system

Abstract

The system according to this embodiment aims to consistently suggest the most suitable hairstyle to the user. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit takes a photo of the user's face and inputs the desired hair length. The analysis unit analyzes the face shape, skin color, height, bone structure, etc., based on the information input by the reception unit and proposes a suitable hairstyle. The generation unit visualizes the hairstyle proposed by the analysis unit as a processed image. The provision unit provides the hairstyle recipe generated by the generation unit to the hairdresser.

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, the method 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, when proposing an optimal hairstyle for a user, it depends on the personal skills of a beautician, so there is a problem that it is difficult to make a stable proposal.

[0005] The system according to the embodiment aims to stably propose an optimal hairstyle for a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit takes a photograph of the user's face and inputs the desired hair length. The analysis unit analyzes the face shape, skin color, height, bone structure, etc., based on the information input by the reception unit and proposes a suitable hairstyle. The generation unit visualizes the hairstyle proposed by the analysis unit as a processed image. The provision unit provides the hairstyle recipe generated by the generation unit to a hairdresser. [Effects of the Invention]

[0007] The system according to this embodiment can consistently suggest the most suitable hairstyle to the user. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 hairstyle suggestion system according to an embodiment of the present invention is a system in which a user takes a photo of their face and tells the system which hair length they want, and the generating AI suggests a suitable hairstyle based on the user's face shape, skin color, height, and bone structure. The hairstyle suggestion system allows the user to take a photo of their face and tell the system which hair length they want, and the generating AI suggests a suitable hairstyle based on the user's face shape, skin color, height, and bone structure. The suggestions are visualized as processed images, and the hairdresser is also provided with a recipe for the hairstyle. This helps the user discover a new self. Because the suggestions are based on a vast amount of fashion data learned by the generating AI, as well as measurements of facial feature placement and body balance, the system provides more stable suggestions that are less dependent on individual skill than human suggestions. For example, the hairstyle suggestion system allows the user to take a photo of their face and tell the system which hair length they want. This information is input into the generating AI. Next, the generating AI analyzes information such as face shape, skin color, height, and bone structure and suggests a hairstyle that suits the user. For example, if the user has a round face and light skin tone, the generating AI will select the most suitable hairstyle based on that information. The generating AI visualizes the suggested hairstyle as a processed image. This allows users to visualize hairstyles that suit them. Furthermore, hairdressers are provided with a recipe for the hairstyle, enabling them to accurately reproduce the hairstyle desired by the user. This service uses a vast amount of fashion data learned by the generative AI, along with measurements of facial feature placement and body balance, to provide suggestions that are more stable and less dependent on individual skill than human suggestions. For example, the generative AI can learn from past fashion data and suggest hairstyles that reflect current trends and fashions. This service is also offered as a subscription service at hair salons. Users can order hairstyles from electronic medical records created at SB Shop and reach hair salons through a cashless payment network. This allows users to easily find hairstyles that suit them, and enables hairdressers to provide accurate hairstyles. In this way, the generative AI service that suggests the optimal hairstyle from a facial photograph helps users discover a new version of themselves and provides hairdressers with accurate hairstyle recipes, resulting in stable suggestions that are less dependent on individual skill.This allows the hairstyle suggestion system to propose the most suitable hairstyle based on the user's facial photograph, providing visualizations and recipes, enabling consistent suggestions regardless of individual skill.

[0029] The hairstyle suggestion system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit takes a photo of the user's face and inputs the desired hair length. For example, a smartphone camera or a digital camera can be used to take the user's face photo. The reception unit can, for example, take a photo of the face using a smartphone camera and input the desired hair length. Alternatively, the reception unit can take a photo of the face using a digital camera and input the desired hair length. Furthermore, the reception unit can take a photo of the face using a webcam and input the desired hair length. For example, the reception unit can take a photo of the face using a smartphone camera and input the desired hair length. Alternatively, a photo of the face using a digital camera can be taken and the desired hair length can be input. Alternatively, a photo of the face using a webcam can be taken and the desired hair length can be input. The analysis unit analyzes the information input by the reception unit, such as face shape, skin color, height, and bone structure diagnosis, and suggests a suitable hairstyle. For example, the analysis unit analyzes the face shape and suggests a hairstyle that suits the user. Alternatively, the analysis unit can analyze the skin color and suggest a hairstyle that suits the user. Furthermore, the analysis unit can analyze the user's height and suggest a hairstyle that suits them. For example, the analysis unit can analyze the user's face shape and suggest a hairstyle that suits them. It can also analyze the user's skin tone and suggest a hairstyle that suits them. It can also analyze the user's height and suggest a hairstyle that suits them. The generation unit visualizes the hairstyle suggested by the analysis unit as a processed image. For example, the generation unit visualizes the suggested hairstyle as a processed image. The generation unit can also visualize the suggested hairstyle as a 3D model. Furthermore, the generation unit can also visualize the suggested hairstyle as an animation. For example, the generation unit visualizes the suggested hairstyle as a processed image. It can also visualize the suggested hairstyle as a 3D model. It can also visualize the suggested hairstyle as an animation. The supply unit provides the hairdresser with the hairstyle recipe generated by the generation unit. For example, the supply unit provides the hairdresser with the generated hairstyle recipe via email. The supply unit can also print out the generated hairstyle recipe and provide it to the hairdresser.Furthermore, the service provider can also provide the generated hairstyle recipes to hairdressers via a web application. For example, the service provider can provide the generated hairstyle recipes to hairdressers via email. It can also print out the generated hairstyle recipes and provide them to hairdressers. It can also provide the generated hairstyle recipes to hairdressers via a web application. As a result, the hairstyle suggestion system according to this embodiment can suggest the optimal hairstyle based on the user's facial photograph, visualize it, and provide a recipe, enabling stable suggestions that are not dependent on the individual's skill.

[0030] The reception desk takes a photo of the user's face and inputs their desired hair length. The user can use a smartphone camera or a digital camera for the photo. Specifically, when using a smartphone camera, the user launches the application and takes a photo of their face from the front, following the guidelines. The application automatically adjusts the position and angle of the face to obtain the optimal photo. When using a digital camera, the user takes a high-resolution photo and uploads it to the system. When using a webcam, the user can use a PC or tablet to take a photo of their face in real time and send it directly to the system. This allows the reception desk to obtain photos from a variety of devices, enhancing user convenience. Furthermore, when inputting the desired hair length, the user can use sliders or dropdown menus to select specific lengths and styles. For example, options such as short, medium, and long are available, and even more detailed length and style specifications are possible. This allows the reception desk to accurately reflect the user's preferences and provide the information necessary for the next analysis step.

[0031] The analysis department analyzes information entered by the reception department, including face shape, skin tone, height, and bone structure diagnosis, to suggest suitable hairstyles. Specifically, it uses AI to analyze facial photographs and identify the user's face shape. Face shapes include round, oval, square, and heart shapes, each with its own suitable hairstyle. The AI ​​detects facial contours and features to suggest the optimal hairstyle. To analyze skin tone, the AI ​​analyzes the color tone of the facial photograph and suggests hair colors and styles that match the user's skin tone. Furthermore, height and bone structure diagnosis are also considered. For example, a tall user may be suggested a voluminous hairstyle to create balance. Bone structure diagnosis selects the optimal hairstyle based on information such as shoulder width and neck length. This allows the analysis department to analyze each user's individual characteristics in detail and suggest the most suitable hairstyle. In addition, the analysis department can utilize past data and trend information to suggest hairstyles that incorporate the latest trends. For example, it can consider the current season and trendy styles to provide the user with the most suitable hairstyle. This enables the analysis department to achieve optimal hairstyle suggestions that combine each user's individual characteristics with the latest trends.

[0032] The generation unit visualizes the hairstyles suggested by the analysis unit as processed images. Specifically, it uses AI to synthesize the suggested hairstyles onto the user's face photo, generating a realistic image. For example, it overlays the suggested hairstyle onto the user's face photo to achieve a natural look. It can also be visualized as a 3D model, allowing the user to rotate it 360 degrees and view the hairstyle from various angles. Furthermore, visualization as animation allows users to realistically experience the movement and volume of the hairstyle. This enables the generation unit to provide users with visually easy-to-understand hairstyle suggestions, making it easier for them to imagine the actual finished look. In addition, the generation unit offers variations in different hair colors and styles, allowing users to choose the best hairstyle from multiple options. For example, even with the same hairstyle, users can try different hair colors or with or without highlights. This allows the generation unit to provide users with diverse options and help them find the perfect hairstyle.

[0033] The service provider provides hairdressers with hairstyle recipes generated by the generation unit. Specifically, it sends the generated hairstyle recipes to hairdressers via email, along with detailed cutting and styling instructions. It is also possible to provide hairdressers with printed copies of the recipes, allowing them to refer to paper-based materials while performing the treatment. Furthermore, by providing recipes through a web application, hairdressers can access the latest information in real time. For example, the web application may include 3D models and animations of hairstyles, which hairdressers can use as references while performing the treatment. This allows the service provider to provide hairdressers with detailed and easy-to-understand information, helping them achieve hairstyles that meet the user's wishes. In addition, the service provider can collect feedback from hairdressers and continuously improve the accuracy and content of the recipes. For example, based on the results of actual treatments performed by hairdressers and user satisfaction, the service provider can revise the recipe content and make more effective suggestions. This allows the service provider to provide high-quality service to both hairdressers and users, improving the reliability and satisfaction of the entire system.

[0034] The reception desk can analyze the user's past hairstyle history and select the optimal shooting method. For example, the reception desk can analyze the trends of hairstyles the user has chosen in the past and select the optimal shooting angle based on those trends. The reception desk can also set lighting conditions suitable for a specific hairstyle based on the user's past hairstyle history. Furthermore, the reception desk can select a background suitable for a specific hairstyle based on the user's past hairstyle history. For example, the reception desk can analyze the trends of hairstyles the user has chosen in the past and select the optimal shooting angle based on those trends. It can also set lighting conditions suitable for a specific hairstyle based on the user's past hairstyle history. It can also select a background suitable for a specific hairstyle based on the user's past hairstyle history. By selecting the optimal shooting method based on past hairstyle history, it becomes possible to suggest more appropriate hairstyles. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past hairstyle history data into a generating AI and have the generating AI select the optimal shooting method.

[0035] The reception desk can filter facial photographs based on the user's current fashion style and makeup. For example, the reception desk can apply the optimal filter to match the user's current fashion style and take a facial photograph. Alternatively, the reception desk can select the optimal filter based on the user's makeup shades and take a facial photograph. Furthermore, the reception desk can apply a specific filter based on the user's fashion style and makeup and take a facial photograph. For example, the reception desk can apply the optimal filter to match the user's current fashion style and take a facial photograph. It can also select the optimal filter based on the user's makeup shades and take a facial photograph. It can also apply a specific filter based on the user's fashion style and makeup and take a facial photograph. This allows for more appropriate hairstyle suggestions by filtering according to the user's fashion style and makeup. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's fashion style and makeup data into a generating AI and have the generating AI select the optimal filter.

[0036] The reception desk can select a highly relevant background based on the user's geographical location when taking a facial photograph. For example, if the user is in an urban area, the reception desk will prioritize selecting an urban background for the facial photograph. Similarly, if the user is in a natural environment, the reception desk can prioritize selecting a natural background for the facial photograph. Furthermore, if the user is participating in a specific event, the reception desk can prioritize selecting a background related to that event for the facial photograph. This allows for more appropriate hairstyle suggestions by selecting backgrounds based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the AI ​​select a highly relevant background.

[0037] The reception desk can analyze the user's social media activity when taking a facial photograph and suggest relevant styles. For example, the reception desk can analyze the content of the user's social media posts and suggest the most suitable style based on that content. It can also analyze the reactions of the user's social media followers and suggest the most suitable style based on those reactions. Furthermore, the reception desk can analyze the user's social media trends and suggest the most suitable style based on those trends. For example, the reception desk can analyze the content of the user's social media posts and suggest the most suitable style based on that content. It can also analyze the reactions of the user's social media followers and suggest the most suitable style based on those reactions. It can also analyze the user's social media trends and suggest the most suitable style based on those trends. This makes it possible to suggest more appropriate hairstyles by providing style suggestions 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 using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform style suggestions related to the user's social media activity.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of face shape and skin tone during the analysis. For example, if the face shape is round, the analysis unit will perform a detailed analysis of hairstyles suitable for round faces. The analysis unit can also perform a detailed analysis of hairstyles suitable for light skin tones if the skin tone is light. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of face shape and skin tone. For example, if the face shape is round, the analysis unit will perform a detailed analysis of hairstyles suitable for round faces. If the skin tone is light, it can also perform a detailed analysis of hairstyles suitable for light skin tones. The level of detail of the analysis can also be adjusted based on the importance of face shape and skin tone. This makes it possible to suggest more appropriate hairstyles by performing analysis based on the importance of face shape and skin tone. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input face shape and skin tone data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the user's lifestyle during analysis. For example, if the user has an active lifestyle, the analysis unit will analyze hairstyles suitable for an active lifestyle. Similarly, if the user has a quiet lifestyle, the analysis unit can analyze hairstyles suitable for a quiet lifestyle. Furthermore, the analysis unit can apply different analysis algorithms depending on the user's lifestyle. For example, if the user has an active lifestyle, the analysis unit will analyze hairstyles suitable for an active lifestyle. If the user has a quiet lifestyle, the analysis unit can analyze hairstyles suitable for a quiet lifestyle. Different analysis algorithms can be applied depending on the user's lifestyle. This allows for more appropriate hairstyle suggestions by performing analysis tailored to the user's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user lifestyle data into a generating AI and have the generating AI apply different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the user's past hairstyle history. For example, the analysis unit analyzes the trends of hairstyles the user has chosen in the past and determines the priority of analysis based on those trends. The analysis unit can also prioritize analysis suitable for a specific hairstyle based on the user's past hairstyle history. Furthermore, the analysis unit can prioritize analysis suitable for a specific hairstyle based on the user's past hairstyle history. For example, the analysis unit analyzes the trends of hairstyles the user has chosen in the past and determines the priority of analysis based on those trends. It can also prioritize analysis suitable for a specific hairstyle based on the user's past hairstyle history. It can also prioritize analysis suitable for a specific hairstyle based on the user's past hairstyle history. This makes it possible to suggest more appropriate hairstyles by performing analysis based on past hairstyle history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past hairstyle history data into a generating AI and have the generating AI determine the priority of analysis.

[0041] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, the analysis unit can adjust the order of analysis based on the user's face shape and skin tone. It can also adjust the order of analysis based on the user's lifestyle. Furthermore, the analysis unit can adjust the order of analysis based on the user's past hairstyle history. For example, the analysis unit can adjust the order of analysis based on the user's face shape and skin tone. It can also adjust the order of analysis based on the user's lifestyle. It can also adjust the order of analysis based on the user's past hairstyle history. This makes it possible to suggest more appropriate hairstyles by performing analysis based on user relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The generation unit can adjust the level of detail of the generated images based on the importance of the hairstyles. For example, if the hairstyle is an important element, the generation unit will generate a detailed hairstyle. If the hairstyle is not very important, the generation unit can also generate a simplified hairstyle. Furthermore, the generation unit can adjust the level of detail of the generated images based on the importance of the hairstyles. For example, if the hairstyle is an important element, the generation unit will generate a detailed hairstyle. If the hairstyle is not very important, it can also generate a simplified hairstyle. The level of detail of the generated images can also be adjusted based on the importance of the hairstyles. This makes it possible to suggest more appropriate hairstyles by generating images based on the importance of hairstyles. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hairstyle importance data into a generation AI and have the generation AI adjust the level of detail of the generated images.

[0043] The generation unit can apply different generation algorithms depending on the hairstyle category during generation. For example, in the case of short hair, the generation unit applies a generation algorithm suitable for short hair. Similarly, in the case of long hair, the generation unit can apply a generation algorithm suitable for long hair. Furthermore, the generation unit can apply different generation algorithms depending on the hairstyle category. For example, in the case of short hair, the generation unit applies a generation algorithm suitable for short hair. In the case of long hair, it can apply a generation algorithm suitable for long hair. Different generation algorithms can be applied depending on the hairstyle category. This makes it possible to suggest more appropriate hairstyles by generating images according to the hairstyle category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hairstyle category data into a generation AI and cause the generation AI to execute the application of different generation algorithms.

[0044] The generation unit can determine the generation priority based on the hairstyle submission date during generation. For example, if the hairstyle submission date is early, the generation unit will prioritize generation. Conversely, if the hairstyle submission date is late, the generation unit can also postpone generation. Furthermore, the generation unit can determine the generation priority based on the hairstyle submission date. For example, if the hairstyle submission date is early, the generation unit will prioritize generation. If the hairstyle submission date is late, the generation unit can also postpone generation. The generation priority can also be determined based on the hairstyle submission date. This makes it possible to suggest more appropriate hairstyles by generating images based on the hairstyle submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hairstyle submission date data into a generation AI and have the generation AI determine the generation priority.

[0045] The generation unit can adjust the generation order based on the relevance of hairstyles during generation. For example, the generation unit can prioritize generation when the relevance of hairstyles is high. It can also postpone generation when the relevance of hairstyles is low. Furthermore, the generation unit can adjust the generation order based on the relevance of hairstyles. For example, the generation unit can prioritize generation when the relevance of hairstyles is high. It can also postpone generation when the relevance of hairstyles is low. It can also adjust the generation order based on the relevance of hairstyles. This makes it possible to suggest more appropriate hairstyles by generating images based on the relevance of hairstyles. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hairstyle relevance data into a generation AI and have the generation AI adjust the generation order.

[0046] The service provider can adjust the level of detail provided based on the importance of the hairstyle. For example, if the hairstyle is an important element, the service provider will provide a detailed recipe. If the hairstyle is not very important, the service provider can also provide a concise recipe. Furthermore, the service provider can adjust the level of detail provided based on the importance of the hairstyle. For example, if the hairstyle is an important element, the service provider will provide a detailed recipe. If the hairstyle is not very important, the service provider can also provide a concise recipe. The service provider can also adjust the level of detail provided based on the importance of the hairstyle. This makes it possible to suggest more appropriate hairstyles by providing recipes based on the importance of the hairstyle. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input hairstyle importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0047] The service provider can apply different service algorithms depending on the hairstyle category at the time of service. For example, in the case of short hair, the service provider can apply a service algorithm suitable for short hair. Similarly, in the case of long hair, the service provider can apply a service algorithm suitable for long hair. Furthermore, the service provider can apply different service algorithms depending on the hairstyle category. For example, in the case of short hair, the service provider can apply a service algorithm suitable for short hair. In the case of long hair, it can apply a service algorithm suitable for long hair. Different service algorithms can also be applied depending on the hairstyle category. This allows for more appropriate hairstyle suggestions by providing recipes tailored to the hairstyle category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input hairstyle category data into a generating AI and have the generating AI apply different service algorithms.

[0048] The service provider can determine the priority of service based on the timing of hairstyle submissions. For example, if a hairstyle is submitted early, the service provider will prioritize its service. Conversely, if a hairstyle is submitted late, the service provider may postpone its service. Furthermore, the service provider can also determine the priority of service based on the timing of hairstyle submissions. For example, if a hairstyle is submitted early, the service provider will prioritize its service. If a hairstyle is submitted late, the service provider may postpone its service. The service provider can also determine the priority of service based on the timing of hairstyle submissions. This allows for more appropriate hairstyle suggestions by providing recipes based on the timing of hairstyle submissions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input hairstyle submission timing data into a generating AI and have the generating AI determine the priority of service.

[0049] The service provider can adjust the order of service based on the relevance of hairstyles at the time of service. For example, the service provider can prioritize service when the relevance of hairstyles is high. It can also postpone service when the relevance of hairstyles is low. Furthermore, the service provider can adjust the order of service based on the relevance of hairstyles. For example, the service provider can prioritize service when the relevance of hairstyles is high. It can also postpone service when the relevance of hairstyles is low. The service provider can also adjust the order of service based on the relevance of hairstyles. This allows for more appropriate hairstyle suggestions by providing recipes based on hairstyle relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input hairstyle relevance data into a generating AI and have the generating AI adjust the order of service.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] A hairstyle suggestion system can analyze a user's past hairstyle history and suggest the most suitable hairstyle based on that history. For example, it can analyze the trends of hairstyles the user has chosen in the past and suggest a new hairstyle based on those trends. It can also suggest styling methods suitable for a specific hairstyle based on the user's past hairstyle history. Furthermore, it can suggest hair care products suitable for a specific hairstyle based on the user's past hairstyle history. By providing optimal hairstyle suggestions based on past hairstyle history, it becomes possible to make suggestions that are more satisfying to the user.

[0052] A hairstyle suggestion system can suggest hairstyles based on a user's current fashion style and makeup. For example, it can suggest the most suitable hairstyle to match the user's current fashion style. It can also suggest the most suitable hairstyle based on the user's makeup colors. Furthermore, it can suggest a specific hairstyle based on the user's fashion style and makeup. This allows for a more consistent style by suggesting hairstyles that match the user's fashion style and makeup.

[0053] The hairstyle suggestion system can suggest hairstyles suitable for the local climate and culture based on the user's geographical location. For example, if the user lives in a humid area, it can suggest hairstyles that are resistant to humidity. Similarly, if the user lives in a cold region, it can suggest hairstyles suitable for the cold. Furthermore, if the user lives in a specific cultural area, it can suggest hairstyles appropriate for that culture. This allows for more practical and appropriate suggestions by providing hairstyle recommendations based on the user's geographical location.

[0054] The hairstyle suggestion system can analyze a user's social media activity and suggest hairstyles based on that activity. For example, it can analyze the content of a user's social media posts and suggest the most suitable hairstyle based on that content. It can also analyze the reactions of the user's social media followers and suggest the most suitable hairstyle based on those reactions. Furthermore, it can analyze the trends on the user's social media and suggest the most suitable hairstyle based on those trends. By providing hairstyle suggestions based on the user's social media activity, it becomes possible to make more appropriate suggestions.

[0055] The hairstyle suggestion system can propose different hairstyles depending on the user's lifestyle. For example, if a user has an active lifestyle, it can suggest a hairstyle suitable for that lifestyle. Similarly, if a user has a quiet lifestyle, it can suggest a hairstyle suitable for that lifestyle. Furthermore, it can suggest different hairstyles depending on the user's lifestyle. This allows for more appropriate suggestions by tailoring the hairstyle recommendations to the user's lifestyle.

[0056] The hairstyle suggestion system can adjust the order of hairstyle suggestions based on the user's past hairstyle history. For example, it can analyze the trends of hairstyles the user has chosen in the past and determine the suggestion order based on those trends. It can also prioritize suggestions that are suitable for specific hairstyles based on the user's past hairstyle history. By providing a suggestion order based on past hairstyle history, it becomes possible to suggest more appropriate hairstyles.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk takes a photo of the user's face and enters their desired hair length. For example, the user can take a photo of their face using a smartphone camera, digital camera, or webcam and enter their desired hair length. Step 2: The analysis unit analyzes the information entered by the reception unit, including face shape, skin tone, height, and bone structure diagnosis, and suggests a suitable hairstyle. For example, it can analyze face shape, skin tone, and height to suggest a hairstyle that suits the user. Step 3: The generation unit visualizes the hairstyle proposed by the analysis unit as a processed image. For example, the proposed hairstyle can be visualized as a processed image, a 3D model, or an animation. Step 4: The supply unit provides the hairdresser with the hairstyle recipe generated by the generation unit. For example, the generated hairstyle recipe can be provided to the hairdresser via email, print, or web application.

[0059] (Example of form 2) The hairstyle suggestion system according to an embodiment of the present invention is a system in which a user takes a photo of their face and tells the system which hair length they want, and the generating AI suggests a suitable hairstyle based on the user's face shape, skin color, height, and bone structure. The hairstyle suggestion system allows the user to take a photo of their face and tell the system which hair length they want, and the generating AI suggests a suitable hairstyle based on the user's face shape, skin color, height, and bone structure. The suggestions are visualized as processed images, and the hairdresser is also provided with a recipe for the hairstyle. This helps the user discover a new self. Because the suggestions are based on a vast amount of fashion data learned by the generating AI, as well as measurements of facial feature placement and body balance, the system provides more stable suggestions that are less dependent on individual skill than human suggestions. For example, the hairstyle suggestion system allows the user to take a photo of their face and tell the system which hair length they want. This information is input into the generating AI. Next, the generating AI analyzes information such as face shape, skin color, height, and bone structure and suggests a hairstyle that suits the user. For example, if the user has a round face and light skin tone, the generating AI will select the most suitable hairstyle based on that information. The generating AI visualizes the suggested hairstyle as a processed image. This allows users to visualize hairstyles that suit them. Furthermore, hairdressers are provided with a recipe for the hairstyle, enabling them to accurately reproduce the hairstyle desired by the user. This service uses a vast amount of fashion data learned by the generative AI, along with measurements of facial feature placement and body balance, to provide suggestions that are more stable and less dependent on individual skill than human suggestions. For example, the generative AI can learn from past fashion data and suggest hairstyles that reflect current trends and fashions. This service is also offered as a subscription service at hair salons. Users can order hairstyles from electronic medical records created at SB Shop and reach hair salons through a cashless payment network. This allows users to easily find hairstyles that suit them, and enables hairdressers to provide accurate hairstyles. In this way, the generative AI service that suggests the optimal hairstyle from a facial photograph helps users discover a new version of themselves and provides hairdressers with accurate hairstyle recipes, resulting in stable suggestions that are less dependent on individual skill.This allows the hairstyle suggestion system to propose the most suitable hairstyle based on the user's facial photograph, providing visualizations and recipes, enabling consistent suggestions regardless of individual skill.

[0060] The hairstyle suggestion system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit takes a photo of the user's face and inputs the desired hair length. For example, a smartphone camera or a digital camera can be used to take the user's face photo. The reception unit can, for example, take a photo of the face using a smartphone camera and input the desired hair length. Alternatively, the reception unit can take a photo of the face using a digital camera and input the desired hair length. Furthermore, the reception unit can take a photo of the face using a webcam and input the desired hair length. For example, the reception unit can take a photo of the face using a smartphone camera and input the desired hair length. Alternatively, a photo of the face using a digital camera can be taken and the desired hair length can be input. Alternatively, a photo of the face using a webcam can be taken and the desired hair length can be input. The analysis unit analyzes the information input by the reception unit, such as face shape, skin color, height, and bone structure diagnosis, and suggests a suitable hairstyle. For example, the analysis unit analyzes the face shape and suggests a hairstyle that suits the user. Alternatively, the analysis unit can analyze the skin color and suggest a hairstyle that suits the user. Furthermore, the analysis unit can analyze the user's height and suggest a hairstyle that suits them. For example, the analysis unit can analyze the user's face shape and suggest a hairstyle that suits them. It can also analyze the user's skin tone and suggest a hairstyle that suits them. It can also analyze the user's height and suggest a hairstyle that suits them. The generation unit visualizes the hairstyle suggested by the analysis unit as a processed image. For example, the generation unit visualizes the suggested hairstyle as a processed image. The generation unit can also visualize the suggested hairstyle as a 3D model. Furthermore, the generation unit can also visualize the suggested hairstyle as an animation. For example, the generation unit visualizes the suggested hairstyle as a processed image. It can also visualize the suggested hairstyle as a 3D model. It can also visualize the suggested hairstyle as an animation. The supply unit provides the hairdresser with the hairstyle recipe generated by the generation unit. For example, the supply unit provides the hairdresser with the generated hairstyle recipe via email. The supply unit can also print out the generated hairstyle recipe and provide it to the hairdresser.Furthermore, the service provider can also provide the generated hairstyle recipes to hairdressers via a web application. For example, the service provider can provide the generated hairstyle recipes to hairdressers via email. It can also print out the generated hairstyle recipes and provide them to hairdressers. It can also provide the generated hairstyle recipes to hairdressers via a web application. As a result, the hairstyle suggestion system according to this embodiment can suggest the optimal hairstyle based on the user's facial photograph, visualize it, and provide a recipe, enabling stable suggestions that are not dependent on the individual's skill.

[0061] The reception desk takes a photo of the user's face and inputs their desired hair length. The user can use a smartphone camera or a digital camera for the photo. Specifically, when using a smartphone camera, the user launches the application and takes a photo of their face from the front, following the guidelines. The application automatically adjusts the position and angle of the face to obtain the optimal photo. When using a digital camera, the user takes a high-resolution photo and uploads it to the system. When using a webcam, the user can use a PC or tablet to take a photo of their face in real time and send it directly to the system. This allows the reception desk to obtain photos from a variety of devices, enhancing user convenience. Furthermore, when inputting the desired hair length, the user can use sliders or dropdown menus to select specific lengths and styles. For example, options such as short, medium, and long are available, and even more detailed length and style specifications are possible. This allows the reception desk to accurately reflect the user's preferences and provide the information necessary for the next analysis step.

[0062] The analysis department analyzes information entered by the reception department, including face shape, skin tone, height, and bone structure diagnosis, to suggest suitable hairstyles. Specifically, it uses AI to analyze facial photographs and identify the user's face shape. Face shapes include round, oval, square, and heart shapes, each with its own suitable hairstyle. The AI ​​detects facial contours and features to suggest the optimal hairstyle. To analyze skin tone, the AI ​​analyzes the color tone of the facial photograph and suggests hair colors and styles that match the user's skin tone. Furthermore, height and bone structure diagnosis are also considered. For example, a tall user may be suggested a voluminous hairstyle to create balance. Bone structure diagnosis selects the optimal hairstyle based on information such as shoulder width and neck length. This allows the analysis department to analyze each user's individual characteristics in detail and suggest the most suitable hairstyle. In addition, the analysis department can utilize past data and trend information to suggest hairstyles that incorporate the latest trends. For example, it can consider the current season and trendy styles to provide the user with the most suitable hairstyle. This enables the analysis department to achieve optimal hairstyle suggestions that combine each user's individual characteristics with the latest trends.

[0063] The generation unit visualizes the hairstyles suggested by the analysis unit as processed images. Specifically, it uses AI to synthesize the suggested hairstyles onto the user's face photo, generating a realistic image. For example, it overlays the suggested hairstyle onto the user's face photo to achieve a natural look. It can also be visualized as a 3D model, allowing the user to rotate it 360 degrees and view the hairstyle from various angles. Furthermore, visualization as animation allows users to realistically experience the movement and volume of the hairstyle. This enables the generation unit to provide users with visually easy-to-understand hairstyle suggestions, making it easier for them to imagine the actual finished look. In addition, the generation unit offers variations in different hair colors and styles, allowing users to choose the best hairstyle from multiple options. For example, even with the same hairstyle, users can try different hair colors or with or without highlights. This allows the generation unit to provide users with diverse options and help them find the perfect hairstyle.

[0064] The service provider provides hairdressers with hairstyle recipes generated by the generation unit. Specifically, it sends the generated hairstyle recipes to hairdressers via email, along with detailed cutting and styling instructions. It is also possible to provide hairdressers with printed copies of the recipes, allowing them to refer to paper-based materials while performing the treatment. Furthermore, by providing recipes through a web application, hairdressers can access the latest information in real time. For example, the web application may include 3D models and animations of hairstyles, which hairdressers can use as references while performing the treatment. This allows the service provider to provide hairdressers with detailed and easy-to-understand information, helping them achieve hairstyles that meet the user's wishes. In addition, the service provider can collect feedback from hairdressers and continuously improve the accuracy and content of the recipes. For example, based on the results of actual treatments performed by hairdressers and user satisfaction, the service provider can revise the recipe content and make more effective suggestions. This allows the service provider to provide high-quality service to both hairdressers and users, improving the reliability and satisfaction of the entire system.

[0065] The reception desk can estimate the user's emotions and adjust the timing of facial photo capture based on the estimated emotions. For example, if the user is relaxed, the reception desk will take a facial photo at a relaxed moment to capture a natural expression. If the user is tense, the reception desk can provide a relaxing environment to alleviate tension before taking a facial photo. Furthermore, if the user is in a hurry, the reception desk can give simple instructions to take a facial photo quickly. This allows for capturing natural expressions by taking facial photos at the optimal timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's facial expression data into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0066] The reception desk can analyze the user's past hairstyle history and select the optimal shooting method. For example, the reception desk can analyze the trends of hairstyles the user has chosen in the past and select the optimal shooting angle based on those trends. The reception desk can also set lighting conditions suitable for a specific hairstyle based on the user's past hairstyle history. Furthermore, the reception desk can select a background suitable for a specific hairstyle based on the user's past hairstyle history. For example, the reception desk can analyze the trends of hairstyles the user has chosen in the past and select the optimal shooting angle based on those trends. It can also set lighting conditions suitable for a specific hairstyle based on the user's past hairstyle history. It can also select a background suitable for a specific hairstyle based on the user's past hairstyle history. By selecting the optimal shooting method based on past hairstyle history, it becomes possible to suggest more appropriate hairstyles. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past hairstyle history data into a generating AI and have the generating AI select the optimal shooting method.

[0067] The reception desk can filter facial photographs based on the user's current fashion style and makeup. For example, the reception desk can apply the optimal filter to match the user's current fashion style and take a facial photograph. Alternatively, the reception desk can select the optimal filter based on the user's makeup shades and take a facial photograph. Furthermore, the reception desk can apply a specific filter based on the user's fashion style and makeup and take a facial photograph. For example, the reception desk can apply the optimal filter to match the user's current fashion style and take a facial photograph. It can also select the optimal filter based on the user's makeup shades and take a facial photograph. It can also apply a specific filter based on the user's fashion style and makeup and take a facial photograph. This allows for more appropriate hairstyle suggestions by filtering according to the user's fashion style and makeup. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's fashion style and makeup data into a generating AI and have the generating AI select the optimal filter.

[0068] The reception desk can estimate the user's emotions and determine the priority of facial photos to take based on those emotions. For example, if the user is relaxed, the reception desk will prioritize taking facial photos at relaxed moments to capture natural expressions. If the user is tense, the reception desk can provide a relaxing environment to alleviate tension before prioritizing facial photos. Furthermore, if the user is in a hurry, the reception desk can give simple instructions to quickly take facial photos and prioritize those photos. This allows for more appropriate hairstyle suggestions by prioritizing facial photos according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the reception area may be performed using AI, or not using AI. For example, the reception area may input user facial expression data into the generative AI and have the generative AI estimate the user's emotions.

[0069] The reception desk can select a highly relevant background based on the user's geographical location when taking a facial photograph. For example, if the user is in an urban area, the reception desk will prioritize selecting an urban background for the facial photograph. Similarly, if the user is in a natural environment, the reception desk can prioritize selecting a natural background for the facial photograph. Furthermore, if the user is participating in a specific event, the reception desk can prioritize selecting a background related to that event for the facial photograph. This allows for more appropriate hairstyle suggestions by selecting backgrounds based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the AI ​​select a highly relevant background.

[0070] The reception desk can analyze the user's social media activity when taking a facial photograph and suggest relevant styles. For example, the reception desk can analyze the content of the user's social media posts and suggest the most suitable style based on that content. It can also analyze the reactions of the user's social media followers and suggest the most suitable style based on those reactions. Furthermore, the reception desk can analyze the user's social media trends and suggest the most suitable style based on those trends. For example, the reception desk can analyze the content of the user's social media posts and suggest the most suitable style based on that content. It can also analyze the reactions of the user's social media followers and suggest the most suitable style based on those reactions. It can also analyze the user's social media trends and suggest the most suitable style based on those trends. This makes it possible to suggest more appropriate hairstyles by providing style suggestions 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 using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform style suggestions related to the user's social media activity.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is tense, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise and easy-to-understand analysis results. This allows for more appropriate hairstyle suggestions by providing analysis results tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of face shape and skin tone during the analysis. For example, if the face shape is round, the analysis unit will perform a detailed analysis of hairstyles suitable for round faces. The analysis unit can also perform a detailed analysis of hairstyles suitable for light skin tones if the skin tone is light. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of face shape and skin tone. For example, if the face shape is round, the analysis unit will perform a detailed analysis of hairstyles suitable for round faces. If the skin tone is light, it can also perform a detailed analysis of hairstyles suitable for light skin tones. The level of detail of the analysis can also be adjusted based on the importance of face shape and skin tone. This makes it possible to suggest more appropriate hairstyles by performing analysis based on the importance of face shape and skin tone. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input face shape and skin tone data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the user's lifestyle during analysis. For example, if the user has an active lifestyle, the analysis unit will analyze hairstyles suitable for an active lifestyle. Similarly, if the user has a quiet lifestyle, the analysis unit can analyze hairstyles suitable for a quiet lifestyle. Furthermore, the analysis unit can apply different analysis algorithms depending on the user's lifestyle. For example, if the user has an active lifestyle, the analysis unit will analyze hairstyles suitable for an active lifestyle. If the user has a quiet lifestyle, the analysis unit can analyze hairstyles suitable for a quiet lifestyle. Different analysis algorithms can be applied depending on the user's lifestyle. This allows for more appropriate hairstyle suggestions by performing analysis tailored to the user's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user lifestyle data into a generating AI and have the generating AI apply different analysis algorithms.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is tense, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise and easy-to-understand analysis results. This allows for more appropriate hairstyle suggestions by providing analysis results tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0075] The analysis unit can determine the priority of analysis based on the user's past hairstyle history. For example, the analysis unit analyzes the trends of hairstyles the user has chosen in the past and determines the priority of analysis based on those trends. The analysis unit can also prioritize analysis suitable for a specific hairstyle based on the user's past hairstyle history. Furthermore, the analysis unit can prioritize analysis suitable for a specific hairstyle based on the user's past hairstyle history. For example, the analysis unit analyzes the trends of hairstyles the user has chosen in the past and determines the priority of analysis based on those trends. It can also prioritize analysis suitable for a specific hairstyle based on the user's past hairstyle history. It can also prioritize analysis suitable for a specific hairstyle based on the user's past hairstyle history. This makes it possible to suggest more appropriate hairstyles by performing analysis based on past hairstyle history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past hairstyle history data into a generating AI and have the generating AI determine the priority of analysis.

[0076] The analysis unit can adjust the order of analysis based on user relevance during the analysis process. For example, the analysis unit can adjust the order of analysis based on the user's face shape and skin tone. It can also adjust the order of analysis based on the user's lifestyle. Furthermore, the analysis unit can adjust the order of analysis based on the user's past hairstyle history. For example, the analysis unit can adjust the order of analysis based on the user's face shape and skin tone. It can also adjust the order of analysis based on the user's lifestyle. It can also adjust the order of analysis based on the user's past hairstyle history. This makes it possible to suggest more appropriate hairstyles by performing analysis based on user relevance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0077] The generation unit can estimate the user's emotions and adjust the way the generated images are represented based on the estimated emotions. For example, if the user is relaxed, the generation unit will take a photo of their face at a relaxed moment to elicit a natural expression. If the user is tense, the generation unit can provide a relaxing environment to alleviate tension and then take a photo of their face. Furthermore, if the user is in a hurry, the generation unit can give simple instructions to quickly take a photo of their face. For example, if the user is relaxed, the generation unit will take a photo of their face at a relaxed moment to elicit a natural expression. If the user is tense, the generation unit can provide a relaxing environment to alleviate tension and then take a photo of their face. If the user is in a hurry, the generation unit can give simple instructions to quickly take a photo of their face. This allows for more appropriate hairstyle suggestions by generating images that respond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the way the image is represented.

[0078] The generation unit can adjust the level of detail of the generated images based on the importance of the hairstyles. For example, if the hairstyle is an important element, the generation unit will generate a detailed hairstyle. If the hairstyle is not very important, the generation unit can also generate a simplified hairstyle. Furthermore, the generation unit can adjust the level of detail of the generated images based on the importance of the hairstyles. For example, if the hairstyle is an important element, the generation unit will generate a detailed hairstyle. If the hairstyle is not very important, it can also generate a simplified hairstyle. The level of detail of the generated images can also be adjusted based on the importance of the hairstyles. This makes it possible to suggest more appropriate hairstyles by generating images based on the importance of hairstyles. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hairstyle importance data into a generation AI and have the generation AI adjust the level of detail of the generated images.

[0079] The generation unit can apply different generation algorithms depending on the hairstyle category during generation. For example, in the case of short hair, the generation unit applies a generation algorithm suitable for short hair. Similarly, in the case of long hair, the generation unit can apply a generation algorithm suitable for long hair. Furthermore, the generation unit can apply different generation algorithms depending on the hairstyle category. For example, in the case of short hair, the generation unit applies a generation algorithm suitable for short hair. In the case of long hair, it can apply a generation algorithm suitable for long hair. Different generation algorithms can be applied depending on the hairstyle category. This makes it possible to suggest more appropriate hairstyles by generating images according to the hairstyle category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hairstyle category data into a generation AI and cause the generation AI to execute the application of different generation algorithms.

[0080] The generation unit can estimate the user's emotions and adjust the length of the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed image. If the user is tense, the generation unit can also generate a concise image. Furthermore, if the user is in a hurry, the generation unit can generate an image that gets straight to the point. For example, if the user is relaxed, the generation unit can generate a detailed image. If the user is tense, it can also generate a concise image. If the user is in a hurry, it can also generate an image that gets straight to the point. This allows for more appropriate hairstyle suggestions by generating images that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the images.

[0081] The generation unit can determine the generation priority based on the hairstyle submission date during generation. For example, if the hairstyle submission date is early, the generation unit will prioritize generation. Conversely, if the hairstyle submission date is late, the generation unit can also postpone generation. Furthermore, the generation unit can determine the generation priority based on the hairstyle submission date. For example, if the hairstyle submission date is early, the generation unit will prioritize generation. If the hairstyle submission date is late, the generation unit can also postpone generation. The generation priority can also be determined based on the hairstyle submission date. This makes it possible to suggest more appropriate hairstyles by generating images based on the hairstyle submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hairstyle submission date data into a generation AI and have the generation AI determine the generation priority.

[0082] The generation unit can adjust the generation order based on the relevance of hairstyles during generation. For example, the generation unit can prioritize generation when the relevance of hairstyles is high. It can also postpone generation when the relevance of hairstyles is low. Furthermore, the generation unit can adjust the generation order based on the relevance of hairstyles. For example, the generation unit can prioritize generation when the relevance of hairstyles is high. It can also postpone generation when the relevance of hairstyles is low. It can also adjust the generation order based on the relevance of hairstyles. This makes it possible to suggest more appropriate hairstyles by generating images based on the relevance of hairstyles. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hairstyle relevance data into a generation AI and have the generation AI adjust the generation order.

[0083] The service provider can estimate the user's emotions and adjust the way the recipes are presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed recipe. If the user is stressed, the service provider can provide a concise and easy-to-understand recipe. Furthermore, if the user is in a hurry, the service provider can provide a recipe that gets straight to the point. For example, if the user is relaxed, the service provider can provide a detailed recipe. If the user is stressed, the service provider can provide a concise and easy-to-understand recipe. If the user is in a hurry, the service provider can provide a recipe that gets straight to the point. By providing recipes that match the user's emotions, it becomes possible to suggest more appropriate hairstyles. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the recipes are presented.

[0084] The service provider can adjust the level of detail provided based on the importance of the hairstyle. For example, if the hairstyle is an important element, the service provider will provide a detailed recipe. If the hairstyle is not very important, the service provider can also provide a concise recipe. Furthermore, the service provider can adjust the level of detail provided based on the importance of the hairstyle. For example, if the hairstyle is an important element, the service provider will provide a detailed recipe. If the hairstyle is not very important, the service provider can also provide a concise recipe. The service provider can also adjust the level of detail provided based on the importance of the hairstyle. This makes it possible to suggest more appropriate hairstyles by providing recipes based on the importance of the hairstyle. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input hairstyle importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0085] The service provider can apply different service algorithms depending on the hairstyle category at the time of service. For example, in the case of short hair, the service provider can apply a service algorithm suitable for short hair. Similarly, in the case of long hair, the service provider can apply a service algorithm suitable for long hair. Furthermore, the service provider can apply different service algorithms depending on the hairstyle category. For example, in the case of short hair, the service provider can apply a service algorithm suitable for short hair. In the case of long hair, it can apply a service algorithm suitable for long hair. Different service algorithms can also be applied depending on the hairstyle category. This allows for more appropriate hairstyle suggestions by providing recipes tailored to the hairstyle category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input hairstyle category data into a generating AI and have the generating AI apply different service algorithms.

[0086] The service provider can estimate the user's emotions and adjust the length of the recipes offered based on those emotions. For example, if the user is relaxed, the service provider can offer a detailed recipe. If the user is stressed, the service provider can offer a concise and easy-to-understand recipe. Furthermore, if the user is in a hurry, the service provider can offer a concise and to-the-point recipe. This allows for more appropriate hairstyle suggestions by providing recipes tailored 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-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the recipes.

[0087] The service provider can determine the priority of service based on the timing of hairstyle submissions. For example, if a hairstyle is submitted early, the service provider will prioritize its service. Conversely, if a hairstyle is submitted late, the service provider may postpone its service. Furthermore, the service provider can also determine the priority of service based on the timing of hairstyle submissions. For example, if a hairstyle is submitted early, the service provider will prioritize its service. If a hairstyle is submitted late, the service provider may postpone its service. The service provider can also determine the priority of service based on the timing of hairstyle submissions. This allows for more appropriate hairstyle suggestions by providing recipes based on the timing of hairstyle submissions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input hairstyle submission timing data into a generating AI and have the generating AI determine the priority of service.

[0088] The service provider can adjust the order of service based on the relevance of hairstyles at the time of service. For example, the service provider can prioritize service when the relevance of hairstyles is high. It can also postpone service when the relevance of hairstyles is low. Furthermore, the service provider can adjust the order of service based on the relevance of hairstyles. For example, the service provider can prioritize service when the relevance of hairstyles is high. It can also postpone service when the relevance of hairstyles is low. The service provider can also adjust the order of service based on the relevance of hairstyles. This allows for more appropriate hairstyle suggestions by providing recipes based on hairstyle relevance. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input hairstyle relevance data into a generating AI and have the generating AI adjust the order of service.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The hairstyle suggestion system can estimate the user's current mood and emotions when taking a photo of their face, and provide the optimal shooting environment based on those emotions. For example, if the user is relaxed, it can play relaxing music to elicit a natural expression. If the user is tense, it can provide a relaxing scent to ease their tension. Furthermore, if the user is in a hurry, it can give simple instructions to take the photo quickly. By providing the optimal shooting environment according to the user's emotions, it is possible to take more natural and attractive facial photos.

[0091] A hairstyle suggestion system can analyze a user's past hairstyle history and suggest the most suitable hairstyle based on that history. For example, it can analyze the trends of hairstyles the user has chosen in the past and suggest a new hairstyle based on those trends. It can also suggest styling methods suitable for a specific hairstyle based on the user's past hairstyle history. Furthermore, it can suggest hair care products suitable for a specific hairstyle based on the user's past hairstyle history. By providing optimal hairstyle suggestions based on past hairstyle history, it becomes possible to make suggestions that are more satisfying to the user.

[0092] A hairstyle suggestion system can suggest hairstyles based on a user's current fashion style and makeup. For example, it can suggest the most suitable hairstyle to match the user's current fashion style. It can also suggest the most suitable hairstyle based on the user's makeup colors. Furthermore, it can suggest a specific hairstyle based on the user's fashion style and makeup. This allows for a more consistent style by suggesting hairstyles that match the user's fashion style and makeup.

[0093] The hairstyle suggestion system can estimate the user's emotions and adjust hairstyle suggestions based on those emotions. For example, if the user is relaxed, it can suggest bolder hairstyles. If the user is stressed, it can suggest more conservative hairstyles. Furthermore, if the user is in a hurry, it can suggest hairstyles that are easy to style. By providing hairstyle suggestions that match the user's emotions, it becomes possible to make more appropriate suggestions.

[0094] The hairstyle suggestion system can suggest hairstyles suitable for the local climate and culture based on the user's geographical location. For example, if the user lives in a humid area, it can suggest hairstyles that are resistant to humidity. Similarly, if the user lives in a cold region, it can suggest hairstyles suitable for the cold. Furthermore, if the user lives in a specific cultural area, it can suggest hairstyles appropriate for that culture. This allows for more practical and appropriate suggestions by providing hairstyle recommendations based on the user's geographical location.

[0095] The hairstyle suggestion system can analyze a user's social media activity and suggest hairstyles based on that activity. For example, it can analyze the content of a user's social media posts and suggest the most suitable hairstyle based on that content. It can also analyze the reactions of the user's social media followers and suggest the most suitable hairstyle based on those reactions. Furthermore, it can analyze the trends on the user's social media and suggest the most suitable hairstyle based on those trends. By providing hairstyle suggestions based on the user's social media activity, it becomes possible to make more appropriate suggestions.

[0096] The hairstyle suggestion system can estimate the user's emotions and adjust the way hairstyles are visualized based on those emotions. For example, if the user is relaxed, it can provide a detailed 3D model. If the user is stressed, it can provide a simple 2D image. Furthermore, if the user is in a hurry, it can provide a concise animation. By providing visualization methods that match the user's emotions, it becomes possible to suggest more appropriate hairstyles.

[0097] The hairstyle suggestion system can propose different hairstyles depending on the user's lifestyle. For example, if a user has an active lifestyle, it can suggest a hairstyle suitable for that lifestyle. Similarly, if a user has a quiet lifestyle, it can suggest a hairstyle suitable for that lifestyle. Furthermore, it can suggest different hairstyles depending on the user's lifestyle. This allows for more appropriate suggestions by tailoring the hairstyle recommendations to the user's lifestyle.

[0098] The hairstyle suggestion system can estimate the user's emotions and adjust the order of hairstyle suggestions based on those emotions. For example, if the user is relaxed, it will suggest more hairstyles. Conversely, if the user is stressed, it may suggest fewer hairstyles. Furthermore, if the user is in a hurry, it can prioritize suggesting the most suitable hairstyle. By providing a suggestion order that matches the user's emotions, it becomes possible to suggest more appropriate hairstyles.

[0099] The hairstyle suggestion system can adjust the order of hairstyle suggestions based on the user's past hairstyle history. For example, it can analyze the trends of hairstyles the user has chosen in the past and determine the suggestion order based on those trends. It can also prioritize suggestions that are suitable for specific hairstyles based on the user's past hairstyle history. By providing a suggestion order based on past hairstyle history, it becomes possible to suggest more appropriate hairstyles.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The reception desk takes a photo of the user's face and enters their desired hair length. For example, the user can take a photo of their face using a smartphone camera, digital camera, or webcam and enter their desired hair length. Step 2: The analysis unit analyzes the information entered by the reception unit, including face shape, skin tone, height, and bone structure diagnosis, and suggests a suitable hairstyle. For example, it can analyze face shape, skin tone, and height to suggest a hairstyle that suits the user. Step 3: The generation unit visualizes the hairstyle proposed by the analysis unit as a processed image. For example, the proposed hairstyle can be visualized as a processed image, a 3D model, or an animation. Step 4: The supply unit provides the hairdresser with the hairstyle recipe generated by the generation unit. For example, the generated hairstyle recipe can be provided to the hairdresser via email, print, or web application.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit takes a facial photograph using the camera 42 of the smart device 14 and inputs the desired hair length. The analysis unit analyzes the face shape, skin color, height, bone structure diagnosis, etc., using the identification processing unit 290 of the data processing unit 12 and proposes a suitable hairstyle. The generation unit visualizes the proposed hairstyle as a processed image using the identification processing unit 290 of the data processing unit 12. The provision unit provides the generated hairstyle recipe to the hairdresser via the communication I / F 44 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.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit takes a facial photograph using the camera 42 of the smart glasses 214 and inputs the desired hair length. The analysis unit analyzes the face shape, skin color, height, bone structure diagnosis, etc., using the identification processing unit 290 of the data processing unit 12 and proposes a suitable hairstyle. The generation unit visualizes the proposed hairstyle as a processed image using the identification processing unit 290 of the data processing unit 12. The provision unit provides the generated hairstyle recipe to the hairdresser via the communication I / F 44 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.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit takes a facial photograph using the camera 42 of the headset terminal 314 and inputs the desired hair length. The analysis unit analyzes the face shape, skin color, height, bone structure diagnosis, etc., using the identification processing unit 290 of the data processing unit 12 and proposes a suitable hairstyle. The generation unit visualizes the proposed hairstyle as a processed image using the identification processing unit 290 of the data processing unit 12. The provision unit provides the generated hairstyle recipe to the hairdresser via the communication I / F 44 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.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit takes a facial photograph using the camera 42 of the robot 414 and inputs the desired hair length. The analysis unit analyzes the face shape, skin color, height, bone structure diagnosis, etc., using the identification processing unit 290 of the data processing unit 12 and proposes a suitable hairstyle. The generation unit visualizes the proposed hairstyle as a processed image using the identification processing unit 290 of the data processing unit 12. The provision unit provides the generated hairstyle recipe to the hairdresser via the communication I / F 44 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) The reception area takes a photo of the user's face and inputs the desired hair length, Based on the information entered by the reception department, the analysis department analyzes face shape, skin tone, height, bone structure, etc., and proposes a suitable hairstyle. A generation unit visualizes the hairstyle proposed by the analysis unit as a processed image, The system includes a providing unit that provides a hairdresser with a hairstyle recipe generated by the generating unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of taking facial photos based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) 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 4) The aforementioned reception unit is When taking a photo of your face, the system filters the image based on the user's current fashion style and makeup. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of facial photos to take based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When taking a facial photograph, a highly relevant background is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When taking a profile picture, the system analyzes the user's social media activity and suggests relevant styles. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of face shape and skin tone. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the analysis priority is determined based on the user's past hairstyle history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on user relevance. 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 images are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, 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 During generation, 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 generated images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the generation priority is determined based on when the hairstyle was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the generation order is adjusted based on the relevance of hairstyles. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the recipes are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, 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 22) The aforementioned supply unit is, When providing a hairstyle, a different distribution algorithm is applied depending on the hairstyle category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the recipes offered based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we will determine the priority of service based on when the hairstyle was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When serving, we adjust the order of service based on the relevance of the hairstyles. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 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 face and inputs the desired hair length, Based on the information entered by the reception department, the analysis department analyzes face shape, skin tone, height, bone structure, etc., and proposes a suitable hairstyle. A generation unit visualizes the hairstyle proposed by the analysis unit as a processed image, The system includes a providing unit that provides a hairdresser with a hairstyle recipe generated by the generating unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of taking facial photos based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is The system analyzes the user's past hairstyle history and selects the optimal shooting method. The system according to feature 1.

4. The aforementioned reception unit is When taking a photo of your face, the system filters the image based on the user's current fashion style and makeup. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of facial photos to take based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When taking a facial photograph, a highly relevant background is selected based on the user's geographical location information. The system according to feature 1.

7. The aforementioned reception unit is When taking a profile picture, the system analyzes the user's social media activity and suggests relevant styles. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

Images