

[0056] When providing makeup, the providing unit can customize the content to be provided by analyzing the user's social media activity. For example, the providing unit provides makeup by referring to images shared by the user on social media. For example, the providing unit provides makeup by referring to images shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related makeup styles. For example, the providing unit can analyze the user's social media activity and provide related makeup styles. The providing unit can also provide makeup by referring to the activity of the user's friends on social media. For example, the providing unit provides makeup by referring to the activity of the user's friends on social media. In this way, the content to be provided can be customized by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and have the generation AI customize the content to be provided.

[0057] When providing makeup, the providing unit can customize the providing method by reflecting the user's past feedback. The providing unit, for example, selects the optimal makeup providing method based on feedback provided by the user in the past. For example, the providing unit selects the optimal makeup providing method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the makeup providing method. For example, the providing unit analyzes the user's past feedback and customizes the makeup providing method. The providing unit can also update the makeup to be provided based on the user's past feedback. For example, the providing unit updates the makeup to be provided based on the user's past feedback. In this way, the provision method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into a generation AI and have the generation AI customize the provision method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When inputting a facial image of a user, the reception unit can detect the user's skin condition and suggest optimal makeup based on the skin condition. For example, if the user's skin is dry, the reception unit can suggest a foundation with a moisturizing effect. Also, if the user's skin is oily, the reception unit can suggest a foundation with a matte finish. Furthermore, if the user's skin has redness, the reception unit can suggest a green base to cover the redness. This makes it possible to suggest makeup that suits the user's skin condition.

[0060] When analyzing the user's facial image, the analysis unit can estimate the user's age and suggest makeup appropriate for that age. For example, the analysis unit can suggest bright and fresh makeup to a young user. The analysis unit can also suggest chic and subdued makeup to a middle-aged user. Furthermore, the analysis unit can also suggest makeup that emphasizes the firmness of the skin to an elderly user. This makes it possible to suggest makeup appropriate for the user's age.

[0061] The suggestion unit can suggest makeup based on the user's lifestyle. For example, if the user has an active lifestyle, the suggestion unit can suggest makeup that lasts for a long time. Also, if the user has a desk-based lifestyle, the suggestion unit can suggest makeup that gives a natural finish. Furthermore, if the user is attending a night event, the suggestion unit can suggest glamorous makeup. This makes it possible to suggest makeup that suits the user's lifestyle.

[0062] When analyzing the user's facial image, the analysis unit can evaluate the symmetry of the user's face and suggest makeup based on the symmetry. For example, if the user's face is symmetrical, the analysis unit can suggest simple makeup. If the user's face is asymmetrical, the analysis unit can also suggest makeup that corrects the asymmetry. Furthermore, if the user's face has significant asymmetry, the analysis unit can also suggest makeup that emphasizes specific features. This makes it possible to suggest makeup that suits the user's facial symmetry.

[0063] The providing unit can refer to the user's past makeup history and customize the makeup presentation method based on the past makeup history. For example, the providing unit can prioritize and provide makeup styles that the user has previously preferred. The providing unit can also exclude makeup styles that the user has previously avoided. Furthermore, the providing unit can analyze the user's past makeup history and suggest new makeup styles. This enables customization based on the user's past makeup history.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit inputs a facial image of the user. The facial image of the user may be in, for example, JPEG format, PNG format, a range of resolutions, etc., but is not limited to these examples. For example, the reception unit allows the user to take a facial image using a smartphone or camera and upload it to the system. The reception unit can also input a facial image of the user taken from the front into the system. Step 2: The analysis unit uses the generation AI to analyze the facial image input by the reception unit and extract facial features. Facial features include, but are not limited to, facial shape, eye position, nose shape, and mouth shape. For example, the analysis unit analyzes facial shape and identifies face types such as round, oval, and square. The analysis unit can also analyze eye position and identify the distance between and height of the eyes. The analysis unit can also analyze nose shape and identify the width and height of the nose. Step 3: The suggestion unit uses the generation AI to suggest optimal makeup based on the facial features extracted by the analysis unit. Examples of makeup include, but are not limited to, foundation, eye shadow, and lipstick. For example, the suggestion unit suggests the optimal foundation color and application method based on the user's facial shape. The suggestion unit can also suggest the optimal eye shadow color and application method based on the user's facial shape. The suggestion unit can also suggest the optimal lipstick color and application method based on the user's facial shape. Step 4: The providing unit provides the makeup suggested by the suggestion unit to the user. Providing includes, but is not limited to, providing online, providing in a physical store, and the like. For example, the providing unit provides the suggested makeup to the user online. The providing unit can also provide the suggested makeup in a physical store.

[0066] (Example 2) A makeup advice system according to an embodiment of the present invention automatically reads a user's facial image, analyzes the user's facial features, and proposes and provides optimal makeup. The makeup advice system inputs the user's facial image, analyzes the facial features, and proposes optimal makeup to the user. For example, the makeup advice system allows the user to input their own facial image. For example, the user may take a facial image using a smartphone or camera and upload it to the system. This information is then input into the system. The makeup advice system then analyzes the input facial image. The system extracts facial features and analyzes the user's face type. For example, the system analyzes features such as the face shape, eye position, nose shape, and mouth shape. This allows the system to identify the user's face type. The makeup advice system then provides detailed advice on makeup that suits the user based on the user's face type. For example, the system suggests optimal foundation color and application method, eye shadow color and application method, lip color and application method, etc. based on the user's face shape. This allows the user to easily apply makeup that suits them. The makeup advice system then provides the proposed makeup to the user. For example, the system may refer to a database of past makeup to propose makeup that best suits the user's face type. This allows the user to find the makeup that best suits them. This allows the makeup advice system to easily apply makeup that suits the user's face. This allows the makeup advice system to automatically analyze the user's facial image and suggest and provide the most suitable makeup. For example, even a beginner at makeup can apply professional makeup by following the system's advice. In addition, because the system suggests makeup based on the user's face type, it can provide personalized makeup advice tailored to each individual user.

[0067] A makeup advice system according to an embodiment includes a receiving unit, an analysis unit, a suggestion unit, and a providing unit. The receiving unit inputs a facial image of a user. The facial image of the user may be in, for example, JPEG format, PNG format, or a range of resolutions, but is not limited to these examples. The receiving unit, for example, allows the user to take a facial image using a smartphone or camera and upload it to the system. The receiving unit can also input a facial image of the user taken from the front to the system. The analysis unit uses a generation AI to analyze the facial image input by the receiving unit and extract facial features. The facial features include, for example, face shape, eye position, nose shape, mouth shape, etc., but are not limited to these examples. For example, the analysis unit analyzes the face shape and identifies a face type such as round, oval, or square. The analysis unit can also analyze the eye position and identify the distance between the eyes and their height. The analysis unit can also analyze the nose shape and identify the width and height of the nose. The suggestion unit uses the generation AI to suggest optimal makeup based on the facial features extracted by the analysis unit. Examples of makeup include, but are not limited to, foundation, eye shadow, and lipstick. For example, the suggestion unit suggests an optimal foundation color and application method based on the user's facial shape. The suggestion unit can also suggest an optimal eye shadow color and application method based on the user's facial shape. The suggestion unit can also suggest an optimal lipstick color and application method based on the user's facial shape. The provision unit provides the user with the makeup suggested by the suggestion unit. Providing the makeup includes, but is not limited to, online provision and provision in a physical store. For example, the provision unit can provide the suggested makeup to the user online. The provision unit can also provide the suggested makeup in a physical store. In this way, the makeup advice system according to the embodiment can automatically analyze a user's facial image and suggest and provide optimal makeup. For example, even a makeup beginner can perform professional makeup by following the system's advice. Furthermore, because the system suggests makeup based on the user's facial type, it can provide personalized makeup advice tailored to each individual user.

[0068] The analysis unit can extract features such as facial shape, eye position, nose shape, and mouth shape. For example, the analysis unit analyzes facial shape to identify a face type such as round, oval, or square. For example, the analysis unit analyzes facial shape to identify a face type such as round, oval, or square. The analysis unit can also analyze eye position to identify the distance between the eyes and the height of the eyes. For example, the analysis unit analyzes eye position to identify the distance between the eyes and the height of the eyes. The analysis unit can also analyze nose shape to identify the width and height of the nose. For example, the analysis unit analyzes nose shape to identify the width and height of the nose. The analysis unit can also analyze mouth shape to identify the width and height of the mouth. For example, the analysis unit analyzes mouth shape to identify the width and height of the mouth. This allows for the extraction of detailed facial features, enabling more accurate makeup suggestions. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a facial image into the generation AI to extract features such as facial shape, eye position, nose shape, and mouth shape, and have the generation AI extract the features.

[0069] The suggestion unit can suggest a foundation color and application method, an eyeshadow color and application method, and a lip color and application method based on the shape of the user's face. The suggestion unit, for example, suggests an optimal foundation color based on the shape of the user's face. For example, the suggestion unit can suggest an optimal foundation color based on the shape of the user's face. The suggestion unit can also suggest an optimal foundation application method based on the shape of the user's face. For example, the suggestion unit can suggest an optimal eyeshadow color based on the shape of the user's face. The suggestion unit can also suggest an optimal eyeshadow color based on the shape of the user's face. For example, the suggestion unit can suggest an optimal eyeshadow application method based on the shape of the user's face. For example, the suggestion unit can suggest an optimal eyeshadow application method based on the shape of the user's face. The suggestion unit can also suggest an optimal lipstick color based on the shape of the user's face. For example, the suggestion unit can suggest an optimal lipstick color based on the shape of the user's face. The suggestion unit can also suggest an optimal lipstick application method based on the shape of the user's face. For example, the suggestion unit suggests an optimal way to apply lipstick based on the shape of the user's face. This makes it possible to suggest optimal makeup based on the shape of the user's face. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input facial feature data into a generation AI and have the generation AI make suggestions in order to suggest optimal foundation colors and application methods, eyeshadow colors and application methods, and lipstick colors and application methods based on the shape of the user's face.

[0070] The suggestion unit can refer to a past makeup database and suggest makeup that is suitable for the user's face type. The suggestion unit can, for example, refer to a past makeup database and suggest makeup that is most suitable for the user's face type. For example, the suggestion unit can refer to a past makeup database and suggest makeup that is most suitable for the user's face type. The suggestion unit can also refer to a past makeup database and suggest makeup that is most suitable for the user's face type. For example, the suggestion unit can refer to a past makeup database and suggest makeup that is most suitable for the user's face type. The suggestion unit can also refer to a past makeup database and suggest makeup that is most suitable for the user's face type. For example, the suggestion unit can refer to a past makeup database and suggest makeup that is most suitable for the user's face type. In this way, by referring to the past makeup database, it is possible to suggest makeup that is optimal for the user. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the past makeup database into a generation AI, which can then suggest makeup that is most suitable for the user's face type.

[0071] The providing unit can provide the suggested makeup to the user. The providing unit, for example, provides the suggested makeup to the user online. For example, the providing unit provides the suggested makeup to the user online. The providing unit can also provide the suggested makeup in a physical store. For example, the providing unit provides the suggested makeup in a physical store. The providing unit can also customize a method of providing the suggested makeup online to the user. For example, the providing unit customizes a method of providing the suggested makeup online to the user online. The providing unit can also customize a method of providing the suggested makeup in a physical store to the user. For example, the providing unit customizes a method of providing the suggested makeup in a physical store to the user in the physical store. This allows the user to easily apply makeup by providing the suggested makeup to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the suggested makeup to a generation AI and have the generation AI provide it to the user.

[0072] The reception unit can estimate the user's emotions and adjust the timing of inputting a facial image based on the emotion data. For example, when the user is relaxed, the reception unit sets the timing of prompting the user to input a facial image to be slow. For example, when the user is relaxed, the reception unit sets the timing of prompting the user to input a facial image to be slow. Furthermore, when the user is nervous, the reception unit can prompt the user to input a facial image quickly. For example, when the user is nervous, the reception unit prompts the user to input a facial image quickly. Furthermore, when the user is excited, the reception unit can temporarily pause the input of a facial image to allow the user to calm down. For example, when the user is excited, the reception unit temporarily pauses the input of a facial image to allow the user to calm down. In this way, by adjusting the input timing of a facial image according to the user's emotions, the facial image can be input at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the timing of inputting the facial image based on the result.

[0073] The reception unit can analyze the user's past facial image input history and select an input method. The reception unit, for example, automatically sets a camera angle that the user used in the past. For example, the reception unit automatically sets a camera angle that the user used in the past. The reception unit can also automatically apply a filter that the user used preferentially in the past. For example, the reception unit automatically applies a filter that the user used preferentially in the past. The reception unit can also suggest an optimal input timing by referring to time periods in which the user previously inputted data. For example, the reception unit suggests an optimal input timing by referring to time periods in which the user previously inputted data. In this way, the optimal input method can be selected by analyzing the past facial image input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past facial image input history to a generation AI, causing the generation AI to select an optimal input method.

[0074] The reception unit can perform filtering based on the user's current environment when inputting a facial image. The reception unit automatically adjusts optimal brightness based on the user's current lighting conditions, for example. For example, the reception unit automatically adjusts optimal brightness based on the user's current lighting conditions. The reception unit can also apply a filter that blurs the background when the user's background is cluttered. For example, the reception unit can apply a filter that blurs the background when the user's background is cluttered. The reception unit can also perform noise cancellation when the user's environmental sound is loud. For example, the reception unit performs noise cancellation when the user's environmental sound is loud. This allows filtering based on the user's current environment to input a more appropriate facial image. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's current environmental data to a generation AI and have the generation AI perform filtering.

[0075] The reception unit can select an input means according to the user's input method when inputting a facial image. For example, if the user selects voice input, the reception unit provides voice guidance to support the input of the facial image. For example, if the user selects voice input, the reception unit provides voice guidance to support the input of the facial image. Furthermore, if the user selects text input, the reception unit can display simple instructions to prompt the user to input a facial image. For example, if the user selects text input, the reception unit displays simple instructions to prompt the user to input a facial image. Furthermore, the reception unit can automatically adjust optimal camera settings if the user selects image input. For example, if the user selects image input, the reception unit automatically adjusts optimal camera settings. This allows the user to input a more appropriate facial image by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method data to a generation AI, causing the generation AI to select the optimal input means.

[0076] The reception unit can estimate the user's emotions and determine the priority of facial images to be input based on the emotion data. For example, when the user is relaxed, the reception unit prioritizes input of facial images. For example, when the user is relaxed, the reception unit prioritizes input of facial images. Furthermore, when the user is nervous, the reception unit can prioritize other input methods and postpone input of facial images. For example, when the user is nervous, the reception unit prioritizes other input methods and postpones input of facial images. Furthermore, when the user is excited, the reception unit can temporarily suspend input of facial images. For example, when the user is excited, the reception unit temporarily suspends input of facial images. This allows more appropriate facial images to be input by determining the priority of facial images according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI, have the generation AI estimate the emotion, and determine the priority of the facial images based on the result.

[0077] When inputting a facial image, the reception unit can prioritize inputting highly relevant images by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes suggesting makeup styles related to that area. For example, when the user is in a specific area, the reception unit prioritizes suggesting makeup styles related to that area. Furthermore, when the user is traveling, the reception unit can also suggest makeup styles that match the culture of the travel destination. For example, when the user is traveling, the reception unit can suggest makeup styles that match the culture of the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize suggesting everyday makeup styles. For example, when the user is at home, the reception unit prioritizes suggesting everyday makeup styles. In this way, highly relevant images can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI, causing the generation AI to prioritize inputting highly relevant images.

[0078] When inputting a facial image, the reception unit can analyze the user's social media activity and input related images. The reception unit, for example, inputs the facial image with reference to images shared by the user on social media. For example, the reception unit inputs the facial image with reference to images shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related makeup styles. For example, the reception unit can analyze the user's social media activity and suggest related makeup styles. The reception unit can also input the facial image with reference to the activity of the user's friends on social media. For example, the reception unit inputs the facial image with reference to the activity of the user's friends on social media. In this way, related images can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to a generation AI, which can input related images.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting a facial image. The reception unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially provide an input method that the user previously preferred. For example, the reception unit preferentially provides an input method that the user previously preferred. The reception unit can also analyze the user's past feedback and improve the input method. For example, the reception unit analyzes the user's past feedback and improves the input method. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data to a generation AI and have the generation AI customize the input method.

[0080] The analysis unit can estimate the user's emotions and adjust the facial feature analysis method based on the emotion data. For example, when the user is relaxed, the analysis unit performs a detailed analysis. For example, when the user is relaxed, the analysis unit performs a detailed analysis. The analysis unit can also perform a simplified analysis when the user is nervous. For example, when the user is nervous, the analysis unit performs a simplified analysis. The analysis unit can also temporarily suspend analysis when the user is excited. For example, when the user is excited, the analysis unit temporarily suspends analysis. This allows for more appropriate analysis by adjusting the facial feature analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI, have the generation AI estimate the emotion, and adjust the method of analyzing facial features based on the results.

[0081] When analyzing facial features, the analysis unit can extract detailed features such as face shape, eye position, nose shape, and mouth shape. The analysis unit, for example, analyzes face shape to identify face types such as round, oval, and square. For example, the analysis unit analyzes face shape to identify face types such as round, oval, and square. The analysis unit can also analyze eye position to identify eye spacing and height. For example, the analysis unit analyzes eye position to identify eye spacing and height. The analysis unit can also analyze nose shape to identify nose width and height. For example, the analysis unit analyzes nose shape to identify nose width and height. The analysis unit can also analyze mouth shape to identify mouth width and height. For example, the analysis unit analyzes mouth shape to identify mouth width and height. This extracts detailed facial features, improving the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a facial image into the generation AI to extract detailed features such as the shape of the face, the position of the eyes, the shape of the nose, and the shape of the mouth, and have the generation AI extract the features.

[0082] When analyzing facial features, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial image data. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial image data and comparing it with the current facial image. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past facial image data and comparing it with the current facial image. The analysis unit can also analyze facial changes based on the user's past facial image data. For example, the analysis unit analyzes facial changes based on the user's past facial image data. The analysis unit can also refer to the user's past facial image data to more accurately extract facial features. For example, the analysis unit can refer to the user's past facial image data to more accurately extract facial features. By referring to the past facial image data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past facial image data into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0083] When analyzing facial features, the analysis unit can integrate images taken under different lighting conditions and angles to perform the analysis. For example, the analysis unit can integrate facial images taken under different lighting conditions to improve the accuracy of the analysis. For example, the analysis unit can integrate facial images taken under different lighting conditions to improve the accuracy of the analysis. The analysis unit can also integrate facial images taken from different angles to more accurately analyze facial features. For example, the analysis unit can integrate facial images taken from different angles to more accurately analyze facial features. The analysis unit can also analyze facial features by correcting for differences in lighting conditions and angles. For example, the analysis unit analyzes facial features by correcting for differences in lighting conditions and angles. In this way, the accuracy of the analysis is improved by integrating images taken under different lighting conditions and angles. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data taken under different lighting conditions and angles to a generation AI, which can then integrate and analyze the data.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotion data. For example, when the user is relaxed, the analysis unit displays detailed analysis results. For example, when the user is relaxed, the analysis unit displays detailed analysis results. Furthermore, when the user is nervous, the analysis unit can display simplified analysis results. For example, when the user is nervous, the analysis unit displays simplified analysis results. Furthermore, when the user is excited, the analysis unit can temporarily suspend the display of the analysis results. For example, when the user is excited, the analysis unit temporarily suspends the display of the analysis results. This allows for a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI, have the generation AI estimate the emotion, and adjust the way the analysis results are displayed based on the results.

[0085] When analyzing facial features, the analysis unit can take into account the user's geographical location information. For example, when the user is in a specific region, the analysis unit can perform the analysis taking into account the makeup style of that region. For example, when the user is in a specific region, the analysis unit can perform the analysis taking into account the makeup style of that region. Furthermore, when the user is traveling, the analysis unit can perform an analysis tailored to the culture of the destination. For example, when the user is traveling, the analysis unit can perform an analysis tailored to the culture of the destination. Furthermore, when the user is at home, the analysis unit can perform an analysis taking into account the user's everyday makeup style. For example, when the user is at home, the analysis unit can perform an analysis taking into account the user's everyday makeup style. This allows for more appropriate analysis by taking into account the user's geographical location information. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's geographical location information to a generation AI and have the generation AI perform the analysis.

[0086] When analyzing facial features, the analysis unit can improve the accuracy of the analysis by referring to related literature and databases. For example, the analysis unit refers to related literature and performs the analysis while taking into account the latest makeup trends. For example, the analysis unit refers to related literature and performs the analysis while taking into account the latest makeup trends. The analysis unit can also refer to a database and improve the accuracy of the analysis based on past makeup data. For example, the analysis unit can refer to a database and improve the accuracy of the analysis based on past makeup data. The analysis unit can also analyze facial features more accurately by referring to related research data. For example, the analysis unit can analyze facial features more accurately by referring to related research data. As a result, the accuracy of the analysis is improved by referring to related literature and databases. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature and databases into a generation AI, and the generation AI can improve the accuracy of the analysis.

[0087] When analyzing facial features, the analysis unit can perform the analysis while taking into account the user's lifestyle and habits. The analysis unit, for example, takes into account the user's lifestyle and suggests an everyday makeup style. For example, the analysis unit takes into account the user's lifestyle and suggests an everyday makeup style. The analysis unit can also take into account the user's lifestyle and suggest a makeup style suitable for a special event. For example, the analysis unit takes into account the user's lifestyle and suggests a makeup style suitable for a special event. The analysis unit can also take into account the user's occupation and suggest a makeup style suitable for the workplace. For example, the analysis unit takes into account the user's occupation and suggests a makeup style suitable for the workplace. This allows for more appropriate analysis by taking into account the user's lifestyle and habits. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's lifestyle and habits data into a generation AI and have the generation AI perform the analysis.

[0088] The suggestion unit can estimate the user's emotions and adjust the makeup suggestion method based on the emotion data. For example, when the user is relaxed, the suggestion unit makes detailed makeup suggestions. For example, when the user is relaxed, the suggestion unit makes detailed makeup suggestions. The suggestion unit can also make simplified makeup suggestions when the user is nervous. For example, when the user is nervous, the suggestion unit makes simplified makeup suggestions. The suggestion unit can also temporarily suspend makeup suggestions when the user is excited. For example, when the user is excited, the suggestion unit temporarily suspends makeup suggestions. This allows for more appropriate suggestions to be made by adjusting the makeup suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI, have the generation AI estimate the emotion, and adjust the makeup suggestion method based on the results.

[0089] When suggesting makeup, the suggestion unit can suggest a foundation color and application method based on the user's facial shape. The suggestion unit, for example, suggests an optimal foundation color based on the user's facial shape. For example, the suggestion unit suggests an optimal foundation color based on the user's facial shape. The suggestion unit can also suggest an optimal foundation application method based on the user's facial shape. For example, the suggestion unit suggests an optimal foundation application method based on the user's facial shape. The suggestion unit can also suggest an amount of foundation to use based on the user's facial shape. For example, the suggestion unit suggests an amount of foundation to use based on the user's facial shape. This allows for more appropriate makeup application by suggesting an optimal foundation color and application method based on the user's facial shape. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's facial shape data into a generation AI, causing the generation AI to suggest an optimal foundation color and application method.

[0090] When suggesting makeup, the suggestion unit can suggest an eyeshadow color and application method based on the shape and position of the user's eyes. The suggestion unit, for example, suggests an optimal eyeshadow color based on the shape of the user's eyes. For example, the suggestion unit suggests an optimal eyeshadow color based on the shape of the user's eyes. The suggestion unit can also suggest an optimal eyeshadow application method based on the position of the user's eyes. For example, the suggestion unit suggests an optimal eyeshadow application method based on the position of the user's eyes. The suggestion unit can also suggest an amount of eyeshadow to use based on the shape and position of the user's eyes. For example, the suggestion unit suggests an amount of eyeshadow to use based on the shape and position of the user's eyes. This allows for more appropriate makeup by suggesting an optimal eyeshadow color and application method based on the shape and position of the user's eyes. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data on the shape and position of the user's eyes into a generation AI, causing the generation AI to suggest an optimal eyeshadow color and application method.

[0091] When suggesting makeup, the suggestion unit can suggest a lip color and application method based on the shape of the user's lips. The suggestion unit, for example, suggests an optimal lip color based on the shape of the user's lips. For example, the suggestion unit can suggest an optimal lip color based on the shape of the user's lips. The suggestion unit can also suggest an optimal lip application method based on the shape of the user's lips. For example, the suggestion unit can suggest an optimal lip application method based on the shape of the user's lips. The suggestion unit can also suggest an amount of lip product to use based on the shape of the user's lips. For example, the suggestion unit can suggest an amount of lip product to use based on the shape of the user's lips. This allows for more appropriate makeup by suggesting an optimal lip color and application method based on the shape of the user's lips. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data of the user's lip shape into a generation AI, which can then suggest an optimal lip color and application method.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the emotion data. For example, when the user is relaxed, the suggestion unit makes detailed makeup suggestions. For example, when the user is relaxed, the suggestion unit makes detailed makeup suggestions. The suggestion unit can also make simplified makeup suggestions when the user is nervous. For example, when the user is nervous, the suggestion unit makes simplified makeup suggestions. The suggestion unit can also temporarily suspend makeup suggestions when the user is excited. For example, when the user is excited, the suggestion unit temporarily suspends makeup suggestions. This allows for adjusting the length of suggestions according to the user's emotions, thereby making more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI, have the generation AI estimate the emotion, and adjust the length of the suggestion based on the result.

[0093] When suggesting makeup, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past makeup history. For example, the suggestion unit can suggest optimal makeup by referring to the user's past makeup history. For example, the suggestion unit can suggest optimal makeup by referring to the user's past makeup history. The suggestion unit can also customize the makeup suggestion based on the user's past makeup history. For example, the suggestion unit customizes the makeup suggestion based on the user's past makeup history. The suggestion unit can also analyze the user's past makeup history and improve the makeup suggestion. For example, the suggestion unit analyzes the user's past makeup history and improves the makeup suggestion. In this way, the accuracy of the suggestion is improved by referring to the user's past makeup history. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past makeup history data into a generation AI, which can improve the accuracy of the suggestion.

[0094] When suggesting makeup, the suggestion unit can make suggestions taking into account the user's lifestyle and habits. The suggestion unit, for example, takes into account the user's lifestyle and suggests an everyday makeup style. For example, the suggestion unit takes into account the user's lifestyle and suggests an everyday makeup style. The suggestion unit can also take into account the user's lifestyle and suggest a makeup style suitable for a special event. For example, the suggestion unit takes into account the user's lifestyle and suggests a makeup style suitable for a special event. The suggestion unit can also take into account the user's occupation and suggest a makeup style suitable for the workplace. For example, the suggestion unit takes into account the user's occupation and suggests a makeup style suitable for the workplace. This allows for more appropriate suggestions to be made by taking into account the user's lifestyle and habits. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's lifestyle and habits data into a generation AI and have the generation AI make suggestions.

[0095] When suggesting makeup, the suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, the suggestion unit uses simple terms to make suggestions to makeup beginners. For example, the suggestion unit uses simple terms to make suggestions to makeup beginners. The suggestion unit can also use detailed technical terms to make suggestions to users with experience in makeup. For example, the suggestion unit uses detailed technical terms to make suggestions to users with experience in makeup. The suggestion unit can also customize the content of the suggestions depending on the user's level of expertise. In this way, by adjusting the use of technical terms in the suggestions depending on the user's level of expertise, more appropriate suggestions can be made. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI, causing the generation AI to adjust the use of technical terms in the suggestions.

[0096] The providing unit can estimate the user's emotions and adjust the makeup application method based on the emotion data. For example, when the user is relaxed, the providing unit provides detailed makeup. For example, when the user is relaxed, the providing unit provides detailed makeup. Furthermore, when the user is nervous, the providing unit can provide simplified makeup. For example, when the user is nervous, the providing unit provides simplified makeup. Furthermore, the providing unit can temporarily suspend the provision of makeup when the user is excited. For example, when the user is excited, the providing unit temporarily suspends the provision of makeup. This allows for more appropriate makeup application by adjusting the makeup application method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generating AI, have the generating AI estimate the emotion, and adjust the makeup providing method based on the results.

[0097] When providing makeup, the providing unit can select the optimal makeup providing method by referring to the user's past makeup history. The providing unit, for example, refers to the user's past makeup history and selects the optimal makeup providing method. For example, the providing unit refers to the user's past makeup history and selects the optimal makeup providing method. The providing unit can also customize the makeup providing method based on the user's past makeup history. For example, the providing unit customizes the makeup providing method based on the user's past makeup history. The providing unit can also analyze the user's past makeup history and improve the makeup providing method. For example, the providing unit analyzes the user's past makeup history and improves the makeup providing method. In this way, the optimal makeup providing method can be selected by referring to the user's past makeup history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past makeup history data into the generation AI, causing the generation AI to select the optimal makeup providing method.

[0098] When providing makeup, the providing unit can customize the provided content based on the user's current environment. The providing unit provides optimal makeup based on, for example, the user's current lighting conditions. For example, the providing unit provides optimal makeup based on the user's current lighting conditions. Furthermore, if the user's background is cluttered, the providing unit can provide makeup by applying a filter that blurs the background. For example, if the user's background is cluttered, the providing unit can provide makeup by applying a filter that blurs the background. Furthermore, if the user's environmental noise is loud, the providing unit can provide makeup by performing noise cancellation. For example, if the user's environmental noise is loud, the providing unit can provide makeup by performing noise cancellation. This allows the provided content to be customized based on the user's current environment, thereby providing more appropriate content. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the user's current environmental data into a generation AI, causing the generation AI to customize the provided content.

[0099] The providing unit can improve the providing method by reflecting user feedback when providing makeup. For example, if a user provides feedback on the provided makeup, the providing unit improves the providing method based on that feedback. For example, if a user provides feedback on the provided makeup, the providing unit improves the providing method based on that feedback. The providing unit can also analyze the user's feedback and customize the makeup providing method. For example, the providing unit analyzes the user's feedback and customizes the makeup providing method. The providing unit can also update the makeup to be provided based on the user's feedback. For example, the providing unit updates the makeup to be provided based on the user's feedback. In this way, the providing method can be improved by reflecting the user's feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generating AI, causing the generating AI to improve the providing method.

[0100] The providing unit can estimate the user's emotions and determine the priority of makeup to provide based on the emotion data. For example, when the user is relaxed, the providing unit prioritizes providing detailed makeup. For example, when the user is relaxed, the providing unit prioritizes providing detailed makeup. The providing unit can also prioritize providing simplified makeup when the user is nervous. For example, when the user is nervous, the providing unit prioritizes providing simplified makeup. The providing unit can also temporarily suspend providing makeup when the user is excited. For example, when the user is excited, the providing unit temporarily suspends providing makeup. This allows for more appropriate makeup to be provided by determining the priority of makeup according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generating AI, have the generating AI estimate the emotion, and determine the priority of the makeup to be provided based on the results.

[0101] When providing makeup, the providing unit can select the optimal providing method by taking into account the user's geographical location information. For example, when the user is in a specific region, the providing unit selects the providing method by taking into account the makeup style of the region. For example, when the user is in a specific region, the providing unit selects the providing method by taking into account the makeup style of the region. Furthermore, when the user is traveling, the providing unit can provide makeup that matches the culture of the destination. For example, when the user is traveling, the providing unit provides makeup that matches the culture of the destination. Furthermore, when the user is at home, the providing unit can select the providing method by taking into account the user's everyday makeup style. For example, when the user is at home, the providing unit selects the providing method by taking into account the user's everyday makeup style. In this way, the optimal providing method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI, causing the generation AI to select the optimal providing method.

[0102] When providing makeup, the providing unit can customize the content to be provided by analyzing the user's social media activity. For example, the providing unit provides makeup by referring to images shared by the user on social media. For example, the providing unit provides makeup by referring to images shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related makeup styles. For example, the providing unit can analyze the user's social media activity and provide related makeup styles. The providing unit can also provide makeup by referring to the activity of the user's friends on social media. For example, the providing unit provides makeup by referring to the activity of the user's friends on social media. In this way, the content to be provided can be customized by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and have the generation AI customize the content to be provided.

[0103] When providing makeup, the providing unit can customize the providing method by reflecting the user's past feedback. The providing unit, for example, selects the optimal makeup providing method based on feedback provided by the user in the past. For example, the providing unit selects the optimal makeup providing method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the makeup providing method. For example, the providing unit analyzes the user's past feedback and customizes the makeup providing method. The providing unit can also update the makeup to be provided based on the user's past feedback. For example, the providing unit updates the makeup to be provided based on the user's past feedback. In this way, the provision method can be customized by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into a generation AI and have the generation AI customize the provision method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit takes a facial image of the user using the camera 42 of the smart device 14 and uploads it to the system via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes facial features using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal makeup based on the analyzed facial features. The provision unit provides the suggested makeup to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit takes a facial image of the user using the camera 42 of the smart glasses 214 and uploads it to the system via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes facial features using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal makeup based on the analyzed facial features. The provision unit provides the suggested makeup to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit takes a facial image of the user using the camera 42 of the headset type terminal 314 and uploads it to the system via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes facial features using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal makeup based on the analyzed facial features. The provision unit provides the suggested makeup to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit takes a facial image of the user using the camera 42 of the robot 414 and uploads it to the system via the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes facial features using a generative AI. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal makeup based on the analyzed facial features. The provision unit uses the speaker 240 of the robot 414 to provide the suggested makeup to the user.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] When inputting a facial image of a user, the reception unit can detect the user's skin condition and suggest optimal makeup based on the skin condition. For example, if the user's skin is dry, the reception unit can suggest a foundation with a moisturizing effect. Also, if the user's skin is oily, the reception unit can suggest a foundation with a matte finish. Furthermore, if the user's skin has redness, the reception unit can suggest a green base to cover the redness. This makes it possible to suggest makeup that suits the user's skin condition.

[0106] When analyzing the user's facial image, the analysis unit can estimate the user's age and suggest makeup appropriate for that age. For example, the analysis unit can suggest bright and fresh makeup to a young user. The analysis unit can also suggest chic and subdued makeup to a middle-aged user. Furthermore, the analysis unit can also suggest makeup that emphasizes the firmness of the skin to an elderly user. This makes it possible to suggest makeup appropriate for the user's age.

[0107] The suggestion unit can estimate the user's emotions and adjust the makeup suggestions based on the emotion data. For example, the suggestion unit can suggest glamorous makeup if the user is relaxed. The suggestion unit can also suggest natural makeup if the user is nervous. Furthermore, the suggestion unit can also suggest subdued makeup if the user is excited. This makes it possible to suggest makeup that matches the user's emotions.

[0108] The suggestion unit can suggest makeup based on the user's lifestyle. For example, if the user has an active lifestyle, the suggestion unit can suggest makeup that lasts for a long time. Also, if the user has a desk-based lifestyle, the suggestion unit can suggest makeup that gives a natural finish. Furthermore, if the user is attending a night event, the suggestion unit can suggest glamorous makeup. This makes it possible to suggest makeup that suits the user's lifestyle.

[0109] The providing unit can estimate the user's emotions and adjust the makeup application method based on the emotion data. For example, if the user is relaxed, the providing unit can provide detailed makeup. If the user is nervous, the providing unit can also provide simplified makeup. Furthermore, if the user is excited, the providing unit can temporarily suspend the application of makeup. This makes it possible to provide makeup that corresponds to the user's emotions.

[0110] When inputting a facial image of a user, the reception unit detects the user's facial expression and can suggest optimal makeup based on the expression. For example, if the user is smiling, the reception unit can suggest a bright lip color. If the user has a serious expression, the reception unit can also suggest a subdued eye shadow color. Furthermore, if the user has a surprised expression, the reception unit can also suggest a natural makeup look. This makes it possible to suggest makeup that matches the user's facial expression.

[0111] When analyzing the user's facial image, the analysis unit can evaluate the symmetry of the user's face and suggest makeup based on the symmetry. For example, if the user's face is symmetrical, the analysis unit can suggest simple makeup. If the user's face is asymmetrical, the analysis unit can also suggest makeup that corrects the asymmetry. Furthermore, if the user's face has significant asymmetry, the analysis unit can also suggest makeup that emphasizes specific features. This makes it possible to suggest makeup that suits the user's facial symmetry.

[0112] The suggestion unit can estimate the user's emotions and adjust the order of makeup suggestions based on the emotion data. For example, if the user is relaxed, the suggestion unit can first suggest detailed makeup steps. If the user is nervous, the suggestion unit can also first suggest simple makeup steps. Furthermore, if the user is excited, the suggestion unit can also suggest makeup steps in stages. This makes it possible to order makeup suggestions according to the user's emotions.

[0113] The providing unit can refer to the user's past makeup history and customize the makeup presentation method based on the past makeup history. For example, the providing unit can prioritize and provide makeup styles that the user has previously preferred. The providing unit can also exclude makeup styles that the user has previously avoided. Furthermore, the providing unit can analyze the user's past makeup history and suggest new makeup styles. This enables customization based on the user's past makeup history.

[0114] The providing unit can estimate the user's emotions and adjust the timing of applying makeup based on the emotion data. For example, if the user is relaxed, the providing unit can quickly apply makeup. If the user is nervous, the providing unit can also slowly apply makeup. Furthermore, if the user is excited, the providing unit can temporarily suspend applying makeup. This makes it possible to adjust the timing of applying makeup according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit inputs a facial image of the user. The facial image of the user may be in, for example, JPEG format, PNG format, a range of resolutions, etc., but is not limited to these examples. For example, the reception unit allows the user to take a facial image using a smartphone or camera and upload it to the system. The reception unit can also input a facial image of the user taken from the front into the system. Step 2: The analysis unit uses the generation AI to analyze the facial image input by the reception unit and extract facial features. Facial features include, but are not limited to, facial shape, eye position, nose shape, and mouth shape. For example, the analysis unit analyzes facial shape and identifies face types such as round, oval, and square. The analysis unit can also analyze eye position and identify the distance between and height of the eyes. The analysis unit can also analyze nose shape and identify the width and height of the nose. Step 3: The suggestion unit uses the generation AI to suggest optimal makeup based on the facial features extracted by the analysis unit. Examples of makeup include, but are not limited to, foundation, eye shadow, and lipstick. For example, the suggestion unit suggests the optimal foundation color and application method based on the user's facial shape. The suggestion unit can also suggest the optimal eye shadow color and application method based on the user's facial shape. The suggestion unit can also suggest the optimal lipstick color and application method based on the user's facial shape. Step 4: The providing unit provides the makeup suggested by the suggestion unit to the user. Providing includes, but is not limited to, providing online, providing in a physical store, and the like. For example, the providing unit provides the suggested makeup to the user online. The providing unit can also provide the suggested makeup in a physical store.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a face image of a user; an analysis unit that analyzes the face image input by the reception unit and extracts facial features; a suggestion unit that suggests makeup based on the facial features extracted by the analysis unit; a providing unit that provides the user with the makeup suggested by the suggestion unit. A system characterized by:

2. The analysis unit Extract features such as face shape, eye position, nose shape, and mouth shape 2. The system of claim 1.

3. The proposal unit Based on the user's face shape, the app suggests foundation colors and application methods, eyeshadow colors and application methods, and lip colors and application methods.

2. The system of claim 1.

4. The proposal unit Refers to a database of past makeup looks and suggests makeup that suits the user's face type 2. The system of claim 1.

5. The providing unit Providing suggested makeup to users 2. The system of claim 1.

6. The reception unit The system estimates the user's emotions and adjusts the timing of inputting a facial image based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past facial image input history and select the input method 2. The system of claim 1.

8. The reception unit When entering a face image, filtering is performed based on the user's current environment.

2. The system of claim 1.

Images