system

Abstract

The system according to this embodiment aims to enable people with hearing impairments or those who have difficulty communicating to achieve their ideal hairstyle. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a cutting unit, and a presentation unit. The reception unit takes a photograph of the user's hair and inputs the ideal hairstyle and color. The generation unit analyzes the information input by the reception unit and generates an output image of the ideal hairstyle. The cutting unit automatically cuts the hair based on the output image generated by the generation unit. The presentation unit presents the output image generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for people with hearing impairments or those who have difficulty communicating to achieve an ideal hairstyle in a beauty salon.

[0005] The system according to the embodiment aims to enable people with hearing impairments or those who have difficulty communicating to achieve an ideal hairstyle.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a cutting unit, and a presentation unit. The reception unit takes a photograph of the user's hair and inputs the desired hairstyle and color. The generation unit analyzes the information input by the reception unit and generates an output image of the ideal hairstyle. The cutting unit automatically cuts the hair based on the output image generated by the generation unit. The presentation unit presents the output image generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows people with hearing impairments or those who have difficulty communicating to achieve their ideal hairstyle. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​hair salon according to an embodiment of the present invention is a system that reduces communication costs at hair salons and helps people with hearing impairments or those who have difficulty communicating to achieve their ideal hairstyle. In the AI ​​hair salon, the user takes a photo of their hair using a touch panel and inputs their ideal hairstyle, color, etc. The generating AI analyzes the input information and generates an output image of the ideal hairstyle. If this output image is the hairstyle the user desires, the user presses OK. The machine automatically cuts the hair based on the generated output image. This mechanism makes it easy for people with hearing impairments or those who have difficulty communicating to achieve their ideal hairstyle. For example, the user takes a photo of their hair using a touch panel. At this time, it is important for the user to accurately photograph the condition of their hair. For example, it is recommended to take photos from multiple angles, such as bangs, side hair, and back hair. This information is input into the generating AI. Next, the user inputs their ideal hairstyle, color, etc. using the touch panel. For example, various parameters such as short hair, long hair, perm, straight hair, black hair, blonde hair, etc., can be input. This information is also input into the generating AI. The AI ​​analyzes the input information and generates an output image of the ideal hairstyle. For example, if a user requests a short blonde haircut, an image of that hairstyle is generated. This output image is presented to the user on a touch panel. The user checks if the presented output image matches their desired hairstyle. If it does, they press OK. If not, they can adjust the parameters again to generate a new output image. The machine then automatically cuts the hair based on the generated output image. For example, a robotic arm cuts the user's hair to achieve the ideal hairstyle. During this process, the output image changes in real time, allowing the user to monitor the progress and feel at ease while getting their hair cut. This makes it easy for people with hearing difficulties or those who have trouble communicating to achieve their ideal hairstyle. It is also expected to reduce communication costs and labor costs.

[0029] The AI ​​hair salon according to this embodiment comprises a reception unit, a generation unit, a cutting unit, and a display unit. The reception unit takes a photograph of the user's hair and inputs the user's desired hairstyle and color. The user's hair photograph may include, but is not limited to, photographs taken from multiple angles, such as bangs, side hair, and back hair. The reception unit allows the user to take a photograph of their hair using, for example, a touch panel. The reception unit also allows the user to input their desired hairstyle and color. For example, various parameters such as short hair, long hair, perm, straight hair, black hair, and blonde hair can be input. The generation unit uses a generation AI to analyze the information input by the reception unit and generates an output image of the ideal hairstyle. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate an image of the hairstyle based on the user's wishes. The generation unit can also use a multimodal generation AI to generate an output image of the ideal hairstyle. For example, if the user desires a short blonde hairstyle, the generation AI will generate an image of the hairstyle based on those wishes. The presentation unit presents the output image generated by the generation unit to the user. The presentation unit displays the output image on a touch panel, for example. The user checks if the presented output image is the hairstyle they want. If it is the hairstyle they want, they press OK. If it is not the hairstyle they want, they can adjust the parameters again to generate a new output image. The cutting unit automatically cuts the hair based on the output image generated by the generation unit. The cutting unit cuts the user's hair using a robotic arm, for example. Because the robotic arm cuts the hair based on the generated output image, the user can achieve their ideal hairstyle. As a result, the AI ​​hair salon according to this embodiment makes it easy for people with hearing difficulties or those who have difficulty communicating to achieve their ideal hairstyle.

[0030] The reception desk takes photos of the user's hair and inputs their desired hairstyle and color. These photos may include, but are not limited to, shots taken from multiple angles, such as bangs, side hair, and back hair. The reception desk allows users to take photos of their hair using a touch panel, for example. The reception desk also allows users to input their desired hairstyle and color. Various parameters can be entered, such as short hair, long hair, perm, straight hair, black hair, blonde hair, etc. Furthermore, the reception desk can collect additional information such as the user's face shape, hair type, and head shape. This provides the generation unit with the basic data necessary to create more accurate hairstyle output images. For example, the user can use the touch panel to select their face shape (round, oval, square, etc.) and hair type (straight, curly, fine, thick, etc.). The reception desk also records the user's past hairstyle history and preferred styles for future reference. This eliminates the need for users to input detailed information each time, providing a smoother experience. Furthermore, the reception area is equipped with a function to scan and import reference images and magazine clippings brought in by the user, allowing for a more concrete reflection of the user's wishes. This enables the reception area to respond to the diverse needs of users and efficiently collect basic data to realize their ideal hairstyle.

[0031] The generation unit uses a generation AI to analyze the information entered by the reception unit and generate an output image of the ideal hairstyle. The generation AI can, for example, use a text generation AI (e.g., LLM) to generate an image of a hairstyle based on the user's wishes. The generation unit can also use a multimodal generation AI to generate an output image of the ideal hairstyle. For example, if the user requests a short blonde hairstyle, the generation AI will generate an image of a hairstyle based on that request. The generation AI also considers additional information such as the user's face shape, hair type, and head shape to suggest the most suitable hairstyle. For example, it can suggest a hairstyle that slims the face for a user with a round face, and a style that is easy to manage for a user with curly hair. The generation AI also takes into account the user's past hairstyle history and preferred styles to customize the output image to meet the user's individual needs. Furthermore, the generation unit displays the generated output image from multiple angles so that the user can check the overall balance. For example, it can generate images from the front, side, and back so that the user can check how it looks from each angle. Furthermore, the generation unit also provides an interface for users to fine-tune their desired hair color and style, allowing them to pursue their ideal hairstyle down to the smallest detail. This enables the generation unit to meet diverse user needs and generate ideal hairstyles with high precision.

[0032] The display unit presents the output image generated by the generation unit to the user. For example, the display unit displays the output image on a touch panel. The user checks if the presented output image is the hairstyle they want. If it is, they press OK. If it is not the hairstyle they want, they can adjust the parameters again to generate a new output image. Furthermore, the display unit has an AR (augmented reality) function that allows the user to virtually try on the generated hairstyle. This allows the user to overlay the generated hairstyle onto their own face and see in real time how it will actually look. For example, the camera can be activated on the touch panel, the user's face can be displayed, and the generated hairstyle can be overlaid on it. This allows the user to have a more concrete image and can be used as a reference when making a final decision. The display unit also provides detailed information and advice on the generated hairstyle. For example, it displays hair care products suitable for the selected hairstyle, styling methods, and maintenance frequency, providing the user with information to maintain the hairstyle. Furthermore, the display unit also has a function that allows the user to share the generated hairstyle with friends and family. For example, images of the generated hairstyle can be shared on social media or by email, allowing the user to refer to the opinions of others. This allows the display unit to support users in selecting their ideal hairstyle, thereby increasing their satisfaction.

[0033] The cutting unit automatically cuts hair based on the output image generated by the generation unit. For example, the cutting unit uses a robotic arm to cut the user's hair. Because the robotic arm cuts hair based on the generated output image, the user can achieve their ideal hairstyle. The robotic arm is equipped with high-precision sensors and cameras, which detect the user's head shape and hair length in real time while working. This allows for precise control of the cutting position and angle, resulting in the ideal hairstyle. Furthermore, the cutting unit has the ability to automatically switch between multiple cutting tools, selecting the optimal tool according to hair length and style. For example, it uses fine clippers for short haircuts and sharp scissors for long hair. The cutting unit can also adjust the cutting intensity and speed according to the user's hair type and condition. For example, it can be set to cut gently for fine hair and firmly for thick hair. Additionally, the cutting unit can automatically perform finishing touches after cutting. For example, it can tidy and style the hair and apply finishing treatments, ensuring the user leaves the salon with perfect hair. This allows the cutting section to achieve the user's ideal hairstyle with high precision, thereby increasing satisfaction.

[0034] The generation unit generates an output image of the ideal hairstyle using a generation AI. For example, the generation unit uses a generation AI to generate an image of a hairstyle based on the user's wishes. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate an image of a hairstyle based on the user's wishes. The generation unit can also use a multimodal generation AI to generate an output image of the ideal hairstyle. For example, if the user requests a short blonde hairstyle, the generation AI will generate an image of a hairstyle based on that request. This allows for the generation of an output image of the ideal hairstyle with high accuracy by using a generation AI.

[0035] The display unit presents the generated output image to the user. The display unit displays the output image on a touch panel, for example. The user checks if the presented output image is the hairstyle they want. If it is the hairstyle they want, they press OK. If it is not the hairstyle they want, they can adjust the parameters again to generate a new output image. This allows the user to check the generated output image. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0036] The cutting unit automatically cuts the hair based on the generated output image. The cutting unit cuts the user's hair using, for example, a robotic arm. Because the robotic arm cuts the hair based on the generated output image, the user can achieve their ideal hairstyle. Thus, by automatically cutting the hair based on the generated output image, the ideal hairstyle can be achieved. Some or all of the above processing in the cutting unit may be performed using, for example, AI, or without using AI.

[0037] The reception desk takes a photo of the user's hair and inputs their desired hairstyle and color. The reception desk allows the user to take a photo of their hair using, for example, a touch panel. The reception desk also allows the user to input their desired hairstyle and color. For example, various parameters such as short hair, long hair, perm, straight hair, black hair, blonde hair can be entered. This allows the user to take a photo of their hair and input their desired hairstyle and color. Some or all of the above processing at the reception desk may be performed using, for example, AI, or not using AI.

[0038] The generation unit updates the output image in real time. The generation unit generates and updates images of hairstyles based on the user's preferences in real time, for example, using a generation AI. The generation AI generates and updates images of hairstyles based on the user's preferences in real time, for example, using a text generation AI (e.g., LLM). The generation unit can also update the output image of the ideal hairstyle in real time using a multimodal generation AI. This allows the user to get their hair cut with peace of mind while checking the progress by updating the output image in real time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0039] The display unit confirms the user's progress. The display unit, for example, displays the progress of the output image on a touch panel. The user can cut their hair with peace of mind while checking the displayed progress. This allows the user to confirm the progress. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0040] The reception desk analyzes the user's past hairstyle history and selects the optimal shooting method. For example, the reception desk may take a photo from the same angle as a photo of the user's hairstyle taken in the past. Alternatively, the reception desk may select the most flattering angle from the user's past hairstyle history and take a photo from there. Furthermore, the reception desk may analyze the user's past hairstyle history and take multiple photos from different angles. This allows the reception desk to select the optimal shooting method based on the user's past hairstyle history. Some or all of the above processing at the reception desk may be performed using AI, for example, or without using AI.

[0041] The reception desk automatically selects the optimal angle for hair photography based on the user's face and head shape. For example, the reception desk analyzes the user's face shape and takes the photo at the most balanced angle. It can also consider the user's head shape and take the photo at the most natural-looking angle. Furthermore, the reception desk can comprehensively assess the user's face and head shape and take multiple photos at the optimal angle. This allows for hair photography at the optimal angle based on the user's face and head shape. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0042] The reception desk prioritizes suggesting highly relevant hairstyles when a hair photograph is taken, taking into account the user's geographical location. For example, if the user is in an urban area, the reception desk will suggest hairstyles based on the latest trends. If the user is in a suburban area, the reception desk can also suggest natural-style hairstyles. Furthermore, if the user is in a specific region, the reception desk can suggest hairstyles popular in that region. This allows the reception desk to suggest highly relevant hairstyles based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0043] The reception desk analyzes the user's social media activity when taking a hair photo and suggests relevant hairstyles. For example, the reception desk may suggest hairstyles based on hairstyles the user has "liked" on social media. It can also suggest hairstyles based on influencers the user follows. Furthermore, the reception desk can analyze hairstyles in photos posted by the user and suggest relevant hairstyles. This allows for the suggestion of relevant hairstyles based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0044] The generation unit adjusts the level of detail based on the importance of the hairstyle when generating the output image. For example, if the hairstyle desired by the user is complex, the generation unit will generate a detailed output image. Conversely, if the hairstyle desired by the user is simple, the generation unit can also generate a concise output image. Furthermore, if the user has specific preferences regarding certain parts, the generation unit can also represent those parts in detail. This allows the level of detail of the output image to be adjusted based on the importance of the hairstyle. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0045] The generation unit applies different generation algorithms depending on the hairstyle category when generating the output image. For example, for short hair, the generation unit applies a simple algorithm. For long hair, it can also apply a more detailed algorithm. Furthermore, for permed hair, it can apply an algorithm specifically designed for curl representation. This allows the generation unit to apply the most suitable generation algorithm for each hairstyle category. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0046] The generation unit determines priorities based on the trend period of hairstyles when generating output images. For example, the generation unit prioritizes generating hairstyles based on the latest trends. It can also generate hairstyles based on past trends. Furthermore, the generation unit can prioritize generating hairstyles appropriate for the season. This allows the priority of output images to be determined based on the trend period of hairstyles. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0047] The generation unit adjusts the order of output images based on the relevance of hairstyles. For example, the generation unit prioritizes generating hairstyles that are closest to the user's desired hairstyle. It can also generate hairstyles that are highly relevant based on hairstyles the user has previously selected. Furthermore, the generation unit can prioritize generating hairstyles that best suit the user's face shape. This allows the order of output images to be adjusted based on the relevance of hairstyles. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0048] The cutting unit analyzes the user's past hairstyle history to select the optimal cutting method when cutting hair. For example, the cutting unit can cut the hair in the same style based on hairstyles the user has liked in the past. Alternatively, the cutting unit can select the most suitable style from the user's past hairstyle history and cut the hair. Furthermore, the cutting unit can analyze the user's past hairstyle history and suggest a different style before cutting the hair. This allows the cutting unit to select the optimal cutting method based on the user's past hairstyle history. Some or all of the above processing in the cutting unit may be performed using AI, for example, or without using AI.

[0049] The cutting unit customizes the cutting method based on the user's current hair condition when cutting hair. For example, if the user's hair is damaged, the cutting unit will cut the hair in a way that minimizes damage. If the user's hair is healthy, the cutting unit can also cut the hair in a normal way. Furthermore, if the user's hair is fine, the cutting unit can cut the hair in a way that adds volume. This allows for the customization of the optimal cutting method based on the user's current hair condition. Some or all of the above processes in the cutting unit may be performed using AI, for example, or without AI.

[0050] The cutting unit selects the optimal cutting method when cutting hair, taking into account the user's geographical location. For example, if the user is in an urban area, the cutting unit may select a cutting method based on the latest trends. If the user is in a suburban area, the cutting unit may also select a cutting method that is more natural in style. Furthermore, if the user is in a specific region, the cutting unit may select a cutting method that is popular in that region. This allows the optimal cutting method to be selected based on the user's geographical location. Some or all of the above processing in the cutting unit may be performed using AI, for example, or without AI.

[0051] The cutting unit analyzes the user's social media activity and proposes a cutting method when cutting hair. For example, the cutting unit proposes a cutting method based on hairstyles the user has "liked" on social media. It can also propose a cutting method based on the hairstyles of influencers the user follows. Furthermore, the cutting unit can analyze hairstyles in photos posted by the user and propose relevant cutting methods. This allows the system to propose the optimal cutting method based on the user's social media activity. Some or all of the above processing in the cutting unit may be performed using AI, for example, or without AI.

[0052] The display unit, when displaying the output image, selects the optimal display method by referring to the user's past hairstyle history. For example, the display unit may display the same style based on the hairstyle the user has liked in the past. Alternatively, the display unit can select and display the most suitable style from the user's past hairstyle history. Furthermore, the display unit can analyze the user's past hairstyle history and suggest and display different styles. This allows the display unit to select the optimal display method based on the user's past hairstyle history. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0053] The display unit customizes the display method based on the user's current hair condition when displaying the output image. For example, if the user's hair is damaged, the display unit will display it in a way that minimizes damage. The display unit can also display the user's hair in a normal way if the user's hair is healthy. Furthermore, if the user's hair is thin, the display unit can display it in a way that adds volume. This allows for the customization of the optimal display method based on the user's current hair condition. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0054] The display unit selects the optimal display method when displaying the output image, taking into account the user's geographical location information. For example, if the user is in an urban area, the display unit provides a display method based on the latest trends. It can also provide a natural-style display method if the user is in a suburban area. Furthermore, if the user is in a specific region, the display unit can provide a display method popular in that region. This allows the display unit to provide the optimal display method based on the user's geographical location information. Some or all of the processing described above in the display unit may be performed using AI, for example, or without AI.

[0055] The display unit analyzes the user's social media activity and proposes a display method when displaying the output image. For example, the display unit proposes a display method based on hairstyles that the user has "liked" on social media. It can also propose a display method based on the hairstyles of influencers that the user follows. Furthermore, the display unit can analyze hairstyles in photos posted by the user and propose relevant display methods. This allows the display unit to propose the optimal display method based on the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

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

[0057] The AI-powered hair salon can also monitor the health of the user's hair and suggest the most suitable hair care products. For example, the reception unit analyzes a photo of the user's hair and evaluates the level of damage. Next, the generation unit generates the most suitable hair care products for the user based on the evaluation results. The presentation unit presents the user with a list of the generated hair care products, from which the user can select. This makes it easy for users to find the hair care products best suited to the health of their hair.

[0058] AI-powered hair salons can also record a user's hairstyle history and use this information to suggest future styles. For example, the reception unit saves data on hairstyles the user has chosen in the past. Next, the generation unit analyzes the saved data to understand the user's preferences and current trends. Based on the analysis results, the presentation unit suggests new hairstyles to the user. This makes it easy for users to find hairstyles that suit their preferences and current trends.

[0059] The AI-powered hair salon can also suggest makeup looks tailored to the user's hairstyle. For example, the reception unit takes a photo of the user's face and analyzes the balance between their hairstyle and face. Next, the generation unit generates the optimal makeup style for the user based on the analysis results. The presentation unit then presents the generated makeup styles to the user, who can select from them. This makes it easy for users to find a style that considers the balance between their hairstyle and makeup.

[0060] The AI-powered hair salon can also offer fashion suggestions tailored to the user's hairstyle. For example, the reception unit takes a full-body photo of the user and analyzes the balance between their hairstyle and fashion. Next, the generation unit generates the optimal fashion style for the user based on the analysis results. The presentation unit then presents the generated fashion styles to the user, who can select from them. This makes it easy for users to find a style that considers the balance between their hairstyle and fashion.

[0061] The AI-powered hair salon can also suggest accessories tailored to the user's hairstyle. For example, the reception unit analyzes the user's hairstyle and face shape to select the most suitable accessories. Next, the generation unit presents the selected accessories to the user. The presentation unit then displays a list of generated accessories to the user, from which the user can make a selection. This makes it easy for users to find accessories that match their hairstyle.

[0062] The AI-powered hair salon can also suggest hair accessories that match the user's hairstyle. For example, the reception unit analyzes the user's hairstyle and face shape and selects the most suitable hair accessories. Next, the generation unit presents the selected hair accessories to the user. The presentation unit then presents a list of generated hair accessories to the user, from which the user can make a selection. This makes it easy for users to find hair accessories that suit their hairstyle.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk takes photos of the user's hair and inputs their desired hairstyle and color. The photos of the user's hair include shots taken from multiple angles, such as bangs, side hair, and back hair. The reception desk allows the user to take photos of their hair using a touch panel and input their desired hairstyle and color. For example, various parameters can be entered, such as short hair, long hair, perm, straight hair, black hair, blonde hair, etc. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and generate an output image of the ideal hairstyle. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate images of hairstyles based on the user's wishes. For example, if the user requests a short blonde hairstyle, the generation unit will generate an image of a hairstyle based on that request. Step 3: The display unit presents the output image generated by the generation unit to the user. The display unit displays the output image on the touch panel, and the user checks if the presented output image is the hairstyle they want. If it is the hairstyle they want, they press OK; if not, they can adjust the parameters again to generate a new output image. Step 4: The cutting unit automatically cuts the hair based on the output image generated by the generation unit. The cutting unit uses a robotic arm to cut the user's hair and cuts it based on the generated output image, allowing the user to achieve their ideal hairstyle.

[0065] (Example of form 2) The AI ​​hair salon according to an embodiment of the present invention is a system that reduces communication costs at hair salons and helps people with hearing impairments or those who have difficulty communicating to achieve their ideal hairstyle. In the AI ​​hair salon, the user takes a photo of their hair using a touch panel and inputs their ideal hairstyle, color, etc. The generating AI analyzes the input information and generates an output image of the ideal hairstyle. If this output image is the hairstyle the user desires, the user presses OK. The machine automatically cuts the hair based on the generated output image. This mechanism makes it easy for people with hearing impairments or those who have difficulty communicating to achieve their ideal hairstyle. For example, the user takes a photo of their hair using a touch panel. At this time, it is important for the user to accurately photograph the condition of their hair. For example, it is recommended to take photos from multiple angles, such as bangs, side hair, and back hair. This information is input into the generating AI. Next, the user inputs their ideal hairstyle, color, etc. using the touch panel. For example, various parameters such as short hair, long hair, perm, straight hair, black hair, blonde hair, etc., can be input. This information is also input into the generating AI. The AI ​​analyzes the input information and generates an output image of the ideal hairstyle. For example, if a user requests a short blonde haircut, an image of that hairstyle is generated. This output image is presented to the user on a touch panel. The user checks if the presented output image matches their desired hairstyle. If it does, they press OK. If not, they can adjust the parameters again to generate a new output image. The machine then automatically cuts the hair based on the generated output image. For example, a robotic arm cuts the user's hair to achieve the ideal hairstyle. During this process, the output image changes in real time, allowing the user to monitor the progress and feel at ease while getting their hair cut. This makes it easy for people with hearing difficulties or those who have trouble communicating to achieve their ideal hairstyle. It is also expected to reduce communication costs and labor costs.

[0066] The AI ​​hair salon according to this embodiment comprises a reception unit, a generation unit, a cutting unit, and a display unit. The reception unit takes a photograph of the user's hair and inputs the user's desired hairstyle and color. The user's hair photograph may include, but is not limited to, photographs taken from multiple angles, such as bangs, side hair, and back hair. The reception unit allows the user to take a photograph of their hair using, for example, a touch panel. The reception unit also allows the user to input their desired hairstyle and color. For example, various parameters such as short hair, long hair, perm, straight hair, black hair, and blonde hair can be input. The generation unit uses a generation AI to analyze the information input by the reception unit and generates an output image of the ideal hairstyle. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate an image of the hairstyle based on the user's wishes. The generation unit can also use a multimodal generation AI to generate an output image of the ideal hairstyle. For example, if the user desires a short blonde hairstyle, the generation AI will generate an image of the hairstyle based on those wishes. The presentation unit presents the output image generated by the generation unit to the user. The presentation unit displays the output image on a touch panel, for example. The user checks if the presented output image is the hairstyle they want. If it is the hairstyle they want, they press OK. If it is not the hairstyle they want, they can adjust the parameters again to generate a new output image. The cutting unit automatically cuts the hair based on the output image generated by the generation unit. The cutting unit cuts the user's hair using a robotic arm, for example. Because the robotic arm cuts the hair based on the generated output image, the user can achieve their ideal hairstyle. As a result, the AI ​​hair salon according to this embodiment makes it easy for people with hearing difficulties or those who have difficulty communicating to achieve their ideal hairstyle.

[0067] The reception desk takes photos of the user's hair and inputs their desired hairstyle and color. These photos may include, but are not limited to, shots taken from multiple angles, such as bangs, side hair, and back hair. The reception desk allows users to take photos of their hair using a touch panel, for example. The reception desk also allows users to input their desired hairstyle and color. Various parameters can be entered, such as short hair, long hair, perm, straight hair, black hair, blonde hair, etc. Furthermore, the reception desk can collect additional information such as the user's face shape, hair type, and head shape. This provides the generation unit with the basic data necessary to create more accurate hairstyle output images. For example, the user can use the touch panel to select their face shape (round, oval, square, etc.) and hair type (straight, curly, fine, thick, etc.). The reception desk also records the user's past hairstyle history and preferred styles for future reference. This eliminates the need for users to input detailed information each time, providing a smoother experience. Furthermore, the reception area is equipped with a function to scan and import reference images and magazine clippings brought in by the user, allowing for a more concrete reflection of the user's wishes. This enables the reception area to respond to the diverse needs of users and efficiently collect basic data to realize their ideal hairstyle.

[0068] The generation unit uses a generation AI to analyze the information entered by the reception unit and generate an output image of the ideal hairstyle. The generation AI can, for example, use a text generation AI (e.g., LLM) to generate an image of a hairstyle based on the user's wishes. The generation unit can also use a multimodal generation AI to generate an output image of the ideal hairstyle. For example, if the user requests a short blonde hairstyle, the generation AI will generate an image of a hairstyle based on that request. The generation AI also considers additional information such as the user's face shape, hair type, and head shape to suggest the most suitable hairstyle. For example, it can suggest a hairstyle that slims the face for a user with a round face, and a style that is easy to manage for a user with curly hair. The generation AI also takes into account the user's past hairstyle history and preferred styles to customize the output image to meet the user's individual needs. Furthermore, the generation unit displays the generated output image from multiple angles so that the user can check the overall balance. For example, it can generate images from the front, side, and back so that the user can check how it looks from each angle. Furthermore, the generation unit also provides an interface for users to fine-tune their desired hair color and style, allowing them to pursue their ideal hairstyle down to the smallest detail. This enables the generation unit to meet diverse user needs and generate ideal hairstyles with high precision.

[0069] The display unit presents the output image generated by the generation unit to the user. For example, the display unit displays the output image on a touch panel. The user checks if the presented output image is the hairstyle they want. If it is, they press OK. If it is not the hairstyle they want, they can adjust the parameters again to generate a new output image. Furthermore, the display unit has an AR (augmented reality) function that allows the user to virtually try on the generated hairstyle. This allows the user to overlay the generated hairstyle onto their own face and see in real time how it will actually look. For example, the camera can be activated on the touch panel, the user's face can be displayed, and the generated hairstyle can be overlaid on it. This allows the user to have a more concrete image and can be used as a reference when making a final decision. The display unit also provides detailed information and advice on the generated hairstyle. For example, it displays hair care products suitable for the selected hairstyle, styling methods, and maintenance frequency, providing the user with information to maintain the hairstyle. Furthermore, the display unit also has a function that allows the user to share the generated hairstyle with friends and family. For example, images of the generated hairstyle can be shared on social media or by email, allowing the user to refer to the opinions of others. This allows the display unit to support users in selecting their ideal hairstyle, thereby increasing their satisfaction.

[0070] The cutting unit automatically cuts hair based on the output image generated by the generation unit. For example, the cutting unit uses a robotic arm to cut the user's hair. Because the robotic arm cuts hair based on the generated output image, the user can achieve their ideal hairstyle. The robotic arm is equipped with high-precision sensors and cameras, which detect the user's head shape and hair length in real time while working. This allows for precise control of the cutting position and angle, resulting in the ideal hairstyle. Furthermore, the cutting unit has the ability to automatically switch between multiple cutting tools, selecting the optimal tool according to hair length and style. For example, it uses fine clippers for short haircuts and sharp scissors for long hair. The cutting unit can also adjust the cutting intensity and speed according to the user's hair type and condition. For example, it can be set to cut gently for fine hair and firmly for thick hair. Additionally, the cutting unit can automatically perform finishing touches after cutting. For example, it can tidy and style the hair and apply finishing treatments, ensuring the user leaves the salon with perfect hair. This allows the cutting section to achieve the user's ideal hairstyle with high precision, thereby increasing satisfaction.

[0071] The generation unit generates an output image of the ideal hairstyle using a generation AI. For example, the generation unit uses a generation AI to generate an image of a hairstyle based on the user's wishes. The generation AI uses, for example, a text generation AI (e.g., LLM) to generate an image of a hairstyle based on the user's wishes. The generation unit can also use a multimodal generation AI to generate an output image of the ideal hairstyle. For example, if the user requests a short blonde hairstyle, the generation AI will generate an image of a hairstyle based on that request. This allows for the generation of an output image of the ideal hairstyle with high accuracy by using a generation AI.

[0072] The display unit presents the generated output image to the user. The display unit displays the output image on a touch panel, for example. The user checks if the presented output image is the hairstyle they want. If it is the hairstyle they want, they press OK. If it is not the hairstyle they want, they can adjust the parameters again to generate a new output image. This allows the user to check the generated output image. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0073] The cutting unit automatically cuts the hair based on the generated output image. The cutting unit cuts the user's hair using, for example, a robotic arm. Because the robotic arm cuts the hair based on the generated output image, the user can achieve their ideal hairstyle. Thus, by automatically cutting the hair based on the generated output image, the ideal hairstyle can be achieved. Some or all of the above processing in the cutting unit may be performed using, for example, AI, or without using AI.

[0074] The reception desk takes a photo of the user's hair and inputs their desired hairstyle and color. The reception desk allows the user to take a photo of their hair using, for example, a touch panel. The reception desk also allows the user to input their desired hairstyle and color. For example, various parameters such as short hair, long hair, perm, straight hair, black hair, blonde hair can be entered. This allows the user to take a photo of their hair and input their desired hairstyle and color. Some or all of the above processing at the reception desk may be performed using, for example, AI, or not using AI.

[0075] The generation unit updates the output image in real time. The generation unit generates and updates images of hairstyles based on the user's preferences in real time, for example, using a generation AI. The generation AI generates and updates images of hairstyles based on the user's preferences in real time, for example, using a text generation AI (e.g., LLM). The generation unit can also update the output image of the ideal hairstyle in real time using a multimodal generation AI. This allows the user to get their hair cut with peace of mind while checking the progress by updating the output image in real time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0076] The display unit confirms the user's progress. The display unit, for example, displays the progress of the output image on a touch panel. The user can cut their hair with peace of mind while checking the displayed progress. This allows the user to confirm the progress. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0077] The reception desk estimates the user's emotions and adjusts the timing of hair photography based on the estimated emotions. For example, if the user is nervous, the reception desk will allow time for the user to relax before starting the photo shoot. Alternatively, if the user is relaxed, the reception desk can start the photo shoot immediately. Furthermore, if the user is in a hurry, the reception desk can take the photo shoot quickly. This allows for hair photography to be performed at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0078] The reception desk analyzes the user's past hairstyle history and selects the optimal shooting method. For example, the reception desk may take a photo from the same angle as a photo of the user's hairstyle taken in the past. Alternatively, the reception desk may select the most flattering angle from the user's past hairstyle history and take a photo from there. Furthermore, the reception desk may analyze the user's past hairstyle history and take multiple photos from different angles. This allows the reception desk to select the optimal shooting method based on the user's past hairstyle history. Some or all of the above processing at the reception desk may be performed using AI, for example, or without using AI.

[0079] The reception desk automatically selects the optimal angle for hair photography based on the user's face and head shape. For example, the reception desk analyzes the user's face shape and takes the photo at the most balanced angle. It can also consider the user's head shape and take the photo at the most natural-looking angle. Furthermore, the reception desk can comprehensively assess the user's face and head shape and take multiple photos at the optimal angle. This allows for hair photography at the optimal angle based on the user's face and head shape. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0080] The reception desk estimates the user's emotions and determines the priority of hair photos to take based on the estimated emotions. For example, if the user is nervous, the reception desk will allow time for relaxation before taking important photos. Alternatively, if the user is relaxed, the reception desk may prioritize taking important photos. Furthermore, if the user is in a hurry, the reception desk may take the most important photos first. This allows the priority of hair photos to be taken to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0081] The reception desk prioritizes suggesting highly relevant hairstyles when a hair photograph is taken, taking into account the user's geographical location. For example, if the user is in an urban area, the reception desk will suggest hairstyles based on the latest trends. If the user is in a suburban area, the reception desk can also suggest natural-style hairstyles. Furthermore, if the user is in a specific region, the reception desk can suggest hairstyles popular in that region. This allows the reception desk to suggest highly relevant hairstyles based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0082] The reception desk analyzes the user's social media activity when taking a hair photo and suggests relevant hairstyles. For example, the reception desk may suggest hairstyles based on hairstyles the user has "liked" on social media. It can also suggest hairstyles based on influencers the user follows. Furthermore, the reception desk can analyze hairstyles in photos posted by the user and suggest relevant hairstyles. This allows for the suggestion of relevant hairstyles based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not.

[0083] The generation unit estimates the user's emotions and adjusts the representation of the output image based on the estimated emotions. For example, if the user is relaxed, the generation unit generates an output image with soft colors. If the user is excited, the generation unit can also generate an output image with vivid colors. Furthermore, if the user is tense, the generation unit can also generate an output image with calm colors. This allows the representation of the output image to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0084] The generation unit adjusts the level of detail based on the importance of the hairstyle when generating the output image. For example, if the hairstyle desired by the user is complex, the generation unit will generate a detailed output image. Conversely, if the hairstyle desired by the user is simple, the generation unit can also generate a concise output image. Furthermore, if the user has specific preferences regarding certain parts, the generation unit can also represent those parts in detail. This allows the level of detail of the output image to be adjusted based on the importance of the hairstyle. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0085] The generation unit applies different generation algorithms depending on the hairstyle category when generating the output image. For example, for short hair, the generation unit applies a simple algorithm. For long hair, it can also apply a more detailed algorithm. Furthermore, for permed hair, it can apply an algorithm specifically designed for curl representation. This allows the generation unit to apply the most suitable generation algorithm for each hairstyle category. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The generation unit estimates the user's emotions and adjusts the length of the output image based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer output image. If the user is in a hurry, the generation unit can also generate a shorter output image. Furthermore, if the user is excited, the generation unit can generate a visually stimulating output image. This allows the length of the output image to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0087] The generation unit determines priorities based on the trend period of hairstyles when generating output images. For example, the generation unit prioritizes generating hairstyles based on the latest trends. It can also generate hairstyles based on past trends. Furthermore, the generation unit can prioritize generating hairstyles appropriate for the season. This allows the priority of output images to be determined based on the trend period of hairstyles. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The generation unit adjusts the order of output images based on the relevance of hairstyles. For example, the generation unit prioritizes generating hairstyles that are closest to the user's desired hairstyle. It can also generate hairstyles that are highly relevant based on hairstyles the user has previously selected. Furthermore, the generation unit can prioritize generating hairstyles that best suit the user's face shape. This allows the order of output images to be adjusted based on the relevance of hairstyles. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The cutting unit estimates the user's emotions and adjusts the hair cutting method based on the estimated emotions. For example, if the user is nervous, the cutting unit will cut the hair at a slow pace. If the user is relaxed, the cutting unit can also cut the hair at a normal pace. Furthermore, if the user is in a hurry, the cutting unit can cut the hair quickly. This allows the optimal hair cutting method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cutting unit may be performed using AI, for example, or without AI.

[0090] The cutting unit analyzes the user's past hairstyle history to select the optimal cutting method when cutting hair. For example, the cutting unit can cut the hair in the same style based on hairstyles the user has liked in the past. Alternatively, the cutting unit can select the most suitable style from the user's past hairstyle history and cut the hair. Furthermore, the cutting unit can analyze the user's past hairstyle history and suggest a different style before cutting the hair. This allows the cutting unit to select the optimal cutting method based on the user's past hairstyle history. Some or all of the above processing in the cutting unit may be performed using AI, for example, or without using AI.

[0091] The cutting unit customizes the cutting method based on the user's current hair condition when cutting hair. For example, if the user's hair is damaged, the cutting unit will cut the hair in a way that minimizes damage. If the user's hair is healthy, the cutting unit can also cut the hair in a normal way. Furthermore, if the user's hair is fine, the cutting unit can cut the hair in a way that adds volume. This allows for the customization of the optimal cutting method based on the user's current hair condition. Some or all of the above processes in the cutting unit may be performed using AI, for example, or without AI.

[0092] The cutting unit estimates the user's emotions and determines the priority of hair cutting based on the estimated emotions. For example, if the user is tense, the cutting unit will allow time for the user to relax before cutting their hair. Conversely, if the user is relaxed, the cutting unit can cut their hair immediately. Furthermore, if the user is in a hurry, the cutting unit can prioritize cutting the most important parts. This allows the cutting unit to determine the priority of hair cutting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cutting unit may be performed using AI, for example, or not using AI.

[0093] The cutting unit selects the optimal cutting method when cutting hair, taking into account the user's geographical location. For example, if the user is in an urban area, the cutting unit may select a cutting method based on the latest trends. If the user is in a suburban area, the cutting unit may also select a cutting method that is more natural in style. Furthermore, if the user is in a specific region, the cutting unit may select a cutting method that is popular in that region. This allows the optimal cutting method to be selected based on the user's geographical location. Some or all of the above processing in the cutting unit may be performed using AI, for example, or without AI.

[0094] The cutting unit analyzes the user's social media activity and proposes a cutting method when cutting hair. For example, the cutting unit proposes a cutting method based on hairstyles the user has "liked" on social media. It can also propose a cutting method based on the hairstyles of influencers the user follows. Furthermore, the cutting unit can analyze hairstyles in photos posted by the user and propose relevant cutting methods. This allows the system to propose the optimal cutting method based on the user's social media activity. Some or all of the above processing in the cutting unit may be performed using AI, for example, or without AI.

[0095] The display unit estimates the user's emotions and adjusts the display method of the output image based on the estimated user emotions. For example, if the user is relaxed, the display unit provides a display method with soft colors. If the user is excited, the display unit can also provide a display method with vivid colors. Furthermore, if the user is tense, the display unit can also provide a display method with calm colors. This allows the display method to be provided optimally according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI.

[0096] The display unit, when displaying the output image, selects the optimal display method by referring to the user's past hairstyle history. For example, the display unit may display the same style based on the hairstyle the user has liked in the past. Alternatively, the display unit can select and display the most suitable style from the user's past hairstyle history. Furthermore, the display unit can analyze the user's past hairstyle history and suggest and display different styles. This allows the display unit to select the optimal display method based on the user's past hairstyle history. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0097] The display unit customizes the display method based on the user's current hair condition when displaying the output image. For example, if the user's hair is damaged, the display unit will display it in a way that minimizes damage. The display unit can also display the user's hair in a normal way if the user's hair is healthy. Furthermore, if the user's hair is thin, the display unit can display it in a way that adds volume. This allows for the customization of the optimal display method based on the user's current hair condition. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0098] The presentation unit estimates the user's emotions and adjusts the display order of the output images based on the estimated emotions. For example, if the user is tense, the presentation unit provides a simple and highly visible display order. If the user is relaxed, the presentation unit can also provide a display order that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a concise display order. This allows for the provision of an optimal display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the presentation unit may be performed using AI, for example, or without AI.

[0099] The display unit selects the optimal display method when displaying the output image, taking into account the user's geographical location information. For example, if the user is in an urban area, the display unit provides a display method based on the latest trends. It can also provide a natural-style display method if the user is in a suburban area. Furthermore, if the user is in a specific region, the display unit can provide a display method popular in that region. This allows the display unit to provide the optimal display method based on the user's geographical location information. Some or all of the processing described above in the display unit may be performed using AI, for example, or without AI.

[0100] The display unit analyzes the user's social media activity and proposes a display method when displaying the output image. For example, the display unit proposes a display method based on hairstyles that the user has "liked" on social media. It can also propose a display method based on the hairstyles of influencers that the user follows. Furthermore, the display unit can analyze hairstyles in photos posted by the user and propose relevant display methods. This allows the display unit to propose the optimal display method based on the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The AI-powered hair salon can also monitor the health of the user's hair and suggest the most suitable hair care products. For example, the reception unit analyzes a photo of the user's hair and evaluates the level of damage. Next, the generation unit generates the most suitable hair care products for the user based on the evaluation results. The presentation unit presents the user with a list of the generated hair care products, from which the user can select. This makes it easy for users to find the hair care products best suited to the health of their hair.

[0103] AI-powered hair salons can also record a user's hairstyle history and use this information to suggest future styles. For example, the reception unit saves data on hairstyles the user has chosen in the past. Next, the generation unit analyzes the saved data to understand the user's preferences and current trends. Based on the analysis results, the presentation unit suggests new hairstyles to the user. This makes it easy for users to find hairstyles that suit their preferences and current trends.

[0104] The AI-powered hair salon can also suggest makeup looks tailored to the user's hairstyle. For example, the reception unit takes a photo of the user's face and analyzes the balance between their hairstyle and face. Next, the generation unit generates the optimal makeup style for the user based on the analysis results. The presentation unit then presents the generated makeup styles to the user, who can select from them. This makes it easy for users to find a style that considers the balance between their hairstyle and makeup.

[0105] The AI-powered hair salon can also estimate the user's emotions and suggest hairstyles based on those emotions. For example, the reception unit analyzes the user's facial expressions and tone of voice to estimate their emotions. Next, the generation unit generates the most suitable hairstyle for the user based on the estimated emotions. The presentation unit presents the generated hairstyles to the user, who can then choose from them. This makes it easy for users to find a hairstyle that matches their emotions.

[0106] The AI-powered hair salon can also offer fashion suggestions tailored to the user's hairstyle. For example, the reception unit takes a full-body photo of the user and analyzes the balance between their hairstyle and fashion. Next, the generation unit generates the optimal fashion style for the user based on the analysis results. The presentation unit then presents the generated fashion styles to the user, who can select from them. This makes it easy for users to find a style that considers the balance between their hairstyle and fashion.

[0107] The AI-powered hair salon can also estimate the user's emotions and provide hair care advice based on those emotions. For example, the reception unit analyzes the user's facial expressions and tone of voice to estimate their emotions. Next, the generation unit generates the most suitable hair care method for the user based on the estimated emotions. The presentation unit presents the generated hair care methods to the user, who can then choose from them. This makes it easy for users to find a hair care method that suits their emotions.

[0108] The AI-powered hair salon can also suggest accessories tailored to the user's hairstyle. For example, the reception unit analyzes the user's hairstyle and face shape to select the most suitable accessories. Next, the generation unit presents the selected accessories to the user. The presentation unit then displays a list of generated accessories to the user, from which the user can make a selection. This makes it easy for users to find accessories that match their hairstyle.

[0109] The AI-powered hair salon can also estimate the user's emotions and provide relaxing music based on those emotions. For example, the reception unit analyzes the user's facial expressions and tone of voice to estimate their emotions. Next, the generation unit generates music that is best suited to the user based on the estimated emotions. The presentation unit provides the generated music to the user, allowing them to relax while getting their hair cut. This allows the user to relax while listening to music that matches their emotions.

[0110] The AI-powered hair salon can also suggest hair accessories that match the user's hairstyle. For example, the reception unit analyzes the user's hairstyle and face shape and selects the most suitable hair accessories. Next, the generation unit presents the selected hair accessories to the user. The presentation unit then presents a list of generated hair accessories to the user, from which the user can make a selection. This makes it easy for users to find hair accessories that suit their hairstyle.

[0111] The AI ​​hair salon can also estimate the user's emotions and suggest hairstyle changes based on those emotions. For example, the reception unit analyzes the user's facial expressions and tone of voice to estimate their emotions. Next, the generation unit generates the most suitable hairstyle change for the user based on the estimated emotions. The presentation unit presents the generated hairstyle changes to the user, who can then select from them. This makes it easy for users to find a hairstyle change that matches their emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk takes photos of the user's hair and inputs their desired hairstyle and color. The photos of the user's hair include shots taken from multiple angles, such as bangs, side hair, and back hair. The reception desk allows the user to take photos of their hair using a touch panel and input their desired hairstyle and color. For example, various parameters can be entered, such as short hair, long hair, perm, straight hair, black hair, blonde hair, etc. Step 2: The generation unit uses a generation AI to analyze the information entered by the reception unit and generate an output image of the ideal hairstyle. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate images of hairstyles based on the user's wishes. For example, if the user requests a short blonde hairstyle, the generation unit will generate an image of a hairstyle based on that request. Step 3: The display unit presents the output image generated by the generation unit to the user. The display unit displays the output image on the touch panel, and the user checks if the presented output image is the hairstyle they want. If it is the hairstyle they want, they press OK; if not, they can adjust the parameters again to generate a new output image. Step 4: The cutting unit automatically cuts the hair based on the output image generated by the generation unit. The cutting unit uses a robotic arm to cut the user's hair and cuts it based on the generated output image, allowing the user to achieve their ideal hairstyle.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] Each of the multiple elements described above, including the reception unit, generation unit, presentation unit, and cutting unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, in the reception unit, the user takes a picture of their hair using the touch panel 38A of the smart device 14 and inputs their ideal hairstyle and color. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an output image of the ideal hairstyle using generation AI. The presentation unit presents the generated output image to the user using the display 40A of the smart device 14. The cutting unit automatically cuts the hair based on the generated output image using the robotic arm of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, presentation unit, and cutting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit allows the user to take a picture of their hair using the touch panel of the smart glasses 214 and input their desired hairstyle and color. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an output image of the ideal hairstyle using generation AI. The presentation unit presents the generated output image to the user using the display of the smart glasses 214. The cutting unit automatically cuts the hair based on the generated output image using the robotic arm of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, presentation unit, and cutting unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, in the reception unit, the user takes a picture of their hair using the touch panel of the headset terminal 314 and inputs their ideal hairstyle and color. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an output image of the ideal hairstyle using generation AI. The presentation unit presents the generated output image to the user using the display of the headset terminal 314. The cutting unit automatically cuts the hair based on the generated output image using the robotic arm of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, generation unit, presentation unit, and cutting unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, in the reception unit, the user takes a picture of their hair using the touch panel of the robot 414 and inputs their ideal hairstyle and color. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an output image of the ideal hairstyle using generation AI. The presentation unit presents the generated output image to the user using the display of the robot 414. The cutting unit automatically cuts the hair based on the generated output image using the robotic arm of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The reception area takes a photo of the user's hair and inputs their desired hairstyle and color, A generation unit analyzes the information input by the reception unit and generates an output image of the ideal hairstyle, A cutting unit that automatically cuts hair based on the output image generated by the generation unit, The system includes a presentation unit that presents the output image generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates output images of the ideal hairstyle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is, The generated output image is presented to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned cut portion is Automatically cuts hair based on the generated output image. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The user takes a photo of their hair and enters their desired hairstyle and color. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The output image is updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is, Check the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of hair photography based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system analyzes the user's past hairstyle history and selects the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When taking photos of hair, the system automatically selects the optimal angle based on the user's face shape and head shape. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the hair photos to take based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When taking photos of hair, the system prioritizes suggesting hairstyles that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When taking photos of hair, the system analyzes the user's social media activity and suggests relevant hairstyles. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the way the output image is represented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating the output image, adjust the level of detail based on the importance of the hairstyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating the output image, different generation algorithms are applied depending on the hairstyle category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the output image based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating output images, prioritization is determined based on the current trend of hairstyles. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating the output image, the order is adjusted based on the relevance of the hairstyles. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned cut portion is It estimates the user's emotions and adjusts the hair cutting method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned cut portion is When cutting hair, the system analyzes the user's past hairstyle history to select the optimal cutting method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned cut portion is When cutting hair, the cutting method is customized based on the user's current hair condition. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned cut portion is The system estimates the user's emotions and determines the priority of hair cutting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned cut portion is When cutting hair, the optimal cutting method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned cut portion is When cutting hair, we analyze the user's social media activity and suggest cutting methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, It estimates the user's emotions and adjusts how the output image is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When displaying the output image, the system selects the optimal display method by referring to the user's past hairstyle history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, When displaying the output image, the display method is customized based on the user's current hair condition. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, It estimates the user's emotions and adjusts the display order of output images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When displaying the output image, the system selects the optimal display method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is, When displaying the output image, the system analyzes the user's social media activity and suggests display methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area takes a photo of the user's hair and inputs their desired hairstyle and color, A generation unit analyzes the information input by the reception unit and generates an output image of the ideal hairstyle, A cutting unit that automatically cuts hair based on the output image generated by the generation unit, The system includes a presentation unit that presents the output image generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is The AI ​​generates an output image of the ideal hairstyle. The system according to feature 1.

3. The aforementioned display unit is, The generated output image is presented to the user. The system according to feature 1.

4. The aforementioned cut portion is Automatically cuts hair based on the generated output image. The system according to feature 1.

5. The aforementioned reception unit is The user takes a photo of their hair and enters their desired hairstyle and color. The system according to feature 1.

6. The generating unit is The output image is updated in real time. The system according to feature 1.

7. The aforementioned display unit is, Check the user's progress. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of hair photography based on those emotions. The system according to feature 1.

Images