System

Abstract

To provide a system that gives real-time comments on makeup color selection and application methods on the basis of a user's facial bone structure and personal color.SOLUTION: A system comprises: an acquisition unit; a diagnosis unit; an analysis unit; and a pointing-out unit. The acquisition unit acquires an image of the user's face. The diagnosis unit analyzes the image acquired by the acquisition unit and diagnoses the user's facial bone structure and personal color. The analysis unit analyzes makeup color selection and application methods on the basis of the diagnosis results of the diagnosis unit. The pointing-out unit gives comments on the basis of the analysis results of the analysis 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the problem that it is difficult to provide real-time makeup suggestions based on a user's facial structure and personal color.

[0005] The system according to the embodiment aims to provide real-time advice on makeup colors and application methods based on the user's facial structure and personal color. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a diagnosis unit, an analysis unit, and a suggestion unit. The acquisition unit acquires an image of a user's face. The diagnosis unit analyzes the image acquired by the acquisition unit and diagnoses the facial bone structure and personal color. The analysis unit analyzes the color and application of makeup based on the result of the diagnosis by the diagnosis unit. The suggestion unit makes suggestions based on the result of the analysis by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can provide real-time advice on makeup colors and application methods based on the user's facial structure and personal color. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A makeup advice system according to an embodiment of the present invention is a system that diagnoses the facial bone structure and personal color by holding a camera over the user, and provides real-time guidance on the current makeup color and application method. In the makeup advice system, a user holds a camera over their face, and an AI analyzes the image captured by the camera and diagnoses the facial bone structure and personal color. Furthermore, the AI ​​analyzes the current makeup color and application method and provides appropriate guidance in real time. For example, a user holds a camera over their face. At this time, the camera captures the entire face of the user. For example, a smartphone camera is used to capture a photo of the user's face. This image is input to the AI. Next, the AI ​​analyzes the input image and diagnoses the facial bone structure and personal color. The AI ​​analyzes the shape and color tone of the face and identifies the user's bone structure and personal color. For example, the AI ​​analyzes the facial contour and skin color to diagnose the user's bone structure type and personal color. Furthermore, the AI ​​analyzes the current makeup color and application method and provides appropriate guidance in real time. The AI ​​analyzes the makeup color and application method and provides the user with suggestions for improvement. For example, if the color of lipstick does not match the user's personal color, the AI ​​will suggest changing the color. Also, if the way eyeshadow is applied is inappropriate, it will suggest the appropriate way to apply it. This system allows users to receive makeup advice in real time based on their facial structure and personal color. This makes it possible to apply makeup that suits them better, improving the quality of their makeup. This allows the makeup advice system to diagnose the user's facial structure and personal color, and provide advice on the color and application of makeup in real time.

[0029] The makeup advice system according to the embodiment includes an acquisition unit, a diagnosis unit, an analysis unit, and a suggestion unit. The acquisition unit acquires an image of the user's face. The image of the user's face includes, but is not limited to, an image acquired using a smartphone camera. The acquisition unit can, for example, take a picture of the user's face using a smartphone camera and acquire the image. The acquisition unit can also acquire an image of the user's face using a digital camera or a webcam. The diagnosis unit analyzes the image acquired by the acquisition unit and diagnoses the facial bone structure and personal color. The diagnosis unit, for example, uses AI to analyze the shape and color tone of the face and identifies the user's bone structure and personal color. For example, the diagnosis unit can analyze the facial contour and skin color to diagnose the user's bone structure type and personal color. The analysis unit analyzes the current makeup color and application method based on the result diagnosed by the diagnosis unit. The analysis unit, for example, uses AI to analyze the makeup color and application method and suggests improvements to the user. For example, the analysis unit can suggest that the color of the lipstick should be changed if the color of the lipstick does not match the personal color. Also, the analysis unit can suggest an appropriate application method if the method of applying eyeshadow is inappropriate. The suggestion unit makes appropriate suggestions based on the results of the analysis by the analysis unit. The suggestion unit can, for example, use AI to suggest improvements to the makeup and provide appropriate advice to the user. For example, the suggestion unit can suggest that the color of the lipstick should be changed if the color of the lipstick does not match the personal color. Also, the suggestion unit can suggest an appropriate application method if the method of applying eyeshadow is inappropriate. As a result, the makeup advice system according to the embodiment can diagnose the facial structure and personal color of the user and suggest the color and application method of the makeup in real time.

[0030] The acquisition unit acquires an image of the user's face. The image of the user's face includes, for example, an image acquired using a smartphone camera, but is not limited to such an example. The acquisition unit can, for example, photograph the user's face using a smartphone camera and acquire the image. The acquisition unit can also acquire an image of the user's face using a digital camera or a webcam. Specifically, when using a smartphone camera, the user starts a dedicated application and follows a guide to photograph images from multiple angles, such as the front, left and right sides, and an angle from above. This allows the acquisition unit to collect detailed information about the user's face. Similarly, when using a digital camera or a webcam, images are acquired from multiple angles using dedicated software, and these images are uploaded to a cloud server. The acquisition unit centrally manages these images and allows the analysis unit and diagnosis unit to access them. Furthermore, the acquisition unit also has a function of automatically checking the resolution and quality of the image and prompting re-imaging as necessary. This allows the acquisition unit to secure high-quality image data and improve the accuracy of the entire system.

[0031] The diagnosis unit analyzes the image acquired by the acquisition unit and diagnoses the facial bone structure and personal color. For example, the diagnosis unit uses AI to analyze the shape and color tone of the face and identify the user's bone structure and personal color. Specifically, the AI ​​uses deep learning technology to extract feature points such as the facial outline, eyes, nose, and mouth, and classifies the facial bone structure type based on these feature points. For example, if the face outline is round, it is classified as "round type", and if the chin is pointed, it is classified as "heart type". In addition, for diagnosing personal colors, skin color, eye color, hair color, etc. are analyzed and classified into four seasonal types: spring, summer, autumn, and winter. Based on this information, the diagnosis unit generates basic data for suggesting the color and style of makeup that is optimal for the user. Furthermore, the diagnosis unit can learn from past diagnosis results and user feedback to continuously improve the accuracy of the diagnosis. For example, it analyzes before and after images of makeup provided by the user and verifies the accuracy of the diagnosis results. In addition, the diagnosis unit is equipped with a correction algorithm to perform diagnosis with high accuracy even under different lighting conditions and backgrounds. This enables the diagnostic unit to provide consistent diagnostic results even in diverse environments, thereby gaining the trust of users.

[0032] The analysis unit analyzes the current color and application of makeup based on the results of the diagnosis by the diagnosis unit. The analysis unit, for example, uses AI to analyze the color and application of makeup and points out areas for improvement to the user. Specifically, the AI ​​uses image recognition technology to detect each part of makeup (lipstick, eyeshadow, cheeks, etc.) from an image of the user's face and analyzes the color and application of each part. For example, if the color of the lipstick does not match the personal color, it can suggest that the color be changed. Also, if the way eyeshadow is applied is inappropriate, it can suggest an appropriate way to apply it. The analysis unit evaluates whether the color of the makeup is in harmony with the skin color and eye color based on the image of the user's face, and suggests changing the color if necessary. Furthermore, the analysis unit evaluates whether the way the makeup is applied matches the facial structure, and can suggest, for example, a way to apply eyeshadow to emphasize the eyes and a shading method to make the face look smaller. Based on these analysis results, the analysis unit presents specific areas for improvement to the user and supports the improvement of makeup skills. The analysis unit can also refer to the user's past makeup history and suggest makeup according to trends and seasons. This allows the analysis unit to always provide the user with the latest makeup information and give advice tailored to individual needs.

[0033] The suggestion unit gives appropriate suggestions based on the results of the analysis by the analysis unit. The suggestion unit, for example, uses AI to suggest areas for improvement in makeup and provides appropriate advice to the user. Specifically, if the lip color does not match the personal color, the suggestion unit can suggest a change in the color. In addition, if the eyeshadow application method is inappropriate, the suggestion unit can also suggest an appropriate application method. The suggestion unit provides specific advice to the user based on the data provided by the analysis unit. For example, when changing the lip color, the suggestion unit suggests a specific color name and brand, making it easy for the user to carry out. In addition, for how to apply eyeshadow, the suggestion unit provides a step-by-step guide so that the user can accurately reproduce it. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, the suggestion unit analyzes before-and-after images provided by the user and evaluates the effectiveness of the advice. In addition, the suggestion unit can provide customized advice according to the user's preferences and lifestyle. For example, the suggestion unit takes into account the difference between work makeup and private makeup and provides appropriate advice for each. As a result, the suggestion unit can provide specific and practical advice to the user and support the improvement of makeup skills.

[0034] The acquisition unit can acquire an image of the user's face using a camera. The acquisition unit can, for example, photograph the user's face using a smartphone camera and acquire the image. The acquisition unit can also acquire an image of the user's face using a digital camera or a webcam. For example, the acquisition unit can photograph the user's face using a smartphone camera and acquire the image. The acquisition unit can also acquire an image of the user's face using a digital camera or a webcam. As a result, the image of the user's face can be accurately acquired by using the camera. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input image data acquired by the camera to the generation AI and cause the generation AI to execute a process of extracting facial features from the image data.

[0035] The diagnosis unit can analyze the shape and color tone of the face and diagnose the bone structure and personal color of the user. The diagnosis unit can, for example, use AI to analyze the shape and color tone of the face and identify the bone structure and personal color of the user. For example, the diagnosis unit can analyze the contours of the face and the color tone of the skin and diagnose the bone structure type and personal color of the user. The diagnosis unit can, for example, use AI to analyze the shape and color tone of the face and identify the bone structure and personal color of the user. For example, the diagnosis unit can analyze the contours of the face and the color tone of the skin and diagnose the bone structure type and personal color of the user. As a result, the bone structure and personal color of the user can be accurately diagnosed by analyzing the shape and color tone of the face. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input face image data to the generation AI and cause the generation AI to execute the analysis of the shape and color tone of the face.

[0036] The analysis unit can analyze the color and application method of the current makeup. The analysis unit, for example, uses AI to analyze the color and application method of the makeup, and points out the improvement points to the user. For example, if the color of the lipstick does not match the personal color, the analysis unit can point out the color to be changed. Also, if the application method of the eyeshadow is inappropriate, the analysis unit can suggest an appropriate application method. The analysis unit, for example, uses AI to analyze the color and application method of the makeup, and points out the improvement points to the user. For example, if the color of the lipstick does not match the personal color, the analysis unit can point out the color to be changed. Also, if the application method of the eyeshadow is inappropriate, the analysis unit can suggest an appropriate application method. In this way, by analyzing the color and application method of the current makeup, appropriate indications can be made. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input image data of the makeup to the generation AI, and cause the generation AI to execute an analysis of the color and application method of the makeup.

[0037] The suggestion unit can point out improvements to the makeup based on the analysis results. The suggestion unit, for example, uses AI to point out improvements to the makeup and provides appropriate advice to the user. For example, if the lip color does not match the personal color, the suggestion unit can suggest a change to the color. Also, if the eyeshadow application method is inappropriate, the suggestion unit can suggest an appropriate application method. The suggestion unit, for example, uses AI to point out improvements to the makeup and provides appropriate advice to the user. For example, if the lip color does not match the personal color, the suggestion unit can suggest a change to the color. Also, if the eyeshadow application method is inappropriate, the suggestion unit can suggest an appropriate application method. In this way, appropriate advice can be provided to the user by pointing out improvements to the makeup based on the analysis results. Part or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the analysis results to the generation AI and cause the generation AI to execute suggestions for improvements to the makeup.

[0038] The indication unit can indicate to change the color of the lipstick if the color does not match the personal color. The indication unit, for example, uses AI to indicate to change the color of the lipstick if the color does not match the personal color. For example, the indication unit can indicate to change the color of the lipstick if the color does not match the personal color. Also, the indication unit can indicate to change the color of the lipstick if the color does not match the personal color. In this way, by indicating to change the color of the lipstick if the color does not match the personal color, appropriate advice can be provided to the user. A part or all of the above-mentioned processing in the indication unit may be performed, for example, using AI, or may be performed without using AI. For example, the indication unit can input image data of the lipstick color to the generation AI and cause the generation AI to change the color of the lipstick.

[0039] The indication unit can suggest an appropriate application method when the eyeshadow application method is inappropriate. The indication unit, for example, uses AI to suggest an appropriate application method when the eyeshadow application method is inappropriate. For example, the indication unit can suggest an appropriate application method when the eyeshadow application method is inappropriate. Furthermore, the indication unit can suggest an appropriate application method when the eyeshadow application method is inappropriate. As a result, when the eyeshadow application method is inappropriate, an appropriate application method is suggested, thereby making it possible to provide appropriate advice to the user. A part or all of the above-mentioned processing in the indication unit may be performed, for example, using AI, or may be performed without using AI. For example, the indication unit can input image data of the eyeshadow application method to the generation AI and cause the generation AI to execute a suggestion of an appropriate application method.

[0040] The acquisition unit can analyze the user's past image acquisition history and select the optimal acquisition method. The acquisition unit can, for example, use AI to analyze the user's past image acquisition history and select the optimal acquisition method. For example, the acquisition unit automatically sets the camera angle that the user has used in the past. In addition, the acquisition unit can reproduce the lighting conditions under which the user has obtained the best results in the past. Furthermore, the acquisition unit can suggest the optimal image acquisition method by referring to the settings of the device that the user has used in the past. As a result, the optimal acquisition method can be selected by analyzing the past image acquisition history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input past image acquisition history data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0041] The acquisition unit can perform filtering based on the user's current environment and lighting conditions when acquiring an image. The acquisition unit performs filtering based on the user's current environment and lighting conditions when acquiring an image, for example, using AI. For example, when the user is outdoors, the acquisition unit acquires an optimal image using natural light. In addition, when the user is indoors, the acquisition unit can apply a filter that minimizes the influence of artificial lighting. Furthermore, when the user is in a dark environment, the acquisition unit can acquire an image by applying a filter that reduces noise. As a result, an optimal image can be acquired by filtering based on the current environment and lighting conditions. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input data on the environment and lighting conditions to the generation AI and cause the generation AI to execute the filtering process.

[0042] The acquisition unit can preferentially acquire highly relevant images in consideration of the geographical location information of the user when acquiring images. The acquisition unit, for example, uses AI to preferentially acquire highly relevant images in consideration of the geographical location information of the user when acquiring images. For example, when the user is in a specific area, the acquisition unit preferentially acquires images that match the characteristics of the area. In addition, when the user is traveling, the acquisition unit can acquire images in consideration of the background of a tourist spot. Furthermore, when the user is at home, the acquisition unit can acquire images that are optimal for an indoor environment. As a result, highly relevant images can be preferentially acquired by considering the geographical location information. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to execute acquisition of highly relevant images.

[0043] The acquisition unit can analyze the social media activity of the user at the time of acquiring the image and acquire related images. The acquisition unit can, for example, use AI to analyze the social media activity of the user at the time of acquiring the image and acquire related images. For example, the acquisition unit can acquire images in a similar style by referring to the style of an image recently posted by the user. In addition, the acquisition unit can acquire images in which the user incorporates trends that are popular on social media. Furthermore, the acquisition unit can acquire an optimal image based on feedback from the user's followers. In this way, related images can be acquired by analyzing social media activities. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input social media activity data to the generation AI and cause the generation AI to execute acquisition of related images.

[0044] The diagnosis unit can adjust the level of detail of the diagnosis based on the importance of the facial bone structure and the personal color at the time of diagnosis. The diagnosis unit adjusts the level of detail of the diagnosis based on the importance of the facial bone structure and the personal color at the time of diagnosis, for example, using AI. For example, the diagnosis unit performs a detailed bone structure analysis when the facial bone structure is important in the diagnosis. Also, the diagnosis unit can perform a detailed color tone analysis when the personal color is important in the diagnosis. Furthermore, the diagnosis unit can perform a balanced diagnosis when both the bone structure and the personal color are important. As a result, by adjusting the level of detail of the diagnosis based on the importance of the facial bone structure and the personal color, a more appropriate diagnosis result can be provided. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input data on the facial bone structure and the personal color to the generation AI and cause the generation AI to adjust the level of detail of the diagnosis.

[0045] The diagnostic unit can apply different diagnostic algorithms according to the categories of face shape and color tone at the time of diagnosis. The diagnostic unit can apply different diagnostic algorithms according to the categories of face shape and color tone at the time of diagnosis using, for example, AI. For example, the diagnostic unit applies a diagnostic algorithm specialized for round faces to a user with a round face. In addition, the diagnostic unit can apply a diagnostic algorithm specialized for square faces to a user with a square face. Furthermore, the diagnostic unit can apply a diagnostic algorithm specialized for fair skin to a user with fair skin. In this way, by applying different diagnostic algorithms according to the categories of face shape and color tone, a more appropriate diagnostic result can be provided. Part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input data on face shape and color tone to the generation AI and cause the generation AI to apply different diagnostic algorithms.

[0046] The diagnosis unit can determine the priority of diagnosis based on the time of submission of the face shape and color tone at the time of diagnosis. The diagnosis unit determines the priority of diagnosis based on the time of submission of the face shape and color tone at the time of diagnosis, for example, using AI. For example, the diagnosis unit prioritizes and diagnoses images taken recently. In addition, the diagnosis unit can prioritize and diagnose images taken before a specific event. Furthermore, the diagnosis unit can prioritize and diagnose images taken at a date and time specified by the user. As a result, by determining the priority of diagnosis based on the time of submission, a more appropriate diagnosis result can be provided. Part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input data on the time of submission to the generation AI and cause the generation AI to execute the determination of the priority of diagnosis.

[0047] The diagnosis unit can adjust the order of diagnosis results based on the relevance of face shape and color tone at the time of diagnosis. The diagnosis unit adjusts the order of diagnosis results based on the relevance of face shape and color tone at the time of diagnosis, for example, using AI. For example, when face shape is important in diagnosis, the diagnosis unit displays diagnosis results related to shape first. Also, when personal color is important in diagnosis, the diagnosis unit can display diagnosis results related to color tone first. Furthermore, when both bone structure and personal color are important, the diagnosis unit can display diagnosis results in a balanced order. Thereby, by adjusting the order of diagnosis results based on the relevance, more appropriate diagnosis results can be provided. Part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input data on face shape and color tone to the generation AI and cause the generation AI to adjust the order of diagnosis results.

[0048] The analysis unit can improve the accuracy of the analysis by considering the interrelationship between the color of makeup and the application method during the analysis. The analysis unit can improve the accuracy of the analysis by considering the interrelationship between the color of makeup and the application method during the analysis, for example, using AI. For example, the analysis unit performs the analysis by considering the interrelationship between the color of lipstick and the color of eyeshadow. The analysis unit can also perform the analysis by considering the interrelationship between the application method of blush and foundation. Furthermore, the analysis unit can perform the analysis by considering the overall makeup balance. As a result, the accuracy of the analysis is improved by considering the interrelationship between the color of makeup and the application method. A part or all of the above-mentioned processing in the analysis unit may be performed by using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the color of makeup and the application method to the generation AI and cause the generation AI to perform an analysis of the interrelationship.

[0049] The analysis unit can perform the analysis while taking into consideration the attribute information of the submitter, such as the color of makeup and the application method, during the analysis. The analysis unit can perform the analysis while taking into consideration the attribute information of the submitter, such as the color of makeup and the application method, during the analysis, for example, using AI. For example, the analysis unit performs the analysis while taking into consideration the age of the submitter. The analysis unit can also perform the analysis while taking into consideration the gender of the submitter. Furthermore, the analysis unit can perform the analysis while taking into consideration the skin type of the submitter. In this way, by taking into consideration the attribute information of the submitter, it is possible to provide a more appropriate analysis result. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the attribute information data of the submitter to the generation AI and cause the generation AI to execute the analysis.

[0050] The analysis unit can perform the analysis while taking into consideration the geographical distribution of makeup colors and application methods. The analysis unit can perform the analysis while taking into consideration the geographical distribution of makeup colors and application methods, for example, using AI. For example, the analysis unit performs the analysis while taking into consideration makeup colors that are popular in a specific region. The analysis unit can also perform the analysis while taking into consideration the general application method of makeup in a specific region. Furthermore, the analysis unit can perform the analysis while taking into consideration makeup trends for each region. As a result, by taking the geographical distribution into consideration, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input geographical distribution data to the generation AI and cause the generation AI to execute the analysis.

[0051] The analysis unit can improve the accuracy of the analysis by referring to related literature on the color and application of makeup during the analysis. The analysis unit can improve the accuracy of the analysis by referring to related literature on the color and application of makeup during the analysis, for example, using AI. For example, the analysis unit performs the analysis by referring to literature on the latest makeup trends. The analysis unit can also perform the analysis by referring to literature on the color theory of makeup. Furthermore, the analysis unit can perform the analysis by referring to literature on makeup techniques. As a result, the accuracy of the analysis is improved by referring to related literature. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input related literature data to the generation AI and cause the generation AI to execute the analysis.

[0052] The indication unit can adjust the level of detail of the indication based on the importance of the color and application of the makeup when making an indication. The indication unit can adjust the level of detail of the indication based on the importance of the color and application of the makeup when making an indication, for example, using AI. For example, the indication unit provides detailed indication when the color of the lip is important. Furthermore, the indication unit can provide detailed indication when the application of eyeshadow is important. Furthermore, the indication unit can provide balanced indication when the overall makeup balance is important. As a result, by adjusting the level of detail of the indication based on the importance, more appropriate indication can be provided. A part or all of the above-mentioned processing in the indication unit may be performed, for example, using AI, or may be performed without using AI. For example, the indication unit can input data on the color and application of the makeup to the generation AI and cause the generation AI to adjust the level of detail of the indication.

[0053] The indication unit can apply different indication algorithms depending on the category of makeup color or application when making an indication. The indication unit can apply different indication algorithms depending on the category of makeup color or application when making an indication using, for example, AI. For example, the indication unit applies an indication algorithm specialized for lip makeup. The indication unit can also apply an indication algorithm specialized for eye makeup. Furthermore, the indication unit can apply an indication algorithm specialized for cheek makeup. In this way, by applying different indication algorithms depending on the category, more appropriate indication can be provided. Part or all of the above-mentioned processing in the indication unit can be performed using, for example, AI, or can be performed without using AI. For example, the indication unit can input data on makeup color and application to the generation AI and cause the generation AI to apply different indication algorithms.

[0054] The pointing unit can determine the priority of the points based on the time of submission of the makeup color and application method when making a point. The pointing unit can determine the priority of the points based on the time of submission of the makeup color and application method when making a point using, for example, AI. For example, the pointing unit prioritizes points for recent makeup. Also, the pointing unit can prioritize points for makeup before a specific event. Furthermore, the pointing unit can prioritize points for makeup at a date and time specified by the user. In this way, by determining the priority of points based on the time of submission, more appropriate points can be provided. Part or all of the above-mentioned processing in the pointing unit can be performed using, for example, AI, or can be performed without using AI. For example, the pointing unit can input data on the time of submission to the generation AI and cause the generation AI to determine the priority of points.

[0055] The indication unit can adjust the order of the indication contents based on the relevance of the makeup color and application method when making indications. The indication unit can adjust the order of the indication contents based on the relevance of the makeup color and application method when making indications using, for example, AI. For example, if the lip color is important, the indication unit can make indications related to the lip first. Also, if the application method of eyeshadow is important, the indication unit can make indications related to the eyeshadow first. Furthermore, if the overall makeup balance is important, the indication unit can make indications in a balanced order. As a result, by adjusting the order of the indication contents based on the relevance, more appropriate indications can be provided. Part or all of the above-mentioned processing in the indication unit can be performed using, for example, AI, or can be performed without using AI. For example, the indication unit can input data on the makeup color and application method to the generation AI and cause the generation AI to adjust the order of the indication contents.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The makeup advice system may further include a health examination unit that diagnoses the health condition of the user's skin. The health examination unit may analyze the image acquired by the acquisition unit and diagnose the health condition of the skin, such as dryness, age spots, and wrinkles. For example, the health examination unit may analyze the moisture content of the skin and, if dry, indicate the need for moisturizing. Also, if age spots or wrinkles are found, it may suggest the use of an appropriate skin care product. Furthermore, the health examination unit may analyze the tone and texture of the skin and provide advice for maintaining healthy skin. This allows the user to receive advice not only about makeup but also about the health condition of the skin.

[0058] When acquiring an image of the user's face, the acquisition unit can automatically recognize facial expressions and provide different makeup advice for different expressions. For example, the acquisition unit acquires images of the user when they are smiling and when they are serious, and provides makeup advice suitable for each expression. The acquisition unit can also acquire images of the user when they have a surprised or sad expression, and point out areas for improvement in makeup according to each expression. Furthermore, the acquisition unit can analyze the movement of the facial muscles when the user makes different expressions, and suggest makeup techniques according to the expression. This allows the user to receive makeup advice suitable for various expressions.

[0059] The diagnostic unit can analyze not only the shape and color tone of the user's face, but also the hairstyle and hair color, and provide comprehensive styling advice. For example, the diagnostic unit can analyze whether the user's hairstyle matches the shape of the face, and suggest a change in hairstyle if necessary. Also, if the hair color does not match the personal color, it can suggest an appropriate hair color. Furthermore, the diagnostic unit can analyze the texture and volume of the hair, and provide styling advice based on the health of the hair. This allows the user to receive comprehensive advice not only on makeup, but also on hairstyle and hair color.

[0060] The analysis unit can analyze not only the color and application method of the user's makeup, but also the ingredients and quality of the cosmetics used, and provide appropriate advice. For example, the analysis unit can analyze the ingredients of the foundation used by the user, and if it contains ingredients that do not suit the user's skin, it can suggest a different product. It can also analyze the ingredients of lipstick or eye shadow, and if it contains ingredients that pose an allergic risk, it can warn the user. Furthermore, the analysis unit can evaluate the quality of the cosmetics used and suggest higher quality products. This allows the user to receive appropriate advice not only on makeup techniques, but also on the cosmetics they use.

[0061] When pointing out areas for improvement in the user's makeup, the suggestion unit can provide advice based on the user's lifestyle and daily activities. For example, when the user plays sports, the suggestion unit can suggest makeup products that are sweat-resistant. Also, when the user is going out for a long time, the suggestion unit can suggest makeup products that last. Furthermore, when the user meets many people for work, the suggestion unit can suggest makeup techniques that give a professional impression. In this way, the user can receive makeup advice that suits his or her lifestyle.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The acquisition unit acquires an image of the user's face. The image of the user's face includes, but is not limited to, an image acquired using a smartphone camera. The acquisition unit can acquire the image of the user's face using a smartphone camera, a digital camera, a webcam, or the like. Step 2: The diagnosis unit analyzes the image acquired by the acquisition unit and diagnoses the facial bone structure and personal color. The diagnosis unit uses AI to analyze the shape and color of the face and identify the user's bone structure and personal color. For example, the diagnosis unit analyzes the facial contours and skin color to diagnose the user's bone structure type and personal color. Step 3: The analysis unit analyzes the current makeup color and application method based on the results of the diagnosis by the diagnosis unit. The analysis unit uses AI to analyze the makeup color and application method, and suggests areas for improvement to the user. For example, if the lip color does not match the user's personal color, it will suggest that the color be changed. Also, if the eyeshadow application method is inappropriate, it will suggest the appropriate way to apply it. Step 4: The suggestion unit gives appropriate suggestions based on the results of the analysis by the analysis unit. The suggestion unit uses AI to suggest areas for improvement in makeup and provide appropriate advice to the user. For example, if the lip color does not match the user's personal color, the suggestion unit will suggest that the color be changed. Also, if the eyeshadow application method is inappropriate, the suggestion unit will suggest the appropriate way to apply it.

[0064] (Example 2) A makeup advice system according to an embodiment of the present invention is a system that diagnoses the facial bone structure and personal color by holding a camera over the user, and provides real-time guidance on the current makeup color and application method. In the makeup advice system, a user holds a camera over their face, and an AI analyzes the image captured by the camera and diagnoses the facial bone structure and personal color. Furthermore, the AI ​​analyzes the current makeup color and application method and provides appropriate guidance in real time. For example, a user holds a camera over their face. At this time, the camera captures the entire face of the user. For example, a smartphone camera is used to capture a photo of the user's face. This image is input to the AI. Next, the AI ​​analyzes the input image and diagnoses the facial bone structure and personal color. The AI ​​analyzes the shape and color tone of the face and identifies the user's bone structure and personal color. For example, the AI ​​analyzes the facial contour and skin color to diagnose the user's bone structure type and personal color. Furthermore, the AI ​​analyzes the current makeup color and application method and provides appropriate guidance in real time. The AI ​​analyzes the makeup color and application method and provides the user with suggestions for improvement. For example, if the color of lipstick does not match the user's personal color, the AI ​​will suggest changing the color. Also, if the way eyeshadow is applied is inappropriate, it will suggest the appropriate way to apply it. This system allows users to receive makeup advice in real time based on their facial structure and personal color. This makes it possible to apply makeup that suits them better, improving the quality of their makeup. This allows the makeup advice system to diagnose the user's facial structure and personal color, and provide advice on the color and application of makeup in real time.

[0065] The makeup advice system according to the embodiment includes an acquisition unit, a diagnosis unit, an analysis unit, and a suggestion unit. The acquisition unit acquires an image of the user's face. The image of the user's face includes, but is not limited to, an image acquired using a smartphone camera. The acquisition unit can, for example, take a picture of the user's face using a smartphone camera and acquire the image. The acquisition unit can also acquire an image of the user's face using a digital camera or a webcam. The diagnosis unit analyzes the image acquired by the acquisition unit and diagnoses the facial bone structure and personal color. The diagnosis unit, for example, uses AI to analyze the shape and color tone of the face and identifies the user's bone structure and personal color. For example, the diagnosis unit can analyze the facial contour and skin color to diagnose the user's bone structure type and personal color. The analysis unit analyzes the current makeup color and application method based on the result diagnosed by the diagnosis unit. The analysis unit, for example, uses AI to analyze the makeup color and application method and suggests improvements to the user. For example, the analysis unit can suggest that the color of the lipstick should be changed if the color of the lipstick does not match the personal color. Also, the analysis unit can suggest an appropriate application method if the method of applying eyeshadow is inappropriate. The suggestion unit makes appropriate suggestions based on the results of the analysis by the analysis unit. The suggestion unit can, for example, use AI to suggest improvements to the makeup and provide appropriate advice to the user. For example, the suggestion unit can suggest that the color of the lipstick should be changed if the color of the lipstick does not match the personal color. Also, the suggestion unit can suggest an appropriate application method if the method of applying eyeshadow is inappropriate. As a result, the makeup advice system according to the embodiment can diagnose the facial structure and personal color of the user and suggest the color and application method of the makeup in real time.

[0066] The acquisition unit acquires an image of the user's face. The image of the user's face includes, for example, an image acquired using a smartphone camera, but is not limited to such an example. The acquisition unit can, for example, photograph the user's face using a smartphone camera and acquire the image. The acquisition unit can also acquire an image of the user's face using a digital camera or a webcam. Specifically, when using a smartphone camera, the user starts a dedicated application and follows a guide to photograph images from multiple angles, such as the front, left and right sides, and an angle from above. This allows the acquisition unit to collect detailed information about the user's face. Similarly, when using a digital camera or a webcam, images are acquired from multiple angles using dedicated software, and these images are uploaded to a cloud server. The acquisition unit centrally manages these images and allows the analysis unit and diagnosis unit to access them. Furthermore, the acquisition unit also has a function of automatically checking the resolution and quality of the image and prompting re-imaging as necessary. This allows the acquisition unit to secure high-quality image data and improve the accuracy of the entire system.

[0067] The diagnosis unit analyzes the image acquired by the acquisition unit and diagnoses the facial bone structure and personal color. For example, the diagnosis unit uses AI to analyze the shape and color tone of the face and identify the user's bone structure and personal color. Specifically, the AI ​​uses deep learning technology to extract feature points such as the facial outline, eyes, nose, and mouth, and classifies the facial bone structure type based on these feature points. For example, if the face outline is round, it is classified as "round type", and if the chin is pointed, it is classified as "heart type". In addition, for diagnosing personal colors, skin color, eye color, hair color, etc. are analyzed and classified into four seasonal types: spring, summer, autumn, and winter. Based on this information, the diagnosis unit generates basic data for suggesting the color and style of makeup that is optimal for the user. Furthermore, the diagnosis unit can learn from past diagnosis results and user feedback to continuously improve the accuracy of the diagnosis. For example, it analyzes before and after images of makeup provided by the user and verifies the accuracy of the diagnosis results. In addition, the diagnosis unit is equipped with a correction algorithm to perform diagnosis with high accuracy even under different lighting conditions and backgrounds. This enables the diagnostic unit to provide consistent diagnostic results even in diverse environments, thereby gaining the trust of users.

[0068] The analysis unit analyzes the current color and application of makeup based on the results of the diagnosis by the diagnosis unit. The analysis unit, for example, uses AI to analyze the color and application of makeup and points out areas for improvement to the user. Specifically, the AI ​​uses image recognition technology to detect each part of makeup (lipstick, eyeshadow, cheeks, etc.) from an image of the user's face and analyzes the color and application of each part. For example, if the color of the lipstick does not match the personal color, it can suggest that the color be changed. Also, if the way eyeshadow is applied is inappropriate, it can suggest an appropriate way to apply it. The analysis unit evaluates whether the color of the makeup is in harmony with the skin color and eye color based on the image of the user's face, and suggests changing the color if necessary. Furthermore, the analysis unit evaluates whether the way the makeup is applied matches the facial structure, and can suggest, for example, a way to apply eyeshadow to emphasize the eyes and a shading method to make the face look smaller. Based on these analysis results, the analysis unit presents specific areas for improvement to the user and supports the improvement of makeup skills. The analysis unit can also refer to the user's past makeup history and suggest makeup according to trends and seasons. This allows the analysis unit to always provide the user with the latest makeup information and give advice tailored to individual needs.

[0069] The suggestion unit gives appropriate suggestions based on the results of the analysis by the analysis unit. The suggestion unit, for example, uses AI to suggest areas for improvement in makeup and provides appropriate advice to the user. Specifically, if the lip color does not match the personal color, the suggestion unit can suggest a change in the color. In addition, if the eyeshadow application method is inappropriate, the suggestion unit can also suggest an appropriate application method. The suggestion unit provides specific advice to the user based on the data provided by the analysis unit. For example, when changing the lip color, the suggestion unit suggests a specific color name and brand, making it easy for the user to carry out. In addition, for how to apply eyeshadow, the suggestion unit provides a step-by-step guide so that the user can accurately reproduce it. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, the suggestion unit analyzes before-and-after images provided by the user and evaluates the effectiveness of the advice. In addition, the suggestion unit can provide customized advice according to the user's preferences and lifestyle. For example, the suggestion unit takes into account the difference between work makeup and private makeup and provides appropriate advice for each. As a result, the suggestion unit can provide specific and practical advice to the user and support the improvement of makeup skills.

[0070] The acquisition unit can acquire an image of the user's face using a camera. The acquisition unit can, for example, photograph the user's face using a smartphone camera and acquire the image. The acquisition unit can also acquire an image of the user's face using a digital camera or a webcam. For example, the acquisition unit can photograph the user's face using a smartphone camera and acquire the image. The acquisition unit can also acquire an image of the user's face using a digital camera or a webcam. As a result, the image of the user's face can be accurately acquired by using the camera. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input image data acquired by the camera to the generation AI and cause the generation AI to execute a process of extracting facial features from the image data.

[0071] The diagnosis unit can analyze the shape and color tone of the face and diagnose the bone structure and personal color of the user. The diagnosis unit can, for example, use AI to analyze the shape and color tone of the face and identify the bone structure and personal color of the user. For example, the diagnosis unit can analyze the contours of the face and the color tone of the skin and diagnose the bone structure type and personal color of the user. The diagnosis unit can, for example, use AI to analyze the shape and color tone of the face and identify the bone structure and personal color of the user. For example, the diagnosis unit can analyze the contours of the face and the color tone of the skin and diagnose the bone structure type and personal color of the user. As a result, the bone structure and personal color of the user can be accurately diagnosed by analyzing the shape and color tone of the face. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input face image data to the generation AI and cause the generation AI to execute the analysis of the shape and color tone of the face.

[0072] The analysis unit can analyze the color and application method of the current makeup. The analysis unit, for example, uses AI to analyze the color and application method of the makeup, and points out the improvement points to the user. For example, if the color of the lipstick does not match the personal color, the analysis unit can point out the color to be changed. Also, if the application method of the eyeshadow is inappropriate, the analysis unit can suggest an appropriate application method. The analysis unit, for example, uses AI to analyze the color and application method of the makeup, and points out the improvement points to the user. For example, if the color of the lipstick does not match the personal color, the analysis unit can point out the color to be changed. Also, if the application method of the eyeshadow is inappropriate, the analysis unit can suggest an appropriate application method. In this way, by analyzing the color and application method of the current makeup, appropriate indications can be made. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input image data of the makeup to the generation AI, and cause the generation AI to execute an analysis of the color and application method of the makeup.

[0073] The suggestion unit can point out improvements to the makeup based on the analysis results. The suggestion unit, for example, uses AI to point out improvements to the makeup and provides appropriate advice to the user. For example, if the lip color does not match the personal color, the suggestion unit can suggest a change to the color. Also, if the eyeshadow application method is inappropriate, the suggestion unit can suggest an appropriate application method. The suggestion unit, for example, uses AI to point out improvements to the makeup and provides appropriate advice to the user. For example, if the lip color does not match the personal color, the suggestion unit can suggest a change to the color. Also, if the eyeshadow application method is inappropriate, the suggestion unit can suggest an appropriate application method. In this way, appropriate advice can be provided to the user by pointing out improvements to the makeup based on the analysis results. Part or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the analysis results to the generation AI and cause the generation AI to execute suggestions for improvements to the makeup.

[0074] The indication unit can indicate to change the color of the lipstick if the color does not match the personal color. The indication unit, for example, uses AI to indicate to change the color of the lipstick if the color does not match the personal color. For example, the indication unit can indicate to change the color of the lipstick if the color does not match the personal color. Also, the indication unit can indicate to change the color of the lipstick if the color does not match the personal color. In this way, by indicating to change the color of the lipstick if the color does not match the personal color, appropriate advice can be provided to the user. A part or all of the above-mentioned processing in the indication unit may be performed, for example, using AI, or may be performed without using AI. For example, the indication unit can input image data of the lipstick color to the generation AI and cause the generation AI to change the color of the lipstick.

[0075] The indication unit can suggest an appropriate application method when the eyeshadow application method is inappropriate. The indication unit, for example, uses AI to suggest an appropriate application method when the eyeshadow application method is inappropriate. For example, the indication unit can suggest an appropriate application method when the eyeshadow application method is inappropriate. Furthermore, the indication unit can suggest an appropriate application method when the eyeshadow application method is inappropriate. As a result, when the eyeshadow application method is inappropriate, an appropriate application method is suggested, thereby making it possible to provide appropriate advice to the user. A part or all of the above-mentioned processing in the indication unit may be performed, for example, using AI, or may be performed without using AI. For example, the indication unit can input image data of the eyeshadow application method to the generation AI and cause the generation AI to execute a suggestion of an appropriate application method.

[0076] The acquisition unit can estimate the user's emotion and determine the timing of acquiring the face image based on the estimated user's emotion. The acquisition unit can estimate the user's emotion using, for example, AI and adjust the timing of acquiring the face image based on the estimated user's emotion. For example, when the user is relaxed, the acquisition unit acquires images at intervals of several seconds in order to capture a natural expression. In addition, when the user is tense, the acquisition unit can acquire images after giving the user time to relax. Furthermore, when the user is in a hurry, the acquisition unit can quickly acquire images and perform processing in a minimum amount of time. As a result, by adjusting the timing of acquiring images based on the user's emotion, a more natural expression can be captured. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input image data acquired by a camera to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The acquisition unit can analyze the user's past image acquisition history and select the optimal acquisition method. The acquisition unit can, for example, use AI to analyze the user's past image acquisition history and select the optimal acquisition method. For example, the acquisition unit automatically sets the camera angle that the user has used in the past. In addition, the acquisition unit can reproduce the lighting conditions under which the user has obtained the best results in the past. Furthermore, the acquisition unit can suggest the optimal image acquisition method by referring to the settings of the device that the user has used in the past. As a result, the optimal acquisition method can be selected by analyzing the past image acquisition history. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input past image acquisition history data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0078] The acquisition unit can perform filtering based on the user's current environment and lighting conditions when acquiring an image. The acquisition unit performs filtering based on the user's current environment and lighting conditions when acquiring an image, for example, using AI. For example, when the user is outdoors, the acquisition unit acquires an optimal image using natural light. In addition, when the user is indoors, the acquisition unit can apply a filter that minimizes the influence of artificial lighting. Furthermore, when the user is in a dark environment, the acquisition unit can acquire an image by applying a filter that reduces noise. As a result, an optimal image can be acquired by filtering based on the current environment and lighting conditions. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input data on the environment and lighting conditions to the generation AI and cause the generation AI to execute the filtering process.

[0079] The acquisition unit can estimate the user's emotion and determine the order of images to be acquired based on the estimated user's emotion. The acquisition unit can estimate the user's emotion using, for example, AI and determine the priority order of images to be acquired based on the estimated user's emotion. For example, when the user is relaxed, the acquisition unit acquires images with a priority on natural facial expressions. Also, when the user is nervous, the acquisition unit can acquire multiple images to capture the moment of relaxation. Furthermore, when the user is in a hurry, the acquisition unit can acquire images with a priority on the most important parts. This allows more appropriate images to be acquired by determining the priority order of images based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input image data acquired by a camera to the generation AI and cause the generation AI to execute emotion estimation.

[0080] The acquisition unit can preferentially acquire highly relevant images in consideration of the geographical location information of the user when acquiring images. The acquisition unit, for example, uses AI to preferentially acquire highly relevant images in consideration of the geographical location information of the user when acquiring images. For example, when the user is in a specific area, the acquisition unit preferentially acquires images that match the characteristics of the area. In addition, when the user is traveling, the acquisition unit can acquire images in consideration of the background of a tourist spot. Furthermore, when the user is at home, the acquisition unit can acquire images that are optimal for an indoor environment. As a result, highly relevant images can be preferentially acquired by considering the geographical location information. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI, or may be performed without using AI. For example, the acquisition unit can input geographical location information data to the generation AI and cause the generation AI to execute acquisition of highly relevant images.

[0081] The acquisition unit can analyze the social media activity of the user at the time of acquiring the image and acquire related images. The acquisition unit can, for example, use AI to analyze the social media activity of the user at the time of acquiring the image and acquire related images. For example, the acquisition unit can acquire images in a similar style by referring to the style of an image recently posted by the user. In addition, the acquisition unit can acquire images in which the user incorporates trends that are popular on social media. Furthermore, the acquisition unit can acquire an optimal image based on feedback from the user's followers. In this way, related images can be acquired by analyzing social media activities. Part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input social media activity data to the generation AI and cause the generation AI to execute acquisition of related images.

[0082] The diagnosis unit can estimate the user's emotion and determine the expression method of the diagnosis result based on the estimated user's emotion. The diagnosis unit can estimate the user's emotion using, for example, AI, and adjust the expression method of the diagnosis result based on the estimated user's emotion. For example, the diagnosis unit provides a detailed diagnosis result when the user is relaxed. Furthermore, the diagnosis unit can provide a concise and easy-to-understand diagnosis result when the user is nervous. Furthermore, the diagnosis unit can provide a diagnosis result that focuses on the main points when the user is in a hurry. This allows a more appropriate diagnosis result to be provided by adjusting the expression method of the diagnosis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input emotion data to the generation AI and cause the generation AI to adjust the expression method of the diagnosis result.

[0083] The diagnosis unit can adjust the level of detail of the diagnosis based on the importance of the facial bone structure and the personal color at the time of diagnosis. The diagnosis unit adjusts the level of detail of the diagnosis based on the importance of the facial bone structure and the personal color at the time of diagnosis, for example, using AI. For example, the diagnosis unit performs a detailed bone structure analysis when the facial bone structure is important in the diagnosis. Also, the diagnosis unit can perform a detailed color tone analysis when the personal color is important in the diagnosis. Furthermore, the diagnosis unit can perform a balanced diagnosis when both the bone structure and the personal color are important. As a result, by adjusting the level of detail of the diagnosis based on the importance of the facial bone structure and the personal color, a more appropriate diagnosis result can be provided. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input data on the facial bone structure and the personal color to the generation AI and cause the generation AI to adjust the level of detail of the diagnosis.

[0084] The diagnostic unit can apply different diagnostic algorithms according to the categories of face shape and color tone at the time of diagnosis. The diagnostic unit can apply different diagnostic algorithms according to the categories of face shape and color tone at the time of diagnosis using, for example, AI. For example, the diagnostic unit applies a diagnostic algorithm specialized for round faces to a user with a round face. In addition, the diagnostic unit can apply a diagnostic algorithm specialized for square faces to a user with a square face. Furthermore, the diagnostic unit can apply a diagnostic algorithm specialized for fair skin to a user with fair skin. In this way, by applying different diagnostic algorithms according to the categories of face shape and color tone, a more appropriate diagnostic result can be provided. Part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnostic unit can input data on face shape and color tone to the generation AI and cause the generation AI to apply different diagnostic algorithms.

[0085] The diagnosis unit can estimate the user's emotion and determine the length of the diagnosis result based on the estimated user's emotion. The diagnosis unit can estimate the user's emotion using, for example, AI, and adjust the length of the diagnosis result based on the estimated user's emotion. For example, the diagnosis unit provides a detailed diagnosis result when the user is relaxed. Furthermore, the diagnosis unit can provide a concise and easy-to-understand diagnosis result when the user is nervous. Furthermore, the diagnosis unit can provide a diagnosis result that focuses on the main points when the user is in a hurry. This allows a more appropriate diagnosis result to be provided by adjusting the length of the diagnosis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, AI, or may be performed without using AI. For example, the diagnosis unit can input emotion data to the generation AI and cause the generation AI to adjust the length of the diagnosis result.

[0086] The diagnosis unit can determine the priority of diagnosis based on the time of submission of the face shape and color tone at the time of diagnosis. The diagnosis unit determines the priority of diagnosis based on the time of submission of the face shape and color tone at the time of diagnosis, for example, using AI. For example, the diagnosis unit prioritizes and diagnoses images taken recently. In addition, the diagnosis unit can prioritize and diagnose images taken before a specific event. Furthermore, the diagnosis unit can prioritize and diagnose images taken at a date and time specified by the user. As a result, by determining the priority of diagnosis based on the time of submission, a more appropriate diagnosis result can be provided. Part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input data on the time of submission to the generation AI and cause the generation AI to execute the determination of the priority of diagnosis.

[0087] The diagnosis unit can adjust the order of diagnosis results based on the relevance of face shape and color tone at the time of diagnosis. The diagnosis unit adjusts the order of diagnosis results based on the relevance of face shape and color tone at the time of diagnosis, for example, using AI. For example, when face shape is important in diagnosis, the diagnosis unit displays diagnosis results related to shape first. Also, when personal color is important in diagnosis, the diagnosis unit can display diagnosis results related to color tone first. Furthermore, when both bone structure and personal color are important, the diagnosis unit can display diagnosis results in a balanced order. Thereby, by adjusting the order of diagnosis results based on the relevance, more appropriate diagnosis results can be provided. Part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input data on face shape and color tone to the generation AI and cause the generation AI to adjust the order of diagnosis results.

[0088] The analysis unit can estimate the user's emotion and determine the analysis criteria for the color and application of makeup based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, AI, and adjust the analysis criteria for the color and application of makeup based on the estimated user's emotion. For example, the analysis unit applies detailed analysis criteria when the user is relaxed. Furthermore, the analysis unit can apply concise analysis criteria when the user is nervous. Furthermore, the analysis unit can apply analysis criteria that focus on the main points when the user is in a hurry. This allows for adjusting the analysis criteria based on the user's emotion to provide a more appropriate analysis result. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input emotion data to the generation AI and cause the generation AI to adjust the analysis criteria.

[0089] The analysis unit can improve the accuracy of the analysis by considering the interrelationship between the color of makeup and the application method during the analysis. The analysis unit can improve the accuracy of the analysis by considering the interrelationship between the color of makeup and the application method during the analysis, for example, using AI. For example, the analysis unit performs the analysis by considering the interrelationship between the color of lipstick and the color of eyeshadow. The analysis unit can also perform the analysis by considering the interrelationship between the application method of blush and foundation. Furthermore, the analysis unit can perform the analysis by considering the overall makeup balance. As a result, the accuracy of the analysis is improved by considering the interrelationship between the color of makeup and the application method. A part or all of the above-mentioned processing in the analysis unit may be performed by using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the color of makeup and the application method to the generation AI and cause the generation AI to perform an analysis of the interrelationship.

[0090] The analysis unit can perform the analysis while taking into consideration the attribute information of the submitter, such as the color of makeup and the application method, during the analysis. The analysis unit can perform the analysis while taking into consideration the attribute information of the submitter, such as the color of makeup and the application method, during the analysis, for example, using AI. For example, the analysis unit performs the analysis while taking into consideration the age of the submitter. The analysis unit can also perform the analysis while taking into consideration the gender of the submitter. Furthermore, the analysis unit can perform the analysis while taking into consideration the skin type of the submitter. In this way, by taking into consideration the attribute information of the submitter, it is possible to provide a more appropriate analysis result. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the attribute information data of the submitter to the generation AI and cause the generation AI to execute the analysis.

[0091] The analysis unit can estimate the user's emotion and determine the display order of the analysis results based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, AI, and adjust the display order of the analysis results based on the estimated user's emotion. For example, the analysis unit displays detailed analysis results first when the user is relaxed. Also, the analysis unit can display concise analysis results first when the user is nervous. Furthermore, the analysis unit can display analysis results with the main points first when the user is in a hurry. This allows more appropriate analysis results to be provided by adjusting the display order of the analysis results based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input emotion data to the generation AI and cause the generation AI to adjust the display order of the analysis results.

[0092] The analysis unit can perform the analysis while taking into consideration the geographical distribution of makeup colors and application methods. The analysis unit can perform the analysis while taking into consideration the geographical distribution of makeup colors and application methods, for example, using AI. For example, the analysis unit performs the analysis while taking into consideration makeup colors that are popular in a specific region. The analysis unit can also perform the analysis while taking into consideration the general application method of makeup in a specific region. Furthermore, the analysis unit can perform the analysis while taking into consideration makeup trends for each region. As a result, by taking the geographical distribution into consideration, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input geographical distribution data to the generation AI and cause the generation AI to execute the analysis.

[0093] The analysis unit can improve the accuracy of the analysis by referring to related literature on the color and application of makeup during the analysis. The analysis unit can improve the accuracy of the analysis by referring to related literature on the color and application of makeup during the analysis, for example, using AI. For example, the analysis unit performs the analysis by referring to literature on the latest makeup trends. The analysis unit can also perform the analysis by referring to literature on the color theory of makeup. Furthermore, the analysis unit can perform the analysis by referring to literature on makeup techniques. As a result, the accuracy of the analysis is improved by referring to related literature. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input related literature data to the generation AI and cause the generation AI to execute the analysis.

[0094] The indication unit can estimate the user's emotion and determine the expression method of the indication content based on the estimated user's emotion. The indication unit can estimate the user's emotion using, for example, AI, and adjust the expression method of the indication content based on the estimated user's emotion. For example, the indication unit provides detailed indication content when the user is relaxed. Furthermore, the indication unit can provide concise and easy-to-understand indication content when the user is nervous. Furthermore, the indication unit can provide indication content that covers the main points when the user is in a hurry. This allows more appropriate indication to be provided by adjusting the expression method of the indication content based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the indication unit may be performed using, for example, AI, or may be performed without using AI. For example, the indication unit can input emotion data to the generation AI and cause the generation AI to adjust the expression method of the indication content.

[0095] The indication unit can adjust the level of detail of the indication based on the importance of the color and application of the makeup when making an indication. The indication unit can adjust the level of detail of the indication based on the importance of the color and application of the makeup when making an indication, for example, using AI. For example, the indication unit provides detailed indication when the color of the lip is important. Furthermore, the indication unit can provide detailed indication when the application of eyeshadow is important. Furthermore, the indication unit can provide balanced indication when the overall makeup balance is important. As a result, by adjusting the level of detail of the indication based on the importance, more appropriate indication can be provided. A part or all of the above-mentioned processing in the indication unit may be performed, for example, using AI, or may be performed without using AI. For example, the indication unit can input data on the color and application of the makeup to the generation AI and cause the generation AI to adjust the level of detail of the indication.

[0096] The indication unit can apply different indication algorithms depending on the category of makeup color or application when making an indication. The indication unit can apply different indication algorithms depending on the category of makeup color or application when making an indication using, for example, AI. For example, the indication unit applies an indication algorithm specialized for lip makeup. The indication unit can also apply an indication algorithm specialized for eye makeup. Furthermore, the indication unit can apply an indication algorithm specialized for cheek makeup. In this way, by applying different indication algorithms depending on the category, more appropriate indication can be provided. Part or all of the above-mentioned processing in the indication unit can be performed using, for example, AI, or can be performed without using AI. For example, the indication unit can input data on makeup color and application to the generation AI and cause the generation AI to apply different indication algorithms.

[0097] The indication unit can estimate the user's emotion and determine the length of the indication content based on the estimated user's emotion. The indication unit can estimate the user's emotion using, for example, AI and adjust the length of the indication content based on the estimated user's emotion. For example, the indication unit provides detailed indication content when the user is relaxed. Furthermore, the indication unit can provide concise and easy-to-understand indication content when the user is nervous. Furthermore, the indication unit can provide indication content that focuses on the main points when the user is in a hurry. This allows more appropriate indication to be provided by adjusting the length of the indication content based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the indication unit may be performed using, for example, AI, or may be performed without using AI. For example, the indication unit can input emotion data to the generation AI and cause the generation AI to adjust the length of the indication content.

[0098] The pointing unit can determine the priority of the points based on the time of submission of the makeup color and application method when making a point. The pointing unit can determine the priority of the points based on the time of submission of the makeup color and application method when making a point using, for example, AI. For example, the pointing unit prioritizes points for recent makeup. Also, the pointing unit can prioritize points for makeup before a specific event. Furthermore, the pointing unit can prioritize points for makeup at a date and time specified by the user. In this way, by determining the priority of points based on the time of submission, more appropriate points can be provided. Part or all of the above-mentioned processing in the pointing unit can be performed using, for example, AI, or can be performed without using AI. For example, the pointing unit can input data on the time of submission to the generation AI and cause the generation AI to determine the priority of points.

[0099] The indication unit can adjust the order of the indication contents based on the relevance of the makeup color and application method when making indications. The indication unit can adjust the order of the indication contents based on the relevance of the makeup color and application method when making indications using, for example, AI. For example, if the lip color is important, the indication unit can make indications related to the lip first. Also, if the application method of eyeshadow is important, the indication unit can make indications related to the eyeshadow first. Furthermore, if the overall makeup balance is important, the indication unit can make indications in a balanced order. As a result, by adjusting the order of the indication contents based on the relevance, more appropriate indications can be provided. Part or all of the above-mentioned processing in the indication unit can be performed using, for example, AI, or can be performed without using AI. For example, the indication unit can input data on the makeup color and application method to the generation AI and cause the generation AI to adjust the order of the indication contents.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The makeup advice system may further include a health examination unit that diagnoses the health condition of the user's skin. The health examination unit may analyze the image acquired by the acquisition unit and diagnose the health condition of the skin, such as dryness, age spots, and wrinkles. For example, the health examination unit may analyze the moisture content of the skin and, if dry, indicate the need for moisturizing. Also, if age spots or wrinkles are found, it may suggest the use of an appropriate skin care product. Furthermore, the health examination unit may analyze the tone and texture of the skin and provide advice for maintaining healthy skin. This allows the user to receive advice not only about makeup but also about the health condition of the skin.

[0102] When acquiring an image of the user's face, the acquisition unit can automatically recognize facial expressions and provide different makeup advice for different expressions. For example, the acquisition unit acquires images of the user when they are smiling and when they are serious, and provides makeup advice suitable for each expression. The acquisition unit can also acquire images of the user when they have a surprised or sad expression, and point out areas for improvement in makeup according to each expression. Furthermore, the acquisition unit can analyze the movement of the facial muscles when the user makes different expressions, and suggest makeup techniques according to the expression. This allows the user to receive makeup advice suitable for various expressions.

[0103] The diagnostic unit can analyze not only the shape and color tone of the user's face, but also the hairstyle and hair color, and provide comprehensive styling advice. For example, the diagnostic unit can analyze whether the user's hairstyle matches the shape of the face, and suggest a change in hairstyle if necessary. Also, if the hair color does not match the personal color, it can suggest an appropriate hair color. Furthermore, the diagnostic unit can analyze the texture and volume of the hair, and provide styling advice based on the health of the hair. This allows the user to receive comprehensive advice not only on makeup, but also on hairstyle and hair color.

[0104] The analysis unit can analyze not only the color and application method of the user's makeup, but also the ingredients and quality of the cosmetics used, and provide appropriate advice. For example, the analysis unit can analyze the ingredients of the foundation used by the user, and if it contains ingredients that do not suit the user's skin, it can suggest a different product. It can also analyze the ingredients of lipstick or eye shadow, and if it contains ingredients that pose an allergic risk, it can warn the user. Furthermore, the analysis unit can evaluate the quality of the cosmetics used and suggest higher quality products. This allows the user to receive appropriate advice not only on makeup techniques, but also on the cosmetics they use.

[0105] When pointing out areas for improvement in the user's makeup, the suggestion unit can provide advice based on the user's lifestyle and daily activities. For example, when the user plays sports, the suggestion unit can suggest makeup products that are sweat-resistant. Also, when the user is going out for a long time, the suggestion unit can suggest makeup products that last. Furthermore, when the user meets many people for work, the suggestion unit can suggest makeup techniques that give a professional impression. In this way, the user can receive makeup advice that suits his or her lifestyle.

[0106] The acquisition unit can estimate the user's emotions and provide makeup advice based on the estimated user's emotions. For example, when the user is relaxed, the acquisition unit can suggest makeup that matches the relaxed atmosphere. When the user is nervous, the acquisition unit can suggest makeup techniques to relieve the tension. When the user is happy, the acquisition unit can suggest bright makeup that brings out the joy. In this way, makeup advice according to the user's emotions can be provided.

[0107] The diagnosis unit can estimate the user's emotion and adjust the feedback method of the diagnosis result based on the estimated user's emotion. For example, the diagnosis unit can provide detailed feedback when the user is relaxed. Also, when the user is nervous, the diagnosis unit can provide concise and easy-to-understand feedback. Furthermore, when the user is in a hurry, the diagnosis unit can provide feedback that focuses on the main points. In this way, appropriate feedback can be provided based on the user's emotion.

[0108] The analysis unit can estimate the user's emotions and adjust the analysis results of the makeup color and application method based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Also, the analysis unit can provide concise analysis results when the user is nervous. Furthermore, the analysis unit can provide analysis results that focus on the main points when the user is in a hurry. In this way, it is possible to provide appropriate analysis results based on the user's emotions.

[0109] The indication unit can estimate the user's emotion and adjust the tone of the indication content based on the estimated user's emotion. For example, when the user is relaxed, the indication unit can provide indication in a friendly tone. When the user is nervous, the indication unit can provide indication in a gentle tone. Furthermore, when the user is in a hurry, the indication unit can provide indication in a concise and direct tone. In this way, indication can be provided in an appropriate tone based on the user's emotion.

[0110] The indication unit can estimate the user's emotion and adjust the order of indication contents based on the estimated user's emotion. For example, when the user is relaxed, the indication unit can provide detailed indication contents first. When the user is nervous, the indication unit can provide concise indication contents first. Furthermore, when the user is in a hurry, the indication unit can provide the main points first. In this way, indications can be given in an appropriate order based on the user's emotion.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The acquisition unit acquires an image of the user's face. The image of the user's face includes, but is not limited to, an image acquired using a smartphone camera. The acquisition unit can acquire the image of the user's face using a smartphone camera, a digital camera, a webcam, or the like. Step 2: The diagnosis unit analyzes the image acquired by the acquisition unit and diagnoses the facial bone structure and personal color. The diagnosis unit uses AI to analyze the shape and color of the face and identify the user's bone structure and personal color. For example, the diagnosis unit analyzes the facial contours and skin color to diagnose the user's bone structure type and personal color. Step 3: The analysis unit analyzes the current makeup color and application method based on the results of the diagnosis by the diagnosis unit. The analysis unit uses AI to analyze the makeup color and application method, and suggests areas for improvement to the user. For example, if the lip color does not match the user's personal color, it will suggest that the color be changed. Also, if the eyeshadow application method is inappropriate, it will suggest the appropriate way to apply it. Step 4: The suggestion unit gives appropriate suggestions based on the results of the analysis by the analysis unit. The suggestion unit uses AI to suggest areas for improvement in makeup and provide appropriate advice to the user. For example, if the lip color does not match the user's personal color, the suggestion unit will suggest that the color be changed. Also, if the eyeshadow application method is inappropriate, the suggestion unit will suggest the appropriate way to apply it.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0114] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the elements including the above-mentioned acquisition unit, diagnosis unit, analysis unit, and indication unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires an image of the user's face using the camera 42 of the smart device 14. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the acquired image, and diagnoses the facial bone structure and personal color. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the current color and application method of makeup based on the diagnosis result. The indication unit is realized by the specific processing unit 290 of the data processing device 12, and makes appropriate indications based on the analysis result. The acquisition unit is also realized, for example, by the control unit 46A of the smart device 14, and estimates the user's emotions and adjusts the timing of image acquisition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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, the RAM 30, and the 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.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] Fig. 4 shows an example of 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned acquisition unit, diagnosis unit, analysis unit, and indication unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires an image of the user's face using the camera 42 of the smart glasses 214. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the acquired image, and diagnoses the facial bone structure and personal color. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the current color and application method of makeup based on the diagnosis result. The indication unit is realized by the specific processing unit 290 of the data processing device 12, and makes appropriate indications based on the analysis result. The acquisition unit is also realized, for example, by the control unit 46A of the smart glasses 214, and estimates the user's emotions and adjusts the timing of image acquisition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned acquisition unit, diagnosis unit, analysis unit, and indication unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires an image of the user's face using the camera 42 of the headset type terminal 314. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the acquired image to diagnose the facial bone structure and personal color. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the current color and application method of makeup based on the diagnosis result. The indication unit is realized by the specific processing unit 290 of the data processing device 12, and makes appropriate indications based on the analysis result. The acquisition unit is also realized, for example, by the control unit 46A of the headset type terminal 314, and estimates the user's emotions and adjusts the timing of image acquisition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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, the RAM 30, and the 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] 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 performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 a voice indicating a user input for 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.

[0163] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0165] Each of the multiple elements including the above-mentioned acquisition unit, diagnosis unit, analysis unit, and indication unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires an image of the user's face using the camera 42 of the robot 414. The diagnosis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the acquired image, and diagnoses the facial bone structure and personal color. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the current color and application method of makeup based on the diagnosis result. The indication unit is realized by the specific processing unit 290 of the data processing device 12, and makes appropriate indications based on the analysis result. The acquisition unit is also realized, for example, by the control unit 46A of the robot 414, and estimates the user's emotions and adjusts the timing of image acquisition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] 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 Universal Serial Bus (USB) 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.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0179] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) An acquisition unit that acquires an image of a user's face; A diagnosis unit that analyzes the image acquired by the acquisition unit and diagnoses a facial bone structure and a personal color; an analysis unit that analyzes the color and application of makeup based on the results of the diagnosis by the diagnosis unit; A suggestion unit that suggests something based on the result of the analysis by the analysis unit. A system characterized by: (Appendix 2) The acquisition unit is Use the camera to capture an image of the user's face 2. The system of claim 1, (Appendix 3) The diagnosis unit includes: Analyzes the shape and color of the face to determine the user's bone structure and personal color 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze your current makeup color and application method 2. The system of claim 1, (Appendix 5) The indication unit is Based on the analysis results, we suggest areas for improvement in makeup 2. The system of claim 1, (Appendix 6) The indication unit is If your lip color doesn't match your personal color, tell her to change it 2. The system of claim 1, (Appendix 7) The indication unit is If you are applying eyeshadow improperly, we will suggest the correct way to do it. 2. The system of claim 1, (Appendix 8) The acquisition unit is A user's emotion is estimated, and the timing of acquiring a face image is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The acquisition unit is Analyze the user's past image acquisition history and select the optimal acquisition method 2. The system of claim 1, (Appendix 10) The acquisition unit is When capturing images, filtering is performed based on the user's current environment and lighting conditions. 2. The system of claim 1, (Appendix 11) The acquisition unit is Estimate the user's emotion and determine the order of images to be acquired based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The acquisition unit is When retrieving images, the system takes into account the user's geographic location information to prioritize retrieving images that are more relevant to the user. 2. The system of claim 1, (Appendix 13) The acquisition unit is When capturing an image, the user's social media activity is analyzed to capture related images. 2. The system of claim 1, (Appendix 14) The diagnosis unit includes: A method for expressing a diagnosis result based on a user's estimated emotion is determined. 2. The system of claim 1, (Appendix 15) The diagnosis unit includes: During diagnosis, the level of detail is adjusted based on the importance of facial bone structure and personal color. 2. The system of claim 1, (Appendix 16) The diagnosis unit includes: During diagnosis, different diagnostic algorithms are applied depending on the face shape and color tone category. 2. The system of claim 1, (Appendix 17) The diagnosis unit includes: A user's emotion is estimated, and a length of a diagnosis result is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 18) The diagnosis unit includes: At the time of diagnosis, diagnostic prioritization is based on face shape and facial tone submission time 2. The system of claim 1, (Appendix 19) The diagnosis unit includes: During diagnosis, the order of diagnosis results is adjusted based on the relevance of face shape and color tone 2. The system of claim 1, (Appendix 20) The analysis unit is Estimate the user's emotions and determine analysis criteria for makeup color and application method based on the estimated user emotions. 2. The system of claim 1, (Appendix 21) The analysis unit is Improve the accuracy of the analysis by taking into account the correlation between makeup colors and application methods. 2. The system of claim 1, (Appendix 22) The analysis unit is When analyzing, the submitter's attribute information such as makeup color and application method is taken into consideration. 2. The system of claim 1, (Appendix 23) The analysis unit is Inferring user emotions and determining the display order of analysis results based on the estimated user emotions 2. The system of claim 1, (Appendix 24) The analysis unit is When analyzing, the geographic distribution of makeup colors and application methods is taken into account. 2. The system of claim 1, (Appendix 25) The analysis unit is During the analysis, we will refer to related literature on makeup colors and application methods to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 26) The indication unit is Infer the user's emotions and decide how to express the complaint based on the estimated user's emotions 2. The system of claim 1, (Appendix 27) The indication unit is When making a comment, the level of detail is adjusted based on the importance of the makeup color and application method. 2. The system of claim 1, (Appendix 28) The indication unit is When pointing out flaws, different algorithms are applied depending on the category of makeup color or application style. 2. The system of claim 1, (Appendix 29) The indication unit is Infer the user's emotion and determine the length of the remark content based on the estimated user emotion 2. The system of claim 1, (Appendix 30) The indication unit is When a problem is reported, the priority of the problem is decided based on the time of submission of the makeup color and application method. 2. The system of claim 1, (Appendix 31) The indication unit is When making corrections, the order of corrections is adjusted based on the relevance of makeup colors and application methods 2. The system of claim 1, [Explanation of symbols]

[0185] 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 acquisition unit that acquires an image of a user's face; A diagnosis unit that analyzes the image acquired by the acquisition unit and diagnoses a facial bone structure and a personal color; an analysis unit that analyzes the color and application of makeup based on the results of the diagnosis by the diagnosis unit; A suggestion unit that suggests something based on the result of the analysis by the analysis unit. A system characterized by:

2. The acquisition unit is Capture an image of the user's face using a camera 2. The system of claim 1 .

3. The diagnosis unit includes: Analyzes the shape and color of the face to determine the user's bone structure and personal color 2. The system of claim 1 .

4. The analysis unit is Analyze your current makeup color and application method 2. The system of claim 1 .

5. The indication unit is Based on the analysis results, we will suggest areas for improvement in makeup.

2. The system of claim 1 .

6. The indication unit is If your lip color doesn't match your personal color, tell her to change it 2. The system of claim 1 .

7. The indication unit is If you are applying eyeshadow improperly, we will suggest the correct way to do it.

2. The system of claim 1 .

8. The acquisition unit is A user's emotion is estimated, and the timing of acquiring a face image is determined based on the estimated user's emotion.

2. The system of claim 1 .

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