system

Abstract

The system according to this embodiment aims to accurately communicate the hairstyle desired by the user. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, an instruction unit, and a synthesis unit. The reception unit receives an image including the user's face. The suggestion unit analyzes the image received by the reception unit and suggests a hairstyle that suits the user. The instruction unit instructs a cutting method based on the hairstyle suggested by the suggestion unit. The synthesis unit synthesizes the hairstyle as it would look if the hair were grown out, based on the cutting method instructed by the instruction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult for a user to accurately convey a desired hairstyle to a stylist.

[0005] The system according to the embodiment aims to accurately convey a desired hairstyle of a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a suggestion unit, an instruction unit, and a synthesis unit. The reception unit receives an image including the user's face. The suggestion unit analyzes the image received by the reception unit and suggests a hairstyle that suits the user. The instruction unit instructs a cutting method based on the hairstyle suggested by the suggestion unit. The synthesis unit synthesizes the hairstyle as it would look if the hair were grown out, based on the cutting method instructed by the instruction unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately communicate the hairstyle the user desires. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The hairstyle suggestion system according to an embodiment of the present invention is a system for solving the problem of "desired hairstyles" that are difficult to communicate to a stylist at a hair salon. This hairstyle suggestion system allows the user to upload multiple images, including their own face, and then suggests hairstyles after a haircut in various patterns. Furthermore, it not only instructs the stylist on how to cut the hair, but also synthesizes a hairstyle that the user will have if they grow their hair out in the future. For example, the user uploads multiple images, including their own face. Next, the generating AI analyzes these images and suggests hairstyles that suit the user in various patterns. The user selects their desired style from the suggested hairstyles. Based on the selected hairstyle, the generating AI instructs the stylist on specific cutting methods. Furthermore, the generating AI synthesizes a hairstyle that the user will have if they grow their hair out and provides it to the user. This allows the user to specifically communicate their desired hairstyle to the stylist and achieve the hairstyle they envisioned. In addition, since they can check their future hairstyle if they grow their hair out in advance, they can plan their hairstyle for the long term. For example, the user uploads multiple selfies, and the generating AI analyzes these photos and suggests hairstyles that suit the user. The user selects their desired hairstyle from the suggested options, and the generating AI instructs the stylist on specific cutting techniques. Furthermore, the generating AI also creates a simulated hairstyle for when the user's hair grows out and provides this to the user. This allows the user to clearly communicate their desired hairstyle to the stylist, enabling them to achieve their ideal look. In this way, the hairstyle suggestion system provides hairstyles that meet the user's preferences and facilitates smooth communication with the stylist.

[0029] The hairstyle suggestion system according to this embodiment comprises a reception unit, a suggestion unit, an instruction unit, and a synthesis unit. The reception unit receives an image including the user's face. The image including the user's face includes, but is not limited to, images in JPEG or PNG format. The reception unit allows, for example, the user to upload an image using a smartphone or personal computer. The reception unit can also automatically adjust the image resolution and convert it to a format suitable for analysis. For example, the reception unit can use an algorithm to convert a low-resolution image to a high-resolution image. The suggestion unit uses a generation AI to analyze the image received by the reception unit and suggests a hairstyle that suits the user. The suggestion unit can, for example, use a face recognition algorithm to extract the features of the user's face and suggest a hairstyle based on that. The suggestion unit can also suggest a hairstyle considering the user's hair type and current trends. For example, the suggestion unit can analyze the thickness and texture of the user's hair and suggest the optimal hairstyle based on that. The instruction unit instructs the stylist on specific cutting methods based on the hairstyle suggested by the suggestion unit. The instruction unit provides detailed instructions, for example, on the length of the cut and the type of style. The instruction unit can also provide instructions on the cutting procedure and the tools to be used. For example, the instruction unit can specify the order of the cuts and the types of scissors and clippers to use. The synthesis unit synthesizes the hairstyle as the hair would appear if it were grown out, based on the cutting method instructed by the instruction unit. The synthesis unit can synthesize a hairstyle that takes into account the rate at which the hair grows, for example, by using a simulation algorithm. The synthesis unit can also synthesize a hairstyle that takes into account the shape of the user's face and hair type. For example, the synthesis unit can synthesize a hairstyle based on the contours of the user's face and the thickness of their hair. As a result, the hairstyle suggestion system according to this embodiment can analyze an image including the user's face and propose a hairstyle, provide instructions on the cutting method, and synthesize a hairstyle.

[0030] The reception unit accepts images that include the user's face. Images containing the user's face include, but are not limited to, images in JPEG or PNG format. The reception unit allows users to upload images using, for example, a smartphone or personal computer. Specifically, it provides an interface for users to upload images through a dedicated application or website. The reception unit can also automatically adjust the image resolution and convert it to a format suitable for analysis. For example, the reception unit can use algorithms to convert low-resolution images to high-resolution images. This includes super-resolution techniques using deep learning, which interpolate details in the image and produce a sharper image. The reception unit also performs image format conversion, converting the image to a format that the analysis unit can process optimally. For example, converting a JPEG image to PNG format can prevent information loss due to compression. Furthermore, as a pre-processing step for the image, the reception unit can use a face detection algorithm to automatically cut out the user's face and remove the background. This allows the analysis unit to extract facial features more accurately.

[0031] The suggestion department uses generative AI to analyze images received by the reception department and propose hairstyles that suit the user. For example, the suggestion department extracts the user's facial features using a facial recognition algorithm and proposes hairstyles based on that. Specifically, the generative AI analyzes features such as the user's face shape, eye position, nose shape, and mouth position, and generates the optimal hairstyle based on this information. The generative AI can refer to hairstyles of other users with similar facial features based on a past database. The suggestion department can also propose hairstyles considering the user's hair type and current trends. For example, the suggestion department analyzes the thickness and texture of the user's hair and proposes the optimal hairstyle based on that. The generative AI analyzes information such as the user's hair thickness, texture, and color, and generates the optimal hairstyle for these elements. Furthermore, the suggestion department can incorporate the latest fashion data to propose hairstyles that are in line with current trends and seasons. This allows the suggestion department to propose personalized and trend-appropriate hairstyles to users.

[0032] The instruction unit provides specific cutting instructions to the stylist based on the hairstyle proposed by the suggestion unit. For example, the instruction unit provides detailed instructions on the length and style of the cut. Specifically, it provides detailed instructions on how long to cut and what shape to shape each part of the proposed hairstyle. The instruction unit can also provide instructions on the cutting procedure and the tools to be used. For example, it can specify the order of cuts and the types of scissors and clippers to use. Furthermore, the instruction unit can provide tips and advice for the stylist when cutting. For example, it can instruct on the angle and pressure to apply when cutting a specific section, and how to cut along the flow of the hair. In this way, the instruction unit supports the stylist in accurately reproducing the proposed hairstyle. The instruction unit can also provide real-time feedback to the stylist while they are cutting. For example, it can monitor the progress of the cut and issue corrective instructions as needed. In this way, the instruction unit helps the stylist provide high-quality cuts.

[0033] The compositing unit synthesizes hairstyles based on the cutting method instructed by the instruction unit, representing what the hair will look like when grown out. For example, the compositing unit uses a simulation algorithm to synthesize hairstyles that take into account the rate of hair growth. Specifically, the compositing unit analyzes the user's hair growth pattern and rate, and simulates what the hairstyle will look like in the future. The compositing unit can also synthesize hairstyles considering the user's face shape and hair type. For example, it can synthesize hairstyles based on the user's face contour and hair thickness. This allows the user to preview what their hairstyle will look like in the future. Furthermore, the compositing unit also provides a simulation function that allows users to try different hairstyles. For example, users can try hairstyles of different lengths and styles, which can help them choose the optimal hairstyle. The compositing unit displays the changes in hairstyle in real time, making it intuitively understandable to the user. This allows the compositing unit to visually confirm future hairstyles and support the user in selecting the best hairstyle.

[0034] The reception desk can accept multiple images, including the user's face. For example, the reception desk can allow the user to upload multiple images taken from different angles. The reception desk can also accept images taken at different times of day. For example, it can accept images taken in the morning and at night. This allows for more accurate hairstyle suggestions by accepting multiple images. 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 multiple images uploaded by the user into a generating AI and have the generating AI perform image analysis.

[0035] The suggestion unit can analyze images received by the reception unit and suggest hairstyles that suit the user, categorized by pattern. For example, the suggestion unit can suggest hairstyles based on face shape. For example, it can suggest hairstyles based on face shape such as round, oval, or square. The suggestion unit can also suggest hairstyles based on hair type. For example, it can suggest hairstyles based on hair type such as straight, curly, or wavy. Furthermore, the suggestion unit can also suggest hairstyles based on trends. For example, it can suggest hairstyles based on the latest trends. This expands the user's choices by suggesting hairstyles that suit them, categorized by pattern. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input images received by the reception unit into a generation AI and have the generation AI suggest hairstyles that suit the user.

[0036] The instruction unit can instruct the stylist on specific cutting methods based on the hairstyle proposed by the suggestion unit. For example, the instruction unit can specify the length and type of style to be cut. For example, the instruction unit can specify styles such as short cuts, bobs, and long hair. The instruction unit can also instruct on the cutting procedure and the tools to be used. For example, the instruction unit can instruct on the order of cutting and the types of scissors and clippers to be used. This allows the stylist to perform the cut accurately by providing specific cutting instructions. Some or all of the above processing in the instruction unit may be performed using, for example, a generating AI, or without a generating AI. For example, the instruction unit can input the hairstyle proposed by the suggestion unit into the generating AI and have the generating AI execute instructions on specific cutting methods.

[0037] The synthesis unit can synthesize hairstyles based on the cutting method instructed by the instruction unit, representing what the hair would look like when grown out. The synthesis unit can, for example, use a simulation algorithm to synthesize hairstyles that take into account the rate at which hair grows. For example, the synthesis unit can simulate how much the user's hair grows in a month and synthesize hairstyles based on that simulation. The synthesis unit can also synthesize hairstyles considering the shape of the user's face and hair type. For example, the synthesis unit can synthesize hairstyles based on the contours of the user's face and the thickness of their hair. This allows the user to preview what their hairstyle will look like when their hair is grown out. Some or all of the above-described processes in the synthesis unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the synthesis unit can input the cutting method instructed by the instruction unit into a generation AI and have the generation AI synthesize hairstyles for when the hair is grown out.

[0038] The reception desk can analyze a user's past image upload history and select the optimal reception method. For example, the reception desk can set the optimal notification timing based on the time periods when the user frequently uploaded in the past. The reception desk can also prioritize suggesting upload methods that the user has used in the past (camera, gallery, etc.). Furthermore, the reception desk can suggest the optimal reception method for specific days of the week or time periods based on the user's past upload history. This improves user convenience by selecting the optimal reception method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past upload history data into a generating AI and have the generating AI select the optimal reception method.

[0039] The reception unit can filter images based on the user's current hairstyle and facial features upon receipt. For example, the reception unit can analyze the user's current hairstyle and perform appropriate filtering. It can also select the most suitable image based on the user's facial features. Furthermore, the reception unit can combine the user's current hairstyle and facial features to accept the most suitable image. This allows for the acceptance of more appropriate images by filtering based on the current hairstyle and facial features. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current hairstyle and facial feature data into a generating AI and have the generating AI perform the filtering.

[0040] The reception unit can prioritize receiving images that are highly relevant based on the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize receiving images related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving images taken at home. This allows for the reception of more appropriate images by prioritizing highly relevant images based on geographical location information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant images.

[0041] The reception unit can analyze the user's social media activity when receiving images and accept relevant images. For example, the reception unit can prioritize receiving images that the user has shared on social media. It can also prioritize receiving images that the user has tagged on social media. Furthermore, it can prioritize receiving images that the user has "liked" on social media. This allows for the efficient reception of relevant images by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant images.

[0042] The suggestion unit can adjust the level of detail of its suggestions based on the user's face shape and hair type. For example, the suggestion unit can suggest the optimal hairstyle based on the user's face shape. It can also suggest an appropriate hairstyle based on the user's hair type. Furthermore, the suggestion unit can combine the user's face shape and hair type to suggest the optimal hairstyle. By adjusting the level of detail of the suggestions based on face shape and hair type, it becomes possible to suggest more appropriate hairstyles. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's face shape and hair type data into a generative AI and have the generative AI perform the adjustment of the level of detail of the suggestions.

[0043] The suggestion unit can apply different suggestion algorithms depending on the user's age and gender when making suggestions. For example, the suggestion unit can suggest an appropriate hairstyle based on the user's age. It can also suggest the optimal hairstyle based on the user's gender. Furthermore, the suggestion unit can suggest the optimal hairstyle by combining the user's age and gender. This allows for more appropriate hairstyle suggestions by applying different suggestion algorithms depending on age and gender. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's age and gender data into a generative AI and have the generative AI execute the application of the suggestion algorithm.

[0044] The suggestion unit can determine the priority of suggestions based on the user's past hairstyle history. For example, the suggestion unit can make the most suitable suggestions based on the hairstyles the user has previously chosen. The suggestion unit can also prioritize suggesting the most popular styles from the user's past hairstyle history. Furthermore, the suggestion unit can analyze the user's past hairstyle history and make the most suitable suggestions. This allows for more appropriate hairstyle suggestions by determining the priority of suggestions based on past hairstyle history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past hairstyle history data into a generative AI and have the generative AI determine the priority of suggestions.

[0045] The suggestion unit can adjust the order of suggestions based on the user's lifestyle and occupation. For example, the suggestion unit can suggest the most suitable hairstyle based on the user's lifestyle. It can also suggest an appropriate hairstyle based on the user's occupation. Furthermore, the suggestion unit can combine the user's lifestyle and occupation to suggest the most suitable hairstyle. By adjusting the order of suggestions based on lifestyle and occupation, it becomes possible to suggest a more appropriate hairstyle. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's lifestyle and occupation data into a generative AI and have the generative AI adjust the order of suggestions.

[0046] The instruction unit can select a cutting method based on the user's hair type and hair health when an instruction is given. For example, the instruction unit can select the optimal cutting method based on the user's hair type. It can also select an appropriate cutting method based on the user's hair health. Furthermore, the instruction unit can select the optimal cutting method by combining the user's hair type and hair health. This allows for the instruction of a more appropriate cutting method by selecting a cutting method based on hair type and hair health. Some or all of the above processing in the instruction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the instruction unit can input the user's hair type and hair health data into a generative AI and have the generative AI select a cutting method.

[0047] The instruction unit can apply different cutting methods depending on the shape and bone structure of the user's face when instructions are given. For example, the instruction unit can apply the optimal cutting method based on the shape of the user's face. It can also apply an appropriate cutting method based on the user's bone structure. Furthermore, the instruction unit can apply the optimal cutting method by combining the shape of the user's face and bone structure. This allows for the instruction of a more appropriate cutting method by applying different cutting methods depending on the shape and bone structure of the face. Some or all of the above processing in the instruction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the instruction unit can input the user's face shape and bone structure data into a generative AI and have the generative AI execute the application of the cutting method.

[0048] The instruction unit can adjust the cutting method based on the user's past cutting history when an instruction is given. For example, the instruction unit adjusts the optimal cutting method based on the user's past cutting history. The instruction unit can also prioritize and instruct the most successful cutting method from the user's past cutting history. Furthermore, the instruction unit can analyze the user's past cutting history and adjust the optimal cutting method. This allows for the instruction of a more appropriate cutting method by adjusting the cutting method based on past cutting history. Some or all of the above processing in the instruction unit may be performed using, for example, a generating AI, or without a generating AI. For example, the instruction unit can input the user's past cutting history data into a generating AI and have the generating AI perform the adjustment of the cutting method.

[0049] The instruction unit can customize the cutting method based on the user's lifestyle and occupation when instructions are given. For example, the instruction unit can customize the optimal cutting method based on the user's lifestyle. It can also customize an appropriate cutting method based on the user's occupation. Furthermore, the instruction unit can combine the user's lifestyle and occupation to customize the optimal cutting method. This allows for more appropriate cutting methods to be instructed by customizing the cutting method based on lifestyle and occupation. Some or all of the above processing in the instruction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the instruction unit can input the user's lifestyle and occupation data into a generative AI and have the generative AI perform the customization of the cutting method.

[0050] The synthesis unit can adjust the level of detail of the synthesis based on the user's hair type and hair health during the synthesis process. For example, the synthesis unit can perform detailed hairstyle synthesis based on the user's hair type. It can also perform appropriate hairstyle synthesis based on the user's hair health. Furthermore, the synthesis unit can combine the user's hair type and hair health to perform optimal hairstyle synthesis. This allows for more appropriate hairstyle synthesis by adjusting the level of detail of the synthesis based on hair type and hair health. Some or all of the above-described processes in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input the user's hair type and hair health data into a generation AI and have the generation AI perform the adjustment of the level of detail of the synthesis.

[0051] The synthesis unit can apply different synthesis algorithms depending on the user's face shape and bone structure during synthesis. For example, the synthesis unit can synthesize the optimal hairstyle based on the user's face shape. It can also synthesize an appropriate hairstyle based on the user's bone structure. Furthermore, the synthesis unit can combine the user's face shape and bone structure to synthesize the optimal hairstyle. This makes it possible to synthesize a more appropriate hairstyle by applying different synthesis algorithms depending on the face shape and bone structure. Some or all of the above processing in the synthesis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the synthesis unit can input the user's face shape and bone structure data into a generative AI and have the generative AI execute the application of the synthesis algorithm.

[0052] The synthesis unit can adjust the synthesis order based on the user's past hairstyle history during synthesis. For example, the synthesis unit can synthesize the optimal hairstyle based on the user's past hairstyle history. The synthesis unit can also prioritize the synthesis of the most popular hairstyles from the user's past hairstyle history. Furthermore, the synthesis unit can analyze the user's past hairstyle history and synthesize the optimal hairstyle. This allows for the synthesis of more appropriate hairstyles by adjusting the synthesis order based on past hairstyle history. Some or all of the above processing in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input the user's past hairstyle history data into a generation AI and have the generation AI perform the adjustment of the synthesis order.

[0053] The synthesis unit can customize the synthesis method based on the user's lifestyle and occupation during the synthesis process. For example, the synthesis unit can synthesize the optimal hairstyle based on the user's lifestyle. It can also synthesize an appropriate hairstyle based on the user's occupation. Furthermore, the synthesis unit can combine the user's lifestyle and occupation to synthesize the optimal hairstyle. This allows for the synthesis of more appropriate hairstyles by customizing the synthesis method based on lifestyle and occupation. Some or all of the above-described processes in the synthesis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the synthesis unit can input the user's lifestyle and occupation data into a generative AI and have the generative AI perform the customization of the synthesis method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze a user's past hairstyle history and select the optimal reception method. For example, it can set the optimal notification timing based on the times of day the user frequently uploaded in the past. The reception desk can also prioritize suggesting upload methods the user has used in the past (camera, gallery, etc.). Furthermore, the reception desk can suggest the optimal reception method for specific days of the week or times of day based on the user's past upload history. This improves user convenience by selecting the optimal reception method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past upload history data into a generating AI and have the generating AI select the optimal reception method.

[0056] The reception unit can prioritize receiving images that are highly relevant based on the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving images related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving images taken at home. This allows for the reception of more appropriate images by prioritizing highly relevant images based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant images.

[0057] The suggestion unit can apply different suggestion algorithms depending on the user's age and gender when making suggestions. For example, it can suggest an appropriate hairstyle based on the user's age. It can also suggest the optimal hairstyle based on the user's gender. Furthermore, it can suggest the optimal hairstyle by combining the user's age and gender. This allows for more appropriate hairstyle suggestions by applying different suggestion algorithms depending on age and gender. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without one. For example, the suggestion unit can input the user's age and gender data into a generative AI and have the generative AI apply the suggestion algorithm.

[0058] The instruction unit can select a cutting method based on the user's hair type and hair health when an instruction is given. For example, it can select the optimal cutting method based on the user's hair type. The instruction unit can also select an appropriate cutting method based on the user's hair health. Furthermore, the instruction unit can select the optimal cutting method by combining the user's hair type and hair health. This allows for more appropriate cutting methods to be instructed by selecting a cutting method based on hair type and hair health. Some or all of the above processing in the instruction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the instruction unit can input the user's hair type and hair health data into a generative AI and have the generative AI select a cutting method.

[0059] The synthesis unit can adjust the level of detail of the synthesis based on the user's hair type and hair health during the synthesis process. For example, it can perform detailed hairstyle synthesis based on the user's hair type. The synthesis unit can also perform appropriate hairstyle synthesis based on the user's hair health. Furthermore, the synthesis unit can combine the user's hair type and hair health to perform optimal hairstyle synthesis. This allows for more appropriate hairstyle synthesis by adjusting the level of detail of the synthesis based on hair type and hair health. Some or all of the above-described processes in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input the user's hair type and hair health data into a generation AI and have the generation AI perform the adjustment of the level of detail of the synthesis.

[0060] The synthesis unit can adjust the synthesis order based on the user's past hairstyle history. For example, it can synthesize the most suitable hairstyle based on the user's past hairstyle history. The synthesis unit can also prioritize the synthesis of the most popular hairstyles from the user's past hairstyle history. Furthermore, the synthesis unit can analyze the user's past hairstyle history and synthesize the most suitable hairstyle. This allows for the synthesis of more appropriate hairstyles by adjusting the synthesis order based on past hairstyle history. Some or all of the above processing in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input the user's past hairstyle history data into a generation AI and have the generation AI perform the adjustment of the synthesis order.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception unit accepts images containing the user's face. Images containing the user's face include, but are not limited to, images in JPEG or PNG format. The reception unit allows, for example, the user to upload images using a smartphone or personal computer. The reception unit can also automatically adjust the image resolution and convert it to a format suitable for analysis. For example, the reception unit can use an algorithm to convert low-resolution images to high-resolution images. Step 2: The suggestion department uses a generation AI to analyze the image received by the reception department and proposes a hairstyle that suits the user. For example, the suggestion department may use a facial recognition algorithm to extract the user's facial features and propose a hairstyle based on that. The suggestion department can also propose a hairstyle that takes into account the user's hair type and current trends. For example, the suggestion department may analyze the thickness and texture of the user's hair and propose the optimal hairstyle based on that. Step 3: The instruction unit instructs the stylist on specific cutting methods based on the hairstyle proposed by the suggestion unit. For example, the instruction unit may specify the length and style of the cut. The instruction unit can also provide instructions on the cutting procedure and the tools to be used. For example, the instruction unit may specify the order of the cuts and the types of scissors and clippers to be used. Step 4: The compositing unit synthesizes the hairstyle as the hair would look if it were grown out, based on the cutting method instructed by the instruction unit. The compositing unit can, for example, use a simulation algorithm to synthesize a hairstyle that takes into account the rate at which the hair grows. The compositing unit can also synthesize a hairstyle that takes into account the shape of the user's face and hair type. For example, the compositing unit can synthesize a hairstyle based on the contours of the user's face and the thickness of their hair.

[0063] (Example of form 2) The hairstyle suggestion system according to an embodiment of the present invention is a system for solving the problem of "desired hairstyles" that are difficult to communicate to a stylist at a hair salon. This hairstyle suggestion system allows the user to upload multiple images, including their own face, and then suggests hairstyles after a haircut in various patterns. Furthermore, it not only instructs the stylist on how to cut the hair, but also synthesizes a hairstyle that the user will have if they grow their hair out in the future. For example, the user uploads multiple images, including their own face. Next, the generating AI analyzes these images and suggests hairstyles that suit the user in various patterns. The user selects their desired style from the suggested hairstyles. Based on the selected hairstyle, the generating AI instructs the stylist on specific cutting methods. Furthermore, the generating AI synthesizes a hairstyle that the user will have if they grow their hair out and provides it to the user. This allows the user to specifically communicate their desired hairstyle to the stylist and achieve the hairstyle they envisioned. In addition, since they can check their future hairstyle if they grow their hair out in advance, they can plan their hairstyle for the long term. For example, the user uploads multiple selfies, and the generating AI analyzes these photos and suggests hairstyles that suit the user. The user selects their desired hairstyle from the suggested options, and the generating AI instructs the stylist on specific cutting techniques. Furthermore, the generating AI also creates a simulated hairstyle for when the user's hair grows out and provides this to the user. This allows the user to clearly communicate their desired hairstyle to the stylist, enabling them to achieve their ideal look. In this way, the hairstyle suggestion system provides hairstyles that meet the user's preferences and facilitates smooth communication with the stylist.

[0064] The hairstyle suggestion system according to this embodiment comprises a reception unit, a suggestion unit, an instruction unit, and a synthesis unit. The reception unit receives an image including the user's face. The image including the user's face includes, but is not limited to, images in JPEG or PNG format. The reception unit allows, for example, the user to upload an image using a smartphone or personal computer. The reception unit can also automatically adjust the image resolution and convert it to a format suitable for analysis. For example, the reception unit can use an algorithm to convert a low-resolution image to a high-resolution image. The suggestion unit uses a generation AI to analyze the image received by the reception unit and suggests a hairstyle that suits the user. The suggestion unit can, for example, use a face recognition algorithm to extract the features of the user's face and suggest a hairstyle based on that. The suggestion unit can also suggest a hairstyle considering the user's hair type and current trends. For example, the suggestion unit can analyze the thickness and texture of the user's hair and suggest the optimal hairstyle based on that. The instruction unit instructs the stylist on specific cutting methods based on the hairstyle suggested by the suggestion unit. The instruction unit provides detailed instructions, for example, on the length of the cut and the type of style. The instruction unit can also provide instructions on the cutting procedure and the tools to be used. For example, the instruction unit can specify the order of the cuts and the types of scissors and clippers to use. The synthesis unit synthesizes the hairstyle as the hair would appear if it were grown out, based on the cutting method instructed by the instruction unit. The synthesis unit can synthesize a hairstyle that takes into account the rate at which the hair grows, for example, by using a simulation algorithm. The synthesis unit can also synthesize a hairstyle that takes into account the shape of the user's face and hair type. For example, the synthesis unit can synthesize a hairstyle based on the contours of the user's face and the thickness of their hair. As a result, the hairstyle suggestion system according to this embodiment can analyze an image including the user's face and propose a hairstyle, provide instructions on the cutting method, and synthesize a hairstyle.

[0065] The reception unit accepts images that include the user's face. Images containing the user's face include, but are not limited to, images in JPEG or PNG format. The reception unit allows users to upload images using, for example, a smartphone or personal computer. Specifically, it provides an interface for users to upload images through a dedicated application or website. The reception unit can also automatically adjust the image resolution and convert it to a format suitable for analysis. For example, the reception unit can use algorithms to convert low-resolution images to high-resolution images. This includes super-resolution techniques using deep learning, which interpolate details in the image and produce a sharper image. The reception unit also performs image format conversion, converting the image to a format that the analysis unit can process optimally. For example, converting a JPEG image to PNG format can prevent information loss due to compression. Furthermore, as a pre-processing step for the image, the reception unit can use a face detection algorithm to automatically cut out the user's face and remove the background. This allows the analysis unit to extract facial features more accurately.

[0066] The suggestion department uses generative AI to analyze images received by the reception department and propose hairstyles that suit the user. For example, the suggestion department extracts the user's facial features using a facial recognition algorithm and proposes hairstyles based on that. Specifically, the generative AI analyzes features such as the user's face shape, eye position, nose shape, and mouth position, and generates the optimal hairstyle based on this information. The generative AI can refer to hairstyles of other users with similar facial features based on a past database. The suggestion department can also propose hairstyles considering the user's hair type and current trends. For example, the suggestion department analyzes the thickness and texture of the user's hair and proposes the optimal hairstyle based on that. The generative AI analyzes information such as the user's hair thickness, texture, and color, and generates the optimal hairstyle for these elements. Furthermore, the suggestion department can incorporate the latest fashion data to propose hairstyles that are in line with current trends and seasons. This allows the suggestion department to propose personalized and trend-appropriate hairstyles to users.

[0067] The instruction unit provides specific cutting instructions to the stylist based on the hairstyle proposed by the suggestion unit. For example, the instruction unit provides detailed instructions on the length and style of the cut. Specifically, it provides detailed instructions on how long to cut and what shape to shape each part of the proposed hairstyle. The instruction unit can also provide instructions on the cutting procedure and the tools to be used. For example, it can specify the order of cuts and the types of scissors and clippers to use. Furthermore, the instruction unit can provide tips and advice for the stylist when cutting. For example, it can instruct on the angle and pressure to apply when cutting a specific section, and how to cut along the flow of the hair. In this way, the instruction unit supports the stylist in accurately reproducing the proposed hairstyle. The instruction unit can also provide real-time feedback to the stylist while they are cutting. For example, it can monitor the progress of the cut and issue corrective instructions as needed. In this way, the instruction unit helps the stylist provide high-quality cuts.

[0068] The compositing unit synthesizes hairstyles based on the cutting method instructed by the instruction unit, representing what the hair will look like when grown out. For example, the compositing unit uses a simulation algorithm to synthesize hairstyles that take into account the rate of hair growth. Specifically, the compositing unit analyzes the user's hair growth pattern and rate, and simulates what the hairstyle will look like in the future. The compositing unit can also synthesize hairstyles considering the user's face shape and hair type. For example, it can synthesize hairstyles based on the user's face contour and hair thickness. This allows the user to preview what their hairstyle will look like in the future. Furthermore, the compositing unit also provides a simulation function that allows users to try different hairstyles. For example, users can try hairstyles of different lengths and styles, which can help them choose the optimal hairstyle. The compositing unit displays the changes in hairstyle in real time, making it intuitively understandable to the user. This allows the compositing unit to visually confirm future hairstyles and support the user in selecting the best hairstyle.

[0069] The reception desk can accept multiple images, including the user's face. For example, the reception desk can allow the user to upload multiple images taken from different angles. The reception desk can also accept images taken at different times of day. For example, it can accept images taken in the morning and at night. This allows for more accurate hairstyle suggestions by accepting multiple images. 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 multiple images uploaded by the user into a generating AI and have the generating AI perform image analysis.

[0070] The suggestion unit can analyze images received by the reception unit and suggest hairstyles that suit the user, categorized by pattern. For example, the suggestion unit can suggest hairstyles based on face shape. For example, it can suggest hairstyles based on face shape such as round, oval, or square. The suggestion unit can also suggest hairstyles based on hair type. For example, it can suggest hairstyles based on hair type such as straight, curly, or wavy. Furthermore, the suggestion unit can also suggest hairstyles based on trends. For example, it can suggest hairstyles based on the latest trends. This expands the user's choices by suggesting hairstyles that suit them, categorized by pattern. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input images received by the reception unit into a generation AI and have the generation AI suggest hairstyles that suit the user.

[0071] The instruction unit can instruct the stylist on specific cutting methods based on the hairstyle proposed by the suggestion unit. For example, the instruction unit can specify the length and type of style to be cut. For example, the instruction unit can specify styles such as short cuts, bobs, and long hair. The instruction unit can also instruct on the cutting procedure and the tools to be used. For example, the instruction unit can instruct on the order of cutting and the types of scissors and clippers to be used. This allows the stylist to perform the cut accurately by providing specific cutting instructions. Some or all of the above processing in the instruction unit may be performed using, for example, a generating AI, or without a generating AI. For example, the instruction unit can input the hairstyle proposed by the suggestion unit into the generating AI and have the generating AI execute instructions on specific cutting methods.

[0072] The synthesis unit can synthesize hairstyles based on the cutting method instructed by the instruction unit, representing what the hair would look like when grown out. The synthesis unit can, for example, use a simulation algorithm to synthesize hairstyles that take into account the rate at which hair grows. For example, the synthesis unit can simulate how much the user's hair grows in a month and synthesize hairstyles based on that simulation. The synthesis unit can also synthesize hairstyles considering the shape of the user's face and hair type. For example, the synthesis unit can synthesize hairstyles based on the contours of the user's face and the thickness of their hair. This allows the user to preview what their hairstyle will look like when their hair is grown out. Some or all of the above-described processes in the synthesis unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the synthesis unit can input the cutting method instructed by the instruction unit into a generation AI and have the generation AI synthesize hairstyles for when the hair is grown out.

[0073] The reception unit can estimate the user's emotions and adjust the timing of image submission based on the estimated emotions. For example, if the user is relaxed, the reception unit can send a notification prompting them to upload an image. It can also suggest that the user postpone uploading an image if they are stressed. Furthermore, if the user is in a hurry, the reception unit can provide a simple interface for quick image uploads. This allows for more appropriate image submission timing by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can analyze a user's past image upload history and select the optimal reception method. For example, the reception desk can set the optimal notification timing based on the time periods when the user frequently uploaded in the past. The reception desk can also prioritize suggesting upload methods that the user has used in the past (camera, gallery, etc.). Furthermore, the reception desk can suggest the optimal reception method for specific days of the week or time periods based on the user's past upload history. This improves user convenience by selecting the optimal reception method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past upload history data into a generating AI and have the generating AI select the optimal reception method.

[0075] The reception unit can filter images based on the user's current hairstyle and facial features upon receipt. For example, the reception unit can analyze the user's current hairstyle and perform appropriate filtering. It can also select the most suitable image based on the user's facial features. Furthermore, the reception unit can combine the user's current hairstyle and facial features to accept the most suitable image. This allows for the acceptance of more appropriate images by filtering based on the current hairstyle and facial features. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current hairstyle and facial feature data into a generating AI and have the generating AI perform the filtering.

[0076] The reception unit can estimate the user's emotions and determine the priority of images to accept based on the estimated emotions. For example, if the user is relaxed, the reception unit may prioritize accepting the most recent images. If the user is stressed, the reception unit may also prioritize accepting older images. Furthermore, if the user is in a hurry, the reception unit may prioritize accepting the easiest images to upload. This allows for the acceptance of more appropriate images by prioritizing images according 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit can prioritize receiving images that are highly relevant based on the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize receiving images related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving images taken at home. This allows for the reception of more appropriate images by prioritizing highly relevant images based on geographical location information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant images.

[0078] The reception unit can analyze the user's social media activity when receiving images and accept relevant images. For example, the reception unit can prioritize receiving images that the user has shared on social media. It can also prioritize receiving images that the user has tagged on social media. Furthermore, it can prioritize receiving images that the user has "liked" on social media. This allows for the efficient reception of relevant images by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant images.

[0079] The suggestion unit can estimate the user's emotions and adjust its hairstyle suggestion method based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can suggest detailed hairstyles. If the user is stressed, it can suggest simpler hairstyles. Furthermore, if the user is in a hurry, the suggestion unit can provide quick suggestions. By adjusting the suggestion method according to 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 suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The suggestion unit can adjust the level of detail of its suggestions based on the user's face shape and hair type. For example, the suggestion unit can suggest the optimal hairstyle based on the user's face shape. It can also suggest an appropriate hairstyle based on the user's hair type. Furthermore, the suggestion unit can combine the user's face shape and hair type to suggest the optimal hairstyle. By adjusting the level of detail of the suggestions based on face shape and hair type, it becomes possible to suggest more appropriate hairstyles. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's face shape and hair type data into a generative AI and have the generative AI perform the adjustment of the level of detail of the suggestions.

[0081] The suggestion unit can apply different suggestion algorithms depending on the user's age and gender when making suggestions. For example, the suggestion unit can suggest an appropriate hairstyle based on the user's age. It can also suggest the optimal hairstyle based on the user's gender. Furthermore, the suggestion unit can suggest the optimal hairstyle by combining the user's age and gender. This allows for more appropriate hairstyle suggestions by applying different suggestion algorithms depending on age and gender. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's age and gender data into a generative AI and have the generative AI execute the application of the suggestion algorithm.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, it can provide concise suggestions. Furthermore, if the user is in a hurry, it can provide suggestions quickly. By adjusting the length of suggestions according to the user's emotions, it becomes possible to suggest more appropriate hairstyles. 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 processing described above in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The suggestion unit can determine the priority of suggestions based on the user's past hairstyle history. For example, the suggestion unit can make the most suitable suggestions based on the hairstyles the user has previously chosen. The suggestion unit can also prioritize suggesting the most popular styles from the user's past hairstyle history. Furthermore, the suggestion unit can analyze the user's past hairstyle history and make the most suitable suggestions. This allows for more appropriate hairstyle suggestions by determining the priority of suggestions based on past hairstyle history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past hairstyle history data into a generative AI and have the generative AI determine the priority of suggestions.

[0084] The suggestion unit can adjust the order of suggestions based on the user's lifestyle and occupation. For example, the suggestion unit can suggest the most suitable hairstyle based on the user's lifestyle. It can also suggest an appropriate hairstyle based on the user's occupation. Furthermore, the suggestion unit can combine the user's lifestyle and occupation to suggest the most suitable hairstyle. By adjusting the order of suggestions based on lifestyle and occupation, it becomes possible to suggest a more appropriate hairstyle. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's lifestyle and occupation data into a generative AI and have the generative AI adjust the order of suggestions.

[0085] The instruction unit can estimate the user's emotions and adjust the instructions for the cutting method based on the estimated emotions. For example, if the user is relaxed, the instruction unit can provide detailed instructions for the cutting method. If the user is stressed, the instruction unit can also provide simpler instructions for the cutting method. Furthermore, if the user is in a hurry, the instruction unit can provide quick instructions for the cutting method. This allows for more appropriate instructions by adjusting the cutting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The 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 instruction unit may be performed using a generative AI, or not. For example, the instruction unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The instruction unit can select a cutting method based on the user's hair type and hair health when an instruction is given. For example, the instruction unit can select the optimal cutting method based on the user's hair type. It can also select an appropriate cutting method based on the user's hair health. Furthermore, the instruction unit can select the optimal cutting method by combining the user's hair type and hair health. This allows for the instruction of a more appropriate cutting method by selecting a cutting method based on hair type and hair health. Some or all of the above processing in the instruction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the instruction unit can input the user's hair type and hair health data into a generative AI and have the generative AI select a cutting method.

[0087] The instruction unit can apply different cutting methods depending on the shape and bone structure of the user's face when instructions are given. For example, the instruction unit can apply the optimal cutting method based on the shape of the user's face. It can also apply an appropriate cutting method based on the user's bone structure. Furthermore, the instruction unit can apply the optimal cutting method by combining the shape of the user's face and bone structure. This allows for the instruction of a more appropriate cutting method by applying different cutting methods depending on the shape and bone structure of the face. Some or all of the above processing in the instruction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the instruction unit can input the user's face shape and bone structure data into a generative AI and have the generative AI execute the application of the cutting method.

[0088] The instruction unit can estimate the user's emotions and determine the priority of cutting methods based on the estimated emotions. For example, if the user is relaxed, the instruction unit may prioritize the most recent cutting method. If the user is stressed, the instruction unit may also prioritize cutting methods that have been successful in the past. Furthermore, if the user is in a hurry, the instruction unit may prioritize the cutting method that can be executed most quickly. In this way, by prioritizing cutting methods according to the user's emotions, a more appropriate cutting method can be recommended. 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 instruction unit may be performed using a generative AI, or not using a generative AI. For example, the instruction unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The instruction unit can adjust the cutting method based on the user's past cutting history when an instruction is given. For example, the instruction unit adjusts the optimal cutting method based on the user's past cutting history. The instruction unit can also prioritize and instruct the most successful cutting method from the user's past cutting history. Furthermore, the instruction unit can analyze the user's past cutting history and adjust the optimal cutting method. This allows for the instruction of a more appropriate cutting method by adjusting the cutting method based on past cutting history. Some or all of the above processing in the instruction unit may be performed using, for example, a generating AI, or without a generating AI. For example, the instruction unit can input the user's past cutting history data into a generating AI and have the generating AI perform the adjustment of the cutting method.

[0090] The instruction unit can customize the cutting method based on the user's lifestyle and occupation when instructions are given. For example, the instruction unit can customize the optimal cutting method based on the user's lifestyle. It can also customize an appropriate cutting method based on the user's occupation. Furthermore, the instruction unit can combine the user's lifestyle and occupation to customize the optimal cutting method. This allows for more appropriate cutting methods to be instructed by customizing the cutting method based on lifestyle and occupation. Some or all of the above processing in the instruction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the instruction unit can input the user's lifestyle and occupation data into a generative AI and have the generative AI perform the customization of the cutting method.

[0091] The compositing unit can estimate the user's emotions and adjust the hairstyle compositing method based on the estimated emotions. For example, if the user is relaxed, the compositing unit can perform detailed hairstyle compositing. If the user is stressed, the compositing unit can also perform simple hairstyle compositing. Furthermore, if the user is in a hurry, the compositing unit can perform rapid hairstyle compositing. This allows for more appropriate hairstyle compositing by adjusting the hairstyle compositing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the compositing unit may be performed using a generative AI, or not using a generative AI. For example, the compositing unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The synthesis unit can adjust the level of detail of the synthesis based on the user's hair type and hair health during the synthesis process. For example, the synthesis unit can perform detailed hairstyle synthesis based on the user's hair type. It can also perform appropriate hairstyle synthesis based on the user's hair health. Furthermore, the synthesis unit can combine the user's hair type and hair health to perform optimal hairstyle synthesis. This allows for more appropriate hairstyle synthesis by adjusting the level of detail of the synthesis based on hair type and hair health. Some or all of the above-described processes in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input the user's hair type and hair health data into a generation AI and have the generation AI perform the adjustment of the level of detail of the synthesis.

[0093] The synthesis unit can apply different synthesis algorithms depending on the user's face shape and bone structure during synthesis. For example, the synthesis unit can synthesize the optimal hairstyle based on the user's face shape. It can also synthesize an appropriate hairstyle based on the user's bone structure. Furthermore, the synthesis unit can combine the user's face shape and bone structure to synthesize the optimal hairstyle. This makes it possible to synthesize a more appropriate hairstyle by applying different synthesis algorithms depending on the face shape and bone structure. Some or all of the above processing in the synthesis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the synthesis unit can input the user's face shape and bone structure data into a generative AI and have the generative AI execute the application of the synthesis algorithm.

[0094] The synthesis unit can estimate the user's emotions and determine the priority of hairstyles to synthesize based on the estimated emotions. For example, if the user is relaxed, the synthesis unit will prioritize synthesizing the latest hairstyles. If the user is stressed, the synthesis unit can also prioritize synthesizing hairstyles that have been successful in the past. Furthermore, if the user is in a hurry, the synthesis unit can also prioritize synthesizing the hairstyle that can be synthesized most quickly. This allows for the synthesis of more appropriate hairstyles by prioritizing hairstyles according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the synthesis unit may be performed using a generative AI, or not. For example, the synthesis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The synthesis unit can adjust the synthesis order based on the user's past hairstyle history during synthesis. For example, the synthesis unit can synthesize the optimal hairstyle based on the user's past hairstyle history. The synthesis unit can also prioritize the synthesis of the most popular hairstyles from the user's past hairstyle history. Furthermore, the synthesis unit can analyze the user's past hairstyle history and synthesize the optimal hairstyle. This allows for the synthesis of more appropriate hairstyles by adjusting the synthesis order based on past hairstyle history. Some or all of the above processing in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input the user's past hairstyle history data into a generation AI and have the generation AI perform the adjustment of the synthesis order.

[0096] The synthesis unit can customize the synthesis method based on the user's lifestyle and occupation during the synthesis process. For example, the synthesis unit can synthesize the optimal hairstyle based on the user's lifestyle. It can also synthesize an appropriate hairstyle based on the user's occupation. Furthermore, the synthesis unit can combine the user's lifestyle and occupation to synthesize the optimal hairstyle. This allows for the synthesis of more appropriate hairstyles by customizing the synthesis method based on lifestyle and occupation. Some or all of the above-described processes in the synthesis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the synthesis unit can input the user's lifestyle and occupation data into a generative AI and have the generative AI perform the customization of the synthesis method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The suggestion unit can estimate the user's emotions and suggest hairstyles based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest a bolder, more adventurous hairstyle. If the user is stressed, the suggestion unit can suggest a simple, easy-to-maintain hairstyle. Furthermore, if the user is in a hurry, the suggestion unit can suggest a hairstyle that can be done quickly. By adjusting hairstyle suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The reception desk can analyze a user's past hairstyle history and select the optimal reception method. For example, it can set the optimal notification timing based on the times of day the user frequently uploaded in the past. The reception desk can also prioritize suggesting upload methods the user has used in the past (camera, gallery, etc.). Furthermore, the reception desk can suggest the optimal reception method for specific days of the week or times of day based on the user's past upload history. This improves user convenience by selecting the optimal reception method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past upload history data into a generating AI and have the generating AI select the optimal reception method.

[0100] The suggestion unit can estimate the user's emotions and adjust its hairstyle suggestion method based on the estimated emotions. For example, if the user is relaxed, it can suggest detailed hairstyles. If the user is stressed, it can suggest simpler hairstyles. Furthermore, if the user is in a hurry, it can suggest hairstyles quickly. By adjusting the suggestion method according to the user's emotions, it becomes possible to suggest more appropriate hairstyles. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The instruction unit can estimate the user's emotions and adjust the instructions for the cutting method based on the estimated emotions. For example, if the user is relaxed, it can provide detailed instructions for the cutting method. If the user is stressed, it can provide simpler instructions. Furthermore, if the user is in a hurry, it can provide quick instructions for the cutting method. By adjusting the instructions for the cutting method according to the user's emotions, a more appropriate cutting method can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using a generative AI, or not. For example, the instruction unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] The synthesis unit can estimate the user's emotions and adjust the hairstyle synthesis method based on the estimated emotions. For example, if the user is relaxed, a detailed hairstyle synthesis can be performed. If the user is stressed, a simple hairstyle synthesis can be performed. Furthermore, if the user is in a hurry, a hairstyle synthesis can be performed quickly. By adjusting the hairstyle synthesis method according to the user's emotions, a more appropriate hairstyle synthesis becomes possible. 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 synthesis unit may be performed using a generative AI, or not using a generative AI. For example, the synthesis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The reception unit can prioritize receiving images that are highly relevant based on the user's geographical location. For example, if the user is in a specific region, it can prioritize receiving images related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving images related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving images taken at home. This allows for the reception of more appropriate images by prioritizing highly relevant images based on geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant images.

[0104] The suggestion unit can apply different suggestion algorithms depending on the user's age and gender when making suggestions. For example, it can suggest an appropriate hairstyle based on the user's age. It can also suggest the optimal hairstyle based on the user's gender. Furthermore, it can suggest the optimal hairstyle by combining the user's age and gender. This allows for more appropriate hairstyle suggestions by applying different suggestion algorithms depending on age and gender. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without one. For example, the suggestion unit can input the user's age and gender data into a generative AI and have the generative AI apply the suggestion algorithm.

[0105] The instruction unit can select a cutting method based on the user's hair type and hair health when an instruction is given. For example, it can select the optimal cutting method based on the user's hair type. The instruction unit can also select an appropriate cutting method based on the user's hair health. Furthermore, the instruction unit can select the optimal cutting method by combining the user's hair type and hair health. This allows for more appropriate cutting methods to be instructed by selecting a cutting method based on hair type and hair health. Some or all of the above processing in the instruction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the instruction unit can input the user's hair type and hair health data into a generative AI and have the generative AI select a cutting method.

[0106] The synthesis unit can adjust the level of detail of the synthesis based on the user's hair type and hair health during the synthesis process. For example, it can perform detailed hairstyle synthesis based on the user's hair type. The synthesis unit can also perform appropriate hairstyle synthesis based on the user's hair health. Furthermore, the synthesis unit can combine the user's hair type and hair health to perform optimal hairstyle synthesis. This allows for more appropriate hairstyle synthesis by adjusting the level of detail of the synthesis based on hair type and hair health. Some or all of the above-described processes in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input the user's hair type and hair health data into a generation AI and have the generation AI perform the adjustment of the level of detail of the synthesis.

[0107] The synthesis unit can adjust the synthesis order based on the user's past hairstyle history. For example, it can synthesize the most suitable hairstyle based on the user's past hairstyle history. The synthesis unit can also prioritize the synthesis of the most popular hairstyles from the user's past hairstyle history. Furthermore, the synthesis unit can analyze the user's past hairstyle history and synthesize the most suitable hairstyle. This allows for the synthesis of more appropriate hairstyles by adjusting the synthesis order based on past hairstyle history. Some or all of the above processing in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input the user's past hairstyle history data into a generation AI and have the generation AI perform the adjustment of the synthesis order.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception unit accepts images containing the user's face. Images containing the user's face include, but are not limited to, images in JPEG or PNG format. The reception unit allows, for example, the user to upload images using a smartphone or personal computer. The reception unit can also automatically adjust the image resolution and convert it to a format suitable for analysis. For example, the reception unit can use an algorithm to convert low-resolution images to high-resolution images. Step 2: The suggestion department uses a generation AI to analyze the image received by the reception department and proposes a hairstyle that suits the user. For example, the suggestion department may use a facial recognition algorithm to extract the user's facial features and propose a hairstyle based on that. The suggestion department can also propose a hairstyle that takes into account the user's hair type and current trends. For example, the suggestion department may analyze the thickness and texture of the user's hair and propose the optimal hairstyle based on that. Step 3: The instruction unit instructs the stylist on specific cutting methods based on the hairstyle proposed by the suggestion unit. For example, the instruction unit may specify the length and style of the cut. The instruction unit can also provide instructions on the cutting procedure and the tools to be used. For example, the instruction unit may specify the order of the cuts and the types of scissors and clippers to be used. Step 4: The compositing unit synthesizes the hairstyle as the hair would look if it were grown out, based on the cutting method instructed by the instruction unit. The compositing unit can, for example, use a simulation algorithm to synthesize a hairstyle that takes into account the rate at which the hair grows. The compositing unit can also synthesize a hairstyle that takes into account the shape of the user's face and hair type. For example, the compositing unit can synthesize a hairstyle based on the contours of the user's face and the thickness of their hair.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, proposal unit, instruction unit, and synthesis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, enabling the user to upload images using a smartphone or personal computer. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses generation AI to suggest a hairstyle that suits the user. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12, which instructs the stylist on a specific cutting method. The synthesis unit is implemented by the specific processing unit 290 of the data processing unit 12, which synthesizes a hairstyle for when the hair is grown out. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, suggestion unit, instruction unit, and synthesis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to upload images using a smartphone or personal computer. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses generating AI to suggest a hairstyle that suits the user. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12, which instructs the stylist on a specific cutting method. The synthesis unit is implemented by the specific processing unit 290 of the data processing unit 12, which synthesizes a hairstyle for when the hair is grown out. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, suggestion unit, instruction unit, and synthesis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, enabling the user to upload images using a smartphone or personal computer. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generation AI to suggest a hairstyle that suits the user. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which instructs the stylist on a specific cutting method. The synthesis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which synthesizes a hairstyle for when the hair is grown out. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, proposal unit, instruction unit, and synthesis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, enabling the user to upload images using a smartphone or personal computer. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses generation AI to suggest a hairstyle that suits the user. The instruction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which instructs the stylist on a specific cutting method. The synthesis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which synthesizes a hairstyle for when the hair is grown out. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A reception area that accepts images including the user's face, The reception unit analyzes the images received by the reception unit and proposes hairstyles that suit the user, An instruction unit that provides instructions on how to cut a hairstyle based on the hairstyle proposed by the aforementioned proposal unit, The system includes a synthesis unit that synthesizes the hairstyle when the hair is grown out based on the cutting method instructed by the instruction unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Accepts multiple images, including the user's face. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, The system analyzes the images received by the reception desk and suggests hairstyles that suit the user, categorized by pattern. The system described in Appendix 1, characterized by the features described herein. (Note 4) The indicator unit is, Based on the hairstyle proposed by the aforementioned proposal department, the stylist is instructed on the specific cutting method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned synthesis section is Based on the cutting method instructed by the aforementioned instruction unit, the hairstyle for when the hair is grown out is synthesized. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of image requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system analyzes the user's past image upload history and selects the optimal acceptance method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When an image is submitted, it is filtered based on the user's current hairstyle and facial features. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of images to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving images, the system prioritizes accepting images that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving images, the system analyzes the user's social media activity and accepts relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the hairstyle suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's face shape and hair type. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, the priority of suggestions is determined based on the user's past hairstyle history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the user's lifestyle and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The indicator unit is, The system estimates the user's emotions and adjusts the instructions for cutting based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The indicator unit is, When giving instructions, the cutting method is selected based on the user's hair type and hair health. The system described in Appendix 1, characterized by the features described herein. (Note 20) The indicator unit is, When giving instructions, different cutting methods are applied depending on the user's face shape and bone structure. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, The system estimates the user's emotions and determines the priority of editing methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, When instructions are given, the cutting method is adjusted based on the user's past cutting history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, When giving instructions, the cutting method is customized based on the user's lifestyle and occupation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned synthesis section is The system estimates the user's emotions and adjusts the hairstyle synthesis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned synthesis section is During synthesis, the level of detail is adjusted based on the user's hair type and hair health. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned synthesis section is During the synthesis process, different synthesis algorithms are applied depending on the user's facial shape and bone structure. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned synthesis section is The system estimates the user's emotions and determines the priority of hairstyles to synthesize based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned synthesis section is During the compositing process, the order of compositing is adjusted based on the user's past hairstyle history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned synthesis section is During synthesis, the synthesis method is customized based on the user's lifestyle and occupation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 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. A reception area that accepts images including the user's face, The reception unit analyzes the images received by the reception unit and proposes hairstyles that suit the user, An instruction unit that provides instructions on how to cut a hairstyle based on the hairstyle proposed by the aforementioned proposal unit, The system includes a synthesis unit that synthesizes the hairstyle when the hair is grown out based on the cutting method instructed by the instruction unit. A system characterized by the following features.

2. The aforementioned reception unit is Accepts multiple images, including the user's face. The system according to feature 1.

3. The aforementioned proposal section is, The system analyzes the images received by the reception desk and suggests hairstyles that suit the user, categorized by pattern. The system according to feature 1.

4. The indicator unit is, Based on the hairstyle proposed by the aforementioned proposal department, the stylist is instructed on the specific cutting method. The system according to feature 1.

5. The aforementioned synthesis section is Based on the cutting method instructed by the aforementioned instruction unit, the hairstyle for when the hair is grown out is synthesized. The system according to feature 1.

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

7. The aforementioned reception unit is The system analyzes the user's past image upload history and selects the optimal acceptance method. The system according to feature 1.

8. The aforementioned reception unit is When an image is submitted, it is filtered based on the user's current hairstyle and facial features. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of images to accept based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving images, the system prioritizes accepting images that are highly relevant based on the user's geographical location. The system according to feature 1.

Images