System

Abstract

An object of a system according to an embodiment is to accurately convey a hairstyle and a hair color desired by a user.SOLUTION: A system includes a collection unit, a generation unit, and a projection unit. The collection unit inputs an image of a hairstyle or a hair color desired by the user as a text or an image. The generation unit analyzes the information collected by the collection unit and generates a sample avatar similar to the user. The projection unit projects the avatar generated by the generation unit, and the hairdresser refers to the avatar.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately communicate the desired hairstyle and hair color at a hair salon.

[0005] The system according to the embodiment aims to accurately convey the user's desired hairstyle and hair color. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a projection unit. The collection unit inputs the user's desired hairstyle and hair color as text or an image. The generation unit analyzes the information collected by the collection unit and generates a sample avatar that resembles the user. The projection unit projects the avatar generated by the generation unit, allowing the hairdresser to use it as a reference. [Effects of the Invention]

[0007] The system according to the embodiment can accurately convey the user's desired hairstyle and hair color. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention solves worries and problems related to hairstyles and hair colors. In this system, a user inputs their ideal hairstyle and hair color image using an app. A generation AI analyzes the information and generates a sample avatar that looks exactly like the user. The generated avatar can be checked before going to a salon, further enhancing the user's ideal look. The visualized avatar can then be projected at the salon, allowing for accurate haircuts and dyeing. For example, a user can input their ideal hairstyle and hair color using an app. The user can input specific images using text or images. The generation AI then analyzes the input information and generates a sample avatar that looks exactly like the user. Based on the generated avatar, the user can further enhance their ideal look. The visualized avatar can then be projected at the salon, allowing for accurate haircuts and dyeing. This allows the system to accurately visualize the user's ideal hairstyle and hair color and communicate them to the hairdresser. For example, when trying out a new hairstyle or hair color, checking the avatar beforehand can help prevent mistakes. In addition, users can visualize their ideal hairstyle and hair color, which improves the experience at the salon.

[0029] A hairstyle and hair color system according to an embodiment includes a collection unit, a generation unit, and a projection unit. The collection unit inputs a user's desired hairstyle and hair color as text or an image. For example, the collection unit can input specific requests such as "I want a short haircut with bangs swept diagonally" or "I want to dye my hair a light brown." The generation unit uses a generation AI to analyze the information collected by the collection unit and generate a sample avatar that resembles the user. For example, the generation AI generates a realistic avatar based on the user's facial photo and hairstyle information. The generation unit can also use the generation AI to generate an avatar that matches the user's desired hairstyle and hair color. For example, if the user requests a short haircut, the generation AI generates an avatar that matches that hairstyle. The projection unit projects the avatar generated by the generation unit, allowing a hairdresser to use it as a reference. For example, the projection unit provides the generated avatar to a hairdresser and reproduces the specific hairstyle and hair color. This allows the hairstyle and hair color system according to an embodiment to specifically imagine the user's ideal hairstyle and hair color and accurately convey that to the hairdresser.

[0030] The collection unit can input the user's desired hairstyle and hair color image in the form of text or an image. For example, the collection unit can input a specific request such as "I want a short cut with bangs swept diagonally" or "I want to dye my hair light brown." The collection unit can clarify the specific format and method of input, such as the text format and image resolution. This allows the user to input a specific image into the collection unit.

[0031] The generation unit can use generation AI to generate a realistic avatar based on the user's facial photo and hairstyle information. The generation unit, for example, uses generation AI to generate a realistic avatar based on the user's facial photo and hairstyle information. The generation AI can generate an avatar based on the user's facial photo and hairstyle information. The generation AI can generate an avatar using technologies such as deep learning and GAN (generative artificial network). This makes it possible to generate a realistic avatar based on the user's facial photo and hairstyle information.

[0032] The generation unit can use generation AI to generate an avatar based on the hairstyle and hair color desired by the user. The generation unit, for example, uses generation AI to generate an avatar that matches the hairstyle and hair color desired by the user. The generation AI generates an avatar based on the hairstyle and hair color desired by the user. The generation AI can generate an avatar using technologies such as deep learning and GAN (generative artificial network). This makes it possible to generate an avatar that matches the hairstyle and hair color desired by the user.

[0033] The projection unit provides the generated avatar to the hairdresser, allowing the hairdresser to recreate a specific hairstyle and hair color. The projection unit, for example, provides the generated avatar to the hairdresser, allowing the hairdresser to recreate a specific hairstyle and hair color. The projection unit allows the hairdresser to recreate a specific hairstyle and hair color based on the generated avatar. The projection unit, for example, uses a screen or projector to project the avatar, allowing the hairdresser to use it as a reference. This allows the hairdresser to recreate a specific hairstyle and hair color.

[0034] The projection unit can reproduce a desired hairstyle and hair color based on an avatar brought by a user. The projection unit reproduces a specific hairstyle and hair color based on, for example, an avatar brought by a user. The projection unit allows a hairdresser to reproduce a specific hairstyle and hair color based on an avatar brought by a user. The projection unit, for example, uses a screen or projector to project the avatar, allowing the hairdresser to use it as a reference. In this way, an ideal hairstyle and hair color can be reproduced based on an avatar brought by a user.

[0035] The collection unit can analyze the user's past hairstyle and hair color history and select an appropriate collection method. For example, the collection unit prioritizes collection of the most successful styles based on hairstyles and hair colors tried by the user in the past. The collection unit can avoid collecting information about hairstyles and hair colors that the user was dissatisfied with in the past in order to avoid such information. The collection unit can suggest optimal hairstyles and hair colors according to seasons and events based on the user's past history. This allows the optimal collection method to be selected based on the user's past history.

[0036] When collecting images of desired hairstyles and hair colors, the collection unit can filter them based on the user's current fashion or lifestyle. For example, if the user prefers casual fashion, the collection unit will prioritize collecting hairstyles and hair colors that match that style. If the user is considering using the device in a business setting, the collection unit can collect formal hairstyles and hair colors. The collection unit can suggest optimal hairstyles and hair colors based on the user's lifestyle (outdoorsy, indoorsy, etc.). This allows the collection of optimal images based on the user's fashion and lifestyle.

[0037] When collecting images of desired hairstyles and hair colors, the collection unit can select an appropriate collection means according to the user's input method. For example, if the user selects voice input, the collection unit collects ideal hairstyles and hair colors using voice recognition technology. If the user selects text input, the collection unit can collect based on detailed text information. If the user selects image input, the collection unit can collect ideal hairstyles and hair colors using image analysis technology. This makes it possible to select the optimal collection means according to the user's input method.

[0038] When collecting images of desired hairstyles and hair colors, the collection unit can prioritize collection of highly relevant images based on the user's geographical location information. For example, if the user lives in an urban area, the collection unit collects hairstyles and hair colors based on the latest trends. If the user lives in a rural area, the collection unit can collect hairstyles and hair colors that suit the local culture and customs. If the user is traveling, the collection unit can collect hairstyles and hair colors based on the local trends and culture. This makes it possible to collect highly relevant images based on the user's geographical location information.

[0039] When collecting images of desired hairstyles and hair colors, the collection unit can analyze the user's social media activities and collect related images. For example, the collection unit collects images based on hairstyles and hair colors that the user has "liked" on social media. The collection unit can collect images based on the hairstyles and hair colors of influencers that the user follows. The collection unit can analyze the content of the user's posts on social media and collect related hairstyles and hair colors. This makes it possible to collect related images based on the user's social media activities.

[0040] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting images of desired hairstyles and hair colors. The collection unit adjusts the collection method, for example, based on feedback provided by the user in the past. The collection unit can preferentially collect hairstyles and hair colors that the user was satisfied with in the past. The collection unit can avoid collecting information about hairstyles and hair colors that the user was dissatisfied with in the past, thereby customizing the collection method based on the user's past feedback.

[0041] When generating an avatar, the generation unit can set the level of detail of the avatar based on the importance of hairstyle and hair color. For example, if a user has strong preferences for hairstyle, the generation unit can generate an avatar with enhanced hairstyle details. If a user has strong preferences for hair color, the generation unit can generate an avatar with enhanced hair color details. If a user has preferences for both hairstyle and hair color, the generation unit can generate an avatar with enhanced details for both. This allows the level of detail of the avatar to be adjusted based on the importance of hairstyle and hair color.

[0042] When generating an avatar, the generation unit can use different generation algorithms depending on the hairstyle or hair color category. For example, if the hair is short, the generation unit can generate an avatar using a specific algorithm. If the hair is long, the generation unit can generate an avatar using a different algorithm. If the hair is colorful, the generation unit can generate an avatar using an algorithm specialized for color. This allows the optimal generation algorithm to be applied depending on the hairstyle or hair color category.

[0043] When generating an avatar, the generation unit can improve the accuracy of generation based on the user's past avatar generation results. For example, the generation unit generates a more accurate avatar based on data of avatars generated by the user in the past. The generation unit can generate an avatar by incorporating features of avatars that the user was satisfied with in the past. The generation unit can generate an avatar by avoiding features of avatars that the user was dissatisfied with in the past. In this way, the generation accuracy is improved by referring to the user's past avatar generation results.

[0044] When generating an avatar, the generation unit can set a generation priority based on the time of submission of the hairstyle and hair color. For example, the generation unit prioritizes generation of hairstyles and hair colors that the user submitted early. If the user is in a hurry, the generation unit can prioritize generation regardless of the time of submission. If the user submits in time for a specific event, the generation unit can prioritize generation in time for that event. This allows the generation priority to be determined based on the time of submission of the hairstyle and hair color.

[0045] When generating an avatar, the generation unit can set the generation order based on the association between hairstyles and hair colors. The generation unit generates avatars in the optimal order based on, for example, the association between hairstyles and hair colors desired by the user. The generation unit can generate avatars in the optimal order based on the association between hairstyles and hair colors tried by the user in the past. The generation unit can generate avatars in the optimal order based on the user's lifestyle and fashion. This allows the generation order to be adjusted based on the association between hairstyles and hair colors.

[0046] When generating an avatar, the generation unit may set the use of technical terms in the generation according to the user's level of expertise. For example, if the user is knowledgeable about beauty, the generation unit may generate an avatar that uses a lot of technical terms. If the user is not knowledgeable about beauty, the generation unit may generate an avatar that explains things in simple terms. The generation unit may generate an avatar using appropriate terms according to the user's level of expertise. This allows the use of technical terms in the generation to be adjusted according to the user's level of expertise.

[0047] When projecting an avatar, the projection unit can select an appropriate projection method based on the user's past projection history. For example, the projection unit selects the optimal projection method based on projection methods that the user was satisfied with in the past. The projection unit can refer to that information to avoid projection methods that the user was dissatisfied with in the past. The projection unit can select the optimal projection method under specific conditions from the user's past projection history. This allows the optimal projection method to be selected based on the user's past projection history.

[0048] The projection unit can adjust the projection content according to the user's current task when projecting the avatar. For example, if the user requests a haircut at a hair salon, the projection unit can provide projection content focusing on the hairstyle. If the user requests a hair color change, the projection unit can provide projection content focusing on the hair color. If the user requests styling advice, the projection unit can provide projection content related to styling. This allows the projection content to be customized according to the user's current task.

[0049] The projection unit can improve the projection method based on user feedback when projecting an avatar. For example, if the user is satisfied with the projection content, the projection unit stores the method and uses the same method next time. If the user is dissatisfied with the projection content, the projection unit can improve the projection method based on the feedback. The projection unit can analyze the user feedback, find common areas for improvement, and optimize the projection method. This allows the projection method to be improved based on user feedback.

[0050] The projection unit can select the optimal projection method when projecting an avatar, taking into consideration the user's device information. For example, if the user is using a smartphone, the projection unit can provide a projection method that matches the screen size. If the user is using a tablet, the projection unit can provide a projection method that is optimized for a large screen. If the user is using a smartwatch, the projection unit can provide a simple and highly visible projection method. This allows the optimal projection method to be selected based on the user's device information.

[0051] The projection unit can set the projection content to be multilingual in accordance with the user's language setting when projecting the avatar. The projection unit can automatically set the projection content based on, for example, the language setting of the user's device. The projection unit can provide a language switching function when the user uses multiple languages. When the user selects a specific language, the projection unit can provide the projection content in that language. This allows the projection content to be multilingual in accordance with the user's language setting.

[0052] When projecting the avatar, the projection unit can set the projection content based on the user's communication history with the hairdresser. The projection unit can adjust the projection content based on, for example, the content of conversations the user has had with the hairdresser in the past. The projection unit can optimize the projection content based on feedback the user has provided to the hairdresser. The projection unit can provide projection content according to specific requests from the user's communication history with the hairdresser. This allows the projection content to be adjusted based on the user's communication history with the hairdresser.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The generation unit can generate an optimal avatar based on the user's past hairstyle and hair color history. For example, the generation unit can prioritize the generation of the most successful style based on hairstyles and hair colors that the user has tried in the past. The generation unit can avoid hairstyles and hair colors that the user has been dissatisfied with in the past by not reflecting that information. The generation unit can suggest optimal hairstyles and hair colors according to seasons and events based on the user's past history. This allows the generation of an optimal avatar based on the user's past history.

[0055] The collection unit can suggest optimal hairstyles and hair colors based on the user's current fashion and lifestyle. For example, if the user prefers casual fashion, the collection unit can suggest hairstyles and hair colors that suit that style. If the user is considering using the device in a business setting, the collection unit can suggest formal hairstyles and hair colors. The collection unit can suggest optimal hairstyles and hair colors based on the user's lifestyle (outdoorsy, indoorsy, etc.). This makes it possible to make optimal suggestions based on the user's fashion and lifestyle.

[0056] The collection unit can prioritize collection of images of hairstyles and hair colors that are highly relevant based on the user's geographical location information. For example, if the user lives in an urban area, the collection unit can collect hairstyles and hair colors based on the latest trends. If the user lives in a rural area, the collection unit can collect hairstyles and hair colors that suit the local culture and customs. If the user is traveling, the collection unit can collect hairstyles and hair colors based on the local trends and culture. This makes it possible to collect highly relevant images based on the user's geographical location information.

[0057] When generating an avatar, the generation unit can use different generation algorithms depending on the hairstyle or hair color category. For example, if the hair is short, the avatar can be generated using a specific algorithm. If the hair is long, the generation unit can generate the avatar using a different algorithm. If the hair is colorful, the generation unit can generate the avatar using an algorithm specialized for color. This allows the optimal generation algorithm to be applied depending on the hairstyle or hair color category.

[0058] When generating an avatar, the generation unit can improve the accuracy of the generation based on the user's past avatar generation results. For example, a more accurate avatar can be generated based on data of avatars generated by the user in the past. The generation unit can generate an avatar by incorporating features of avatars that the user was satisfied with in the past. The generation unit can generate an avatar by avoiding features of avatars that the user was dissatisfied with in the past. In this way, the generation accuracy can be improved by referring to the user's past avatar generation results.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit inputs the user's desired hairstyle and hair color image as text or an image. For example, the user can input specific requests such as "I want a short cut with bangs swept to the side" or "I want to dye my hair a light brown." Step 2: The generation unit uses the generation AI to analyze the information collected by the collection unit and generate a sample avatar that resembles the user. For example, the generation AI generates a realistic avatar based on the user's facial photo and hairstyle information. It can also generate an avatar that matches the user's desired hairstyle and hair color. Step 3: The projection unit projects the avatar generated by the generation unit, and the hairdresser uses it as a reference. For example, the generated avatar is provided to the hairdresser to reproduce a specific hairstyle and hair color.

[0061] (Example 2) A system according to an embodiment of the present invention solves worries and problems related to hairstyles and hair colors. In this system, a user inputs their ideal hairstyle and hair color image using an app. A generation AI analyzes the information and generates a sample avatar that looks exactly like the user. The generated avatar can be checked before going to a salon, further enhancing the user's ideal look. The visualized avatar can then be projected at the salon, allowing for accurate haircuts and dyeing. For example, a user can input their ideal hairstyle and hair color using an app. The user can input specific images using text or images. The generation AI then analyzes the input information and generates a sample avatar that looks exactly like the user. Based on the generated avatar, the user can further enhance their ideal look. The visualized avatar can then be projected at the salon, allowing for accurate haircuts and dyeing. This allows the system to accurately visualize the user's ideal hairstyle and hair color and communicate them to the hairdresser. For example, when trying out a new hairstyle or hair color, checking the avatar beforehand can help prevent mistakes. In addition, users can visualize their ideal hairstyle and hair color, which improves the experience at the salon.

[0062] A hairstyle and hair color system according to an embodiment includes a collection unit, a generation unit, and a projection unit. The collection unit inputs a user's desired hairstyle and hair color as text or an image. For example, the collection unit can input specific requests such as "I want a short haircut with bangs swept diagonally" or "I want to dye my hair a light brown." The generation unit uses a generation AI to analyze the information collected by the collection unit and generate a sample avatar that resembles the user. For example, the generation AI generates a realistic avatar based on the user's facial photo and hairstyle information. The generation unit can also use the generation AI to generate an avatar that matches the user's desired hairstyle and hair color. For example, if the user requests a short haircut, the generation AI generates an avatar that matches that hairstyle. The projection unit projects the avatar generated by the generation unit, allowing a hairdresser to use it as a reference. For example, the projection unit provides the generated avatar to a hairdresser and reproduces the specific hairstyle and hair color. This allows the hairstyle and hair color system according to an embodiment to specifically imagine the user's ideal hairstyle and hair color and accurately convey that to the hairdresser.

[0063] The collection unit can input the user's desired hairstyle and hair color image in the form of text or an image. For example, the collection unit can input a specific request such as "I want a short cut with bangs swept diagonally" or "I want to dye my hair light brown." The collection unit can clarify the specific format and method of input, such as the text format and image resolution. This allows the user to input a specific image into the collection unit.

[0064] The generation unit can use generation AI to generate a realistic avatar based on the user's facial photo and hairstyle information. The generation unit, for example, uses generation AI to generate a realistic avatar based on the user's facial photo and hairstyle information. The generation AI can generate an avatar based on the user's facial photo and hairstyle information. The generation AI can generate an avatar using technologies such as deep learning and GAN (generative artificial network). This makes it possible to generate a realistic avatar based on the user's facial photo and hairstyle information.

[0065] The generation unit can use generation AI to generate an avatar based on the hairstyle and hair color desired by the user. The generation unit, for example, uses generation AI to generate an avatar that matches the hairstyle and hair color desired by the user. The generation AI generates an avatar based on the hairstyle and hair color desired by the user. The generation AI can generate an avatar using technologies such as deep learning and GAN (generative artificial network). This makes it possible to generate an avatar that matches the hairstyle and hair color desired by the user.

[0066] The projection unit provides the generated avatar to the hairdresser, allowing the hairdresser to recreate a specific hairstyle and hair color. The projection unit, for example, provides the generated avatar to the hairdresser, allowing the hairdresser to recreate a specific hairstyle and hair color. The projection unit allows the hairdresser to recreate a specific hairstyle and hair color based on the generated avatar. The projection unit, for example, uses a screen or projector to project the avatar, allowing the hairdresser to use it as a reference. This allows the hairdresser to recreate a specific hairstyle and hair color.

[0067] The projection unit can reproduce a desired hairstyle and hair color based on an avatar brought by a user. The projection unit reproduces a specific hairstyle and hair color based on, for example, an avatar brought by a user. The projection unit allows a hairdresser to reproduce a specific hairstyle and hair color based on an avatar brought by a user. The projection unit, for example, uses a screen or projector to project the avatar, allowing the hairdresser to use it as a reference. In this way, an ideal hairstyle and hair color can be reproduced based on an avatar brought by a user.

[0068] The collection unit can estimate the user's emotions and adjust the timing of collecting images of desired hairstyles and hair colors based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can delay the timing of collection to allow the user to provide more detailed information. If the user is feeling stressed, the collection unit can advance the timing of collection to quickly acquire information. If the user is excited, the collection unit can adjust the timing of collection to allow the user to provide information calmly. This makes it possible to adjust the collection timing according to the user's emotions.

[0069] The collection unit can analyze the user's past hairstyle and hair color history and select an appropriate collection method. For example, the collection unit prioritizes collection of the most successful styles based on hairstyles and hair colors tried by the user in the past. The collection unit can avoid collecting information about hairstyles and hair colors that the user was dissatisfied with in the past in order to avoid such information. The collection unit can suggest optimal hairstyles and hair colors according to seasons and events based on the user's past history. This allows the optimal collection method to be selected based on the user's past history.

[0070] When collecting images of desired hairstyles and hair colors, the collection unit can filter them based on the user's current fashion or lifestyle. For example, if the user prefers casual fashion, the collection unit will prioritize collecting hairstyles and hair colors that match that style. If the user is considering using the device in a business setting, the collection unit can collect formal hairstyles and hair colors. The collection unit can suggest optimal hairstyles and hair colors based on the user's lifestyle (outdoorsy, indoorsy, etc.). This allows the collection of optimal images based on the user's fashion and lifestyle.

[0071] When collecting images of desired hairstyles and hair colors, the collection unit can select an appropriate collection means according to the user's input method. For example, if the user selects voice input, the collection unit collects ideal hairstyles and hair colors using voice recognition technology. If the user selects text input, the collection unit can collect based on detailed text information. If the user selects image input, the collection unit can collect ideal hairstyles and hair colors using image analysis technology. This makes it possible to select the optimal collection means according to the user's input method.

[0072] The collection unit can estimate the user's emotions and set a priority order for images to be collected based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can prioritize collecting detailed images. If the user is stressed, the collection unit can prioritize collecting simple images. If the user is excited, the collection unit can prioritize collecting visually appealing images. This makes it possible to determine the priority order for images to be collected according to the user's emotions.

[0073] When collecting images of desired hairstyles and hair colors, the collection unit can prioritize collection of highly relevant images based on the user's geographical location information. For example, if the user lives in an urban area, the collection unit collects hairstyles and hair colors based on the latest trends. If the user lives in a rural area, the collection unit can collect hairstyles and hair colors that suit the local culture and customs. If the user is traveling, the collection unit can collect hairstyles and hair colors based on the local trends and culture. This makes it possible to collect highly relevant images based on the user's geographical location information.

[0074] When collecting images of desired hairstyles and hair colors, the collection unit can analyze the user's social media activities and collect related images. For example, the collection unit collects images based on hairstyles and hair colors that the user has "liked" on social media. The collection unit can collect images based on the hairstyles and hair colors of influencers that the user follows. The collection unit can analyze the content of the user's posts on social media and collect related hairstyles and hair colors. This makes it possible to collect related images based on the user's social media activities.

[0075] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting images of desired hairstyles and hair colors. The collection unit adjusts the collection method, for example, based on feedback provided by the user in the past. The collection unit can preferentially collect hairstyles and hair colors that the user was satisfied with in the past. The collection unit can avoid collecting information about hairstyles and hair colors that the user was dissatisfied with in the past, thereby customizing the collection method based on the user's past feedback.

[0076] The generation unit can estimate the user's emotion and set the avatar's expression method based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate an avatar with soft colors and a calm expression. If the user is excited, the generation unit can generate an avatar with vivid colors and a lively expression. If the user is stressed, the generation unit can generate an avatar with calm colors and a relaxed expression. This allows the avatar's expression method to be adjusted according to the user's emotion.

[0077] When generating an avatar, the generation unit can set the level of detail of the avatar based on the importance of hairstyle and hair color. For example, if a user has strong preferences for hairstyle, the generation unit can generate an avatar with enhanced hairstyle details. If a user has strong preferences for hair color, the generation unit can generate an avatar with enhanced hair color details. If a user has preferences for both hairstyle and hair color, the generation unit can generate an avatar with enhanced details for both. This allows the level of detail of the avatar to be adjusted based on the importance of hairstyle and hair color.

[0078] When generating an avatar, the generation unit can use different generation algorithms depending on the hairstyle or hair color category. For example, if the hair is short, the generation unit can generate an avatar using a specific algorithm. If the hair is long, the generation unit can generate an avatar using a different algorithm. If the hair is colorful, the generation unit can generate an avatar using an algorithm specialized for color. This allows the optimal generation algorithm to be applied depending on the hairstyle or hair color category.

[0079] When generating an avatar, the generation unit can improve the accuracy of generation based on the user's past avatar generation results. For example, the generation unit generates a more accurate avatar based on data of avatars generated by the user in the past. The generation unit can generate an avatar by incorporating features of avatars that the user was satisfied with in the past. The generation unit can generate an avatar by avoiding features of avatars that the user was dissatisfied with in the past. In this way, the generation accuracy is improved by referring to the user's past avatar generation results.

[0080] The generation unit can estimate the user's emotion and set the length of the avatar based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a longer avatar. If the user is in a hurry, the generation unit can generate a shorter avatar. If the user is excited, the generation unit can generate an avatar with a visually stimulating effect. This allows the length of the avatar to be adjusted according to the user's emotion.

[0081] When generating an avatar, the generation unit can set a generation priority based on the time of submission of the hairstyle and hair color. For example, the generation unit prioritizes generation of hairstyles and hair colors that the user submitted early. If the user is in a hurry, the generation unit can prioritize generation regardless of the time of submission. If the user submits in time for a specific event, the generation unit can prioritize generation in time for that event. This allows the generation priority to be determined based on the time of submission of the hairstyle and hair color.

[0082] When generating an avatar, the generation unit can set the generation order based on the association between hairstyles and hair colors. The generation unit generates avatars in the optimal order based on, for example, the association between hairstyles and hair colors desired by the user. The generation unit can generate avatars in the optimal order based on the association between hairstyles and hair colors tried by the user in the past. The generation unit can generate avatars in the optimal order based on the user's lifestyle and fashion. This allows the generation order to be adjusted based on the association between hairstyles and hair colors.

[0083] When generating an avatar, the generation unit may set the use of technical terms in the generation according to the user's level of expertise. For example, if the user is knowledgeable about beauty, the generation unit may generate an avatar that uses a lot of technical terms. If the user is not knowledgeable about beauty, the generation unit may generate an avatar that explains things in simple terms. The generation unit may generate an avatar using appropriate terms according to the user's level of expertise. This allows the use of technical terms in the generation to be adjusted according to the user's level of expertise.

[0084] The projection unit can estimate the user's emotion and set the avatar projection method based on the estimated user's emotion. For example, if the user is relaxed, the projection unit can project an avatar with soft colors and a calm expression. If the user is excited, the projection unit can project an avatar with vivid colors and a lively expression. If the user is stressed, the projection unit can project an avatar with calm colors and a relaxed expression. This allows the avatar projection method to be adjusted according to the user's emotion.

[0085] When projecting an avatar, the projection unit can select an appropriate projection method based on the user's past projection history. For example, the projection unit selects the optimal projection method based on projection methods that the user was satisfied with in the past. The projection unit can refer to that information to avoid projection methods that the user was dissatisfied with in the past. The projection unit can select the optimal projection method under specific conditions from the user's past projection history. This allows the optimal projection method to be selected based on the user's past projection history.

[0086] The projection unit can adjust the projection content according to the user's current task when projecting the avatar. For example, if the user requests a haircut at a hair salon, the projection unit can provide projection content focusing on the hairstyle. If the user requests a hair color change, the projection unit can provide projection content focusing on the hair color. If the user requests styling advice, the projection unit can provide projection content related to styling. This allows the projection content to be customized according to the user's current task.

[0087] The projection unit can improve the projection method based on user feedback when projecting an avatar. For example, if the user is satisfied with the projection content, the projection unit stores the method and uses the same method next time. If the user is dissatisfied with the projection content, the projection unit can improve the projection method based on the feedback. The projection unit can analyze the user feedback, find common areas for improvement, and optimize the projection method. This allows the projection method to be improved based on user feedback.

[0088] The projection unit can estimate the user's emotion and set the projection order of the avatars based on the estimated user's emotion. For example, if the user is relaxed, the projection unit can project the avatars in an order that includes detailed information. If the user is in a hurry, the projection unit can project the avatars in an order that highlights the main points. If the user is excited, the projection unit can project the avatars in a visually appealing order. This allows the projection order of the avatars to be adjusted according to the user's emotion.

[0089] The projection unit can select the optimal projection method when projecting an avatar, taking into consideration the user's device information. For example, if the user is using a smartphone, the projection unit can provide a projection method that matches the screen size. If the user is using a tablet, the projection unit can provide a projection method that is optimized for a large screen. If the user is using a smartwatch, the projection unit can provide a simple and highly visible projection method. This allows the optimal projection method to be selected based on the user's device information.

[0090] The projection unit can set the projection content to be multilingual in accordance with the user's language setting when projecting the avatar. The projection unit can automatically set the projection content based on, for example, the language setting of the user's device. The projection unit can provide a language switching function when the user uses multiple languages. When the user selects a specific language, the projection unit can provide the projection content in that language. This allows the projection content to be multilingual in accordance with the user's language setting.

[0091] When projecting the avatar, the projection unit can set the projection content based on the user's communication history with the hairdresser. The projection unit can adjust the projection content based on, for example, the content of conversations the user has had with the hairdresser in the past. The projection unit can optimize the projection content based on feedback the user has provided to the hairdresser. The projection unit can provide projection content according to specific requests from the user's communication history with the hairdresser. This allows the projection content to be adjusted based on the user's communication history with the hairdresser. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, and projection unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit inputs the user's desired hairstyle and hair color as text or an image using the reception device 38 of the smart device 14. For example, the generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 using a generation AI and generates a sample avatar that resembles the user. For example, the projection unit projects the generated avatar using the output device 40 of the smart device 14, so that the hairdresser can use it as reference. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, and projection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit inputs the user's desired hairstyle and hair color as text or an image using the microphone 238 of the smart glasses 214. For example, the generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 using a generation AI and generates a sample avatar that resembles the user. For example, the projection unit projects the generated avatar using the speaker 240 of the smart glasses 214, so that the hairdresser can use it as reference. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and projection unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit inputs the user's desired hairstyle and hair color as text or an image using the microphone 238 of the headset-type terminal 314. For example, the generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 using a generation AI, and generates a sample avatar that resembles the user. For example, the projection unit projects the generated avatar using the display 343 of the headset-type terminal 314, so that the hairdresser can use it as reference. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and projection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the microphone 238 of the robot 414 to input an image of the user's desired hairstyle and hair color as text or an image. For example, the generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 using a generation AI and generates a sample avatar that resembles the user. For example, the projection unit projects the generated avatar using the display device of the robot 414, so that the hairdresser can use it as reference.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The collection unit can estimate the user's current mood and physical condition and suggest hairstyles and hair colors based on the estimated information. For example, if the user is tired, the collection unit can suggest low-maintenance hairstyles and hair colors. If the user is energetic, the collection unit can suggest bolder hairstyles and hair colors. If the user is relaxed, the collection unit can suggest hairstyles and hair colors that suit the relaxed atmosphere. This makes it possible to make optimal suggestions based on the user's mood and physical condition.

[0094] The generation unit can generate an optimal avatar based on the user's past hairstyle and hair color history. For example, the generation unit can prioritize the generation of the most successful style based on hairstyles and hair colors that the user has tried in the past. The generation unit can avoid hairstyles and hair colors that the user has been dissatisfied with in the past by not reflecting that information. The generation unit can suggest optimal hairstyles and hair colors according to seasons and events based on the user's past history. This allows the generation of an optimal avatar based on the user's past history.

[0095] The projection unit can estimate the user's emotions and adjust the projection method of the avatar based on the estimated user's emotions. For example, if the user is relaxed, the projection unit can project an avatar with soft colors and a calm expression. If the user is excited, the projection unit can project an avatar with vivid colors and a lively expression. If the user is stressed, the projection unit can project an avatar with calm colors and a relaxed expression. This allows the projection method of the avatar to be adjusted according to the user's emotions.

[0096] The collection unit can suggest optimal hairstyles and hair colors based on the user's current fashion and lifestyle. For example, if the user prefers casual fashion, the collection unit can suggest hairstyles and hair colors that suit that style. If the user is considering using the device in a business setting, the collection unit can suggest formal hairstyles and hair colors. The collection unit can suggest optimal hairstyles and hair colors based on the user's lifestyle (outdoorsy, indoorsy, etc.). This makes it possible to make optimal suggestions based on the user's fashion and lifestyle.

[0097] The generation unit can estimate the user's emotion and set the avatar's expression method based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate an avatar with soft colors and a calm expression. If the user is excited, the generation unit can generate an avatar with bright colors and a lively expression. If the user is stressed, the generation unit can generate an avatar with calm colors and a relaxed expression. This allows the avatar's expression method to be adjusted according to the user's emotion.

[0098] The collection unit can prioritize collection of images of hairstyles and hair colors that are highly relevant based on the user's geographical location information. For example, if the user lives in an urban area, the collection unit can collect hairstyles and hair colors based on the latest trends. If the user lives in a rural area, the collection unit can collect hairstyles and hair colors that suit the local culture and customs. If the user is traveling, the collection unit can collect hairstyles and hair colors based on the local trends and culture. This makes it possible to collect highly relevant images based on the user's geographical location information.

[0099] When generating an avatar, the generation unit can use different generation algorithms depending on the hairstyle or hair color category. For example, if the hair is short, the avatar can be generated using a specific algorithm. If the hair is long, the generation unit can generate the avatar using a different algorithm. If the hair is colorful, the generation unit can generate the avatar using an algorithm specialized for color. This allows the optimal generation algorithm to be applied depending on the hairstyle or hair color category.

[0100] The collection unit can estimate the user's emotions and set a priority order for images to be collected based on the estimated user's emotions. For example, if the user is relaxed, detailed images can be collected with priority. If the user is stressed, the collection unit can collect simple images with priority. If the user is excited, the collection unit can collect visually appealing images with priority. In this way, the priority order for images to be collected can be determined according to the user's emotions.

[0101] When generating an avatar, the generation unit can improve the accuracy of the generation based on the user's past avatar generation results. For example, a more accurate avatar can be generated based on data of avatars generated by the user in the past. The generation unit can generate an avatar by incorporating features of avatars that the user was satisfied with in the past. The generation unit can generate an avatar by avoiding features of avatars that the user was dissatisfied with in the past. In this way, the generation accuracy can be improved by referring to the user's past avatar generation results.

[0102] The projection unit can estimate the user's emotion and set the projection order of the avatars based on the estimated emotion of the user. For example, if the user is relaxed, the projection unit can project the avatars in an order that includes detailed information. If the user is in a hurry, the projection unit can project the avatars in an order that highlights the main points. If the user is excited, the projection unit can project the avatars in a visually appealing order. This allows the projection order of the avatars to be adjusted according to the user's emotion.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit inputs the user's desired hairstyle and hair color image as text or an image. For example, the user can input specific requests such as "I want a short cut with bangs swept to the side" or "I want to dye my hair a light brown." Step 2: The generation unit uses the generation AI to analyze the information collected by the collection unit and generate a sample avatar that resembles the user. For example, the generation AI generates a realistic avatar based on the user's facial photo and hairstyle information. It can also generate an avatar that matches the user's desired hairstyle and hair color. Step 3: The projection unit projects the avatar generated by the generation unit, and the hairdresser uses it as a reference. For example, the generated avatar is provided to the hairdresser to reproduce a specific hairstyle and hair color.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0128] The 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.

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

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0132] Fig. 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] 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.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that inputs the user's desired hairstyle and hair color image in the form of text or image; a generation unit that analyzes the information collected by the collection unit and generates a sample avatar that resembles the user; and a projection unit that projects the avatar generated by the generation unit so that a hairdresser can use it as reference.

2. 2. The system according to claim 1, wherein the collection unit inputs the user's desired hairstyle and hair color image in the form of text or an image.

3. The system according to claim 1, wherein the generation unit generates a realistic avatar based on a user's facial photograph and hairstyle information using a generation AI.

4. The system according to claim 1, wherein the generation unit generates an avatar based on a hairstyle and hair color desired by the user using a generation AI.

5. The system according to claim 1 , wherein the projection unit provides the generated avatar to a hairdresser to reproduce detailed hairstyle and hair color.

6. The system according to claim 1 , wherein the projection unit reproduces a desired hairstyle and hair color based on an avatar brought by the user.

7. The system according to claim 1 , wherein the collection unit estimates the user's emotions and adjusts the timing for collecting images of desired hairstyles and hair colors based on the estimated user emotions.

8. The system according to claim 1 , wherein the collection unit analyzes a history of the user's past hairstyles and hair colors and selects an appropriate collection method.

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