System

Abstract

An object of a system according to an embodiment is to allow a user to easily find makeup that suits the user.SOLUTION: A system includes an analysis unit, a generation unit, a presentation unit, and a provision unit. The analysis unit analyzes a face photograph of a user. The generation unit generates a post-makeup image based on the face photograph analyzed by the analysis unit. The presentation unit presents the post-makeup image generated by the generation unit to the user. The provision unit provides a service based on the post-makeup image presented by the presentation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have made it difficult for users to find makeup that suits them.

[0005] The system according to the embodiment aims to enable users to easily find makeup that suits them. [Means for solving the problem]

[0006] A system according to an embodiment includes an analysis unit, a generation unit, a presentation unit, and a provision unit. The analysis unit analyzes a facial photograph of a user. The generation unit generates a post-makeup image based on the facial photograph analyzed by the analysis unit. The presentation unit presents the post-makeup image generated by the generation unit to the user. The provision unit provides a service based on the post-makeup image presented by the presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily find makeup that suits them. [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 suggestion system according to an embodiment of the present invention allows a user to upload a photo of their face, and the system presents multiple makeup-based images and suggests the optimal makeup look. The makeup suggestion system allows a user to upload multiple photos of themselves, and a generation AI analyzes these photos to generate multiple makeup-based images that suit the user. The generated images are presented to the user, allowing the user to select the most suitable makeup look from among them. After the suggestions are made, the user can choose from two services. The first is to apply the makeup themselves while watching a video. The generation AI provides detailed instructions on the selected makeup look, allowing the user to follow the video while applying the makeup. The second is to have a professional makeup artist apply the makeup look in-store and learn how to do it. The user can have a professional makeup artist actually apply the selected makeup look and learn the steps. For example, the makeup suggestion system allows a user to upload multiple photos of their face. Uploading photos from different angles and facial expressions enables more accurate analysis. For example, users can upload photos from the front, an oblique view, or a smiling face. The makeup suggestion system then analyzes the uploaded photos using the generation AI. The generative AI analyzes the user's facial features and generates multiple makeup looks that suit them. For example, it analyzes eye shape, skin color, and facial contours, and generates multiple makeup looks based on that information. The generated makeup looks are then presented to the user. The user can select the look that best suits them from the presented images. For example, makeup looks tailored to different situations, such as natural makeup, party makeup, and office makeup, are presented. After the suggestions are made, the user can choose from the following two services. The first is to apply the makeup themselves while watching a video. The video provided by the generative AI provides detailed instructions for the selected makeup look, allowing the user to apply the makeup while watching the video. For example, the video explains specific steps, such as how to apply eyeshadow and lipstick. The second is to have a professional makeup artist apply the makeup in-store and learn how to do it.Users can learn the steps by having a professional makeup artist apply the makeup they select. For example, a professional makeup artist demonstrates how to apply eyeshadow. This system allows users to easily find the makeup that suits them best. Learning the makeup steps also improves their self-application skills. For example, even users who are not confident in their usual makeup application can learn how to create makeup that suits them by watching videos provided by the generation AI or receiving instruction from a professional makeup artist. This allows the makeup suggestion system to analyze users' facial photos and generate, display, and provide images of the makeup look they have created. For example, if a user uploads multiple photos of themselves, the generation AI can analyze these photos and generate multiple images of the makeup look that suit the user. The generated images are then presented to the user, who can select the most suitable makeup look from among them. Furthermore, users can choose to apply the makeup themselves while watching the video or have a professional makeup artist apply it in-store. This allows users to easily find the makeup that suits them best, and learning the makeup steps also improves their self-application skills.

[0029] A makeup suggestion system according to an embodiment includes an analysis unit, a generation unit, a presentation unit, and a providing unit. The analysis unit analyzes a facial photograph of a user. The facial photograph of the user includes, but is not limited to, a photograph taken from the front, a photograph taken from an oblique angle, and a photograph of a smiling face. The analysis unit extracts facial features using, for example, a facial recognition algorithm. The analysis unit can also detect facial landmarks using a feature extraction method. For example, the analysis unit extracts features such as eye shape, skin color, and facial contours. The generation unit generates a makeup-applied image based on the facial photograph analyzed by the analysis unit. The generation unit generates the makeup-applied image using, for example, an image generation algorithm. The generation unit can also generate different makeup styles based on the type of data used. For example, the generation unit generates different makeup styles such as natural makeup, party makeup, and office makeup. The presentation unit presents the makeup-applied image generated by the generation unit to the user. The presentation unit displays the makeup-applied image using, for example, a display device. The presentation unit can also display different makeup styles based on a display format. For example, the presentation unit displays an image of the user with makeup applied according to the screen size and resolution. The provision unit provides a service based on the image of the user with makeup applied presented by the presentation unit. The provision unit provides a video based on, for example, makeup selected by the user. The provision unit can also provide a service provided by a professional makeup artist. For example, the provision unit can provide a video that explains in detail the makeup steps selected by the user. The provision unit can also provide a service in which a professional makeup artist actually applies the makeup. As a result, the makeup suggestion system according to the embodiment can analyze a user's facial photo and generate, present, and provide a service of an image of the user with makeup applied. For example, by uploading multiple photos including the user's face, the generation AI can analyze these photos and generate multiple images of the user with makeup applied that suit the user. The generated images are presented to the user, and the user can select the most suitable makeup from among them. Furthermore, the user can choose whether to apply makeup themselves while watching a video or to have a professional makeup artist apply their makeup in a store.This allows users to easily find makeup that suits them, and by learning the makeup steps, they can improve their own makeup application skills.

[0030] The analysis unit can extract the user's facial features. The analysis unit extracts the facial features using, for example, a facial recognition algorithm. For example, the analysis unit extracts features such as eye shape, skin color, and facial contours. The analysis unit can also detect facial landmarks using a feature extraction method. For example, the analysis unit detects landmarks such as the position of the eyes, the shape of the nose, and the shape of the mouth. The analysis unit can also extract facial shape features. For example, the analysis unit extracts shape features such as the facial contour and the height of the cheeks. By extracting the user's facial features, more appropriate makeup suggestions can be made. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input a photo of the user's face into the generation AI and cause the generation AI to extract facial features.

[0031] The generation unit can generate multiple after-makeup images based on the extracted features. The generation unit generates the after-makeup images using, for example, an image generation algorithm. For example, the generation unit generates different makeup styles based on the extracted features. The generation unit can also generate different makeup styles based on the type of data used. For example, the generation unit generates different makeup styles such as natural makeup, party makeup, and office makeup. The generation unit can also generate the after-makeup images using a generation AI. For example, the generation unit inputs the extracted features into the generation AI, which then generates the after-makeup images. This allows the user to have a variety of options by generating multiple after-makeup images based on the extracted features. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the extracted features into the generation AI, which then generates the after-makeup images.

[0032] The presentation unit can display the generated post-makeup image to the user. The presentation unit displays the post-makeup image using, for example, a display device. For example, the presentation unit displays the post-makeup image according to the screen size and resolution. The presentation unit can also display different makeup styles based on the display format. For example, the presentation unit displays different makeup styles such as natural makeup, party makeup, and office makeup. This allows the user to select the most suitable makeup by displaying the generated post-makeup image to the user. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit inputs the generated post-makeup image to AI, which then displays the image.

[0033] The providing unit may include a video providing unit that provides a video based on the makeup selected by the user. The video providing unit provides, for example, a video that explains in detail the steps of the makeup selected by the user. For example, the video providing unit uses a video to explain specific steps, such as how to apply eyeshadow or lipstick. The video providing unit may also generate a video using a generation AI. For example, the video providing unit inputs the makeup steps into the generation AI, which then generates a video. This allows the user to apply the makeup by themselves by providing a video based on the makeup selected by the user. Some or all of the above-described processing in the video providing unit may be performed using, or without, the generation AI. For example, the video providing unit inputs the makeup steps into the generation AI, which then generates a video.

[0034] The providing unit may include an artist providing unit that provides services by a professional makeup artist based on makeup selected by the user. The artist providing unit, for example, provides a service in which a professional makeup artist actually applies makeup selected by the user. For example, the artist providing unit may have a professional makeup artist demonstrate how to apply eyeshadow. The artist providing unit may also provide makeup steps using a generation AI. For example, the artist providing unit inputs makeup steps into the generation AI, which then provides the steps. This allows the user to learn professional techniques by providing services by a professional makeup artist based on the makeup selected by the user. Some or all of the above-described processing in the artist providing unit may be performed using, or without, the generation AI. For example, the artist providing unit inputs makeup steps into the generation AI, which then provides the steps.

[0035] When analyzing a facial photo, the analysis unit can improve the analysis accuracy by referring to the user's past makeup history. For example, the analysis unit refers to makeup styles the user has tried in the past, and the generation AI performs analysis based on that data. For example, the analysis unit allows the generation AI to perform analysis while taking into account the makeup colors and styles that the user has previously preferred. The analysis unit can also eliminate makeup styles that the user has avoided in the past and allow the generation AI to suggest optimal makeup. For example, the analysis unit improves the analysis accuracy by referring to the user's past makeup history. In this way, by referring to the user's past makeup history, the analysis accuracy is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's past makeup history into the generation AI and cause the generation AI to improve the analysis accuracy.

[0036] When analyzing a facial photo, the analysis unit can take different lighting conditions and backgrounds into consideration. For example, if a user uploads a photo taken under different lighting conditions, the analysis unit causes the generation AI to correct for the effects of lighting before performing the analysis. For example, if a user uploads a photo taken against a different background, the analysis unit causes the generation AI to remove the background and analyze the facial features. Furthermore, if a user uploads a photo taken indoors or outdoors, the analysis unit can also cause the generation AI to take environmental differences into consideration when performing the analysis. For example, the analysis unit performs the analysis while taking different lighting conditions and backgrounds into consideration. This improves the accuracy of the analysis by taking different lighting conditions and backgrounds into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input different lighting conditions and backgrounds into the generation AI and have the generation AI perform the analysis.

[0037] When analyzing a facial photo, the analysis unit can reflect the user's facial movements and changes in facial expression in the analysis. For example, if the user uploads a photo of them smiling, the analysis unit causes the generation AI to reflect the characteristics of the smile in the analysis. For example, if the user uploads photos with different facial expressions, the analysis unit causes the generation AI to reflect the characteristics of each facial expression in the analysis. Furthermore, if the user uploads a photo taken while moving their face, the analysis unit can also cause the generation AI to reflect the characteristics of the movement in the analysis. For example, the analysis unit reflects the user's facial movements and changes in facial expression in the analysis. In this way, by reflecting the user's facial movements and changes in facial expression in the analysis, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's facial movements and changes in facial expression into the generation AI and cause the generation AI to perform the analysis.

[0038] When analyzing a facial photo, the analysis unit can reflect regional makeup trends by taking into account the user's geographical location information. For example, if the user lives in an urban area, the analysis unit causes the generation AI to reflect urban makeup trends. For example, if the user lives in a rural area, the analysis unit causes the generation AI to reflect regional makeup trends. Furthermore, if the user lives overseas, the analysis unit can also cause the generation AI to reflect makeup trends of that country. For example, the analysis unit reflects regional makeup trends by taking into account the user's geographical location information. This enables analysis that reflects regional makeup trends by taking into account the user's geographical location information. Some or all of the above-described processing by the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and cause the generation AI to perform analysis.

[0039] When analyzing a facial photo, the analysis unit can analyze the user's social media activity and suggest a related makeup style. The analysis unit, for example, reflects the styles of makeup artists the user follows on social media. For example, the analysis unit reflects makeup styles that the user has "liked" on social media. The analysis unit can also reflect makeup styles that the user has shared on social media. For example, the analysis unit analyzes the user's social media activity and suggests a related makeup style. In this way, related makeup styles can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media activity into the generation AI and cause the generation AI to suggest a makeup style.

[0040] When analyzing a facial photo, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, the analysis unit causes the generation AI to adjust the analysis method based on feedback provided by the user in the past. For example, the analysis unit reflects makeup styles that the user previously preferred. The analysis unit can also eliminate makeup styles that the user previously avoided, allowing the generation AI to suggest optimal makeup. For example, the analysis unit customizes the analysis method by reflecting the user's past feedback. This allows the analysis method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past feedback into the generation AI and have the generation AI customize the analysis method.

[0041] When generating a post-makeup image, the generation unit can generate different makeup styles based on the user's facial features. For example, the generation unit generates different eyeshadow styles to match the user's eye shape. For example, the generation unit generates different foundation shades to match the user's skin color. The generation unit can also generate different blush application methods to match the user's facial contours. For example, the generation unit generates different makeup styles based on the user's facial features. This allows the user to have a variety of options by generating different makeup styles based on the user's facial features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's facial features into the generation AI and cause the generation AI to generate different makeup styles.

[0042] When generating a post-makeup image, the generation unit can adjust the makeup tone according to the user's skin condition and the season. For example, if the user's skin is dry, the generation unit causes the generation AI to suggest makeup with a moisturizing effect. For example, in summer, the generation unit causes the generation AI to suggest makeup with cool tones. In addition, in winter, the generation unit can also suggest makeup with warm tones. For example, the generation unit adjusts the makeup tone according to the user's skin condition and the season. This makes it possible to suggest more appropriate makeup. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's skin condition and the season into the generation AI and cause the generation AI to adjust the makeup tone.

[0043] When generating a post-makeup image, the generation unit can improve generation accuracy by referring to the user's past makeup history. For example, the generation unit generates optimal makeup using a generation AI based on makeup styles the user has tried in the past. For example, the generation unit reflects makeup colors and styles that the user has previously preferred. The generation unit can also eliminate makeup styles that the user has avoided in the past and have the generation AI suggest optimal makeup. For example, the generation unit improves generation accuracy by referring to the user's past makeup history. In this way, by referring to the user's past makeup history, generation accuracy is improved. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's past makeup history into the generation AI and cause the generation AI to improve generation accuracy.

[0044] When generating a post-makeup image, the generation unit can reflect regional makeup trends by taking into account the user's geographical location information. For example, if the user lives in an urban area, the generation unit causes the generation AI to reflect urban makeup trends. For example, if the user lives in a rural area, the generation unit causes the generation AI to reflect regional makeup trends. Furthermore, if the user lives overseas, the generation unit can also cause the generation AI to reflect makeup trends of that country. For example, the generation unit reflects regional makeup trends by taking into account the user's geographical location information. This makes it possible to generate an image that reflects regional makeup trends by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to generate an image.

[0045] When generating a post-makeup image, the generation unit can analyze the user's social media activity and suggest a related makeup style. The generation unit, for example, reflects the style of a makeup artist the user follows on social media. For example, the generation unit reflects the makeup style that the user has "liked" on social media. The generation unit can also reflect the makeup style that the user has shared on social media. For example, the generation unit analyzes the user's social media activity and suggests a related makeup style. In this way, related makeup styles can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's social media activity into the generation AI and cause the generation AI to suggest a makeup style.

[0046] When generating an image after makeup application, the generation unit can customize the generation method by reflecting the user's past feedback. In the generation unit, for example, the generation AI adjusts the generation method based on feedback provided by the user in the past. For example, the generation unit reflects makeup styles that the user has previously preferred. The generation unit can also eliminate makeup styles that the user has previously avoided and have the generation AI suggest the most suitable makeup. For example, the generation unit customizes the generation method by reflecting the user's past feedback. In this way, the generation method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past feedback into the generation AI and have the generation AI customize the generation method.

[0047] When displaying the post-makeup image, the presentation unit can select the optimal display method by referring to the user's past selection history. The presentation unit, for example, allows the generation AI to select the optimal display method based on makeup styles previously selected by the user. For example, the presentation unit reflects display methods previously preferred by the user. The presentation unit can also eliminate display methods previously avoided by the user and allow the generation AI to suggest the optimal display method. For example, the presentation unit selects the optimal display method by referring to the user's past selection history. In this way, the optimal display method can be selected by referring to the user's past selection history. Some or all of the above-described processing in the presentation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the presentation unit can input the user's past selection history into the generation AI and cause the generation AI to select the display method.

[0048] The presentation unit can customize the display content according to the user's current makeup preferences when displaying the post-makeup image. For example, if the user prefers natural makeup, the presentation unit causes the generation AI to preferentially display natural makeup styles. For example, if the user prefers party makeup, the presentation unit causes the generation AI to preferentially display party makeup styles. Furthermore, if the user prefers office makeup, the presentation unit can also cause the generation AI to preferentially display office makeup styles. For example, the presentation unit customizes the display content according to the user's current makeup preferences. This enables a more appropriate display by customizing the display content according to the user's current makeup preferences. Some or all of the above-described processing in the presentation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the presentation unit can input the user's current makeup preferences into the generation AI and cause the generation AI to customize the display content.

[0049] The presentation unit can improve the display method by reflecting user feedback when displaying the post-makeup image. For example, the presentation unit causes the generation AI to adjust the display method based on feedback previously provided by the user. For example, the presentation unit reflects the display method preferred by the user. The presentation unit can also eliminate display methods avoided by the user and have the generation AI suggest the most appropriate display method. For example, the presentation unit improves the display method by reflecting user feedback. In this way, the display method can be improved by reflecting user feedback. Some or all of the above-described processing in the presentation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the presentation unit can input user feedback into the generation AI and cause the generation AI to improve the display method.

[0050] When displaying the post-makeup image, the presentation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display method that matches the screen size. For example, if the user is using a tablet, the presentation unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a simple and highly visible display method. For example, the presentation unit selects the optimal display method by taking into account the user's device information. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above-described processing in the presentation unit may be performed using, or without, the generation AI. For example, the presentation unit can input the user's device information into the generation AI and cause the generation AI to select the display method.

[0051] The presentation unit can make the display content multilingual according to the user's language setting when displaying the post-makeup image. The presentation unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the presentation unit provides a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the presentation unit can provide the display content in that language. For example, the presentation unit makes the display content multilingual according to the user's language setting. This makes it possible to accommodate a greater number of users by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the presentation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the presentation unit can input the user's language setting into the generation AI and cause the generation AI to perform multilingual support for the display content.

[0052] The presentation unit can customize the display method by reflecting the user's past feedback when displaying the post-makeup image. In the presentation unit, for example, the generation AI adjusts the display method based on feedback provided by the user in the past. For example, the presentation unit reflects the user's preferred display method. The presentation unit can also eliminate display methods avoided by the user and have the generation AI suggest the optimal display method. For example, the presentation unit customizes the display method by reflecting the user's past feedback. In this way, the display method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the presentation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the presentation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the display method.

[0053] When providing a service, the providing unit can refer to the user's past makeup history to provide the optimal service. For example, the providing unit allows the generation AI to provide the optimal makeup procedure based on makeup styles the user has tried in the past. For example, the providing unit reflects the makeup colors and styles that the user has previously preferred. The providing unit can also eliminate makeup styles that the user has avoided in the past and allow the generation AI to propose the optimal makeup procedure. For example, the providing unit refers to the user's past makeup history to provide the optimal service. In this way, the optimal service can be provided by referring to the user's past makeup history. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past makeup history into the generation AI and cause the generation AI to provide the optimal service.

[0054] The providing unit can customize the service content according to the user's current living situation and schedule when providing the service. For example, if the user is busy, the providing unit may have the generating AI provide a makeup routine that can be completed in a short time. For example, if the user has time, the providing unit may have the generating AI provide detailed makeup routines. The providing unit may also have the generating AI suggest an optimal makeup routine according to the user's schedule. For example, the providing unit customizes the service content according to the user's current living situation and schedule. This allows for the provision of more appropriate services by customizing the service content according to the user's current living situation and schedule. Some or all of the above-described processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit may input the user's current living situation and schedule into the generating AI and have the generating AI customize the service content.

[0055] The providing unit can improve the service content by reflecting user feedback when providing the service. In the providing unit, for example, the generation AI adjusts the service content based on feedback previously provided by the user. For example, the providing unit reflects service content preferred by the user. The providing unit can also eliminate service content avoided by the user and have the generation AI propose optimal service content. For example, the providing unit improves the service content by reflecting user feedback. In this way, the service content can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the service content.

[0056] When providing a service, the providing unit can provide an optimal service by taking into account the user's geographical location information. For example, if the user lives in an urban area, the providing unit can provide a service in which the generation AI reflects urban makeup trends. For example, if the user lives in a rural area, the providing unit can provide a service in which the generation AI reflects local makeup trends. Furthermore, if the user lives overseas, the providing unit can provide a service in which the generation AI reflects the makeup trends of that country. For example, the providing unit can provide an optimal service by taking into account the user's geographical location information. In this way, the optimal service can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI and cause the generation AI to provide the optimal service.

[0057] When providing a service, the providing unit can analyze the user's social media activity and suggest related services. For example, the providing unit provides a service that reflects the style of a makeup artist the user follows on social media. For example, the providing unit can provide a service that reflects a makeup style that the user has "liked" on social media. The providing unit can also provide a service that reflects a makeup style that the user has shared on social media. For example, the providing unit analyzes the user's social media activity and suggests related services. In this way, related services can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity into the generation AI and cause the generation AI to suggest related services.

[0058] The providing unit can customize the service content by reflecting the user's past feedback when providing the service. In the providing unit, for example, the generation AI adjusts the service content based on feedback provided by the user in the past. For example, the providing unit reflects the service content preferred by the user. The providing unit can also eliminate service content avoided by the user and have the generation AI propose the most suitable service content. For example, the providing unit customizes the service content by reflecting the user's past feedback. In this way, the service content can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the service content.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When analyzing the user's facial photograph, the analysis unit can also evaluate the user's skin health condition. For example, the analysis unit can analyze the dryness and oil content of the user's skin and suggest appropriate skin care products. The analysis unit can also evaluate the user's skin tone, blemishes, and wrinkles and suggest makeup based on the results. Furthermore, the analysis unit can predict the duration and effect of makeup application based on the user's skin health condition. This allows the user to select makeup that best suits their skin condition.

[0061] The analysis unit can take into account the asymmetry of the user's face when analyzing the user's facial photo. For example, the analysis unit can analyze the difference in the position and size of the user's left and right eyes and suggest makeup based on that. The analysis unit can also analyze the difference in the contours of the left and right sides of the user's face and suggest shading and highlighting based on that. Furthermore, the analysis unit can adjust the balance of the makeup based on the asymmetry of the user's face. This allows the user to select makeup that best suits their facial features.

[0062] When displaying the generated post-makeup image to the user, the presentation unit can customize the display method based on the user's visual preferences. For example, if the user prefers bright colors, the presentation unit causes the generation AI to display the post-makeup image with a bright background. For example, if the user prefers simple designs, the presentation unit causes the generation AI to display the post-makeup image with a simple layout. Furthermore, if the user prefers detailed information, the presentation unit can also provide a display method in which the generation AI includes a detailed explanation of the makeup. This allows for a more appropriate display by customizing the display method according to the user's visual preferences.

[0063] The providing unit can provide a list of cosmetics to be used for makeup based on the makeup selected by the user. For example, the providing unit generates a list of necessary cosmetics based on the makeup procedure selected by the user. For example, the providing unit can suggest recommended cosmetic brands and products based on the makeup style selected by the user. The providing unit can also suggest cosmetics of appropriate colors based on the makeup shade selected by the user. This allows the user to easily gather the necessary cosmetics.

[0064] The providing unit can predict the duration of makeup based on the makeup selected by the user. For example, the providing unit predicts how long the makeup will last based on the makeup steps selected by the user. For example, the providing unit predicts the duration of makeup based on the makeup style selected by the user. The providing unit can also predict the duration of makeup based on the shade of makeup selected by the user. This allows the user to know the duration of makeup and touch up their makeup at an appropriate time.

[0065] The analysis unit can take the user's age into consideration when analyzing the user's facial photo. For example, the analysis unit can suggest an appropriate makeup style based on the user's age. For example, the analysis unit can suggest a makeup style that is in line with trends to a younger user. The analysis unit can also suggest a makeup style that takes into account skin health to a middle-aged or older user. This makes it possible to suggest the most appropriate makeup style depending on the user's age.

[0066] The providing unit can suggest a makeup aftercare method based on the makeup selected by the user. For example, the providing unit can suggest an appropriate cleansing method for removing makeup based on the makeup procedure selected by the user. For example, the providing unit can suggest a skin moisturizing method based on the makeup style selected by the user. The providing unit can also suggest a skin care product for even skin tone based on the shade of makeup selected by the user. This allows the user to perform appropriate makeup aftercare.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The analysis unit analyzes the user's facial photograph. The photographs include those taken from the front, at an angle, and with a smiling face. The analysis unit uses a facial recognition algorithm to extract facial features and detect facial landmarks. For example, it extracts features such as eye shape, skin color, and facial contours. Step 2: The generator generates a makeup-applied image based on the facial photograph analyzed by the analyzer. The generator uses an image generation algorithm to generate a makeup-applied image and generates different makeup styles based on the type of data used. For example, different makeup styles such as natural makeup, party makeup, and office makeup are generated. Step 3: The presentation unit presents the post-makeup image generated by the generation unit to the user. The presentation unit displays the post-makeup image using a display device, and displays different makeup styles based on the display format. For example, the post-makeup image is displayed according to the screen size and resolution. Step 4: The providing unit provides a service based on the image of the user with makeup presented by the presenting unit. The providing unit provides a video based on the makeup selected by the user, and provides a service by a professional makeup artist. For example, the providing unit provides a video that explains in detail the steps of the makeup selected by the user, and provides a service in which a professional makeup artist actually applies the makeup.

[0069] (Example 2) A makeup suggestion system according to an embodiment of the present invention allows a user to upload a photo of their face, and the system presents multiple makeup-based images and suggests the optimal makeup look. The makeup suggestion system allows a user to upload multiple photos of themselves, and a generation AI analyzes these photos to generate multiple makeup-based images that suit the user. The generated images are presented to the user, allowing the user to select the most suitable makeup look from among them. After the suggestions are made, the user can choose from two services. The first is to apply the makeup themselves while watching a video. The generation AI provides detailed instructions on the selected makeup look, allowing the user to follow the video while applying the makeup. The second is to have a professional makeup artist apply the makeup look in-store and learn how to do it. The user can have a professional makeup artist actually apply the selected makeup look and learn the steps. For example, the makeup suggestion system allows a user to upload multiple photos of their face. Uploading photos from different angles and facial expressions enables more accurate analysis. For example, users can upload photos from the front, an oblique view, or a smiling face. The makeup suggestion system then analyzes the uploaded photos using the generation AI. The generative AI analyzes the user's facial features and generates multiple makeup looks that suit them. For example, it analyzes eye shape, skin color, and facial contours, and generates multiple makeup looks based on that information. The generated makeup looks are then presented to the user. The user can select the look that best suits them from the presented images. For example, makeup looks tailored to different situations, such as natural makeup, party makeup, and office makeup, are presented. After the suggestions are made, the user can choose from the following two services. The first is to apply the makeup themselves while watching a video. The video provided by the generative AI provides detailed instructions for the selected makeup look, allowing the user to apply the makeup while watching the video. For example, the video explains specific steps, such as how to apply eyeshadow and lipstick. The second is to have a professional makeup artist apply the makeup in-store and learn how to do it.Users can learn the steps by having a professional makeup artist apply the makeup they select. For example, a professional makeup artist demonstrates how to apply eyeshadow. This system allows users to easily find the makeup that suits them best. Learning the makeup steps also improves their self-application skills. For example, even users who are not confident in their usual makeup application can learn how to create makeup that suits them by watching videos provided by the generation AI or receiving instruction from a professional makeup artist. This allows the makeup suggestion system to analyze users' facial photos and generate, display, and provide images of the makeup look they have created. For example, if a user uploads multiple photos of themselves, the generation AI can analyze these photos and generate multiple images of the makeup look that suit the user. The generated images are then presented to the user, who can select the most suitable makeup look from among them. Furthermore, users can choose to apply the makeup themselves while watching the video or have a professional makeup artist apply it in-store. This allows users to easily find the makeup that suits them best, and learning the makeup steps also improves their self-application skills.

[0070] A makeup suggestion system according to an embodiment includes an analysis unit, a generation unit, a presentation unit, and a providing unit. The analysis unit analyzes a facial photograph of a user. The facial photograph of the user includes, but is not limited to, a photograph taken from the front, a photograph taken from an oblique angle, and a photograph of a smiling face. The analysis unit extracts facial features using, for example, a facial recognition algorithm. The analysis unit can also detect facial landmarks using a feature extraction method. For example, the analysis unit extracts features such as eye shape, skin color, and facial contours. The generation unit generates a makeup-applied image based on the facial photograph analyzed by the analysis unit. The generation unit generates the makeup-applied image using, for example, an image generation algorithm. The generation unit can also generate different makeup styles based on the type of data used. For example, the generation unit generates different makeup styles such as natural makeup, party makeup, and office makeup. The presentation unit presents the makeup-applied image generated by the generation unit to the user. The presentation unit displays the makeup-applied image using, for example, a display device. The presentation unit can also display different makeup styles based on a display format. For example, the presentation unit displays an image of the user with makeup applied according to the screen size and resolution. The provision unit provides a service based on the image of the user with makeup applied presented by the presentation unit. The provision unit provides a video based on, for example, makeup selected by the user. The provision unit can also provide a service provided by a professional makeup artist. For example, the provision unit can provide a video that explains in detail the makeup steps selected by the user. The provision unit can also provide a service in which a professional makeup artist actually applies the makeup. As a result, the makeup suggestion system according to the embodiment can analyze a user's facial photo and generate, present, and provide a service of an image of the user with makeup applied. For example, by uploading multiple photos including the user's face, the generation AI can analyze these photos and generate multiple images of the user with makeup applied that suit the user. The generated images are presented to the user, and the user can select the most suitable makeup from among them. Furthermore, the user can choose whether to apply makeup themselves while watching a video or to have a professional makeup artist apply their makeup in a store.This allows users to easily find makeup that suits them, and by learning the makeup steps, they can improve their own makeup application skills.

[0071] The analysis unit can extract the user's facial features. The analysis unit extracts the facial features using, for example, a facial recognition algorithm. For example, the analysis unit extracts features such as eye shape, skin color, and facial contours. The analysis unit can also detect facial landmarks using a feature extraction method. For example, the analysis unit detects landmarks such as the position of the eyes, the shape of the nose, and the shape of the mouth. The analysis unit can also extract facial shape features. For example, the analysis unit extracts shape features such as the facial contour and the height of the cheeks. By extracting the user's facial features, more appropriate makeup suggestions can be made. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input a photo of the user's face into the generation AI and cause the generation AI to extract facial features.

[0072] The generation unit can generate multiple after-makeup images based on the extracted features. The generation unit generates the after-makeup images using, for example, an image generation algorithm. For example, the generation unit generates different makeup styles based on the extracted features. The generation unit can also generate different makeup styles based on the type of data used. For example, the generation unit generates different makeup styles such as natural makeup, party makeup, and office makeup. The generation unit can also generate the after-makeup images using a generation AI. For example, the generation unit inputs the extracted features into the generation AI, which then generates the after-makeup images. This allows the user to have a variety of options by generating multiple after-makeup images based on the extracted features. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the extracted features into the generation AI, which then generates the after-makeup images.

[0073] The presentation unit can display the generated post-makeup image to the user. The presentation unit displays the post-makeup image using, for example, a display device. For example, the presentation unit displays the post-makeup image according to the screen size and resolution. The presentation unit can also display different makeup styles based on the display format. For example, the presentation unit displays different makeup styles such as natural makeup, party makeup, and office makeup. This allows the user to select the most suitable makeup by displaying the generated post-makeup image to the user. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit inputs the generated post-makeup image to AI, which then displays the image.

[0074] The providing unit may include a video providing unit that provides a video based on the makeup selected by the user. The video providing unit provides, for example, a video that explains in detail the steps of the makeup selected by the user. For example, the video providing unit uses a video to explain specific steps, such as how to apply eyeshadow or lipstick. The video providing unit may also generate a video using a generation AI. For example, the video providing unit inputs the makeup steps into the generation AI, which then generates a video. This allows the user to apply the makeup by themselves by providing a video based on the makeup selected by the user. Some or all of the above-described processing in the video providing unit may be performed using, or without, the generation AI. For example, the video providing unit inputs the makeup steps into the generation AI, which then generates a video.

[0075] The providing unit may include an artist providing unit that provides services by a professional makeup artist based on makeup selected by the user. The artist providing unit, for example, provides a service in which a professional makeup artist actually applies makeup selected by the user. For example, the artist providing unit may have a professional makeup artist demonstrate how to apply eyeshadow. The artist providing unit may also provide makeup steps using a generation AI. For example, the artist providing unit inputs makeup steps into the generation AI, which then provides the steps. This allows the user to learn professional techniques by providing services by a professional makeup artist based on the makeup selected by the user. Some or all of the above-described processing in the artist providing unit may be performed using, or without, the generation AI. For example, the artist providing unit inputs makeup steps into the generation AI, which then provides the steps.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis method of the facial photo based on the estimated user emotions. For example, if the user is nervous, the analysis unit adjusts the generation AI to analyze facial features in a more relaxed state. For example, if the user is relaxed, the analysis unit can cause the generation AI to analyze detailed features and provide more accurate makeup suggestions. Furthermore, if the user is in a hurry, the analysis unit can cause the generation AI to perform analysis quickly and provide results in a short time. For example, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. This allows for more appropriate analysis results to be provided by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 can be performed, for example, using the generation AI, or without the generation AI. For example, the analysis unit can input a photo of the user's face into the generation AI and have the generation AI estimate emotions and adjust the analysis method.

[0077] When analyzing a facial photo, the analysis unit can improve the analysis accuracy by referring to the user's past makeup history. For example, the analysis unit refers to makeup styles the user has tried in the past, and the generation AI performs analysis based on that data. For example, the analysis unit allows the generation AI to perform analysis while taking into account the makeup colors and styles that the user has previously preferred. The analysis unit can also eliminate makeup styles that the user has avoided in the past and allow the generation AI to suggest optimal makeup. For example, the analysis unit improves the analysis accuracy by referring to the user's past makeup history. In this way, by referring to the user's past makeup history, the analysis accuracy is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's past makeup history into the generation AI and cause the generation AI to improve the analysis accuracy.

[0078] When analyzing a facial photo, the analysis unit can take different lighting conditions and backgrounds into consideration. For example, if a user uploads a photo taken under different lighting conditions, the analysis unit causes the generation AI to correct for the effects of lighting before performing the analysis. For example, if a user uploads a photo taken against a different background, the analysis unit causes the generation AI to remove the background and analyze the facial features. Furthermore, if a user uploads a photo taken indoors or outdoors, the analysis unit can also cause the generation AI to take environmental differences into consideration when performing the analysis. For example, the analysis unit performs the analysis while taking different lighting conditions and backgrounds into consideration. This improves the accuracy of the analysis by taking different lighting conditions and backgrounds into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input different lighting conditions and backgrounds into the generation AI and have the generation AI perform the analysis.

[0079] When analyzing a facial photo, the analysis unit can reflect the user's facial movements and changes in facial expression in the analysis. For example, if the user uploads a photo of them smiling, the analysis unit causes the generation AI to reflect the characteristics of the smile in the analysis. For example, if the user uploads photos with different facial expressions, the analysis unit causes the generation AI to reflect the characteristics of each facial expression in the analysis. Furthermore, if the user uploads a photo taken while moving their face, the analysis unit can also cause the generation AI to reflect the characteristics of the movement in the analysis. For example, the analysis unit reflects the user's facial movements and changes in facial expression in the analysis. In this way, by reflecting the user's facial movements and changes in facial expression in the analysis, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's facial movements and changes in facial expression into the generation AI and cause the generation AI to perform the analysis.

[0080] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can cause the generation AI to prioritize detailed analysis results. For example, if the user is in a hurry, the analysis unit can cause the generation AI to prioritize concise analysis results. Furthermore, if the user is excited, the analysis unit can cause the generation AI to prioritize visually appealing analysis results. For example, the analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. This allows the optimal results to be provided to the user by prioritizing the analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotions into the generation AI and have the generation AI determine the priority of the analysis results.

[0081] When analyzing a facial photo, the analysis unit can reflect regional makeup trends by taking into account the user's geographical location information. For example, if the user lives in an urban area, the analysis unit causes the generation AI to reflect urban makeup trends. For example, if the user lives in a rural area, the analysis unit causes the generation AI to reflect regional makeup trends. Furthermore, if the user lives overseas, the analysis unit can also cause the generation AI to reflect makeup trends of that country. For example, the analysis unit reflects regional makeup trends by taking into account the user's geographical location information. This enables analysis that reflects regional makeup trends by taking into account the user's geographical location information. Some or all of the above-described processing by the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and cause the generation AI to perform analysis.

[0082] When analyzing a facial photo, the analysis unit can analyze the user's social media activity and suggest a related makeup style. The analysis unit, for example, reflects the styles of makeup artists the user follows on social media. For example, the analysis unit reflects makeup styles that the user has "liked" on social media. The analysis unit can also reflect makeup styles that the user has shared on social media. For example, the analysis unit analyzes the user's social media activity and suggests a related makeup style. In this way, related makeup styles can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media activity into the generation AI and cause the generation AI to suggest a makeup style.

[0083] When analyzing a facial photo, the analysis unit can customize the analysis method by reflecting the user's past feedback. For example, the analysis unit causes the generation AI to adjust the analysis method based on feedback provided by the user in the past. For example, the analysis unit reflects makeup styles that the user previously preferred. The analysis unit can also eliminate makeup styles that the user previously avoided, allowing the generation AI to suggest optimal makeup. For example, the analysis unit customizes the analysis method by reflecting the user's past feedback. This allows the analysis method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past feedback into the generation AI and have the generation AI customize the analysis method.

[0084] The generation unit can estimate the user's emotions and adjust the generation method for the post-makeup image based on the estimated user's emotions. For example, if the user is relaxed, the generation AI generates a natural makeup style. For example, if the user is excited, the generation unit generates a glamorous makeup style. Furthermore, if the user is in a hurry, the generation AI can generate a simple and quickly applicable makeup style. For example, the generation unit estimates the user's emotions and adjusts the generation method based on the estimated emotions. This allows for the generation of a more appropriate post-makeup image by adjusting the generation method 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 generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotions into the generation AI and cause the generation AI to adjust the generation method.

[0085] When generating a post-makeup image, the generation unit can generate different makeup styles based on the user's facial features. For example, the generation unit generates different eyeshadow styles to match the user's eye shape. For example, the generation unit generates different foundation shades to match the user's skin color. The generation unit can also generate different blush application methods to match the user's facial contours. For example, the generation unit generates different makeup styles based on the user's facial features. This allows the user to have a variety of options by generating different makeup styles based on the user's facial features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's facial features into the generation AI and cause the generation AI to generate different makeup styles.

[0086] When generating a post-makeup image, the generation unit can adjust the makeup tone according to the user's skin condition and the season. For example, if the user's skin is dry, the generation unit causes the generation AI to suggest makeup with a moisturizing effect. For example, in summer, the generation unit causes the generation AI to suggest makeup with cool tones. In addition, in winter, the generation unit can also suggest makeup with warm tones. For example, the generation unit adjusts the makeup tone according to the user's skin condition and the season. This makes it possible to suggest more appropriate makeup. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's skin condition and the season into the generation AI and cause the generation AI to adjust the makeup tone.

[0087] When generating a post-makeup image, the generation unit can improve generation accuracy by referring to the user's past makeup history. For example, the generation unit generates optimal makeup using a generation AI based on makeup styles the user has tried in the past. For example, the generation unit reflects makeup colors and styles that the user has previously preferred. The generation unit can also eliminate makeup styles that the user has avoided in the past and have the generation AI suggest optimal makeup. For example, the generation unit improves generation accuracy by referring to the user's past makeup history. In this way, by referring to the user's past makeup history, generation accuracy is improved. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's past makeup history into the generation AI and cause the generation AI to improve generation accuracy.

[0088] The generation unit can estimate the user's emotions and determine the priority of the post-makeup images to be generated based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can prioritize generating detailed makeup styles. For example, if the user is in a hurry, the generation unit can prioritize generating simple makeup styles. Alternatively, if the user is excited, the generation AI can prioritize generating flashy makeup styles. For example, the generation unit can estimate the user's emotions and determine the priority of the post-makeup images to be generated based on the estimated emotions. This allows the user to receive optimal results by prioritizing the post-makeup images to be generated according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 generation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotions into the generation AI and have the generation AI determine the priority of the post-makeup images to be generated.

[0089] When generating a post-makeup image, the generation unit can reflect regional makeup trends by taking into account the user's geographical location information. For example, if the user lives in an urban area, the generation unit causes the generation AI to reflect urban makeup trends. For example, if the user lives in a rural area, the generation unit causes the generation AI to reflect regional makeup trends. Furthermore, if the user lives overseas, the generation unit can also cause the generation AI to reflect makeup trends of that country. For example, the generation unit reflects regional makeup trends by taking into account the user's geographical location information. This makes it possible to generate an image that reflects regional makeup trends by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to generate an image.

[0090] When generating a post-makeup image, the generation unit can analyze the user's social media activity and suggest a related makeup style. The generation unit, for example, reflects the style of a makeup artist the user follows on social media. For example, the generation unit reflects the makeup style that the user has "liked" on social media. The generation unit can also reflect the makeup style that the user has shared on social media. For example, the generation unit analyzes the user's social media activity and suggests a related makeup style. In this way, related makeup styles can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's social media activity into the generation AI and cause the generation AI to suggest a makeup style.

[0091] When generating an image after makeup application, the generation unit can customize the generation method by reflecting the user's past feedback. In the generation unit, for example, the generation AI adjusts the generation method based on feedback provided by the user in the past. For example, the generation unit reflects makeup styles that the user has previously preferred. The generation unit can also eliminate makeup styles that the user has previously avoided and have the generation AI suggest the most suitable makeup. For example, the generation unit customizes the generation method by reflecting the user's past feedback. In this way, the generation method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's past feedback into the generation AI and have the generation AI customize the generation method.

[0092] The presentation unit can estimate the user's emotions and adjust the display method of the post-makeup image based on the estimated user emotions. For example, if the user is relaxed, the generation AI displays a detailed makeup style. For example, if the user is in a hurry, the presentation unit can display a simple makeup style. Furthermore, if the user is excited, the presentation unit can display a flashy makeup style. For example, the presentation unit can estimate the user's emotions and adjust the display method of the post-makeup image based on the estimated emotions. This enables a more appropriate display by adjusting the display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 presentation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the presentation unit can input the user's emotions into the generation AI and have the generation AI adjust the display method.

[0093] When displaying the post-makeup image, the presentation unit can select the optimal display method by referring to the user's past selection history. The presentation unit, for example, allows the generation AI to select the optimal display method based on makeup styles previously selected by the user. For example, the presentation unit reflects display methods previously preferred by the user. The presentation unit can also eliminate display methods previously avoided by the user and allow the generation AI to suggest the optimal display method. For example, the presentation unit selects the optimal display method by referring to the user's past selection history. In this way, the optimal display method can be selected by referring to the user's past selection history. Some or all of the above-described processing in the presentation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the presentation unit can input the user's past selection history into the generation AI and cause the generation AI to select the display method.

[0094] The presentation unit can customize the display content according to the user's current makeup preferences when displaying the post-makeup image. For example, if the user prefers natural makeup, the presentation unit causes the generation AI to preferentially display natural makeup styles. For example, if the user prefers party makeup, the presentation unit causes the generation AI to preferentially display party makeup styles. Furthermore, if the user prefers office makeup, the presentation unit can also cause the generation AI to preferentially display office makeup styles. For example, the presentation unit customizes the display content according to the user's current makeup preferences. This enables a more appropriate display by customizing the display content according to the user's current makeup preferences. Some or all of the above-described processing in the presentation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the presentation unit can input the user's current makeup preferences into the generation AI and cause the generation AI to customize the display content.

[0095] The presentation unit can improve the display method by reflecting user feedback when displaying the post-makeup image. For example, the presentation unit causes the generation AI to adjust the display method based on feedback previously provided by the user. For example, the presentation unit reflects the display method preferred by the user. The presentation unit can also eliminate display methods avoided by the user and have the generation AI suggest the most appropriate display method. For example, the presentation unit improves the display method by reflecting user feedback. In this way, the display method can be improved by reflecting user feedback. Some or all of the above-described processing in the presentation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the presentation unit can input user feedback into the generation AI and cause the generation AI to improve the display method.

[0096] The presentation unit can estimate the user's emotions and adjust the display order of the makeup-applied images based on the estimated user's emotions. For example, when the user is relaxed, the generation AI prioritizes displaying detailed makeup styles. For example, when the user is in a hurry, the presentation unit can prioritize displaying simple makeup styles. Furthermore, when the user is excited, the presentation unit can prioritize displaying flashy makeup styles. For example, the presentation unit can estimate the user's emotions and adjust the display order of the makeup-applied images based on the estimated emotions. This allows for more appropriate display by adjusting the display order according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using, for example, the generation AI. For example, the presentation unit can input the user's emotions into the generation AI and have the generation AI adjust the display order.

[0097] When displaying the post-makeup image, the presentation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display method that matches the screen size. For example, if the user is using a tablet, the presentation unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a simple and highly visible display method. For example, the presentation unit selects the optimal display method by taking into account the user's device information. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above-described processing in the presentation unit may be performed using, or without, the generation AI. For example, the presentation unit can input the user's device information into the generation AI and cause the generation AI to select the display method.

[0098] The presentation unit can make the display content multilingual according to the user's language setting when displaying the post-makeup image. The presentation unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the presentation unit provides a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the presentation unit can provide the display content in that language. For example, the presentation unit makes the display content multilingual according to the user's language setting. This makes it possible to accommodate a greater number of users by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the presentation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the presentation unit can input the user's language setting into the generation AI and cause the generation AI to perform multilingual support for the display content.

[0099] The presentation unit can customize the display method by reflecting the user's past feedback when displaying the post-makeup image. In the presentation unit, for example, the generation AI adjusts the display method based on feedback provided by the user in the past. For example, the presentation unit reflects the user's preferred display method. The presentation unit can also eliminate display methods avoided by the user and have the generation AI suggest the optimal display method. For example, the presentation unit customizes the display method by reflecting the user's past feedback. In this way, the display method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the presentation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the presentation unit can input the user's past feedback into the generation AI and cause the generation AI to customize the display method.

[0100] The providing unit can estimate the user's emotions and adjust the service provision method based on the estimated user's emotions. For example, if the user is relaxed, the generating AI can provide detailed makeup instructions. For example, if the user is in a hurry, the generating AI can provide concise makeup instructions. Furthermore, if the user is excited, the generating AI can provide visually appealing makeup instructions. For example, the providing unit can estimate the user's emotions and adjust the service provision method based on the estimated emotions. This allows for more appropriate service to be provided by adjusting the service provision method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generating AI, or can be performed without using the generating AI. For example, the providing unit can input the user's emotions into the generating AI and cause the generating AI to adjust the service provision method.

[0101] When providing a service, the providing unit can refer to the user's past makeup history to provide the optimal service. For example, the providing unit allows the generation AI to provide the optimal makeup procedure based on makeup styles the user has tried in the past. For example, the providing unit reflects the makeup colors and styles that the user has previously preferred. The providing unit can also eliminate makeup styles that the user has avoided in the past and allow the generation AI to propose the optimal makeup procedure. For example, the providing unit refers to the user's past makeup history to provide the optimal service. In this way, the optimal service can be provided by referring to the user's past makeup history. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past makeup history into the generation AI and cause the generation AI to provide the optimal service.

[0102] The providing unit can customize the service content according to the user's current living situation and schedule when providing the service. For example, if the user is busy, the providing unit may have the generating AI provide a makeup routine that can be completed in a short time. For example, if the user has time, the providing unit may have the generating AI provide detailed makeup routines. The providing unit may also have the generating AI suggest an optimal makeup routine according to the user's schedule. For example, the providing unit customizes the service content according to the user's current living situation and schedule. This allows for the provision of more appropriate services by customizing the service content according to the user's current living situation and schedule. Some or all of the above-described processing in the providing unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the providing unit may input the user's current living situation and schedule into the generating AI and have the generating AI customize the service content.

[0103] The providing unit can improve the service content by reflecting user feedback when providing the service. In the providing unit, for example, the generation AI adjusts the service content based on feedback previously provided by the user. For example, the providing unit reflects service content preferred by the user. The providing unit can also eliminate service content avoided by the user and have the generation AI propose optimal service content. For example, the providing unit improves the service content by reflecting user feedback. In this way, the service content can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input user feedback into the generation AI and cause the generation AI to improve the service content.

[0104] The providing unit can estimate the user's emotions and determine the priority of services based on the estimated user's emotions. For example, when the user is relaxed, the generating AI can prioritize detailed makeup instructions. For example, when the user is in a hurry, the generating AI can prioritize simple makeup instructions. Furthermore, when the user is excited, the generating AI can prioritize visually appealing makeup instructions. For example, the providing unit can estimate the user's emotions and determine the priority of services based on the estimated emotions. This allows for more appropriate services to be provided by determining the priority of services according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using the generating AI, for example, or without the generating AI. For example, the providing unit can input the user's emotions into the generating AI and have the generating AI determine the priority of services.

[0105] When providing a service, the providing unit can provide an optimal service by taking into account the user's geographical location information. For example, if the user lives in an urban area, the providing unit can provide a service in which the generation AI reflects urban makeup trends. For example, if the user lives in a rural area, the providing unit can provide a service in which the generation AI reflects local makeup trends. Furthermore, if the user lives overseas, the providing unit can provide a service in which the generation AI reflects the makeup trends of that country. For example, the providing unit can provide an optimal service by taking into account the user's geographical location information. In this way, the optimal service can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI and cause the generation AI to provide the optimal service.

[0106] When providing a service, the providing unit can analyze the user's social media activity and suggest related services. For example, the providing unit provides a service that reflects the style of a makeup artist the user follows on social media. For example, the providing unit can provide a service that reflects a makeup style that the user has "liked" on social media. The providing unit can also provide a service that reflects a makeup style that the user has shared on social media. For example, the providing unit analyzes the user's social media activity and suggests related services. In this way, related services can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity into the generation AI and cause the generation AI to suggest related services.

[0107] The providing unit can customize the service content by reflecting the user's past feedback when providing the service. In the providing unit, for example, the generation AI adjusts the service content based on feedback provided by the user in the past. For example, the providing unit reflects the service content preferred by the user. The providing unit can also eliminate service content avoided by the user and have the generation AI propose the most suitable service content. For example, the providing unit customizes the service content by reflecting the user's past feedback. In this way, the service content can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback into the generation AI and cause the generation AI to customize the service content. === Hard Collateral 1-1 === Each of the multiple elements including the above-described analysis unit, generation unit, presentation 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 analysis unit can acquire a facial photograph of the user using the camera 42 of the smart device 14 and analyze facial features using the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a made-up image based on the facial photograph analyzed by the specific processing unit 290 of the data processing device 12. The presentation unit can present the generated made-up image to the user using, for example, the display 40A of the smart device 14. For example, the provision unit can provide a video based on makeup selected by the user using the control unit 46A of the smart device 14. In addition, the provision unit can also provide services by a professional makeup artist using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described analysis unit, generation unit, presentation 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 analysis unit can acquire a facial photograph of the user using the camera 42 of the smart glasses 214 and analyze facial features using the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a made-up image based on the facial photograph analyzed by the specific processing unit 290 of the data processing device 12. The presentation unit can present the generated made-up image to the user using, for example, the display of the smart glasses 214. For example, the provision unit can provide a video based on makeup selected by the user using the control unit 46A of the smart glasses 214. In addition, the provision unit can also provide services from a professional makeup artist using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-described analysis unit, generation unit, presentation 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 analysis unit can acquire a facial photograph of the user using the camera 42 of the headset type terminal 314 and analyze the facial features using the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a made-up image based on the facial photograph analyzed by the specific processing unit 290 of the data processing device 12. The presentation unit can present the generated made-up image to the user using, for example, the display 343 of the headset type terminal 314. For example, the provision unit can provide a video based on the makeup selected by the user using the control unit 46A of the headset type terminal 314. Furthermore, the provision unit can also provide services by a professional makeup artist using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-described analysis unit, generation unit, presentation 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 analysis unit can acquire a facial photograph of the user using the camera 42 of the robot 414 and analyze facial features using the specific processing unit 290 of the data processing device 12. For example, the generation unit can generate a made-up image based on the facial photograph analyzed by the specific processing unit 290 of the data processing device 12. The presentation unit can present the generated made-up image to the user using, for example, the display of the robot 414. For example, the provision unit can provide a video based on the makeup selected by the user using the control unit 46A of the robot 414. Furthermore, the provision unit can also provide services by a professional makeup artist using the specific processing unit 290 of the data processing device 12.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When analyzing the user's facial photograph, the analysis unit can also evaluate the user's skin health condition. For example, the analysis unit can analyze the dryness and oil content of the user's skin and suggest appropriate skin care products. The analysis unit can also evaluate the user's skin tone, blemishes, and wrinkles and suggest makeup based on the results. Furthermore, the analysis unit can predict the duration and effect of makeup application based on the user's skin health condition. This allows the user to select makeup that best suits their skin condition.

[0110] The analysis unit can take into account the asymmetry of the user's face when analyzing the user's facial photo. For example, the analysis unit can analyze the difference in the position and size of the user's left and right eyes and suggest makeup based on that. The analysis unit can also analyze the difference in the contours of the left and right sides of the user's face and suggest shading and highlighting based on that. Furthermore, the analysis unit can adjust the balance of the makeup based on the asymmetry of the user's face. This allows the user to select makeup that best suits their facial features.

[0111] The generation unit can estimate the user's emotions and adjust the color tone of the post-makeup image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit causes the generation AI to generate a makeup style with soft colors. For example, if the user is excited, the generation unit causes the generation AI to generate a makeup style with vivid colors. In addition, if the user is sad, the generation unit can also cause the generation AI to generate a makeup style with subdued colors. This makes it possible to generate a more appropriate post-makeup image by adjusting the color tone according to the user's emotions.

[0112] When displaying the generated post-makeup image to the user, the presentation unit can customize the display method based on the user's visual preferences. For example, if the user prefers bright colors, the presentation unit causes the generation AI to display the post-makeup image with a bright background. For example, if the user prefers simple designs, the presentation unit causes the generation AI to display the post-makeup image with a simple layout. Furthermore, if the user prefers detailed information, the presentation unit can also provide a display method in which the generation AI includes a detailed explanation of the makeup. This allows for a more appropriate display by customizing the display method according to the user's visual preferences.

[0113] The providing unit can provide a list of cosmetics to be used for makeup based on the makeup selected by the user. For example, the providing unit generates a list of necessary cosmetics based on the makeup procedure selected by the user. For example, the providing unit can suggest recommended cosmetic brands and products based on the makeup style selected by the user. The providing unit can also suggest cosmetics of appropriate colors based on the makeup shade selected by the user. This allows the user to easily gather the necessary cosmetics.

[0114] The providing unit can predict the duration of makeup based on the makeup selected by the user. For example, the providing unit predicts how long the makeup will last based on the makeup steps selected by the user. For example, the providing unit predicts the duration of makeup based on the makeup style selected by the user. The providing unit can also predict the duration of makeup based on the shade of makeup selected by the user. This allows the user to know the duration of makeup and touch up their makeup at an appropriate time.

[0115] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit causes the generation AI to display detailed analysis results. For example, if the user is in a hurry, the analysis unit causes the generation AI to display concise analysis results. Also, if the user is excited, the analysis unit can cause the generation AI to display visually appealing analysis results. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions.

[0116] The analysis unit can take the user's age into consideration when analyzing the user's facial photo. For example, the analysis unit can suggest an appropriate makeup style based on the user's age. For example, the analysis unit can suggest a makeup style that is in line with trends to a younger user. The analysis unit can also suggest a makeup style that takes into account skin health to a middle-aged or older user. This makes it possible to suggest the most appropriate makeup style depending on the user's age.

[0117] The generation unit can estimate the user's emotions and adjust the filter effect of the post-makeup image based on the estimated user's emotions. For example, if the user is relaxed, the generation AI applies a soft filter effect. For example, if the user is excited, the generation unit can apply a vivid filter effect. Also, if the user is sad, the generation unit can apply a calm filter effect. This allows for the generation of a more appropriate post-makeup image by adjusting the filter effect according to the user's emotions.

[0118] The providing unit can suggest a makeup aftercare method based on the makeup selected by the user. For example, the providing unit can suggest an appropriate cleansing method for removing makeup based on the makeup procedure selected by the user. For example, the providing unit can suggest a skin moisturizing method based on the makeup style selected by the user. The providing unit can also suggest a skin care product for even skin tone based on the shade of makeup selected by the user. This allows the user to perform appropriate makeup aftercare.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The analysis unit analyzes the user's facial photograph. The photographs include those taken from the front, at an angle, and with a smiling face. The analysis unit uses a facial recognition algorithm to extract facial features and detect facial landmarks. For example, it extracts features such as eye shape, skin color, and facial contours. Step 2: The generator generates a makeup-applied image based on the facial photograph analyzed by the analyzer. The generator uses an image generation algorithm to generate a makeup-applied image and generates different makeup styles based on the type of data used. For example, different makeup styles such as natural makeup, party makeup, and office makeup are generated. Step 3: The presentation unit presents the post-makeup image generated by the generation unit to the user. The presentation unit displays the post-makeup image using a display device, and displays different makeup styles based on the display format. For example, the post-makeup image is displayed according to the screen size and resolution. Step 4: The providing unit provides a service based on the image of the user with makeup presented by the presenting unit. The providing unit provides a video based on the makeup selected by the user, and provides a service by a professional makeup artist. For example, the providing unit provides a video that explains in detail the steps of the makeup selected by the user, and provides a service in which a professional makeup artist actually applies the makeup.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0128] The 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.

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

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

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

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 AI 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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 AI 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes a facial photo of a user; a generation unit that generates a makeup-applied image based on the facial photograph analyzed by the analysis unit; a presentation unit that presents the post-makeup image generated by the generation unit to a user; a providing unit that provides a service based on the after-makeup image presented by the presenting unit. system.

2. The analysis unit Extracting the user's facial features The system of claim 1 .

3. The generation unit Generate multiple makeup images based on extracted features The system of claim 1 .

4. The presentation unit Display the generated makeup image to the user The system of claim 1 .

5. The providing unit A video providing unit is provided that provides videos based on the makeup selected by the user. The system of claim 1 .

6. The providing unit An artist providing unit that provides services by professional makeup artists based on the makeup selected by the user. The system of claim 1 .

7. The analysis unit Estimate the user's emotions and adjust the facial photo analysis method based on the estimated user emotions. The system of claim 1 .

8. The analysis unit When analyzing facial photos, the accuracy of the analysis can be improved by referring to the user's past makeup history. The system of claim 1 .

Images