system

Abstract

The system according to this embodiment aims to suggest an appropriate hairstyle based on the user's preferences and constraints. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a constraint input unit, a proposal unit, and a display unit. The acquisition unit acquires a video of a user. The analysis unit analyzes the video acquired by the acquisition unit. The constraint input unit inputs the conditions for a hairstyle desired by the user. The proposal unit proposes a hairstyle suitable for the user based on the results of the analysis by the analysis unit and the conditions input by the constraint input unit. The display unit displays the hairstyle proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to suggest an appropriate hairstyle based on a user's preferences and constraints.

[0005] The system according to this embodiment aims to suggest an appropriate hairstyle based on the user's preferences and constraints. [Means for solving the problem]

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a constraint input unit, a suggestion unit, and a display unit. The acquisition unit acquires the user's video. The analysis unit analyzes the video acquired by the acquisition unit. The constraint input unit inputs the conditions for the hairstyle desired by the user. The suggestion unit suggests a hairstyle suitable for the user based on the results analyzed by the analysis unit and the conditions input by the constraint input unit. The display unit displays the hairstyle suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an appropriate hairstyle based on the user's preferences and constraints. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, a hairstyle suggestion system uses AI to suggest a hairstyle based on a user's own video (even an unedited photo) captured with a smartphone camera and constraints (such as school or company rules). In this hairstyle suggestion system, a user takes a video of themselves with a smartphone camera and inputs the video into an AI for analysis. Based on the analyzed video, the AI ​​determines the user's facial shape, hair type, etc., and suggests an appropriate hairstyle based on the constraints (e.g., school or company rules) entered by the user. This suggestion is provided in 2D, 3D, or VR format. This allows users to easily find the optimal hairstyle based on their own video and constraints. For example, a user takes a video of themselves with a smartphone camera. The quality and resolution of the video must also be taken into consideration. For example, technology is needed to enable the AI ​​to accurately analyze low-resolution video. The video is then input into an AI, which analyzes the video. The AI ​​uses video quality improvement technology to determine the user's facial shape, hair type, etc. Furthermore, the AI ​​suggests an appropriate hairstyle based on the constraints (e.g., school or company rules) entered by the user. A specific method for entering constraints must also be described. For example, it is necessary to clarify how users input school or company rules, whether by text input or by selecting from a list of options. It is important to provide specific examples of how suggested hairstyles are displayed in 2D, 3D, and VR formats. The algorithms and criteria used by the AI ​​to suggest hairstyles should also be mentioned. For example, specific algorithms and technologies should be described, such as how the AI ​​determines face shape and hair type. It is also important to describe how the suggested hairstyles are presented to users, for example, how they are displayed on a smartphone screen. Furthermore, the user experience should be described in detail, including how users can view the suggested hairstyles, for example, whether they can be rotated 360 degrees and whether they can be zoomed in or out. It is also recommended to provide guidelines or reference materials for hairdressers to use when recreating the suggested hairstyle, such as the accuracy with which the suggested hairstyles are actually reproduced. This allows the hairstyle suggestion system to suggest and display the optimal hairstyle based on the user's video and constraints.

[0029] A hairstyle suggestion system according to an embodiment includes an acquisition unit, an analysis unit, a constraint input unit, a suggestion unit, and a display unit. The acquisition unit acquires a video of a user. The video of the user may include, but is not limited to, still images, videos, and real-time videos. The acquisition unit may capture the video of the user using, for example, a smartphone camera. The acquisition unit may also load video already saved by the user. The acquisition unit may use technology to improve the resolution and quality of the video. For example, the acquisition unit may use technology to convert low-resolution video to high-resolution video. The analysis unit analyzes the video acquired by the acquisition unit. The analysis unit may determine the shape of the user's face using, for example, face recognition technology. The analysis unit may also use technology to extract hair texture characteristics. For example, the analysis unit may use technology to analyze hair thickness and density. The analysis unit may also use technology to improve the accuracy of the analysis using technology to improve the quality of the video. The constraint input unit inputs the conditions for the hairstyle desired by the user. The constraint input unit may input constraints, for example, by text input or by selecting from options. For example, when a user inputs school rules or company rules, the user can freely input them using a text box. The constraint input unit can also provide a format in which the user can select constraints from options. For example, the constraint input unit can display a list of school rules or company rules, and the user can select the appropriate item. The suggestion unit suggests a hairstyle suitable for the user based on the results of the analysis by the analysis unit and the conditions input by the constraint input unit. The suggestion unit, for example, uses AI to suggest a hairstyle. The AI ​​selects an optimal hairstyle based on the user's face shape, hair type, and constraints. For example, the AI ​​can use an algorithm to suggest a hairstyle that suits the face shape. The AI ​​can also use an algorithm to suggest a hairstyle according to the hair type. Furthermore, the AI ​​can use an algorithm to suggest a hairstyle based on the constraints. The display unit displays the hairstyle suggested by the suggestion unit. The display unit, for example, displays the hairstyle on a smartphone screen. The display unit can display the hairstyle in any of 2D, 3D, and VR formats. For example, the display unit can display the hairstyle superimposed on the user's face as a 2D display.Furthermore, the display unit can display the user's face in 3D, overlaying it with a hairstyle. In addition, the display unit can display a VR version, allowing the user to view the hairstyle using a VR device. As a result, the hairstyle suggestion system according to this embodiment can suggest and display the optimal hairstyle based on the user's video and constraints.

[0030] The acquisition unit may include a quality enhancement unit that adjusts the resolution and color tone of the video. The quality enhancement unit can, for example, adjust the resolution of the video. For example, the quality enhancement unit can use a technique to convert low-resolution video to high-resolution video. The quality enhancement unit can also adjust the color tone of the video. For example, the quality enhancement unit can use a technique to adjust color temperature, saturation, and contrast. Furthermore, the quality enhancement unit can also use a technique to reduce noise in the video. For example, the quality enhancement unit can use a filtering technique to remove noise from the video. By improving the quality of the video, more accurate analysis and suggestions become possible. Some or all of the above processing in the quality enhancement unit may be performed using AI, for example, or without AI. For example, the quality enhancement unit can convert low-resolution video to high-resolution video using an AI model to improve the resolution of the video.

[0031] The constraint input unit may include an interface unit that provides an interface for the user to input constraints. The interface unit provides, for example, a GUI (Graphical User Interface). For example, the interface unit displays text boxes and options for the user to input constraints. The interface unit may also provide voice input. For example, the interface unit may use a microphone to allow the user to input constraints by voice. The interface unit may also provide touch operation. For example, the interface unit may provide an interface for the user to input constraints using a touch screen. This makes it possible to provide an interface that allows the user to easily input constraints. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit may convert the voice input into text using an AI model for analyzing the user's voice input.

[0032] The display unit may include a rotation display unit that allows the user to rotate the proposed hairstyle 360 ​​degrees to check it. The rotation display unit provides, for example, an interface that allows the user to rotate the hairstyle 360 ​​degrees to check it. For example, the rotation display unit allows the user to rotate the hairstyle by swiping the screen with their finger. The rotation display unit also allows the user to rotate the hairstyle using a mouse. Furthermore, the rotation display unit also allows the user to rotate the hairstyle using a VR device. For example, the rotation display unit allows the user to wear the VR device and rotate the hairstyle in accordance with head movement. This allows the user to rotate the proposed hairstyle 360 ​​degrees to check it. Some or all of the above-described processing in the rotation display unit may be performed, for example, using AI or without AI. For example, the rotation display unit may rotate the hairstyle 360 ​​degrees using an AI model for generating a 3D model of the hairstyle.

[0033] The display unit may include a scaling unit that can scale the proposed hairstyle. The scaling unit, for example, provides an interface that allows the user to scale and check the hairstyle. For example, the scaling unit allows the user to scale the hairstyle by pinching in and out on the screen with their fingers. The scaling unit also allows the user to scale the hairstyle using a mouse wheel. The scaling unit also allows the user to scale the hairstyle using a VR device. For example, the scaling unit allows the user to wear the VR device and scale the hairstyle according to hand movements. This allows the user to scale and check the proposed hairstyle. Some or all of the above-described processing in the scaling unit may be performed using, for example, AI, or may be performed without AI. For example, the scaling unit may scale the hairstyle using an AI model for generating a 3D model of the hairstyle.

[0034] The reproduction confirmation unit may include a reproduction confirmation unit that simulates the accuracy with which a proposed hairstyle is actually reproduced. The reproduction confirmation unit, for example, provides an interface for simulating the accuracy with which the proposed hairstyle is actually reproduced. For example, the reproduction confirmation unit displays a 3D model for the user to simulate the hairstyle. The reproduction confirmation unit may also provide guidelines for the user to simulate the hairstyle. For example, the reproduction confirmation unit may provide reference materials for a hairdresser to use when reproducing the hairstyle. Furthermore, the reproduction confirmation unit may also provide criteria for evaluating the reproduction accuracy of the hairstyle. For example, the reproduction confirmation unit may evaluate the reproduction accuracy based on factors such as the length, shape, and color of the hairstyle. This makes it possible to confirm the accuracy with which the proposed hairstyle is actually reproduced. Some or all of the above-described processing in the reproduction confirmation unit may be performed, for example, using AI or without AI. For example, the reproduction confirmation unit may simulate the reproduction accuracy using an AI model for evaluating the reproduction accuracy of the hairstyle.

[0035] The acquisition unit can analyze the user's past video acquisition history and select an acquisition method suitable for the user. The acquisition unit, for example, automatically sets a camera angle that the user has previously preferred. For example, the acquisition unit suggests an optimal camera angle based on the user's past video acquisition history. The acquisition unit can also suggest optimal camera settings based on the resolution and quality of videos the user has previously acquired. For example, the acquisition unit analyzes the user's past video acquisition history and sets optimal resolution and quality. Furthermore, the acquisition unit can predict the timing to capture the most natural facial expression based on the user's past video acquisition history and adjust the acquisition method. For example, the acquisition unit suggests the optimal shooting timing based on the user's past video acquisition history. This makes it possible to select an optimal acquisition method based on the user's past video acquisition history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past video acquisition history to a generation AI and cause the generation AI to select an optimal acquisition method.

[0036] The acquisition unit can perform filtering based on the user's current environment and lighting conditions when acquiring video. For example, if the user is shooting outdoors, the acquisition unit performs filtering that makes use of natural light. For example, the acquisition unit adjusts the filtering according to the intensity and direction of natural light. Furthermore, if the user is shooting indoors, the acquisition unit can perform filtering that matches the color temperature of the lighting. For example, the acquisition unit adjusts the filtering based on the color temperature of the indoor lighting. Furthermore, if the user is shooting in a dark place, the acquisition unit can perform filtering that reduces noise. For example, the acquisition unit uses a filtering technique for reducing noise in dark places. This allows optimal filtering to be performed according to the user's environment and lighting conditions. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's environment and lighting conditions into the generation AI and leave the execution of optimal filtering to the generation AI.

[0037] When acquiring video, the acquisition unit can prioritize acquiring highly relevant video based on the user's geographical location information. For example, when the user is in a specific location, the acquisition unit prioritizes acquiring backgrounds related to that location. For example, the acquisition unit prioritizes capturing backgrounds of specific tourist spots or famous places. Furthermore, when the user is traveling, the acquisition unit can also prioritize acquiring backgrounds of tourist spots. For example, the acquisition unit prioritizes capturing backgrounds of tourist spots or famous places at the travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring indoor backgrounds. For example, the acquisition unit prioritizes capturing backgrounds suitable for the indoor environment of the home. This makes it possible to acquire optimal video based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant video.

[0038] When acquiring video, the acquisition unit can analyze the user's social media activity and acquire related video. For example, the acquisition unit can acquire similar video based on photos shared by the user on social media. For example, the acquisition unit can analyze the characteristics of photos shared by the user to acquire similar video. The acquisition unit can also acquire related video based on the content posted by accounts the user follows on social media. For example, the acquisition unit can analyze the content posted by the followed accounts to acquire related video. Furthermore, the acquisition unit can also acquire related video based on posts the user has "liked" on social media. For example, the acquisition unit can analyze the characteristics of "liked" posts to acquire related video. This makes it possible to acquire optimal video based on the user's social media activity. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related video.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. For example, the analysis unit performs a detailed analysis for important video. For example, the analysis unit displays detailed analysis results for important video. The analysis unit can also perform a concise analysis for general video. For example, the analysis unit displays concise analysis results for general video. Furthermore, the analysis unit can perform a particularly detailed analysis for video in which the user is particularly interested. For example, the analysis unit adjusts the level of detail of the analysis based on the user's level of interest. This makes it possible to adjust the optimal level of detail of the analysis depending on the importance of the video. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model for evaluating the importance of the video.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. For example, in the case of portrait video, the analysis unit applies a face recognition algorithm. For example, the analysis unit displays the face recognition results for the portrait video. The analysis unit can also apply a landscape analysis algorithm in the case of landscape video. For example, the analysis unit displays the analysis results for the landscape video. The analysis unit can also apply an animal recognition algorithm in the case of animal video. For example, the analysis unit displays the recognition results for the animal video. This makes it possible to apply the optimal analysis algorithm depending on the category of the video. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can select an appropriate analysis algorithm using an AI model for evaluating the category of the video.

[0041] The analysis unit can determine the priority of analysis based on when the video was shot. For example, the analysis unit may prioritize the analysis of recently shot video. For example, the analysis unit may display the analysis results of recently shot video. The analysis unit can also prioritize the analysis of video shot at a specific event. For example, the analysis unit may display the analysis results of video shot at a specific event. Furthermore, the analysis unit may prioritize the analysis of video from a period of particular interest to the user. For example, the analysis unit may determine the priority of analysis based on the user's level of interest. This allows for the determination of the optimal analysis priority based on when the video was shot. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to evaluate when the video was shot to determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the videos during the analysis. For example, the analysis unit may prioritize the analysis of videos that the user has shown particular interest in. For example, the analysis unit may adjust the order of analysis based on the user's level of interest. The analysis unit can also group highly relevant videos together for analysis. For example, the analysis unit may analyze highly relevant videos all at once. Furthermore, the analysis unit may postpone the analysis of less relevant videos. For example, the analysis unit may postpone the analysis of less relevant videos. This allows for the adjustment of the optimal analysis order based on the relevance of the videos. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to evaluate the relevance of videos to adjust the order of analysis.

[0043] The constraint input unit can select the optimal input method by referring to the user's past constraint input history when a constraint is entered. For example, the constraint input unit can automatically display constraints previously entered by the user as candidates. For example, the constraint input unit can suggest the optimal input method based on the past constraint input history. The constraint input unit can also prioritize suggesting input methods (text, choices, etc.) previously used by the user. For example, the constraint input unit can suggest the optimal input method based on past input methods. Furthermore, the constraint input unit can predict and suggest constraints to be used during a specific time period based on the user's past constraint input history. For example, the constraint input unit can suggest constraints suitable for a specific time period based on past input history. This allows the optimal input method to be selected based on the user's past constraint input history. Some or all of the above processing in the constraint input unit may be performed using AI, for example, or without AI. For example, the constraint input unit can input the user's past constraint input history into a generating AI and have the generating AI select the optimal input method.

[0044] The constraint input unit can customize the constraint input method based on the user's current situation when a constraint is entered. For example, if the user is at work, the constraint input unit can automatically suggest constraints based on workplace rules. For example, the constraint input unit can suggest the optimal constraints based on workplace rules. The constraint input unit can also automatically suggest constraints based on school rules if the user is at school. For example, the constraint input unit can suggest the optimal constraints based on school rules. Furthermore, if the user is at home, the constraint input unit can suggest general constraints. For example, the constraint input unit can suggest constraints suitable for the home environment. This allows the system to provide the optimal constraint input method according to the user's current situation. Some or all of the above processing in the constraint input unit may be performed using AI, for example, or without AI. For example, the constraint input unit can input the user's current situation data into a generating AI and have the generating AI customize the optimal constraint input method.

[0045] The constraint input unit can input optimal constraints taking into account the user's geographical location information when inputting constraints. For example, when the user is in a specific location, the constraint input unit automatically suggests constraints related to that location. For example, the constraint input unit suggests optimal constraints based on the rules and constraints of the specific location. Furthermore, when the user is traveling, the constraint input unit can also suggest constraints based on the rules of the travel destination. For example, the constraint input unit suggests optimal constraints based on the rules of the travel destination. Furthermore, when the user is at home, the constraint input unit can also suggest general constraints. For example, the constraint input unit suggests constraints suitable for the situation at home. This makes it possible to input optimal constraints based on the user's geographical location information. Some or all of the above-mentioned processing in the constraint input unit may be performed using, for example, AI, or may be performed without using AI. For example, the constraint input unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest optimal constraints.

[0046] When inputting constraints, the constraint input unit can analyze the user's social media activity and input relevant constraints. The constraint input unit can, for example, propose relevant constraints based on posts shared by the user on social media. For example, the constraint input unit can analyze the content of posts shared by the user and propose relevant constraints. The constraint input unit can also propose relevant constraints based on the content of posts from accounts the user follows on social media. For example, the constraint input unit can analyze the content of posts from the followed accounts and propose relevant constraints. The constraint input unit can also propose relevant constraints based on posts the user has "liked" on social media. For example, the constraint input unit can analyze the content of "liked" posts and propose relevant constraints. This makes it possible to input optimal constraints based on the user's social media activity. Some or all of the above-described processing in the constraint input unit can be performed using, or without, AI. For example, the constraint input unit can input the user's social media activity data to the generation AI and cause the generation AI to propose relevant constraints.

[0047] The suggestion section can adjust the level of detail of its suggestions based on the importance of the hairstyle. For example, if a hairstyle is important, the suggestion section will provide detailed suggestions. For example, the suggestion section will display detailed suggestions for important hairstyles. The suggestion section can also provide concise suggestions for common hairstyles. For example, the suggestion section will display concise suggestions for common hairstyles. Furthermore, the suggestion section can provide particularly detailed suggestions for hairstyles that the user is especially interested in. For example, the suggestion section will adjust the level of detail of its suggestions based on the user's level of interest. This allows for the adjustment of the optimal level of detail of suggestions according to the importance of the hairstyle. Some or all of the above processing in the suggestion section may be performed using AI, for example, or without AI. For example, the suggestion section can adjust the level of detail of its suggestions using an AI model to evaluate the importance of hairstyles.

[0048] The suggestion unit can apply different suggestion algorithms depending on the hairstyle category when making suggestions. For example, in the case of short hair, the suggestion unit applies a suggestion algorithm specialized for short hair. For example, the suggestion unit displays the suggestion results for short hair. The suggestion unit can also apply a suggestion algorithm specialized for long hair. For example, the suggestion unit displays the suggestion results for long hair. Furthermore, the suggestion unit can also apply a suggestion algorithm specialized for curly hair. For example, the suggestion unit displays the suggestion results for curly hair. This allows the optimal suggestion algorithm to be applied according to the hairstyle category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can select an appropriate suggestion algorithm using an AI model to evaluate hairstyle categories.

[0049] The suggestion unit can determine the priority of suggestions based on the timing of the hairstyle suggestion. For example, if a user is participating in a specific event, the suggestion unit will prioritize suggesting hairstyles suitable for that event. For example, the suggestion unit will display suggestions for hairstyles suitable for a specific event. The suggestion unit can also prioritize suggesting hairstyles that the user uses on a daily basis. For example, the suggestion unit will display suggestions for hairstyles used on a daily basis. Furthermore, the suggestion unit can also prioritize suggesting hairstyles that are suitable for a specific season. For example, the suggestion unit will display suggestions for hairstyles suitable for a specific season. This allows for the determination of the optimal priority of suggestions based on the timing of the hairstyle suggestion. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can determine the priority of suggestions using an AI model to evaluate the timing of hairstyle suggestions.

[0050] The suggestion function can adjust the order of suggestions based on the relevance of the hairstyles. For example, the suggestion function may prioritize suggesting hairstyles that the user has shown particular interest in. For example, the suggestion function may adjust the order of suggestions based on the user's level of interest. The suggestion function can also group highly relevant hairstyles together for suggestion. For example, the suggestion function may suggest highly relevant hairstyles all at once. Furthermore, the suggestion function may postpone suggesting less relevant hairstyles. For example, the suggestion function may postpone suggesting less relevant hairstyles. This allows for the optimal order of suggestions to be adjusted based on the relevance of the hairstyles. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function may use an AI model to evaluate the relevance of hairstyles to adjust the order of suggestions.

[0051] The display unit can select the optimal display method by referring to the user's past display history when displaying information. For example, the display unit can automatically set a display method that the user has previously preferred. For example, the display unit can suggest the optimal display method based on the past display history. The display unit can also prioritize suggesting display options (2D, 3D, VR, etc.) that the user has previously used. For example, the display unit can suggest the optimal display method based on past display options. Furthermore, the display unit can suggest the display method with the highest visibility based on the user's past display history. For example, the display unit can suggest the optimal display method based on the past display history. This allows the display unit to select the optimal display method based on the user's past display history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past display history into a generating AI and have the generating AI select the optimal display method.

[0052] The display unit can customize the display means based on the user's current device information when displaying information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, the display unit can provide a display method optimized for the screen size of a smartphone. The display unit can also provide a display method optimized for a larger screen if the user is using a tablet. For example, the display unit can provide a display method optimized for the screen size of a tablet. Furthermore, if the user is using a VR device, the display unit can provide a display method optimized for VR. For example, the display unit can provide a display method optimized for a VR device. This makes it possible to provide the optimal display means based on the user's current device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI perform the customization of the optimal display means.

[0053] The display unit can select the optimal display method during display, taking into account the user's geographical location information. For example, when the user is in a specific location, the display unit provides a display method related to that location. For example, the display unit displays information related to the specific location. Furthermore, when the user is traveling, the display unit can also provide a display method based on information about the travel destination. For example, the display unit provides the optimal display method based on information about the travel destination. Furthermore, when the user is at home, the display unit can also provide a general display method. For example, the display unit provides a display method suitable for the situation at home. This makes it possible to provide the optimal display method based on the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's geographical location information to a generation AI and cause the generation AI to select the optimal display method.

[0054] The display unit can analyze the user's social media activity and provide a relevant display method when displaying the content. The display unit can provide the relevant display method, for example, based on posts shared by the user on social media. For example, the display unit can analyze the content of the posts shared by the user and provide the relevant display method. The display unit can also provide the relevant display method by referring to the content of posts from accounts the user follows on social media. For example, the display unit can analyze the content of posts from the followed accounts and provide the relevant display method. The display unit can also provide the relevant display method based on posts the user has "liked" on social media. For example, the display unit can analyze the content of the "liked" posts and provide the relevant display method. This makes it possible to provide an optimal display method based on the user's social media activity. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the user's social media activity data into a generation AI and cause the generation AI to provide a relevant display method.

[0055] When improving the quality of video, the quality improvement unit can select the optimal method by referring to the user's past video quality improvement history. The quality improvement unit, for example, automatically sets a quality improvement method that the user has previously preferred. For example, the quality improvement unit suggests an optimal method based on the past quality improvement history. The quality improvement unit can also prioritize quality improvement options that the user has previously used. For example, the quality improvement unit suggests an optimal method based on the past quality improvement options. Furthermore, the quality improvement unit can suggest the most effective method based on the user's past video quality improvement history. For example, the quality improvement unit suggests an optimal method based on the past quality improvement history. This makes it possible to select the optimal method based on the user's past video quality improvement history. Some or all of the above-described processing in the quality improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the quality improvement unit can input the user's past quality improvement history into a generation AI and have the generation AI select the optimal method.

[0056] When improving the quality of video, the quality improvement unit can customize the quality improvement means based on the user's current environment. For example, if the user is shooting outdoors, the quality improvement unit performs quality improvement that takes advantage of natural light. For example, the quality improvement unit adjusts the quality improvement according to the intensity and direction of natural light. Furthermore, if the user is shooting indoors, the quality improvement unit can also perform quality improvement that matches the color temperature of the lighting. For example, the quality improvement unit adjusts the quality improvement based on the color temperature of the indoor lighting. Furthermore, if the user is shooting in a dark place, the quality improvement unit can perform quality improvement that reduces noise. For example, the quality improvement unit uses quality improvement technology to reduce noise in dark places. This makes it possible to provide optimal quality improvement means based on the user's current environment. Some or all of the above-described processing in the quality improvement unit may be performed using, or without, AI. For example, the quality improvement unit can input the user's environmental data into the generation AI and cause the generation AI to customize the optimal quality improvement means.

[0057] When improving the quality of video, the quality improvement unit can select the optimal quality improvement method by taking into account the user's geographical location information. For example, when the user is in a specific location, the quality improvement unit provides a quality improvement method related to that location. For example, the quality improvement unit provides the optimal quality improvement method based on the environment of the specific location. Furthermore, when the user is traveling, the quality improvement unit can provide a quality improvement method based on information about the travel destination. For example, the quality improvement unit provides the optimal quality improvement method based on the environment of the travel destination. Furthermore, when the user is at home, the quality improvement unit can provide a general quality improvement method. For example, the quality improvement unit provides a quality improvement method suitable for the home environment. This makes it possible to provide the optimal quality improvement method based on the user's geographical location information. Some or all of the above-mentioned processing in the quality improvement unit may be performed using, or without, AI. For example, the quality improvement unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal quality improvement method.

[0058] When improving the quality of a video, the quality improvement unit can analyze a user's social media activities and provide relevant quality improvement measures. The quality improvement unit can provide relevant quality improvement methods, for example, based on posts shared by the user on social media. For example, the quality improvement unit can analyze the content of posts shared by the user and provide relevant quality improvement methods. The quality improvement unit can also provide relevant quality improvement methods based on the content of posts from accounts the user follows on social media. For example, the quality improvement unit can analyze the content of posts from the followed accounts and provide relevant quality improvement methods. The quality improvement unit can also provide relevant quality improvement methods based on posts the user "likes" on social media. For example, the quality improvement unit can analyze the content of "liked" posts and provide relevant quality improvement methods. This makes it possible to provide optimal quality improvement measures based on the user's social media activities. Some or all of the above-described processing in the quality improvement unit can be performed using, for example, AI, or without AI. For example, the quality improvement unit can input the user's social media activity data into a generation AI and cause the generation AI to provide relevant quality improvement measures.

[0059] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can automatically set the interface that the user has previously preferred to use. For example, the interface unit can suggest the optimal interface based on the past operation history. The interface unit can also preferentially suggest operation options that the user has previously used. For example, the interface unit can suggest the optimal interface based on past operation options. Furthermore, the interface unit can suggest the most efficient interface from the user's past operation history. For example, the interface unit can suggest the optimal interface based on the past operation history. This allows the user to select the optimal display method based on their past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's past operation history into a generating AI and have the generating AI select the optimal display method.

[0060] The interface unit can customize the display means based on the user's current device information when displaying the interface. For example, if the user is using a smartphone, the interface unit can provide an interface that matches the screen size. For example, the interface unit can provide an interface optimized for the screen size of a smartphone. The interface unit can also provide an interface optimized for a larger screen if the user is using a tablet. For example, the interface unit can provide an interface optimized for the screen size of a tablet. Furthermore, if the user is using a VR device, the interface unit can provide an interface optimized for VR. For example, the interface unit can provide an interface optimized for a VR device. This makes it possible to provide the optimal display means based on the user's current device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's device information into a generating AI and have the generating AI perform the customization of the optimal display means.

[0061] The interface unit can select the optimal display method when displaying the interface, taking into account the user's geographical location information. For example, if the user is in a specific location, the interface unit can provide an interface related to that location. For example, the interface unit can display information related to that specific location. Furthermore, if the user is traveling, the interface unit can provide an interface based on information about the travel destination. For example, the interface unit can provide the optimal interface based on information about the travel destination. In addition, if the user is at home, the interface unit can provide a general interface. For example, the interface unit can provide an interface suitable for the home environment. This allows the interface unit to provide the optimal display method based on the user's geographical location information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0062] The interface unit can analyze the user's social media activity and provide relevant display methods when displaying the interface. For example, the interface unit can provide relevant interfaces based on posts shared by the user on social media. For example, the interface unit can analyze the content of posts shared by the user and provide relevant interfaces. The interface unit can also provide relevant interfaces based on the content of posts from accounts followed by the user on social media. For example, the interface unit can analyze the content of posts from followed accounts and provide relevant interfaces. Furthermore, the interface unit can also provide relevant interfaces based on posts that the user has "liked" on social media. For example, the interface unit can analyze the content of "liked" posts and provide relevant interfaces. This makes it possible to provide the optimal display method based on the user's social media activity. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant display methods.

[0063] The rotation display unit can select the optimal method by referring to the user's past rotation display history when rotating the display. For example, the rotation display unit automatically sets the rotation display method that the user has previously preferred. For example, the rotation display unit suggests the optimal method based on the past rotation display history. The rotation display unit can also prioritize suggesting rotation display options that the user has previously used. For example, the rotation display unit suggests the optimal method based on the past rotation display options. Furthermore, the rotation display unit can suggest the most effective method based on the user's past rotation display history. For example, the rotation display unit suggests the optimal method based on the past rotation display history. This makes it possible to select the optimal method based on the user's past rotation display history. Some or all of the above-described processing in the rotation display unit may be performed using, for example, AI, or may be performed without using AI. For example, the rotation display unit can input the user's past rotation display history into a generation AI and cause the generation AI to select the optimal method.

[0064] The rotation display unit can customize the rotation display means based on the user's current device information during rotation display. For example, if the user is using a smartphone, the rotation display unit provides a rotated display that matches the screen size. For example, the rotation display unit provides a rotated display optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the rotation display unit can also provide a rotated display optimized for a larger screen. For example, the rotation display unit provides a rotated display optimized for the tablet screen size. Furthermore, if the user is using a VR device, the rotation display unit can also provide a rotated display optimized for VR. For example, the rotation display unit provides a rotated display optimized for the VR device. This makes it possible to provide an optimal rotation display means based on the user's current device information. Some or all of the above-described processing in the rotation display unit may be performed using, for example, AI, or may be performed without using AI. For example, the rotation display unit can input the user's device information to a generation AI and cause the generation AI to customize the optimal rotation display means.

[0065] The rotating display unit can select the optimal rotating display method when rotating the display, taking into account the user's geographical location information. For example, if the user is in a specific location, the rotating display unit can provide a rotating display method related to that location. For example, the rotating display unit can provide the optimal rotating display method based on the environment of that specific location. Furthermore, if the user is traveling, the rotating display unit can also provide a rotating display method based on information about the travel destination. For example, the rotating display unit can provide the optimal rotating display method based on the environment of the travel destination. In addition, if the user is at home, the rotating display unit can provide a general rotating display method. For example, the rotating display unit can provide a rotating display method suitable for the home environment. This makes it possible to provide the optimal rotating display method based on the user's geographical location information. Some or all of the above processing in the rotating display unit may be performed using AI, for example, or without AI. For example, the rotating display unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal rotating display method.

[0066] The rotating display unit can analyze the user's social media activity and provide relevant rotating display means during rotating display. For example, the rotating display unit can provide relevant rotating display methods based on posts shared by the user on social media. For example, the rotating display unit can analyze the content of posts shared by the user and provide relevant rotating display methods. The rotating display unit can also provide relevant rotating display methods based on the content of posts from accounts followed by the user on social media. For example, the rotating display unit can analyze the content of posts from followed accounts and provide relevant rotating display methods. Furthermore, the rotating display unit can also provide relevant rotating display methods based on posts that the user has "liked" on social media. For example, the rotating display unit can analyze the content of "liked" posts and provide relevant rotating display methods. This makes it possible to provide the optimal rotating display means based on the user's social media activity. Some or all of the above processing in the rotating display unit may be performed using AI, for example, or without AI. For example, the rotating display unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant rotating display means.

[0067] The zoom function can select the optimal method when zooming in or out by referring to the user's past zoom history. For example, the zoom function can automatically set the zoom method that the user has previously preferred. For example, the zoom function can suggest the optimal method based on the user's past zoom history. The zoom function can also prioritize suggesting zoom options that the user has previously used. For example, the zoom function can suggest the optimal method based on past zoom options. Furthermore, the zoom function can suggest the most effective method from the user's past zoom history. For example, the zoom function can suggest the optimal method based on the user's past zoom history. This allows the optimal method to be selected based on the user's past zoom history. Some or all of the above processing in the zoom function may be performed using AI, for example, or without AI. For example, the zoom function can input the user's past zoom history into a generating AI and have the generating AI select the optimal method.

[0068] The scaling unit can customize the scaling means based on the user's current device information when scaling. For example, if the user is using a smartphone, the scaling unit can provide scaling that matches the screen size. For example, the scaling unit can provide scaling optimized for the screen size of a smartphone. The scaling unit can also provide scaling optimized for a larger screen if the user is using a tablet. For example, the scaling unit can provide scaling optimized for the screen size of a tablet. Furthermore, if the user is using a VR device, the scaling unit can provide scaling optimized for VR. For example, the scaling unit can provide scaling optimized for a VR device. This makes it possible to provide the optimal scaling means based on the user's current device information. Some or all of the above processing in the scaling unit may be performed using AI, for example, or without AI. For example, the scaling unit can input the user's device information into a generating AI and have the generating AI perform the customization of the optimal scaling means.

[0069] When scaling, the scaling unit can select an optimal scaling method taking into account the user's geographical location information. For example, when the user is in a specific location, the scaling unit provides a scaling method related to that location. For example, the scaling unit provides an optimal scaling method based on the environment of the specific location. Furthermore, when the user is traveling, the scaling unit can provide a scaling method based on information about the travel destination. For example, the scaling unit provides an optimal scaling method based on the environment of the travel destination. Furthermore, when the user is at home, the scaling unit can provide a general scaling method. For example, the scaling unit provides a scaling method suitable for the home environment. This makes it possible to provide an optimal scaling method based on the user's geographical location information. Some or all of the above-described processing in the scaling unit may be performed using, or without, AI. For example, the scaling unit can input the user's geographical location information to the generation AI and cause the generation AI to select an optimal scaling method.

[0070] The scaling unit can analyze the user's social media activity during scaling and provide relevant scaling methods. For example, the scaling unit can provide relevant scaling methods based on posts shared by the user on social media. For example, the scaling unit can analyze the content of posts shared by the user and provide relevant scaling methods. The scaling unit can also provide relevant scaling methods based on the content of posts from accounts followed by the user on social media. For example, the scaling unit can analyze the content of posts from followed accounts and provide relevant scaling methods. Furthermore, the scaling unit can also provide relevant scaling methods based on posts that the user has "liked" on social media. For example, the scaling unit can analyze the content of "liked" posts and provide relevant scaling methods. This allows the optimal scaling method to be provided based on the user's social media activity. Some or all of the above processing in the scaling unit may be performed using AI, for example, or without AI. For example, the scaling unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant scaling methods.

[0071] The reproduction confirmation unit can select the optimal method by referring to the user's past reproduction confirmation history when performing reproduction confirmation. The reproduction confirmation unit, for example, automatically sets the reproduction confirmation method that the user has previously preferred. For example, the reproduction confirmation unit suggests the optimal method based on the past reproduction confirmation history. The reproduction confirmation unit can also preferentially suggest reproduction confirmation options that the user has previously used. For example, the reproduction confirmation unit suggests the optimal method based on the past reproduction confirmation options. Furthermore, the reproduction confirmation unit can also suggest the most effective method based on the user's past reproduction confirmation history. For example, the reproduction confirmation unit suggests the optimal method based on the past reproduction confirmation history. This makes it possible to select the optimal method based on the user's past reproduction confirmation history. Some or all of the above-described processing in the reproduction confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproduction confirmation unit can input the user's past reproduction confirmation history into a generation AI and cause the generation AI to select the optimal method.

[0072] The reproduction confirmation unit can customize the reproduction confirmation means based on the user's current device information during reproduction confirmation. For example, if the user is using a smartphone, the reproduction confirmation unit provides a reproduction confirmation tailored to the screen size. For example, the reproduction confirmation unit provides a reproduction confirmation optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the reproduction confirmation unit can also provide a reproduction confirmation optimized for a larger screen. For example, the reproduction confirmation unit provides a reproduction confirmation optimized for the tablet screen size. Furthermore, if the user is using a VR device, the reproduction confirmation unit can also provide a reproduction confirmation optimized for VR. For example, the reproduction confirmation unit provides a reproduction confirmation optimized for the VR device. This makes it possible to provide an optimal reproduction confirmation means based on the user's current device information. Some or all of the above-described processing in the reproduction confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the reproduction confirmation unit can input the user's device information to the generation AI and cause the generation AI to customize the optimal reproduction confirmation means.

[0073] The reproduction confirmation unit can select the optimal reproduction confirmation method during reproduction confirmation, taking into account the user's geographical location information. For example, when the user is in a specific location, the reproduction confirmation unit provides a reproduction confirmation method related to the location. For example, the reproduction confirmation unit provides the optimal reproduction confirmation method based on the environment of the specific location. Furthermore, when the user is traveling, the reproduction confirmation unit can provide a reproduction confirmation method based on information about the travel destination. For example, the reproduction confirmation unit provides the optimal reproduction confirmation method based on the environment of the travel destination. Furthermore, when the user is at home, the reproduction confirmation unit can provide a general reproduction confirmation method. For example, the reproduction confirmation unit provides a reproduction confirmation method suitable for the home environment. This makes it possible to provide the optimal reproduction confirmation method based on the user's geographical location information. Some or all of the above-described processing in the reproduction confirmation unit may be performed using AI, for example, or without AI. For example, the reproduction confirmation unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal reproduction confirmation method.

[0074] During reproduction confirmation, the reproduction confirmation unit can analyze the user's social media activities and provide a related reproduction confirmation method. The reproduction confirmation unit can provide a related reproduction confirmation method, for example, based on posts shared by the user on social media. For example, the reproduction confirmation unit can analyze the content of the posts shared by the user and provide a related reproduction confirmation method. The reproduction confirmation unit can also provide a related reproduction confirmation method based on the content of posts from accounts the user follows on social media. For example, the reproduction confirmation unit can analyze the content of posts from the followed accounts and provide a related reproduction confirmation method. The reproduction confirmation unit can also provide a related reproduction confirmation method based on posts the user "liked" on social media. For example, the reproduction confirmation unit can analyze the content of the "liked" posts and provide a related reproduction confirmation method. This makes it possible to provide an optimal reproduction confirmation method based on the user's social media activities. Some or all of the above-described processing in the reproduction confirmation unit can be performed using, for example, AI, or without AI. For example, the reproduction confirmation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide a related reproduction confirmation method.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The acquisition unit can analyze the user's past hairstyle history and suggest hairstyles that suit the user. For example, the acquisition unit collects data on hairstyles previously selected by the user and provides it to the analysis unit. The acquisition unit can also evaluate the success rate and satisfaction level of hairstyles that the user has tried in the past and reflect this in the suggestion unit. Furthermore, the acquisition unit can suggest hairstyles that suit the season or event based on the user's past hairstyle history. This makes it possible to suggest the optimal hairstyle based on the user's past hairstyle history.

[0077] The analysis unit can suggest hairstyles by combining the user's hairstyle history with current trends. For example, the analysis unit analyzes the user's past hairstyle history and compares it with current trends. The analysis unit can also suggest hairstyles that match the trends based on the user's hair type and face shape. Furthermore, the analysis unit can suggest hairstyles that suit the season or event based on the user's hairstyle history and trends. This makes it possible to suggest the optimal hairstyle by combining the user's hairstyle history with current trends.

[0078] The acquisition unit can suggest hairstyles that suit local trends and culture based on the user's geographical location information. For example, if the user is in a specific area, the acquisition unit can suggest hairstyles based on local trends. Also, if the user is traveling, the acquisition unit can suggest hairstyles based on the culture and trends of the destination. Furthermore, if the user is at home, the acquisition unit can suggest hairstyles that are common in the area. This makes it possible to suggest optimal hairstyles based on the user's geographical location information.

[0079] The analysis unit can analyze the user's social media activity and suggest hairstyles that are suitable for the user. For example, the analysis unit can suggest similar hairstyles based on photos the user has shared on social media. The analysis unit can also suggest related hairstyles based on the hairstyles of accounts the user follows. Furthermore, the analysis unit can also suggest related hairstyles based on posts the user has "liked." This makes it possible to suggest optimal hairstyles based on the user's social media activity.

[0080] The constraint input section can refer to the user's past constraint input history and suggest the most suitable constraints. For example, the constraint input section can automatically display constraints previously entered by the user as candidates. It can also prioritize suggesting input methods (text, multiple-choice, etc.) previously used by the user. Furthermore, the constraint input section can predict and suggest constraints to be used during specific time periods based on the user's past constraint input history. This allows for the suggestion of optimal constraints based on the user's past constraint input history.

[0081] The suggestion section can customize how suggestions are displayed based on the user's current device information. For example, if the user is using a smartphone, the suggestion section will provide suggestions tailored to the screen size. It can also provide suggestions optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a VR device, the suggestion section can provide suggestions optimized for VR. This allows the system to provide the most suitable suggestion display method based on the user's current device information.

[0082] The display unit can refer to the user's past viewing history and select the optimal display method. For example, the display unit can automatically set the display method that the user has preferred in the past. It can also prioritize suggesting display options (2D, 3D, VR, etc.) that the user has used in the past. Furthermore, the display unit can suggest the most visually appealing display method based on the user's past viewing history. This allows the system to select the optimal display method based on the user's past viewing history.

[0083] The processing flow of the first embodiment will be briefly explained below.

[0084] Step 1: The acquisition unit acquires the user's video. The user's video may include still images, videos, real-time video, etc. The acquisition unit can capture the user's video using a smartphone camera, or it can read video that the user has already saved. Furthermore, the acquisition unit can use technology to improve the resolution and quality of the video. Step 2: The analysis unit analyzes the video captured by the capture unit. The analysis unit can use facial recognition technology to determine the user's face shape and technology to extract hair characteristics. For example, technology to analyze hair thickness and density can be used. Furthermore, the analysis unit can improve the accuracy of the analysis by using technology to improve the quality of the video. Step 3: The constraint input unit inputs the conditions for the hairstyle desired by the user. The constraint input unit can input constraints in the form of text input or selection from options. For example, when the user inputs school rules or company rules, the user can freely input them using the text box. The constraint input unit can also display a list of school rules or company rules, allowing the user to select the appropriate item. Step 4: The suggestion unit suggests a hairstyle suitable for the user based on the results of the analysis by the analysis unit and the conditions entered by the constraint input unit. The suggestion unit uses AI to suggest hairstyles and select the optimal hairstyle based on the user's face shape, hair type, and constraints. For example, an algorithm can be used to suggest hairstyles that suit the face shape and hair type. Step 5: The display unit displays the hairstyle suggested by the suggestion unit. The display unit displays the hairstyle on the smartphone screen, and can display the hairstyle in any of the following formats: 2D display, 3D display, and VR display. For example, the display can be a 2D display in which the hairstyle is superimposed on the user's face, or a 3D display in which the user's face is displayed three-dimensionally and the hairstyle is superimposed on top of it. Furthermore, the VR display allows the user to check the hairstyle using a VR device.

[0085] (Example 2) In an embodiment of the present invention, a hairstyle suggestion system uses AI to suggest a hairstyle based on a user's own video (even an unedited photo) captured with a smartphone camera and constraints (such as school or company rules). In this hairstyle suggestion system, a user takes a video of themselves with a smartphone camera and inputs the video into an AI for analysis. Based on the analyzed video, the AI ​​determines the user's facial shape, hair type, etc., and suggests an appropriate hairstyle based on the constraints (e.g., school or company rules) entered by the user. This suggestion is provided in 2D, 3D, or VR format. This allows users to easily find the optimal hairstyle based on their own video and constraints. For example, a user takes a video of themselves with a smartphone camera. The quality and resolution of the video must also be taken into consideration. For example, technology is needed to enable the AI ​​to accurately analyze low-resolution video. The video is then input into an AI, which analyzes the video. The AI ​​uses video quality improvement technology to determine the user's facial shape, hair type, etc. Furthermore, the AI ​​suggests an appropriate hairstyle based on the constraints (e.g., school or company rules) entered by the user. A specific method for entering constraints must also be described. For example, it is necessary to clarify how users input school or company rules, whether by text input or by selecting from a list of options. It is important to provide specific examples of how suggested hairstyles are displayed in 2D, 3D, and VR formats. The algorithms and criteria used by the AI ​​to suggest hairstyles should also be mentioned. For example, specific algorithms and technologies should be described, such as how the AI ​​determines face shape and hair type. It is also important to describe how the suggested hairstyles are presented to users, for example, how they are displayed on a smartphone screen. Furthermore, the user experience should be described in detail, including how users can view the suggested hairstyles, for example, whether they can be rotated 360 degrees and whether they can be zoomed in or out. It is also recommended to provide guidelines or reference materials for hairdressers to use when recreating the suggested hairstyle, such as the accuracy with which the suggested hairstyles are actually reproduced. This allows the hairstyle suggestion system to suggest and display the optimal hairstyle based on the user's video and constraints.

[0086] A hairstyle suggestion system according to an embodiment includes an acquisition unit, an analysis unit, a constraint input unit, a suggestion unit, and a display unit. The acquisition unit acquires a video of a user. The video of the user may include, but is not limited to, still images, videos, and real-time videos. The acquisition unit may capture the video of the user using, for example, a smartphone camera. The acquisition unit may also load video already saved by the user. The acquisition unit may use technology to improve the resolution and quality of the video. For example, the acquisition unit may use technology to convert low-resolution video to high-resolution video. The analysis unit analyzes the video acquired by the acquisition unit. The analysis unit may determine the shape of the user's face using, for example, face recognition technology. The analysis unit may also use technology to extract hair texture characteristics. For example, the analysis unit may use technology to analyze hair thickness and density. The analysis unit may also use technology to improve the accuracy of the analysis using technology to improve the quality of the video. The constraint input unit inputs the conditions for the hairstyle desired by the user. The constraint input unit may input constraints, for example, by text input or by selecting from options. For example, when a user inputs school rules or company rules, the user can freely input them using a text box. The constraint input unit can also provide a format in which the user can select constraints from options. For example, the constraint input unit can display a list of school rules or company rules, and the user can select the appropriate item. The suggestion unit suggests a hairstyle suitable for the user based on the results of the analysis by the analysis unit and the conditions input by the constraint input unit. The suggestion unit, for example, uses AI to suggest a hairstyle. The AI ​​selects an optimal hairstyle based on the user's face shape, hair type, and constraints. For example, the AI ​​can use an algorithm to suggest a hairstyle that suits the face shape. The AI ​​can also use an algorithm to suggest a hairstyle according to the hair type. Furthermore, the AI ​​can use an algorithm to suggest a hairstyle based on the constraints. The display unit displays the hairstyle suggested by the suggestion unit. The display unit, for example, displays the hairstyle on a smartphone screen. The display unit can display the hairstyle in any of 2D, 3D, and VR formats. For example, the display unit can display the hairstyle superimposed on the user's face as a 2D display.Furthermore, the display unit can display the user's face in 3D, overlaying it with a hairstyle. In addition, the display unit can display a VR version, allowing the user to view the hairstyle using a VR device. As a result, the hairstyle suggestion system according to this embodiment can suggest and display the optimal hairstyle based on the user's video and constraints.

[0087] The acquisition unit may include a quality enhancement unit that adjusts the resolution and color tone of the video. The quality enhancement unit can, for example, adjust the resolution of the video. For example, the quality enhancement unit can use a technique to convert low-resolution video to high-resolution video. The quality enhancement unit can also adjust the color tone of the video. For example, the quality enhancement unit can use a technique to adjust color temperature, saturation, and contrast. Furthermore, the quality enhancement unit can also use a technique to reduce noise in the video. For example, the quality enhancement unit can use a filtering technique to remove noise from the video. By improving the quality of the video, more accurate analysis and suggestions become possible. Some or all of the above processing in the quality enhancement unit may be performed using AI, for example, or without AI. For example, the quality enhancement unit can convert low-resolution video to high-resolution video using an AI model to improve the resolution of the video.

[0088] The constraint input unit may include an interface unit that provides an interface for the user to input constraints. The interface unit provides, for example, a GUI (Graphical User Interface). For example, the interface unit displays text boxes and options for the user to input constraints. The interface unit may also provide voice input. For example, the interface unit may use a microphone to allow the user to input constraints by voice. The interface unit may also provide touch operation. For example, the interface unit may provide an interface for the user to input constraints using a touch screen. This makes it possible to provide an interface that allows the user to easily input constraints. Some or all of the above-described processing in the interface unit may be performed using, for example, AI, or may be performed without using AI. For example, the interface unit may convert the voice input into text using an AI model for analyzing the user's voice input.

[0089] The display unit may include a rotation display unit that allows the user to rotate the proposed hairstyle 360 ​​degrees to check it. The rotation display unit provides, for example, an interface that allows the user to rotate the hairstyle 360 ​​degrees to check it. For example, the rotation display unit allows the user to rotate the hairstyle by swiping the screen with their finger. The rotation display unit also allows the user to rotate the hairstyle using a mouse. Furthermore, the rotation display unit also allows the user to rotate the hairstyle using a VR device. For example, the rotation display unit allows the user to wear the VR device and rotate the hairstyle in accordance with head movement. This allows the user to rotate the proposed hairstyle 360 ​​degrees to check it. Some or all of the above-described processing in the rotation display unit may be performed, for example, using AI or without AI. For example, the rotation display unit may rotate the hairstyle 360 ​​degrees using an AI model for generating a 3D model of the hairstyle.

[0090] The display unit may include a scaling unit that can scale the proposed hairstyle. The scaling unit, for example, provides an interface that allows the user to scale and check the hairstyle. For example, the scaling unit allows the user to scale the hairstyle by pinching in and out on the screen with their fingers. The scaling unit also allows the user to scale the hairstyle using a mouse wheel. The scaling unit also allows the user to scale the hairstyle using a VR device. For example, the scaling unit allows the user to wear the VR device and scale the hairstyle according to hand movements. This allows the user to scale and check the proposed hairstyle. Some or all of the above-described processing in the scaling unit may be performed using, for example, AI, or may be performed without AI. For example, the scaling unit may scale the hairstyle using an AI model for generating a 3D model of the hairstyle.

[0091] The reproduction confirmation unit may include a reproduction confirmation unit that simulates the accuracy with which a proposed hairstyle is actually reproduced. The reproduction confirmation unit, for example, provides an interface for simulating the accuracy with which the proposed hairstyle is actually reproduced. For example, the reproduction confirmation unit displays a 3D model for the user to simulate the hairstyle. The reproduction confirmation unit may also provide guidelines for the user to simulate the hairstyle. For example, the reproduction confirmation unit may provide reference materials for a hairdresser to use when reproducing the hairstyle. Furthermore, the reproduction confirmation unit may also provide criteria for evaluating the reproduction accuracy of the hairstyle. For example, the reproduction confirmation unit may evaluate the reproduction accuracy based on factors such as the length, shape, and color of the hairstyle. This makes it possible to confirm the accuracy with which the proposed hairstyle is actually reproduced. Some or all of the above-described processing in the reproduction confirmation unit may be performed, for example, using AI or without AI. For example, the reproduction confirmation unit may simulate the reproduction accuracy using an AI model for evaluating the reproduction accuracy of the hairstyle.

[0092] The acquisition unit can estimate emotions from the user's facial expressions and voice and adjust the timing of video acquisition based on the estimated emotions. For example, the acquisition unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expressions. The acquisition unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on heart rate fluctuations. This makes it possible to adjust the optimal video acquisition timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input facial expression data of a user captured by a camera into the generation AI and have the generation AI estimate emotions.

[0093] The acquisition unit can analyze the user's past video acquisition history and select an acquisition method suitable for the user. The acquisition unit, for example, automatically sets a camera angle that the user has previously preferred. For example, the acquisition unit suggests an optimal camera angle based on the user's past video acquisition history. The acquisition unit can also suggest optimal camera settings based on the resolution and quality of videos the user has previously acquired. For example, the acquisition unit analyzes the user's past video acquisition history and sets optimal resolution and quality. Furthermore, the acquisition unit can predict the timing to capture the most natural facial expression based on the user's past video acquisition history and adjust the acquisition method. For example, the acquisition unit suggests the optimal shooting timing based on the user's past video acquisition history. This makes it possible to select an optimal acquisition method based on the user's past video acquisition history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past video acquisition history to a generation AI and cause the generation AI to select an optimal acquisition method.

[0094] The acquisition unit can perform filtering based on the user's current environment and lighting conditions when acquiring video. For example, if the user is shooting outdoors, the acquisition unit performs filtering that makes use of natural light. For example, the acquisition unit adjusts the filtering according to the intensity and direction of natural light. Furthermore, if the user is shooting indoors, the acquisition unit can perform filtering that matches the color temperature of the lighting. For example, the acquisition unit adjusts the filtering based on the color temperature of the indoor lighting. Furthermore, if the user is shooting in a dark place, the acquisition unit can perform filtering that reduces noise. For example, the acquisition unit uses a filtering technique for reducing noise in dark places. This allows optimal filtering to be performed according to the user's environment and lighting conditions. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's environment and lighting conditions into the generation AI and leave the execution of optimal filtering to the generation AI.

[0095] The acquisition unit can estimate the user's emotions and determine the priority of the videos to be acquired based on the estimated user emotions. For example, when the user is relaxed, the acquisition unit prioritizes acquiring natural facial expressions. For example, the acquisition unit adjusts the timing to capture a relaxed facial expression. Furthermore, when the user is nervous, the acquisition unit can acquire multiple videos and prioritize the most natural facial expression. For example, the acquisition unit analyzes multiple videos of the user in a nervous state and selects the most natural facial expression. Furthermore, when the user is in a hurry, the acquisition unit can prioritize videos that can be acquired quickly. For example, the acquisition unit acquires multiple videos in a short period of time and selects the most optimal video from among them. This allows the priority of the optimal videos to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed, for example, using AI or without AI. For example, the acquisition unit can input user emotion data into the generation AI and have the generation AI determine the priority of the images.

[0096] When acquiring video, the acquisition unit can prioritize acquiring highly relevant video based on the user's geographical location information. For example, when the user is in a specific location, the acquisition unit prioritizes acquiring backgrounds related to that location. For example, the acquisition unit prioritizes capturing backgrounds of specific tourist spots or famous places. Furthermore, when the user is traveling, the acquisition unit can also prioritize acquiring backgrounds of tourist spots. For example, the acquisition unit prioritizes capturing backgrounds of tourist spots or famous places at the travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring indoor backgrounds. For example, the acquisition unit prioritizes capturing backgrounds suitable for the indoor environment of the home. This makes it possible to acquire optimal video based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant video.

[0097] When acquiring video, the acquisition unit can analyze the user's social media activity and acquire related video. For example, the acquisition unit can acquire similar video based on photos shared by the user on social media. For example, the acquisition unit can analyze the characteristics of photos shared by the user to acquire similar video. The acquisition unit can also acquire related video based on the content posted by accounts the user follows on social media. For example, the acquisition unit can analyze the content posted by the followed accounts to acquire related video. Furthermore, the acquisition unit can also acquire related video based on posts the user has "liked" on social media. For example, the acquisition unit can analyze the characteristics of "liked" posts to acquire related video. This makes it possible to acquire optimal video based on the user's social media activity. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related video.

[0098] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit displays detailed analysis results for a relaxed state. Furthermore, when the user is tense, the analysis unit can provide concise and to-the-point analysis results. For example, the analysis unit displays concise analysis results for a tense state. Furthermore, when the user is in a hurry, the analysis unit can quickly provide analysis results. For example, the analysis unit displays quick analysis results for a hurry. This allows the optimal way to present the analysis to the user's emotions to be adjusted. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0099] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. For example, the analysis unit performs a detailed analysis for important video. For example, the analysis unit displays detailed analysis results for important video. The analysis unit can also perform a concise analysis for general video. For example, the analysis unit displays concise analysis results for general video. Furthermore, the analysis unit can perform a particularly detailed analysis for video in which the user is particularly interested. For example, the analysis unit adjusts the level of detail of the analysis based on the user's level of interest. This makes it possible to adjust the optimal level of detail of the analysis depending on the importance of the video. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model for evaluating the importance of the video.

[0100] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. For example, in the case of portrait video, the analysis unit applies a face recognition algorithm. For example, the analysis unit displays the face recognition results for the portrait video. The analysis unit can also apply a landscape analysis algorithm in the case of landscape video. For example, the analysis unit displays the analysis results for the landscape video. The analysis unit can also apply an animal recognition algorithm in the case of animal video. For example, the analysis unit displays the recognition results for the animal video. This makes it possible to apply the optimal analysis algorithm depending on the category of the video. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can select an appropriate analysis algorithm using an AI model for evaluating the category of the video.

[0101] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit performs a detailed analysis. For example, the analysis unit displays detailed analysis results for the relaxed state. The analysis unit can also perform a concise analysis for the tense state. For example, the analysis unit displays concise analysis results for the tense state. The analysis unit can also perform a quick analysis for the user in a hurry. For example, the analysis unit displays quick analysis results for the user in a hurry. This allows the length of the analysis to be optimally adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the analysis.

[0102] The analysis unit can determine the priority of analysis based on when the video was shot. For example, the analysis unit may prioritize the analysis of recently shot video. For example, the analysis unit may display the analysis results of recently shot video. The analysis unit can also prioritize the analysis of video shot at a specific event. For example, the analysis unit may display the analysis results of video shot at a specific event. Furthermore, the analysis unit may prioritize the analysis of video from a period of particular interest to the user. For example, the analysis unit may determine the priority of analysis based on the user's level of interest. This allows for the determination of the optimal analysis priority based on when the video was shot. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to evaluate when the video was shot to determine the priority of analysis.

[0103] The analysis unit can adjust the order of analysis based on the relevance of the videos during the analysis. For example, the analysis unit may prioritize the analysis of videos that the user has shown particular interest in. For example, the analysis unit may adjust the order of analysis based on the user's level of interest. The analysis unit can also group highly relevant videos together for analysis. For example, the analysis unit may analyze highly relevant videos all at once. Furthermore, the analysis unit may postpone the analysis of less relevant videos. For example, the analysis unit may postpone the analysis of less relevant videos. This allows for the adjustment of the optimal analysis order based on the relevance of the videos. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use an AI model to evaluate the relevance of videos to adjust the order of analysis.

[0104] The constraint input unit can estimate the user's emotions and adjust the constraint input method based on the estimated user's emotions. For example, when the user is relaxed, the constraint input unit provides detailed input options. For example, the constraint input unit displays detailed input options for a relaxed state. Furthermore, when the user is nervous, the constraint input unit can provide a concise and to-the-point input method. For example, the constraint input unit displays a concise input method for a nervous state. Furthermore, when the user is in a hurry, the constraint input unit can provide a method that allows quick input. For example, the constraint input unit displays a quick input method for a hurry. This makes it possible to adjust the optimal constraint input method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the constraint input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the constraint input unit can input the user's emotional data to the generation AI and cause the generation AI to adjust the constraint input method.

[0105] The constraint input unit can select the optimal input method by referring to the user's past constraint input history when a constraint is entered. For example, the constraint input unit can automatically display constraints previously entered by the user as candidates. For example, the constraint input unit can suggest the optimal input method based on the past constraint input history. The constraint input unit can also prioritize suggesting input methods (text, choices, etc.) previously used by the user. For example, the constraint input unit can suggest the optimal input method based on past input methods. Furthermore, the constraint input unit can predict and suggest constraints to be used during a specific time period based on the user's past constraint input history. For example, the constraint input unit can suggest constraints suitable for a specific time period based on past input history. This allows the optimal input method to be selected based on the user's past constraint input history. Some or all of the above processing in the constraint input unit may be performed using AI, for example, or without AI. For example, the constraint input unit can input the user's past constraint input history into a generating AI and have the generating AI select the optimal input method.

[0106] The constraint input unit can customize the constraint input method based on the user's current situation when a constraint is entered. For example, if the user is at work, the constraint input unit can automatically suggest constraints based on workplace rules. For example, the constraint input unit can suggest the optimal constraints based on workplace rules. The constraint input unit can also automatically suggest constraints based on school rules if the user is at school. For example, the constraint input unit can suggest the optimal constraints based on school rules. Furthermore, if the user is at home, the constraint input unit can suggest general constraints. For example, the constraint input unit can suggest constraints suitable for the home environment. This allows the system to provide the optimal constraint input method according to the user's current situation. Some or all of the above processing in the constraint input unit may be performed using AI, for example, or without AI. For example, the constraint input unit can input the user's current situation data into a generating AI and have the generating AI customize the optimal constraint input method.

[0107] The constraint input unit can estimate the user's emotions and determine the priority of constraints based on the estimated user's emotions. For example, when the user is relaxed, the constraint input unit prioritizes input of detailed constraints. For example, the constraint input unit displays detailed constraints for a relaxed state. Furthermore, when the user is tense, the constraint input unit can also prioritize input of concise constraints. For example, the constraint input unit displays concise constraints for a tense state. Furthermore, when the user is in a hurry, the constraint input unit can also prioritize constraints that can be input quickly. For example, the constraint input unit displays quick constraints for a hurry. This makes it possible to determine the optimal priority of constraints according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the constraint input unit can be performed, for example, using AI or without AI. For example, the constraint input unit can input the user's emotional data to the generation AI and cause the generation AI to determine the priority of the constraints.

[0108] The constraint input unit can input optimal constraints taking into account the user's geographical location information when inputting constraints. For example, when the user is in a specific location, the constraint input unit automatically suggests constraints related to that location. For example, the constraint input unit suggests optimal constraints based on the rules and constraints of the specific location. Furthermore, when the user is traveling, the constraint input unit can also suggest constraints based on the rules of the travel destination. For example, the constraint input unit suggests optimal constraints based on the rules of the travel destination. Furthermore, when the user is at home, the constraint input unit can also suggest general constraints. For example, the constraint input unit suggests constraints suitable for the situation at home. This makes it possible to input optimal constraints based on the user's geographical location information. Some or all of the above-mentioned processing in the constraint input unit may be performed using, for example, AI, or may be performed without using AI. For example, the constraint input unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest optimal constraints.

[0109] When inputting constraints, the constraint input unit can analyze the user's social media activity and input relevant constraints. The constraint input unit can, for example, propose relevant constraints based on posts shared by the user on social media. For example, the constraint input unit can analyze the content of posts shared by the user and propose relevant constraints. The constraint input unit can also propose relevant constraints based on the content of posts from accounts the user follows on social media. For example, the constraint input unit can analyze the content of posts from the followed accounts and propose relevant constraints. The constraint input unit can also propose relevant constraints based on posts the user has "liked" on social media. For example, the constraint input unit can analyze the content of "liked" posts and propose relevant constraints. This makes it possible to input optimal constraints based on the user's social media activity. Some or all of the above-described processing in the constraint input unit can be performed using, or without, AI. For example, the constraint input unit can input the user's social media activity data to the generation AI and cause the generation AI to propose relevant constraints.

[0110] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, the suggestion unit can display detailed suggestions when the user is relaxed. The suggestion unit can also provide concise and to-the-point suggestions when the user is tense. For example, the suggestion unit can display concise suggestions when the user is tense. Furthermore, the suggestion unit can provide quick suggestions when the user is in a hurry. For example, the suggestion unit can display quick suggestions when the user is in a hurry. This allows the optimal way suggestions are presented to match 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the way suggestions are presented.

[0111] The suggestion section can adjust the level of detail of its suggestions based on the importance of the hairstyle. For example, if a hairstyle is important, the suggestion section will provide detailed suggestions. For example, the suggestion section will display detailed suggestions for important hairstyles. The suggestion section can also provide concise suggestions for common hairstyles. For example, the suggestion section will display concise suggestions for common hairstyles. Furthermore, the suggestion section can provide particularly detailed suggestions for hairstyles that the user is especially interested in. For example, the suggestion section will adjust the level of detail of its suggestions based on the user's level of interest. This allows for the adjustment of the optimal level of detail of suggestions according to the importance of the hairstyle. Some or all of the above processing in the suggestion section may be performed using AI, for example, or without AI. For example, the suggestion section can adjust the level of detail of its suggestions using an AI model to evaluate the importance of hairstyles.

[0112] The suggestion unit can apply different suggestion algorithms depending on the hairstyle category when making suggestions. For example, in the case of short hair, the suggestion unit applies a suggestion algorithm specialized for short hair. For example, the suggestion unit displays the suggestion results for short hair. The suggestion unit can also apply a suggestion algorithm specialized for long hair. For example, the suggestion unit displays the suggestion results for long hair. Furthermore, the suggestion unit can also apply a suggestion algorithm specialized for curly hair. For example, the suggestion unit displays the suggestion results for curly hair. This allows the optimal suggestion algorithm to be applied according to the hairstyle category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can select an appropriate suggestion algorithm using an AI model to evaluate hairstyle categories.

[0113] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit makes detailed suggestions. For example, the suggestion unit displays detailed suggestions for a relaxed state. Furthermore, when the user is nervous, the suggestion unit can make concise and to-the-point suggestions. For example, the suggestion unit displays concise suggestions for a nervous state. Furthermore, when the user is in a hurry, the suggestion unit can make quick suggestions. For example, the suggestion unit displays quick suggestions for a hurry. This allows the length of the suggestions to be optimally adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0114] The suggestion unit can determine the priority of suggestions based on the timing of the hairstyle suggestion. For example, if a user is participating in a specific event, the suggestion unit will prioritize suggesting hairstyles suitable for that event. For example, the suggestion unit will display suggestions for hairstyles suitable for a specific event. The suggestion unit can also prioritize suggesting hairstyles that the user uses on a daily basis. For example, the suggestion unit will display suggestions for hairstyles used on a daily basis. Furthermore, the suggestion unit can also prioritize suggesting hairstyles that are suitable for a specific season. For example, the suggestion unit will display suggestions for hairstyles suitable for a specific season. This allows for the determination of the optimal priority of suggestions based on the timing of the hairstyle suggestion. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can determine the priority of suggestions using an AI model to evaluate the timing of hairstyle suggestions.

[0115] The suggestion function can adjust the order of suggestions based on the relevance of the hairstyles. For example, the suggestion function may prioritize suggesting hairstyles that the user has shown particular interest in. For example, the suggestion function may adjust the order of suggestions based on the user's level of interest. The suggestion function can also group highly relevant hairstyles together for suggestion. For example, the suggestion function may suggest highly relevant hairstyles all at once. Furthermore, the suggestion function may postpone suggesting less relevant hairstyles. For example, the suggestion function may postpone suggesting less relevant hairstyles. This allows for the optimal order of suggestions to be adjusted based on the relevance of the hairstyles. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function may use an AI model to evaluate the relevance of hairstyles to adjust the order of suggestions.

[0116] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, the display unit can provide a detailed display. For example, the display unit can provide a detailed display in a relaxed state. The display unit can also provide a concise and to-the-point display if the user is tense. For example, the display unit can provide a concise display in a tense state. Furthermore, if the user is in a hurry, the display unit can provide a rapid display. For example, the display unit can provide a rapid display in a hurry state. This allows the display method to be adjusted to the optimal level according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI adjust the display method.

[0117] The display unit can select the optimal display method by referring to the user's past display history when displaying information. For example, the display unit can automatically set a display method that the user has previously preferred. For example, the display unit can suggest the optimal display method based on the past display history. The display unit can also prioritize suggesting display options (2D, 3D, VR, etc.) that the user has previously used. For example, the display unit can suggest the optimal display method based on past display options. Furthermore, the display unit can suggest the display method with the highest visibility based on the user's past display history. For example, the display unit can suggest the optimal display method based on the past display history. This allows the display unit to select the optimal display method based on the user's past display history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past display history into a generating AI and have the generating AI select the optimal display method.

[0118] The display unit can customize the display means based on the user's current device information when displaying information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. For example, the display unit can provide a display method optimized for the screen size of a smartphone. The display unit can also provide a display method optimized for a larger screen if the user is using a tablet. For example, the display unit can provide a display method optimized for the screen size of a tablet. Furthermore, if the user is using a VR device, the display unit can provide a display method optimized for VR. For example, the display unit can provide a display method optimized for a VR device. This makes it possible to provide the optimal display means based on the user's current device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI perform the customization of the optimal display means.

[0119] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is relaxed, the display unit may prioritize detailed displays. For example, the display unit may provide detailed displays in a relaxed state. The display unit may also prioritize concise displays if the user is tense. For example, the display unit may provide concise displays in a tense state. Furthermore, if the user is in a hurry, the display unit may prioritize a method that allows for quick display. For example, the display unit may provide quick displays in a hurry state. This allows for the determination of the optimal display priority 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 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 display unit may be performed using AI, for example, or not using AI. For example, the display unit may input user emotion data into a generative AI and have the generative AI determine the display priority.

[0120] The display unit can select the optimal display method during display, taking into account the user's geographical location information. For example, when the user is in a specific location, the display unit provides a display method related to that location. For example, the display unit displays information related to the specific location. Furthermore, when the user is traveling, the display unit can also provide a display method based on information about the travel destination. For example, the display unit provides the optimal display method based on information about the travel destination. Furthermore, when the user is at home, the display unit can also provide a general display method. For example, the display unit provides a display method suitable for the situation at home. This makes it possible to provide the optimal display method based on the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's geographical location information to a generation AI and cause the generation AI to select the optimal display method.

[0121] The display unit can analyze the user's social media activity and provide a relevant display method when displaying the content. The display unit can provide the relevant display method, for example, based on posts shared by the user on social media. For example, the display unit can analyze the content of the posts shared by the user and provide the relevant display method. The display unit can also provide the relevant display method by referring to the content of posts from accounts the user follows on social media. For example, the display unit can analyze the content of posts from the followed accounts and provide the relevant display method. The display unit can also provide the relevant display method based on posts the user has "liked" on social media. For example, the display unit can analyze the content of the "liked" posts and provide the relevant display method. This makes it possible to provide an optimal display method based on the user's social media activity. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or can be performed without using AI. For example, the display unit can input the user's social media activity data into a generation AI and cause the generation AI to provide a relevant display method.

[0122] The quality improvement unit can estimate the user's emotions and adjust the video quality improvement method based on the estimated user's emotions. For example, when the user is relaxed, the quality improvement unit performs detailed quality improvement. For example, the quality improvement unit provides detailed quality improvement when the user is relaxed. Furthermore, when the user is tense, the quality improvement unit can perform concise and to-the-point quality improvement. For example, the quality improvement unit provides concise quality improvement when the user is tense. Furthermore, when the user is in a hurry, the quality improvement unit can perform quick quality improvement. For example, the quality improvement unit provides quick quality improvement when the user is in a hurry. This makes it possible to adjust the optimal video quality improvement method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the quality improvement unit may be performed using, for example, AI, or without AI. For example, the quality improvement department can input user emotion data into a generating AI and have the AI ​​adjust the quality improvement methods.

[0123] When improving the quality of video, the quality improvement unit can select the optimal method by referring to the user's past video quality improvement history. The quality improvement unit, for example, automatically sets a quality improvement method that the user has previously preferred. For example, the quality improvement unit suggests an optimal method based on the past quality improvement history. The quality improvement unit can also prioritize quality improvement options that the user has previously used. For example, the quality improvement unit suggests an optimal method based on the past quality improvement options. Furthermore, the quality improvement unit can suggest the most effective method based on the user's past video quality improvement history. For example, the quality improvement unit suggests an optimal method based on the past quality improvement history. This makes it possible to select the optimal method based on the user's past video quality improvement history. Some or all of the above-described processing in the quality improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the quality improvement unit can input the user's past quality improvement history into a generation AI and have the generation AI select the optimal method.

[0124] When improving the quality of video, the quality improvement unit can customize the quality improvement means based on the user's current environment. For example, if the user is shooting outdoors, the quality improvement unit performs quality improvement that takes advantage of natural light. For example, the quality improvement unit adjusts the quality improvement according to the intensity and direction of natural light. Furthermore, if the user is shooting indoors, the quality improvement unit can also perform quality improvement that matches the color temperature of the lighting. For example, the quality improvement unit adjusts the quality improvement based on the color temperature of the indoor lighting. Furthermore, if the user is shooting in a dark place, the quality improvement unit can perform quality improvement that reduces noise. For example, the quality improvement unit uses quality improvement technology to reduce noise in dark places. This makes it possible to provide optimal quality improvement means based on the user's current environment. Some or all of the above-described processing in the quality improvement unit may be performed using, or without, AI. For example, the quality improvement unit can input the user's environmental data into the generation AI and cause the generation AI to customize the optimal quality improvement means.

[0125] The quality improvement unit can estimate the user's emotions and determine the priority of video quality improvement based on the estimated user's emotions. For example, when the user is relaxed, the quality improvement unit prioritizes detailed quality improvement. For example, the quality improvement unit provides detailed quality improvement in a relaxed state. The quality improvement unit can also prioritize concise quality improvement when the user is tense. For example, the quality improvement unit provides concise quality improvement in a tense state. Furthermore, the quality improvement unit can prioritize a quick quality improvement method when the user is in a hurry. For example, the quality improvement unit provides quick quality improvement in a hurry. This makes it possible to determine the optimal quality improvement priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the quality improvement unit may be performed using, for example, AI, or without AI. For example, the quality improvement unit can input user emotional data into the generation AI and have the generation AI determine the priority of quality improvement.

[0126] When improving the quality of video, the quality improvement unit can select the optimal quality improvement method by taking into account the user's geographical location information. For example, when the user is in a specific location, the quality improvement unit provides a quality improvement method related to that location. For example, the quality improvement unit provides the optimal quality improvement method based on the environment of the specific location. Furthermore, when the user is traveling, the quality improvement unit can provide a quality improvement method based on information about the travel destination. For example, the quality improvement unit provides the optimal quality improvement method based on the environment of the travel destination. Furthermore, when the user is at home, the quality improvement unit can provide a general quality improvement method. For example, the quality improvement unit provides a quality improvement method suitable for the home environment. This makes it possible to provide the optimal quality improvement method based on the user's geographical location information. Some or all of the above-mentioned processing in the quality improvement unit may be performed using, or without, AI. For example, the quality improvement unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal quality improvement method.

[0127] When improving the quality of a video, the quality improvement unit can analyze a user's social media activities and provide relevant quality improvement measures. The quality improvement unit can provide relevant quality improvement methods, for example, based on posts shared by the user on social media. For example, the quality improvement unit can analyze the content of posts shared by the user and provide relevant quality improvement methods. The quality improvement unit can also provide relevant quality improvement methods based on the content of posts from accounts the user follows on social media. For example, the quality improvement unit can analyze the content of posts from the followed accounts and provide relevant quality improvement methods. The quality improvement unit can also provide relevant quality improvement methods based on posts the user "likes" on social media. For example, the quality improvement unit can analyze the content of "liked" posts and provide relevant quality improvement methods. This makes it possible to provide optimal quality improvement measures based on the user's social media activities. Some or all of the above-described processing in the quality improvement unit can be performed using, for example, AI, or without AI. For example, the quality improvement unit can input the user's social media activity data into a generation AI and cause the generation AI to provide relevant quality improvement measures.

[0128] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is relaxed, the interface unit can provide a detailed interface. For example, the interface unit can display a detailed interface in a relaxed state. The interface unit can also provide a concise and to-the-point interface if the user is tense. For example, the interface unit can display a concise interface in a tense state. Furthermore, if the user is in a hurry, the interface unit can provide a fast-operating interface. For example, the interface unit can display a fast interface in a hurry state. This allows the interface display method to be adjusted to the optimal level according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user emotion data into the generative AI and have the generative AI adjust the interface display method.

[0129] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can automatically set the interface that the user has previously preferred to use. For example, the interface unit can suggest the optimal interface based on the past operation history. The interface unit can also preferentially suggest operation options that the user has previously used. For example, the interface unit can suggest the optimal interface based on past operation options. Furthermore, the interface unit can suggest the most efficient interface from the user's past operation history. For example, the interface unit can suggest the optimal interface based on the past operation history. This allows the user to select the optimal display method based on their past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's past operation history into a generating AI and have the generating AI select the optimal display method.

[0130] The interface unit can customize the display means based on the user's current device information when displaying the interface. For example, if the user is using a smartphone, the interface unit can provide an interface that matches the screen size. For example, the interface unit can provide an interface optimized for the screen size of a smartphone. The interface unit can also provide an interface optimized for a larger screen if the user is using a tablet. For example, the interface unit can provide an interface optimized for the screen size of a tablet. Furthermore, if the user is using a VR device, the interface unit can provide an interface optimized for VR. For example, the interface unit can provide an interface optimized for a VR device. This makes it possible to provide the optimal display means based on the user's current device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's device information into a generating AI and have the generating AI perform the customization of the optimal display means.

[0131] The interface unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, when the user is relaxed, the interface unit provides detailed operation procedures. For example, the interface unit displays detailed operation procedures for a relaxed state. Furthermore, when the user is tense, the interface unit can provide concise and to-the-point operation procedures. For example, the interface unit displays concise operation procedures for a tense state. Furthermore, when the user is in a hurry, the interface unit can provide procedures that can be performed quickly. For example, the interface unit displays quick operation procedures for a hurry. This makes it possible to adjust the interface operation procedures optimally according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's emotion data into the generation AI and have the generation AI adjust the interface operation procedures.

[0132] The interface unit can select the optimal display method when displaying the interface, taking into account the user's geographical location information. For example, if the user is in a specific location, the interface unit can provide an interface related to that location. For example, the interface unit can display information related to that specific location. Furthermore, if the user is traveling, the interface unit can provide an interface based on information about the travel destination. For example, the interface unit can provide the optimal interface based on information about the travel destination. In addition, if the user is at home, the interface unit can provide a general interface. For example, the interface unit can provide an interface suitable for the home environment. This allows the interface unit to provide the optimal display method based on the user's geographical location information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0133] The interface unit can analyze the user's social media activity and provide relevant display methods when displaying the interface. For example, the interface unit can provide relevant interfaces based on posts shared by the user on social media. For example, the interface unit can analyze the content of posts shared by the user and provide relevant interfaces. The interface unit can also provide relevant interfaces based on the content of posts from accounts followed by the user on social media. For example, the interface unit can analyze the content of posts from followed accounts and provide relevant interfaces. Furthermore, the interface unit can also provide relevant interfaces based on posts that the user has "liked" on social media. For example, the interface unit can analyze the content of "liked" posts and provide relevant interfaces. This makes it possible to provide the optimal display method based on the user's social media activity. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant display methods.

[0134] The rotating display unit can estimate the user's emotions and adjust the rotation display method based on the estimated user emotions. For example, if the user is relaxed, the rotating display unit can provide a detailed rotation display. For example, the rotating display unit can provide a detailed rotation display in a relaxed state. The rotating display unit can also provide a concise and to-the-point rotation display if the user is tense. For example, the rotating display unit can provide a concise rotation display in a tense state. Furthermore, if the user is in a hurry, the rotating display unit can provide a rapid rotation display. For example, the rotating display unit can provide a rapid rotation display in a hurry state. This allows the optimal rotation display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the rotating display unit may be performed using AI, for example, or without AI. For example, the rotating display unit can input user emotion data into a generating AI, which can then adjust the rotation display method.

[0135] The rotation display unit can select the optimal method by referring to the user's past rotation display history when rotating the display. For example, the rotation display unit automatically sets the rotation display method that the user has previously preferred. For example, the rotation display unit suggests the optimal method based on the past rotation display history. The rotation display unit can also prioritize suggesting rotation display options that the user has previously used. For example, the rotation display unit suggests the optimal method based on the past rotation display options. Furthermore, the rotation display unit can suggest the most effective method based on the user's past rotation display history. For example, the rotation display unit suggests the optimal method based on the past rotation display history. This makes it possible to select the optimal method based on the user's past rotation display history. Some or all of the above-described processing in the rotation display unit may be performed using, for example, AI, or may be performed without using AI. For example, the rotation display unit can input the user's past rotation display history into a generation AI and cause the generation AI to select the optimal method.

[0136] The rotation display unit can customize the rotation display means based on the user's current device information during rotation display. For example, if the user is using a smartphone, the rotation display unit provides a rotated display that matches the screen size. For example, the rotation display unit provides a rotated display optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the rotation display unit can also provide a rotated display optimized for a larger screen. For example, the rotation display unit provides a rotated display optimized for the tablet screen size. Furthermore, if the user is using a VR device, the rotation display unit can also provide a rotated display optimized for VR. For example, the rotation display unit provides a rotated display optimized for the VR device. This makes it possible to provide an optimal rotation display means based on the user's current device information. Some or all of the above-described processing in the rotation display unit may be performed using, for example, AI, or may be performed without using AI. For example, the rotation display unit can input the user's device information to a generation AI and cause the generation AI to customize the optimal rotation display means.

[0137] The rotating display unit can estimate the user's emotions and determine the priority of the rotated display based on the estimated user's emotions. For example, when the user is relaxed, the rotating display unit prioritizes a detailed rotated display. For example, the rotating display unit provides a detailed rotated display in a relaxed state. Furthermore, when the user is tense, the rotating display unit can prioritize a concise rotated display. For example, the rotating display unit provides a concise rotated display in a tense state. Furthermore, when the user is in a hurry, the rotating display unit can prioritize a method of quickly rotating the display. For example, the rotating display unit provides a quick rotated display in a hurry. This makes it possible to determine the optimal priority of the rotated display according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the rotating display unit can be performed using, for example, AI, or without AI. For example, the rotation display unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the rotation display.

[0138] The rotating display unit can select the optimal rotating display method when rotating the display, taking into account the user's geographical location information. For example, if the user is in a specific location, the rotating display unit can provide a rotating display method related to that location. For example, the rotating display unit can provide the optimal rotating display method based on the environment of that specific location. Furthermore, if the user is traveling, the rotating display unit can also provide a rotating display method based on information about the travel destination. For example, the rotating display unit can provide the optimal rotating display method based on the environment of the travel destination. In addition, if the user is at home, the rotating display unit can provide a general rotating display method. For example, the rotating display unit can provide a rotating display method suitable for the home environment. This makes it possible to provide the optimal rotating display method based on the user's geographical location information. Some or all of the above processing in the rotating display unit may be performed using AI, for example, or without AI. For example, the rotating display unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal rotating display method.

[0139] The rotating display unit can analyze the user's social media activity and provide relevant rotating display means during rotating display. For example, the rotating display unit can provide relevant rotating display methods based on posts shared by the user on social media. For example, the rotating display unit can analyze the content of posts shared by the user and provide relevant rotating display methods. The rotating display unit can also provide relevant rotating display methods based on the content of posts from accounts followed by the user on social media. For example, the rotating display unit can analyze the content of posts from followed accounts and provide relevant rotating display methods. Furthermore, the rotating display unit can also provide relevant rotating display methods based on posts that the user has "liked" on social media. For example, the rotating display unit can analyze the content of "liked" posts and provide relevant rotating display methods. This makes it possible to provide the optimal rotating display means based on the user's social media activity. Some or all of the above processing in the rotating display unit may be performed using AI, for example, or without AI. For example, the rotating display unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant rotating display means.

[0140] The zoom function can estimate the user's emotions and adjust the zoom method based on the estimated emotions. For example, if the user is relaxed, the zoom function can perform detailed zooming. For example, the zoom function can provide detailed zooming in a relaxed state. The zoom function can also perform concise and to the point if the user is tense. For example, the zoom function can provide concise zooming in a tense state. Furthermore, if the user is in a hurry, the zoom function can perform rapid zooming. For example, the zoom function can provide rapid zooming in a hurry state. This allows the optimal zoom method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the zoom function may be performed using AI, for example, or without AI. For example, the scaling unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the method of scaling.

[0141] The zoom function can select the optimal method when zooming in or out by referring to the user's past zoom history. For example, the zoom function can automatically set the zoom method that the user has previously preferred. For example, the zoom function can suggest the optimal method based on the user's past zoom history. The zoom function can also prioritize suggesting zoom options that the user has previously used. For example, the zoom function can suggest the optimal method based on past zoom options. Furthermore, the zoom function can suggest the most effective method from the user's past zoom history. For example, the zoom function can suggest the optimal method based on the user's past zoom history. This allows the optimal method to be selected based on the user's past zoom history. Some or all of the above processing in the zoom function may be performed using AI, for example, or without AI. For example, the zoom function can input the user's past zoom history into a generating AI and have the generating AI select the optimal method.

[0142] The scaling unit can customize the scaling means based on the user's current device information when scaling. For example, if the user is using a smartphone, the scaling unit can provide scaling that matches the screen size. For example, the scaling unit can provide scaling optimized for the screen size of a smartphone. The scaling unit can also provide scaling optimized for a larger screen if the user is using a tablet. For example, the scaling unit can provide scaling optimized for the screen size of a tablet. Furthermore, if the user is using a VR device, the scaling unit can provide scaling optimized for VR. For example, the scaling unit can provide scaling optimized for a VR device. This makes it possible to provide the optimal scaling means based on the user's current device information. Some or all of the above processing in the scaling unit may be performed using AI, for example, or without AI. For example, the scaling unit can input the user's device information into a generating AI and have the generating AI perform the customization of the optimal scaling means.

[0143] The zoom function can estimate the user's emotions and determine zoom priorities based on the estimated emotions. For example, if the user is relaxed, the zoom function may prioritize detailed zoom. For example, it may provide detailed zoom in a relaxed state. The zoom function may also prioritize concise zoom if the user is tense. For example, it may provide concise zoom in a tense state. Furthermore, if the user is in a hurry, the zoom function may prioritize a method of rapid zoom. For example, it may provide rapid zoom in a hurry state. This allows for the determination of the optimal zoom priority 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 zoom function may be performed using AI, for example, or without AI. For example, the scaling unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of scaling.

[0144] When scaling, the scaling unit can select an optimal scaling method taking into account the user's geographical location information. For example, when the user is in a specific location, the scaling unit provides a scaling method related to that location. For example, the scaling unit provides an optimal scaling method based on the environment of the specific location. Furthermore, when the user is traveling, the scaling unit can provide a scaling method based on information about the travel destination. For example, the scaling unit provides an optimal scaling method based on the environment of the travel destination. Furthermore, when the user is at home, the scaling unit can provide a general scaling method. For example, the scaling unit provides a scaling method suitable for the home environment. This makes it possible to provide an optimal scaling method based on the user's geographical location information. Some or all of the above-described processing in the scaling unit may be performed using, or without, AI. For example, the scaling unit can input the user's geographical location information to the generation AI and cause the generation AI to select an optimal scaling method.

[0145] The scaling unit can analyze the user's social media activity during scaling and provide relevant scaling methods. For example, the scaling unit can provide relevant scaling methods based on posts shared by the user on social media. For example, the scaling unit can analyze the content of posts shared by the user and provide relevant scaling methods. The scaling unit can also provide relevant scaling methods based on the content of posts from accounts followed by the user on social media. For example, the scaling unit can analyze the content of posts from followed accounts and provide relevant scaling methods. Furthermore, the scaling unit can also provide relevant scaling methods based on posts that the user has "liked" on social media. For example, the scaling unit can analyze the content of "liked" posts and provide relevant scaling methods. This allows the optimal scaling method to be provided based on the user's social media activity. Some or all of the above processing in the scaling unit may be performed using AI, for example, or without AI. For example, the scaling unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant scaling methods.

[0146] The reproduction confirmation unit can estimate the user's emotion and adjust the reproduction confirmation method based on the estimated user's emotion. For example, when the user is relaxed, the reproduction confirmation unit performs detailed reproduction confirmation. For example, the reproduction confirmation unit provides detailed reproduction confirmation in a relaxed state. Furthermore, when the user is tense, the reproduction confirmation unit can perform brief and to-the-point reproduction confirmation. For example, the reproduction confirmation unit provides brief reproduction confirmation in a tense state. Furthermore, when the user is in a hurry, the reproduction confirmation unit can perform quick reproduction confirmation. For example, the reproduction confirmation unit provides quick reproduction confirmation in a hurry. This makes it possible to adjust the reproduction confirmation method optimally according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reproduction confirmation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reproduction verification unit can input user emotion data into the generating AI and have the generating AI adjust the reproduction verification method.

[0147] The reproduction confirmation unit can select the optimal method by referring to the user's past reproduction confirmation history when performing reproduction confirmation. The reproduction confirmation unit, for example, automatically sets the reproduction confirmation method that the user has previously preferred. For example, the reproduction confirmation unit suggests the optimal method based on the past reproduction confirmation history. The reproduction confirmation unit can also preferentially suggest reproduction confirmation options that the user has previously used. For example, the reproduction confirmation unit suggests the optimal method based on the past reproduction confirmation options. Furthermore, the reproduction confirmation unit can also suggest the most effective method based on the user's past reproduction confirmation history. For example, the reproduction confirmation unit suggests the optimal method based on the past reproduction confirmation history. This makes it possible to select the optimal method based on the user's past reproduction confirmation history. Some or all of the above-described processing in the reproduction confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the reproduction confirmation unit can input the user's past reproduction confirmation history into a generation AI and cause the generation AI to select the optimal method.

[0148] The reproduction confirmation unit can customize the reproduction confirmation means based on the user's current device information during reproduction confirmation. For example, if the user is using a smartphone, the reproduction confirmation unit provides a reproduction confirmation tailored to the screen size. For example, the reproduction confirmation unit provides a reproduction confirmation optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the reproduction confirmation unit can also provide a reproduction confirmation optimized for a larger screen. For example, the reproduction confirmation unit provides a reproduction confirmation optimized for the tablet screen size. Furthermore, if the user is using a VR device, the reproduction confirmation unit can also provide a reproduction confirmation optimized for VR. For example, the reproduction confirmation unit provides a reproduction confirmation optimized for the VR device. This makes it possible to provide an optimal reproduction confirmation means based on the user's current device information. Some or all of the above-described processing in the reproduction confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the reproduction confirmation unit can input the user's device information to the generation AI and cause the generation AI to customize the optimal reproduction confirmation means.

[0149] The reproduction confirmation unit can estimate the user's emotions and determine the priority of reproduction confirmation based on the estimated user's emotions. For example, when the user is relaxed, the reproduction confirmation unit prioritizes detailed reproduction confirmation. For example, the reproduction confirmation unit provides detailed reproduction confirmation in a relaxed state. Furthermore, when the user is tense, the reproduction confirmation unit can prioritize concise reproduction confirmation. For example, the reproduction confirmation unit provides concise reproduction confirmation in a tense state. Furthermore, when the user is in a hurry, the reproduction confirmation unit can prioritize a method of performing quick reproduction confirmation. For example, the reproduction confirmation unit provides quick reproduction confirmation in a hurry. This makes it possible to determine the optimal priority of reproduction confirmation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reproduction confirmation unit can be performed, for example, using AI or without AI. For example, the reproduction verification unit can input user emotion data into a generating AI and have the generating AI determine the priority of reproduction verification.

[0150] The reproduction confirmation unit can select the optimal reproduction confirmation method during reproduction confirmation, taking into account the user's geographical location information. For example, when the user is in a specific location, the reproduction confirmation unit provides a reproduction confirmation method related to the location. For example, the reproduction confirmation unit provides the optimal reproduction confirmation method based on the environment of the specific location. Furthermore, when the user is traveling, the reproduction confirmation unit can provide a reproduction confirmation method based on information about the travel destination. For example, the reproduction confirmation unit provides the optimal reproduction confirmation method based on the environment of the travel destination. Furthermore, when the user is at home, the reproduction confirmation unit can provide a general reproduction confirmation method. For example, the reproduction confirmation unit provides a reproduction confirmation method suitable for the home environment. This makes it possible to provide the optimal reproduction confirmation method based on the user's geographical location information. Some or all of the above-described processing in the reproduction confirmation unit may be performed using AI, for example, or without AI. For example, the reproduction confirmation unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal reproduction confirmation method.

[0151] During reproduction confirmation, the reproduction confirmation unit can analyze the user's social media activities and provide a related reproduction confirmation method. The reproduction confirmation unit can provide a related reproduction confirmation method, for example, based on posts shared by the user on social media. For example, the reproduction confirmation unit can analyze the content of the posts shared by the user and provide a related reproduction confirmation method. The reproduction confirmation unit can also provide a related reproduction confirmation method based on the content of posts from accounts the user follows on social media. For example, the reproduction confirmation unit can analyze the content of posts from the followed accounts and provide a related reproduction confirmation method. The reproduction confirmation unit can also provide a related reproduction confirmation method based on posts the user "liked" on social media. For example, the reproduction confirmation unit can analyze the content of the "liked" posts and provide a related reproduction confirmation method. This makes it possible to provide an optimal reproduction confirmation method based on the user's social media activities. Some or all of the above-described processing in the reproduction confirmation unit can be performed using, for example, AI, or without AI. For example, the reproduction confirmation unit can input the user's social media activity data into the generation AI and cause the generation AI to provide a related reproduction confirmation method. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, constraint input unit, suggestion unit, and display unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can capture a video of the user using the camera 42 of the smart device 14 and improve the resolution and quality of the video using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the user's face shape and hair type using the specific processing unit 290 of the data processing device 12. For example, the constraint input unit can allow the user to input school rules or company rules using the touch panel 38A of the smart device 14. For example, the suggestion unit can suggest a hairstyle using AI using the specific processing unit 290 of the data processing device 12. For example, the display unit can display the suggested hairstyle in 2D, 3D, or VR format using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, constraint input unit, suggestion unit, and display unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can capture video of the user using the camera 42 of the smart glasses 214 and improve the resolution and quality of the video using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the user's face shape and hair type using the specific processing unit 290 of the data processing device 12. For example, the constraint input unit can allow the user to input school rules or company rules by voice using the microphone 238 of the smart glasses 214. For example, the suggestion unit can suggest a hairstyle using AI using the specific processing unit 290 of the data processing device 12. For example, the display unit can display the suggested hairstyle in 2D, 3D, or VR format using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, constraint input unit, suggestion unit, and display unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit can capture video of the user using the camera 42 of the headset-type terminal 314 and improve the resolution and quality of the video using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the user's face shape and hair type using the specific processing unit 290 of the data processing device 12. For example, the constraint input unit can allow the user to input school rules or company rules by voice using the microphone 238 of the headset-type terminal 314. For example, the suggestion unit can suggest a hairstyle using AI using the specific processing unit 290 of the data processing device 12. For example, the display unit can display the suggested hairstyle in 2D, 3D, or VR format using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, constraint input unit, suggestion unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can capture video of the user using the camera 42 of the robot 414 and improve the resolution and quality of the video using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the user's face shape and hair type using the specific processing unit 290 of the data processing device 12. For example, the constraint input unit can allow the user to input school rules or company rules by voice using the microphone 238 of the robot 414. For example, the suggestion unit can suggest a hairstyle using AI using the specific processing unit 290 of the data processing device 12. For example, the display unit can display the suggested hairstyle in 2D, 3D, or VR format using the display of the robot 414.

[0152] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0153] The acquisition unit can analyze the user's past hairstyle history and suggest hairstyles that suit the user. For example, the acquisition unit collects data on hairstyles previously selected by the user and provides it to the analysis unit. The acquisition unit can also evaluate the success rate and satisfaction level of hairstyles that the user has tried in the past and reflect this in the suggestion unit. Furthermore, the acquisition unit can suggest hairstyles that suit the season or event based on the user's past hairstyle history. This makes it possible to suggest the optimal hairstyle based on the user's past hairstyle history.

[0154] The analysis unit can suggest hairstyles by combining the user's hairstyle history with current trends. For example, the analysis unit analyzes the user's past hairstyle history and compares it with current trends. The analysis unit can also suggest hairstyles that match the trends based on the user's hair type and face shape. Furthermore, the analysis unit can suggest hairstyles that suit the season or event based on the user's hairstyle history and trends. This makes it possible to suggest the optimal hairstyle by combining the user's hairstyle history with current trends.

[0155] The constraint input unit can estimate the user's emotions and adjust the constraint input method based on those emotions. For example, if the user is relaxed, the constraint input unit can provide detailed constraint input options. If the user is stressed, it can provide a concise and to-the-point constraint input method. Furthermore, if the user is in a hurry, it can provide a constraint input method that allows for quick input. This allows the system to provide the optimal constraint input method according to the user's emotions.

[0156] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions. If the user is stressed, it can provide concise and to-the-point suggestions. Furthermore, if the user is in a hurry, it can provide suggestions quickly. This allows the system to provide the most appropriate way to present suggestions according to the user's emotions.

[0157] The display unit can estimate the user's emotions and adjust the display method based on those emotions. For example, if the user is relaxed, the display unit will provide detailed information. If the user is stressed, the display unit can provide concise and to-the-point information. Furthermore, if the user is in a hurry, the display unit can provide information quickly. This allows the system to provide the optimal display method according to the user's emotions.

[0158] The acquisition unit can suggest hairstyles that suit local trends and culture based on the user's geographical location information. For example, if the user is in a specific area, the acquisition unit can suggest hairstyles based on local trends. Also, if the user is traveling, the acquisition unit can suggest hairstyles based on the culture and trends of the destination. Furthermore, if the user is at home, the acquisition unit can suggest hairstyles that are common in the area. This makes it possible to suggest optimal hairstyles based on the user's geographical location information.

[0159] The analysis unit can analyze the user's social media activity and suggest hairstyles that are suitable for the user. For example, the analysis unit can suggest similar hairstyles based on photos the user has shared on social media. The analysis unit can also suggest related hairstyles based on the hairstyles of accounts the user follows. Furthermore, the analysis unit can also suggest related hairstyles based on posts the user has "liked." This makes it possible to suggest optimal hairstyles based on the user's social media activity.

[0160] The constraint input section can refer to the user's past constraint input history and suggest the most suitable constraints. For example, the constraint input section can automatically display constraints previously entered by the user as candidates. It can also prioritize suggesting input methods (text, multiple-choice, etc.) previously used by the user. Furthermore, the constraint input section can predict and suggest constraints to be used during specific time periods based on the user's past constraint input history. This allows for the suggestion of optimal constraints based on the user's past constraint input history.

[0161] The suggestion section can customize how suggestions are displayed based on the user's current device information. For example, if the user is using a smartphone, the suggestion section will provide suggestions tailored to the screen size. It can also provide suggestions optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a VR device, the suggestion section can provide suggestions optimized for VR. This allows the system to provide the most suitable suggestion display method based on the user's current device information.

[0162] The display unit can refer to the user's past viewing history and select the optimal display method. For example, the display unit can automatically set the display method that the user has preferred in the past. It can also prioritize suggesting display options (2D, 3D, VR, etc.) that the user has used in the past. Furthermore, the display unit can suggest the most visually appealing display method based on the user's past viewing history. This allows the system to select the optimal display method based on the user's past viewing history.

[0163] The processing flow of the second embodiment will be briefly explained below.

[0164] Step 1: The acquisition unit acquires the user's video. The user's video may include still images, videos, real-time video, etc. The acquisition unit can capture the user's video using a smartphone camera, or it can read video that the user has already saved. Furthermore, the acquisition unit can use technology to improve the resolution and quality of the video. Step 2: The analysis unit analyzes the video captured by the capture unit. The analysis unit can use facial recognition technology to determine the user's face shape and technology to extract hair characteristics. For example, technology to analyze hair thickness and density can be used. Furthermore, the analysis unit can improve the accuracy of the analysis by using technology to improve the quality of the video. Step 3: The constraint input unit inputs the conditions for the hairstyle desired by the user. The constraint input unit can input constraints in the form of text input or selection from options. For example, when the user inputs school rules or company rules, the user can freely input them using the text box. The constraint input unit can also display a list of school rules or company rules, allowing the user to select the appropriate item. Step 4: The suggestion unit suggests a hairstyle suitable for the user based on the results of the analysis by the analysis unit and the conditions entered by the constraint input unit. The suggestion unit uses AI to suggest hairstyles and select the optimal hairstyle based on the user's face shape, hair type, and constraints. For example, an algorithm can be used to suggest hairstyles that suit the face shape and hair type. Step 5: The display unit displays the hairstyle suggested by the suggestion unit. The display unit displays the hairstyle on the smartphone screen, and can display the hairstyle in any of the following formats: 2D display, 3D display, and VR display. For example, the display can be a 2D display in which the hairstyle is superimposed on the user's face, or a 3D display in which the user's face is displayed three-dimensionally and the hairstyle is superimposed on top of it. Furthermore, the VR display allows the user to check the hairstyle using a VR device.

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0167] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0170] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0183] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0186] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0192] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0193] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0194] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0195] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0196] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0197] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0198] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0199] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0202] 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.

[0203] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0204] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0205] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0206] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0207] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0208] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0209] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0210] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0212] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0213] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0214] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0215] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0216] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0217] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0218] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0219] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0220] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0221] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0222] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0223] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0224] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0225] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0226] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0227] 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.

[0228] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0229] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0230] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0231] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0232] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0233] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0234] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0235] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0236] [Explanation of symbols]

[0237] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires a user's video; an analysis unit that analyzes the video acquired by the acquisition unit; a constraint input unit for inputting conditions for a hairstyle desired by a user; a suggestion unit that suggests a hairstyle suitable for the user based on the analysis result by the analysis unit and the conditions input by the constraint input unit; a display unit that displays the hairstyle suggested by the suggestion unit. A system characterized by:

2. Equipped with a quality improvement section that adjusts the image resolution and color tone The system of claim 1 .

3. An interface section is provided that provides an interface for users to input constraints. The system of claim 1 .

4. It has a rotating display that allows users to rotate the proposed hairstyle 360 ​​degrees to check it. The system of claim 1 .

5. Equipped with a scaling unit that can scale the proposed hairstyle The system of claim 1 .

6. It has a reproduction confirmation unit that simulates how accurately the proposed hairstyle will actually be reproduced. The system of claim 1 .

7. The acquisition unit Estimates the user's emotions from their facial expressions and voice, and adjusts the timing of video capture based on the estimated emotions. The system of claim 1 .

8. The acquisition unit Analyze the user's past video acquisition history and select the acquisition method that best suits the user. The system of claim 1 .

9. The acquisition unit When capturing video, filtering is performed based on the user's current environment and lighting conditions to suit the user's environment and lighting conditions. The system of claim 1 .

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