system

Abstract

The system according to this embodiment aims to allow customers to check styles that suit them in real time and to provide personalized suggestions through collaboration with hairdressers. [Solution] The system according to the embodiment comprises a comparison unit, a linking unit, and a suggestion unit. The comparison unit compares styles in real time. The linking unit allows the hairdresser to provide feedback on the styles suggested by the comparison unit. The suggestion unit provides personalized suggestions based on the feedback obtained from the linking unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a customer to choose a style that suits them, and there may be a gap in the finish due to insufficient communication with a beautician and problems with the timing of feedback.

[0005] The system according to the embodiment aims to enable a customer to confirm a style that suits them in real time and make a personalized proposal through cooperation with a beautician.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a comparison unit, a linking unit, and a suggestion unit. The comparison unit compares styles in real time. The linking unit allows the hairdresser to provide feedback on the styles suggested by the comparison unit. The suggestion unit provides personalized suggestions based on the feedback received from the linking unit. [Effects of the Invention]

[0007] The system according to this embodiment allows customers to check styles that suit them in real time and receive personalized suggestions through collaboration with hairdressers. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) MirrorStyle AI: Personal Beauty Experience System, according to an embodiment of the present invention, is a system that revolutionizes style selection in hair salons. This system utilizes AI technology to allow customers to check hairstyles and colors that suit them in real time. First, there is the challenge that it is difficult for customers to find hairstyles and colors that suit them. It is difficult to tell if a style will suit them from magazine images alone, and the suggested style may differ from the actual result. Such gaps arise from insufficient communication with the hairdresser and problems with the timing of feedback, which can lead to decreased customer satisfaction. MirrorStyle AI: Personal Beauty Experience System provides the following configuration and functions. First, it provides a real-time style comparison function. Customers can check the hairstyles and colors suggested by the AI ​​on their actual appearance in a real mirror. Rather than abstract images, customers can try multiple styles tailored to their hair type, bone structure, and individuality, enabling them to make the optimal choice. Next, it provides a function for collaboration with hairdressers. Hairdressers can provide immediate feedback on the styles and colors suggested by the AI, enabling suggestions that accurately reflect the customer's wishes. This allows hairdressers and customers to share the same finished image, significantly reducing the gap in the final result. Furthermore, it provides a personalized experience. By offering suggestions based on hair type, bone structure, personality, and preferences, the system increases the likelihood of customers finding a style that truly suits them. The ability to compare multiple options enhances the enjoyment of style selection. This system improves customer satisfaction and enhances the efficiency and skills of hairdressers. Furthermore, the personalized beauty experience utilizing the latest technology differentiates salons from others and leads to the development of new customer segments. As a result, MirrorStyle AI: Personal Beauty Experience System allows customers to see hairstyles and colors that suit them in real time. For example, customers can see styles tailored to their hair type and bone structure. It also allows hairdressers and customers to share the same desired finished look. Moreover, it increases the likelihood of customers finding a style that truly suits them.

[0029] The MirrorStyle AI Personal Beauty Experience System according to this embodiment comprises a comparison unit, a collaboration unit, and a suggestion unit. The comparison unit compares styles in real time. The comparison unit can, for example, use AI to analyze an image of the customer's face and display multiple hairstyles and colors in real time. The comparison unit can, for example, use a smart mirror or augmented reality (AR) technology to allow the customer to see the hairstyle and color on their actual appearance. The collaboration unit allows the hairdresser to provide feedback on the styles suggested by the comparison unit. The collaboration unit can, for example, allow the hairdresser to listen to the customer's requests on the spot and provide feedback on the suggested styles. The collaboration unit can provide feedback in the form of, for example, verbal, text, or images. The suggestion unit makes personalized suggestions based on the feedback obtained by the collaboration unit. The suggestion unit can, for example, use AI to analyze information such as the customer's hair type, bone structure, personality, and preferences and suggest the optimal style. The suggestion unit can, for example, make suggestions that allow the customer to compare multiple patterns, increasing the probability that the customer will find a style that truly suits them. As a result, the MirrorStyle AI: Personal Beauty Experience System according to this embodiment allows customers to check hairstyles and colors that suit them in real time. For example, customers can check styles that suit their hair type and bone structure. In addition, the hairdresser and the customer can share the same finished image. Furthermore, the probability of customers finding a style that truly suits them is increased.

[0030] The comparison unit compares styles in real time. Specifically, it uses AI to analyze images of the customer's face and display multiple hairstyles and colors in real time. The AI ​​first acquires an image of the customer's face and analyzes its features using facial recognition technology. This extracts information such as the customer's face shape, bone structure, and skin color. Next, the AI ​​compares this information with a pre-registered database of hairstyles and colors to select the most suitable style for the customer. For example, the AI ​​suggests styles such as short hair for round faces and long hair for long faces, depending on the customer's face shape. It also suggests colors such as brown for light skin tones and black for dark skin tones, depending on skin color. Furthermore, the comparison unit uses smart mirrors and augmented reality (AR) technology to allow customers to check hairstyles and colors on their actual appearance. The smart mirror has a display built into the mirror surface and overlays hairstyles and colors onto the customer's face image. This allows customers to check their appearance as if they had actually changed their hairstyle or color. AR technology overlays hairstyles and colors onto the customer's face through the camera of a smartphone or tablet. This allows customers to easily try out styles anywhere. This allows the comparison unit to provide customers with an environment where they can check hairstyles and colors that suit them in real time, thereby improving the accuracy of their style selection.

[0031] The collaboration department allows hairdressers to provide feedback on styles proposed by the comparison department. Specifically, hairdressers can listen to customer requests on the spot and provide feedback on the proposed styles. The collaboration department can provide feedback in various forms, such as verbally, in text, or with images. Hairdressers consider customer requests, hair type, bone structure, and individuality to provide appropriate advice on the proposed styles. For example, if a customer wants a short haircut, the hairdresser will determine whether a short haircut suits them based on the customer's face shape and hair type, and propose an appropriate style. Also, if a customer wants a specific color, the hairdresser will determine whether that color suits them based on the customer's skin tone and hair type, and propose an appropriate color. Furthermore, when hairdressers provide feedback, the collaboration department can use AI to analyze customer information and provide more accurate feedback. For example, the AI ​​can analyze the customer's past style history and preferences and provide appropriate advice to the hairdresser. This allows the collaboration department to provide an environment where hairdressers and customers can share the same finished image, improving the accuracy of style selection.

[0032] The Proposal Department provides personalized suggestions based on feedback received from the Collaboration Department. Specifically, it uses AI to analyze information such as the customer's hair type, bone structure, personality, and preferences to suggest the optimal style. The AI ​​first collects information such as the customer's hair type, bone structure, personality, and preferences, and then selects the optimal style based on this information. For example, if the customer's hair is fine and soft, the AI ​​will suggest a voluminous style; if the hair is thick and coarse, it will suggest a straight style. It will also suggest a style that complements the customer's facial features, based on their bone structure. Furthermore, it will suggest styles that are in line with trends and the season, based on the customer's personality and preferences. The Proposal Department provides suggestions that allow customers to compare multiple patterns, increasing the probability that customers will find a style that truly suits them. For example, the AI ​​will suggest several styles to the customer and explain the advantages and disadvantages of each style. Based on these suggestions, the customer can choose the style that suits them best. In addition, the Proposal Department can collect customer feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to increase the probability that customers will find a style that truly suits them, thereby increasing customer satisfaction.

[0033] The comparison unit allows users to check their hairstyle and color in front of a real mirror, seeing themselves in the mirror. The comparison unit can, for example, use a smart mirror to allow customers to check their hairstyle and color in the mirror. The comparison unit can, for example, use augmented reality (AR) technology to allow customers to check their hairstyle and color in the mirror. The comparison unit can, for example, use AI to analyze an image of the customer's face and display hairstyles and colors in real time. This allows customers to check styles that suit their hair type and bone structure. Real mirrors include, but are not limited to, smart mirrors and augmented reality (AR) technology.

[0034] The collaborative system allows hairdressers to provide on-the-spot feedback, accurately reflecting customer requests. For example, the collaborative system enables hairdressers to listen to customer requests in real time and provide feedback on proposed styles. Feedback can be provided in various forms, such as verbally, in text, or with images. The collaborative system can also analyze hairdresser feedback using AI to accurately reflect customer requests. This ensures that hairdressers and customers share the same vision of the final result. On-the-spot feedback includes, but is not limited to, feedback during treatment or consultation.

[0035] The suggestion department makes suggestions based on hair type, bone structure, personality, and preferences. For example, the suggestion department can use AI to analyze information such as the customer's hair type, bone structure, personality, and preferences, and propose the optimal style. For example, the suggestion department can make suggestions that allow customers to compare multiple patterns, increasing the probability that customers will find a style that truly suits them. For example, the suggestion department can use AI to propose styles based on the customer's hair type and bone structure. This increases the probability that customers will find a style that truly suits them. Hair type includes, but is not limited to, straight hair, curly hair, thickness, and hardness. Bone structure includes, but is not limited to, face shape and head shape. Personality includes, but is not limited to, fashion style and lifestyle. Preferences include, but is not limited to, questionnaires and past selection history.

[0036] The proposal department can make suggestions that allow customers to compare multiple patterns. For example, the proposal department can use AI to generate multiple hairstyle and color patterns and propose them to customers. For example, the proposal department can make suggestions that allow customers to compare variations in color and style. For example, the proposal department can use AI to suggest multiple styles based on the customer's preferences. This enhances the customer's enjoyment of choosing a style. Multiple patterns include, but are not limited to, variations in color and style.

[0037] The comparison unit analyzes the customer's past style history and prioritizes displaying the most suitable styles. For example, the comparison unit can use AI to analyze the customer's past style history and prioritize displaying similar styles. For example, the comparison unit can suggest styles suitable for each season based on the customer's past style history. For example, the comparison unit can analyze the customer's past style history and prioritize displaying styles that align with current trends. This enables the display of optimal styles based on the customer's past style history. Past style history includes, but is not limited to, photos and text records.

[0038] The comparison unit analyzes the customer's facial expressions and posture in real time when comparing styles and proposes the optimal style. For example, the comparison unit can use AI to analyze the customer's facial expressions and propose a style that enhances their smile. For example, the comparison unit can use AI to analyze the customer's posture and propose a style that suits their posture. For example, the comparison unit can use AI to comprehensively analyze the customer's facial expressions and posture and propose the most balanced style. This makes it possible to propose the optimal style based on the customer's facial expressions and posture. Facial expressions include, but are not limited to, facial expression recognition technology and emotion analysis. Posture includes, but are not limited to, posture recognition technology and skeletal analysis.

[0039] The comparison unit prioritizes displaying region-specific styles when comparing styles, taking into account the customer's geographical location. For example, the comparison unit can use AI to analyze the customer's geographical location and suggest region-specific styles. For example, if the customer lives in an urban area, the comparison unit can prioritize displaying urban styles. For example, if the customer lives by the sea, the comparison unit can prioritize displaying resort-style styles. For example, if the customer lives in a cold region, the comparison unit can prioritize displaying warm styles. This enables the display of region-specific styles. Geographical location information includes, but is not limited to, GPS data and location services.

[0040] The comparison unit analyzes the customer's social media activity when comparing styles and displays relevant styles. For example, the comparison unit can use AI to analyze the customer's social media activity and suggest relevant styles. For example, the comparison unit can display styles of influencers the customer follows on social media. For example, the comparison unit can prioritize displaying styles that the customer has "liked" on social media. For example, the comparison unit can analyze the customer's social media activity and display styles that are in line with current trends. This enables style display based on the customer's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers.

[0041] The collaboration department analyzes the hairdresser's past feedback history and selects the optimal feedback method. For example, the collaboration department can use AI to analyze the hairdresser's past feedback history and propose the optimal feedback method. For example, the collaboration department can select a feedback method that yields high customer satisfaction based on the hairdresser's past feedback history. For example, the collaboration department can analyze the hairdresser's past feedback history and select an efficient feedback method. This makes it possible to select the optimal feedback method based on the hairdresser's past feedback history. Past feedback history includes, but is not limited to, text records and audio recordings.

[0042] The collaborative unit analyzes the customer's real-time response during feedback and adjusts the feedback content accordingly. For example, the collaborative unit can use AI to analyze the customer's facial expressions and voice to estimate real-time responses. For example, if the customer is satisfied, the collaborative unit can emphasize positive feedback. For example, if the customer is dissatisfied, the collaborative unit can specifically point out areas for improvement. The collaborative unit can analyze the customer's real-time response and provide appropriate feedback. This makes it possible to adjust the feedback content based on the customer's real-time response. Real-time responses include, but are not limited to, facial expression analysis and voice analysis.

[0043] The collaboration unit provides region-specific feedback by considering the geographical location information of the hairdresser during the feedback process. For example, the collaboration unit can use AI to analyze the geographical location information of a hairdresser and provide region-specific feedback. For example, the collaboration unit can provide urban style feedback to a hairdresser in an urban area. For example, the collaboration unit can provide resort-style style feedback to a hairdresser by the sea. For example, the collaboration unit can provide warm style feedback to a hairdresser in a cold region. This makes it possible to provide region-specific feedback. Geographical location information includes, but is not limited to, GPS data and location services.

[0044] The Collaboration Department analyzes the hairdresser's social media activity during feedback and provides relevant feedback. For example, the Collaboration Department can use AI to analyze the hairdresser's social media activity and provide relevant feedback. For example, the Collaboration Department can provide feedback on the styles of influencers that the hairdresser follows on social media. For example, the Collaboration Department can provide feedback on styles that the hairdresser has "liked" on social media. For example, the Collaboration Department can analyze the hairdresser's social media activity and provide trend-aligned feedback. This makes it possible to provide feedback based on the hairdresser's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers.

[0045] The proposal department analyzes the customer's past style history when making a proposal to provide the most suitable suggestions. For example, the proposal department can use AI to analyze the customer's past style history and suggest similar styles. For example, the proposal department can suggest styles suitable for each season based on the customer's past style history. For example, the proposal department can analyze the customer's past style history and suggest styles that are in line with current trends. This enables the provision of optimal suggestions based on the customer's past style history. Past style history includes, but is not limited to, photographs and text records.

[0046] The proposal department customizes proposals based on the customer's current lifestyle and areas of interest. For example, the proposal department can use AI to analyze the customer's current lifestyle and areas of interest and make optimal proposals. For example, if the customer is busy with work, the proposal department can suggest an easy-to-maintain style. For example, if the customer enjoys sports as a hobby, the proposal department can suggest a style that allows for easy movement. For example, if the customer plans to attend a specific event, the proposal department can suggest a style suitable for that event. This makes it possible to customize proposals based on the customer's current lifestyle and areas of interest. Current lifestyle includes, but is not limited to, surveys and interviews. Areas of interest include, but is not limited to, surveys and social media activity.

[0047] The proposal department considers the customer's geographical location when making proposals, providing region-specific suggestions. For example, the proposal department can use AI to analyze the customer's geographical location and propose region-specific styles. For example, the proposal department can propose an urban style to a customer living in an urban area. For example, the proposal department can propose a resort-style to a customer living by the sea. For example, the proposal department can propose a warm style to a customer living in a cold region. This enables region-specific suggestions. Geographical location information includes, but is not limited to, GPS data and location services.

[0048] The proposal department analyzes the customer's social media activity and makes relevant suggestions when making proposals. For example, the proposal department can use AI to analyze the customer's social media activity and suggest relevant styles. For example, the proposal department can suggest styles from influencers the customer follows on social media. For example, the proposal department can prioritize suggesting styles that the customer has "liked" on social media. For example, the proposal department can analyze the customer's social media activity and suggest styles that are in line with current trends. This makes it possible to make suggestions based on the customer's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The proposal department can analyze a customer's past style history and make optimal suggestions. For example, it can use AI to analyze a customer's past style history and suggest similar styles. It can also suggest styles suitable for each season based on a customer's past style history. By analyzing a customer's past style history, it can suggest styles that are in line with current trends. This makes it possible to make optimal suggestions based on a customer's past style history.

[0051] The comparison section can prioritize displaying region-specific styles by considering the customer's geographical location. For example, it can use AI to analyze the customer's geographical location and suggest region-specific styles. If the customer lives in an urban area, urban styles can be prioritized. If the customer lives by the sea, resort-style styles can be prioritized. If the customer lives in a cold region, warm styles can be prioritized. This enables the display of region-specific styles.

[0052] The collaborative unit can analyze the customer's real-time reactions during feedback and adjust the feedback content accordingly. For example, it can use AI to analyze the customer's facial expressions and voice to estimate their real-time reactions. If the customer is satisfied, positive feedback can be emphasized. If the customer is dissatisfied, specific areas for improvement can be pointed out. By analyzing the customer's real-time reactions, appropriate feedback can be provided. This makes it possible to adjust the feedback content based on the customer's real-time reactions.

[0053] The proposal department can customize proposals based on the customer's current lifestyle and areas of interest. For example, it can use AI to analyze the customer's current lifestyle and areas of interest and provide optimal suggestions. If the customer is busy with work, it can suggest a style that is easy to maintain. If the customer enjoys sports as a hobby, it can suggest a style that allows for easy movement. If the customer has plans to attend a specific event, it can suggest a style that is suitable for that event. This makes it possible to customize proposals based on the customer's current lifestyle and areas of interest.

[0054] The comparison unit can analyze the customer's facial expression and posture in real time when comparing styles, and suggest the optimal style. For example, it can use AI to analyze the customer's facial expression and suggest a style that enhances their smile. It can also use AI to analyze the customer's posture and suggest a style that suits their posture. Furthermore, it can use AI to comprehensively analyze the customer's facial expression and posture and suggest the most balanced style. This makes it possible to suggest the optimal style based on the customer's facial expression and posture.

[0055] The collaborative department can analyze a hairdresser's past feedback history and select the optimal feedback method. For example, it can use AI to analyze a hairdresser's past feedback history and propose the most suitable feedback method. It can select feedback methods that result in high customer satisfaction based on a hairdresser's past feedback history. It can analyze a hairdresser's past feedback history and select an efficient feedback method. This makes it possible to select the optimal feedback method based on a hairdresser's past feedback history.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The comparison section compares styles in real time. For example, it can use AI to analyze a customer's facial image and display multiple hairstyles and colors in real time. Furthermore, smart mirrors and augmented reality (AR) technology are used to allow customers to see the hairstyles and colors on their actual appearance. Step 2: The collaboration department has the hairdresser provide feedback on the style proposed by the comparison department. For example, the hairdresser can listen to the customer's requests on the spot and provide feedback on the proposed style. The feedback can be provided verbally, in text, or in the form of images. Step 3: The proposal department provides personalized suggestions based on feedback received from the collaboration department. For example, it can use AI to analyze information such as the customer's hair type, bone structure, personality, and preferences to suggest the optimal style. Furthermore, it can provide suggestions that allow customers to compare multiple patterns, increasing the likelihood that they will find a style that truly suits them.

[0058] (Example of form 2) MirrorStyle AI: Personal Beauty Experience System, according to an embodiment of the present invention, is a system that revolutionizes style selection in hair salons. This system utilizes AI technology to allow customers to check hairstyles and colors that suit them in real time. First, there is the challenge that it is difficult for customers to find hairstyles and colors that suit them. It is difficult to tell if a style will suit them from magazine images alone, and the suggested style may differ from the actual result. Such gaps arise from insufficient communication with the hairdresser and problems with the timing of feedback, which can lead to decreased customer satisfaction. MirrorStyle AI: Personal Beauty Experience System provides the following configuration and functions. First, it provides a real-time style comparison function. Customers can check the hairstyles and colors suggested by the AI ​​on their actual appearance in a real mirror. Rather than abstract images, customers can try multiple styles tailored to their hair type, bone structure, and individuality, enabling them to make the optimal choice. Next, it provides a function for collaboration with hairdressers. Hairdressers can provide immediate feedback on the styles and colors suggested by the AI, enabling suggestions that accurately reflect the customer's wishes. This allows hairdressers and customers to share the same finished image, significantly reducing the gap in the final result. Furthermore, it provides a personalized experience. By offering suggestions based on hair type, bone structure, personality, and preferences, the system increases the likelihood of customers finding a style that truly suits them. The ability to compare multiple options enhances the enjoyment of style selection. This system improves customer satisfaction and enhances the efficiency and skills of hairdressers. Furthermore, the personalized beauty experience utilizing the latest technology differentiates salons from others and leads to the development of new customer segments. As a result, MirrorStyle AI: Personal Beauty Experience System allows customers to see hairstyles and colors that suit them in real time. For example, customers can see styles tailored to their hair type and bone structure. It also allows hairdressers and customers to share the same desired finished look. Moreover, it increases the likelihood of customers finding a style that truly suits them.

[0059] The MirrorStyle AI Personal Beauty Experience System according to this embodiment comprises a comparison unit, a collaboration unit, and a suggestion unit. The comparison unit compares styles in real time. The comparison unit can, for example, use AI to analyze an image of the customer's face and display multiple hairstyles and colors in real time. The comparison unit can, for example, use a smart mirror or augmented reality (AR) technology to allow the customer to see the hairstyle and color on their actual appearance. The collaboration unit allows the hairdresser to provide feedback on the styles suggested by the comparison unit. The collaboration unit can, for example, allow the hairdresser to listen to the customer's requests on the spot and provide feedback on the suggested styles. The collaboration unit can provide feedback in the form of, for example, verbal, text, or images. The suggestion unit makes personalized suggestions based on the feedback obtained by the collaboration unit. The suggestion unit can, for example, use AI to analyze information such as the customer's hair type, bone structure, personality, and preferences and suggest the optimal style. The suggestion unit can, for example, make suggestions that allow the customer to compare multiple patterns, increasing the probability that the customer will find a style that truly suits them. As a result, the MirrorStyle AI: Personal Beauty Experience System according to this embodiment allows customers to check hairstyles and colors that suit them in real time. For example, customers can check styles that suit their hair type and bone structure. In addition, the hairdresser and the customer can share the same finished image. Furthermore, the probability of customers finding a style that truly suits them is increased.

[0060] The comparison unit compares styles in real time. Specifically, it uses AI to analyze images of the customer's face and display multiple hairstyles and colors in real time. The AI ​​first acquires an image of the customer's face and analyzes its features using facial recognition technology. This extracts information such as the customer's face shape, bone structure, and skin color. Next, the AI ​​compares this information with a pre-registered database of hairstyles and colors to select the most suitable style for the customer. For example, the AI ​​suggests styles such as short hair for round faces and long hair for long faces, depending on the customer's face shape. It also suggests colors such as brown for light skin tones and black for dark skin tones, depending on skin color. Furthermore, the comparison unit uses smart mirrors and augmented reality (AR) technology to allow customers to check hairstyles and colors on their actual appearance. The smart mirror has a display built into the mirror surface and overlays hairstyles and colors onto the customer's face image. This allows customers to check their appearance as if they had actually changed their hairstyle or color. AR technology overlays hairstyles and colors onto the customer's face through the camera of a smartphone or tablet. This allows customers to easily try out styles anywhere. This allows the comparison unit to provide customers with an environment where they can check hairstyles and colors that suit them in real time, thereby improving the accuracy of their style selection.

[0061] The collaboration department allows hairdressers to provide feedback on styles proposed by the comparison department. Specifically, hairdressers can listen to customer requests on the spot and provide feedback on the proposed styles. The collaboration department can provide feedback in various forms, such as verbally, in text, or with images. Hairdressers consider customer requests, hair type, bone structure, and individuality to provide appropriate advice on the proposed styles. For example, if a customer wants a short haircut, the hairdresser will determine whether a short haircut suits them based on the customer's face shape and hair type, and propose an appropriate style. Also, if a customer wants a specific color, the hairdresser will determine whether that color suits them based on the customer's skin tone and hair type, and propose an appropriate color. Furthermore, when hairdressers provide feedback, the collaboration department can use AI to analyze customer information and provide more accurate feedback. For example, the AI ​​can analyze the customer's past style history and preferences and provide appropriate advice to the hairdresser. This allows the collaboration department to provide an environment where hairdressers and customers can share the same finished image, improving the accuracy of style selection.

[0062] The Proposal Department provides personalized suggestions based on feedback received from the Collaboration Department. Specifically, it uses AI to analyze information such as the customer's hair type, bone structure, personality, and preferences to suggest the optimal style. The AI ​​first collects information such as the customer's hair type, bone structure, personality, and preferences, and then selects the optimal style based on this information. For example, if the customer's hair is fine and soft, the AI ​​will suggest a voluminous style; if the hair is thick and coarse, it will suggest a straight style. It will also suggest a style that complements the customer's facial features, based on their bone structure. Furthermore, it will suggest styles that are in line with trends and the season, based on the customer's personality and preferences. The Proposal Department provides suggestions that allow customers to compare multiple patterns, increasing the probability that customers will find a style that truly suits them. For example, the AI ​​will suggest several styles to the customer and explain the advantages and disadvantages of each style. Based on these suggestions, the customer can choose the style that suits them best. In addition, the Proposal Department can collect customer feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to increase the probability that customers will find a style that truly suits them, thereby increasing customer satisfaction.

[0063] The comparison unit allows users to check their hairstyle and color in front of a real mirror, seeing themselves in the mirror. The comparison unit can, for example, use a smart mirror to allow customers to check their hairstyle and color in the mirror. The comparison unit can, for example, use augmented reality (AR) technology to allow customers to check their hairstyle and color in the mirror. The comparison unit can, for example, use AI to analyze an image of the customer's face and display hairstyles and colors in real time. This allows customers to check styles that suit their hair type and bone structure. Real mirrors include, but are not limited to, smart mirrors and augmented reality (AR) technology.

[0064] The collaborative system allows hairdressers to provide on-the-spot feedback, accurately reflecting customer requests. For example, the collaborative system enables hairdressers to listen to customer requests in real time and provide feedback on proposed styles. Feedback can be provided in various forms, such as verbally, in text, or with images. The collaborative system can also analyze hairdresser feedback using AI to accurately reflect customer requests. This ensures that hairdressers and customers share the same vision of the final result. On-the-spot feedback includes, but is not limited to, feedback during treatment or consultation.

[0065] The suggestion department makes suggestions based on hair type, bone structure, personality, and preferences. For example, the suggestion department can use AI to analyze information such as the customer's hair type, bone structure, personality, and preferences, and propose the optimal style. For example, the suggestion department can make suggestions that allow customers to compare multiple patterns, increasing the probability that customers will find a style that truly suits them. For example, the suggestion department can use AI to propose styles based on the customer's hair type and bone structure. This increases the probability that customers will find a style that truly suits them. Hair type includes, but is not limited to, straight hair, curly hair, thickness, and hardness. Bone structure includes, but is not limited to, face shape and head shape. Personality includes, but is not limited to, fashion style and lifestyle. Preferences include, but is not limited to, questionnaires and past selection history.

[0066] The proposal department can make suggestions that allow customers to compare multiple patterns. For example, the proposal department can use AI to generate multiple hairstyle and color patterns and propose them to customers. For example, the proposal department can make suggestions that allow customers to compare variations in color and style. For example, the proposal department can use AI to suggest multiple styles based on the customer's preferences. This enhances the customer's enjoyment of choosing a style. Multiple patterns include, but are not limited to, variations in color and style.

[0067] The comparison unit estimates the customer's emotions and adjusts the style display method based on the estimated emotions. The comparison unit can, for example, use AI to analyze the customer's facial expressions and voice to estimate emotions. For example, if the customer is nervous, the comparison unit can display a simple and highly visible style. For example, if the customer is relaxed, the comparison unit can display a style that includes detailed information. For example, if the customer is excited, the comparison unit can display a style with visually stimulating effects. This enables style display according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Customer emotions include, but are not limited to, facial expression analysis and voice analysis.

[0068] The comparison unit analyzes the customer's past style history and prioritizes displaying the most suitable styles. For example, the comparison unit can use AI to analyze the customer's past style history and prioritize displaying similar styles. For example, the comparison unit can suggest styles suitable for each season based on the customer's past style history. For example, the comparison unit can analyze the customer's past style history and prioritize displaying styles that align with current trends. This enables the display of optimal styles based on the customer's past style history. Past style history includes, but is not limited to, photos and text records.

[0069] The comparison unit analyzes the customer's facial expressions and posture in real time when comparing styles and proposes the optimal style. For example, the comparison unit can use AI to analyze the customer's facial expressions and propose a style that enhances their smile. For example, the comparison unit can use AI to analyze the customer's posture and propose a style that suits their posture. For example, the comparison unit can use AI to comprehensively analyze the customer's facial expressions and posture and propose the most balanced style. This makes it possible to propose the optimal style based on the customer's facial expressions and posture. Facial expressions include, but are not limited to, facial expression recognition technology and emotion analysis. Posture includes, but are not limited to, posture recognition technology and skeletal analysis.

[0070] The comparison unit estimates the customer's emotions and determines style priorities based on the estimated emotions. The comparison unit can, for example, use AI to analyze the customer's facial expressions and voice to estimate emotions. For example, if the customer is nervous, the comparison unit can prioritize displaying calm styles. For example, if the customer is relaxed, the comparison unit can prioritize displaying adventurous styles. For example, if the customer is excited, the comparison unit can prioritize displaying visually stimulating styles. This makes it possible to prioritize styles according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Customer emotions include, but are not limited to, facial expression analysis and voice analysis.

[0071] The comparison unit prioritizes displaying region-specific styles when comparing styles, taking into account the customer's geographical location. For example, the comparison unit can use AI to analyze the customer's geographical location and suggest region-specific styles. For example, if the customer lives in an urban area, the comparison unit can prioritize displaying urban styles. For example, if the customer lives by the sea, the comparison unit can prioritize displaying resort-style styles. For example, if the customer lives in a cold region, the comparison unit can prioritize displaying warm styles. This enables the display of region-specific styles. Geographical location information includes, but is not limited to, GPS data and location services.

[0072] The comparison unit analyzes the customer's social media activity when comparing styles and displays relevant styles. For example, the comparison unit can use AI to analyze the customer's social media activity and suggest relevant styles. For example, the comparison unit can display styles of influencers the customer follows on social media. For example, the comparison unit can prioritize displaying styles that the customer has "liked" on social media. For example, the comparison unit can analyze the customer's social media activity and display styles that are in line with current trends. This enables style display based on the customer's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers.

[0073] The collaborative unit estimates the hairdresser's emotions and adjusts the feedback method based on the estimated emotions. For example, the collaborative unit can use AI to analyze the hairdresser's facial expressions and voice to estimate emotions. For example, if the hairdresser is tired, the collaborative unit can provide a concise and efficient feedback method. For example, if the hairdresser is relaxed, the collaborative unit can provide a detailed feedback method. For example, if the hairdresser is nervous, the collaborative unit can provide a calm feedback method. This makes it possible to adjust the feedback method according to the hairdresser's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The hairdresser's emotions include, but are not limited to, facial expression analysis and voice analysis.

[0074] The collaboration department analyzes the hairdresser's past feedback history and selects the optimal feedback method. For example, the collaboration department can use AI to analyze the hairdresser's past feedback history and propose the optimal feedback method. For example, the collaboration department can select a feedback method that yields high customer satisfaction based on the hairdresser's past feedback history. For example, the collaboration department can analyze the hairdresser's past feedback history and select an efficient feedback method. This makes it possible to select the optimal feedback method based on the hairdresser's past feedback history. Past feedback history includes, but is not limited to, text records and audio recordings.

[0075] The collaborative unit analyzes the customer's real-time response during feedback and adjusts the feedback content accordingly. For example, the collaborative unit can use AI to analyze the customer's facial expressions and voice to estimate real-time responses. For example, if the customer is satisfied, the collaborative unit can emphasize positive feedback. For example, if the customer is dissatisfied, the collaborative unit can specifically point out areas for improvement. The collaborative unit can analyze the customer's real-time response and provide appropriate feedback. This makes it possible to adjust the feedback content based on the customer's real-time response. Real-time responses include, but are not limited to, facial expression analysis and voice analysis.

[0076] The collaborative unit estimates the hairdresser's emotions and determines the priority of feedback based on the estimated emotions. For example, the collaborative unit can use AI to analyze the hairdresser's facial expressions and voice to estimate emotions. For example, if the hairdresser is tired, the collaborative unit can prioritize providing important feedback. For example, if the hairdresser is relaxed, the collaborative unit can prioritize providing detailed feedback. For example, if the hairdresser is nervous, the collaborative unit can prioritize providing concise feedback. This makes it possible to prioritize feedback according to the hairdresser's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The hairdresser's emotions include, but are not limited to, facial expression analysis and voice analysis.

[0077] The collaboration unit provides region-specific feedback by considering the geographical location information of the hairdresser during the feedback process. For example, the collaboration unit can use AI to analyze the geographical location information of a hairdresser and provide region-specific feedback. For example, the collaboration unit can provide urban style feedback to a hairdresser in an urban area. For example, the collaboration unit can provide resort-style style feedback to a hairdresser by the sea. For example, the collaboration unit can provide warm style feedback to a hairdresser in a cold region. This makes it possible to provide region-specific feedback. Geographical location information includes, but is not limited to, GPS data and location services.

[0078] The Collaboration Department analyzes the hairdresser's social media activity during feedback and provides relevant feedback. For example, the Collaboration Department can use AI to analyze the hairdresser's social media activity and provide relevant feedback. For example, the Collaboration Department can provide feedback on the styles of influencers that the hairdresser follows on social media. For example, the Collaboration Department can provide feedback on styles that the hairdresser has "liked" on social media. For example, the Collaboration Department can analyze the hairdresser's social media activity and provide trend-aligned feedback. This makes it possible to provide feedback based on the hairdresser's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers.

[0079] The proposal department estimates the customer's emotions and adjusts the presentation of the proposal based on the estimated emotions. For example, the proposal department can use AI to analyze the customer's facial expressions and voice to estimate their emotions. For example, if the customer is nervous, the proposal department can make simple and highly visual proposals. For example, if the customer is relaxed, the proposal department can make proposals that include detailed information. For example, if the customer is excited, the proposal department can make proposals with visually stimulating effects. This makes it possible to adjust the presentation of proposals according to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Customer emotions include, but are not limited to, facial expression analysis and voice analysis.

[0080] The proposal department analyzes the customer's past style history when making a proposal to provide the most suitable suggestions. For example, the proposal department can use AI to analyze the customer's past style history and suggest similar styles. For example, the proposal department can suggest styles suitable for each season based on the customer's past style history. For example, the proposal department can analyze the customer's past style history and suggest styles that are in line with current trends. This enables the provision of optimal suggestions based on the customer's past style history. Past style history includes, but is not limited to, photographs and text records.

[0081] The proposal department customizes proposals based on the customer's current lifestyle and areas of interest. For example, the proposal department can use AI to analyze the customer's current lifestyle and areas of interest and make optimal proposals. For example, if the customer is busy with work, the proposal department can suggest an easy-to-maintain style. For example, if the customer enjoys sports as a hobby, the proposal department can suggest a style that allows for easy movement. For example, if the customer plans to attend a specific event, the proposal department can suggest a style suitable for that event. This makes it possible to customize proposals based on the customer's current lifestyle and areas of interest. Current lifestyle includes, but is not limited to, surveys and interviews. Areas of interest include, but is not limited to, surveys and social media activity.

[0082] The suggestion department estimates the customer's emotions and prioritizes suggestions based on those emotions. For example, the suggestion department can use AI to analyze the customer's facial expressions and voice to estimate their emotions. For example, if the customer is nervous, the suggestion department can prioritize suggesting a calm style. For example, if the customer is relaxed, the suggestion department can prioritize suggesting an adventurous style. For example, if the customer is excited, the suggestion department can prioritize suggesting a visually stimulating style. This makes it possible to prioritize suggestions according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Customer emotions include, but are not limited to, facial expression analysis and voice analysis.

[0083] The proposal department considers the customer's geographical location when making proposals, providing region-specific suggestions. For example, the proposal department can use AI to analyze the customer's geographical location and propose region-specific styles. For example, the proposal department can propose an urban style to a customer living in an urban area. For example, the proposal department can propose a resort-style to a customer living by the sea. For example, the proposal department can propose a warm style to a customer living in a cold region. This enables region-specific suggestions. Geographical location information includes, but is not limited to, GPS data and location services.

[0084] The proposal department analyzes the customer's social media activity and makes relevant suggestions when making proposals. For example, the proposal department can use AI to analyze the customer's social media activity and suggest relevant styles. For example, the proposal department can suggest styles from influencers the customer follows on social media. For example, the proposal department can prioritize suggesting styles that the customer has "liked" on social media. For example, the proposal department can analyze the customer's social media activity and suggest styles that are in line with current trends. This makes it possible to make suggestions based on the customer's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The comparison unit can estimate the customer's emotions and adjust the style display method based on the estimated emotions. For example, it can use AI to analyze the customer's facial expressions and voice to estimate their emotions. If the customer is nervous, a simple and highly visible style display can be shown. If the customer is relaxed, a style display with detailed information can be shown. If the customer is excited, a style display with visually stimulating effects can be shown. This makes it possible to display styles according to the customer's emotions.

[0087] The proposal department can analyze a customer's past style history and make optimal suggestions. For example, it can use AI to analyze a customer's past style history and suggest similar styles. It can also suggest styles suitable for each season based on a customer's past style history. By analyzing a customer's past style history, it can suggest styles that are in line with current trends. This makes it possible to make optimal suggestions based on a customer's past style history.

[0088] The collaborative unit can estimate the emotions of the hairdresser and adjust the feedback method based on the estimated emotions. For example, it can use AI to analyze the hairdresser's facial expressions and voice to estimate their emotions. If the hairdresser is tired, it can provide concise and efficient feedback. If the hairdresser is relaxed, it can provide detailed feedback. If the hairdresser is tense, it can provide calm feedback. This makes it possible to adjust the feedback method according to the hairdresser's emotions.

[0089] The comparison section can prioritize displaying region-specific styles by considering the customer's geographical location. For example, it can use AI to analyze the customer's geographical location and suggest region-specific styles. If the customer lives in an urban area, urban styles can be prioritized. If the customer lives by the sea, resort-style styles can be prioritized. If the customer lives in a cold region, warm styles can be prioritized. This enables the display of region-specific styles.

[0090] The proposal department can estimate the customer's emotions and adjust the way the proposal is presented based on those estimated emotions. For example, it can use AI to analyze the customer's facial expressions and voice to estimate their emotions. If the customer is nervous, it can present a simple and highly visual proposal. If the customer is relaxed, it can present a proposal that includes detailed information. If the customer is excited, it can present a proposal with visually stimulating effects. This makes it possible to adjust the way the proposal is presented according to the customer's emotions.

[0091] The collaborative unit can analyze the customer's real-time reactions during feedback and adjust the feedback content accordingly. For example, it can use AI to analyze the customer's facial expressions and voice to estimate their real-time reactions. If the customer is satisfied, positive feedback can be emphasized. If the customer is dissatisfied, specific areas for improvement can be pointed out. By analyzing the customer's real-time reactions, appropriate feedback can be provided. This makes it possible to adjust the feedback content based on the customer's real-time reactions.

[0092] The proposal department can customize proposals based on the customer's current lifestyle and areas of interest. For example, it can use AI to analyze the customer's current lifestyle and areas of interest and provide optimal suggestions. If the customer is busy with work, it can suggest a style that is easy to maintain. If the customer enjoys sports as a hobby, it can suggest a style that allows for easy movement. If the customer has plans to attend a specific event, it can suggest a style that is suitable for that event. This makes it possible to customize proposals based on the customer's current lifestyle and areas of interest.

[0093] The comparison unit can analyze the customer's facial expression and posture in real time when comparing styles, and suggest the optimal style. For example, it can use AI to analyze the customer's facial expression and suggest a style that enhances their smile. It can also use AI to analyze the customer's posture and suggest a style that suits their posture. Furthermore, it can use AI to comprehensively analyze the customer's facial expression and posture and suggest the most balanced style. This makes it possible to suggest the optimal style based on the customer's facial expression and posture.

[0094] The proposal department can estimate the customer's emotions and prioritize proposals based on those emotions. For example, it can use AI to analyze the customer's facial expressions and voice to estimate their emotions. If the customer is nervous, it can prioritize suggesting a calm style. If the customer is relaxed, it can prioritize suggesting an adventurous style. If the customer is excited, it can prioritize suggesting a visually stimulating style. This makes it possible to prioritize proposals according to the customer's emotions.

[0095] The collaborative department can analyze a hairdresser's past feedback history and select the optimal feedback method. For example, it can use AI to analyze a hairdresser's past feedback history and propose the most suitable feedback method. It can select feedback methods that result in high customer satisfaction based on a hairdresser's past feedback history. It can analyze a hairdresser's past feedback history and select an efficient feedback method. This makes it possible to select the optimal feedback method based on a hairdresser's past feedback history.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The comparison section compares styles in real time. For example, it can use AI to analyze a customer's facial image and display multiple hairstyles and colors in real time. Furthermore, smart mirrors and augmented reality (AR) technology are used to allow customers to see the hairstyles and colors on their actual appearance. Step 2: The collaboration department has the hairdresser provide feedback on the style proposed by the comparison department. For example, the hairdresser can listen to the customer's requests on the spot and provide feedback on the proposed style. The feedback can be provided verbally, in text, or in the form of images. Step 3: The proposal department provides personalized suggestions based on feedback received from the collaboration department. For example, it can use AI to analyze information such as the customer's hair type, bone structure, personality, and preferences to suggest the optimal style. Furthermore, it can provide suggestions that allow customers to compare multiple patterns, increasing the likelihood that they will find a style that truly suits them.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements described above, including the comparison unit, the collaboration unit, and the proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the comparison unit can analyze an image of a customer's face using the camera 42 and display 40A of the smart device 14 and display multiple hairstyles and colors in real time. The collaboration unit can, for example, use the microphone 38B and touch panel 38A of the smart device 14 to allow a hairdresser to hear the customer's requests on the spot and provide feedback on the proposed style. The proposal unit can, for example, use the specific processing unit 290 of the data processing unit 12 to analyze information such as the customer's hair type, bone structure, personality, and preferences and propose the optimal style. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the comparison unit, the collaboration unit, and the proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the comparison unit can analyze an image of a customer's face using the camera 42 and display of the smart glasses 214 and display multiple hairstyles and colors in real time. The collaboration unit can, for example, use the microphone 238 of the smart glasses 214 to allow a hairdresser to hear the customer's requests on the spot and provide feedback on the proposed style. The proposal unit can, for example, use the specific processing unit 290 of the data processing unit 12 to analyze information such as the customer's hair type, bone structure, personality, and preferences and propose the optimal style. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the comparison unit, the collaboration unit, and the proposal unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the comparison unit can analyze an image of a customer's face using the camera 42 and display 343 of the headset terminal 314 and display multiple hairstyles and colors in real time. The collaboration unit can, for example, use the microphone 238 of the headset terminal 314 to allow a hairdresser to hear the customer's requests on the spot and provide feedback on the proposed style. The proposal unit can, for example, use the specific processing unit 290 of the data processing unit 12 to analyze information such as the customer's hair type, bone structure, personality, and preferences and propose the optimal style. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

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

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the comparison unit, the collaboration unit, and the proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the comparison unit can analyze an image of a customer's face using the camera 42 and display of the robot 414 and display multiple hairstyles and colors in real time. The collaboration unit can, for example, use the microphone 238 of the robot 414 to allow a hairdresser to hear the customer's requests on the spot and provide feedback on the proposed style. The proposal unit can, for example, use the specific processing unit 290 of the data processing unit 12 to analyze information such as the customer's hair type, bone structure, personality, and preferences and propose the optimal style. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A comparison section that compares styles in real time, The aforementioned comparison unit provides a collaboration unit in which the hairdresser provides feedback on the proposed style, The system includes a proposal unit that makes personalized suggestions based on feedback obtained by the aforementioned collaboration unit. A system characterized by the following features. (Note 2) The comparison unit is, You can check your hairstyle and color in front of a realistic mirror, seeing yourself in the mirror. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned linkage unit is, The hairdresser provides feedback on the spot, accurately reflecting the customer's requests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We offer suggestions based on hair type, bone structure, personality, and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose a way to compare multiple patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The comparison unit is, It estimates customer emotions and adjusts how styles are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The comparison unit is, We analyze the customer's past style history and prioritize displaying the most suitable styles. The system described in Appendix 1, characterized by the features described herein. (Note 8) The comparison unit is, When comparing styles, the system analyzes the customer's facial expressions and posture in real time to suggest the optimal style. The system described in Appendix 1, characterized by the features described herein. (Note 9) The comparison unit is, Estimate customer emotions and prioritize styles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The comparison unit is, When comparing styles, the system prioritizes displaying region-specific styles, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The comparison unit is, When comparing styles, the system analyzes the customer's social media activity and displays relevant styles. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned linkage unit is, The system estimates the hairdresser's emotions and adjusts the feedback method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned linkage unit is, Analyze the hairdresser's past feedback history to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned linkage unit is, During the feedback process, we analyze the customer's real-time reactions and adjust the feedback accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned linkage unit is, The system estimates the hairdresser's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned linkage unit is, When providing feedback, the hairdresser's geographical location is taken into consideration to provide region-specific feedback. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned linkage unit is, When providing feedback, we analyze the hairdresser's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the customer's emotions and adjust the way we present our proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we analyze the customer's past style history to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we customize the content based on the customer's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate customer emotions and prioritize proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we take the customer's geographical location into consideration and provide region-specific suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the client's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A comparison section that compares styles in real time, The aforementioned comparison unit provides a collaboration unit in which the hairdresser provides feedback on the proposed style, The system includes a proposal unit that makes personalized suggestions based on feedback obtained by the aforementioned collaboration unit. A system characterized by the following features.

2. The comparison unit is, You can check your hairstyle and color in front of a realistic mirror, seeing yourself in the mirror. The system according to feature 1.

3. The aforementioned linkage unit is, The hairdresser provides feedback on the spot, accurately reflecting the customer's requests. The system according to feature 1.

4. The aforementioned proposal section is, We offer suggestions based on hair type, bone structure, personality, and preferences. The system according to feature 1.

5. The aforementioned proposal section is, We propose a way to compare multiple patterns. The system according to feature 1.

6. The comparison unit is, It estimates customer emotions and adjusts how styles are displayed based on those estimated emotions. The system according to feature 1.

7. The comparison unit is, We analyze the customer's past style history and prioritize displaying the most suitable styles. The system according to feature 1.

8. The comparison unit is, When comparing styles, the system analyzes the customer's facial expressions and posture in real time to suggest the optimal style. The system according to feature 1.

9. The comparison unit is, Estimate customer emotions and prioritize styles based on those estimated emotions. The system according to feature 1.

10. The comparison unit is, When comparing styles, the system prioritizes displaying region-specific styles, taking into account the customer's geographical location. The system according to feature 1.

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