system

Abstract

The system according to this embodiment aims to accurately capture customer preferences and provide suggestions based on pre-visualization of styles and trend information. [Solution] The system according to the embodiment comprises a reception unit, a display unit, a suggestion unit, and a management unit. The reception unit receives customer requests by voice. The display unit displays style changes in real time on a mirror based on the requests received by the reception unit. The suggestion unit analyzes trend information and the customer's past history and presents the optimal style suggestion. The management unit manages the customer's past treatment history.

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 prior art, there were problems such as it being difficult to accurately capture the customer's wishes and to make proposals based on pre-visualization of styles and trend information.

[0005] The system according to the embodiment aims to accurately capture the customer's wishes and make proposals based on pre-visualization of styles and trend information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a display unit, a suggestion unit, and a management unit. The reception unit receives customer requests by voice. The display unit displays style changes in real time on a mirror based on the requests received by the reception unit. The suggestion unit analyzes trend information and the customer's past history and presents the optimal style suggestion. The management unit manages the customer's past treatment history. [Effects of the Invention]

[0007] The system according to this embodiment can accurately capture customer preferences and make suggestions based on pre-visualization of styles and trend information. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that combines a voice assistant and a smart mirror and uses AI to suggest styles based on customer preferences. This system receives customer requests by voice, displays style changes in real time on the mirror, and analyzes trend information and the customer's past history to present the optimal style. It also manages the customer's past treatment history and provides technical advice to the hairdresser. For example, this system utilizes a voice assistant to receive customer requests by voice. Furthermore, it uses smart mirror technology to display style changes in real time on the mirror, allowing the customer to visually confirm them. Next, it performs AI-driven trend analysis and style suggestions. The AI ​​analyzes trend information and the customer's past history and presents the optimal style. For example, by providing simulations of styles and colors based on the customer's requests, the customer can form a concrete image. Furthermore, it provides treatment assistance and history management. The AI ​​provides technical advice to the hairdresser and manages the customer's past treatment history. This allows the hairdresser to perform treatments efficiently. As a result, the system can improve customer satisfaction and decision-making ability.

[0029] The system according to this embodiment comprises a reception unit, a display unit, a suggestion unit, and a management unit. The reception unit receives customer requests by voice. For example, the reception unit can receive customer requests using voice recognition technology. The reception unit can also classify customer requests according to the type of voice command. For example, when a customer voice-states a request such as "cut" or "color," the reception unit takes appropriate action. The display unit projects style changes onto a mirror in real time based on the requests received by the reception unit. For example, the display unit can project style changes in real time using display technology that minimizes latency. The display unit can also display style changes in high resolution depending on the display technology used. For example, the display unit instantly projects the style selected by the customer onto a mirror, allowing the customer to confirm the change. The suggestion unit analyzes trend information and the customer's past history to present the optimal style suggestion. For example, the suggestion unit can obtain trend information from fashion magazines or online databases and use that information to present the optimal style suggestion to the customer. Furthermore, the proposal department can present optimal style suggestions based on the customer's preferences and current trends. For example, the proposal department can analyze the customer's past history and suggest styles and colors that the customer would like. The management department manages the customer's past treatment history. For example, the management department can manage information such as the date of treatment, the treatment content, and the person in charge, enabling centralized management of the customer's past treatment history. The management department can also provide technical advice to hairdressers based on the customer's past treatment history. For example, the management department can analyze the customer's past treatment history and suggest improvements to treatment methods and tools to be used. As a result, the system according to this embodiment can accurately capture customer requests and enable pre-visualization of styles and suggestions based on trend information.

[0030] The reception desk receives customer requests via voice. For example, the reception desk can use voice recognition technology to receive customer requests. Specifically, natural language processing (NLP) technology is used for voice recognition, which converts the customer's utterances into text data. This text data is further analyzed, and the customer's request is classified into a specific category. For example, if a customer voice-states a request such as "cut" or "color," the voice recognition technology analyzes this and classifies it into categories such as cut or color. This allows the reception desk to accurately understand the customer's request and provide appropriate service. The reception desk can also classify customer requests according to the type of voice command. For example, if a customer says, "I'd like a cut," the voice recognition technology extracts the keyword "cut" and classifies it as a request for a cut. Furthermore, the reception desk can process customer requests in real time and respond quickly. For example, if a customer says, "I'd like a cut and a color," the reception desk can simultaneously receive both requests and provide appropriate service. This allows the reception desk to accurately and quickly receive customer requests, improving customer satisfaction.

[0031] The display unit projects style changes onto the mirror in real time based on requests received by the reception desk. For example, the display unit can project style changes in real time using display technology that minimizes latency. Specifically, the display unit uses a high-resolution display that can instantly project the style chosen by the customer onto the mirror. This allows the customer to check the style change in real time. The display unit can also display style changes in high resolution depending on the display technology used. For example, the display unit can use a 4K resolution display to clearly show even the smallest details of the style. Furthermore, the display unit can display the customer's face and hairstyle as a 3D model, allowing the customer to see the chosen style in three dimensions. This allows the customer to visualize the style change more concretely, increasing their satisfaction. In addition, the display unit can display the customer's chosen style from multiple angles, allowing the customer to see the overall look of the style. This allows the customer to check the style change in more detail and choose a style they are satisfied with.

[0032] The suggestion department analyzes trend information and the customer's past history to propose the most suitable style. For example, the suggestion department can obtain trend information from fashion magazines and online databases and use that information to propose the most suitable style to the customer. Specifically, the suggestion department collects trend information from the latest fashion magazines and online databases and analyzes this information using AI. The AI ​​analyzes the trend information and can propose the most suitable style based on the customer's preferences and current styles. For example, the suggestion department can analyze the customer's past history and propose styles and colors that the customer prefers. Furthermore, the suggestion department can analyze the customer's facial features such as face shape, hair type, and skin color and propose the most suitable style based on these features. This allows the suggestion department to propose individually optimized style suggestions to each customer, thereby improving customer satisfaction. In addition, the suggestion department can record styles and colors that the customer has tried in the past based on their past history and propose new style suggestions based on this information. This allows the suggestion department to propose more personalized style suggestions based on the customer's preferences and history.

[0033] The management department manages the past treatment history of customers. For example, the management department manages information such as the date of treatment, the treatment content, and the person in charge, allowing for centralized management of a customer's past treatment history. Specifically, the management department stores the customer's treatment history in a database and centrally manages this information. This allows the management department to quickly search for a customer's past treatment history and provide the necessary information. In addition, the management department can provide technical advice to hairdressers based on the customer's past treatment history. For example, the management department analyzes the customer's past treatment history and suggests areas for improvement in treatment methods and tools to be used. This allows the management department to provide technical support to hairdressers and improve the quality of treatments. Furthermore, the management department can suggest repeat treatments to customers based on their past treatment history. For example, the management department suggests the next treatment based on the styles and colors the customer has tried in the past. This allows the management department to provide continuous service to customers and improve customer satisfaction.

[0034] The proposal department can provide style and color simulations based on customer requests. For example, the proposal department uses software to perform style and color simulations based on customer requests. For instance, the proposal department takes the customer's desired style and color as input and displays the simulation results. Furthermore, the proposal department can consider information such as the customer's face shape and hair type to improve the accuracy of the simulation. For example, the proposal department simulates the optimal style and color based on the customer's face shape and hair type. This allows the proposal department to provide specific style and color simulations based on customer requests.

[0035] The management department can provide technical advice to hairdressers. For example, the management department can provide hairdressers with suggestions for improving treatment methods. For example, the management department can also suggest tools to be used during treatments. Furthermore, the management department can provide advice to improve the efficiency of treatments. For example, the management department can advise on treatment procedures and time allocation. In this way, the efficiency and precision of treatments are improved by the management department providing technical advice to hairdressers. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input suggestions for improving treatment methods into AI, and the AI ​​can output the optimal improvements.

[0036] The display unit can reflect style changes in the mirror in real time. The display unit can reflect style changes in real time using, for example, display technology that minimizes latency. For example, the display unit can instantly reflect the style selected by the customer in the mirror, allowing the customer to see the change. The display unit can also display style changes in high resolution, depending on the display technology used. For example, the display unit can display the style selected by the customer in high resolution, allowing the customer to see the change in detail. This allows the display unit to visually confirm style changes in real time. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the display unit can input the style selected by the customer into a generative AI, and the generative AI can display style changes in real time.

[0037] The suggestion unit can analyze trend information and the customer's past history to present the most suitable style. For example, the suggestion unit can obtain trend information from fashion magazines or online databases and use it to present the most suitable style to the customer. For example, the suggestion unit can present the most suitable style based on the customer's preferences and current trends. The suggestion unit can also analyze the customer's past history and suggest styles and colors that the customer would like. For example, the suggestion unit can make suggestions based on the customer's past history, taking into account the styles and colors the customer has chosen in the past. In this way, the suggestion unit can present the most suitable style by analyzing trend information and the customer's past history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input trend information and the customer's past history into a generative AI, which can then output the most suitable style.

[0038] The management department can manage a customer's past treatment history. For example, the management department manages information such as the date of treatment, the treatment content, and the person in charge, and centrally manages the customer's past treatment history. For example, the management department can store the customer's past treatment history in a database and refer to it as needed. The management department can also provide technical advice to beauticians based on the customer's past treatment history. For example, the management department can analyze the customer's past treatment history and suggest areas for improvement in treatment methods and tools to be used. In this way, the management department can improve the consistency and efficiency of treatments by managing the customer's past treatment history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the customer's past treatment history into AI, and the AI ​​can output the optimal management method.

[0039] The reception department can analyze the customer's past request history and select the optimal reception method. For example, the reception department can automatically display styles that the customer has frequently requested in the past as candidates. For example, the reception department can prioritize suggesting specific styles based on the customer's past request history. The reception department can also efficiently receive requests by selecting the optimal reception method based on the customer's past request history. For example, the reception department can analyze the customer's past request history and select the optimal reception method. In this way, the reception department can select the optimal reception method by analyzing the customer's past request history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the customer's past request history into AI, and the AI ​​can output the optimal reception method.

[0040] The reception desk can filter requests based on the customer's current lifestyle and areas of interest. For example, the reception desk can suggest suitable styles based on the customer's current lifestyle (work, hobbies, etc.). For example, the reception desk can suggest relevant styles based on the customer's areas of interest (fashion, sports, etc.). The reception desk can also filter and suggest the most suitable styles by considering the customer's lifestyle and areas of interest. For example, the reception desk can suggest the most suitable styles based on the customer's lifestyle and areas of interest. In this way, the reception desk can suggest the most suitable styles based on the customer's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the customer's lifestyle and areas of interest into an AI, which can then filter and suggest the most suitable styles.

[0041] The reception desk can prioritize requests that are highly relevant, taking into account the customer's geographical location when receiving requests. For example, the reception desk can prioritize suggesting nearby trending styles based on the customer's current location. For example, the reception desk can prioritize suggesting highly relevant styles, taking into account the customer's geographical location. The reception desk can also suggest the most suitable style based on the customer's geographical location and prioritize its acceptance. For example, the reception desk can suggest the most suitable style based on the customer's geographical location. In this way, the reception desk can prioritize requests that are highly relevant by taking into account the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location into AI, which can then prioritize receiving requests that are highly relevant.

[0042] The reception department can analyze the customer's social media activity when receiving a request and accept relevant requests. For example, the reception department can analyze the customer's social media activity and suggest relevant styles. For example, the reception department can suggest the optimal style based on the customer's interests on social media. The reception department can also prioritize accepting relevant requests by considering the customer's social media activity. For example, the reception department can suggest the optimal style based on the customer's social media activity. In this way, the reception department can accept relevant requests by analyzing the customer's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input data on the customer's social media activity into AI, and the AI ​​can accept relevant requests.

[0043] The display unit can adjust the level of detail of the display based on the importance of the style when displaying it. For example, the display unit provides a detailed display in the case of a significant style change. For example, the display unit provides a concise display in the case of a minor style change. The display unit can also adjust the level of detail of the display according to the importance of the style. For example, the display unit adjusts the level of detail of the display based on the importance of the style. This allows the display unit to provide a more appropriate display by adjusting the level of detail of the display according to the importance of the style. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input style importance data into the AI, and the AI ​​can adjust the level of detail of the display.

[0044] The display unit can apply different display algorithms depending on the style category during display. For example, in the case of hairstyles, the display unit can apply an algorithm that displays the texture and color of the hair in detail. For example, in the case of makeup, the display unit can apply an algorithm that displays the skin tone and color in detail. The display unit can also apply an algorithm that displays the texture and color of clothing in detail in the case of fashion. For example, the display unit can apply an algorithm that displays the fashion style in detail. In this way, the display unit can provide a more appropriate display by applying different display algorithms depending on the style category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input style category data into the AI, and the AI ​​can apply different display algorithms.

[0045] The display unit can determine the display priority based on the submission date of the styles when displaying them. For example, the display unit may prioritize displaying recently submitted styles. For example, the display unit may postpone displaying older styles. The display unit can also determine the display priority based on the submission date. For example, the display unit may determine the display priority based on the submission date. This allows the display unit to display styles more appropriately by determining the display priority based on the submission date. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input data on the submission date of styles into AI, and the AI ​​can determine the display priority.

[0046] The display unit can adjust the display order based on the relationships between styles during display. For example, the display unit may prioritize displaying styles that are most relevant to the customer's request. For example, the display unit may postpone displaying styles that are less relevant. The display unit can also adjust the display order based on the relationships between styles. For example, the display unit may adjust the display order based on the relationships between styles. This allows the display unit to provide a more appropriate display by adjusting the display order based on the relationships between styles. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input style relationship data into AI, and the AI ​​may adjust the display order.

[0047] The proposal function can adjust the level of detail of a proposal based on the importance of the style. For example, the proposal function will provide a detailed proposal for significant style changes. For example, the proposal function will provide a concise proposal for minor style changes. The proposal function can also adjust the level of detail of a proposal according to the importance of the style. For example, the proposal function adjusts the level of detail of a proposal based on the importance of the style. This allows the proposal function to provide more appropriate proposals by adjusting the level of detail of a proposal according to the importance of the style. Some or all of the above processing in the proposal function may be performed using AI, for example, or not using AI. For example, the proposal function can input style importance data into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0048] The suggestion unit can apply different suggestion algorithms depending on the style category when making suggestions. For example, in the case of hairstyles, the suggestion unit can apply an algorithm that suggests hair texture and color in detail. For example, in the case of makeup, the suggestion unit can apply an algorithm that suggests skin tone and color in detail. The suggestion unit can also apply an algorithm that suggests clothing texture and color in detail in the case of fashion. For example, the suggestion unit can apply an algorithm that suggests fashion styles in detail. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the style category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input style category data into AI, and the AI ​​can apply different suggestion algorithms.

[0049] The proposal department can prioritize proposals based on when styles are submitted. For example, the proposal department might prioritize recently submitted styles. For example, it might postpone older styles. The proposal department can also prioritize proposals based on submission date. For example, the proposal department can prioritize proposals based on submission date. This allows the proposal department to make more appropriate proposals by prioritizing proposals based on when styles are submitted. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input data on when styles are submitted into an AI, and the AI ​​can determine the priority of proposals.

[0050] The proposal department can adjust the order of proposals based on the relevance of styles during the proposal process. For example, the proposal department will prioritize proposing styles that are most relevant to the customer's needs. For example, the proposal department will postpone proposing styles that are less relevant. The proposal department can also adjust the order of proposals based on the relevance of styles. For example, the proposal department will adjust the order of proposals based on the relevance of styles. This allows the proposal department to make more appropriate proposals by adjusting the order of proposals based on the relevance of styles. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input style relevance data into AI, and the AI ​​can adjust the order of proposals.

[0051] The management department can optimize its management algorithms by referring to past management data during management. For example, the management department can select the optimal management algorithm based on past management data. For example, the management department can optimize its management algorithms by referring to past management data. The management department can also analyze past management data and propose the optimal management algorithm. For example, the management department can select the optimal management algorithm based on past management data. In this way, the management department can optimize its management algorithms by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past management data into AI, and the AI ​​can output the optimal management algorithm.

[0052] The management department can weight the management data based on when the treatment history was submitted. For example, the management department can prioritize weighting recent treatment histories. For example, the management department can reduce the weight of older treatment histories. The management department can also weight the management data based on the submission date. For example, the management department can weight the management data based on the submission date. This allows the management department to perform more appropriate management by weighting the management data based on when the treatment history was submitted. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input data on when the treatment history was submitted into the AI, and the AI ​​can weight the management data.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze customer voice data and learn the customer's accent and speaking style. For example, the reception desk can perform more accurate speech recognition based on the customer's accent. Furthermore, by learning the customer's speaking style, the reception desk can more accurately understand the customer's requests. In addition, the reception desk can adjust the tone and pace of the voice guide based on the customer's speaking style. As a result, by analyzing customer voice data, the reception desk can provide more appropriate speech recognition and voice guidance.

[0055] The management department can analyze a customer's treatment history and learn their preferences and tendencies. For example, the management department can analyze the styles and colors a customer has chosen in the past to understand their preferences. Furthermore, based on the customer's treatment history, the management department can learn the customer's preferred styles and color tendencies. In addition, the management department can make suggestions to the stylists based on the customer's preferences and tendencies. This allows the management department to understand customer preferences and tendencies by analyzing their treatment history and make more appropriate suggestions.

[0056] The suggestion department can learn the customer's preferred styles and colors based on their past treatment history. For example, it can analyze the styles and colors the customer has chosen in the past to understand their preferences. Furthermore, it can learn the customer's preferred styles and colors based on their treatment history. In addition, the suggestion department can present the most suitable style options based on the customer's preferences and tendencies. As a result, by analyzing the customer's past treatment history, the suggestion department can understand their preferences and tendencies and present more appropriate style options.

[0057] The reception desk can learn the customer's preferred styles and colors based on their past request history. For example, the reception desk can analyze the styles and colors the customer has requested in the past to understand their preferences. Furthermore, the reception desk can learn the customer's preferred styles and colors based on their request history. In addition, the reception desk can suggest the most suitable styles and colors based on the customer's preferences and preferences. In this way, the reception desk can understand the customer's preferences and preferences by analyzing their past request history and make more appropriate suggestions.

[0058] The management department can learn about customers' preferred styles and colors based on their treatment history. For example, the management department can analyze the styles and colors customers have chosen in the past to understand their preferences. Furthermore, the management department can make suggestions to hairdressers based on customers' preferences and tendencies. In this way, by analyzing customers' treatment history, the management department can understand their preferences and tendencies and make more appropriate suggestions.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives customer requests via voice. For example, the reception desk can use voice recognition technology to receive customer requests. The reception desk can also categorize customer requests according to the type of voice command. For example, if a customer voice-states a request such as "cut" or "color," the reception desk will respond accordingly. Step 2: The display unit projects the style changes onto the mirror in real time based on the request received by the reception unit. For example, the display unit can project the style changes in real time using display technology that minimizes latency. Depending on the display technology used, the display unit can also display the style changes in high resolution. For example, the display unit can instantly project the style chosen by the customer onto the mirror, allowing the customer to confirm the change. Step 3: The proposal department analyzes trend information and the customer's past history to present the optimal style. For example, the proposal department can obtain trend information from fashion magazines and online databases and use that to present the optimal style to the customer. Alternatively, the proposal department can present the optimal style based on the customer's preferences and current trends. For example, the proposal department can analyze the customer's past history and suggest styles and colors that the customer prefers. Step 4: The management department manages the customer's past treatment history. For example, the management department can manage information such as the date of treatment, the treatment content, and the person in charge, allowing for centralized management of the customer's past treatment history. The management department can also provide technical advice to hairdressers based on the customer's past treatment history. For example, the management department can analyze the customer's past treatment history and suggest areas for improvement in treatment methods and tools to be used.

[0061] (Example of form 2) The system according to an embodiment of the present invention is a system that combines a voice assistant and a smart mirror and uses AI to suggest styles based on customer preferences. This system receives customer requests by voice, displays style changes in real time on the mirror, and analyzes trend information and the customer's past history to present the optimal style. It also manages the customer's past treatment history and provides technical advice to the hairdresser. For example, this system utilizes a voice assistant to receive customer requests by voice. Furthermore, it uses smart mirror technology to display style changes in real time on the mirror, allowing the customer to visually confirm them. Next, it performs AI-driven trend analysis and style suggestions. The AI ​​analyzes trend information and the customer's past history and presents the optimal style. For example, by providing simulations of styles and colors based on the customer's requests, the customer can form a concrete image. Furthermore, it provides treatment assistance and history management. The AI ​​provides technical advice to the hairdresser and manages the customer's past treatment history. This allows the hairdresser to perform treatments efficiently. As a result, the system can improve customer satisfaction and decision-making ability.

[0062] The system according to this embodiment comprises a reception unit, a display unit, a suggestion unit, and a management unit. The reception unit receives customer requests by voice. For example, the reception unit can receive customer requests using voice recognition technology. The reception unit can also classify customer requests according to the type of voice command. For example, when a customer voice-states a request such as "cut" or "color," the reception unit takes appropriate action. The display unit projects style changes onto a mirror in real time based on the requests received by the reception unit. For example, the display unit can project style changes in real time using display technology that minimizes latency. The display unit can also display style changes in high resolution depending on the display technology used. For example, the display unit instantly projects the style selected by the customer onto a mirror, allowing the customer to confirm the change. The suggestion unit analyzes trend information and the customer's past history to present the optimal style suggestion. For example, the suggestion unit can obtain trend information from fashion magazines or online databases and use that information to present the optimal style suggestion to the customer. Furthermore, the proposal department can present optimal style suggestions based on the customer's preferences and current trends. For example, the proposal department can analyze the customer's past history and suggest styles and colors that the customer would like. The management department manages the customer's past treatment history. For example, the management department can manage information such as the date of treatment, the treatment content, and the person in charge, enabling centralized management of the customer's past treatment history. The management department can also provide technical advice to hairdressers based on the customer's past treatment history. For example, the management department can analyze the customer's past treatment history and suggest improvements to treatment methods and tools to be used. As a result, the system according to this embodiment can accurately capture customer requests and enable pre-visualization of styles and suggestions based on trend information.

[0063] The reception desk receives customer requests via voice. For example, the reception desk can use voice recognition technology to receive customer requests. Specifically, natural language processing (NLP) technology is used for voice recognition, which converts the customer's utterances into text data. This text data is further analyzed, and the customer's request is classified into a specific category. For example, if a customer voice-states a request such as "cut" or "color," the voice recognition technology analyzes this and classifies it into categories such as cut or color. This allows the reception desk to accurately understand the customer's request and provide appropriate service. The reception desk can also classify customer requests according to the type of voice command. For example, if a customer says, "I'd like a cut," the voice recognition technology extracts the keyword "cut" and classifies it as a request for a cut. Furthermore, the reception desk can process customer requests in real time and respond quickly. For example, if a customer says, "I'd like a cut and a color," the reception desk can simultaneously receive both requests and provide appropriate service. This allows the reception desk to accurately and quickly receive customer requests, improving customer satisfaction.

[0064] The display unit projects style changes onto the mirror in real time based on requests received by the reception desk. For example, the display unit can project style changes in real time using display technology that minimizes latency. Specifically, the display unit uses a high-resolution display that can instantly project the style chosen by the customer onto the mirror. This allows the customer to check the style change in real time. The display unit can also display style changes in high resolution depending on the display technology used. For example, the display unit can use a 4K resolution display to clearly show even the smallest details of the style. Furthermore, the display unit can display the customer's face and hairstyle as a 3D model, allowing the customer to see the chosen style in three dimensions. This allows the customer to visualize the style change more concretely, increasing their satisfaction. In addition, the display unit can display the customer's chosen style from multiple angles, allowing the customer to see the overall look of the style. This allows the customer to check the style change in more detail and choose a style they are satisfied with.

[0065] The suggestion department analyzes trend information and the customer's past history to propose the most suitable style. For example, the suggestion department can obtain trend information from fashion magazines and online databases and use that information to propose the most suitable style to the customer. Specifically, the suggestion department collects trend information from the latest fashion magazines and online databases and analyzes this information using AI. The AI ​​analyzes the trend information and can propose the most suitable style based on the customer's preferences and current styles. For example, the suggestion department can analyze the customer's past history and propose styles and colors that the customer prefers. Furthermore, the suggestion department can analyze the customer's facial features such as face shape, hair type, and skin color and propose the most suitable style based on these features. This allows the suggestion department to propose individually optimized style suggestions to each customer, thereby improving customer satisfaction. In addition, the suggestion department can record styles and colors that the customer has tried in the past based on their past history and propose new style suggestions based on this information. This allows the suggestion department to propose more personalized style suggestions based on the customer's preferences and history.

[0066] The management department manages the past treatment history of customers. For example, the management department manages information such as the date of treatment, the treatment content, and the person in charge, allowing for centralized management of a customer's past treatment history. Specifically, the management department stores the customer's treatment history in a database and centrally manages this information. This allows the management department to quickly search for a customer's past treatment history and provide the necessary information. In addition, the management department can provide technical advice to hairdressers based on the customer's past treatment history. For example, the management department analyzes the customer's past treatment history and suggests areas for improvement in treatment methods and tools to be used. This allows the management department to provide technical support to hairdressers and improve the quality of treatments. Furthermore, the management department can suggest repeat treatments to customers based on their past treatment history. For example, the management department suggests the next treatment based on the styles and colors the customer has tried in the past. This allows the management department to provide continuous service to customers and improve customer satisfaction.

[0067] The proposal department can provide style and color simulations based on customer requests. For example, the proposal department uses software to perform style and color simulations based on customer requests. For instance, the proposal department takes the customer's desired style and color as input and displays the simulation results. Furthermore, the proposal department can consider information such as the customer's face shape and hair type to improve the accuracy of the simulation. For example, the proposal department simulates the optimal style and color based on the customer's face shape and hair type. This allows the proposal department to provide specific style and color simulations based on customer requests.

[0068] The management department can provide technical advice to hairdressers. For example, the management department can provide hairdressers with suggestions for improving treatment methods. For example, the management department can also suggest tools to be used during treatments. Furthermore, the management department can provide advice to improve the efficiency of treatments. For example, the management department can advise on treatment procedures and time allocation. In this way, the efficiency and precision of treatments are improved by the management department providing technical advice to hairdressers. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input suggestions for improving treatment methods into AI, and the AI ​​can output the optimal improvements.

[0069] The display unit can reflect style changes in the mirror in real time. The display unit can reflect style changes in real time using, for example, display technology that minimizes latency. For example, the display unit can instantly reflect the style selected by the customer in the mirror, allowing the customer to see the change. The display unit can also display style changes in high resolution, depending on the display technology used. For example, the display unit can display the style selected by the customer in high resolution, allowing the customer to see the change in detail. This allows the display unit to visually confirm style changes in real time. Some or all of the above processing in the display unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the display unit can input the style selected by the customer into a generative AI, and the generative AI can display style changes in real time.

[0070] The suggestion unit can analyze trend information and the customer's past history to present the most suitable style. For example, the suggestion unit can obtain trend information from fashion magazines or online databases and use it to present the most suitable style to the customer. For example, the suggestion unit can present the most suitable style based on the customer's preferences and current trends. The suggestion unit can also analyze the customer's past history and suggest styles and colors that the customer would like. For example, the suggestion unit can make suggestions based on the customer's past history, taking into account the styles and colors the customer has chosen in the past. In this way, the suggestion unit can present the most suitable style by analyzing trend information and the customer's past history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input trend information and the customer's past history into a generative AI, which can then output the most suitable style.

[0071] The management department can manage a customer's past treatment history. For example, the management department manages information such as the date of treatment, the treatment content, and the person in charge, and centrally manages the customer's past treatment history. For example, the management department can store the customer's past treatment history in a database and refer to it as needed. The management department can also provide technical advice to beauticians based on the customer's past treatment history. For example, the management department can analyze the customer's past treatment history and suggest areas for improvement in treatment methods and tools to be used. In this way, the management department can improve the consistency and efficiency of treatments by managing the customer's past treatment history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the customer's past treatment history into AI, and the AI ​​can output the optimal management method.

[0072] The reception desk can estimate the customer's emotions and adjust the way requests are received based on the estimated emotions. For example, if the customer is nervous, the reception desk can provide a relaxing voice guide and take the request. For example, if the customer is relaxed, the reception desk can take the request by asking more questions to elicit more detailed requests. The reception desk can also take the request quickly with concise questions if the customer is in a hurry. For example, if the customer is in a hurry, the reception desk can ask questions to take the request in a short amount of time. In this way, the reception desk can take requests more appropriately by adjusting the way requests are received according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input customer voice data into a generating AI, which can then estimate the customer's emotions and adjust the way requests are handled based on the results.

[0073] The reception department can analyze the customer's past request history and select the optimal reception method. For example, the reception department can automatically display styles that the customer has frequently requested in the past as candidates. For example, the reception department can prioritize suggesting specific styles based on the customer's past request history. The reception department can also efficiently receive requests by selecting the optimal reception method based on the customer's past request history. For example, the reception department can analyze the customer's past request history and select the optimal reception method. In this way, the reception department can select the optimal reception method by analyzing the customer's past request history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the customer's past request history into AI, and the AI ​​can output the optimal reception method.

[0074] The reception desk can filter requests based on the customer's current lifestyle and areas of interest. For example, the reception desk can suggest suitable styles based on the customer's current lifestyle (work, hobbies, etc.). For example, the reception desk can suggest relevant styles based on the customer's areas of interest (fashion, sports, etc.). The reception desk can also filter and suggest the most suitable styles by considering the customer's lifestyle and areas of interest. For example, the reception desk can suggest the most suitable styles based on the customer's lifestyle and areas of interest. In this way, the reception desk can suggest the most suitable styles based on the customer's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the customer's lifestyle and areas of interest into an AI, which can then filter and suggest the most suitable styles.

[0075] The reception desk can estimate the customer's emotions and prioritize requests based on those emotions. For example, if the customer is nervous, the reception desk will prioritize requests that will help them relax. For example, if the customer is relaxed, the reception desk will prioritize detailed requests. The reception desk can also prioritize requests that can be handled quickly if the customer is in a hurry. For example, if the customer is in a hurry, the reception desk will ask questions to process the request quickly. This allows the reception desk to process requests more appropriately by prioritizing them according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input customer voice data into a generative AI, which will estimate the customer's emotions and determine the priority of requests based on the result.

[0076] The reception desk can prioritize requests that are highly relevant, taking into account the customer's geographical location when receiving requests. For example, the reception desk can prioritize suggesting nearby trending styles based on the customer's current location. For example, the reception desk can prioritize suggesting highly relevant styles, taking into account the customer's geographical location. The reception desk can also suggest the most suitable style based on the customer's geographical location and prioritize its acceptance. For example, the reception desk can suggest the most suitable style based on the customer's geographical location. In this way, the reception desk can prioritize requests that are highly relevant by taking into account the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location into AI, which can then prioritize receiving requests that are highly relevant.

[0077] The reception department can analyze the customer's social media activity when receiving a request and accept relevant requests. For example, the reception department can analyze the customer's social media activity and suggest relevant styles. For example, the reception department can suggest the optimal style based on the customer's interests on social media. The reception department can also prioritize accepting relevant requests by considering the customer's social media activity. For example, the reception department can suggest the optimal style based on the customer's social media activity. In this way, the reception department can accept relevant requests by analyzing the customer's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input data on the customer's social media activity into AI, and the AI ​​can accept relevant requests.

[0078] The display unit can estimate the customer's emotions and adjust the display's presentation based on the estimated emotions. For example, if the customer is tense, the display unit may provide a display with calm colors. For example, if the customer is relaxed, the display unit may provide a display with bright colors. The display unit can also provide a visually stimulating display if the customer is excited. For example, if the customer is excited, the display unit may provide a visually stimulating display. This allows the display unit to provide a more appropriate display by adjusting the presentation based on the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input customer emotion data into a generative AI, the generative AI can estimate the customer's emotions, and the display's presentation can be adjusted based on the result.

[0079] The display unit can adjust the level of detail of the display based on the importance of the style when displaying it. For example, the display unit provides a detailed display in the case of a significant style change. For example, the display unit provides a concise display in the case of a minor style change. The display unit can also adjust the level of detail of the display according to the importance of the style. For example, the display unit adjusts the level of detail of the display based on the importance of the style. This allows the display unit to provide a more appropriate display by adjusting the level of detail of the display according to the importance of the style. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input style importance data into the AI, and the AI ​​can adjust the level of detail of the display.

[0080] The display unit can apply different display algorithms depending on the style category during display. For example, in the case of hairstyles, the display unit can apply an algorithm that displays the texture and color of the hair in detail. For example, in the case of makeup, the display unit can apply an algorithm that displays the skin tone and color in detail. The display unit can also apply an algorithm that displays the texture and color of clothing in detail in the case of fashion. For example, the display unit can apply an algorithm that displays the fashion style in detail. In this way, the display unit can provide a more appropriate display by applying different display algorithms depending on the style category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input style category data into the AI, and the AI ​​can apply different display algorithms.

[0081] The display unit can estimate the customer's emotions and adjust the length of the display based on the estimated emotions. For example, if the customer is in a hurry, the display unit provides a short, concise display. For example, if the customer is relaxed, the display unit provides a detailed display. The display unit can also provide a visually stimulating display if the customer is excited. For example, if the display unit is excited, the display unit provides a visually stimulating display. This allows the display unit to provide a more appropriate display by adjusting the length of the display according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input customer emotion data into a generative AI, the generative AI can estimate the customer's emotions, and the length of the display can be adjusted based on the result.

[0082] The display unit can determine the display priority based on the submission date of the styles when displaying them. For example, the display unit may prioritize displaying recently submitted styles. For example, the display unit may postpone displaying older styles. The display unit can also determine the display priority based on the submission date. For example, the display unit may determine the display priority based on the submission date. This allows the display unit to display styles more appropriately by determining the display priority based on the submission date. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input data on the submission date of styles into AI, and the AI ​​can determine the display priority.

[0083] The display unit can adjust the display order based on the relationships between styles during display. For example, the display unit may prioritize displaying styles that are most relevant to the customer's request. For example, the display unit may postpone displaying styles that are less relevant. The display unit can also adjust the display order based on the relationships between styles. For example, the display unit may adjust the display order based on the relationships between styles. This allows the display unit to provide a more appropriate display by adjusting the display order based on the relationships between styles. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input style relationship data into AI, and the AI ​​may adjust the display order.

[0084] The suggestion unit can estimate the customer's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the customer is nervous, the suggestion unit can provide simple and highly visual suggestions. For example, if the customer is relaxed, the suggestion unit can provide detailed suggestions. The suggestion unit can also provide visually stimulating suggestions if the customer is excited. For example, if the suggestion unit is excited, the suggestion unit can provide visually stimulating suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents its suggestions according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input customer emotion data into a generative AI, the generative AI can estimate the customer's emotions, and the way it presents its suggestions can be adjusted based on the result.

[0085] The proposal function can adjust the level of detail of a proposal based on the importance of the style. For example, the proposal function will provide a detailed proposal for significant style changes. For example, the proposal function will provide a concise proposal for minor style changes. The proposal function can also adjust the level of detail of a proposal according to the importance of the style. For example, the proposal function adjusts the level of detail of a proposal based on the importance of the style. This allows the proposal function to provide more appropriate proposals by adjusting the level of detail of a proposal according to the importance of the style. Some or all of the above processing in the proposal function may be performed using AI, for example, or not using AI. For example, the proposal function can input style importance data into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0086] The suggestion unit can apply different suggestion algorithms depending on the style category when making suggestions. For example, in the case of hairstyles, the suggestion unit can apply an algorithm that suggests hair texture and color in detail. For example, in the case of makeup, the suggestion unit can apply an algorithm that suggests skin tone and color in detail. The suggestion unit can also apply an algorithm that suggests clothing texture and color in detail in the case of fashion. For example, the suggestion unit can apply an algorithm that suggests fashion styles in detail. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the style category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input style category data into AI, and the AI ​​can apply different suggestion algorithms.

[0087] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the customer is in a hurry, the suggestion unit will provide a short, to-the-point suggestion. For example, if the customer is relaxed, the suggestion unit will provide a detailed suggestion. The suggestion unit can also provide a visually stimulating suggestion if the customer is excited. For example, if the suggestion unit is excited, the suggestion unit will provide a visually stimulating suggestion. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of the suggestion according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input customer emotion data into a generative AI, which will estimate the customer's emotions and adjust the length of the suggestion based on the result.

[0088] The proposal department can prioritize proposals based on when styles are submitted. For example, the proposal department might prioritize recently submitted styles. For example, it might postpone older styles. The proposal department can also prioritize proposals based on submission date. For example, the proposal department can prioritize proposals based on submission date. This allows the proposal department to make more appropriate proposals by prioritizing proposals based on when styles are submitted. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input data on when styles are submitted into an AI, and the AI ​​can determine the priority of proposals.

[0089] The proposal department can adjust the order of proposals based on the relevance of styles during the proposal process. For example, the proposal department will prioritize proposing styles that are most relevant to the customer's needs. For example, the proposal department will postpone proposing styles that are less relevant. The proposal department can also adjust the order of proposals based on the relevance of styles. For example, the proposal department will adjust the order of proposals based on the relevance of styles. This allows the proposal department to make more appropriate proposals by adjusting the order of proposals based on the relevance of styles. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input style relevance data into AI, and the AI ​​can adjust the order of proposals.

[0090] The management department can estimate customer emotions and select management data based on the estimated emotions. For example, if a customer is tense, the management department will prioritize selecting management data that promotes relaxation. For example, if a customer is relaxed, the management department will select detailed management data. The management department can also select visually stimulating management data if a customer is excited. For example, if a customer is excited, the management department will select visually stimulating management data. This allows the management department to provide more appropriate management by selecting management data according to customer emotions. Customer 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. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input customer emotion data into a generative AI, the generative AI can estimate the customer's emotions, and the management department can select management data based on the result.

[0091] The management department can optimize its management algorithms by referring to past management data during management. For example, the management department can select the optimal management algorithm based on past management data. For example, the management department can optimize its management algorithms by referring to past management data. The management department can also analyze past management data and propose the optimal management algorithm. For example, the management department can select the optimal management algorithm based on past management data. In this way, the management department can optimize its management algorithms by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past management data into AI, and the AI ​​can output the optimal management algorithm.

[0092] The management department can estimate the customer's emotions and adjust the frequency of management based on the estimated emotions. For example, if the customer is stressed, the management department may update management data frequently to provide reassurance. For example, if the customer is relaxed, the management department may reduce the frequency of management and update only when necessary. The management department may also update management data at a moderate frequency to provide information if the customer is agitated. For example, if the customer is agitated, the management department may update management data at a moderate frequency. This allows the management department to provide more appropriate management by adjusting the frequency of management according to the customer's emotions. The estimation of customer emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input customer emotion data into a generative AI, the generative AI can estimate the customer's emotions, and adjust the frequency of management based on the result.

[0093] The management department can weight the management data based on when the treatment history was submitted. For example, the management department can prioritize weighting recent treatment histories. For example, the management department can reduce the weight of older treatment histories. The management department can also weight the management data based on the submission date. For example, the management department can weight the management data based on the submission date. This allows the management department to perform more appropriate management by weighting the management data based on when the treatment history was submitted. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input data on when the treatment history was submitted into the AI, and the AI ​​can weight the management data.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can analyze customer voice data and learn the customer's accent and speaking style. For example, the reception desk can perform more accurate speech recognition based on the customer's accent. Furthermore, by learning the customer's speaking style, the reception desk can more accurately understand the customer's requests. In addition, the reception desk can adjust the tone and pace of the voice guide based on the customer's speaking style. As a result, by analyzing customer voice data, the reception desk can provide more appropriate speech recognition and voice guidance.

[0096] The proposal department can estimate the customer's emotions and adjust the timing of its proposals based on those emotions. For example, if the customer is relaxed, the proposal department can delay making a proposal. Conversely, if the customer is in a hurry, it can make a proposal quickly. Furthermore, if the customer is excited, the proposal department can also adjust the timing of its proposals. This allows the proposal department to make more appropriate proposals by adjusting the timing of its proposals according to the customer's emotions.

[0097] The management department can analyze a customer's treatment history and learn their preferences and tendencies. For example, the management department can analyze the styles and colors a customer has chosen in the past to understand their preferences. Furthermore, based on the customer's treatment history, the management department can learn the customer's preferred styles and color tendencies. In addition, the management department can make suggestions to the stylists based on the customer's preferences and tendencies. This allows the management department to understand customer preferences and tendencies by analyzing their treatment history and make more appropriate suggestions.

[0098] The display unit can estimate the customer's emotions and adjust the display's color scheme based on those emotions. For example, if the customer is relaxed, the display unit can provide a calm color scheme. If the customer is excited, the display unit can provide a bright color scheme. Furthermore, if the customer is tense, the display unit can provide a visually relaxing color scheme. In this way, the display unit can provide a more appropriate display by adjusting the color scheme according to the customer's emotions.

[0099] The suggestion department can learn the customer's preferred styles and colors based on their past treatment history. For example, it can analyze the styles and colors the customer has chosen in the past to understand their preferences. Furthermore, it can learn the customer's preferred styles and colors based on their treatment history. In addition, the suggestion department can present the most suitable style options based on the customer's preferences and tendencies. As a result, by analyzing the customer's past treatment history, the suggestion department can understand their preferences and tendencies and present more appropriate style options.

[0100] The management department can estimate customer emotions and adjust how management data is displayed based on those estimates. For example, if a customer is relaxed, the management department can display detailed management data. If a customer is in a hurry, it can display concise management data. Furthermore, if a customer is excited, it can display visually stimulating management data. This allows the management department to provide more appropriate management by adjusting how management data is displayed according to customer emotions.

[0101] The reception desk can learn the customer's preferred styles and colors based on their past request history. For example, the reception desk can analyze the styles and colors the customer has requested in the past to understand their preferences. Furthermore, the reception desk can learn the customer's preferred styles and colors based on their request history. In addition, the reception desk can suggest the most suitable styles and colors based on the customer's preferences and preferences. In this way, the reception desk can understand the customer's preferences and preferences by analyzing their past request history and make more appropriate suggestions.

[0102] The proposal department can estimate the customer's emotions and adjust the content of its proposals based on those emotions. For example, if the customer is relaxed, the proposal department can offer detailed proposals. If the customer is in a hurry, it can offer concise proposals. Furthermore, if the customer is excited, it can offer visually stimulating proposals. In this way, the proposal department can provide more appropriate proposals by adjusting the content of its proposals according to the customer's emotions.

[0103] The management department can learn about customers' preferred styles and colors based on their treatment history. For example, the management department can analyze the styles and colors customers have chosen in the past to understand their preferences. Furthermore, the management department can make suggestions to hairdressers based on customers' preferences and tendencies. In this way, by analyzing customers' treatment history, the management department can understand their preferences and tendencies and make more appropriate suggestions.

[0104] The display unit can estimate the customer's emotions and adjust the content of the display based on those emotions. For example, if the customer is relaxed, the display unit can provide detailed information. If the customer is in a hurry, it can provide concise information. Furthermore, if the customer is excited, it can provide visually stimulating information. In this way, the display unit can provide more appropriate information by adjusting the content of the display according to the customer's emotions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives customer requests via voice. For example, the reception desk can use voice recognition technology to receive customer requests. The reception desk can also categorize customer requests according to the type of voice command. For example, if a customer voice-states a request such as "cut" or "color," the reception desk will respond accordingly. Step 2: The display unit projects the style changes onto the mirror in real time based on the request received by the reception unit. For example, the display unit can project the style changes in real time using display technology that minimizes latency. Depending on the display technology used, the display unit can also display the style changes in high resolution. For example, the display unit can instantly project the style chosen by the customer onto the mirror, allowing the customer to confirm the change. Step 3: The proposal department analyzes trend information and the customer's past history to present the optimal style. For example, the proposal department can obtain trend information from fashion magazines and online databases and use that to present the optimal style to the customer. Alternatively, the proposal department can present the optimal style based on the customer's preferences and current trends. For example, the proposal department can analyze the customer's past history and suggest styles and colors that the customer prefers. Step 4: The management department manages the customer's past treatment history. For example, the management department can manage information such as the date of treatment, the treatment content, and the person in charge, allowing for centralized management of the customer's past treatment history. The management department can also provide technical advice to hairdressers based on the customer's past treatment history. For example, the management department can analyze the customer's past treatment history and suggest areas for improvement in treatment methods and tools to be used.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, display unit, proposal unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14, and receives customer requests by voice. The display unit is implemented by, for example, the display 40A of the smart device 14, and reflects style changes in a mirror in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and presents the optimal style suggestion by analyzing trend information and the customer's past history. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and manages the customer's past treatment history and provides technical advice to the beautician. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, display unit, suggestion unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214, which receive customer requests by voice. The display unit is implemented, for example, by the display of the smart glasses 214, which reflects style changes in a mirror in real time. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes trend information and the customer's past history to present the optimal style suggestion. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the customer's past treatment history and provides technical advice to the beautician. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, display unit, proposal unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314, and receives customer requests by voice. The display unit is implemented by, for example, the display 343 of the headset terminal 314, and reflects style changes in a mirror in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and presents the optimal style proposal by analyzing trend information and the customer's past history. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and manages the customer's past treatment history and provides technical advice to the beautician. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, display unit, proposal unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414, which receive customer requests by voice. The display unit is implemented by, for example, the display of the robot 414, which reflects style changes in a mirror in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes trend information and the customer's past history to present the optimal style suggestion. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which manages the customer's past treatment history and provides technical advice to the beautician. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception desk that takes customer requests by voice, A display unit that projects style changes onto a mirror in real time based on requests received by the aforementioned reception unit, The proposal department analyzes trend information and customer history to present optimal style suggestions, It includes a management department that manages the customer's past treatment history. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We provide style and color simulations based on customer requests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, Providing technical advice to hairdressers The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is The changes in style are reflected in the mirror in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We analyze trend information and the customer's past history to suggest the optimal style. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Manage the customer's past treatment history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate customer emotions and adjust how requests are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the customer's past request history and select the most suitable method of receiving their request. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a request, filtering is performed based on the customer's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the customer's emotions and prioritizes the requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a request, we analyze the customer's social media activity and accept relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned display unit is We estimate customer emotions and adjust the way the display is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned display unit is When displaying, adjust the level of detail based on the importance of the style. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display unit is When displaying, apply different display algorithms depending on the style category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is It estimates customer emotions and adjusts the display length based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is When displaying styles, the display priority is determined based on when the styles were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying, adjust the display order based on style relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the style. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the style category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate the customer's emotions and adjust the length of the suggestion based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the styles were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the styles. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, The system estimates customer emotions and selects management data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, During management, the management algorithm is optimized by referring to past management data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, Estimate customer sentiment and adjust the frequency of interventions based on the estimated customer sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, During management, management data is weighted based on when the treatment history was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that takes customer requests by voice, A display unit that projects style changes onto a mirror in real time based on requests received by the aforementioned reception unit, The proposal department analyzes trend information and customer history to present optimal style suggestions, It includes a management department that manages the customer's past treatment history. A system characterized by the following features.

2. The aforementioned proposal section is, We provide style and color simulations based on customer requests. The system according to feature 1.

3. The aforementioned management department, Providing technical advice to hairdressers The system according to feature 1.

4. The aforementioned display unit is The changes in style are reflected in the mirror in real time. The system according to feature 1.

5. The aforementioned proposal section is, We analyze trend information and the customer's past history to suggest the optimal style. The system according to feature 1.

6. The aforementioned management department, Manage the customer's past treatment history. The system according to feature 1.

7. The aforementioned reception unit is We estimate customer emotions and adjust how requests are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the customer's past request history and select the most suitable method of receiving their request. The system according to feature 1.

9. The aforementioned reception unit is When receiving a request, filtering is performed based on the customer's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the customer's emotions and prioritizes the requests to be accepted based on those estimated emotions. The system according to feature 1.

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