System

Abstract

An object of the system according to the embodiment is to fill a gap between a hairstyle desired by a user and an actual cut result.SOLUTION: A system includes a cut image input unit, a cut plan generation unit, and a cut plan provision unit. The cut image input unit inputs a hairstyle desired by a user. The cut plan generation unit analyzes the cut images input by the cut image input unit and generates an optimal cut plan. The cutting plan providing unit provides the hairdresser with the cutting plan generated by the cutting plan generation unit.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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional techniques, there is a risk of a gap occurring between the hairstyle desired by the user and the actual haircut result.

[0005] The system according to the embodiment aims to bridge the gap between a user's desired hairstyle and the actual haircut result. [Means for solving the problem]

[0006] The system according to the embodiment includes a haircut image input unit, a haircut plan generation unit, and a haircut plan providing unit. The haircut image input unit inputs a desired hairstyle from a user. The haircut plan generation unit analyzes the haircut image input by the haircut image input unit and generates an optimal haircut plan. The haircut plan providing unit provides the haircut plan generated by the haircut plan generation unit to a hairdresser. [Effects of the Invention]

[0007] The system according to the embodiment can bridge the gap between the user's desired hairstyle and the actual haircut result. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The AI ​​haircut ordering system according to an embodiment of the present invention is a system that bridges the gap between the user's desired hairstyle image and the actual haircut result. This system allows the user to input the desired hairstyle into the AI, which then analyzes the information to generate an optimal haircut plan and provides it to the hairdresser. This allows the AI ​​haircut ordering system to realize a haircut that meets the user's wishes.

[0029] The AI ​​haircut ordering system according to the embodiment includes a haircut image input unit, a haircut plan generation unit, and a haircut plan provision unit. The haircut image input unit inputs a desired hairstyle. For example, the user can input text such as "I want my bangs short and have volume all over." The user can also upload an image of the desired hairstyle. The user can also input their preference by voice. For example, the user can input by voice such as "I want my bangs short and have volume all over." The haircut plan generation unit analyzes the haircut image input by the haircut image input unit and generates an optimal haircut plan. For example, the AI ​​can use image analysis technology to analyze the image uploaded by the user and specifically plan which parts of the hair to cut and how. The AI ​​can also use natural language processing technology to analyze the text input by the user and understand the desired style. The AI ​​can also use voice analysis technology to analyze the voice input by the user and understand the desired style. The haircut plan provision unit provides the haircut plan generated by the haircut plan generation unit to the hairdresser. For example, a plan generated by AI may include specific instructions such as "cut the bangs by 2 cm and layer the sides." This allows the AI ​​haircut ordering system according to the embodiment to provide a haircut that meets the user's wishes. For example, a cut that accurately reflects the style desired by the user can be achieved, improving satisfaction. In addition, a specific haircut plan is provided to hairdressers, making their work more efficient.

[0030] The haircut image input unit can learn the user's past haircut history or preferred styles and propose individually optimized haircut images. For example, the haircut image input unit stores the user's past haircut history in a database, and the AI ​​proposes individually optimized haircut images based on that data. For example, a new haircut plan is generated based on styles that have been well-received in the past. In addition, to learn the user's preferred styles, the AI ​​analyzes the user's past haircut history and preferred styles and proposes individually optimized haircut images. For example, it analyzes the user's preferred hairstyle trends and generates haircut plans based on that. In addition, the AI ​​learns the user's past haircut history and preferred styles and proposes individually optimized haircut images. For example, it generates the next haircut plan based on the user's previously selected styles and feedback. This allows for the provision of more personalized haircut plans by proposing haircut images that reflect the user's past haircut history and preferences.

[0031] The haircut image input unit analyzes the voice data input by the user and can more accurately understand the user's intentions from the tone and strength of the voice. For example, if the user emphasizes, "I want more volume," the AI ​​generates a haircut plan that reflects that intention. In addition, using voice analysis technology, the AI ​​analyzes the tone and strength of the voice data input by the user and more accurately understands the user's intentions. For example, if the user speaks in a relaxed tone, the AI ​​will suggest a haircut style that gives a relaxed impression. On the other hand, if the user speaks in an excited tone, the AI ​​will generate a haircut plan that reflects that emotion. In this way, by analyzing the user's voice data, the AI ​​can more accurately understand the haircut image and generate a haircut plan.

[0032] The haircut image input unit can generate a hairstyle desired by the user as a 3D model and provide an interface that allows the user to check the hairstyle in real time. The haircut image input unit, for example, generates a hairstyle desired by the user as a 3D model and provides an interface that allows the user to check the hairstyle in real time. For example, when the user inputs the desired style, a 3D model is instantly generated and the user can check it. In addition, using 3D modeling technology, the user's desired hairstyle is generated in real time and can be checked on the interface. For example, when the user selects a style, the 3D model is dynamically updated. In addition, the haircut image input unit generates a hairstyle desired by the user as a 3D model and provides an interface that allows the user to check the hairstyle in real time. For example, when the user tries out a different style, the 3D model is instantly updated. This allows the user to check the desired hairstyle as a 3D model in real time, improving the accuracy of haircut plans.

[0033] The haircut image input unit can provide the user with a variety of style options by referencing hairstyle databases for different cultures or regions. The haircut image input unit, for example, can provide the user with a variety of style options by referencing hairstyle databases for different cultures or regions. For example, styles for each region, such as Asia, Europe, and Africa, can be proposed. The haircut image input unit can also utilize the hairstyle database to provide the user with styles from different cultures or regions. For example, a variety of options can be presented, ranging from traditional styles to the latest trends. The haircut image input unit can also provide the user with a variety of style options by referencing hairstyle databases for different cultures or regions. For example, if the user desires a style from a particular region, styles can be proposed from the database for that region. In this way, the haircut image input unit can provide the user with a variety of style options by referencing hairstyle databases for different cultures or regions.

[0034] The haircut plan generation unit can use generation AI to automatically generate a haircut plan that is optimal for the user's face shape or hair type. The haircut plan generation unit, for example, uses generation AI to automatically generate a haircut plan that is optimal for the user's face shape and hair type. For example, for a user with a round face, it proposes a haircut plan that makes the face look slimmer. The generation AI also analyzes the user's face shape and hair type and automatically generates the optimal haircut plan. For example, for a user with thin hair, it proposes a haircut plan that adds volume. The generation AI also automatically generates a haircut plan that is optimal for the user's face shape and hair type. For example, for a user with a square face, it proposes a haircut plan that gives a soft impression. In this way, by automatically generating a haircut plan that is optimal for the user's face shape and hair type, it is possible to provide a haircut that is more satisfying.

[0035] The haircut plan generation unit can propose the optimal haircut plan based on the user's lifestyle or occupation. The haircut plan generation unit proposes the optimal haircut plan based on the user's lifestyle and occupation, for example. For example, a clean-looking haircut plan is proposed for a business person. The generation AI also proposes the optimal haircut plan taking the user's lifestyle and occupation into consideration. For example, an easy-to-maintain haircut plan is proposed for a user with an active lifestyle. The AI ​​also proposes the optimal haircut plan based on the user's lifestyle and occupation. For example, a unique haircut plan is proposed for a user with a creative occupation. In this way, by proposing the optimal haircut plan based on the user's lifestyle and occupation, it is possible to provide a haircut that meets the user's needs.

[0036] When generating a haircut plan, the haircut plan generation unit can recommend the most suitable hairdresser based on the techniques or styles of different hairdressers. For example, when generating a haircut plan, the haircut plan generation unit considers the techniques and styles of different hairdressers to recommend the most suitable hairdresser. For example, it recommends a hairdresser who is good at layered cuts. The generation AI also analyzes the techniques and styles of different hairdressers to recommend the most suitable hairdresser. For example, it recommends a hairdresser who is good at short cuts. Also, when generating a haircut plan, it considers the techniques and styles of different hairdressers to recommend the most suitable hairdresser. For example, it recommends a hairdresser who is good at coloring. In this way, it is possible to recommend the most suitable hairdresser by considering the techniques and styles of different hairdressers.

[0037] The haircut plan generation unit can generate multiple haircut plans and provide an interface that allows the user to select from them. The haircut plan generation unit, for example, generates multiple haircut plans and provides an interface that allows the user to select from them. For example, haircut plans of different styles can be presented and the user can select from them. The generation AI also generates multiple haircut plans and provides an interface that allows the user to select from them. For example, haircut plans for short, medium, and long hair can be presented. The haircut plan generation unit can also generate multiple haircut plans and provide an interface that allows the user to select from them. For example, haircut plans with different coloring can be presented and the user can select from them. In this way, by providing multiple haircut plans, the user can select the most suitable plan.

[0038] The haircut plan providing unit can use the generation AI to customize the haircut plan according to the skill level of the hairdresser. The haircut plan providing unit, for example, uses the generation AI to customize the haircut plan according to the skill level of the hairdresser. For example, a simple haircut plan is provided for a novice hairdresser. The generation AI also analyzes the hairdresser's skill level and customizes the haircut plan. For example, an advanced haircut plan is provided for an experienced hairdresser. The generation AI also customizes the haircut plan according to the hairdresser's skill level. For example, a haircut plan that makes use of a particular technique is provided for a hairdresser who is skilled in that technique. In this way, by customizing the haircut plan according to the hairdresser's skill level, a more appropriate haircut can be provided.

[0039] The haircut plan providing unit can collect hairdressers' feedback on the haircut plan and reflect it in the generation of the next plan. The haircut plan providing unit, for example, collects hairdressers' feedback on the haircut plan and reflects it in the generation of the next plan. For example, if the hairdresser comments, "This part should be shorter," this will be reflected in the next plan. The hairdressers' feedback is also collected and the generation AI reflects it in the next haircut plan. For example, if the hairdresser comments, "This technique is difficult," the technique will be adjusted in the next plan. The hairdressers' feedback on the haircut plan is also collected and reflected in the generation of the next plan. For example, if the hairdresser comments, "This style is popular," this style will be incorporated into the next plan. In this way, by collecting hairdressers' feedback and reflecting it in the generation of the next plan, more accurate haircut plans can be provided.

[0040] The haircut plan providing unit can show the haircutting steps using 3D animation so that the hairdresser can easily understand the haircut plan. The haircut plan providing unit, for example, shows the haircutting steps using 3D animation so that the hairdresser can easily understand the haircut plan. For example, the haircutting steps for cutting bangs are visually explained using 3D animation. The haircut plan providing unit also uses 3D animation to show the steps so that the hairdresser can easily understand the haircut plan. For example, the haircutting steps for layered hair are explained in detail using 3D animation. The haircutting steps are also shown using 3D animation so that the hairdresser can easily understand the haircut plan. For example, the haircutting steps for side hair are visually explained using 3D animation. In this way, showing the haircutting steps using 3D animation makes it easier for the hairdresser to understand the haircut plan.

[0041] The haircut plan providing unit can provide haircut plans to multiple hairdressers and hold a competition to select the optimal plan. The haircut plan providing unit, for example, provides haircut plans to multiple hairdressers and holds a competition to select the optimal plan. For example, multiple hairdressers offer the same plan, and the user selects the plan that satisfies them the most. Also, a haircut plan competition is held between hairdressers to select the optimal plan. For example, plans offered by different hairdressers are compared, and the user selects the plan that they like best. Also, the haircut plans are provided to multiple hairdressers and a competition is held to select the optimal plan. For example, the user tries multiple plans and selects the plan that satisfies them the most. In this way, the optimal haircut plan can be selected by holding a competition among multiple hairdressers.

[0042] The haircut plan providing unit can provide an interface that allows a hairdresser to modify a haircut plan in real time. The haircut plan providing unit, for example, provides an interface that allows a hairdresser to modify a haircut plan in real time. For example, the hairdresser can adjust the plan during the cut and have the changes reflected immediately. An interface that allows the hairdresser to modify the haircut plan in real time is also provided, allowing the hairdresser to adjust the plan according to the user's requests. For example, if the user requests a change during the cut, the hairdresser immediately modifies the plan. An interface that allows the hairdresser to modify the haircut plan in real time is also provided. For example, the hairdresser can fine-tune the plan during the cut to suit the user's wishes. In this way, by providing an interface that allows the hairdresser to modify the haircut plan in real time, it is possible to flexibly respond to user requests.

[0043] The haircut plan generation unit can use the generation AI to analyze user feedback and reflect it in the next haircut plan. The haircut plan generation unit, for example, uses the generation AI to analyze user feedback and reflect it in the next haircut plan. For example, if the user comments, "I would have preferred the bangs to be a little shorter," this will be reflected in the next plan. The generation AI also analyzes user feedback and reflects it in the next haircut plan. For example, if the user comments, "I would like more volume on the sides," this will be reflected in the next plan. The generation AI also analyzes user feedback and reflects it in the next haircut plan. For example, if the user comments, "I would like the back to be a little shorter," this will be reflected in the next plan. In this way, by analyzing user feedback and reflecting it in the next haircut plan, a more satisfying haircut can be provided.

[0044] The haircut plan generation unit can clarify areas for improvement by sharing feedback with other users and gaining sympathy. The haircut plan generation unit, for example, can clarify areas for improvement by sharing feedback with other users and gaining sympathy. For example, users who want the same style can share feedback to identify common areas for improvement. Furthermore, by sharing user feedback and gaining sympathy from other users, areas for improvement can be clarified. For example, the feedback can be made public and comments from other users can be collected. Furthermore, by sharing feedback with other users and gaining sympathy, areas for improvement can be clarified. For example, users who use the same hairdresser can share feedback to identify common areas for improvement. In this way, by sharing feedback with other users, areas for improvement can be clarified by gaining sympathy.

[0045] The cutting plan generation unit can develop an algorithm that automatically generates improvements to the cutting plan based on feedback. The cutting plan generation unit, for example, develops an algorithm that automatically generates improvements to the cutting plan based on feedback. For example, it analyzes user comments and automatically generates specific improvements. Also, it develops an algorithm that allows the generation AI to automatically generate improvements to the cutting plan based on user feedback. For example, it analyzes user complaints and automatically generates improvements. Also, it develops an algorithm that automatically generates improvements to the cutting plan based on feedback. For example, it analyzes user wishes and automatically generates improvements to be reflected in the next cutting plan. In this way, by developing an algorithm that automatically generates improvements to the cutting plan based on feedback, the accuracy of the cutting plan is improved.

[0046] The haircut plan generation unit can use the generation AI to compare the haircut image and the actual haircut result in a 3D model to visually show any gaps. The haircut plan generation unit, for example, uses the generation AI to compare the haircut image and the actual haircut result in a 3D model to visually show any gaps. For example, the style desired by the user and the actual haircut result are displayed side by side in a 3D model. A system is also constructed that compares the haircut image and the actual haircut result in a 3D model to visually show any gaps. For example, the style desired by the user and the actual haircut result are compared in a 3D animation. The generation AI can also compare the haircut image and the actual haircut result in a 3D model to visually show any gaps. For example, the style desired by the user and the actual haircut result are rotated and compared in a 3D model. In this way, by comparing the haircut image and the actual haircut result in a 3D model, any gaps can be visually shown.

[0047] The haircut plan generation unit can develop an algorithm that analyzes images of the resulting haircut and identifies specific areas for improvement. The haircut plan generation unit, for example, analyzes images of the resulting haircut and develops an algorithm that identifies specific areas for improvement. For example, it analyzes the length of the bangs and the volume of the sides and identifies areas for improvement. The generation AI also analyzes images of the resulting haircut and develops an algorithm that identifies specific areas for improvement. For example, it analyzes the length of the back and the overall balance and identifies areas for improvement. The generation AI also analyzes images of the resulting haircut and develops an algorithm that identifies specific areas for improvement. For example, it analyzes the consistency of the hair texture and style and identifies areas for improvement. In this way, by developing an algorithm that analyzes images of the resulting haircut and identifies specific areas for improvement, the accuracy of the haircut plan is improved.

[0048] The haircut plan generation unit can clarify areas for improvement by sharing the results of the haircut with other users and gaining their sympathy. The haircut plan generation unit, for example, can clarify areas for improvement by sharing the results of the haircut with other users and gaining their sympathy. For example, users who want the same style can share the results of the haircut and identify common areas for improvement. Furthermore, the haircut results of the users can be shared and the areas for improvement can be clarified by gaining their sympathy. For example, the haircut results can be made public and comments from other users can be collected. Furthermore, the haircut results can be shared with other users and the areas for improvement can be clarified by gaining their sympathy. For example, users who use the same hairdresser can share the results of the haircut and identify common areas for improvement. In this way, by sharing the results of the haircut with other users, the areas for improvement can be clarified by gaining their sympathy.

[0049] The cutting plan generation unit can develop an algorithm that automatically generates the next cutting plan based on the cutting results. The cutting plan generation unit, for example, develops an algorithm that automatically generates the next cutting plan based on the cutting results. For example, it analyzes user feedback and automatically generates the next cutting plan. In addition, the generation AI analyzes the cutting results and develops an algorithm that automatically generates the next cutting plan. For example, it automatically generates a cutting plan that reflects the user's wishes. In addition, it develops an algorithm that automatically generates the next cutting plan based on the cutting results. For example, it analyzes user satisfaction and automatically generates the next cutting plan. In this way, by developing an algorithm that automatically generates the next cutting plan based on the cutting results, the accuracy of the cutting plan is improved.

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

[0051] When a user inputs their desired hairstyle, the haircut image input unit can automatically suggest the most suitable style based on the user's face shape and hair type. For example, if the user has a round face, it can suggest a style that makes the face look slimmer. Also, if the user has thin hair, it can suggest a style that adds volume. Furthermore, the style input by the user can be customized based on the face shape and hair type. This makes it possible to provide a haircut plan that matches the user's individual characteristics.

[0052] When a user inputs their desired hairstyle, the haircut image input unit learns the user's past haircut history and preferred styles and can propose individually optimized haircut images. For example, it can generate a new haircut plan based on styles that have been well-received in the past. In addition, to learn the user's preferred styles, the AI ​​analyzes the user's past haircut history and preferred styles and proposes individually optimized haircut images. This makes it possible to provide a more personalized haircut plan by proposing haircut images that reflect the user's past haircut history and preferences.

[0053] The haircut image input unit can generate a 3D model of the hairstyle the user desires and provide an interface that allows the user to check it in real time. For example, when the user inputs the desired style, a 3D model is instantly generated and the user can check it. In addition, using 3D modeling technology, the haircut image input unit can generate the user's desired hairstyle in real time and allow the user to check it on the interface. This allows the user to check the desired hairstyle as a 3D model in real time, improving the accuracy of the haircut plan.

[0054] The haircut image input unit can provide the user with a variety of style options by referencing hairstyle databases from different cultures or regions. For example, it can propose styles for each region, such as Asia, Europe, and Africa. It also utilizes the hairstyle database to provide the user with styles from different cultures and regions. By referencing hairstyle databases from different cultures and regions, it is possible to provide the user with a variety of style options.

[0055] The haircut image input unit analyzes the voice data entered by the user and can more accurately understand the user's intentions from the tone or strength of the voice. For example, if the user emphasizes, "I want more volume," the AI ​​will generate a haircut plan that reflects that intention. In addition, using voice analysis technology, the AI ​​can analyze the tone and strength of the voice data entered by the user to more accurately understand the user's intentions. This allows the AI ​​to understand a more accurate haircut image and generate a haircut plan by analyzing the user's voice data.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The haircut image input unit allows the user to input the hairstyle they desire. For example, the user can input text such as "I want my bangs to be short and have volume all over." The user can also upload an image of the hairstyle they desire. Furthermore, the user can input their request by voice. For example, the user can input by voice such as "I want my bangs to be short and have volume all over." Step 2: The haircut plan generation unit analyzes the haircut image input by the haircut image input unit and generates an optimal haircut plan. For example, the AI ​​uses image analysis technology to analyze the image uploaded by the user and specifically plan which parts to cut and how. The AI ​​can also use natural language processing technology to analyze the text entered by the user and understand the desired style. Furthermore, the AI ​​can use voice analysis technology to analyze the voice entered by the user and understand the desired style. Step 3: The haircut plan provider provides the haircut plan generated by the haircut plan generator to the hairdresser. For example, the plan generated by the AI ​​includes specific instructions such as "cut the bangs by 2 cm and add layers to the sides."

[0058] (Example 2) The AI ​​haircut ordering system according to an embodiment of the present invention is a system that bridges the gap between the user's desired hairstyle image and the actual haircut result. This system allows the user to input the desired hairstyle into the AI, which then analyzes the information to generate an optimal haircut plan and provides it to the hairdresser. This allows the AI ​​haircut ordering system to realize a haircut that meets the user's wishes.

[0059] The AI ​​haircut ordering system according to the embodiment includes a haircut image input unit, a haircut plan generation unit, and a haircut plan provision unit. The haircut image input unit inputs a desired hairstyle. For example, the user can input text such as "I want my bangs short and have volume all over." The user can also upload an image of the desired hairstyle. The user can also input their preference by voice. For example, the user can input by voice such as "I want my bangs short and have volume all over." The haircut plan generation unit analyzes the haircut image input by the haircut image input unit and generates an optimal haircut plan. For example, the AI ​​can use image analysis technology to analyze the image uploaded by the user and specifically plan which parts of the hair to cut and how. The AI ​​can also use natural language processing technology to analyze the text input by the user and understand the desired style. The AI ​​can also use voice analysis technology to analyze the voice input by the user and understand the desired style. The haircut plan provision unit provides the haircut plan generated by the haircut plan generation unit to the hairdresser. For example, a plan generated by AI may include specific instructions such as "cut the bangs by 2 cm and layer the sides." This allows the AI ​​haircut ordering system according to the embodiment to provide a haircut that meets the user's wishes. For example, a cut that accurately reflects the style desired by the user can be achieved, improving satisfaction. In addition, a specific haircut plan is provided to hairdressers, making their work more efficient.

[0060] The haircut image input unit uses generative AI to perform sentiment analysis on text or images entered by the user and complements a haircut image based on the user's emotions. The haircut image input unit uses generative AI to perform sentiment analysis on text or images entered by the user and complements a haircut image based on the user's emotions. For example, if a user enters "I want to feel refreshed," the AI ​​will suggest a haircut style that reflects positive emotions. The generative AI also performs sentiment analysis on images uploaded by the user and complements a haircut image based on the emotions readable from the image. For example, it will read positive emotions from an image of a smiling face and suggest a haircut style with a bright impression. Furthermore, if a user enters "I want a calm atmosphere," the AI ​​will suggest a haircut style with a relaxed impression. This complements the haircut image based on the user's emotions, making it possible to provide a haircut plan with a higher level of satisfaction.

[0061] The haircut image input unit can learn the user's past haircut history or preferred styles and propose individually optimized haircut images. For example, the haircut image input unit stores the user's past haircut history in a database, and the AI ​​proposes individually optimized haircut images based on that data. For example, a new haircut plan is generated based on styles that have been well-received in the past. In addition, to learn the user's preferred styles, the AI ​​analyzes the user's past haircut history and preferred styles and proposes individually optimized haircut images. For example, it analyzes the user's preferred hairstyle trends and generates haircut plans based on that. In addition, the AI ​​learns the user's past haircut history and preferred styles and proposes individually optimized haircut images. For example, it generates the next haircut plan based on the user's previously selected styles and feedback. This allows for the provision of more personalized haircut plans by proposing haircut images that reflect the user's past haircut history and preferences.

[0062] The haircut image input unit analyzes the voice data input by the user and can more accurately understand the user's intentions from the tone and strength of the voice. For example, if the user emphasizes, "I want more volume," the AI ​​generates a haircut plan that reflects that intention. In addition, using voice analysis technology, the AI ​​analyzes the tone and strength of the voice data input by the user and more accurately understands the user's intentions. For example, if the user speaks in a relaxed tone, the AI ​​will suggest a haircut style that gives a relaxed impression. On the other hand, if the user speaks in an excited tone, the AI ​​will generate a haircut plan that reflects that emotion. In this way, by analyzing the user's voice data, the AI ​​can more accurately understand the haircut image and generate a haircut plan.

[0063] The haircut image input unit can generate a hairstyle desired by the user as a 3D model and provide an interface that allows the user to check the hairstyle in real time. The haircut image input unit, for example, generates a hairstyle desired by the user as a 3D model and provides an interface that allows the user to check the hairstyle in real time. For example, when the user inputs the desired style, a 3D model is instantly generated and the user can check it. In addition, using 3D modeling technology, the user's desired hairstyle is generated in real time and can be checked on the interface. For example, when the user selects a style, the 3D model is dynamically updated. In addition, the haircut image input unit generates a hairstyle desired by the user as a 3D model and provides an interface that allows the user to check the hairstyle in real time. For example, when the user tries out a different style, the 3D model is instantly updated. This allows the user to check the desired hairstyle as a 3D model in real time, improving the accuracy of haircut plans.

[0064] The haircut image input unit can provide the user with a variety of style options by referencing hairstyle databases for different cultures or regions. The haircut image input unit, for example, can provide the user with a variety of style options by referencing hairstyle databases for different cultures or regions. For example, styles for each region, such as Asia, Europe, and Africa, can be proposed. The haircut image input unit can also utilize the hairstyle database to provide the user with styles from different cultures or regions. For example, a variety of options can be presented, ranging from traditional styles to the latest trends. The haircut image input unit can also provide the user with a variety of style options by referencing hairstyle databases for different cultures or regions. For example, if the user desires a style from a particular region, styles can be proposed from the database for that region. In this way, the haircut image input unit can provide the user with a variety of style options by referencing hairstyle databases for different cultures or regions.

[0065] The cut image input unit can use the emotion estimation function to estimate the emotion the user is feeling when entering text in real time and make suggestions to elicit positive emotions. The cut image input unit, for example, uses the emotion estimation function to estimate the emotion the user is feeling when entering text in real time and make suggestions to elicit positive emotions. For example, if the user is nervous, it makes suggestions to help the user relax. The cut image input unit can also estimate the emotion the user is feeling when entering text in real time and provide an interface for eliciting positive emotions. For example, if the user is feeling anxious, it displays a message that gives a sense of security. The emotion estimation function can also be used to estimate the emotion the user is feeling when entering text in real time and make suggestions to elicit positive emotions. For example, if the user is feeling depressed, it displays an encouraging message. In this way, the user's emotions can be estimated in real time and suggestions to elicit positive emotions can be made, thereby improving user satisfaction.

[0066] The haircut plan generation unit can use generation AI to automatically generate a haircut plan that is optimal for the user's face shape or hair type. The haircut plan generation unit, for example, uses generation AI to automatically generate a haircut plan that is optimal for the user's face shape and hair type. For example, for a user with a round face, it proposes a haircut plan that makes the face look slimmer. The generation AI also analyzes the user's face shape and hair type and automatically generates the optimal haircut plan. For example, for a user with thin hair, it proposes a haircut plan that adds volume. The generation AI also automatically generates a haircut plan that is optimal for the user's face shape and hair type. For example, for a user with a square face, it proposes a haircut plan that gives a soft impression. In this way, by automatically generating a haircut plan that is optimal for the user's face shape and hair type, it is possible to provide a haircut that is more satisfying.

[0067] The haircut plan generation unit can propose the optimal haircut plan based on the user's lifestyle or occupation. The haircut plan generation unit proposes the optimal haircut plan based on the user's lifestyle and occupation, for example. For example, a clean-looking haircut plan is proposed for a business person. The generation AI also proposes the optimal haircut plan taking the user's lifestyle and occupation into consideration. For example, an easy-to-maintain haircut plan is proposed for a user with an active lifestyle. The AI ​​also proposes the optimal haircut plan based on the user's lifestyle and occupation. For example, a unique haircut plan is proposed for a user with a creative occupation. In this way, by proposing the optimal haircut plan based on the user's lifestyle and occupation, it is possible to provide a haircut that meets the user's needs.

[0068] When generating a haircut plan, the haircut plan generation unit can recommend the most suitable hairdresser based on the techniques or styles of different hairdressers. For example, when generating a haircut plan, the haircut plan generation unit considers the techniques and styles of different hairdressers to recommend the most suitable hairdresser. For example, it recommends a hairdresser who is good at layered cuts. The generation AI also analyzes the techniques and styles of different hairdressers to recommend the most suitable hairdresser. For example, it recommends a hairdresser who is good at short cuts. Also, when generating a haircut plan, it considers the techniques and styles of different hairdressers to recommend the most suitable hairdresser. For example, it recommends a hairdresser who is good at coloring. In this way, it is possible to recommend the most suitable hairdresser by considering the techniques and styles of different hairdressers.

[0069] The haircut plan generation unit can generate multiple haircut plans and provide an interface that allows the user to select from them. The haircut plan generation unit, for example, generates multiple haircut plans and provides an interface that allows the user to select from them. For example, haircut plans of different styles can be presented and the user can select from them. The generation AI also generates multiple haircut plans and provides an interface that allows the user to select from them. For example, haircut plans for short, medium, and long hair can be presented. The haircut plan generation unit can also generate multiple haircut plans and provide an interface that allows the user to select from them. For example, haircut plans with different coloring can be presented and the user can select from them. In this way, by providing multiple haircut plans, the user can select the most suitable plan.

[0070] The haircut plan generation unit can use the emotion estimation function to monitor the user's emotional reaction to the generated haircut plans in real time and select the optimal plan. The haircut plan generation unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the generated haircut plans in real time and select the optimal plan. For example, a plan to which the user has a positive reaction is prioritized. The haircut plan generation unit also monitors the user's emotional reaction in real time and selects the optimal plan from the generated haircut plans. For example, a plan to which the user has a smiling face is prioritized. The emotion estimation function also monitors the user's emotional reaction to the generated haircut plans in real time and selects the optimal plan. For example, a plan to which the user has a relaxed face is prioritized. In this way, the optimal haircut plan can be selected by monitoring the user's emotional reaction in real time.

[0071] The haircut plan providing unit can use the generation AI to customize the haircut plan according to the skill level of the hairdresser. The haircut plan providing unit, for example, uses the generation AI to customize the haircut plan according to the skill level of the hairdresser. For example, a simple haircut plan is provided for a novice hairdresser. The generation AI also analyzes the hairdresser's skill level and customizes the haircut plan. For example, an advanced haircut plan is provided for an experienced hairdresser. The generation AI also customizes the haircut plan according to the hairdresser's skill level. For example, a haircut plan that makes use of a particular technique is provided for a hairdresser who is skilled in that technique. In this way, by customizing the haircut plan according to the hairdresser's skill level, a more appropriate haircut can be provided.

[0072] The haircut plan providing unit can collect hairdressers' feedback on the haircut plan and reflect it in the generation of the next plan. The haircut plan providing unit, for example, collects hairdressers' feedback on the haircut plan and reflects it in the generation of the next plan. For example, if the hairdresser comments, "This part should be shorter," this will be reflected in the next plan. The hairdressers' feedback is also collected and the generation AI reflects it in the next haircut plan. For example, if the hairdresser comments, "This technique is difficult," the technique will be adjusted in the next plan. The hairdressers' feedback on the haircut plan is also collected and reflected in the generation of the next plan. For example, if the hairdresser comments, "This style is popular," this style will be incorporated into the next plan. In this way, by collecting hairdressers' feedback and reflecting it in the generation of the next plan, more accurate haircut plans can be provided.

[0073] The haircut plan providing unit can show the haircutting steps using 3D animation so that the hairdresser can easily understand the haircut plan. The haircut plan providing unit, for example, shows the haircutting steps using 3D animation so that the hairdresser can easily understand the haircut plan. For example, the haircutting steps for cutting bangs are visually explained using 3D animation. The haircut plan providing unit also uses 3D animation to show the steps so that the hairdresser can easily understand the haircut plan. For example, the haircutting steps for layered hair are explained in detail using 3D animation. The haircutting steps are also shown using 3D animation so that the hairdresser can easily understand the haircut plan. For example, the haircutting steps for side hair are visually explained using 3D animation. In this way, showing the haircutting steps using 3D animation makes it easier for the hairdresser to understand the haircut plan.

[0074] The haircut plan providing unit can provide haircut plans to multiple hairdressers and hold a competition to select the optimal plan. The haircut plan providing unit, for example, provides haircut plans to multiple hairdressers and holds a competition to select the optimal plan. For example, multiple hairdressers offer the same plan, and the user selects the plan that satisfies them the most. Also, a haircut plan competition is held between hairdressers to select the optimal plan. For example, plans offered by different hairdressers are compared, and the user selects the plan that they like best. Also, the haircut plans are provided to multiple hairdressers and a competition is held to select the optimal plan. For example, the user tries multiple plans and selects the plan that satisfies them the most. In this way, the optimal haircut plan can be selected by holding a competition among multiple hairdressers.

[0075] The haircut plan providing unit can provide an interface that allows a hairdresser to modify a haircut plan in real time. The haircut plan providing unit, for example, provides an interface that allows a hairdresser to modify a haircut plan in real time. For example, the hairdresser can adjust the plan during the cut and have the changes reflected immediately. An interface that allows the hairdresser to modify the haircut plan in real time is also provided, allowing the hairdresser to adjust the plan according to the user's requests. For example, if the user requests a change during the cut, the hairdresser immediately modifies the plan. An interface that allows the hairdresser to modify the haircut plan in real time is also provided. For example, the hairdresser can fine-tune the plan during the cut to suit the user's wishes. In this way, by providing an interface that allows the hairdresser to modify the haircut plan in real time, it is possible to flexibly respond to user requests.

[0076] The haircut plan providing unit can use the emotion estimation function to analyze the emotion of the hairdresser when receiving the haircut plan and make suggestions that will elicit a positive response. The haircut plan providing unit, for example, uses the emotion estimation function to analyze the emotion of the hairdresser when receiving the haircut plan and make suggestions that will elicit a positive response. For example, if the hairdresser is nervous, the unit makes suggestions to help the hairdresser relax. The haircut plan providing unit also analyzes the emotion of the hairdresser when receiving the haircut plan in real time and provides an interface for eliciting a positive response. For example, if the hairdresser is feeling anxious, the unit displays a message that gives the hairdresser a sense of security. The haircut plan providing unit also uses the emotion estimation function to analyze the emotion of the hairdresser when receiving the haircut plan and make suggestions that will elicit a positive response. For example, if the hairdresser is feeling depressed, the unit displays an encouraging message. In this way, the hairdresser's emotions are analyzed and suggestions that will elicit a positive response are made, thereby improving the hairdresser's motivation.

[0077] The haircut plan generation unit can use the generation AI to analyze user feedback and reflect it in the next haircut plan. The haircut plan generation unit, for example, uses the generation AI to analyze user feedback and reflect it in the next haircut plan. For example, if the user comments, "I would have preferred the bangs to be a little shorter," this will be reflected in the next plan. The generation AI also analyzes user feedback and reflects it in the next haircut plan. For example, if the user comments, "I would like more volume on the sides," this will be reflected in the next plan. The generation AI also analyzes user feedback and reflects it in the next haircut plan. For example, if the user comments, "I would like the back to be a little shorter," this will be reflected in the next plan. In this way, by analyzing user feedback and reflecting it in the next haircut plan, a more satisfying haircut can be provided.

[0078] The cutting plan generation unit can complement the content of the feedback based on the user's emotional data and identify more accurate areas for improvement. The cutting plan generation unit complements the content of the feedback based on, for example, the user's emotional data and identifies more accurate areas for improvement. For example, if the user is feeling dissatisfied, it identifies areas for improvement that reflect that emotion. It also analyzes the emotional data and complements the content of the user's feedback to identify more accurate areas for improvement. For example, if the user is feeling anxious, it identifies areas for improvement that reflect that emotion. It also complements the content of the feedback based on the user's emotional data and identifies more accurate areas for improvement. For example, if the user is satisfied, it identifies areas for improvement that reflect that emotion. In this way, by complementing the content of the feedback based on the user's emotional data, it is possible to identify more accurate areas for improvement.

[0079] The haircut plan generation unit can clarify areas for improvement by sharing feedback with other users and gaining sympathy. The haircut plan generation unit, for example, can clarify areas for improvement by sharing feedback with other users and gaining sympathy. For example, users who want the same style can share feedback to identify common areas for improvement. Furthermore, by sharing user feedback and gaining sympathy from other users, areas for improvement can be clarified. For example, the feedback can be made public and comments from other users can be collected. Furthermore, by sharing feedback with other users and gaining sympathy, areas for improvement can be clarified. For example, users who use the same hairdresser can share feedback to identify common areas for improvement. In this way, by sharing feedback with other users, areas for improvement can be clarified by gaining sympathy.

[0080] The cutting plan generation unit can develop an algorithm that automatically generates improvements to the cutting plan based on feedback. The cutting plan generation unit, for example, develops an algorithm that automatically generates improvements to the cutting plan based on feedback. For example, it analyzes user comments and automatically generates specific improvements. Also, it develops an algorithm that allows the generation AI to automatically generate improvements to the cutting plan based on user feedback. For example, it analyzes user complaints and automatically generates improvements. Also, it develops an algorithm that automatically generates improvements to the cutting plan based on feedback. For example, it analyzes user wishes and automatically generates improvements to be reflected in the next cutting plan. In this way, by developing an algorithm that automatically generates improvements to the cutting plan based on feedback, the accuracy of the cutting plan is improved.

[0081] The cutting plan generation unit can use the emotion estimation function to analyze the user's emotion at the time of feedback and make suggestions that will elicit positive emotions. The cutting plan generation unit, for example, uses the emotion estimation function to analyze the user's emotion at the time of feedback and make suggestions that will elicit positive emotions. For example, if the user is feeling dissatisfied, a positive suggestion is made. The cutting plan generation unit also analyzes the user's emotion in real time and provides an interface for eliciting positive emotions at the time of feedback. For example, if the user is feeling anxious, a message that gives a sense of security is displayed. The emotion estimation function also analyzes the user's emotion at the time of feedback and makes suggestions that will elicit positive emotions. For example, if the user is feeling depressed, an encouraging message is displayed. In this way, by analyzing the user's emotion at the time of feedback and making suggestions that will elicit positive emotions, user satisfaction is improved.

[0082] The haircut plan generation unit can use the generation AI to compare the haircut image and the actual haircut result in a 3D model to visually show any gaps. The haircut plan generation unit, for example, uses the generation AI to compare the haircut image and the actual haircut result in a 3D model to visually show any gaps. For example, the style desired by the user and the actual haircut result are displayed side by side in a 3D model. A system is also constructed that compares the haircut image and the actual haircut result in a 3D model to visually show any gaps. For example, the style desired by the user and the actual haircut result are compared in a 3D animation. The generation AI can also compare the haircut image and the actual haircut result in a 3D model to visually show any gaps. For example, the style desired by the user and the actual haircut result are rotated and compared in a 3D model. In this way, by comparing the haircut image and the actual haircut result in a 3D model, any gaps can be visually shown.

[0083] The haircut plan generation unit can develop an algorithm that analyzes images of the resulting haircut and identifies specific areas for improvement. The haircut plan generation unit, for example, analyzes images of the resulting haircut and develops an algorithm that identifies specific areas for improvement. For example, it analyzes the length of the bangs and the volume of the sides and identifies areas for improvement. The generation AI also analyzes images of the resulting haircut and develops an algorithm that identifies specific areas for improvement. For example, it analyzes the length of the back and the overall balance and identifies areas for improvement. The generation AI also analyzes images of the resulting haircut and develops an algorithm that identifies specific areas for improvement. For example, it analyzes the consistency of the hair texture and style and identifies areas for improvement. In this way, by developing an algorithm that analyzes images of the resulting haircut and identifies specific areas for improvement, the accuracy of the haircut plan is improved.

[0084] The haircut plan generation unit can evaluate the user's satisfaction with the haircut result based on the user's emotional data and reflect this in the next haircut plan. The haircut plan generation unit, for example, evaluates the user's satisfaction with the haircut result based on the user's emotional data and reflects this in the next haircut plan. For example, if the user is satisfied, a haircut plan that reflects that emotion is proposed. The emotional data is also analyzed to evaluate the user's satisfaction with the haircut result and reflect this in the next haircut plan. For example, if the user is dissatisfied, areas for improvement that reflect that emotion are identified. The haircut plan generation unit also evaluates the user's satisfaction with the haircut result based on the user's emotional data and reflects this in the next haircut plan. For example, if the user is relaxed, a haircut plan that reflects that emotion is proposed. In this way, by evaluating the user's satisfaction with the haircut result based on the user's emotional data and reflecting this in the next haircut plan, a haircut with a higher level of satisfaction can be provided.

[0085] The haircut plan generation unit can clarify areas for improvement by sharing the results of the haircut with other users and gaining their sympathy. The haircut plan generation unit, for example, can clarify areas for improvement by sharing the results of the haircut with other users and gaining their sympathy. For example, users who want the same style can share the results of the haircut and identify common areas for improvement. Furthermore, the haircut results of the users can be shared and the areas for improvement can be clarified by gaining their sympathy. For example, the haircut results can be made public and comments from other users can be collected. Furthermore, the haircut results can be shared with other users and the areas for improvement can be clarified by gaining their sympathy. For example, users who use the same hairdresser can share the results of the haircut and identify common areas for improvement. In this way, by sharing the results of the haircut with other users, the areas for improvement can be clarified by gaining their sympathy.

[0086] The cutting plan generation unit can develop an algorithm that automatically generates the next cutting plan based on the cutting results. The cutting plan generation unit, for example, develops an algorithm that automatically generates the next cutting plan based on the cutting results. For example, it analyzes user feedback and automatically generates the next cutting plan. In addition, the generation AI analyzes the cutting results and develops an algorithm that automatically generates the next cutting plan. For example, it automatically generates a cutting plan that reflects the user's wishes. In addition, it develops an algorithm that automatically generates the next cutting plan based on the cutting results. For example, it analyzes user satisfaction and automatically generates the next cutting plan. In this way, by developing an algorithm that automatically generates the next cutting plan based on the cutting results, the accuracy of the cutting plan is improved.

[0087] The haircut plan generation unit can use the emotion estimation function to monitor the user's emotional reaction to the haircut result in real time and identify optimal areas for improvement. The haircut plan generation unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the haircut result in real time and identify optimal areas for improvement. For example, if the user is dissatisfied, the unit identifies areas for improvement that reflect that emotion. The haircut plan generation unit also monitors the user's emotional reaction in real time and identifies optimal areas for improvement to the haircut result. For example, if the user is satisfied, the unit identifies areas for improvement that reflect that emotion. The emotion estimation function also monitors the user's emotional reaction to the haircut result in real time and identifies optimal areas for improvement. For example, if the user is relaxed, the unit identifies areas for improvement that reflect that emotion. In this way, the optimal areas for improvement can be identified by monitoring the user's emotional reaction to the haircut result in real time.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] When a user inputs their desired hairstyle, the haircut image input unit can automatically suggest the most suitable style based on the user's face shape and hair type. For example, if the user has a round face, it can suggest a style that makes the face look slimmer. Also, if the user has thin hair, it can suggest a style that adds volume. Furthermore, the style input by the user can be customized based on the face shape and hair type. This makes it possible to provide a haircut plan that matches the user's individual characteristics.

[0090] When a user inputs their desired hairstyle, the haircut image input unit learns the user's past haircut history and preferred styles and can propose individually optimized haircut images. For example, it can generate a new haircut plan based on styles that have been well-received in the past. In addition, to learn the user's preferred styles, the AI ​​analyzes the user's past haircut history and preferred styles and proposes individually optimized haircut images. This makes it possible to provide a more personalized haircut plan by proposing haircut images that reflect the user's past haircut history and preferences.

[0091] The haircut image input unit can generate a 3D model of the hairstyle the user desires and provide an interface that allows the user to check it in real time. For example, when the user inputs the desired style, a 3D model is instantly generated and the user can check it. In addition, using 3D modeling technology, the haircut image input unit can generate the user's desired hairstyle in real time and allow the user to check it on the interface. This allows the user to check the desired hairstyle as a 3D model in real time, improving the accuracy of the haircut plan.

[0092] The haircut image input unit can provide the user with a variety of style options by referencing hairstyle databases from different cultures or regions. For example, it can propose styles for each region, such as Asia, Europe, and Africa. It also utilizes the hairstyle database to provide the user with styles from different cultures and regions. By referencing hairstyle databases from different cultures and regions, it is possible to provide the user with a variety of style options.

[0093] The haircut image input unit analyzes the voice data entered by the user and can more accurately understand the user's intentions from the tone or strength of the voice. For example, if the user emphasizes, "I want more volume," the AI ​​will generate a haircut plan that reflects that intention. In addition, using voice analysis technology, the AI ​​can analyze the tone and strength of the voice data entered by the user to more accurately understand the user's intentions. This allows the AI ​​to understand a more accurate haircut image and generate a haircut plan by analyzing the user's voice data.

[0094] The cut image input unit uses an emotion estimation function to estimate the user's emotion in real time when inputting and make suggestions to elicit positive emotions. For example, if the user is nervous, it makes suggestions to help them relax. It also estimates the user's emotion in real time when inputting and provides an interface for eliciting positive emotions. This improves user satisfaction by estimating the user's emotion in real time and making suggestions to elicit positive emotions.

[0095] The haircut plan generation unit can monitor the user's emotional response to the generated haircut plans in real time and select the optimal plan. For example, it can prioritize plans to which the user has a positive response. It can also monitor the user's emotional response in real time and select the optimal plan from the generated haircut plans. In this way, the optimal haircut plan can be selected by monitoring the user's emotional response in real time.

[0096] The cutting plan generation unit can complement the feedback content based on the user's emotional data and identify more accurate areas for improvement. For example, if the user is dissatisfied, the cutting plan generation unit identifies areas for improvement that reflect that emotion. The cutting plan generation unit also analyzes the emotional data and complements the user's feedback content to identify more accurate areas for improvement. In this way, by complementing the feedback content based on the user's emotional data, more accurate areas for improvement can be identified.

[0097] The cutting plan generation unit uses the emotion estimation function to analyze the user's emotions at the time of feedback and make suggestions that elicit positive emotions. For example, if the user is feeling dissatisfied, a positive suggestion is made. The cutting plan generation unit also analyzes the user's emotions in real time and provides an interface for eliciting positive emotions at the time of feedback. This allows the user's emotions at the time of feedback to be analyzed and suggestions that elicit positive emotions to be made, thereby improving user satisfaction.

[0098] The haircut plan generation unit uses the emotion estimation function to monitor the user's emotional response to the haircut results in real time and identify optimal areas for improvement. For example, if the user is dissatisfied, the unit identifies areas for improvement that reflect that emotion. The unit also monitors the user's emotional response in real time and identifies optimal areas for improvement to the haircut results. This makes it possible to identify optimal areas for improvement by monitoring the user's emotional response to the haircut results in real time.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The haircut image input unit allows the user to input the hairstyle they desire. For example, the user can input text such as "I want my bangs to be short and have volume all over." The user can also upload an image of the hairstyle they desire. Furthermore, the user can input their request by voice. For example, the user can input by voice such as "I want my bangs to be short and have volume all over." Step 2: The haircut plan generation unit analyzes the haircut image input by the haircut image input unit and generates an optimal haircut plan. For example, the AI ​​uses image analysis technology to analyze the image uploaded by the user and specifically plan which parts to cut and how. The AI ​​can also use natural language processing technology to analyze the text entered by the user and understand the desired style. Furthermore, the AI ​​can use voice analysis technology to analyze the voice entered by the user and understand the desired style. Step 3: The haircut plan provider provides the haircut plan generated by the haircut plan generator to the hairdresser. For example, the plan generated by the AI ​​includes specific instructions such as "cut the bangs by 2 cm and add layers to the sides."

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] 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.

[0127] 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.

[0128] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

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

[0137] 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.

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

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] 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.

[0143] 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.

[0144] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] 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.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0160] 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.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 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 haircut image input unit for inputting a desired hairstyle by the user; a cut plan generation unit that analyzes the cut image input by the cut image input unit and generates an optimal cut plan; a haircut plan providing unit that provides the haircut plan generated by the haircut plan generating unit to a hairdresser. A system characterized by:

2. The cut image input unit The generation AI performs sentiment analysis on the text or image entered by the user, and complements the cut image based on the user's sentiment.

2. The system of claim 1.

3. The cut image input unit The hairstyle desired by the user is generated as the 3D model, and an interface is provided that allows the user to check the hairstyle in real time.

2. The system of claim 1.

4. The cut plan generation unit Using the generation AI, the haircut plan optimal for the face shape or hair type of the user is automatically generated.

2. The system of claim 1.

5. The cutting plan providing unit The haircut plan is customized according to the skill level of the hairdresser using the generation AI.

2. The system of claim 1.

6. The cut plan generation unit The generation AI is used to analyze the user's feedback and reflect it in the next cutting plan.

2. The system of claim 1.

7. The cut plan generation unit Using the generating AI, the cut image and the cut result are compared with the 3D model, and gaps are visually indicated.

2. The system of claim 1.

8. The cut image input unit The user's emotions are estimated in real time as they input, and suggestions are made to elicit positive emotions.

2. The system of claim 1.

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