System

Abstract

An object of a system according to an embodiment is to enable a user to easily find a fashion that suits the user.SOLUTION: A system according to an embodiment includes an image recognition unit, a personal color diagnosis unit, a skeleton diagnosis unit, and a fashion proposal unit. The image recognition unit analyzes the photograph data of the user. The personal color diagnosis unit diagnoses a personal color based on the data analyzed by the image recognition unit. The skeleton diagnosis unit diagnoses the skeleton based on the data analyzed by the image recognition unit. The fashion proposal part proposes total fashion coordination on the basis of diagnosis results of the personal color diagnosis part and the skeleton diagnosis part.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 making it difficult for users to find fashion that suits them.

[0005] The system according to the embodiment aims to enable users to easily find fashion that suits them. [Means for solving the problem]

[0006] The system according to the embodiment includes an image recognition unit, a personal color diagnosis unit, a body type diagnosis unit, and a fashion suggestion unit. The image recognition unit analyzes a user's photo data. The personal color diagnosis unit diagnoses the user's personal color based on the data analyzed by the image recognition unit. The body type diagnosis unit diagnoses the user's body type based on the data analyzed by the image recognition unit. The fashion suggestion unit suggests a total fashion coordination based on the diagnosis results of the personal color diagnosis unit and the body type diagnosis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily find fashion that suits them. [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 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 fashion support system according to the embodiment of the present invention automatically analyzes a user's photographic data, and a generation AI diagnoses their personal color and physique, and then proposes a total fashion coordination based on the results. This allows the fashion support system to easily find a fashion that suits the user.

[0029] A fashion support system according to an embodiment includes an image recognition unit, a personal color diagnosis unit, a bone structure diagnosis unit, and a fashion suggestion unit. The image recognition unit analyzes a user's photographic data. For example, the image recognition unit detects the user's facial contours, skin color, hair color, and eye color. The image recognition unit can also analyze the user's body proportions. For example, the image recognition unit identifies the facial contours and analyzes skin color. The personal color diagnosis unit diagnoses a personal color based on the data analyzed by the image recognition unit. For example, the personal color diagnosis unit diagnoses the color that best matches the user's skin color, hair color, and eye color. The personal color diagnosis unit can also analyze the user's skin color and classify the user into one of a "spring type," a "summer type," a "fall type," and a "winter type." For example, the personal color diagnosis unit diagnoses a personal color based on skin color. The bone structure diagnosis unit diagnoses a bone structure based on the data analyzed by the image recognition unit. For example, the bone structure diagnosis unit analyzes the user's facial contours and body proportions and classifies the user into one of "straight type," "wavy type," and "natural type." The bone structure diagnosis unit can also perform diagnosis based on the user's body shape and bone structure characteristics. For example, the bone structure diagnosis unit diagnoses bone structure based on the facial contour. The fashion suggestion unit proposes a total fashion coordination based on the diagnosis results of the personal color diagnosis unit and the bone structure diagnosis unit. For example, the fashion suggestion unit proposes a coordination that combines clothes and accessories in a color that matches the user's personal color with clothes in a style that matches the user's bone structure. The fashion suggestion unit can also propose an optimal fashion style based on the user's personal color and bone structure. For example, the fashion suggestion unit proposes a coordination that combines a bright pastel-colored top with simple-lined pants for a user with a "spring type" personal color and a "straight type" bone structure. This allows the fashion support system according to the embodiment to easily find fashion that suits them. For example, the user can upload their own photos through the system and check the diagnosis results and coordination suggestions in real time.Users can also immediately purchase suggested fashion items.

[0030] The image recognition unit analyzes the user's skin texture and hair texture to perform a more detailed personal color diagnosis. For example, the image recognition unit analyzes the skin texture from a photo of the user to identify characteristics such as dry skin or oily skin. For example, the image recognition unit diagnoses the personal color based on the skin texture. The image recognition unit can also analyze the user's hair texture to identify hair thickness and gloss. For example, the image recognition unit diagnoses the personal color based on the hair texture. This enables a detailed personal color diagnosis based on the user's skin and hair texture.

[0031] The image recognition unit can analyze the user's movements and walking style and make fashion suggestions based on the results. For example, the image recognition unit analyzes the user's movements from a photo and suggests sporty fashion if the user is actively moving. For example, the image recognition unit detects active movements from a photo of the user running and suggests sportswear. The image recognition unit can also analyze the user's walking style and suggest fashion that matches the walking style. For example, the image recognition unit suggests a fashion style based on the walking style. This makes it possible to suggest fashion based on the user's movements and walking style.

[0032] The image recognition unit can analyze the user's environment and background and make fashion suggestions according to the situation. For example, the image recognition unit can analyze the background of the user's photo and suggest outdoor styles for photos taken outdoors. For example, the image recognition unit can suggest outdoor wear from photos taken in nature. The image recognition unit can also analyze the user's environment and suggest fashion that suits the environment. For example, the image recognition unit can suggest a fashion style based on the user's environment. This makes it possible to suggest fashion according to the user's environment and situation.

[0033] The personal color diagnosis unit can perform a more personalized diagnosis based on the user's fashion history and favorite colors. The personal color diagnosis unit, for example, analyzes the user's past fashion history and identifies the user's favorite colors and style. For example, the personal color diagnosis unit diagnoses the user's personal color based on the colors and designs of items purchased in the past. The personal color diagnosis unit can also perform a personalized diagnosis taking the user's favorite colors into consideration. For example, the personal color diagnosis unit diagnoses the user's personal color based on the user's favorite colors. This makes it possible to perform a personalized diagnosis based on the user's past fashion history and favorite colors.

[0034] The personal color diagnosis unit can make color suggestions according to the season and trends. The personal color diagnosis unit can suggest trend colors for each season based on the results of the personal color diagnosis, for example. For example, the personal color diagnosis unit can suggest pastel colors in spring and earth colors in autumn. The personal color diagnosis unit can also make color suggestions according to trends. For example, the personal color diagnosis unit can suggest colors based on popular colors. This makes it possible to make color suggestions according to the season and trends.

[0035] The personal color diagnosis unit can suggest makeup and hairstyles based on the diagnosis results. The personal color diagnosis unit, for example, suggests optimal makeup colors based on the results of the personal color diagnosis. For example, the personal color diagnosis unit suggests pink blush and lipstick to a spring-type user. The personal color diagnosis unit can also suggest hairstyles based on the diagnosis results. For example, the personal color diagnosis unit suggests hairstyles that match the user's personal color. This makes it possible to suggest makeup and hairstyles based on the user's personal color.

[0036] The personal color diagnosis unit can suggest colors for interior and accessories based on the diagnosis results. The personal color diagnosis unit can suggest optimal interior colors based on the results of the personal color diagnosis. For example, the personal color diagnosis unit can suggest warm-colored interiors for an autumn-type user. The personal color diagnosis unit can also suggest accessory colors based on the diagnosis results. For example, the personal color diagnosis unit can suggest accessories that match the user's personal color. This makes it possible to suggest colors for interior and accessories based on the user's personal color.

[0037] The skeletal diagnosis unit can perform a more detailed diagnosis based on the user's weight fluctuations and muscle mass. The skeletal diagnosis unit, for example, analyzes the user's weight fluctuations and performs a skeletal diagnosis based on past data. For example, the skeletal diagnosis unit adjusts the diagnosis results taking into account changes in body shape when the user's weight increases or decreases. The skeletal diagnosis unit can also analyze the user's muscle mass and perform a diagnosis based on the muscle mass. For example, the skeletal diagnosis unit performs a skeletal diagnosis based on the muscle mass. This makes it possible to perform a detailed skeletal diagnosis based on the user's weight fluctuations and muscle mass.

[0038] The skeletal diagnosis unit can provide advice for improving the user's posture and correcting their body shape based on the diagnosis results. The skeletal diagnosis unit provides advice for improving the user's posture based on, for example, the results of the skeletal diagnosis. For example, the skeletal diagnosis unit suggests posture correction stretches for a user with a hunched back. The skeletal diagnosis unit can also provide advice for correcting their body shape. For example, the skeletal diagnosis unit suggests the selection of shapewear and exercises. This makes it possible to provide advice for improving the user's posture and correcting their body shape.

[0039] The skeletal diagnosis unit can propose optimal fitness plans and exercises to the user based on the diagnosis results. The skeletal diagnosis unit proposes optimal fitness plans to the user based on, for example, the results of the skeletal diagnosis. For example, the skeletal diagnosis unit proposes strength training to a user with low muscle mass. The skeletal diagnosis unit can also propose optimal exercises to the user. For example, the skeletal diagnosis unit proposes exercises that suit the user's body type. This makes it possible to propose optimal fitness plans and exercises to the user.

[0040] The skeletal diagnosis unit can suggest shoes and accessories that are best suited to the user based on the diagnosis results. The skeletal diagnosis unit, for example, suggests shoes that are best suited to the user based on the results of the skeletal diagnosis. For example, the skeletal diagnosis unit suggests shoes that fit the shape of the foot. The skeletal diagnosis unit can also suggest accessories that are best suited to the user. For example, the skeletal diagnosis unit suggests accessories that fit the user's skeletal structure. This makes it possible to suggest shoes and accessories that are best suited to the user.

[0041] The fashion suggestion unit can suggest more practical total fashion coordination based on the user's lifestyle and occupation. The fashion suggestion unit, for example, analyzes the user's lifestyle and suggests fashion suitable for everyday life. For example, the fashion suggestion unit suggests a casual style that is easy to move in to a user with an active lifestyle. The fashion suggestion unit can also take the user's occupation into consideration and suggest fashion suitable for the workplace. For example, the fashion suggestion unit suggests a formal style to a user who works in an office. This makes it possible to suggest practical fashion based on the user's lifestyle and occupation.

[0042] The fashion suggestion unit can make suggestions based on the user's purchase history and budget. For example, the fashion suggestion unit analyzes the user's purchase history of fashion items and suggests outfits that combine items purchased in the past. For example, the fashion suggestion unit suggests bottoms that go well with tops purchased in the past. The fashion suggestion unit can also take the user's budget into consideration and suggest items that can be purchased within the budget. For example, the fashion suggestion unit suggests items based on the user's budget. This makes it possible to make fashion suggestions based on the user's purchase history and budget.

[0043] The fashion suggestion unit can suggest the most suitable event and date plan to the user based on the coordination result. The fashion suggestion unit, for example, suggests the most suitable event to the user based on the coordination result. For example, the fashion suggestion unit suggests an outdoor event for a casual style. The fashion suggestion unit can also suggest the most suitable date plan to the user. For example, the fashion suggestion unit suggests a date at a high-end restaurant for a formal style. This makes it possible to suggest the most suitable event and date plan to the user.

[0044] The fashion suggestion unit can suggest the optimal hairstyle and makeup for the user based on the coordination result. The fashion suggestion unit, for example, suggests the optimal hairstyle based on the coordination result. For example, the fashion suggestion unit suggests a natural hairstyle for a casual style. The fashion suggestion unit can also suggest the optimal makeup for the user. For example, the fashion suggestion unit suggests chic makeup for a formal style. This makes it possible to suggest the optimal hairstyle and makeup for the user.

[0045] The user interface unit can learn the user's fashion history and preferences and make more personalized suggestions. The user interface unit, for example, adds a function to analyze the user's fashion history and learn the user's past purchase history and preferences. For example, the user interface unit makes personalized suggestions based on the colors and designs of items purchased in the past. The user interface unit can also learn the user's preferences and make more personalized suggestions. For example, the user interface unit makes fashion suggestions based on the user's preferences. This makes it possible to make personalized suggestions based on the user's fashion history and preferences.

[0046] The user interface unit can add a function for users to rate each other's fashion and provide feedback, thereby forming a community. The user interface unit can, for example, add a function that allows users to rate each other's fashion, thereby forming a community. For example, the user interface unit can allow users to "like" or comment on other users' outfits. The user interface unit can also add a feedback function for users to each other, thereby revitalizing the community. For example, the user interface unit can provide a forum for exchanging fashion advice and opinions. This makes it possible to form a community through the fashion rating and feedback function between users.

[0047] The user interface unit can add a virtual try-on function, allowing the user to virtually try on suggested fashions. The user interface unit can add, for example, a virtual try-on function, allowing the user to virtually try on suggested fashions. For example, the user interface unit can overlay suggested clothes on a photo of the user and display them. The user interface unit can also use 3D modeling technology to allow the user to virtually try on clothes. For example, the user interface unit can create a 3D model that matches the user's body type and allow the user to try on suggested fashions. This allows the user to virtually try on suggested fashions.

[0048] The user interface unit can add a purchase link to enable the user to immediately purchase the suggested fashion item. The user interface unit, for example, adds a purchase link to the suggested fashion item to enable the user to immediately purchase it. For example, the user interface unit displays a purchase link for the suggested top. The user interface unit can also enable the user to easily purchase the suggested item based on the purchase link. For example, the user interface unit allows the user to complete the purchase procedure by simply clicking the purchase link. This allows the user to immediately purchase the suggested fashion item.

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

[0050] The fashion suggestion unit can suggest more practical total fashion coordination based on the user's lifestyle and occupation. For example, the fashion suggestion unit analyzes the user's lifestyle and suggests fashion suitable for everyday life. For example, the fashion suggestion unit suggests a casual style that is easy to move in to a user with an active lifestyle. The fashion suggestion unit can also take the user's occupation into consideration and suggest fashion suitable for the workplace. For example, the fashion suggestion unit suggests a formal style to a user who works in an office. This makes it possible to suggest practical fashion based on the user's lifestyle and occupation.

[0051] The fashion suggestion unit can make suggestions based on the user's purchase history and budget. For example, the fashion suggestion unit can analyze the user's purchase history of fashion items and suggest outfits that combine them with previously purchased items. For example, the fashion suggestion unit can suggest bottoms that go well with previously purchased tops. The fashion suggestion unit can also take the user's budget into consideration and suggest items that can be purchased within the budget. For example, the fashion suggestion unit can suggest items based on the user's budget. This makes it possible to make fashion suggestions based on the user's purchase history and budget.

[0052] The fashion suggestion unit can suggest the most suitable event and date plan to the user based on the coordination result. For example, the fashion suggestion unit suggests the most suitable event to the user based on the coordination result. For example, the fashion suggestion unit suggests an outdoor event for a casual style. The fashion suggestion unit can also suggest the most suitable date plan to the user. For example, the fashion suggestion unit suggests a date at a high-end restaurant for a formal style. This makes it possible to suggest the most suitable event and date plan to the user.

[0053] The fashion suggestion unit can suggest the optimal hairstyle and makeup for the user based on the coordination result. For example, the fashion suggestion unit suggests the optimal hairstyle based on the coordination result. For example, the fashion suggestion unit suggests a natural hairstyle for a casual style. The fashion suggestion unit can also suggest the optimal makeup for the user. For example, the fashion suggestion unit suggests chic makeup for a formal style. This makes it possible to suggest the optimal hairstyle and makeup for the user.

[0054] The user interface unit can learn the user's fashion history and preferences and make more personalized suggestions. For example, the user interface unit adds a function to analyze the user's fashion history and learn the user's past purchase history and preferences. For example, the user interface unit makes personalized suggestions based on the colors and designs of items purchased in the past. The user interface unit can also learn the user's preferences and make more personalized suggestions. For example, the user interface unit makes fashion suggestions based on the user's preferences. This makes it possible to make personalized suggestions based on the user's fashion history and preferences.

[0055] The user interface unit can add a function for users to rate each other's fashion and provide feedback, thereby forming a community. For example, the user interface unit can add a function that allows users to rate each other's fashion, thereby forming a community. For example, the user interface unit can allow users to "like" or comment on other users' outfits. The user interface unit can also add a feedback function for users to each other, thereby revitalizing the community. For example, the user interface unit can provide a forum for exchanging fashion advice and opinions. This makes it possible to form a community through the fashion rating and feedback function between users.

[0056] The user interface unit can add a virtual try-on function, allowing the user to virtually try on suggested fashions. For example, the user interface unit can add a virtual try-on function, allowing the user to virtually try on suggested fashions. For example, the user interface unit can overlay suggested clothes on a photo of the user and display them. The user interface unit can also use 3D modeling technology to allow the user to virtually try on clothes. For example, the user interface unit can create a 3D model that matches the user's body type and allow the user to try on suggested fashions. This allows the user to virtually try on suggested fashions.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The image recognition unit analyzes the user's photo data. For example, the image recognition unit can detect the user's facial contours, skin color, hair color, and eye color, and analyze body proportions. Step 2: The personal color diagnosis unit diagnoses the personal color based on the data analyzed by the image recognition unit. For example, it diagnoses the color that best matches the user's skin color, hair color, and eye color, and classifies the user into one of "spring type," "summer type," "autumn type," or "winter type." Step 3: The bone structure diagnosis unit diagnoses the bone structure based on the data analyzed by the image recognition unit. For example, it analyzes the user's facial contours and body proportions and classifies them into one of three types: "straight type," "wavy type," or "natural type." Step 4: The fashion suggestion unit proposes a total fashion coordination based on the diagnosis results of the personal color diagnosis unit and the skeletal structure diagnosis unit. For example, it proposes a coordination that combines clothes and accessories in colors that match the user's personal color and clothes in a style that matches their skeletal structure.

[0059] (Example 2) The fashion support system according to the embodiment of the present invention automatically analyzes a user's photographic data, and a generation AI diagnoses their personal color and physique, and then proposes a total fashion coordination based on the results. This allows the fashion support system to easily find a fashion that suits the user.

[0060] A fashion support system according to an embodiment includes an image recognition unit, a personal color diagnosis unit, a bone structure diagnosis unit, and a fashion suggestion unit. The image recognition unit analyzes a user's photographic data. For example, the image recognition unit detects the user's facial contours, skin color, hair color, and eye color. The image recognition unit can also analyze the user's body proportions. For example, the image recognition unit identifies the facial contours and analyzes skin color. The personal color diagnosis unit diagnoses a personal color based on the data analyzed by the image recognition unit. For example, the personal color diagnosis unit diagnoses the color that best matches the user's skin color, hair color, and eye color. The personal color diagnosis unit can also analyze the user's skin color and classify the user into one of a "spring type," a "summer type," a "fall type," and a "winter type." For example, the personal color diagnosis unit diagnoses a personal color based on skin color. The bone structure diagnosis unit diagnoses a bone structure based on the data analyzed by the image recognition unit. For example, the bone structure diagnosis unit analyzes the user's facial contours and body proportions and classifies the user into one of "straight type," "wavy type," and "natural type." The bone structure diagnosis unit can also perform diagnosis based on the user's body shape and bone structure characteristics. For example, the bone structure diagnosis unit diagnoses bone structure based on the facial contour. The fashion suggestion unit proposes a total fashion coordination based on the diagnosis results of the personal color diagnosis unit and the bone structure diagnosis unit. For example, the fashion suggestion unit proposes a coordination that combines clothes and accessories in a color that matches the user's personal color with clothes in a style that matches the user's bone structure. The fashion suggestion unit can also propose an optimal fashion style based on the user's personal color and bone structure. For example, the fashion suggestion unit proposes a coordination that combines a bright pastel-colored top with simple-lined pants for a user with a "spring type" personal color and a "straight type" bone structure. This allows the fashion support system according to the embodiment to easily find fashion that suits them. For example, the user can upload their own photos through the system and check the diagnosis results and coordination suggestions in real time.Users can also immediately purchase suggested fashion items.

[0061] The image recognition unit can analyze the user's facial expression and posture and make fashion suggestions based on their emotions and mood. For example, the image recognition unit analyzes facial expressions and body posture from photos uploaded by the user to estimate their emotions and mood. For example, the image recognition unit can detect positive emotions from a photo of a smiling person and suggest bright-colored fashion that matches that emotion. The image recognition unit can also analyze the user's posture and suggest casual fashion if the user is in a relaxed posture. For example, the image recognition unit can suggest a fashion style based on the user's posture. This makes it possible to suggest fashion that matches the user's emotions and mood.

[0062] The image recognition unit analyzes the user's skin texture and hair texture to perform a more detailed personal color diagnosis. For example, the image recognition unit analyzes the skin texture from a photo of the user to identify characteristics such as dry skin or oily skin. For example, the image recognition unit diagnoses the personal color based on the skin texture. The image recognition unit can also analyze the user's hair texture to identify hair thickness and gloss. For example, the image recognition unit diagnoses the personal color based on the hair texture. This enables a detailed personal color diagnosis based on the user's skin and hair texture.

[0063] The image recognition unit can analyze the user's movements and walking style and make fashion suggestions based on the results. For example, the image recognition unit analyzes the user's movements from a photo and suggests sporty fashion if the user is actively moving. For example, the image recognition unit detects active movements from a photo of the user running and suggests sportswear. The image recognition unit can also analyze the user's walking style and suggest fashion that matches the walking style. For example, the image recognition unit suggests a fashion style based on the walking style. This makes it possible to suggest fashion based on the user's movements and walking style.

[0064] The image recognition unit can analyze the user's environment and background and make fashion suggestions according to the situation. For example, the image recognition unit can analyze the background of the user's photo and suggest outdoor styles for photos taken outdoors. For example, the image recognition unit can suggest outdoor wear from photos taken in nature. The image recognition unit can also analyze the user's environment and suggest fashion that suits the environment. For example, the image recognition unit can suggest a fashion style based on the user's environment. This makes it possible to suggest fashion according to the user's environment and situation.

[0065] The personal color diagnosis unit can perform a more personalized diagnosis based on the user's fashion history and favorite colors. The personal color diagnosis unit, for example, analyzes the user's past fashion history and identifies the user's favorite colors and style. For example, the personal color diagnosis unit diagnoses the user's personal color based on the colors and designs of items purchased in the past. The personal color diagnosis unit can also perform a personalized diagnosis taking the user's favorite colors into consideration. For example, the personal color diagnosis unit diagnoses the user's personal color based on the user's favorite colors. This makes it possible to perform a personalized diagnosis based on the user's past fashion history and favorite colors.

[0066] The personal color diagnosis unit can make color suggestions according to the season and trends. The personal color diagnosis unit can suggest trend colors for each season based on the results of the personal color diagnosis, for example. For example, the personal color diagnosis unit can suggest pastel colors in spring and earth colors in autumn. The personal color diagnosis unit can also make color suggestions according to trends. For example, the personal color diagnosis unit can suggest colors based on popular colors. This makes it possible to make color suggestions according to the season and trends.

[0067] The personal color diagnosis unit can use the emotion estimation function to make color suggestions based on the user's emotions. The personal color diagnosis unit, for example, uses the emotion estimation function to make color suggestions based on the user's emotions. For example, the personal color diagnosis unit suggests bright colors when positive emotions are strong. The personal color diagnosis unit can also make color suggestions based on the user's emotions. For example, the personal color diagnosis unit makes color suggestions based on the emotion score. This makes it possible to make color suggestions based on the user's emotions.

[0068] The personal color diagnosis unit can suggest makeup and hairstyles based on the diagnosis results. The personal color diagnosis unit, for example, suggests optimal makeup colors based on the results of the personal color diagnosis. For example, the personal color diagnosis unit suggests pink blush and lipstick to a spring-type user. The personal color diagnosis unit can also suggest hairstyles based on the diagnosis results. For example, the personal color diagnosis unit suggests hairstyles that match the user's personal color. This makes it possible to suggest makeup and hairstyles based on the user's personal color.

[0069] The personal color diagnosis unit can suggest colors for interior and accessories based on the diagnosis results. The personal color diagnosis unit can suggest optimal interior colors based on the results of the personal color diagnosis. For example, the personal color diagnosis unit can suggest warm-colored interiors for an autumn-type user. The personal color diagnosis unit can also suggest accessory colors based on the diagnosis results. For example, the personal color diagnosis unit can suggest accessories that match the user's personal color. This makes it possible to suggest colors for interior and accessories based on the user's personal color.

[0070] The personal color diagnosis unit can use the emotion estimation function to suggest colors that will make the user most relaxed. The personal color diagnosis unit, for example, uses the emotion estimation function to suggest colors that will make the user most relaxed. For example, the personal color diagnosis unit suggests blue colors that have a relaxing effect when the emotion score is low. The personal color diagnosis unit can also suggest relaxing colors based on the user's emotions. For example, the personal color diagnosis unit suggests relaxing colors based on the emotion score. This makes it possible to suggest colors that will make the user most relaxed.

[0071] The skeletal diagnosis unit can perform a more detailed diagnosis based on the user's weight fluctuations and muscle mass. The skeletal diagnosis unit, for example, analyzes the user's weight fluctuations and performs a skeletal diagnosis based on past data. For example, the skeletal diagnosis unit adjusts the diagnosis results taking into account changes in body shape when the user's weight increases or decreases. The skeletal diagnosis unit can also analyze the user's muscle mass and perform a diagnosis based on the muscle mass. For example, the skeletal diagnosis unit performs a skeletal diagnosis based on the muscle mass. This makes it possible to perform a detailed skeletal diagnosis based on the user's weight fluctuations and muscle mass.

[0072] The skeletal diagnosis unit can provide advice for improving the user's posture and correcting their body shape based on the diagnosis results. The skeletal diagnosis unit provides advice for improving the user's posture based on, for example, the results of the skeletal diagnosis. For example, the skeletal diagnosis unit suggests posture correction stretches for a user with a hunched back. The skeletal diagnosis unit can also provide advice for correcting their body shape. For example, the skeletal diagnosis unit suggests the selection of shapewear and exercises. This makes it possible to provide advice for improving the user's posture and correcting their body shape.

[0073] The skeletal diagnosis unit can use the emotion estimation function to analyze the user's emotions regarding their body shape and make fashion suggestions that elicit positive emotions. The skeletal diagnosis unit, for example, uses the emotion estimation function to analyze the user's emotions regarding their body shape. For example, the skeletal diagnosis unit can suggest fashion that will make a user who has negative emotions regarding their body shape feel confident. The skeletal diagnosis unit can also make fashion suggestions that elicit positive emotions based on the user's emotions. For example, the skeletal diagnosis unit makes fashion suggestions based on the emotion score. This makes it possible to make fashion suggestions that elicit positive emotions regarding the user's body shape.

[0074] The skeletal diagnosis unit can propose optimal fitness plans and exercises to the user based on the diagnosis results. The skeletal diagnosis unit proposes optimal fitness plans to the user based on, for example, the results of the skeletal diagnosis. For example, the skeletal diagnosis unit proposes strength training to a user with low muscle mass. The skeletal diagnosis unit can also propose optimal exercises to the user. For example, the skeletal diagnosis unit proposes exercises that suit the user's body type. This makes it possible to propose optimal fitness plans and exercises to the user.

[0075] The skeletal diagnosis unit can suggest shoes and accessories that are best suited to the user based on the diagnosis results. The skeletal diagnosis unit, for example, suggests shoes that are best suited to the user based on the results of the skeletal diagnosis. For example, the skeletal diagnosis unit suggests shoes that fit the shape of the foot. The skeletal diagnosis unit can also suggest accessories that are best suited to the user. For example, the skeletal diagnosis unit suggests accessories that fit the user's skeletal structure. This makes it possible to suggest shoes and accessories that are best suited to the user.

[0076] The skeletal diagnosis unit can use the emotion estimation function to suggest a style in which the user can be most confident. The skeletal diagnosis unit, for example, uses the emotion estimation function to suggest a style in which the user can be most confident. For example, the skeletal diagnosis unit preferentially suggests styles with high emotion scores. The skeletal diagnosis unit can also suggest a style in which the user can be most confident based on the user's emotions. For example, the skeletal diagnosis unit suggests a style based on the emotion score. This makes it possible to suggest a style in which the user can be most confident.

[0077] The fashion suggestion unit can suggest more practical total fashion coordination based on the user's lifestyle and occupation. The fashion suggestion unit, for example, analyzes the user's lifestyle and suggests fashion suitable for everyday life. For example, the fashion suggestion unit suggests a casual style that is easy to move in to a user with an active lifestyle. The fashion suggestion unit can also take the user's occupation into consideration and suggest fashion suitable for the workplace. For example, the fashion suggestion unit suggests a formal style to a user who works in an office. This makes it possible to suggest practical fashion based on the user's lifestyle and occupation.

[0078] The fashion suggestion unit can make suggestions based on the user's purchase history and budget. For example, the fashion suggestion unit analyzes the user's purchase history of fashion items and suggests outfits that combine items purchased in the past. For example, the fashion suggestion unit suggests bottoms that go well with tops purchased in the past. The fashion suggestion unit can also take the user's budget into consideration and suggest items that can be purchased within the budget. For example, the fashion suggestion unit suggests items based on the user's budget. This makes it possible to make fashion suggestions based on the user's purchase history and budget.

[0079] The fashion suggestion unit can use the emotion estimation function to suggest outfits based on the user's emotions. The fashion suggestion unit, for example, uses the emotion estimation function to suggest outfits based on the user's emotions. For example, the fashion suggestion unit suggests bright-colored fashion when the user has strong positive emotions. The fashion suggestion unit can also suggest outfits based on the user's emotions. For example, the fashion suggestion unit suggests outfits based on the emotion score. This makes it possible to suggest outfits based on the user's emotions.

[0080] The fashion suggestion unit can suggest the most suitable event and date plan to the user based on the coordination result. The fashion suggestion unit, for example, suggests the most suitable event to the user based on the coordination result. For example, the fashion suggestion unit suggests an outdoor event for a casual style. The fashion suggestion unit can also suggest the most suitable date plan to the user. For example, the fashion suggestion unit suggests a date at a high-end restaurant for a formal style. This makes it possible to suggest the most suitable event and date plan to the user.

[0081] The fashion suggestion unit can suggest the optimal hairstyle and makeup for the user based on the coordination result. The fashion suggestion unit, for example, suggests the optimal hairstyle based on the coordination result. For example, the fashion suggestion unit suggests a natural hairstyle for a casual style. The fashion suggestion unit can also suggest the optimal makeup for the user. For example, the fashion suggestion unit suggests chic makeup for a formal style. This makes it possible to suggest the optimal hairstyle and makeup for the user.

[0082] The fashion suggestion unit can use the emotion estimation function to suggest an outfit that will make the user most relaxed. The fashion suggestion unit, for example, uses the emotion estimation function to suggest an outfit that will make the user most relaxed. For example, the fashion suggestion unit suggests a casual style that has a relaxing effect when the emotion score is low. The fashion suggestion unit can also suggest an outfit that will make the user most relaxed based on the user's emotions. For example, the fashion suggestion unit suggests an outfit based on the emotion score. This makes it possible to suggest an outfit that will make the user most relaxed.

[0083] The user interface unit can learn the user's fashion history and preferences and make more personalized suggestions. The user interface unit, for example, adds a function to analyze the user's fashion history and learn the user's past purchase history and preferences. For example, the user interface unit makes personalized suggestions based on the colors and designs of items purchased in the past. The user interface unit can also learn the user's preferences and make more personalized suggestions. For example, the user interface unit makes fashion suggestions based on the user's preferences. This makes it possible to make personalized suggestions based on the user's fashion history and preferences.

[0084] The user interface unit can add a function for users to rate each other's fashion and provide feedback, thereby forming a community. The user interface unit can, for example, add a function that allows users to rate each other's fashion, thereby forming a community. For example, the user interface unit can allow users to "like" or comment on other users' outfits. The user interface unit can also add a feedback function for users to each other, thereby revitalizing the community. For example, the user interface unit can provide a forum for exchanging fashion advice and opinions. This makes it possible to form a community through the fashion rating and feedback function between users.

[0085] The user interface unit can add a virtual try-on function, allowing the user to virtually try on suggested fashions. The user interface unit can add, for example, a virtual try-on function, allowing the user to virtually try on suggested fashions. For example, the user interface unit can overlay suggested clothes on a photo of the user and display them. The user interface unit can also use 3D modeling technology to allow the user to virtually try on clothes. For example, the user interface unit can create a 3D model that matches the user's body type and allow the user to try on suggested fashions. This allows the user to virtually try on suggested fashions.

[0086] The user interface unit can add a purchase link to enable the user to immediately purchase the suggested fashion item. The user interface unit, for example, adds a purchase link to the suggested fashion item to enable the user to immediately purchase it. For example, the user interface unit displays a purchase link for the suggested top. The user interface unit can also enable the user to easily purchase the suggested item based on the purchase link. For example, the user interface unit allows the user to complete the purchase procedure by simply clicking the purchase link. This allows the user to immediately purchase the suggested fashion item.

[0087] The user interface unit can use the emotion estimation function to propose an interface design that will allow the user to feel most relaxed. The user interface unit, for example, uses the emotion estimation function to propose an interface design that will allow the user to feel most relaxed. For example, the user interface unit displays a design that has a relaxing effect when the emotion score is low. The user interface unit can also propose an interface design based on the user's emotion. For example, the user interface unit changes the design based on the emotion score. This makes it possible to propose an interface design that will allow the user to feel most relaxed.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The fashion suggestion unit can suggest more practical total fashion coordination based on the user's lifestyle and occupation. For example, the fashion suggestion unit analyzes the user's lifestyle and suggests fashion suitable for everyday life. For example, the fashion suggestion unit suggests a casual style that is easy to move in to a user with an active lifestyle. The fashion suggestion unit can also take the user's occupation into consideration and suggest fashion suitable for the workplace. For example, the fashion suggestion unit suggests a formal style to a user who works in an office. This makes it possible to suggest practical fashion based on the user's lifestyle and occupation.

[0090] The fashion suggestion unit can make suggestions based on the user's purchase history and budget. For example, the fashion suggestion unit can analyze the user's purchase history of fashion items and suggest outfits that combine them with previously purchased items. For example, the fashion suggestion unit can suggest bottoms that go well with previously purchased tops. The fashion suggestion unit can also take the user's budget into consideration and suggest items that can be purchased within the budget. For example, the fashion suggestion unit can suggest items based on the user's budget. This makes it possible to make fashion suggestions based on the user's purchase history and budget.

[0091] The fashion suggestion unit can use the emotion estimation function to suggest outfits based on the user's emotions. For example, the fashion suggestion unit uses the emotion estimation function to suggest outfits based on the user's emotions. For example, the fashion suggestion unit suggests bright-colored fashion when the user has strong positive emotions. The fashion suggestion unit can also suggest outfits based on the user's emotions. For example, the fashion suggestion unit suggests outfits based on the emotion score. This makes it possible to suggest outfits based on the user's emotions.

[0092] The fashion suggestion unit can suggest the most suitable event and date plan to the user based on the coordination result. For example, the fashion suggestion unit suggests the most suitable event to the user based on the coordination result. For example, the fashion suggestion unit suggests an outdoor event for a casual style. The fashion suggestion unit can also suggest the most suitable date plan to the user. For example, the fashion suggestion unit suggests a date at a high-end restaurant for a formal style. This makes it possible to suggest the most suitable event and date plan to the user.

[0093] The fashion suggestion unit can suggest the optimal hairstyle and makeup for the user based on the coordination result. For example, the fashion suggestion unit suggests the optimal hairstyle based on the coordination result. For example, the fashion suggestion unit suggests a natural hairstyle for a casual style. The fashion suggestion unit can also suggest the optimal makeup for the user. For example, the fashion suggestion unit suggests chic makeup for a formal style. This makes it possible to suggest the optimal hairstyle and makeup for the user.

[0094] The fashion suggestion unit can use the emotion estimation function to suggest an outfit that will make the user most relaxed. For example, the fashion suggestion unit can use the emotion estimation function to suggest an outfit that will make the user most relaxed. For example, if the emotion score is low, the fashion suggestion unit can suggest a casual style that has a relaxing effect. The fashion suggestion unit can also suggest an outfit that will make the user most relaxed based on the user's emotions. For example, the fashion suggestion unit can suggest an outfit that will make the user most relaxed. This makes it possible to suggest an outfit that will make the user most relaxed.

[0095] The user interface unit can learn the user's fashion history and preferences and make more personalized suggestions. For example, the user interface unit adds a function to analyze the user's fashion history and learn the user's past purchase history and preferences. For example, the user interface unit makes personalized suggestions based on the colors and designs of items purchased in the past. The user interface unit can also learn the user's preferences and make more personalized suggestions. For example, the user interface unit makes fashion suggestions based on the user's preferences. This makes it possible to make personalized suggestions based on the user's fashion history and preferences.

[0096] The user interface unit can add a function for users to rate each other's fashion and provide feedback, thereby forming a community. For example, the user interface unit can add a function that allows users to rate each other's fashion, thereby forming a community. For example, the user interface unit can allow users to "like" or comment on other users' outfits. The user interface unit can also add a feedback function for users to each other, thereby revitalizing the community. For example, the user interface unit can provide a forum for exchanging fashion advice and opinions. This makes it possible to form a community through the fashion rating and feedback function between users.

[0097] The user interface unit can use the emotion estimation function to propose an interface design that will allow the user to feel most relaxed. For example, the user interface unit uses the emotion estimation function to propose an interface design that will allow the user to feel most relaxed. For example, the user interface unit displays a design that has a relaxing effect when the emotion score is low. The user interface unit can also propose an interface design based on the user's emotion. For example, the user interface unit changes the design based on the emotion score. This makes it possible to propose an interface design that will allow the user to feel most relaxed.

[0098] The user interface unit can add a virtual try-on function, allowing the user to virtually try on suggested fashions. For example, the user interface unit can add a virtual try-on function, allowing the user to virtually try on suggested fashions. For example, the user interface unit can overlay suggested clothes on a photo of the user and display them. The user interface unit can also use 3D modeling technology to allow the user to virtually try on clothes. For example, the user interface unit can create a 3D model that matches the user's body type and allow the user to try on suggested fashions. This allows the user to virtually try on suggested fashions.

[0099] The fashion suggestion unit can use the emotion estimation function to suggest an outfit that will make the user most relaxed. For example, the fashion suggestion unit can use the emotion estimation function to suggest an outfit that will make the user most relaxed. For example, if the emotion score is low, the fashion suggestion unit can suggest a casual style that has a relaxing effect. The fashion suggestion unit can also suggest an outfit that will make the user most relaxed based on the user's emotions. For example, the fashion suggestion unit can suggest an outfit that will make the user most relaxed. This makes it possible to suggest an outfit that will make the user most relaxed.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The image recognition unit analyzes the user's photo data. For example, the image recognition unit can detect the user's facial contours, skin color, hair color, and eye color, and analyze body proportions. Step 2: The personal color diagnosis unit diagnoses the personal color based on the data analyzed by the image recognition unit. For example, it diagnoses the color that best matches the user's skin color, hair color, and eye color, and classifies the user into one of "spring type," "summer type," "autumn type," or "winter type." Step 3: The bone structure diagnosis unit diagnoses the bone structure based on the data analyzed by the image recognition unit. For example, it analyzes the user's facial contours and body proportions and classifies them into one of three types: "straight type," "wavy type," or "natural type." Step 4: The fashion suggestion unit proposes a total fashion coordination based on the diagnosis results of the personal color diagnosis unit and the skeletal structure diagnosis unit. For example, it proposes a coordination that combines clothes and accessories in colors that match the user's personal color and clothes in a style that matches their skeletal structure.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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. an image recognition unit that analyzes the user's photo data; a personal color diagnosis unit that diagnoses personal color based on the data analyzed by the image recognition unit; a skeleton diagnosis unit that diagnoses a skeleton based on the data analyzed by the image recognition unit; a fashion suggestion unit that suggests a total fashion coordination based on the diagnosis results of the personal color diagnosis unit and the skeletal structure diagnosis unit. A system characterized by:

2. The image recognition unit Analyzing the skin texture and hair texture of the user and performing a more detailed personal color diagnosis.

2. The system of claim 1.

3. The personal color diagnosis unit Provide a more personalized diagnosis based on the user's fashion history and preferred colors.

2. The system of claim 1.

4. The skeletal diagnosis unit A more detailed diagnosis is performed based on the user's weight fluctuations and muscle mass.

2. The system of claim 1.

5. The fashion suggestion department Proposing more practical total fashion coordination based on the user's lifestyle and occupation 2. The system of claim 1.

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