System

Abstract

An object of the system according to the embodiment is to analyze detailed feature data of a user and propose an optimal hairstyle.SOLUTION: A system includes a feature data collection unit, an analysis unit, and a proposal unit. The feature data collection unit collects detailed feature data such as the way and direction of hair growth of the user, the color of natural hair, the width of the forehead, and the shape of the head. The analysis unit analyzes the feature data collected by the feature data collection unit. The proposing section proposes an optimum hairstyle to the user on the basis of the result analyzed by the analyzing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that when proposing the optimal hairstyle for a user, it relies on the experience of the hairdresser and the user's subjectivity, and there are limitations to the accuracy.

[0005] The system according to the embodiment aims to analyze detailed characteristic data of a user and propose an optimal hairstyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a feature data collection unit, an analysis unit, and a suggestion unit. The feature data collection unit collects detailed feature data such as the user's hair growth pattern and direction, natural hair color, forehead width, and head shape. The analysis unit analyzes the feature data collected by the feature data collection unit. The suggestion unit suggests an optimal hairstyle for the user based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze detailed characteristic data of a user and suggest an optimal hairstyle. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The hairstyle suggestion system according to an embodiment of the present invention collects detailed characteristic data such as the user's hair growth pattern and direction, natural hair color, forehead width, and head shape, and uses a generative AI to analyze the data and suggest the optimal hairstyle. This allows the hairstyle suggestion system to suggest the optimal hairstyle for the user.

[0029] A hairstyle suggestion system according to an embodiment includes a feature data collection unit, an analysis unit, and a suggestion unit. The feature data collection unit collects detailed feature data, such as a user's hair growth pattern and direction, natural hair color, forehead width, and head shape. For example, a user takes a photo of their head with a smartphone and inputs the image data into an AI. The feature data collection unit can also collect detailed feature data using a dedicated scanning device. For example, the scanning device scans the entire head from 360 degrees and generates a detailed 3D model. The feature data collection unit also collects the user's past hairstyle history and provides the generation AI with data reflecting preferences and trends. For example, the user uploads photos of hairstyles they have tried in the past, and the AI ​​analyzes the data. The analysis unit analyzes the feature data collected by the feature data collection unit. For example, the generation AI analyzes the user's hair growth pattern and direction, natural hair color, forehead width, head shape, and other details, and combines them with general classifications such as the user's hair type, hair volume, and face shape. The generation AI performs analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests an optimal hairstyle for the user based on the results of the analysis by the analysis unit. For example, the generation AI may suggest, "This hairstyle is optimal based on your hair growth pattern and direction, natural hair color, forehead width, and head shape." This allows the hairstyle suggestion system according to the embodiment to suggest an optimal hairstyle for the user.

[0030] The feature data collection unit collects 3D scan data of the user's head, enabling more detailed analysis of hair growth and direction. For example, the user scans their head using a dedicated 3D scanning device. The scan data can capture the hair growth and direction, as well as the head shape, in detail. For example, the scanning device scans the entire head from 360 degrees and generates a detailed 3D model. This allows more detailed analysis of hair growth and direction.

[0031] The feature data collection unit collects the user's past hairstyle history and can provide the generation AI with data that reflects preferences and trends. For example, the feature data collection unit allows the user to upload photos of hairstyles they have tried in the past, and the AI ​​analyzes that data. For example, the user can select photos of past hairstyles from their smartphone album, and the AI ​​analyzes them to understand preferences and trends. This makes it possible to provide data that reflects the user's preferences and trends.

[0032] The feature data collection unit can collect data about the user's lifestyle and occupation and reflect it in hairstyle suggestions. For example, the feature data collection unit inputs information about the user's daily activities and occupation, and the AI ​​analyzes that data. For example, the user inputs information such as how often they play sports or their workplace dress code, and the AI ​​suggests hairstyles based on that information. This makes it possible to suggest hairstyles that suit the user's lifestyle and occupation.

[0033] The feature data collection unit collects the opinions of the user's family and friends and inputs them into the generation AI, allowing suggestions to be made from a more multifaceted perspective. For example, the feature data collection unit allows the user to ask family and friends for their opinions on hairstyles and input that feedback into the AI. For example, family and friends enter their opinions through a smartphone app, and the AI ​​analyzes them. This makes it possible to make hairstyle suggestions from a more multifaceted perspective.

[0034] The analysis unit can consider seasonal and climate changes when analyzing the user's feature data and suggest the most suitable hairstyle. For example, the analysis unit considers seasonal climate data when analyzing the user's feature data. For example, it can suggest cool hairstyles in summer and warm hairstyles in winter. This makes it possible to suggest hairstyles that take seasonal and climate changes into account.

[0035] The analysis unit can analyze the user's facial expressions and movements and suggest hairstyle changes according to the movements. For example, the analysis unit can analyze the user's facial expressions and suggest hairstyles according to the expressions. For example, it can suggest hairstyles that look best when smiling. This makes it possible to suggest hairstyle changes according to the user's facial expressions and movements.

[0036] The analysis unit can take into account hairstyle trends in different cultures and regions when analyzing the user's feature data. For example, the analysis unit can reflect trends in different cultures, such as trends in Asia, Europe, and Africa, when analyzing the user's feature data. This makes it possible to make suggestions that take into account hairstyle trends in different cultures and regions.

[0037] When analyzing the user's feature data, the analysis unit can simultaneously consider fashion and makeup trends and make comprehensive style suggestions. For example, when analyzing the user's feature data, the analysis unit considers the latest fashion trends and suggests hairstyles. For example, it suggests hairstyles that match this season's fashion. This makes it possible to make comprehensive style suggestions that take fashion and makeup trends into consideration.

[0038] The suggestion unit can predict future hairstyle trends based on the user's feature data and reflect them in suggestions. The suggestion unit, for example, analyzes the user's feature data and uses AI to predict future hairstyle trends. For example, it suggests hairstyles that will be popular in the next season based on past trend data. This makes it possible to predict future hairstyle trends and reflect them in suggestions.

[0039] The suggestion unit can also simultaneously provide advice on maintaining and caring for the hairstyle based on the user's feature data. For example, the suggestion unit uses AI to provide advice on how to maintain and care for the hairstyle based on the user's feature data. For example, it can suggest shampoos and treatments that are suitable for a specific hairstyle. This makes it possible to provide advice on maintaining and caring for the hairstyle.

[0040] The suggestion unit can suggest the best hairstyle for a specific event or season based on the user's feature data. For example, the suggestion unit uses AI to suggest the best hairstyle for a specific event based on the user's feature data. For example, it suggests the best hairstyle for attending a wedding or party. This makes it possible to suggest the best hairstyle for a specific event or season.

[0041] The suggestion unit can also suggest makeup and fashion that accompanies a change in hairstyle based on the user's feature data. For example, the suggestion unit uses AI to suggest makeup that accompanies a change in hairstyle based on the user's feature data. For example, it can suggest makeup colors and styles that match a new hairstyle. This makes it possible to suggest makeup and fashion that accompanies a change in hairstyle.

[0042] The suggestion unit can visualize the proposed hairstyle as a 3D model so that the user can check it from 360 degrees. For example, the suggestion unit generates the proposed hairstyle as a 3D model so that the user can check it from 360 degrees. For example, the user rotates and checks the 3D model on a smartphone or PC. This allows the proposed hairstyle to be visualized as a 3D model so that the user can check it from 360 degrees.

[0043] The suggestion unit can visualize the proposed hairstyle under different lighting conditions and backgrounds to simulate how it would look in a real environment. For example, the suggestion unit visualizes the proposed hairstyle under different lighting conditions to simulate how it would look in a real environment. For example, the suggestion unit checks how it would look under indoor lighting and natural light. This allows the proposed hairstyle to be visualized under different lighting conditions and backgrounds to simulate how it would look in a real environment.

[0044] The suggestion unit can visualize the proposed hairstyle with animation, allowing the user to check how it looks in motion. For example, the suggestion unit can visualize the proposed hairstyle with animation, allowing the user to check how it looks in motion. For example, the suggestion unit can simulate the movement of the hairstyle when the user shakes their head. This allows the proposed hairstyle to be visualized with animation, allowing the user to check how it looks in motion.

[0045] The suggestion unit may visualize the proposed hairstyle with different hair colors to allow the user to compare the color differences. For example, the suggestion unit may simulate different hair colors such as black hair, brown hair, and blonde hair. This allows the proposed hairstyle with different hair colors to allow the user to compare the color differences.

[0046] The suggestion unit can reflect changes in real time and visualize them instantly when a user customizes a suggested hairstyle. The suggestion unit, for example, builds a system that reflects changes in real time and visualizes them instantly when a user customizes a suggested hairstyle. For example, when a user changes the length of bangs, the change is visualized instantly. This allows changes to be reflected in real time and visualized instantly when a user customizes a suggested hairstyle.

[0047] The suggestion unit can refer to the past customization history and make the optimal suggestion when the user customizes the suggested hairstyle. The suggestion unit, for example, builds a system that refers to the past customization history and makes the optimal suggestion when the user customizes the suggested hairstyle. For example, the suggestion unit suggests the optimal customization based on the history of hairstyles that the user has tried in the past. This allows the suggestion unit to refer to the past customization history and make the optimal suggestion when the user customizes the suggested hairstyle.

[0048] The suggestion unit can provide a function that allows a user to refer to customization examples of other users when customizing the proposed hairstyle. The suggestion unit, for example, provides a function that allows a user to refer to customization examples of other users when customizing the proposed hairstyle. For example, the suggestion unit displays photos and comments of customizations made by other users. This allows a user to refer to customization examples of other users when customizing the proposed hairstyle.

[0049] The suggestion unit enables the generation AI to automatically suggest optimal customization options when a user customizes a proposed hairstyle. The suggestion unit, for example, builds a system in which the generation AI automatically suggests optimal customization options when a user customizes a proposed hairstyle. For example, the suggestion unit suggests optimal customization options based on the user's feature data. This allows the generation AI to automatically suggest optimal customization options when a user customizes a proposed hairstyle.

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

[0051] The feature data collection unit can collect data on the health of the user's hair and provide it to the generation AI. For example, the user can input information about the thickness, density, and degree of damage of their hair, and the AI ​​will analyze that data. This makes it possible to suggest hairstyles based on the health of the hair. In addition, by inputting information about the hair care products used by the user, suggestions can be made that take their effectiveness into account. Furthermore, factors that affect hair health, such as the user's diet and stress level, can also be collected, allowing for a comprehensive analysis.

[0052] The feature data collection unit can collect data on the user's hair growth cycle and provide it to the generation AI. For example, the user can input their hair growth rate and frequency of hair loss, and the AI ​​will analyze that data. This makes it possible to suggest hairstyles based on the hair growth cycle. The AI ​​can also input any hair problems or treatment history the user has experienced in the past, and make suggestions that take that data into consideration. Furthermore, factors that affect hair growth, such as the user's age and hormone balance, can be collected to perform a comprehensive analysis.

[0053] The feature data collection unit can collect data on the user's hair texture and provide it to the generation AI. For example, the user can input the softness, hardness, and smoothness of their hair, and the AI ​​will analyze that data. This makes it possible to suggest hairstyles based on hair texture. It can also input information about the styling products the user uses and make suggestions that take their effects into account. It can also collect environmental factors (humidity, temperature, etc.) that affect the user's hair texture, allowing for a comprehensive analysis.

[0054] The feature data collection unit can collect data on the user's hair pigmentation and provide it to the generation AI. For example, the user can input the degree of hair pigmentation and color changes, and the AI ​​can analyze that data. This makes it possible to suggest hairstyles based on hair pigmentation. The system can also input the user's past hair coloring history and make suggestions that take that data into consideration. Furthermore, it can collect factors that affect the user's hair pigmentation (such as ultraviolet rays and chemicals) and perform a comprehensive analysis.

[0055] The feature data collection unit can collect data on the movement of the user's hair and provide it to the generation AI. For example, the user inputs the ease of movement and elasticity of their hair, and the AI ​​analyzes that data. This makes it possible to suggest hairstyles based on hair movement. The AI ​​can also input feedback on the movement of hairstyles the user has tried in the past, and make suggestions that take that data into consideration. Furthermore, it can collect factors that affect the movement of the user's hair (wind, humidity, etc.), allowing for a comprehensive analysis.

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

[0057] Step 1: The feature data collection unit collects detailed feature data such as the user's hair growth pattern and direction, natural hair color, forehead width, and head shape. For example, the user can take a photo of their head with their smartphone and input the image data into the AI. Detailed feature data can also be collected using a dedicated scanning device. The scanning device scans the entire head from 360 degrees and generates a detailed 3D model. The unit also collects the user's past hairstyle history and provides the generation AI with data that reflects preferences and trends. For example, the user can upload photos of hairstyles they have tried in the past, and the AI ​​will analyze that data. Step 2: The analysis unit analyzes the feature data collected by the feature data collection unit. For example, the generation AI performs a detailed analysis of the user's hair growth pattern and direction, natural hair color, forehead width, head shape, etc., and combines this with general classifications such as the user's hair type, hair volume, and face shape. The generation AI performs the analysis using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The suggestion unit suggests the optimal hairstyle for the user based on the results of the analysis by the analysis unit. For example, the generation AI might suggest, "This hairstyle is optimal based on your hair growth pattern and direction, natural hair color, forehead width, and head shape."

[0058] (Example 2) The hairstyle suggestion system according to an embodiment of the present invention collects detailed characteristic data such as the user's hair growth pattern and direction, natural hair color, forehead width, and head shape, and uses a generative AI to analyze the data and suggest the optimal hairstyle. This allows the hairstyle suggestion system to suggest the optimal hairstyle for the user.

[0059] A hairstyle suggestion system according to an embodiment includes a feature data collection unit, an analysis unit, and a suggestion unit. The feature data collection unit collects detailed feature data, such as a user's hair growth pattern and direction, natural hair color, forehead width, and head shape. For example, a user takes a photo of their head with a smartphone and inputs the image data into an AI. The feature data collection unit can also collect detailed feature data using a dedicated scanning device. For example, the scanning device scans the entire head from 360 degrees and generates a detailed 3D model. The feature data collection unit also collects the user's past hairstyle history and provides the generation AI with data reflecting preferences and trends. For example, the user uploads photos of hairstyles they have tried in the past, and the AI ​​analyzes the data. The analysis unit analyzes the feature data collected by the feature data collection unit. For example, the generation AI analyzes the user's hair growth pattern and direction, natural hair color, forehead width, head shape, and other details, and combines them with general classifications such as the user's hair type, hair volume, and face shape. The generation AI performs analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests an optimal hairstyle for the user based on the results of the analysis by the analysis unit. For example, the generation AI may suggest, "This hairstyle is optimal based on your hair growth pattern and direction, natural hair color, forehead width, and head shape." This allows the hairstyle suggestion system according to the embodiment to suggest an optimal hairstyle for the user.

[0060] The feature data collection unit collects 3D scan data of the user's head, enabling more detailed analysis of hair growth and direction. For example, the user scans their head using a dedicated 3D scanning device. The scan data can capture the hair growth and direction, as well as the head shape, in detail. For example, the scanning device scans the entire head from 360 degrees and generates a detailed 3D model. This allows more detailed analysis of hair growth and direction.

[0061] The feature data collection unit collects the user's past hairstyle history and can provide the generation AI with data that reflects preferences and trends. For example, the feature data collection unit allows the user to upload photos of hairstyles they have tried in the past, and the AI ​​analyzes that data. For example, the user can select photos of past hairstyles from their smartphone album, and the AI ​​analyzes them to understand preferences and trends. This makes it possible to provide data that reflects the user's preferences and trends.

[0062] The feature data collection unit can use the emotion estimation function to analyze which hairstyle the user was most satisfied with in the past and collect the data. For example, the feature data collection unit analyzes the user's emotional response to photos of hairstyles the user has tried in the past. For example, it analyzes the user's facial expressions and voice when looking at the photos to identify the hairstyle that elicited the most positive reaction. This makes it possible to identify the hairstyle that the user was most satisfied with.

[0063] The feature data collection unit can collect data about the user's lifestyle and occupation and reflect it in hairstyle suggestions. For example, the feature data collection unit inputs information about the user's daily activities and occupation, and the AI ​​analyzes that data. For example, the user inputs information such as how often they play sports or their workplace dress code, and the AI ​​suggests hairstyles based on that information. This makes it possible to suggest hairstyles that suit the user's lifestyle and occupation.

[0064] The feature data collection unit collects the opinions of the user's family and friends and inputs them into the generation AI, allowing suggestions to be made from a more multifaceted perspective. For example, the feature data collection unit allows the user to ask family and friends for their opinions on hairstyles and input that feedback into the AI. For example, family and friends enter their opinions through a smartphone app, and the AI ​​analyzes them. This makes it possible to make hairstyle suggestions from a more multifaceted perspective.

[0065] The feature data collection unit uses the emotion estimation function to monitor the user's emotions when selecting a hairstyle in real time and reflect them in data collection. The feature data collection unit, for example, analyzes the user's facial expressions and voice when selecting a hairstyle in real time to collect emotion data. For example, it analyzes the user's facial expressions and tone of voice when viewing a photo of a hairstyle and calculates an emotion score. This allows the user's emotions to be monitored in real time and reflected in data collection.

[0066] The analysis unit can consider seasonal and climate changes when analyzing the user's feature data and suggest the most suitable hairstyle. For example, the analysis unit considers seasonal climate data when analyzing the user's feature data. For example, it can suggest cool hairstyles in summer and warm hairstyles in winter. This makes it possible to suggest hairstyles that take seasonal and climate changes into account.

[0067] The analysis unit can analyze the user's facial expressions and movements and suggest hairstyle changes according to the movements. For example, the analysis unit can analyze the user's facial expressions and suggest hairstyles according to the expressions. For example, it can suggest hairstyles that look best when smiling. This makes it possible to suggest hairstyle changes according to the user's facial expressions and movements.

[0068] The analysis unit can use the emotion estimation function to suggest a hairstyle that corresponds to the user's emotional state. For example, the analysis unit analyzes the user's emotional state in real time and suggests a hairstyle based on the results. For example, when the user is relaxed, the analysis unit suggests a hairstyle that enhances the feeling of relaxation. This makes it possible to suggest a hairstyle that corresponds to the user's emotional state.

[0069] The analysis unit can take into account hairstyle trends in different cultures and regions when analyzing the user's feature data. For example, the analysis unit can reflect trends in different cultures, such as trends in Asia, Europe, and Africa, when analyzing the user's feature data. This makes it possible to make suggestions that take into account hairstyle trends in different cultures and regions.

[0070] When analyzing the user's feature data, the analysis unit can simultaneously consider fashion and makeup trends and make comprehensive style suggestions. For example, when analyzing the user's feature data, the analysis unit considers the latest fashion trends and suggests hairstyles. For example, it suggests hairstyles that match this season's fashion. This makes it possible to make comprehensive style suggestions that take fashion and makeup trends into consideration.

[0071] The analysis unit can use the emotion estimation function to suggest a hairstyle that will make the user feel most relaxed. For example, the analysis unit analyzes the user's emotional state and suggests a hairstyle that will bring out a sense of relaxation. For example, it suggests a hairstyle that looks best when the user is relaxed. This makes it possible to suggest a hairstyle that will make the user feel most relaxed.

[0072] The suggestion unit can predict future hairstyle trends based on the user's feature data and reflect them in suggestions. The suggestion unit, for example, analyzes the user's feature data and uses AI to predict future hairstyle trends. For example, it suggests hairstyles that will be popular in the next season based on past trend data. This makes it possible to predict future hairstyle trends and reflect them in suggestions.

[0073] The suggestion unit can also simultaneously provide advice on maintaining and caring for the hairstyle based on the user's feature data. For example, the suggestion unit uses AI to provide advice on how to maintain and care for the hairstyle based on the user's feature data. For example, it can suggest shampoos and treatments that are suitable for a specific hairstyle. This makes it possible to provide advice on maintaining and caring for the hairstyle.

[0074] The suggestion unit can use the emotion estimation function to suggest a hairstyle that will make the user feel most confident. For example, the suggestion unit analyzes the user's emotional state and suggests a hairstyle that will bring out confidence. For example, the suggestion unit suggests an optimal hairstyle when the user wants to feel confident. This makes it possible to suggest a hairstyle that will make the user feel most confident.

[0075] The suggestion unit can suggest the best hairstyle for a specific event or season based on the user's feature data. For example, the suggestion unit uses AI to suggest the best hairstyle for a specific event based on the user's feature data. For example, it suggests the best hairstyle for attending a wedding or party. This makes it possible to suggest the best hairstyle for a specific event or season.

[0076] The suggestion unit can also suggest makeup and fashion that accompanies a change in hairstyle based on the user's feature data. For example, the suggestion unit uses AI to suggest makeup that accompanies a change in hairstyle based on the user's feature data. For example, it can suggest makeup colors and styles that match a new hairstyle. This makes it possible to suggest makeup and fashion that accompanies a change in hairstyle.

[0077] The suggestion unit can use the emotion estimation function to suggest a hairstyle that the user will enjoy the most. For example, the suggestion unit analyzes the user's emotional state and suggests a hairstyle that will bring out joy. For example, it suggests an optimal hairstyle when the user feels like having fun. This makes it possible to suggest a hairstyle that the user will enjoy the most.

[0078] The suggestion unit can visualize the proposed hairstyle as a 3D model so that the user can check it from 360 degrees. For example, the suggestion unit generates the proposed hairstyle as a 3D model so that the user can check it from 360 degrees. For example, the user rotates and checks the 3D model on a smartphone or PC. This allows the proposed hairstyle to be visualized as a 3D model so that the user can check it from 360 degrees.

[0079] The suggestion unit can visualize the proposed hairstyle under different lighting conditions and backgrounds to simulate how it would look in a real environment. For example, the suggestion unit visualizes the proposed hairstyle under different lighting conditions to simulate how it would look in a real environment. For example, the suggestion unit checks how it would look under indoor lighting and natural light. This allows the proposed hairstyle to be visualized under different lighting conditions and backgrounds to simulate how it would look in a real environment.

[0080] The suggestion unit can use the emotion estimation function to select a visualization method that will elicit the most positive response from the user. For example, the suggestion unit analyzes the user's emotional response to the proposed hairstyle visualization methods and selects the method that will elicit the most positive response. For example, the suggestion unit identifies the visualization method that the user is most satisfied with. This allows the selection of a visualization method that will elicit the most positive response from the user.

[0081] The suggestion unit can visualize the proposed hairstyle with animation, allowing the user to check how it looks in motion. For example, the suggestion unit can visualize the proposed hairstyle with animation, allowing the user to check how it looks in motion. For example, the suggestion unit can simulate the movement of the hairstyle when the user shakes their head. This allows the proposed hairstyle to be visualized with animation, allowing the user to check how it looks in motion.

[0082] The suggestion unit may visualize the proposed hairstyle with different hair colors to allow the user to compare the color differences. For example, the suggestion unit may simulate different hair colors such as black hair, brown hair, and blonde hair. This allows the proposed hairstyle with different hair colors to allow the user to compare the color differences.

[0083] The suggestion unit can use the emotion estimation function to select a visualization method that the user is most interested in. For example, the suggestion unit analyzes the user's emotional reaction to the proposed hairstyle visualization methods and selects the most interesting method. For example, the suggestion unit identifies the visualization method that the user is most interested in. This allows the suggestion unit to select the visualization method that the user is most interested in.

[0084] The suggestion unit can reflect changes in real time and visualize them instantly when a user customizes a suggested hairstyle. The suggestion unit, for example, builds a system that reflects changes in real time and visualizes them instantly when a user customizes a suggested hairstyle. For example, when a user changes the length of bangs, the change is visualized instantly. This allows changes to be reflected in real time and visualized instantly when a user customizes a suggested hairstyle.

[0085] The suggestion unit can refer to the past customization history and make the optimal suggestion when the user customizes the suggested hairstyle. The suggestion unit, for example, builds a system that refers to the past customization history and makes the optimal suggestion when the user customizes the suggested hairstyle. For example, the suggestion unit suggests the optimal customization based on the history of hairstyles that the user has tried in the past. This allows the suggestion unit to refer to the past customization history and make the optimal suggestion when the user customizes the suggested hairstyle.

[0086] The suggestion unit can use the emotion estimation function to suggest customization that will most satisfy the user. For example, the suggestion unit analyzes the user's emotional state and suggests customization that will most satisfy the user. For example, the suggestion unit identifies customization that will give the user a sense of satisfaction and suggests that customization. This makes it possible to suggest customization that will most satisfy the user.

[0087] The suggestion unit can provide a function that allows a user to refer to customization examples of other users when customizing the proposed hairstyle. The suggestion unit, for example, provides a function that allows a user to refer to customization examples of other users when customizing the proposed hairstyle. For example, the suggestion unit displays photos and comments of customizations made by other users. This allows a user to refer to customization examples of other users when customizing the proposed hairstyle.

[0088] The suggestion unit enables the generation AI to automatically suggest optimal customization options when a user customizes a proposed hairstyle. The suggestion unit, for example, builds a system in which the generation AI automatically suggests optimal customization options when a user customizes a proposed hairstyle. For example, the suggestion unit suggests optimal customization options based on the user's feature data. This allows the generation AI to automatically suggest optimal customization options when a user customizes a proposed hairstyle.

[0089] The suggestion unit can use the emotion estimation function to provide a customization process that is most enjoyable for the user. For example, the suggestion unit analyzes the user's emotional state and provides a customization process that is most enjoyable for the user. For example, the suggestion unit identifies a customization process that the user finds enjoyable and suggests that customization process. This makes it possible to provide a customization process that the user finds most enjoyable.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The feature data collection unit can collect data on the health of the user's hair and provide it to the generation AI. For example, the user can input information about the thickness, density, and degree of damage of their hair, and the AI ​​will analyze that data. This makes it possible to suggest hairstyles based on the health of the hair. In addition, by inputting information about the hair care products used by the user, suggestions can be made that take their effectiveness into account. Furthermore, factors that affect hair health, such as the user's diet and stress level, can also be collected, allowing for a comprehensive analysis.

[0092] The feature data collection unit can collect data on the user's hair growth cycle and provide it to the generation AI. For example, the user can input their hair growth rate and frequency of hair loss, and the AI ​​will analyze that data. This makes it possible to suggest hairstyles based on the hair growth cycle. The AI ​​can also input any hair problems or treatment history the user has experienced in the past, and make suggestions that take that data into consideration. Furthermore, factors that affect hair growth, such as the user's age and hormone balance, can be collected to perform a comprehensive analysis.

[0093] The feature data collection unit can collect data on the user's hair texture and provide it to the generation AI. For example, the user can input the softness, hardness, and smoothness of their hair, and the AI ​​will analyze that data. This makes it possible to suggest hairstyles based on hair texture. It can also input information about the styling products the user uses and make suggestions that take their effects into account. It can also collect environmental factors (humidity, temperature, etc.) that affect the user's hair texture, allowing for a comprehensive analysis.

[0094] The feature data collection unit can collect data on the user's hair pigmentation and provide it to the generation AI. For example, the user can input the degree of hair pigmentation and color changes, and the AI ​​can analyze that data. This makes it possible to suggest hairstyles based on hair pigmentation. The system can also input the user's past hair coloring history and make suggestions that take that data into consideration. Furthermore, it can collect factors that affect the user's hair pigmentation (such as ultraviolet rays and chemicals) and perform a comprehensive analysis.

[0095] The feature data collection unit can collect data on the movement of the user's hair and provide it to the generation AI. For example, the user inputs the ease of movement and elasticity of their hair, and the AI ​​analyzes that data. This makes it possible to suggest hairstyles based on hair movement. The AI ​​can also input feedback on the movement of hairstyles the user has tried in the past, and make suggestions that take that data into consideration. Furthermore, it can collect factors that affect the movement of the user's hair (wind, humidity, etc.), allowing for a comprehensive analysis.

[0096] The analysis unit can analyze the user's emotional state and suggest hairstyles that have a stress-reducing effect. For example, when the user is feeling stressed, it can suggest hairstyles that have a relaxing effect. This makes it possible to suggest hairstyles that have a stress-reducing effect according to the user's emotional state. It can also identify the most relaxing hairstyle that the user has tried in the past and make suggestions based on that data. Furthermore, it can suggest hairstyles that have a comprehensive stress-reducing effect by taking into account the user's lifestyle habits and stress factors.

[0097] The analysis unit can analyze the user's emotional state and suggest hairstyles that will boost motivation. For example, when a user feels motivated, it can suggest hairstyles that will boost motivation. This makes it possible to suggest hairstyles that will boost motivation according to the user's emotional state. It can also identify the hairstyle that most motivated the user among those that have been tried in the past and make suggestions based on that data. Furthermore, it can suggest hairstyles that will boost overall motivation by taking into account the user's goals and what they want to achieve.

[0098] The analysis unit can analyze the user's emotional state and suggest hairstyles that will improve concentration. For example, when a user feels like they want to concentrate, it can suggest hairstyles that will improve concentration. This makes it possible to suggest hairstyles that will improve concentration according to the user's emotional state. It can also identify the hairstyle that helped the user concentrate the most among the hairstyles that the user has tried in the past and make suggestions based on that data. Furthermore, it can suggest hairstyles that will improve overall concentration by taking into account the user's work and study environment.

[0099] The analysis unit can analyze the user's emotional state and suggest hairstyles that enhance happiness. For example, when a user wants to feel happy, it can suggest hairstyles that enhance happiness. This makes it possible to suggest hairstyles that enhance happiness according to the user's emotional state. It can also identify the hairstyle that the user has tried in the past that made them feel the happiest, and make suggestions based on that data. Furthermore, it can suggest hairstyles that have an overall effect of enhancing happiness by taking into account the user's lifestyle and hobbies.

[0100] The analysis unit can analyze the user's emotional state and suggest hairstyles that will enhance sociability. For example, if the user feels like being more sociable, it can suggest hairstyles that will enhance sociability. This makes it possible to suggest hairstyles that will enhance sociability according to the user's emotional state. It can also identify the hairstyle that the user has tried in the past that made them most sociable and make suggestions based on that data. Furthermore, it can suggest hairstyles that will enhance overall sociability by taking into account the user's friendships and social events.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The feature data collection unit collects detailed feature data such as the user's hair growth pattern and direction, natural hair color, forehead width, and head shape. For example, the user can take a photo of their head with their smartphone and input the image data into the AI. Detailed feature data can also be collected using a dedicated scanning device. The scanning device scans the entire head from 360 degrees and generates a detailed 3D model. The unit also collects the user's past hairstyle history and provides the generation AI with data that reflects preferences and trends. For example, the user can upload photos of hairstyles they have tried in the past, and the AI ​​will analyze that data. Step 2: The analysis unit analyzes the feature data collected by the feature data collection unit. For example, the generation AI performs a detailed analysis of the user's hair growth pattern and direction, natural hair color, forehead width, head shape, etc., and combines this with general classifications such as the user's hair type, hair volume, and face shape. The generation AI performs the analysis using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The suggestion unit suggests the optimal hairstyle for the user based on the results of the analysis by the analysis unit. For example, the generation AI might suggest, "This hairstyle is optimal based on your hair growth pattern and direction, natural hair color, forehead width, and head shape."

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 feature data collection unit that collects detailed feature data such as the user's hair growth pattern and direction, natural hair color, forehead width, and head shape; an analysis unit that analyzes the feature data collected by the feature data collection unit; a suggestion unit that suggests an optimal hairstyle to the user based on the results of the analysis by the analysis unit. A system characterized by:

2. The feature data collection unit Using the emotion estimation function, analyze which hairstyle the user was most satisfied with in the past and collect that data. The system of claim 1 .

3. The analysis unit When analyzing the user's characteristic data, seasonal and weather changes are taken into consideration to suggest the most suitable hairstyle. The system of claim 1 .

4. The proposal unit Based on the user's characteristic data, future hairstyle trends are predicted and reflected in the proposal. The system of claim 1 .

5. The proposal unit Using an emotion estimation function, select the visualization method that elicits the most positive response from the user. The system of claim 1 .

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