System

Abstract

To provide a system that enables a user and a hairdresser to decide on a hairstyle with common recognition.SOLUTION: A system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a reflection unit. The reception unit receives a user's desired hairstyle. The analysis unit analyzes a facial photo based on information received by the reception unit. The generation unit generates a 3D model of a hairstyle based on the information analyzed by the analysis unit. The reflection unit reflects the 3D model generated by the generation unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult for a hairdresser and a user to reach a common understanding of the hairstyle, which can lead to dissatisfaction with the results.

[0005] The system according to the embodiment aims to allow a user and a hairdresser to decide on a hairstyle with a common understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a reflection unit. The reception unit receives a hairstyle desired by a user. The analysis unit analyzes a face photo based on the information received by the reception unit. The generation unit generates a 3D model of the hairstyle based on the information analyzed by the analysis unit. The reflection unit reflects the 3D model generated by the generation unit in real time. Effect of the Invention

[0007] The system according to the embodiment allows a user and a hairdresser to decide on a hairstyle with a common understanding. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The hairstyle recommendation system according to the embodiment of the present invention is a system in which a user inputs a desired hairstyle in natural language and uploads a face photo, and a generation AI generates a 3D model of the hairstyle and reflects it in the image in real time. This system allows the user and the hairdresser to proceed with the treatment with a common understanding. For example, a user inputs a desired hairstyle in natural language and uploads a face photo. The generation AI analyzes the input natural language request and face photo, and generates a 3D model of a hairstyle that is optimal for the user's face. The generated 3D model is reflected in the user's face photo in real time. This allows the user to visually check what hairstyle suits his or her face. Furthermore, the generation AI takes into account the user's facial features and hair type, etc., and recommends the optimal hairstyle. This service allows the user and the hairdresser to have a common understanding, and the desired hairstyle is accurately conveyed, improving the satisfaction of the treatment. In addition, the user can check in advance what hairstyle suits him or her, making communication at the hairdresser smoother. As a result, the hairstyle recommendation system reflects the user's desired hairstyle in real time, and the treatment can be proceeded with a common understanding.

[0029] The hairstyle recommendation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a reflection unit. The reception unit receives a hairstyle desired by a user. The hairstyle desired by the user includes, for example, length, style, color, and the like, but is not limited to such examples. The reception unit receives, for example, a user's input of a desired hairstyle in natural language and uploads a face photo. The analysis unit analyzes the face photo based on the information received by the reception unit. The analysis unit analyzes the user's facial features and hair type using, for example, a facial feature point extraction method or an analysis algorithm. The generation unit generates a 3D model of a hairstyle based on the information analyzed by the analysis unit using a generation AI. The generation unit generates a 3D model of a hairstyle that is optimal for the user's face using, for example, a text generation AI (for example, LLM). The generation unit can also generate a 3D model taking into account the user's desired hairstyle. The reflection unit reflects the 3D model generated by the generation unit to the user's face photo in real time. The reflection unit, for example, uses a technology for reflecting the generated 3D model in real time to display the 3D model on the user's facial photograph. As a result, the hairstyle recommendation system according to the embodiment can reflect the user's desired hairstyle in real time and proceed with the treatment with a common understanding. A part or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can display the 3D model on the user's facial photograph using an AI model for reflecting the generated 3D model in real time.

[0030] The reception unit receives the user's desired hairstyle. The user's desired hairstyle includes, but is not limited to, for example, length, style, and color. The reception unit receives, for example, the user's desired hairstyle in natural language and uploads a face photo. Specifically, the user uses a smartphone or a personal computer to access a dedicated application or website and inputs the desired hairstyle in text. For example, a specific request such as "shoulder-length, curled style, light brown" can be input. In addition, the user can upload a face photo to enable the system to recommend hairstyles more accurately. It is recommended that the face photo be taken from the front and that the hair not cover the face. Furthermore, the reception unit can also receive information on hairstyles that the user has tried in the past and the current hairstyle. This allows the system to have a deeper understanding of the user's preferences and hair type and make optimal recommendations. The reception unit centrally manages this information and stores it in a database to provide to the analysis unit and generation unit. This allows information on the user's desired hairstyle to be processed accurately and quickly, improving the efficiency of the entire system.

[0031] The analysis unit analyzes the face photo based on the information received by the reception unit. The analysis unit analyzes the user's facial features and hair type, for example, using a facial feature point extraction method or analysis algorithm. Specifically, feature points such as the facial outline, eyes, nose, and mouth are extracted with high accuracy, and the shape and balance of the user's face are analyzed. This provides basic data for proposing a hairstyle that is optimal for the user's face. In addition, image processing technology is used to analyze the hair type, and information such as hair thickness, density, color, and luster is extracted. This makes it possible to propose a style and coloring that is suitable for the user's hair type. Based on these analysis results, the analysis unit lists hairstyle candidates that are optimal for the user's facial shape and hair type. Furthermore, the analysis unit considers the elements of the user's desired hairstyle (length, style, color, etc.) and selects the optimal hairstyle from the listed candidates. The analysis unit performs these processes in real time and can provide quick feedback to the user. By utilizing AI technology, the analysis unit's processing can be improved in accuracy and speed, thereby increasing user satisfaction.

[0032] The generation unit uses a generation AI to generate a 3D model of a hairstyle based on the information analyzed by the analysis unit. The generation unit uses, for example, a text generation AI (e.g., LLM) to generate a 3D model of a hairstyle that is optimal for the user's face. Specifically, the generation AI receives information on the user's facial features and hair type as input, and generates a 3D model of the hairstyle based on this. The generation AI has been trained with a large amount of hairstyle data set in advance and has the ability to generate hairstyles of various styles and colorings. For example, in response to a user's request for a "shoulder-length, curled style, light brown" hairstyle, the generation AI generates a 3D model of a hairstyle that matches that request. The generation unit can also generate a 3D model taking into account the user's desired hairstyle. For example, if a user desires a hairstyle similar to that of a particular celebrity, a 3D model can be generated based on that hairstyle. The generation unit can also update the generated 3D model in real time and make fine adjustments based on user feedback. This allows the generation unit to provide a hairstyle that is closest to the user's desire, thereby increasing user satisfaction.

[0033] The reflecting unit reflects the 3D model generated by the generating unit on the user's facial photograph in real time. The reflecting unit, for example, uses a technology for reflecting the generated 3D model in real time to display the 3D model on the user's facial photograph. Specifically, the reflecting unit overlays the generated 3D model of the hairstyle on the user's facial photograph and displays it in real time. This allows the user to intuitively check how the new hairstyle looks on his or her face. The reflecting unit uses a technology for realizing realistic visual effects such as light reflection and shadow expression in displaying the 3D model. This allows the user to obtain an image close to the actual hairstyle. Furthermore, the reflecting unit provides a function for freely rotating the 3D model so that the user can check the hairstyle from different angles. This makes it easier for the user to grasp the overall picture of the hairstyle. A part or all of the above-mentioned processing in the reflecting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflecting unit can display the 3D model on the user's facial photograph using an AI model for reflecting the generated 3D model in real time. This allows the reflection unit to reflect the user's desired hairstyle in real time, allowing the treatment to be carried out with a common understanding.

[0034] The analysis unit can analyze the facial features or hair type of the user. The analysis unit analyzes the facial features of the user, for example, using a facial feature point extraction method or analysis algorithm. For example, the analysis unit extracts features such as the position of the eyes, the shape of the nose, and the contour of the face. The analysis unit can also analyze the hair type. For example, the analysis unit analyzes the thickness, hardness, and presence or absence of curls of the hair. This allows a 3D model of a more appropriate hairstyle to be generated by analyzing the facial features and hair type of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the extraction of facial feature points into an AI model and analyze the facial features.

[0035] The generation unit can generate a 3D model of a hairstyle suitable for the user's face using the generation AI. The generation unit generates a 3D model of a hairstyle that is optimal for the user's face using, for example, the generation AI. The generation AI generates a 3D model of a hairstyle that is optimal for the user's face using, for example, a text generation AI (for example, LLM). The generation unit can also generate a 3D model by using the generation AI, taking into account the user's desired hairstyle. For example, the generation AI generates a 3D model by taking into account the length, style, color, etc. of the user's desired hairstyle. In this way, by using the generation AI, a 3D model of a hairstyle that is optimal for the user's face can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a user's face photo and information on the desired hairstyle to the generation AI, and generate a 3D model of the hairstyle.

[0036] The reflection unit can reflect the generated 3D model on the user's facial photograph in real time. The reflection unit, for example, uses a technology for reflecting the generated 3D model in real time to display the 3D model on the user's facial photograph. For example, the reflection unit displays the generated 3D model by superimposing it on the user's facial photograph in real time. The reflection unit can also use a technology that minimizes the delay time for displaying the generated 3D model in real time. For example, the reflection unit uses a high-speed rendering technology for displaying the generated 3D model in real time. As a result, by reflecting the generated 3D model in real time, the user can visually check what hairstyle suits his or her face. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflection unit can display the 3D model on the user's facial photograph using an AI model for reflecting the generated 3D model in real time.

[0037] The generation unit can generate a 3D model based on the hairstyle desired by the user. The generation unit generates a 3D model based on the hairstyle desired by the user, for example, using a generation AI. The generation AI generates a 3D model by considering the length, style, color, etc. of the hairstyle desired by the user, for example, using a text generation AI (for example, LLM). The generation unit can also generate a 3D model by considering the hairstyle desired by the user, using the generation AI. For example, the generation AI analyzes detailed requests for the hairstyle desired by the user and generates a 3D model based on the analysis. In this way, a 3D model of a hairstyle with higher satisfaction can be generated by considering the hairstyle desired by the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input information on the hairstyle desired by the user to the generation AI and generate a 3D model of the hairstyle.

[0038] The reception unit can analyze the user's past hairstyle history and select an appropriate reception method. The reception unit can, for example, analyze the user's past hairstyle history and automatically display optimal hairstyle candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest hairstyles that match specific seasons or events from the user's past hairstyle history. In this way, by analyzing the past hairstyle history, it is possible to provide the user with an optimal reception method. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past hairstyle history data into a generation AI and select an optimal reception method.

[0039] The reception unit can perform filtering based on the user's current hairstyle and hair type when receiving a desired hairstyle. The reception unit, for example, automatically recognizes the user's current hairstyle and displays suitable hairstyle candidates based on the recognition. The reception unit can also propose suitable hairstyles in consideration of the user's hair type (straight hair, curly hair, etc.). Furthermore, the reception unit can filter and display feasible hairstyles based on the user's hair length and volume. As a result, feasible hairstyles can be proposed by filtering based on the current hairstyle and hair type. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current hairstyle and hair type to the generation AI and cause the generation AI to perform filtering.

[0040] The reception unit can preferentially receive hairstyles that are highly relevant based on the geographical location information of the user when receiving a desired hairstyle. The reception unit, for example, preferentially displays hairstyles that are popular in the user's area. The reception unit can also suggest hairstyles that are suitable for the climate based on the user's geographical location. Furthermore, the reception unit can also preferentially display hairstyles that match the culture and events of the user's area. In this way, a hairstyle that is highly relevant to the user can be suggested by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and preferentially receive hairstyles that are highly relevant.

[0041] The reception unit can analyze the user's social media activity and receive related hairstyles when receiving a desired hairstyle. The reception unit can, for example, suggest related hairstyles based on hairstyles that the user has "liked" on social media. The reception unit can also suggest related hairstyles based on hairstyles of influencers that the user follows. Furthermore, the reception unit can analyze the content of the user's social media posts and suggest related hairstyles. In this way, a hairstyle related to the user can be suggested by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and receive related hairstyles.

[0042] The analysis unit can improve the accuracy of the analysis when analyzing the facial features and hair type by referring to the user's past hairstyle history. The analysis unit, for example, analyzes changes in the facial features and hair type based on the user's past hairstyle history. The analysis unit can also analyze the reaction to a specific hairstyle from the user's past hairstyle history. Furthermore, the analysis unit can also customize the analysis result by referring to the user's past hairstyle history. In this way, the accuracy of the analysis is improved by referring to the past hairstyle history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's past hairstyle history data into the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can perform the analysis based on the user's lifestyle or environment when analyzing the facial features and hair type. The analysis unit, for example, performs the analysis of the hair type taking into consideration the user's lifestyle (exercise, diet, etc.). The analysis unit can also perform the analysis of the hair type taking into consideration the user's environment (climate, humidity, etc.). Furthermore, the analysis unit can also perform the analysis of the facial features and hair type taking into consideration the user's occupation and lifestyle. This makes it possible to provide more appropriate analysis results by taking into consideration the lifestyle and environment. A part 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 input the user's lifestyle and environmental data into the generation AI and perform the analysis.

[0044] The analysis unit can perform the analysis by taking into consideration the geographical location information of the user when analyzing the facial features and hair type. The analysis unit performs the analysis of the hair type by taking into consideration, for example, the climate of the user's region. The analysis unit can also perform the analysis by taking into consideration specific environmental conditions based on the user's geographical location. Furthermore, the analysis unit can also perform the analysis of the facial features and hair type by taking into consideration the culture and trends of the user's region. As a result, by taking into consideration the geographical location information, more appropriate analysis results can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and perform the analysis.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature when analyzing the facial features and hair type. The analysis unit performs the analysis by referring to, for example, academic papers on the user's hair type. The analysis unit can also perform the analysis based on research data on the user's facial features. Furthermore, the analysis unit can perform the analysis by referring to trend reports on the user's hairstyle. This improves the accuracy of the analysis by referring to the related literature. A part 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 input the user's related literature data into the generation AI to improve the accuracy of the analysis.

[0046] The generation unit can improve the accuracy of generation when generating a 3D model of a hairstyle by referring to the user's past hairstyle history. The generation unit generates an optimal 3D model based on the user's past hairstyle history, for example. The generation unit can also generate a 3D model from the user's past hairstyle history, taking into account the user's reaction to a specific hairstyle. Furthermore, the generation unit can also customize the 3D model to be generated by referring to the user's past hairstyle history. This improves the accuracy of generation by referring to the past hairstyle history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's past hairstyle history data into the generation AI to improve the accuracy of generation.

[0047] The generation unit can generate a 3D model of a hairstyle based on the user's lifestyle or environment when generating the 3D model. The generation unit generates an optimal 3D model, for example, taking into account the user's lifestyle (exercise, diet, etc.). The generation unit can also generate an optimal 3D model, taking into account the user's environment (climate, humidity, etc.). Furthermore, the generation unit can generate an optimal 3D model, taking into account the user's occupation and lifestyle. This allows a more appropriate 3D model to be generated by taking into account the lifestyle and environment. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle and environmental data into the generation AI and perform generation.

[0048] The generation unit can generate a 3D model of a hairstyle by taking into consideration the geographical location information of the user. The generation unit generates an optimal 3D model, for example, by taking into consideration the climate of the user's region. The generation unit can also generate a 3D model that takes into consideration specific environmental conditions based on the geographical location of the user. Furthermore, the generation unit can generate an optimal 3D model by taking into consideration the culture and trends of the user's region. This allows a more appropriate 3D model to be generated by taking into consideration the geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the user to the generation AI and perform generation.

[0049] The generating unit can improve the accuracy of generation when generating a 3D model of a hairstyle by referring to related literature of the user. The generating unit generates a 3D model by referring to, for example, academic papers on the user's hair quality. The generating unit can also generate a 3D model based on research data on the user's facial features. Furthermore, the generating unit can also generate a 3D model by referring to trend reports on the user's hairstyle. This improves the accuracy of generation by referring to related literature. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI or may be performed without using AI. For example, the generating unit can input related literature data of the user to the generating AI to improve the accuracy of generation.

[0050] The reflection unit can improve the accuracy of the reflection when reflecting the 3D model by referring to the user's past hairstyle history. The reflection unit reflects an optimal 3D model based on the user's past hairstyle history, for example. The reflection unit can also reflect the 3D model by considering the reaction to a specific hairstyle from the user's past hairstyle history. Furthermore, the reflection unit can also customize the 3D model to be reflected by referring to the user's past hairstyle history. Thereby, the accuracy of the reflection is improved by referring to the past hairstyle history. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflection unit can input the user's past hairstyle history data to the generation AI to improve the accuracy of the reflection.

[0051] The reflection unit can reflect the 3D model based on the user's lifestyle or environment when reflecting the 3D model. The reflection unit reflects the optimal 3D model, for example, taking into account the user's lifestyle (exercise, diet, etc.). The reflection unit can also reflect the optimal 3D model, taking into account the user's environment (climate, humidity, etc.). Furthermore, the reflection unit can also reflect the optimal 3D model, taking into account the user's occupation and lifestyle. In this way, a more appropriate 3D model can be reflected by taking into account the lifestyle and environment. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflection unit can input the user's lifestyle and environmental data into the generation AI and reflect it.

[0052] The reflection unit can reflect the 3D model while taking into consideration the geographical location information of the user. The reflection unit reflects the optimal 3D model, for example, taking into consideration the climate of the user's region. The reflection unit can also reflect a 3D model that takes into consideration specific environmental conditions based on the user's geographical location. Furthermore, the reflection unit can also reflect the optimal 3D model while taking into consideration the culture and trends of the user's region. This allows a more appropriate 3D model to be reflected by taking into consideration the geographical location information. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflection unit can input the user's geographical location information into the generation AI and reflect it.

[0053] The reflection unit can improve the accuracy of the reflection when reflecting the 3D model by referring to the user's related literature. The reflection unit reflects the 3D model by referring to, for example, academic papers on the user's hair quality. The reflection unit can also reflect the 3D model based on research data on the user's facial features. Furthermore, the reflection unit can also reflect the 3D model by referring to trend reports on the user's hairstyle. This improves the accuracy of the reflection by referring to the related literature. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI or may be performed without using AI. For example, the reflection unit can input the user's related literature data into the generation AI to improve the accuracy of the reflection.

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

[0055] The analysis unit can take into account the user's lifestyle and environment when analyzing the user's facial features and hair type. For example, if the user exercises frequently, the analysis can take into account changes in hair type. Also, if the user lives in a humid area, changes in hair curls and volume can be taken into account. Furthermore, the analysis can take into account the user's diet and stress level. This can provide more accurate analysis results.

[0056] The reflection unit can take into account the geographical location information of the user when reflecting the generated 3D model on the user's facial photo in real time. For example, if the user lives in a humid area, the reflection unit can reflect the hairstyle while taking into account the volume and curl durability of the hairstyle. In addition, if the user lives in a cold area, the heat retention of the hairstyle can be taken into account. Furthermore, the optimal hairstyle can be reflected by taking into account the culture and trends of the user's region. As a result, a more appropriate 3D model can be reflected by taking into account the geographical location information.

[0057] The generation unit can improve the accuracy of generating a 3D hairstyle model by referring to the user's past hairstyle history. For example, a similar hairstyle can be suggested based on data on hairstyles the user has previously chosen. It can also exclude hairstyles that the user has avoided in the past. Furthermore, it can predict and suggest hairstyles suited to specific seasons or events based on the user's past hairstyle history. In this way, it is possible to suggest the most suitable hairstyle for the user by referring to the past hairstyle history.

[0058] When analyzing the user's facial features and hair type, the analysis unit can improve the accuracy of the analysis by referring to the user's past hairstyle history. For example, the analysis unit analyzes changes in facial features and hair type based on data on hairstyles the user has previously chosen. It can also exclude hairstyles that the user has avoided in the past. Furthermore, it can analyze the user's reaction to a specific hairstyle from the user's past hairstyle history and customize the analysis results. In this way, the accuracy of the analysis can be improved by referring to the past hairstyle history.

[0059] The reflection unit can take into account the geographical location information of the user when reflecting the generated 3D model on the user's facial photo in real time. For example, if the user lives in a humid area, the reflection unit can reflect the hairstyle while taking into account the volume and curl durability of the hairstyle. In addition, if the user lives in a cold area, the heat retention of the hairstyle can be taken into account. Furthermore, the optimal hairstyle can be reflected by taking into account the culture and trends of the user's region. As a result, a more appropriate 3D model can be reflected by taking into account the geographical location information.

[0060] The reception unit can analyze the user's social media activity and suggest related hairstyles. For example, related hairstyles can be suggested based on hairstyles that the user has "liked" on social media. Also, related hairstyles can be suggested based on the hairstyles of influencers that the user follows. Furthermore, related hairstyles can be suggested by analyzing the content of the user's social media posts. In this way, hairstyles related to the user can be suggested by analyzing social media activity.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives a hairstyle desired by the user. The hairstyle desired by the user includes, but is not limited to, for example, length, style, and color. The reception unit receives, for example, the user inputting the hairstyle desired in natural language and uploading a face photo. Step 2: The analysis unit analyzes the face photo based on the information received by the reception unit. The analysis unit analyzes the user's facial features and hair type, for example, using a facial feature point extraction method or analysis algorithm. Step 3: The generation unit uses a generation AI to generate a 3D model of a hairstyle based on the information analyzed by the analysis unit. The generation unit uses, for example, a text generation AI (e.g., LLM) to generate a 3D model of a hairstyle that is optimal for the user's face. The generation unit can also generate the 3D model taking into account the user's desired hairstyle. Step 4: The reflecting unit reflects the 3D model generated by the generating unit on the user's facial photograph in real time. For example, the reflecting unit displays the 3D model on the user's facial photograph using a technique for reflecting the generated 3D model in real time. As a result, the hairstyle recommendation system according to the embodiment can reflect the user's desired hairstyle in real time and proceed with the treatment with a common understanding.

[0063] (Example 2) The hairstyle recommendation system according to the embodiment of the present invention is a system in which a user inputs a desired hairstyle in natural language and uploads a face photo, and a generation AI generates a 3D model of the hairstyle and reflects it in the image in real time. This system allows the user and the hairdresser to proceed with the treatment with a common understanding. For example, a user inputs a desired hairstyle in natural language and uploads a face photo. The generation AI analyzes the input natural language request and face photo, and generates a 3D model of a hairstyle that is optimal for the user's face. The generated 3D model is reflected in the user's face photo in real time. This allows the user to visually check what hairstyle suits his or her face. Furthermore, the generation AI takes into account the user's facial features and hair type, etc., and recommends the optimal hairstyle. This service allows the user and the hairdresser to have a common understanding, and the desired hairstyle is accurately conveyed, improving the satisfaction of the treatment. In addition, the user can check in advance what hairstyle suits him or her, making communication at the hairdresser smoother. As a result, the hairstyle recommendation system reflects the user's desired hairstyle in real time, and the treatment can be proceeded with a common understanding.

[0064] The hairstyle recommendation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a reflection unit. The reception unit receives a hairstyle desired by a user. The hairstyle desired by the user includes, for example, length, style, color, and the like, but is not limited to such examples. The reception unit receives, for example, a user's input of a desired hairstyle in natural language and uploads a face photo. The analysis unit analyzes the face photo based on the information received by the reception unit. The analysis unit analyzes the user's facial features and hair type using, for example, a facial feature point extraction method or an analysis algorithm. The generation unit generates a 3D model of a hairstyle based on the information analyzed by the analysis unit using a generation AI. The generation unit generates a 3D model of a hairstyle that is optimal for the user's face using, for example, a text generation AI (for example, LLM). The generation unit can also generate a 3D model taking into account the user's desired hairstyle. The reflection unit reflects the 3D model generated by the generation unit to the user's face photo in real time. The reflection unit, for example, uses a technology for reflecting the generated 3D model in real time to display the 3D model on the user's facial photograph. As a result, the hairstyle recommendation system according to the embodiment can reflect the user's desired hairstyle in real time and proceed with the treatment with a common understanding. A part or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can display the 3D model on the user's facial photograph using an AI model for reflecting the generated 3D model in real time.

[0065] The reception unit receives the user's desired hairstyle. The user's desired hairstyle includes, but is not limited to, for example, length, style, and color. The reception unit receives, for example, the user's desired hairstyle in natural language and uploads a face photo. Specifically, the user uses a smartphone or a personal computer to access a dedicated application or website and inputs the desired hairstyle in text. For example, a specific request such as "shoulder-length, curled style, light brown" can be input. In addition, the user can upload a face photo to enable the system to recommend hairstyles more accurately. It is recommended that the face photo be taken from the front and that the hair not cover the face. Furthermore, the reception unit can also receive information on hairstyles that the user has tried in the past and the current hairstyle. This allows the system to have a deeper understanding of the user's preferences and hair type and make optimal recommendations. The reception unit centrally manages this information and stores it in a database to provide to the analysis unit and generation unit. This allows information on the user's desired hairstyle to be processed accurately and quickly, improving the efficiency of the entire system.

[0066] The analysis unit analyzes the face photo based on the information received by the reception unit. The analysis unit analyzes the user's facial features and hair type, for example, using a facial feature point extraction method or analysis algorithm. Specifically, feature points such as the facial outline, eyes, nose, and mouth are extracted with high accuracy, and the shape and balance of the user's face are analyzed. This provides basic data for proposing a hairstyle that is optimal for the user's face. In addition, image processing technology is used to analyze the hair type, and information such as hair thickness, density, color, and luster is extracted. This makes it possible to propose a style and coloring that is suitable for the user's hair type. Based on these analysis results, the analysis unit lists hairstyle candidates that are optimal for the user's facial shape and hair type. Furthermore, the analysis unit considers the elements of the user's desired hairstyle (length, style, color, etc.) and selects the optimal hairstyle from the listed candidates. The analysis unit performs these processes in real time and can provide quick feedback to the user. By utilizing AI technology, the analysis unit's processing can be improved in accuracy and speed, thereby increasing user satisfaction.

[0067] The generation unit uses a generation AI to generate a 3D model of a hairstyle based on the information analyzed by the analysis unit. The generation unit uses, for example, a text generation AI (e.g., LLM) to generate a 3D model of a hairstyle that is optimal for the user's face. Specifically, the generation AI receives information on the user's facial features and hair type as input, and generates a 3D model of the hairstyle based on this. The generation AI has been trained with a large amount of hairstyle data set in advance and has the ability to generate hairstyles of various styles and colorings. For example, in response to a user's request for a "shoulder-length, curled style, light brown" hairstyle, the generation AI generates a 3D model of a hairstyle that matches that request. The generation unit can also generate a 3D model taking into account the user's desired hairstyle. For example, if a user desires a hairstyle similar to that of a particular celebrity, a 3D model can be generated based on that hairstyle. The generation unit can also update the generated 3D model in real time and make fine adjustments based on user feedback. This allows the generation unit to provide a hairstyle that is closest to the user's desire, thereby increasing user satisfaction.

[0068] The reflecting unit reflects the 3D model generated by the generating unit on the user's facial photograph in real time. The reflecting unit, for example, uses a technology for reflecting the generated 3D model in real time to display the 3D model on the user's facial photograph. Specifically, the reflecting unit overlays the generated 3D model of the hairstyle on the user's facial photograph and displays it in real time. This allows the user to intuitively check how the new hairstyle looks on his or her face. The reflecting unit uses a technology for realizing realistic visual effects such as light reflection and shadow expression in displaying the 3D model. This allows the user to obtain an image close to the actual hairstyle. Furthermore, the reflecting unit provides a function for freely rotating the 3D model so that the user can check the hairstyle from different angles. This makes it easier for the user to grasp the overall picture of the hairstyle. A part or all of the above-mentioned processing in the reflecting unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflecting unit can display the 3D model on the user's facial photograph using an AI model for reflecting the generated 3D model in real time. This allows the reflection unit to reflect the user's desired hairstyle in real time, allowing the treatment to be carried out with a common understanding.

[0069] The analysis unit can analyze the facial features or hair type of the user. The analysis unit analyzes the facial features of the user, for example, using a facial feature point extraction method or analysis algorithm. For example, the analysis unit extracts features such as the position of the eyes, the shape of the nose, and the contour of the face. The analysis unit can also analyze the hair type. For example, the analysis unit analyzes the thickness, hardness, and presence or absence of curls of the hair. This allows a 3D model of a more appropriate hairstyle to be generated by analyzing the facial features and hair type of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the extraction of facial feature points into an AI model and analyze the facial features.

[0070] The generation unit can generate a 3D model of a hairstyle suitable for the user's face using the generation AI. The generation unit generates a 3D model of a hairstyle that is optimal for the user's face using, for example, the generation AI. The generation AI generates a 3D model of a hairstyle that is optimal for the user's face using, for example, a text generation AI (for example, LLM). The generation unit can also generate a 3D model by using the generation AI, taking into account the user's desired hairstyle. For example, the generation AI generates a 3D model by taking into account the length, style, color, etc. of the user's desired hairstyle. In this way, by using the generation AI, a 3D model of a hairstyle that is optimal for the user's face can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a user's face photo and information on the desired hairstyle to the generation AI, and generate a 3D model of the hairstyle.

[0071] The reflection unit can reflect the generated 3D model on the user's facial photograph in real time. The reflection unit, for example, uses a technology for reflecting the generated 3D model in real time to display the 3D model on the user's facial photograph. For example, the reflection unit displays the generated 3D model by superimposing it on the user's facial photograph in real time. The reflection unit can also use a technology that minimizes the delay time for displaying the generated 3D model in real time. For example, the reflection unit uses a high-speed rendering technology for displaying the generated 3D model in real time. As a result, by reflecting the generated 3D model in real time, the user can visually check what hairstyle suits his or her face. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflection unit can display the 3D model on the user's facial photograph using an AI model for reflecting the generated 3D model in real time.

[0072] The generation unit can generate a 3D model based on the hairstyle desired by the user. The generation unit generates a 3D model based on the hairstyle desired by the user, for example, using a generation AI. The generation AI generates a 3D model by considering the length, style, color, etc. of the hairstyle desired by the user, for example, using a text generation AI (for example, LLM). The generation unit can also generate a 3D model by considering the hairstyle desired by the user, using the generation AI. For example, the generation AI analyzes detailed requests for the hairstyle desired by the user and generates a 3D model based on the analysis. In this way, a 3D model of a hairstyle with higher satisfaction can be generated by considering the hairstyle desired by the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the generation unit can input information on the hairstyle desired by the user to the generation AI and generate a 3D model of the hairstyle.

[0073] The reception unit can estimate the user's emotion and adjust the reception method of the desired hairstyle based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the reception method of the desired hairstyle based on the estimated user's emotion. For example, when the user is nervous, the reception unit provides a simple and intuitive interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the desired hairstyle to be input quickly. This improves the satisfaction of the user by providing a reception method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0074] The reception unit can analyze the user's past hairstyle history and select an appropriate reception method. The reception unit can, for example, analyze the user's past hairstyle history and automatically display optimal hairstyle candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest hairstyles that match specific seasons or events from the user's past hairstyle history. In this way, by analyzing the past hairstyle history, it is possible to provide the user with an optimal reception method. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past hairstyle history data into a generation AI and select an optimal reception method.

[0075] The reception unit can perform filtering based on the user's current hairstyle and hair type when receiving a desired hairstyle. The reception unit, for example, automatically recognizes the user's current hairstyle and displays suitable hairstyle candidates based on the recognition. The reception unit can also propose suitable hairstyles in consideration of the user's hair type (straight hair, curly hair, etc.). Furthermore, the reception unit can filter and display feasible hairstyles based on the user's hair length and volume. As a result, feasible hairstyles can be proposed by filtering based on the current hairstyle and hair type. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current hairstyle and hair type to the generation AI and cause the generation AI to perform filtering.

[0076] The reception unit can estimate the user's emotion and determine the priority order of hairstyles to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority order of hairstyles to be received based on the estimated user's emotion. For example, when the user is excited, the reception unit can preferentially display trendy hairstyles. Furthermore, when the user is relaxed, the reception unit can also preferentially display classic hairstyles. Furthermore, when the user is anxious, the reception unit can also preferentially display hairstyles selected in the past. This improves the satisfaction of the user by determining the priority order of hairstyles based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The reception unit can preferentially receive hairstyles that are highly relevant based on the geographical location information of the user when receiving a desired hairstyle. The reception unit, for example, preferentially displays hairstyles that are popular in the user's area. The reception unit can also suggest hairstyles that are suitable for the climate based on the user's geographical location. Furthermore, the reception unit can also preferentially display hairstyles that match the culture and events of the user's area. In this way, a hairstyle that is highly relevant to the user can be suggested by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and preferentially receive hairstyles that are highly relevant.

[0078] The reception unit can analyze the user's social media activity and receive related hairstyles when receiving a desired hairstyle. The reception unit can, for example, suggest related hairstyles based on hairstyles that the user has "liked" on social media. The reception unit can also suggest related hairstyles based on hairstyles of influencers that the user follows. Furthermore, the reception unit can analyze the content of the user's social media posts and suggest related hairstyles. In this way, a hairstyle related to the user can be suggested by analyzing social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and receive related hairstyles.

[0079] The analysis unit can estimate the user's emotion and adjust the analysis method of the facial features and hair type based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis method of the facial features and hair type based on the estimated user's emotion. For example, when the user is nervous, the analysis unit uses a simple analysis method and quickly provides the result. In addition, when the user is relaxed, the analysis unit can also perform a detailed analysis and provide more information. Furthermore, when the user is in a hurry, the analysis unit can also perform an analysis that focuses on important features. This improves the accuracy of the analysis by providing an analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to execute emotion estimation.

[0080] The analysis unit can improve the accuracy of the analysis when analyzing the facial features and hair type by referring to the user's past hairstyle history. The analysis unit, for example, analyzes changes in the facial features and hair type based on the user's past hairstyle history. The analysis unit can also analyze the reaction to a specific hairstyle from the user's past hairstyle history. Furthermore, the analysis unit can also customize the analysis result by referring to the user's past hairstyle history. In this way, the accuracy of the analysis is improved by referring to the past hairstyle history. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's past hairstyle history data into the generation AI to improve the accuracy of the analysis.

[0081] The analysis unit can perform the analysis based on the user's lifestyle or environment when analyzing the facial features and hair type. The analysis unit, for example, performs the analysis of the hair type taking into consideration the user's lifestyle (exercise, diet, etc.). The analysis unit can also perform the analysis of the hair type taking into consideration the user's environment (climate, humidity, etc.). Furthermore, the analysis unit can also perform the analysis of the facial features and hair type taking into consideration the user's occupation and lifestyle. This makes it possible to provide more appropriate analysis results by taking into consideration the lifestyle and environment. A part 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 input the user's lifestyle and environmental data into the generation AI and perform the analysis.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This provides a display method according to the user's emotion, thereby deepening understanding of the analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to execute emotion estimation.

[0083] The analysis unit can perform the analysis by taking into consideration the geographical location information of the user when analyzing the facial features and hair type. The analysis unit performs the analysis of the hair type by taking into consideration, for example, the climate of the user's region. The analysis unit can also perform the analysis by taking into consideration specific environmental conditions based on the user's geographical location. Furthermore, the analysis unit can also perform the analysis of the facial features and hair type by taking into consideration the culture and trends of the user's region. As a result, by taking into consideration the geographical location information, more appropriate analysis results can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and perform the analysis.

[0084] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature when analyzing the facial features and hair type. The analysis unit performs the analysis by referring to, for example, academic papers on the user's hair type. The analysis unit can also perform the analysis based on research data on the user's facial features. Furthermore, the analysis unit can perform the analysis by referring to trend reports on the user's hairstyle. This improves the accuracy of the analysis by referring to the related literature. A part 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 input the user's related literature data into the generation AI to improve the accuracy of the analysis.

[0085] The generation unit can estimate the user's emotion and adjust the generation method of the 3D model of the hairstyle based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the generation method of the 3D model of the hairstyle based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a 3D model that proceeds at a leisurely pace. Also, when the user is in a hurry, the generation unit can generate a 3D model that can be generated in the shortest time. Furthermore, when the user is excited, the generation unit can generate a 3D model with a visually stimulating effect added. This improves the accuracy of generation by providing a generation method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0086] The generation unit can improve the accuracy of generation when generating a 3D model of a hairstyle by referring to the user's past hairstyle history. The generation unit generates an optimal 3D model based on the user's past hairstyle history, for example. The generation unit can also generate a 3D model from the user's past hairstyle history, taking into account the user's reaction to a specific hairstyle. Furthermore, the generation unit can also customize the 3D model to be generated by referring to the user's past hairstyle history. This improves the accuracy of generation by referring to the past hairstyle history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's past hairstyle history data into the generation AI to improve the accuracy of generation.

[0087] The generation unit can generate a 3D model of a hairstyle based on the user's lifestyle or environment when generating the 3D model. The generation unit generates an optimal 3D model, for example, taking into account the user's lifestyle (exercise, diet, etc.). The generation unit can also generate an optimal 3D model, taking into account the user's environment (climate, humidity, etc.). Furthermore, the generation unit can generate an optimal 3D model, taking into account the user's occupation and lifestyle. This allows a more appropriate 3D model to be generated by taking into account the lifestyle and environment. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle and environmental data into the generation AI and perform generation.

[0088] The generation unit can estimate the user's emotion and adjust the display method of the 3D model to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the display method of the 3D model to be generated based on the estimated user's emotion. For example, when the user is nervous, the generation unit provides a display method that is simple and highly visible. Furthermore, when the user is relaxed, the generation unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the generation unit can also provide a display method that focuses on the main points. This improves the visibility of the generated 3D model by providing a display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0089] The generation unit can generate a 3D model of a hairstyle by taking into consideration the geographical location information of the user. The generation unit generates an optimal 3D model, for example, by taking into consideration the climate of the user's region. The generation unit can also generate a 3D model that takes into consideration specific environmental conditions based on the geographical location of the user. Furthermore, the generation unit can generate an optimal 3D model by taking into consideration the culture and trends of the user's region. This allows a more appropriate 3D model to be generated by taking into consideration the geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the user to the generation AI and perform generation.

[0090] The generating unit can improve the accuracy of generation when generating a 3D model of a hairstyle by referring to related literature of the user. The generating unit generates a 3D model by referring to, for example, academic papers on the user's hair quality. The generating unit can also generate a 3D model based on research data on the user's facial features. Furthermore, the generating unit can also generate a 3D model by referring to trend reports on the user's hairstyle. This improves the accuracy of generation by referring to related literature. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI or may be performed without using AI. For example, the generating unit can input related literature data of the user to the generating AI to improve the accuracy of generation.

[0091] The reflection unit can estimate the user's emotion and adjust the reflection method of the 3D model based on the estimated user's emotion. The reflection unit, for example, estimates the user's emotion and adjusts the reflection method of the 3D model based on the estimated user's emotion. For example, when the user is nervous, the reflection unit provides a simple and highly visible reflection method. Furthermore, when the user is relaxed, the reflection unit can also provide a reflection method including detailed information. Furthermore, when the user is in a hurry, the reflection unit can also provide a reflection method that covers the main points. This improves the accuracy of reflection by providing a reflection method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The reflection unit can improve the accuracy of the reflection when reflecting the 3D model by referring to the user's past hairstyle history. The reflection unit reflects an optimal 3D model based on the user's past hairstyle history, for example. The reflection unit can also reflect the 3D model by considering the reaction to a specific hairstyle from the user's past hairstyle history. Furthermore, the reflection unit can also customize the 3D model to be reflected by referring to the user's past hairstyle history. Thereby, the accuracy of the reflection is improved by referring to the past hairstyle history. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflection unit can input the user's past hairstyle history data to the generation AI to improve the accuracy of the reflection.

[0093] The reflection unit can reflect the 3D model based on the user's lifestyle or environment when reflecting the 3D model. The reflection unit reflects the optimal 3D model, for example, taking into account the user's lifestyle (exercise, diet, etc.). The reflection unit can also reflect the optimal 3D model, taking into account the user's environment (climate, humidity, etc.). Furthermore, the reflection unit can also reflect the optimal 3D model, taking into account the user's occupation and lifestyle. In this way, a more appropriate 3D model can be reflected by taking into account the lifestyle and environment. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflection unit can input the user's lifestyle and environmental data into the generation AI and reflect it.

[0094] The reflection unit can estimate the user's emotion and adjust the display method of the reflected 3D model based on the estimated user's emotion. The reflection unit, for example, estimates the user's emotion and adjusts the display method of the reflected 3D model based on the estimated user's emotion. For example, when the user is nervous, the reflection unit provides a display method that is simple and highly visible. Furthermore, when the user is relaxed, the reflection unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the reflection unit can also provide a display method that focuses on the main points. This improves the visibility of the reflected 3D model by providing a display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reflection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reflection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0095] The reflection unit can reflect the 3D model while taking into consideration the geographical location information of the user. The reflection unit reflects the optimal 3D model, for example, taking into consideration the climate of the user's region. The reflection unit can also reflect a 3D model that takes into consideration specific environmental conditions based on the user's geographical location. Furthermore, the reflection unit can also reflect the optimal 3D model while taking into consideration the culture and trends of the user's region. This allows a more appropriate 3D model to be reflected by taking into consideration the geographical location information. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI, or may be performed without using AI. For example, the reflection unit can input the user's geographical location information into the generation AI and reflect it.

[0096] The reflection unit can improve the accuracy of the reflection when reflecting the 3D model by referring to the user's related literature. The reflection unit reflects the 3D model by referring to, for example, academic papers on the user's hair quality. The reflection unit can also reflect the 3D model based on research data on the user's facial features. Furthermore, the reflection unit can also reflect the 3D model by referring to trend reports on the user's hairstyle. This improves the accuracy of the reflection by referring to the related literature. A part or all of the above-mentioned processing in the reflection unit may be performed, for example, using AI or may be performed without using AI. For example, the reflection unit can input the user's related literature data into the generation AI to improve the accuracy of the reflection.

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

[0098] The reception unit can analyze the user's tone of voice and manner of speaking to estimate the user's emotions. For example, if the user is excited, the reception unit can provide more detailed hairstyle options, and if the user is relaxed, it can provide simple options. Also, if the user is feeling anxious, the reception unit can preferentially display hairstyles selected in the past. This makes it possible to respond according to the user's emotions, thereby improving the user's satisfaction.

[0099] The analysis unit can take into account the user's lifestyle and environment when analyzing the user's facial features and hair type. For example, if the user exercises frequently, the analysis can take into account changes in hair type. Also, if the user lives in a humid area, changes in hair curls and volume can be taken into account. Furthermore, the analysis can take into account the user's diet and stress level. This can provide more accurate analysis results.

[0100] The generation unit can estimate the user's emotion and adjust the generation method of the 3D hairstyle model based on the estimated emotion. For example, if the user is relaxed, a 3D model that progresses at a leisurely pace can be generated. If the user is in a hurry, a 3D model that can be generated in the shortest time can be generated. Furthermore, if the user is excited, a 3D model with a visually stimulating effect can be generated. This makes it possible to improve the accuracy of generation by providing a generation method according to the user's emotion.

[0101] The reflection unit can take into account the geographical location information of the user when reflecting the generated 3D model on the user's facial photo in real time. For example, if the user lives in a humid area, the reflection unit can reflect the hairstyle while taking into account the volume and curl durability of the hairstyle. In addition, if the user lives in a cold area, the heat retention of the hairstyle can be taken into account. Furthermore, the optimal hairstyle can be reflected by taking into account the culture and trends of the user's region. As a result, a more appropriate 3D model can be reflected by taking into account the geographical location information.

[0102] The generation unit can improve the accuracy of generating a 3D hairstyle model by referring to the user's past hairstyle history. For example, a similar hairstyle can be suggested based on data on hairstyles the user has previously chosen. It can also exclude hairstyles that the user has avoided in the past. Furthermore, it can predict and suggest hairstyles suited to specific seasons or events based on the user's past hairstyle history. In this way, it is possible to suggest the most suitable hairstyle for the user by referring to the past hairstyle history.

[0103] The reception unit can estimate the user's emotion and adjust the reception method of the desired hairstyle based on the estimated emotion. For example, if the user is nervous, a simple and intuitive interface can be provided to minimize input steps. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input the desired hairstyle. In this way, by providing a reception method according to the user's emotion, the satisfaction of the user can be improved.

[0104] When analyzing the user's facial features and hair type, the analysis unit can improve the accuracy of the analysis by referring to the user's past hairstyle history. For example, the analysis unit analyzes changes in facial features and hair type based on data on hairstyles the user has previously chosen. It can also exclude hairstyles that the user has avoided in the past. Furthermore, it can analyze the user's reaction to a specific hairstyle from the user's past hairstyle history and customize the analysis results. In this way, the accuracy of the analysis can be improved by referring to the past hairstyle history.

[0105] The generation unit can estimate the user's emotion and adjust the generation method of the 3D hairstyle model based on the estimated emotion. For example, if the user is relaxed, a 3D model that progresses at a leisurely pace can be generated. If the user is in a hurry, a 3D model that can be generated in the shortest time can be generated. Furthermore, if the user is excited, a 3D model with a visually stimulating effect can be generated. This makes it possible to improve the accuracy of generation by providing a generation method according to the user's emotion.

[0106] The reflection unit can take into account the geographical location information of the user when reflecting the generated 3D model on the user's facial photo in real time. For example, if the user lives in a humid area, the reflection unit can reflect the hairstyle while taking into account the volume and curl durability of the hairstyle. In addition, if the user lives in a cold area, the heat retention of the hairstyle can be taken into account. Furthermore, the optimal hairstyle can be reflected by taking into account the culture and trends of the user's region. As a result, a more appropriate 3D model can be reflected by taking into account the geographical location information.

[0107] The reception unit can analyze the user's social media activity and suggest related hairstyles. For example, related hairstyles can be suggested based on hairstyles that the user has "liked" on social media. Also, related hairstyles can be suggested based on the hairstyles of influencers that the user follows. Furthermore, related hairstyles can be suggested by analyzing the content of the user's social media posts. In this way, hairstyles related to the user can be suggested by analyzing social media activity.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The reception unit receives a hairstyle desired by the user. The hairstyle desired by the user includes, but is not limited to, for example, length, style, and color. The reception unit receives, for example, the user inputting the hairstyle desired in natural language and uploading a face photo. Step 2: The analysis unit analyzes the face photo based on the information received by the reception unit. The analysis unit analyzes the user's facial features and hair type, for example, using a facial feature point extraction method or analysis algorithm. Step 3: The generation unit uses a generation AI to generate a 3D model of a hairstyle based on the information analyzed by the analysis unit. The generation unit uses, for example, a text generation AI (e.g., LLM) to generate a 3D model of a hairstyle that is optimal for the user's face. The generation unit can also generate the 3D model taking into account the user's desired hairstyle. Step 4: The reflecting unit reflects the 3D model generated by the generating unit on the user's facial photograph in real time. For example, the reflecting unit displays the 3D model on the user's facial photograph using a technique for reflecting the generated 3D model in real time. As a result, the hairstyle recommendation system according to the embodiment can reflect the user's desired hairstyle in real time and proceed with the treatment with a common understanding.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0111] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and reflection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and accepts that the user inputs a desired hairstyle in natural language and uploads a face photo. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial features and hair type using a facial feature point extraction method and analysis algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D model of the hairstyle using a generation AI. The reflection unit is realized, for example, by the output device 40 of the smart device 14, and reflects the generated 3D model on the user's face photo in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and reflection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and accepts the user's input of a desired hairstyle in natural language and upload of a face photo. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial features and hair type using a facial feature point extraction method and analysis algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D model of the hairstyle using a generation AI. The reflection unit is realized, for example, by the speaker 240 of the smart glasses 214, and reflects the generated 3D model on the user's face photo in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and reflection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and accepts the user's input of a desired hairstyle in natural language and upload of a face photo. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial features and hair type using a facial feature point extraction method and analysis algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D model of the hairstyle using a generation AI. The reflection unit is realized, for example, by the display 343 of the headset type terminal 314, and reflects the generated 3D model on the user's face photo in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0149] 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.

[0150] 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0154] 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.

[0155] 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.

[0156] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0158] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for 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.

[0160] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0162] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and reflection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and accepts the user's input of a desired hairstyle in natural language and upload of a face photo. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's facial features and hair type using a facial feature point extraction method and analysis algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D model of the hairstyle using a generation AI. The reflection unit is realized, for example, by the control target 443 of the robot 414, and reflects the generated 3D model on the user's face photo in real time. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A reception unit that receives a hairstyle desired by a user; an analysis unit that analyzes a facial photograph based on the information received by the reception unit; A generation unit that generates a 3D model of a hairstyle based on the information analyzed by the analysis unit; A reflection unit that reflects the 3D model generated by the generation unit in real time. A system characterized by: (Appendix 2) The analysis unit is Analyzing a user's facial features or hair type 2. The system of claim 1, (Appendix 3) The generation unit is Generative AI generates a 3D model of a hairstyle that matches the user's face 2. The system of claim 1, (Appendix 4) The reflection unit is The generated 3D model is reflected on the user's face photo in real time. 2. The system of claim 1, (Appendix 5) The generation unit is Generate a 3D model based on the user's desired hairstyle 2. The system of claim 1, (Appendix 6) The reception unit is To estimate a user's emotion and adjust a method of accepting a desired hairstyle based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past hairstyle history and select the most appropriate reception method 2. The system of claim 1, (Appendix 8) The reception unit is When receiving a desired hairstyle, it filters it based on the user's current hairstyle and hair type. 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and prioritize hairstyles to be accepted based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When receiving a request for a desired hairstyle, hairstyles that are highly relevant based on the user's geographic location information are given priority. 2. The system of claim 1, (Appendix 11) The reception unit is When a user requests a hairstyle, the system analyzes the user's social media activity and requests related hairstyles. 2. The system of claim 1, (Appendix 12) The analysis unit is Estimate the user's emotions and adjust the analysis method of facial features and hair type based on the estimated user emotions. 2. The system of claim 1, (Appendix 13) The analysis unit is When analyzing facial features and hair type, the accuracy of the analysis is improved by referring to the user's past hairstyle history. 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing facial features and hair type, analysis is performed based on the user's lifestyle or environment. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is When analyzing facial features and hair type, the analysis takes into account the user's geographic location information. 2. The system of claim 1, (Appendix 17) The analysis unit is When analyzing facial features and hair type, the accuracy of the analysis is improved by referring to the user's related literature. 2. The system of claim 1, (Appendix 18) The generation unit is Estimate the user's emotion and adjust the generation method of the 3D hairstyle model based on the estimated user emotion. 2. The system of claim 1, (Appendix 19) The generation unit is When generating a 3D hairstyle model, the accuracy of the generation is improved by referring to the user's past hairstyle history. 2. The system of claim 1, (Appendix 20) The generation unit is When generating a 3D hairstyle model, it is generated based on the user's lifestyle and environment. 2. The system of claim 1, (Appendix 21) The generation unit is Estimate the user's emotions and adjust the display method of the generated 3D model based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The generation unit is When generating a 3D hairstyle model, the system takes into account the user's geographic location information. 2. The system of claim 1, (Appendix 23) The generation unit is When generating a 3D hairstyle model, the accuracy of the generation is improved by referring to the user's related literature. 2. The system of claim 1, (Appendix 24) The reflection unit is Estimate the user's emotions and adjust the way the 3D model is reflected based on the estimated user's emotions. 2. The system of claim 1, (Appendix 25) The reflection unit is When reflecting a 3D model, improve the accuracy of reflection by referring to the user's past hairstyle history. 2. The system of claim 1, (Appendix 26) The reflection unit is When updating 3D models, updates are made based on the user's lifestyle or environment. 2. The system of claim 1, (Appendix 27) The reflection unit is Estimate the user's emotions and adjust the way the 3D model is displayed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The reflection unit is When reflecting 3D models, the geographic location information of the user is taken into consideration. 2. The system of claim 1, (Appendix 29) The reflection unit is When updating 3D models, the accuracy of the update can be improved by referring to the user's related literature. 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives a hairstyle desired by a user; an analysis unit that analyzes a facial photograph based on the information received by the reception unit; A generation unit that generates a 3D model of a hairstyle based on the information analyzed by the analysis unit; A reflection unit that reflects the 3D model generated by the generation unit in real time. A system characterized by:

2. The analysis unit is Analyzing a user's facial features or hair type 2. The system of claim 1 .

3. The generation unit is Generative AI generates a 3D model of a hairstyle that matches the user's face 2. The system of claim 1 .

4. The reflection unit is The generated 3D model is reflected on the user's face photo in real time.

2. The system of claim 1 .

5. The generation unit is Generate a 3D model based on the user's desired hairstyle 2. The system of claim 1 .

6. The reception unit is To estimate a user's emotion and adjust a method of accepting a desired hairstyle based on the estimated user's emotion.

2. The system of claim 1 .

7. The reception unit is Analyze the user's past hairstyle history and select the most appropriate reception method 2. The system of claim 1 .

8. The reception unit is When receiving a desired hairstyle, it filters it based on the user's current hairstyle and hair type.

2. The system of claim 1 .

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