System

Abstract

An object of a system according to an embodiment is to enable a user to quickly and accurately achieve a desired hairstyle without going to a beauty salon or a barber shop.SOLUTION: A system includes an interaction unit, an analysis unit, and a cut unit. The interaction unit receives an instruction from a user. The analysis unit analyzes the instruction received by the interaction unit. The cutting unit cuts the hair based on the instruction analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of requiring users to go to a beauty salon or barber shop and to communicate with a hairdresser.

[0005] The system according to the embodiment aims to enable a user to quickly and accurately achieve a desired hairstyle without going to a beauty salon or barber shop. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an analysis unit, and a cutting unit. The dialogue unit receives instructions from a user. The analysis unit analyzes the instructions received by the dialogue unit. The cutting unit cuts hair based on the instructions analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to quickly and accurately achieve a desired hairstyle without going to a beauty salon or barber shop. [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) The automatic haircutting system according to an embodiment of the present invention is a system that accurately cuts a user's desired hairstyle. This system allows the user to communicate with AI about the length and style of the cut, and the robot cuts the hair while sucking it up based on the user's instructions, thereby achieving a fast and accurate haircut. This allows the automatic haircutting system to quickly and accurately cut a user's desired hairstyle.

[0029] The automatic haircutting system according to the embodiment includes a dialogue unit, an analysis unit, and a cutting unit. The dialogue unit receives instructions from a user. For example, it can receive voice instructions. The dialogue unit can also receive touch panel input. The dialogue unit can also receive gesture input. The analysis unit analyzes the instructions received by the dialogue unit. For example, the analysis unit analyzes voice instructions using voice recognition technology. The analysis unit can also analyze the instruction content using natural language processing technology. The analysis unit can also analyze gesture input using image recognition technology. The cutting unit cuts hair based on the instructions analyzed by the analysis unit. For example, the cutting unit cuts hair to a specified length. The cutting unit can also cut hair in a specified style. The cutting unit can also automatically select tools to use and cut hair. This allows hair to be cut based on user instructions.

[0030] The dialogue unit learns the user's past haircut history and can predict and suggest the user's preferred style. For example, the dialogue unit's generation AI learns data on hairstyles the user has chosen in the past, and predicts and suggests the user's preferred style based on that data for the next haircut. For example, if a user has previously preferred short hair, the dialogue unit will suggest the latest short hair style. The dialogue unit also analyzes trends based on the user's past haircut history and suggests the latest style that suits the user's preferences. For example, the dialogue unit makes suggestions that reflect current trends based on styles that were popular in the past. The dialogue unit also learns the user's past haircut history, and the generation AI suggests styles according to the season or event. For example, it suggests cool short styles in the summer and voluminous styles in the winter. This makes it possible to predict and suggest the user's preferred style.

[0031] The dialogue unit can scan the user's face shape or hair type with a camera and suggest the optimal haircut style. For example, the dialogue unit can scan the user's face shape and hair type with a camera, and the generation AI can suggest the optimal haircut style based on that data. For example, for a user with a round face, it can suggest a style that makes the face look slimmer. The dialogue unit can also analyze the hair type data scanned with the camera, and the generation AI can suggest a haircut style based on the damage and volume of the hair. For example, for a user with thin hair and little volume, it can suggest a style that has a voluminous effect. The dialogue unit can also scan the user's face shape and hair type, and the generation AI can suggest trendy styles based on that data. For example, it can suggest the latest hairstyles that suit the face shape. This makes it possible to suggest the optimal haircut style based on the user's face shape and hair type.

[0032] The dialogue unit allows the generation AI to suggest fashion and makeup that suits a style selected by the user based on that style. For example, based on a hairstyle selected by the user, the dialogue unit allows the generation AI to suggest fashion items that suit that style. For example, it may suggest casual clothing that suits short hair. Furthermore, based on a hairstyle selected by the user, the dialogue unit allows the generation AI to suggest makeup that suits that style. For example, it may suggest natural makeup that suits a bob cut. Furthermore, based on the hairstyle selected by the user, the dialogue unit allows the generation AI to suggest accessories that suit that style. For example, it may suggest earrings and necklaces that go well with long hair. This makes it possible to suggest fashion and makeup that suits the style selected by the user.

[0033] The dialogue unit develops a multi-user compatible generation AI with which multiple users can interact simultaneously, allowing them to enjoy a haircut together with family and friends. For example, the dialogue unit develops a generation AI with which multiple users can interact simultaneously, and builds a system for enjoying a haircut together with family and friends. For example, it allows all family members to give haircut instructions at the same time. The dialogue unit also uses a multi-user compatible generation AI to develop a system in which friends can enjoy a haircut together at the same time. For example, friends can enjoy conversations while getting their haircut in the same room. The dialogue unit also develops a generation AI with which multiple users can interact simultaneously, and organizes an event where family and friends can enjoy a haircut together. For example, it plans an event where all family members get a haircut together. This allows multiple users to interact simultaneously, allowing them to enjoy a haircut together with family and friends.

[0034] The analysis unit can translate user instructions in real time, making it possible to accommodate users who speak different languages. The analysis unit, for example, builds a system in which a generation AI translates user instructions in real time, making it possible to accommodate users who speak different languages. For example, Japanese instructions are translated into English for understanding. The analysis unit also translates user instructions in real time, making it possible for the generation AI to accommodate users who speak different languages. For example, English instructions are translated into Spanish for understanding. The analysis unit also develops a system in which a generation AI translates user instructions in real time, making it possible to accommodate users who speak different languages. For example, Chinese instructions are translated into French for understanding. This makes it possible to translate user instructions in real time, making it possible to accommodate users who speak different languages.

[0035] The analysis unit, upon receiving a user's instructions, can display a haircut simulation in real time, allowing the user to check the results. For example, the analysis unit builds a system that displays a haircut simulation in real time, when the generation AI receives a user's instructions. For example, the analysis unit displays the style specified by the user as a 3D model. Furthermore, the analysis unit, upon receiving a user's instructions, can display a haircut simulation in real time, allowing the user to check the results. For example, the analysis unit displays an image of the hair after cutting on a screen. Furthermore, the analysis unit develops a system that displays a haircut simulation in real time, when the generation AI receives a user's instructions. For example, the analysis unit displays the style specified by the user in virtual reality. In this way, the analysis unit, upon receiving a user's instructions, can display a haircut simulation in real time, allowing the user to check the results.

[0036] The cutting unit uses sensors to detect the texture and thickness of the hair when sucking it, and can automatically adjust the optimal cutting method. For example, we will develop a system in which the cutting unit uses sensors to detect the texture and thickness of the hair when the robot sucks it, and automatically adjusts the optimal cutting method. For example, if the hair is thin, the cutting speed will be slowed down. The cutting unit also uses sensors to detect the texture and thickness of the hair, and the robot automatically adjusts the optimal cutting method. For example, if the hair is thick, the cutting force will be increased. We will also develop a system in which the cutting unit uses sensors to detect the texture and thickness of the hair in real time when the robot sucks it, and automatically adjusts the optimal cutting method. For example, the cutting angle will be adjusted depending on the hair texture. This allows the sensor to detect the texture and thickness of the hair, and automatically adjusts the optimal cutting method.

[0037] The cutting unit can achieve a natural finish by taking into account the direction of hair growth. For example, a system will be developed in which the cutting unit takes into account the direction of hair growth when the robot cuts. For example, a natural finish will be achieved by cutting in the direction of hair growth. The cutting unit will also use a sensor to detect the direction of hair growth, and the robot will cut in that direction. For example, the cutting unit will cut in such a way that the hair does not go against the direction of hair growth. The cutting unit will also develop a system in which the robot detects the direction of hair growth in real time when cutting, achieving a natural finish. For example, the angle of the cut will be adjusted to match the direction of hair growth. This will achieve a natural finish by taking into account the direction of hair growth.

[0038] The cutting unit can provide a scalp massage at the same time as sucking up hair, providing a relaxation effect. For example, a system can be constructed in which the robot simultaneously massages the scalp while sucking up hair. For example, a suction function and a massage function can be combined. Furthermore, the cutting unit can provide a scalp massage while sucking up hair. For example, a massage function can be added to a cutting arm that has a suction function. Furthermore, a system can be developed in which the robot simultaneously massages the scalp while sucking up hair, providing a relaxation effect. For example, a program can be incorporated to perform suction and massage simultaneously. This allows the robot to simultaneously massage the scalp while sucking up hair, providing a relaxation effect.

[0039] The cutting unit can add a dyeing or treatment function to change the hair color or texture. For example, a system is constructed in which the cutting unit adds a dyeing function to change the hair color when the robot cuts hair. For example, the hair is dyed at the same time as the cut. The cutting unit also adds a treatment function to the robot to change the hair texture. For example, the hair is treated at the same time as the cut. The cutting unit also develops a system in which the cutting unit adds a dyeing or treatment function to change the hair color or texture when the robot cuts hair. For example, a program is incorporated that dyes or treats the hair at the same time as the cut. This makes it possible to add a dyeing or treatment function to change the hair color or texture.

[0040] The cutting unit can adjust the hair length or style in millimeters based on instructions analyzed by the generative AI. For example, the cutting unit will build a system in which the robot adjusts the hair length and style in millimeters based on instructions analyzed by the generative AI. For example, it will cut the hair length in millimeters as instructed by the user. Furthermore, when the robot cuts, the cutting unit will adjust the hair length and style in millimeters based on instructions analyzed by the generative AI. For example, it will adjust the hair on top in millimeters before cutting. Furthermore, the cutting unit will develop a system in which the robot adjusts the hair length and style in millimeters based on instructions analyzed by the generative AI. For example, it will adjust the hair on the sides in millimeters before cutting. This allows the hair length and style to be adjusted in millimeters based on instructions analyzed by the generative AI.

[0041] The cutting unit 3D scans the shape of the user's head, and the generation AI calculates the optimal cut line. For example, when the robot cuts, the cutting unit 3D scans the shape of the user's head, and the generation AI calculates the optimal cut line. For example, the cutting unit calculates a cut line that matches the head shape. The cutting unit also 3D scans the shape of the user's head, and the generation AI calculates the optimal cut line. For example, the cut line is adjusted based on the head shape. The cutting unit also develops a system when the robot cuts, 3D scans the shape of the user's head, and the generation AI calculates the optimal cut line. For example, the cut line is calculated in real time to match the head shape. This allows the shape of the user's head to be 3D scanned, and the generation AI to calculate the optimal cut line.

[0042] The cutting unit can simulate hair length or style in real time based on instructions analyzed by the generation AI, allowing the user to check it. For example, the cutting unit builds a system that simulates hair length and style in real time when the robot cuts hair based on instructions analyzed by the generation AI. For example, it displays the simulation results before cutting. Furthermore, the cutting unit simulates hair length and style in real time based on instructions analyzed by the generation AI when the robot cuts hair, allowing the user to check it. For example, it displays an image of the hair after cutting on a screen. Furthermore, the cutting unit develops a system that simulates hair length and style in real time when the robot cuts hair based on instructions analyzed by the generation AI. For example, it displays the simulation results in virtual reality. This allows hair length and style to be simulated in real time based on instructions analyzed by the generation AI, allowing the user to check it.

[0043] The cutting unit records the hair length or style and can refer to it the next time it cuts. For example, the cutting unit will build a system that records the hair length and style when the robot cuts. For example, the details of the cut will be saved in a database. The cutting unit will also record the hair length and style and refer to it the next time it cuts. For example, the next cut will be based on the data from the previous cut. Also, a system will be developed where the cutting unit records the hair length and style when the robot cuts and can refer to it the next time it cuts. For example, the cut data will be saved in the cloud and accessed the next time it cuts. This will allow the hair length and style to be recorded and referred to the next time it cuts.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The dialogue unit accepts instructions from a user. For example, it can accept voice instructions. The dialogue unit can also accept touch panel input. The dialogue unit can also accept gesture input. The analysis unit analyzes the instructions accepted by the dialogue unit. For example, the analysis unit analyzes voice instructions using voice recognition technology. The analysis unit can also analyze the instruction content using natural language processing technology. The analysis unit can also analyze gesture input using image recognition technology. The cutting unit cuts hair based on the instructions analyzed by the analysis unit. For example, the cutting unit cuts hair to a specified length. The cutting unit can also cut hair in a specified style. The cutting unit can also automatically select a tool to use and cut hair. This makes it possible to cut hair based on user instructions.

[0046] The dialogue unit learns the user's past haircut history and can predict and suggest the user's preferred style. For example, the generation AI learns data on hairstyles the user has chosen in the past and predicts and suggests the user's preferred style based on that data at the time of the next haircut. For example, if a user has previously preferred short hair, the latest short hair style will be suggested. The dialogue unit also analyzes trends based on the user's past haircut history and suggests the latest style that suits the user's preferences. For example, suggestions that reflect current trends are made based on styles that were popular in the past. The dialogue unit also learns the user's past haircut history and the generation AI suggests styles according to the season or event. For example, it suggests cool short styles in the summer and voluminous styles in the winter. This makes it possible to predict and suggest the user's preferred style.

[0047] The dialogue unit can scan the user's face shape or hair type with a camera and suggest the optimal haircut style. For example, the user's face shape and hair type can be scanned with a camera, and the generation AI can suggest the optimal haircut style based on that data. For example, a style that makes a user with a round face appear thinner can be suggested. The dialogue unit can also analyze the hair type data scanned with the camera, and the generation AI can suggest a haircut style based on the damage and volume of the hair. For example, a style that adds volume can be suggested for a user with thin hair with little volume. The dialogue unit can also scan the user's face shape and hair type, and the generation AI can suggest trendy styles based on that data. For example, the generation AI can suggest the latest hairstyles that suit the face shape. This makes it possible to suggest the optimal haircut style based on the user's face shape and hair type.

[0048] Based on the style selected by the user, the dialogue unit can make suggestions for fashion and makeup that suit that style. For example, based on the hairstyle selected by the user, the dialogue unit can make suggestions for fashion items that suit that style. For example, it can suggest casual clothing that goes well with short hair. Based on the hairstyle selected by the user, the dialogue unit can make suggestions for makeup that suits that style. For example, it can suggest natural makeup that goes well with a bob cut. Based on the hairstyle selected by the user, the dialogue unit can make suggestions for accessories that suit that style. For example, it can suggest earrings and necklaces that go well with long hair. This makes it possible to make suggestions for fashion and makeup that suit the style selected by the user.

[0049] The dialogue unit develops a multi-user compatible generative AI that multiple users can interact with simultaneously, allowing them to enjoy a haircut together with family and friends. For example, the dialogue unit develops a generative AI that multiple users can interact with simultaneously, building a system for enjoying a haircut together with family and friends. For example, it allows all family members to give haircut instructions at the same time. The dialogue unit also uses a multi-user compatible generative AI to develop a system for friends to enjoy a haircut together at the same time. For example, friends can enjoy conversations while getting their haircut in the same room. The dialogue unit also develops a generative AI that multiple users can interact with simultaneously, and organizes an event where family and friends can enjoy a haircut together. For example, it plans an event where all family members get a haircut together. This allows multiple users to interact simultaneously, allowing them to enjoy a haircut together with family and friends.

[0050] The analysis unit can translate user instructions in real time, making it possible to accommodate users who speak different languages. For example, a system is constructed in which a generation AI translates user instructions in real time, making it possible to accommodate users who speak different languages. For example, instructions in Japanese are translated into English for understanding. The analysis unit also translates user instructions in real time, making it possible for the generation AI to accommodate users who speak different languages. For example, instructions in English are translated into Spanish for understanding. The analysis unit also develops a system in which a generation AI translates user instructions in real time, making it possible to accommodate users who speak different languages. For example, instructions in Chinese are translated into French for understanding. This makes it possible to translate user instructions in real time, making it possible to accommodate users who speak different languages.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The dialogue unit accepts user instructions, such as voice instructions, touch panel input, and gesture input. Step 2: The analysis unit analyzes the instructions received by the dialogue unit. For example, the analysis unit can analyze voice instructions using voice recognition technology, analyze the instruction content using natural language processing technology, and analyze gesture input using image recognition technology. Step 3: The cutting unit cuts the hair based on the instructions analyzed by the analysis unit. For example, it can cut the hair to a specified length and style and automatically select the tools to use to cut the hair.

[0053] (Example 2) The automatic haircutting system according to an embodiment of the present invention is a system that accurately cuts a user's desired hairstyle. This system allows the user to communicate with AI about the length and style of the cut, and the robot cuts the hair while sucking it up based on the user's instructions, thereby achieving a fast and accurate haircut. This allows the automatic haircutting system to quickly and accurately cut a user's desired hairstyle.

[0054] The automatic haircutting system according to the embodiment includes a dialogue unit, an analysis unit, and a cutting unit. The dialogue unit receives instructions from a user. For example, it can receive voice instructions. The dialogue unit can also receive touch panel input. The dialogue unit can also receive gesture input. The analysis unit analyzes the instructions received by the dialogue unit. For example, the analysis unit analyzes voice instructions using voice recognition technology. The analysis unit can also analyze the instruction content using natural language processing technology. The analysis unit can also analyze gesture input using image recognition technology. The cutting unit cuts hair based on the instructions analyzed by the analysis unit. For example, the cutting unit cuts hair to a specified length. The cutting unit can also cut hair in a specified style. The cutting unit can also automatically select tools to use and cut hair. This allows hair to be cut based on user instructions.

[0055] The dialogue unit learns the user's past haircut history and can predict and suggest the user's preferred style. For example, the dialogue unit's generation AI learns data on hairstyles the user has chosen in the past, and predicts and suggests the user's preferred style based on that data for the next haircut. For example, if a user has previously preferred short hair, the dialogue unit will suggest the latest short hair style. The dialogue unit also analyzes trends based on the user's past haircut history and suggests the latest style that suits the user's preferences. For example, the dialogue unit makes suggestions that reflect current trends based on styles that were popular in the past. The dialogue unit also learns the user's past haircut history, and the generation AI suggests styles according to the season or event. For example, it suggests cool short styles in the summer and voluminous styles in the winter. This makes it possible to predict and suggest the user's preferred style.

[0056] The dialogue unit can scan the user's face shape or hair type with a camera and suggest the optimal haircut style. For example, the dialogue unit can scan the user's face shape and hair type with a camera, and the generation AI can suggest the optimal haircut style based on that data. For example, for a user with a round face, it can suggest a style that makes the face look slimmer. The dialogue unit can also analyze the hair type data scanned with the camera, and the generation AI can suggest a haircut style based on the damage and volume of the hair. For example, for a user with thin hair and little volume, it can suggest a style that has a voluminous effect. The dialogue unit can also scan the user's face shape and hair type, and the generation AI can suggest trendy styles based on that data. For example, it can suggest the latest hairstyles that suit the face shape. This makes it possible to suggest the optimal haircut style based on the user's face shape and hair type.

[0057] The dialogue unit can use the emotion estimation function to suggest a hairstyle that matches the user's current mood. For example, the dialogue unit uses the emotion estimation function to analyze the user's current mood, and the generation AI suggests a hairstyle that matches that mood. For example, a natural style is suggested for a user who is feeling relaxed. The dialogue unit also uses the emotion estimation function to analyze the user's mood in real time, and the generation AI suggests an optimal hairstyle based on the results. For example, a style with a relaxing effect is suggested for a user who is feeling stressed. This makes it possible to suggest a hairstyle that matches the user's current mood.

[0058] The dialogue unit allows the generation AI to suggest fashion and makeup that suits a style selected by the user based on that style. For example, based on a hairstyle selected by the user, the dialogue unit allows the generation AI to suggest fashion items that suit that style. For example, it may suggest casual clothing that suits short hair. Furthermore, based on a hairstyle selected by the user, the dialogue unit allows the generation AI to suggest makeup that suits that style. For example, it may suggest natural makeup that suits a bob cut. Furthermore, based on the hairstyle selected by the user, the dialogue unit allows the generation AI to suggest accessories that suit that style. For example, it may suggest earrings and necklaces that go well with long hair. This makes it possible to suggest fashion and makeup that suits the style selected by the user.

[0059] The dialogue unit develops a multi-user compatible generation AI with which multiple users can interact simultaneously, allowing them to enjoy a haircut together with family and friends. For example, the dialogue unit develops a generation AI with which multiple users can interact simultaneously, and builds a system for enjoying a haircut together with family and friends. For example, it allows all family members to give haircut instructions at the same time. The dialogue unit also uses a multi-user compatible generation AI to develop a system in which friends can enjoy a haircut together at the same time. For example, friends can enjoy conversations while getting their haircut in the same room. The dialogue unit also develops a generation AI with which multiple users can interact simultaneously, and organizes an event where family and friends can enjoy a haircut together. For example, it plans an event where all family members get a haircut together. This allows multiple users to interact simultaneously, allowing them to enjoy a haircut together with family and friends.

[0060] The dialogue unit uses the emotion estimation function to simultaneously provide relaxing music or video to the user, thereby improving the haircut experience. The dialogue unit, for example, uses the emotion estimation function to build a system that provides relaxing music to the user. For example, it plays music with a relaxing effect based on the user's emotion data. The dialogue unit also uses the emotion estimation function to develop a system that provides relaxing video to the user. For example, it plays relaxing video based on the user's emotion data. The dialogue unit also uses the emotion estimation function to build a system that simultaneously provides relaxing music and video to the user. For example, it plays relaxing music and video based on the user's emotion data. This allows the user to simultaneously provide relaxing music and video, thereby improving the haircut experience.

[0061] The analysis unit analyzes the tone or speed of the user's voice and can engage in a dialogue that will relax the user if the user is feeling nervous or anxious. The analysis unit, for example, builds a system in which the generation AI analyzes the tone and speed of the user's voice and engages in a dialogue that will relax the user if the user is feeling nervous or anxious. For example, if the user's voice is trembling, the analysis unit will use words that have a relaxing effect. The analysis unit also analyzes the tone and speed of the user's voice and engages in a dialogue that will relax the user. For example, if the user's voice is getting faster, the analysis unit will encourage the user to speak more slowly. The analysis unit also develops a system in which the generation AI analyzes the tone and speed of the user's voice in real time and engages in a dialogue that will relax the user if the user is feeling nervous or anxious. For example, if the user's voice is getting higher, the analysis unit will encourage the user to calm down. This makes it possible to analyze the tone and speed of the user's voice and engage in a dialogue that will relax the user if the user is feeling nervous or anxious.

[0062] The analysis unit can translate user instructions in real time, making it possible to accommodate users who speak different languages. The analysis unit, for example, builds a system in which a generation AI translates user instructions in real time, making it possible to accommodate users who speak different languages. For example, Japanese instructions are translated into English for understanding. The analysis unit also translates user instructions in real time, making it possible for the generation AI to accommodate users who speak different languages. For example, English instructions are translated into Spanish for understanding. The analysis unit also develops a system in which a generation AI translates user instructions in real time, making it possible to accommodate users who speak different languages. For example, Chinese instructions are translated into French for understanding. This makes it possible to translate user instructions in real time, making it possible to accommodate users who speak different languages.

[0063] The analysis unit can use the emotion estimation function to fine-tune the haircut style according to the user's emotions. The analysis unit, for example, uses the emotion estimation function to build a system that fine-tunes the haircut style according to the user's emotions. For example, a natural style is suggested for a user who is relaxed. The analysis unit also uses the user's emotion data to have the generation AI fine-tune the haircut style. For example, an active style is suggested for a user who is feeling energetic. The analysis unit also uses the emotion estimation function to develop a system that fine-tunes the haircut style according to the user's emotions. For example, a style that has a relaxing effect is suggested for a user who is feeling stressed. This makes it possible to fine-tune the haircut style according to the user's emotions.

[0064] The analysis unit, upon receiving a user's instructions, can display a haircut simulation in real time, allowing the user to check the results. For example, the analysis unit builds a system that displays a haircut simulation in real time, when the generation AI receives a user's instructions. For example, the analysis unit displays the style specified by the user as a 3D model. Furthermore, the analysis unit, upon receiving a user's instructions, can display a haircut simulation in real time, allowing the user to check the results. For example, the analysis unit displays an image of the hair after cutting on a screen. Furthermore, the analysis unit develops a system that displays a haircut simulation in real time, when the generation AI receives a user's instructions. For example, the analysis unit displays the style specified by the user in virtual reality. In this way, the analysis unit, upon receiving a user's instructions, can display a haircut simulation in real time, allowing the user to check the results.

[0065] The analysis unit can use the emotion estimation function to predict and suggest in advance the haircut style that will satisfy the user most. The analysis unit, for example, uses the emotion estimation function to predict in advance the haircut style that will satisfy the user most, and builds a system in which the generation AI makes suggestions. For example, the analysis unit proposes the optimal style based on past emotional data. The analysis unit also predicts and suggests the haircut style that will satisfy the user most based on the user's emotional data. For example, it analyzes past haircut styles and emotional responses and suggests the style that the user is most satisfied with. The analysis unit also uses the emotion estimation function to predict in advance the haircut style that will satisfy the user most, and develops a system in which the generation AI makes suggestions. For example, it analyzes emotional data in real time and suggests the optimal style. This makes it possible to predict and suggest in advance the haircut style that will satisfy the user most.

[0066] The cutting unit uses sensors to detect the texture and thickness of the hair when sucking it, and can automatically adjust the optimal cutting method. For example, we will develop a system in which the cutting unit uses sensors to detect the texture and thickness of the hair when the robot sucks it, and automatically adjusts the optimal cutting method. For example, if the hair is thin, the cutting speed will be slowed down. The cutting unit also uses sensors to detect the texture and thickness of the hair, and the robot automatically adjusts the optimal cutting method. For example, if the hair is thick, the cutting force will be increased. We will also develop a system in which the cutting unit uses sensors to detect the texture and thickness of the hair in real time when the robot sucks it, and automatically adjusts the optimal cutting method. For example, the cutting angle will be adjusted depending on the hair texture. This allows the sensor to detect the texture and thickness of the hair, and automatically adjusts the optimal cutting method.

[0067] The cutting unit can achieve a natural finish by taking into account the direction of hair growth. For example, a system will be developed in which the cutting unit takes into account the direction of hair growth when the robot cuts. For example, a natural finish will be achieved by cutting in the direction of hair growth. The cutting unit will also use a sensor to detect the direction of hair growth, and the robot will cut in that direction. For example, the cutting unit will cut in such a way that the hair does not go against the direction of hair growth. The cutting unit will also develop a system in which the robot detects the direction of hair growth in real time when cutting, achieving a natural finish. For example, the angle of the cut will be adjusted to match the direction of hair growth. This will achieve a natural finish by taking into account the direction of hair growth.

[0068] The cutting unit can use the emotion estimation function to adjust the robot's movement speed or sound so that the user can relax. The cutting unit, for example, uses the emotion estimation function to build a system that adjusts the robot's movement speed so that the user can relax. For example, if the user is nervous, the movement speed is slowed down. The cutting unit also adjusts the robot's movement sounds based on the user's emotion data. For example, if the user is relaxed, the movement sounds are made quieter. The cutting unit also uses the emotion estimation function to develop a system that adjusts the robot's movement speed and sound in real time so that the user can relax. For example, the movement speed and sound are adjusted in accordance with changes in the user's emotion. This makes it possible to adjust the robot's movement speed and sound so that the user can relax.

[0069] The cutting unit can provide a scalp massage at the same time as sucking up hair, providing a relaxation effect. For example, a system can be constructed in which the robot simultaneously massages the scalp while sucking up hair. For example, a suction function and a massage function can be combined. Furthermore, the cutting unit can provide a scalp massage while sucking up hair. For example, a massage function can be added to a cutting arm that has a suction function. Furthermore, a system can be developed in which the robot simultaneously massages the scalp while sucking up hair, providing a relaxation effect. For example, a program can be incorporated to perform suction and massage simultaneously. This allows the robot to simultaneously massage the scalp while sucking up hair, providing a relaxation effect.

[0070] The cutting unit can add a dyeing or treatment function to change the hair color or texture. For example, a system is constructed in which the cutting unit adds a dyeing function to change the hair color when the robot cuts hair. For example, the hair is dyed at the same time as the cut. The cutting unit also adds a treatment function to the robot to change the hair texture. For example, the hair is treated at the same time as the cut. The cutting unit also develops a system in which the cutting unit adds a dyeing or treatment function to change the hair color or texture when the robot cuts hair. For example, a program is incorporated that dyes or treats the hair at the same time as the cut. This makes it possible to add a dyeing or treatment function to change the hair color or texture.

[0071] The cutting unit can use the emotion estimation function to provide the user with the most relaxing environmental sounds or scents, thereby improving the haircut experience. For example, the cutting unit uses the emotion estimation function to build a system that provides the user with the most relaxing environmental sounds. For example, sounds with a relaxing effect are played based on the user's emotion data. The cutting unit also develops a system that provides a relaxing scent based on the user's emotion data. For example, an aroma is provided when the user is relaxed. The cutting unit also uses the emotion estimation function to build a system that simultaneously provides the user with the most relaxing environmental sounds and scents. For example, sounds and scents with a relaxing effect are simultaneously provided based on the user's emotion data. This allows the user to be provided with the most relaxing environmental sounds and scents, thereby improving the haircut experience.

[0072] The cutting unit can adjust the hair length or style in millimeters based on instructions analyzed by the generative AI. For example, the cutting unit will build a system in which the robot adjusts the hair length and style in millimeters based on instructions analyzed by the generative AI. For example, it will cut the hair length in millimeters as instructed by the user. Furthermore, when the robot cuts, the cutting unit will adjust the hair length and style in millimeters based on instructions analyzed by the generative AI. For example, it will adjust the hair on top in millimeters before cutting. Furthermore, the cutting unit will develop a system in which the robot adjusts the hair length and style in millimeters based on instructions analyzed by the generative AI. For example, it will adjust the hair on the sides in millimeters before cutting. This allows the hair length and style to be adjusted in millimeters based on instructions analyzed by the generative AI.

[0073] The cutting unit 3D scans the shape of the user's head, and the generation AI calculates the optimal cut line. For example, when the robot cuts, the cutting unit 3D scans the shape of the user's head, and the generation AI calculates the optimal cut line. For example, the cutting unit calculates a cut line that matches the head shape. The cutting unit also 3D scans the shape of the user's head, and the generation AI calculates the optimal cut line. For example, the cut line is adjusted based on the head shape. The cutting unit also develops a system when the robot cuts, 3D scans the shape of the user's head, and the generation AI calculates the optimal cut line. For example, the cut line is calculated in real time to match the head shape. This allows the shape of the user's head to be 3D scanned, and the generation AI to calculate the optimal cut line.

[0074] The cutting unit can simulate hair length or style in real time based on instructions analyzed by the generation AI, allowing the user to check it. For example, the cutting unit builds a system that simulates hair length and style in real time when the robot cuts hair based on instructions analyzed by the generation AI. For example, it displays the simulation results before cutting. Furthermore, the cutting unit simulates hair length and style in real time based on instructions analyzed by the generation AI when the robot cuts hair, allowing the user to check it. For example, it displays an image of the hair after cutting on a screen. Furthermore, the cutting unit develops a system that simulates hair length and style in real time when the robot cuts hair based on instructions analyzed by the generation AI. For example, it displays the simulation results in virtual reality. This allows hair length and style to be simulated in real time based on instructions analyzed by the generation AI, allowing the user to check it.

[0075] The cutting unit records the hair length or style and can refer to it the next time it cuts. For example, the cutting unit will build a system that records the hair length and style when the robot cuts. For example, the details of the cut will be saved in a database. The cutting unit will also record the hair length and style and refer to it the next time it cuts. For example, the next cut will be based on the data from the previous cut. Also, a system will be developed where the cutting unit records the hair length and style when the robot cuts and can refer to it the next time it cuts. For example, the cut data will be saved in the cloud and accessed the next time it cuts. This will allow the hair length and style to be recorded and referred to the next time it cuts.

[0076] The haircutting department can use the emotion estimation function to predict and suggest in advance the haircut style that will satisfy the user most. The haircutting department, for example, uses the emotion estimation function to predict in advance the haircut style that will satisfy the user most, and builds a system in which the generation AI makes suggestions. For example, the optimal style is suggested based on past emotional data. The haircutting department also predicts and suggests the haircut style that will satisfy the user most based on the user's emotional data. For example, past haircut styles and emotional responses are analyzed to suggest the style that the user is most satisfied with. The haircutting department also uses the emotion estimation function to predict in advance the haircut style that will satisfy the user most, and develops a system in which the generation AI makes suggestions. For example, emotional data is analyzed in real time to suggest the optimal style. This makes it possible to predict and suggest in advance the haircut style that will satisfy the user most.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The dialogue unit accepts instructions from a user. For example, it can accept voice instructions. The dialogue unit can also accept touch panel input. The dialogue unit can also accept gesture input. The analysis unit analyzes the instructions accepted by the dialogue unit. For example, the analysis unit analyzes voice instructions using voice recognition technology. The analysis unit can also analyze the instruction content using natural language processing technology. The analysis unit can also analyze gesture input using image recognition technology. The cutting unit cuts hair based on the instructions analyzed by the analysis unit. For example, the cutting unit cuts hair to a specified length. The cutting unit can also cut hair in a specified style. The cutting unit can also automatically select a tool to use and cut hair. This makes it possible to cut hair based on user instructions.

[0079] The dialogue unit learns the user's past haircut history and can predict and suggest the user's preferred style. For example, the generation AI learns data on hairstyles the user has chosen in the past and predicts and suggests the user's preferred style based on that data at the time of the next haircut. For example, if a user has previously preferred short hair, the latest short hair style will be suggested. The dialogue unit also analyzes trends based on the user's past haircut history and suggests the latest style that suits the user's preferences. For example, suggestions that reflect current trends are made based on styles that were popular in the past. The dialogue unit also learns the user's past haircut history and the generation AI suggests styles according to the season or event. For example, it suggests cool short styles in the summer and voluminous styles in the winter. This makes it possible to predict and suggest the user's preferred style.

[0080] The dialogue unit can scan the user's face shape or hair type with a camera and suggest the optimal haircut style. For example, the user's face shape and hair type can be scanned with a camera, and the generation AI can suggest the optimal haircut style based on that data. For example, a style that makes a user with a round face appear thinner can be suggested. The dialogue unit can also analyze the hair type data scanned with the camera, and the generation AI can suggest a haircut style based on the damage and volume of the hair. For example, a style that adds volume can be suggested for a user with thin hair with little volume. The dialogue unit can also scan the user's face shape and hair type, and the generation AI can suggest trendy styles based on that data. For example, the generation AI can suggest the latest hairstyles that suit the face shape. This makes it possible to suggest the optimal haircut style based on the user's face shape and hair type.

[0081] The dialogue unit can use the emotion estimation function to suggest a hairstyle that matches the user's current mood. For example, the emotion estimation function can be used to analyze the user's current mood, and the generation AI can suggest a hairstyle that matches that mood. For example, a natural style can be suggested for a user who is feeling relaxed. The dialogue unit can also use the emotion estimation function to allow the generation AI to suggest a haircut style that matches the user's mood based on the user's emotional data. For example, an active style can be suggested for a user who is feeling energetic. The dialogue unit can also use the emotion estimation function to analyze the user's mood in real time, and the generation AI can suggest the optimal hairstyle based on the results. For example, a style with a relaxing effect can be suggested for a user who is feeling stressed. This makes it possible to suggest a hairstyle that matches the user's current mood.

[0082] Based on the style selected by the user, the dialogue unit can make suggestions for fashion and makeup that suit that style. For example, based on the hairstyle selected by the user, the dialogue unit can make suggestions for fashion items that suit that style. For example, it can suggest casual clothing that goes well with short hair. Based on the hairstyle selected by the user, the dialogue unit can make suggestions for makeup that suits that style. For example, it can suggest natural makeup that goes well with a bob cut. Based on the hairstyle selected by the user, the dialogue unit can make suggestions for accessories that suit that style. For example, it can suggest earrings and necklaces that go well with long hair. This makes it possible to make suggestions for fashion and makeup that suit the style selected by the user.

[0083] The dialogue unit develops a multi-user compatible generative AI that multiple users can interact with simultaneously, allowing them to enjoy a haircut together with family and friends. For example, the dialogue unit develops a generative AI that multiple users can interact with simultaneously, building a system for enjoying a haircut together with family and friends. For example, it allows all family members to give haircut instructions at the same time. The dialogue unit also uses a multi-user compatible generative AI to develop a system for friends to enjoy a haircut together at the same time. For example, friends can enjoy conversations while getting their haircut in the same room. The dialogue unit also develops a generative AI that multiple users can interact with simultaneously, and organizes an event where family and friends can enjoy a haircut together. For example, it plans an event where all family members get a haircut together. This allows multiple users to interact simultaneously, allowing them to enjoy a haircut together with family and friends.

[0084] The dialogue unit can use the emotion estimation function to simultaneously provide relaxing music or video to the user, thereby improving the haircut experience. For example, a system is constructed using the emotion estimation function to provide relaxing music to the user. For example, music with a relaxing effect is played based on the user's emotion data. The dialogue unit also uses the emotion estimation function to develop a system to provide relaxing video to the user. For example, video with a relaxing effect is played based on the user's emotion data. The dialogue unit also uses the emotion estimation function to construct a system to simultaneously provide relaxing music and video to the user. For example, music and video with a relaxing effect are played simultaneously based on the user's emotion data. This allows the user to simultaneously provide relaxing music and video, thereby improving the haircut experience.

[0085] The analysis unit analyzes the tone or speed of the user's voice and can engage in a dialogue that will relax them if they are feeling nervous or anxious. For example, we will build a system in which the generation AI analyzes the tone and speed of the user's voice and engages in a dialogue that will relax them if they are feeling nervous or anxious. For example, if the user's voice is trembling, the generation AI will use words that have a relaxing effect. The analysis unit will also analyze the tone and speed of the user's voice and engage in a dialogue that will relax them. For example, if the user's voice is getting faster, the generation AI will encourage them to speak more slowly. We will also develop a system in which the analysis unit analyzes the tone and speed of the user's voice in real time and engages in a dialogue that will relax them if they are feeling nervous or anxious. For example, if the user's voice is getting higher, the generation AI will encourage them to calm down. This makes it possible to analyze the tone and speed of the user's voice and engage in a dialogue that will relax them if they are feeling nervous or anxious.

[0086] The analysis unit can translate user instructions in real time, making it possible to accommodate users who speak different languages. For example, a system is constructed in which a generation AI translates user instructions in real time, making it possible to accommodate users who speak different languages. For example, instructions in Japanese are translated into English for understanding. The analysis unit also translates user instructions in real time, making it possible for the generation AI to accommodate users who speak different languages. For example, instructions in English are translated into Spanish for understanding. The analysis unit also develops a system in which a generation AI translates user instructions in real time, making it possible to accommodate users who speak different languages. For example, instructions in Chinese are translated into French for understanding. This makes it possible to translate user instructions in real time, making it possible to accommodate users who speak different languages.

[0087] The analysis unit can use the emotion estimation function to fine-tune the haircut style according to the user's emotions. For example, a system can be built using the emotion estimation function to fine-tune the haircut style according to the user's emotions. For example, a natural style can be suggested for a user who is relaxed. The analysis unit also uses the user's emotion data to have the generation AI fine-tune the haircut style. For example, an active style can be suggested for a user who is feeling energetic. The analysis unit also uses the emotion estimation function to develop a system that fine-tunes the haircut style according to the user's emotions. For example, a style that has a relaxing effect can be suggested for a user who is feeling stressed. This makes it possible to fine-tune the haircut style according to the user's emotions.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The dialogue unit accepts user instructions, such as voice instructions, touch panel input, and gesture input. Step 2: The analysis unit analyzes the instructions received by the dialogue unit. For example, the analysis unit can analyze voice instructions using voice recognition technology, analyze the instruction content using natural language processing technology, and analyze gesture input using image recognition technology. Step 3: The cutting unit cuts the hair based on the instructions analyzed by the analysis unit. For example, it can cut the hair to a specified length and style and automatically select the tools to use to cut the hair.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 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 dialogue unit that accepts instructions from a user; an analysis unit that analyzes the instruction received by the dialogue unit; a cutting unit that cuts hair based on the instruction analyzed by the analyzing unit. A system characterized by:

2. The dialogue unit Learn the user's past haircut history and predict and suggest the user's preferred style.

2. The system of claim 1.

3. The dialogue unit Scan the user's face shape or hair type with a camera and suggest the best haircut style 2. The system of claim 1.

4. The dialogue unit Suggest a hairstyle that matches the user's current mood 2. The system of claim 1.

5. The dialogue unit Based on the style selected by the user, the generative AI will suggest fashion and makeup that matches that style.

2. The system of claim 1.

6. The dialogue unit Developing a multi-user generative AI that can interact with multiple users simultaneously, allowing them to enjoy haircuts with family and friends 2. The system of claim 1.

7. The dialogue unit Simultaneously provide the user with relaxing music or video to enhance the haircut experience 2. The system of claim 1.

8. The analysis unit The tone or speed of the user's voice is analyzed, and if the user feels nervous or anxious, a dialogue is conducted to relax the user.

2. The system of claim 1.

Images