System

Abstract

An object of the system according to the embodiment is to materialize a hairstyle desired by a user and eliminate a recognition discrepancy with a stylist.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a display unit, and a sharing unit. The reception unit receives a user's request. The analysis unit analyzes the information received by the reception unit. The generation unit generates a hairstyle based on the information analyzed by the analysis unit. The display unit displays the hairstyle generated by the generation unit on a mirror. The sharing unit shares the information on the hairstyle generated by the generation unit with the stylist.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for a beauty salon to realize a user's desired hairstyle and eliminate any misunderstandings between the user and the stylist.

[0005] The system according to the embodiment aims to realize the hairstyle desired by the user and eliminate any discrepancies in understanding between the user and the stylist. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a display unit, and a sharing unit. The reception unit inputs a user's preference. The analysis unit analyzes the information received by the reception unit. The generation unit generates a hairstyle based on the information analyzed by the analysis unit. The display unit displays the hairstyle generated by the generation unit on a mirror. The sharing unit shares the information about the hairstyle generated by the generation unit with a stylist. [Effects of the Invention]

[0007] The system according to the embodiment can realize the hairstyle desired by the user and eliminate any discrepancy in understanding between the user and the stylist. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that uses image generation technology to replace a user's hairstyle and other information reflected in a mirror at a beauty salon through interaction with a dialogue system. The user interacts with the dialogue system and communicates their desired hairstyle, color, length, etc. The dialogue system analyzes the user's input and suggests an appropriate hairstyle. The dialogue system then uses image generation technology to replace the user's hairstyle reflected in the mirror. Specifically, the dialogue system generates the suggested hairstyle using image generation technology and projects it on the mirror in real time. This allows the user to preview their new hairstyle in advance. Furthermore, if the user is satisfied with the proposed hairstyle, the system shares this information with a stylist. The stylist then styles the user's hair in accordance with the user's wishes based on the hairstyle information generated by the dialogue system. For example, if a user inputs "I want a short haircut" into the dialogue system, the dialogue system analyzes the information and suggests an appropriate short haircut style. The short haircut style is then projected on the mirror using image generation technology. If the user is satisfied with the style, the information is shared with the stylist, who then styles the hair based on that information. This not only allows the user to realize their preferred style while they are waiting, but also facilitates communication with the stylist and eliminates misunderstandings.This not only allows the user to realize their preferred style while they are waiting, but also facilitates communication with the stylist and eliminates misunderstandings.

[0029] A beauty salon system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a display unit, and a sharing unit. The reception unit inputs a user's preferences. The user's preferences include, but are not limited to, a hairstyle, color, and length. For example, the reception unit receives the user's desired hairstyle via text input. The reception unit can also receive the user's preferences using voice input. Furthermore, the reception unit can also receive the user's preferences using image input. For example, the reception unit receives an image of the desired hairstyle uploaded by the user. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the user's preferences using a data analysis method. The analysis unit can also analyze the user's preferences using an algorithm. Furthermore, the analysis unit can analyze the user's preferences using AI. For example, the analysis unit receives the user's preferences and performs an analysis using an AI model that inputs an appropriate hairstyle. The generation unit generates a hairstyle based on the information analyzed by the analysis unit. The generation unit generates a hairstyle using, for example, image generation technology. The generation unit can also generate a hairstyle using deep learning. Furthermore, the generation unit can also generate a hairstyle using CG technology. For example, the generation unit performs generation using an AI model that receives information analyzed by the analysis unit as input and outputs an image of a hairstyle. The display unit projects the hairstyle generated by the generation unit onto a mirror. The display unit projects the hairstyle onto the mirror using, for example, display technology. The display unit can also project the hairstyle onto the mirror using projection technology. Furthermore, the display unit can project the hairstyle onto the mirror in real time. For example, the display unit projects an image of the hairstyle generated by the generation unit onto the mirror in real time. The sharing unit shares information about the hairstyle generated by the generation unit with a stylist. The sharing unit shares the information about the hairstyle with the stylist using, for example, a data sharing means. The sharing unit can also share the information about the hairstyle with the stylist using a communication protocol.Furthermore, the sharing unit can also share hairstyle information with stylists using AI. For example, the sharing unit receives the hairstyle information generated by the generation unit and shares it with stylists using an AI model. As a result, the beauty salon system according to the embodiment generates a hairstyle based on the user's preferences and displays it in a mirror, allowing the user to check it in advance.

[0030] The reception unit can receive information on the user's desired hairstyle, color, and length. For example, the reception unit can receive the user's desired hairstyle by text input. The reception unit can also receive the user's desire using voice input. Furthermore, the reception unit can also receive the user's desire using image input. For example, the reception unit can receive an image of the user's desired hairstyle uploaded. By receiving the user's specific desire, a more appropriate hairstyle can be suggested.

[0031] The analysis unit can analyze the information received by the reception unit and suggest a hairstyle. The analysis unit can, for example, analyze the user's preferences using a data analysis method. The analysis unit can also analyze the user's preferences using an algorithm. The analysis unit can also analyze the user's preferences using AI. For example, the analysis unit inputs the user's preferences and performs analysis using an AI model that outputs an appropriate hairstyle. In this way, by analyzing the information from the reception unit, it is possible to suggest the optimal hairstyle for the user.

[0032] The generation unit can generate a hairstyle using image generation technology based on the information analyzed by the analysis unit. The generation unit generates a hairstyle using, for example, image generation technology. The generation unit can also generate a hairstyle using deep learning. Furthermore, the generation unit can generate a hairstyle using CG technology. For example, the generation unit uses an AI model that receives the information analyzed by the analysis unit as input and outputs an image of the hairstyle. In this way, realistic hairstyles can be generated using image generation technology.

[0033] The display unit can display the hairstyle generated by the generation unit on the mirror. The display unit can display the hairstyle on the mirror using, for example, display technology. The display unit can also display the hairstyle on the mirror using projection technology. Furthermore, the display unit can display the hairstyle on the mirror in real time. For example, the display unit displays an image of the hairstyle generated by the generation unit on the mirror in real time. This allows the user to check the hairstyle in real time, thereby improving user satisfaction.

[0034] The sharing unit can share the hairstyle information generated by the generation unit with the stylist. The sharing unit, for example, uses a data sharing means to share the hairstyle information with the stylist. The sharing unit can also share the hairstyle information with the stylist using a communication protocol. Furthermore, the sharing unit can also share the hairstyle information with the stylist using AI. For example, the sharing unit inputs the hairstyle information generated by the generation unit and shares it using an AI model to be shared with the stylist. This eliminates discrepancies in understanding with the stylist and enables styling that meets the user's wishes.

[0035] The reception unit can analyze the user's past hairstyle history and select the optimal reception method. For example, the reception unit can suggest similar styles based on hairstyles the user has chosen in the past. The reception unit can also make suggestions while excluding hairstyles that the user has avoided in the past. The reception unit can also refer to the user's past feedback and prioritize suggestions of preferred styles. This makes it possible to make optimal suggestions based on the user's past history.

[0036] The reception unit can perform filtering based on the user's current fashion and trends. For example, the reception unit can suggest hairstyles that match the clothes the user is currently wearing. The reception unit can also suggest trendy hairstyles taking into account the latest fashion trends. The reception unit can also suggest hairstyles that match the accessories the user owns. This makes it possible to make suggestions that match the current fashion and trends.

[0037] The reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit can use voice recognition technology to receive the desired hairstyle. If the user selects text input, the reception unit can also use keyword search to receive the desired hairstyle. If the user selects image input, the reception unit can also use image analysis technology to receive the desired hairstyle. This allows for flexible reception depending on the user's input method.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the hairstyle. For example, the analysis unit performs a detailed analysis for a hairstyle for an important event. The analysis unit can also perform a simple analysis for an everyday hairstyle. The analysis unit can also perform a detailed analysis for a hairstyle that the user particularly likes. This makes it possible to perform a detailed analysis according to the importance.

[0039] The analysis unit can apply different analysis algorithms depending on the hairstyle category. For example, the analysis unit applies a color analysis algorithm to a colored hairstyle. The analysis unit can also apply a shape analysis algorithm to a cut hairstyle. The analysis unit can also apply a curl analysis algorithm to a permed hairstyle. This allows for appropriate analysis according to the category.

[0040] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit refers to analysis results that the user has previously received favorably. The analysis unit can also exclude analysis results that the user has previously been dissatisfied with. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. This enables highly accurate analysis based on past analysis results.

[0041] The generation unit can adjust the level of detail of the generation based on the importance of the hairstyle. For example, the generation unit performs detailed generation for a hairstyle for an important event. The generation unit can also perform simple generation for an everyday hairstyle. The generation unit can also perform detailed generation for a hairstyle that the user particularly likes. This allows for detailed generation according to the importance.

[0042] The generation unit can apply different generation algorithms depending on the hairstyle category. For example, the generation unit applies a color generation algorithm to a colored hairstyle. The generation unit can also apply a shape generation algorithm to a cut hairstyle. The generation unit can also apply a curl generation algorithm to a permed hairstyle. This allows for appropriate generation according to the category.

[0043] The generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit refers to generation results that the user has previously received favorably. The generation unit can also exclude generation results that the user has previously been dissatisfied with. The generation unit can also adjust the generation algorithm based on the user's past generation results. This enables highly accurate generation based on past generation results.

[0044] The display unit can adjust the level of detail of the display based on the importance of the hairstyle. For example, the display unit displays a detailed view of a hairstyle for an important event. The display unit can also display a simple view of an everyday hairstyle. The display unit can also display a detailed view of a hairstyle that the user particularly likes. This allows for a detailed display according to the importance.

[0045] The display unit can apply different display algorithms depending on the hairstyle category. For example, the display unit applies a color display algorithm to a colored hairstyle. The display unit can also apply a shape display algorithm to a cut hairstyle. The display unit can also apply a curl display algorithm to a permed hairstyle. This allows for appropriate display according to the category.

[0046] The display unit can improve the accuracy of the display by referring to the user's past display results. For example, the display unit refers to display results that the user has previously found popular. The display unit can also exclude display results that the user has previously been dissatisfied with. The display unit can also adjust the display algorithm based on the user's past display results. This enables highly accurate display based on the past display results.

[0047] The display unit can determine the display priority based on the time of submission of the hairstyle. For example, the display unit prioritizes display of hairstyles for important events. The display unit can also postpone display of everyday hairstyles. The display unit can also prioritize display of hairstyles when the user is in a hurry. This makes it possible to display the hairstyles in order of priority based on the time of submission.

[0048] The display unit can adjust the display order based on the relevance of hairstyles. For example, the display unit can prioritize displaying hairstyles that the user particularly likes. The display unit can also postpone hairstyles that the user wants to avoid. The display unit can also prioritize displaying highly relevant hairstyles based on the user's past selection history. This makes it possible to adjust the display order based on relevance.

[0049] The display unit can adjust the use of technical terms in the display according to the user's level of expertise. For example, if the user has expertise in beauty, the display unit uses a lot of technical terms. If the user does not have expertise in beauty, the display unit can also explain things in simple terms. The display unit can also adjust the use of technical terms based on the user's past feedback. This allows for appropriate display according to the user's level of expertise.

[0050] The sharing unit can adjust the level of detail of sharing based on the importance of the hairstyle. For example, the sharing unit can share in detail a hairstyle for an important event. The sharing unit can also share briefly a hairstyle for everyday use. The sharing unit can also share in detail a hairstyle that the user particularly likes. This allows for detailed sharing according to the importance.

[0051] The sharing unit can apply different sharing algorithms depending on the hairstyle category. For example, in the case of a colored hairstyle, the sharing unit can apply a color sharing algorithm. In addition, in the case of a cut hairstyle, the sharing unit can also apply a shape sharing algorithm. In addition, in the case of a permed hairstyle, the sharing unit can also apply a curl sharing algorithm. This allows appropriate sharing according to the category.

[0052] The sharing unit can improve the accuracy of sharing by referring to the user's past sharing results. For example, the sharing unit refers to shared results that the user has previously found popular. The sharing unit can also exclude shared results that the user has previously been dissatisfied with. The sharing unit can also adjust the sharing algorithm based on the user's past sharing results. This enables highly accurate sharing based on past sharing results.

[0053] The sharing unit can determine the priority of sharing based on the time of submission of the hairstyle. For example, the sharing unit prioritizes sharing of hairstyles for important events. The sharing unit can also postpone sharing of everyday hairstyles. The sharing unit can also prioritize sharing when the user is in a hurry. This makes it possible to prioritize sharing based on the time of submission.

[0054] The sharing unit can adjust the sharing order based on the relevance of the hairstyles. For example, the sharing unit prioritizes sharing hairstyles that the user particularly likes. The sharing unit can also postpone hairstyles that the user wants to avoid. The sharing unit can also prioritize sharing highly relevant hairstyles based on the user's past selection history. This makes it possible to adjust the sharing order based on the relevance.

[0055] The sharing unit can adjust the use of technical terms in the sharing depending on the user's level of expertise. For example, if the user has expertise in beauty, the sharing unit will use a lot of technical terms. If the user does not have expertise in beauty, the sharing unit can also explain things in simple terms. The sharing unit can also adjust the use of technical terms based on the user's past feedback. This allows for appropriate sharing depending on the user's level of expertise.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the user's past hairstyle history and select the optimal reception method. For example, it can suggest similar styles based on hairstyles the user has chosen in the past. It can also make suggestions while excluding hairstyles that the user has avoided in the past. The reception unit can also refer to the user's past feedback and prioritize suggestions of preferred styles. This makes it possible to make optimal suggestions based on the user's past history.

[0058] The generation unit can adjust the level of detail of the generation based on the importance of the hairstyle. For example, for a hairstyle for an important event, detailed generation is performed. The generation unit can also perform simple generation for an everyday hairstyle. The generation unit can also perform detailed generation for a hairstyle that the user particularly likes. This makes it possible to generate detailed hairstyles according to the importance.

[0059] The sharing unit can adjust the level of detail of sharing based on the importance of the hairstyle. For example, detailed sharing is performed for a hairstyle for an important event. The sharing unit can also perform simple sharing for an everyday hairstyle. The sharing unit can also perform detailed sharing for a hairstyle that the user particularly likes. This allows for detailed sharing according to the importance.

[0060] The reception unit can perform filtering based on the user's current fashion and trends. For example, it can suggest hairstyles that match the clothes the user is currently wearing. The reception unit can also suggest trendy hairstyles taking into account the latest fashion trends. The reception unit can also suggest hairstyles that match the accessories the user owns. This makes it possible to make suggestions that match the current fashion and trends.

[0061] The analysis unit can apply different analysis algorithms depending on the hairstyle category. For example, for a colored hairstyle, a color analysis algorithm can be applied. For a cut hairstyle, a shape analysis algorithm can be applied. For a permed hairstyle, a curl analysis algorithm can be applied. This allows for appropriate analysis according to the category.

[0062] The display unit can determine the display priority based on the time of submission of the hairstyle. For example, a hairstyle for an important event is displayed with priority. The display unit can also postpone everyday hairstyles. The display unit can also display hairstyles with priority when the user is in a hurry. This makes it possible to display hairstyles with priority based on the time of submission.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit inputs the user's preferences. The user's preferences include hairstyle, color, length, etc. The reception unit receives the user's preferences using text input, voice input, or image input. For example, the user can upload an image of the hairstyle they desire. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's preferences using data analysis methods, algorithms, and AI. For example, the analysis is performed using an AI model that inputs the user's preferences and outputs an appropriate hairstyle. Step 3: The generation unit generates a hairstyle based on the information analyzed by the analysis unit. The generation unit generates the hairstyle using image generation technology, deep learning, and CG technology. For example, the generation is performed using an AI model that takes the information analyzed by the analysis unit as input and outputs an image of the hairstyle. Step 4: The display unit projects the hairstyle generated by the generation unit onto the mirror. The display unit can project the hairstyle onto the mirror using display technology or projection technology. For example, the image of the hairstyle generated by the generation unit is projected onto the mirror in real time. Step 5: The sharing unit shares the hairstyle information generated by the generation unit with the stylist. The sharing unit shares the hairstyle information with the stylist using a data sharing means, a communication protocol, and AI. For example, the hairstyle information generated by the generation unit is input and shared using an AI model to be shared with the stylist.

[0065] (Example 2) A system according to an embodiment of the present invention is a system that uses image generation technology to replace a user's hairstyle and other information reflected in a mirror at a beauty salon through interaction with a dialogue system. The user interacts with the dialogue system and communicates their desired hairstyle, color, length, etc. The dialogue system analyzes the user's input and suggests an appropriate hairstyle. The dialogue system then uses image generation technology to replace the user's hairstyle reflected in the mirror. Specifically, the dialogue system generates the suggested hairstyle using image generation technology and projects it on the mirror in real time. This allows the user to preview their new hairstyle in advance. Furthermore, if the user is satisfied with the proposed hairstyle, the system shares this information with a stylist. The stylist then styles the user's hair in accordance with the user's wishes based on the hairstyle information generated by the dialogue system. For example, if a user inputs "I want a short haircut" into the dialogue system, the dialogue system analyzes the information and suggests an appropriate short haircut style. The short haircut style is then projected on the mirror using image generation technology. If the user is satisfied with the style, the information is shared with the stylist, who then styles the hair based on that information. This not only allows the user to realize their preferred style while they are waiting, but also facilitates communication with the stylist and eliminates misunderstandings.This not only allows the user to realize their preferred style while they are waiting, but also facilitates communication with the stylist and eliminates misunderstandings.

[0066] A beauty salon system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a display unit, and a sharing unit. The reception unit inputs a user's preferences. The user's preferences include, but are not limited to, a hairstyle, color, and length. For example, the reception unit receives the user's desired hairstyle via text input. The reception unit can also receive the user's preferences using voice input. Furthermore, the reception unit can also receive the user's preferences using image input. For example, the reception unit receives an image of the desired hairstyle uploaded by the user. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the user's preferences using a data analysis method. The analysis unit can also analyze the user's preferences using an algorithm. Furthermore, the analysis unit can analyze the user's preferences using AI. For example, the analysis unit receives the user's preferences and performs an analysis using an AI model that inputs an appropriate hairstyle. The generation unit generates a hairstyle based on the information analyzed by the analysis unit. The generation unit generates a hairstyle using, for example, image generation technology. The generation unit can also generate a hairstyle using deep learning. Furthermore, the generation unit can also generate a hairstyle using CG technology. For example, the generation unit performs generation using an AI model that receives information analyzed by the analysis unit as input and outputs an image of a hairstyle. The display unit projects the hairstyle generated by the generation unit onto a mirror. The display unit projects the hairstyle onto the mirror using, for example, display technology. The display unit can also project the hairstyle onto the mirror using projection technology. Furthermore, the display unit can project the hairstyle onto the mirror in real time. For example, the display unit projects an image of the hairstyle generated by the generation unit onto the mirror in real time. The sharing unit shares information about the hairstyle generated by the generation unit with a stylist. The sharing unit shares the information about the hairstyle with the stylist using, for example, a data sharing means. The sharing unit can also share the information about the hairstyle with the stylist using a communication protocol.Furthermore, the sharing unit can also share hairstyle information with stylists using AI. For example, the sharing unit receives the hairstyle information generated by the generation unit and shares it with stylists using an AI model. As a result, the beauty salon system according to the embodiment generates a hairstyle based on the user's preferences and displays it in a mirror, allowing the user to check it in advance.

[0067] The reception unit can receive information on the user's desired hairstyle, color, and length. For example, the reception unit can receive the user's desired hairstyle by text input. The reception unit can also receive the user's desire using voice input. Furthermore, the reception unit can also receive the user's desire using image input. For example, the reception unit can receive an image of the user's desired hairstyle uploaded. By receiving the user's specific desire, a more appropriate hairstyle can be suggested.

[0068] The analysis unit can analyze the information received by the reception unit and suggest a hairstyle. The analysis unit can, for example, analyze the user's preferences using a data analysis method. The analysis unit can also analyze the user's preferences using an algorithm. The analysis unit can also analyze the user's preferences using AI. For example, the analysis unit inputs the user's preferences and performs analysis using an AI model that outputs an appropriate hairstyle. In this way, by analyzing the information from the reception unit, it is possible to suggest the optimal hairstyle for the user.

[0069] The generation unit can generate a hairstyle using image generation technology based on the information analyzed by the analysis unit. The generation unit generates a hairstyle using, for example, image generation technology. The generation unit can also generate a hairstyle using deep learning. Furthermore, the generation unit can generate a hairstyle using CG technology. For example, the generation unit uses an AI model that receives the information analyzed by the analysis unit as input and outputs an image of the hairstyle. In this way, realistic hairstyles can be generated using image generation technology.

[0070] The display unit can display the hairstyle generated by the generation unit on the mirror. The display unit can display the hairstyle on the mirror using, for example, display technology. The display unit can also display the hairstyle on the mirror using projection technology. Furthermore, the display unit can display the hairstyle on the mirror in real time. For example, the display unit displays an image of the hairstyle generated by the generation unit on the mirror in real time. This allows the user to check the hairstyle in real time, thereby improving user satisfaction.

[0071] The sharing unit can share the hairstyle information generated by the generation unit with the stylist. The sharing unit, for example, uses a data sharing means to share the hairstyle information with the stylist. The sharing unit can also share the hairstyle information with the stylist using a communication protocol. Furthermore, the sharing unit can also share the hairstyle information with the stylist using AI. For example, the sharing unit inputs the hairstyle information generated by the generation unit and shares it using an AI model to be shared with the stylist. This eliminates discrepancies in understanding with the stylist and enables styling that meets the user's wishes.

[0072] The reception unit can estimate the user's emotions and adjust the suggested hairstyle based on the estimated user's emotions. For example, if the user is nervous, the reception unit can suggest a simple, calm hairstyle to help the user relax. If the user is excited, the reception unit can also suggest a bolder, more colorful hairstyle. If the user is feeling anxious, the reception unit can also suggest hairstyles that have been successful in the past to give the user a sense of security. This enables suggestions based on the user's emotions, improving satisfaction. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can analyze the user's past hairstyle history and select the optimal reception method. For example, the reception unit can suggest similar styles based on hairstyles the user has chosen in the past. The reception unit can also make suggestions while excluding hairstyles that the user has avoided in the past. The reception unit can also refer to the user's past feedback and prioritize suggestions of preferred styles. This makes it possible to make optimal suggestions based on the user's past history.

[0074] The reception unit can perform filtering based on the user's current fashion and trends. For example, the reception unit can suggest hairstyles that match the clothes the user is currently wearing. The reception unit can also suggest trendy hairstyles taking into account the latest fashion trends. The reception unit can also suggest hairstyles that match the accessories the user owns. This makes it possible to make suggestions that match the current fashion and trends.

[0075] The reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit can use voice recognition technology to receive the desired hairstyle. If the user selects text input, the reception unit can also use keyword search to receive the desired hairstyle. If the user selects image input, the reception unit can also use image analysis technology to receive the desired hairstyle. This allows for flexible reception depending on the user's input method.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is feeling anxious, the analysis unit can also provide analysis results that give a sense of security. This makes it possible to provide analysis results that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the hairstyle. For example, the analysis unit performs a detailed analysis for a hairstyle for an important event. The analysis unit can also perform a simple analysis for an everyday hairstyle. The analysis unit can also perform a detailed analysis for a hairstyle that the user particularly likes. This makes it possible to perform a detailed analysis according to the importance.

[0078] The analysis unit can apply different analysis algorithms depending on the hairstyle category. For example, the analysis unit applies a color analysis algorithm to a colored hairstyle. The analysis unit can also apply a shape analysis algorithm to a cut hairstyle. The analysis unit can also apply a curl analysis algorithm to a permed hairstyle. This allows for appropriate analysis according to the category.

[0079] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit refers to analysis results that the user has previously received favorably. The analysis unit can also exclude analysis results that the user has previously been dissatisfied with. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. This enables highly accurate analysis based on past analysis results.

[0080] The generation unit can estimate the user's emotion and adjust the expression method of the generated hairstyle based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate a hairstyle with a soft color. If the user is excited, the generation unit can also generate a hairstyle with a vivid color. If the user is feeling anxious, the generation unit can also generate a hairstyle with a subdued color. This makes it possible to generate a hairstyle according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] The generation unit can adjust the level of detail of the generation based on the importance of the hairstyle. For example, the generation unit performs detailed generation for a hairstyle for an important event. The generation unit can also perform simple generation for an everyday hairstyle. The generation unit can also perform detailed generation for a hairstyle that the user particularly likes. This allows for detailed generation according to the importance.

[0082] The generation unit can apply different generation algorithms depending on the hairstyle category. For example, the generation unit applies a color generation algorithm to a colored hairstyle. The generation unit can also apply a shape generation algorithm to a cut hairstyle. The generation unit can also apply a curl generation algorithm to a permed hairstyle. This allows for appropriate generation according to the category.

[0083] The generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit refers to generation results that the user has previously received favorably. The generation unit can also exclude generation results that the user has previously been dissatisfied with. The generation unit can also adjust the generation algorithm based on the user's past generation results. This enables highly accurate generation based on past generation results.

[0084] The display unit can estimate the user's emotions and adjust the way the hairstyle is displayed based on the estimated user's emotions. For example, if the user is relaxed, the display unit can display a hairstyle with soft colors. If the user is excited, the display unit can also display a hairstyle with vivid colors. If the user is feeling anxious, the display unit can also display a hairstyle with subdued colors. This makes it possible to display a hairstyle according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The display unit can adjust the level of detail of the display based on the importance of the hairstyle. For example, the display unit displays a detailed view of a hairstyle for an important event. The display unit can also display a simple view of an everyday hairstyle. The display unit can also display a detailed view of a hairstyle that the user particularly likes. This allows for a detailed display according to the importance.

[0086] The display unit can apply different display algorithms depending on the hairstyle category. For example, the display unit applies a color display algorithm to a colored hairstyle. The display unit can also apply a shape display algorithm to a cut hairstyle. The display unit can also apply a curl display algorithm to a permed hairstyle. This allows for appropriate display according to the category.

[0087] The display unit can improve the accuracy of the display by referring to the user's past display results. For example, the display unit refers to display results that the user has previously found popular. The display unit can also exclude display results that the user has previously been dissatisfied with. The display unit can also adjust the display algorithm based on the user's past display results. This enables highly accurate display based on the past display results.

[0088] The display unit can estimate the user's emotions and adjust the length of the hairstyle to be displayed based on the estimated user's emotions. For example, if the user is relaxed, the display unit can display a longer hairstyle. If the user is in a hurry, the display unit can also display a shorter hairstyle. If the user is feeling anxious, the display unit can also display a hairstyle with a length that gives a sense of security. This makes it possible to adjust the length of the hairstyle according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The display unit can determine the display priority based on the time of submission of the hairstyle. For example, the display unit prioritizes display of hairstyles for important events. The display unit can also postpone display of everyday hairstyles. The display unit can also prioritize display of hairstyles when the user is in a hurry. This makes it possible to display the hairstyles in order of priority based on the time of submission.

[0090] The display unit can adjust the display order based on the relevance of hairstyles. For example, the display unit can prioritize displaying hairstyles that the user particularly likes. The display unit can also postpone hairstyles that the user wants to avoid. The display unit can also prioritize displaying highly relevant hairstyles based on the user's past selection history. This makes it possible to adjust the display order based on relevance.

[0091] The display unit can adjust the use of technical terms in the display according to the user's level of expertise. For example, if the user has expertise in beauty, the display unit uses a lot of technical terms. If the user does not have expertise in beauty, the display unit can also explain things in simple terms. The display unit can also adjust the use of technical terms based on the user's past feedback. This allows for appropriate display according to the user's level of expertise.

[0092] The sharing unit can estimate the user's emotions and adjust the way the shared hairstyle is expressed based on the estimated user's emotions. For example, if the user is relaxed, the sharing unit can share a hairstyle with a soft color. If the user is excited, the sharing unit can also share a hairstyle with a bright color. If the user is feeling anxious, the sharing unit can also share a hairstyle with a subdued color. This makes it possible to share hairstyles according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The sharing unit can adjust the level of detail of sharing based on the importance of the hairstyle. For example, the sharing unit can share in detail a hairstyle for an important event. The sharing unit can also share briefly a hairstyle for everyday use. The sharing unit can also share in detail a hairstyle that the user particularly likes. This allows for detailed sharing according to the importance.

[0094] The sharing unit can apply different sharing algorithms depending on the hairstyle category. For example, in the case of a colored hairstyle, the sharing unit can apply a color sharing algorithm. In addition, in the case of a cut hairstyle, the sharing unit can also apply a shape sharing algorithm. In addition, in the case of a permed hairstyle, the sharing unit can also apply a curl sharing algorithm. This allows appropriate sharing according to the category.

[0095] The sharing unit can improve the accuracy of sharing by referring to the user's past sharing results. For example, the sharing unit refers to shared results that the user has previously found popular. The sharing unit can also exclude shared results that the user has previously been dissatisfied with. The sharing unit can also adjust the sharing algorithm based on the user's past sharing results. This enables highly accurate sharing based on past sharing results.

[0096] The sharing unit can estimate the user's emotions and adjust the length of the hairstyle to be shared based on the estimated user's emotions. For example, if the user is relaxed, the sharing unit can share a longer hairstyle. Also, if the user is in a hurry, the sharing unit can share a shorter hairstyle. Also, if the user is feeling anxious, the sharing unit can share a hairstyle of a length that gives a sense of security. This makes it possible to share hairstyle lengths according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The sharing unit can determine the priority of sharing based on the time of submission of the hairstyle. For example, the sharing unit prioritizes sharing of hairstyles for important events. The sharing unit can also postpone sharing of everyday hairstyles. The sharing unit can also prioritize sharing when the user is in a hurry. This makes it possible to prioritize sharing based on the time of submission.

[0098] The sharing unit can adjust the sharing order based on the relevance of the hairstyles. For example, the sharing unit prioritizes sharing hairstyles that the user particularly likes. The sharing unit can also postpone hairstyles that the user wants to avoid. The sharing unit can also prioritize sharing highly relevant hairstyles based on the user's past selection history. This makes it possible to adjust the sharing order based on the relevance.

[0099] The sharing unit can adjust the use of technical terms in the sharing depending on the user's level of expertise. For example, if the user has expertise in beauty, the sharing unit will use a lot of technical terms. If the user does not have expertise in beauty, the sharing unit can also explain things in simple terms. The sharing unit can also adjust the use of technical terms based on the user's past feedback. This allows for appropriate sharing depending on the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, display unit, and sharing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the user's request via text input or voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a hairstyle using image generation technology. The display unit is realized, for example, by the output device 40 of the smart device 14 and displays the generated hairstyle on a mirror. The sharing unit shares information with a stylist, for example, via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, display unit, and sharing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's request by voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's request. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a hairstyle using image generation technology. The display unit is realized by the speaker 240 of the smart glasses 214 and displays the generated hairstyle on a mirror. The sharing unit shares information with a stylist, for example, via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, display unit, and sharing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the user's request by voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's request. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a hairstyle using image generation technology. The display unit is realized by the display 343 of the headset-type terminal 314 and displays the generated hairstyle on a mirror. The sharing unit shares information with a stylist, for example, via the communication I / F 26 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, display unit, and sharing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's request by voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's request. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a hairstyle using image generation technology. The display unit is realized, for example, by the control object 443 of the robot 414 and displays the generated hairstyle on a mirror. The sharing unit shares information with a stylist, for example, via the communication I / F 26 of the data processing device 12.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception unit can also estimate the user's emotions and adjust the reception method based on the estimated emotions. For example, if the user is nervous, the reception unit can provide relaxing music or images. If the user is excited, the reception unit can also speak in a calm tone. Furthermore, if the user is feeling anxious, the reception unit can present past success stories to reassure the user. This allows for flexible responses according to the user's emotions, improving satisfaction.

[0102] The reception unit can analyze the user's past hairstyle history and select the optimal reception method. For example, it can suggest similar styles based on hairstyles the user has chosen in the past. It can also make suggestions while excluding hairstyles that the user has avoided in the past. The reception unit can also refer to the user's past feedback and prioritize suggestions of preferred styles. This makes it possible to make optimal suggestions based on the user's past history.

[0103] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. If the user is feeling anxious, analysis results that give a sense of security can be provided. This makes it possible to provide analysis results that correspond to the user's emotions.

[0104] The generation unit can adjust the level of detail of the generation based on the importance of the hairstyle. For example, for a hairstyle for an important event, detailed generation is performed. The generation unit can also perform simple generation for an everyday hairstyle. The generation unit can also perform detailed generation for a hairstyle that the user particularly likes. This makes it possible to generate detailed hairstyles according to the importance.

[0105] The display unit can estimate the user's emotions and adjust the way hairstyles are displayed based on the estimated emotions. For example, if the user is relaxed, a hairstyle with a soft color can be displayed. If the user is excited, a hairstyle with a vivid color can be displayed. If the user is anxious, a hairstyle with a subdued color can be displayed. This makes it possible to display hairstyles according to the user's emotions.

[0106] The sharing unit can adjust the level of detail of sharing based on the importance of the hairstyle. For example, detailed sharing is performed for a hairstyle for an important event. The sharing unit can also perform simple sharing for an everyday hairstyle. The sharing unit can also perform detailed sharing for a hairstyle that the user particularly likes. This allows for detailed sharing according to the importance.

[0107] The reception unit can perform filtering based on the user's current fashion and trends. For example, it can suggest hairstyles that match the clothes the user is currently wearing. The reception unit can also suggest trendy hairstyles taking into account the latest fashion trends. The reception unit can also suggest hairstyles that match the accessories the user owns. This makes it possible to make suggestions that match the current fashion and trends.

[0108] The analysis unit can apply different analysis algorithms depending on the hairstyle category. For example, for a colored hairstyle, a color analysis algorithm can be applied. For a cut hairstyle, a shape analysis algorithm can be applied. For a permed hairstyle, a curl analysis algorithm can be applied. This allows for appropriate analysis according to the category.

[0109] The generation unit can estimate the user's emotion and adjust the expression method of the generated hairstyle based on the estimated emotion. For example, if the user is relaxed, a hairstyle with soft colors can be generated. If the user is excited, a hairstyle with vivid colors can be generated. If the user is anxious, a hairstyle with subdued colors can be generated. This makes it possible to generate hairstyles according to the user's emotion.

[0110] The display unit can determine the display priority based on the time of submission of the hairstyle. For example, a hairstyle for an important event is displayed with priority. The display unit can also postpone everyday hairstyles. The display unit can also display hairstyles with priority when the user is in a hurry. This makes it possible to display hairstyles with priority based on the time of submission.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit inputs the user's preferences. The user's preferences include hairstyle, color, length, etc. The reception unit receives the user's preferences using text input, voice input, or image input. For example, the user can upload an image of the hairstyle they desire. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's preferences using data analysis methods, algorithms, and AI. For example, the analysis is performed using an AI model that inputs the user's preferences and outputs an appropriate hairstyle. Step 3: The generation unit generates a hairstyle based on the information analyzed by the analysis unit. The generation unit generates the hairstyle using image generation technology, deep learning, and CG technology. For example, the generation is performed using an AI model that takes the information analyzed by the analysis unit as input and outputs an image of the hairstyle. Step 4: The display unit projects the hairstyle generated by the generation unit onto the mirror. The display unit can project the hairstyle onto the mirror using display technology or projection technology. For example, the image of the hairstyle generated by the generation unit is projected onto the mirror in real time. Step 5: The sharing unit shares the hairstyle information generated by the generation unit with the stylist. The sharing unit shares the hairstyle information with the stylist using a data sharing means, a communication protocol, and AI. For example, the hairstyle information generated by the generation unit is input and shared using an AI model to be shared with the stylist.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting user preferences; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a hairstyle based on the information analyzed by the analysis unit; a display unit that displays the hairstyle generated by the generation unit on a mirror; a sharing unit that shares the information about the hairstyle generated by the generation unit with a stylist. A system characterized by:

2. The reception unit Accepts information about the user's desired hairstyle, color, and length 2. The system of claim 1.

3. The analysis unit Analyzing the information received by the reception unit and proposing a hairstyle 2. The system of claim 1.

4. The generation unit A hairstyle is generated using an image generation technique based on the information analyzed by the analysis unit.

2. The system of claim 1.

5. The display unit The hairstyle generated by the generation unit is reflected in a mirror.

2. The system of claim 1.

6. The common part is The hairstyle information generated by the generation unit is shared with a stylist.

2. The system of claim 1.

7. The reception unit Estimating a user's emotions and adjusting the suggested hairstyle based on the estimated user's emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past hairstyle history and select the optimal reception method 2. The system of claim 1.

Images