System

Abstract

To consistently propose a hairstyle, fashion and makeup on the basis of a user's desired image.SOLUTION: The system includes a photograph / moving image acquisition part, an image input part, a hairstyle proposal part, a fashion proposal part, a makeup proposal part, an order sheet generation part, and an item proposal part. The photo / video acquisition unit acquires a photo or a video of a user. The image input unit inputs an image desired by a user. The hairstyle suggestion unit suggests a hairstyle based on the image. The fashion proposal unit proposes a fashion on the basis of the image. The makeup proposal unit proposes makeup based on the image. The order sheet generation unit generates an order sheet to the beauty salon. The item proposal unit proposes an item to be used for the fashion and makeup proposed by the fashion proposal unit and the makeup proposal unit, and a purchase source of the item.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] With conventional technology, it was difficult for users to receive consistent suggestions on hairstyle, fashion, and makeup based on the image they wanted to achieve.

[0005] The system according to the embodiment aims to consistently suggest hairstyles, fashions, and makeup based on the image the user desires to achieve. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo / video acquisition unit, an image input unit, a hairstyle suggestion unit, a fashion suggestion unit, a makeup suggestion unit, an order sheet generation unit, and an item suggestion unit. The photo / video acquisition unit acquires photos or videos of the user. The image input unit inputs the user's desired image. The hairstyle suggestion unit suggests a hairstyle based on the photos or videos acquired by the photo / video acquisition unit and the image input by the image input unit. The fashion suggestion unit suggests fashion based on the photos or videos acquired by the photo / video acquisition unit and the image input by the image input unit. The makeup suggestion unit suggests makeup based on the photos or videos acquired by the photo / video acquisition unit and the image input by the image input unit. The order sheet generation unit generates an order sheet for a beauty salon based on the hairstyle and makeup suggested by the hairstyle suggestion unit and the makeup suggestion unit. The item suggestion unit suggests items to be used in the fashion and makeup suggested by the fashion suggestion unit and the makeup suggestion unit, as well as where to purchase them. [Effects of the Invention]

[0007] The system according to the embodiment can consistently suggest hairstyles, fashions, and makeup based on the image the user wants to achieve. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The image change suggestion system according to an embodiment of the present invention allows users to take photos or videos of themselves, and the AI ​​generator proposes ways to change their entire image, including hairstyle, fashion, and makeup, and also provides an order form for a beauty salon, the items to be used, and where to purchase them. This allows the image change suggestion system to easily enable users to achieve a complete image change.

[0029] The image change suggestion system according to the embodiment includes a photo / video acquisition unit, an image input unit, a hairstyle suggestion unit, a fashion suggestion unit, a makeup suggestion unit, an order sheet generation unit, and an item suggestion unit. The photo / video acquisition unit acquires photos or videos of the user. For example, it takes frontal photos or videos using a smartphone camera. The photo / video acquisition unit can also analyze photos and videos uploaded by the user. For example, the user uploads photos and videos taken in the past to the system and analyzes them. The image input unit inputs the user's desired image. For example, the input is in the form of "casual and stylish" or "adult-looking style." The hairstyle suggestion unit suggests a hairstyle based on the photos or videos acquired by the photo / video acquisition unit and the image input by the image input unit. For example, it analyzes the user's face shape and hair type and suggests multiple hairstyles that suit the user. The fashion suggestion unit suggests fashion based on the photos or videos acquired by the photo / video acquisition unit and the image input by the image input unit. For example, it analyzes the user's body type and suggests casual and stylish clothing or adult-looking clothing. The makeup suggestion unit suggests makeup based on the photo or video acquired by the photo / video acquisition unit and the image input by the image input unit. For example, it analyzes the user's skin color and facial features and suggests the optimal makeup method. The order sheet generation unit generates an order sheet for a beauty salon based on the hairstyle and makeup suggested by the hairstyle suggestion unit and the makeup suggestion unit. For example, it generates an order sheet that describes details of the hairstyle, the coloring agent to be used, and the makeup procedure. The item suggestion unit suggests items to be used in the fashion and makeup suggested by the fashion suggestion unit and the makeup suggestion unit, and where to purchase them. For example, it lists specific brands and places to purchase the suggested clothes, accessories, and makeup products. This allows the makeover suggestion system according to the embodiment to easily achieve a full-body makeover. For example, this is useful when you want to try a new style for a special event or when you want to refresh your everyday style.In addition, users can smoothly place orders and purchase items at beauty salons, improving convenience for them.

[0030] The photo and video acquisition unit can analyze the user's facial expression and posture and automatically suggest the optimal shooting angle and lighting. For example, when a user takes a photo or video, the photo and video acquisition unit uses the generation AI to analyze the user's facial expression and posture in real time and suggest the optimal shooting angle. For example, it may instruct the user to adjust the direction of the face or the angle of the body. The photo and video acquisition unit also suggests lighting adjustments. For example, it may instruct the user to adjust the direction or intensity of the light. This allows the system to automatically suggest the optimal shooting angle and lighting.

[0031] The photo and video acquisition unit provides real-time feedback when taking photos, guiding the user until optimal photos and videos are taken. For example, when a user takes a photo, the generation AI in the photo and video acquisition unit provides real-time feedback, guiding the user until the optimal expression and pose are captured. For example, it may issue instructions such as "Please smile a little more." The photo and video acquisition unit also provides real-time feedback when taking videos. For example, it may issue instructions such as "Please wave at the camera." This allows the user to be guided until the optimal photos and videos are taken.

[0032] The photo and video acquisition unit can automatically combine photos and videos taken with backgrounds to generate images in different situations. For example, the photo and video acquisition unit uses a generation AI to analyze photos taken by the user and automatically combine backgrounds. For example, it can add a background that makes it look like the user is at a beach. The photo and video acquisition unit also combines backgrounds when shooting videos. For example, it can add a background that makes it look like the user is at a party. This makes it possible to generate images in different situations.

[0033] The photo and video acquisition unit tracks the user's movements when taking photos and can suggest dynamic poses and actions. For example, when a user takes a video, the photo and video acquisition unit's generation AI tracks the user's movements and suggests optimal dynamic poses. For example, it issues instructions such as "Wave your hand at the camera." The photo and video acquisition unit also suggests dynamic actions. For example, it issues instructions such as "Try jumping." This makes it possible to suggest dynamic poses and actions.

[0034] The hairstyle suggestion unit can analyze the user's past hairstyle history and make new suggestions based on the most popular style. For example, the hairstyle suggestion unit uses a generation AI to analyze the user's past hairstyle history and identify the most popular style. For example, it makes new suggestions based on hairstyles that have received many likes on social media in the past. The hairstyle suggestion unit also takes user feedback into consideration. For example, it makes suggestions based on hairstyles that have been well-received by the user at a hair salon in the past. This allows it to make new suggestions based on the user's past hairstyle history.

[0035] The hairstyle suggestion unit can take into account the season and trends and suggest hairstyles that match the latest fashions. For example, the generation AI analyzes the season and trends and suggests hairstyles that match the latest fashions. For example, it suggests light short hair in the summer and voluminous long hair in the winter. The hairstyle suggestion unit also takes into account the user's preferences. For example, it makes suggestions based on the user's favorite styles. This makes it possible to suggest hairstyles that match the latest fashions.

[0036] The hairstyle suggestion unit can suggest an appropriate hairstyle by taking into consideration the user's lifestyle and occupation. For example, the generation AI analyzes the user's lifestyle and suggests the most suitable hairstyle. For example, it suggests short hair that is easy to maintain for a user with an active lifestyle. The hairstyle suggestion unit also takes into consideration the user's occupation. For example, it suggests a hairstyle that is suitable for business situations. This makes it possible to suggest a hairstyle that suits the user's lifestyle and occupation.

[0037] The hairstyle suggestion unit can add a function that allows the user to try out the proposed hairstyle in virtual reality (VR). The hairstyle suggestion unit may develop a function that allows the user to try out the proposed hairstyle in VR. For example, the user can wear a VR headset and try out a new hairstyle in a virtual space. The hairstyle suggestion unit can also save the results of trying on hairstyles in VR. For example, the history of hairstyles tried by the user can be saved so that they can be checked later. This allows the user to try out the proposed hairstyle in virtual reality.

[0038] The fashion suggestion unit can analyze the user's past fashion history and make new suggestions based on the most popular styles. For example, the fashion suggestion unit uses a generation AI to analyze the user's past fashion history and identify the most popular styles. For example, new suggestions are made based on fashions that have received many likes on social media in the past. The fashion suggestion unit also takes user feedback into consideration. For example, suggestions are made based on the fashions that the user was most satisfied with in the past. This makes it possible to make new suggestions based on past fashion history.

[0039] The fashion suggestion unit can analyze the user's past fashion history and make new suggestions based on the most popular styles. For example, the fashion suggestion unit uses a generation AI to analyze the user's past fashion history and identify the most popular styles. For example, new suggestions are made based on fashions that have received many likes on social media in the past. The fashion suggestion unit also takes user feedback into consideration. For example, suggestions are made based on the fashions that the user was most satisfied with in the past. This makes it possible to make new suggestions based on past fashion history.

[0040] The fashion suggestion unit can suggest the most suitable fashion style by taking into account the season and weather. For example, the fashion suggestion unit uses a generative AI to analyze the season and weather and suggest the most suitable fashion style. For example, it suggests clothes made of light materials in summer and clothes made of warm materials in winter. The fashion suggestion unit also takes into account the user's preferences. For example, it makes suggestions based on the user's favorite styles. This makes it possible to suggest the most suitable fashion style to suit the season and weather.

[0041] The fashion suggestion unit can take into account the user's planned activities and suggest an appropriate fashion style. For example, the generation AI analyzes the user's planned activities and suggests the most suitable fashion style. For example, it suggests business casual for work and casual elegant for dates. The fashion suggestion unit also takes into account the user's preferences. For example, it makes suggestions based on the user's favorite styles. This makes it possible to suggest an appropriate fashion style that matches the user's planned activities.

[0042] The fashion suggestion unit can add a function that allows the user to try on suggested fashions in virtual reality (VR). The fashion suggestion unit, for example, develops a function that allows the user to try on suggested fashions in VR. For example, the user can wear a VR headset and try on new fashions in a virtual space. The fashion suggestion unit can also save the results of trying on clothes in VR. For example, the history of fashions that the user has tried on can be saved so that they can be checked later. This allows the user to try on suggested fashions in virtual reality.

[0043] The makeup suggestion unit takes into account the season and trends and can suggest makeup that matches the latest trends. For example, the makeup suggestion unit uses a generative AI to analyze the season and trends and suggest makeup that matches the latest trends. For example, it suggests light makeup in summer and heavy makeup in winter. The makeup suggestion unit also takes into account the user's preferences. For example, it makes suggestions based on the user's favorite makeup style. This makes it possible to suggest makeup that matches the latest trends.

[0044] The makeup suggestion unit can suggest appropriate makeup products by taking into account the user's skin condition and allergy information. For example, the makeup suggestion unit uses a generative AI to analyze the user's skin condition and suggest the most suitable makeup products. For example, it might suggest a foundation with moisturizing effects for dry skin. The makeup suggestion unit also takes into account the user's allergy information. For example, it might suggest makeup products that contain ingredients that do not cause allergies. This makes it possible to suggest appropriate makeup products based on the user's skin condition and allergy information.

[0045] The makeup suggestion unit can add a function that allows the user to try out the suggested makeup in virtual reality (VR). The makeup suggestion unit may develop a function that allows the user to try out the suggested makeup in VR. For example, the user can wear a VR headset and try out new makeup in a virtual space. The makeup suggestion unit can also save the results of the try-on in VR. For example, the history of the makeup the user has tried can be saved so that it can be checked later. This allows the user to try out the suggested makeup in virtual reality.

[0046] The order sheet generation unit can reflect the user's past salon history and stylist evaluations. For example, the order sheet generation unit uses a generation AI to analyze the user's past salon history and reflect this in the order sheet. For example, the order sheet generation unit can suggest the optimal style based on the evaluations of salons and stylists visited in the past. The order sheet generation unit also takes user feedback into consideration. For example, it can make suggestions based on the style that the user was most satisfied with in the past. This makes it possible to reflect the user's past salon history and stylist evaluations.

[0047] The order sheet generation unit can add a function that digitizes order sheets and shares them with beauty salons in real time. The order sheet generation unit, for example, develops a system that can digitize order sheets and share them with beauty salons in real time. For example, a user creates an order sheet on a smartphone and sends it to the beauty salon. The order sheet generation unit can also use a sharing platform. For example, the order sheet can be shared using cloud storage. This allows the order sheet to be digitized and shared with the beauty salon in real time.

[0048] The order sheet generation unit can attach a 3D model of the proposed style to the order sheet to enable the stylist to visually understand it. For example, the order sheet generation unit can create a 3D model of the hairstyle desired by the user and add it to the order sheet. The order sheet generation unit can also adjust the level of detail of the 3D model. For example, a high-precision 3D model that reproduces the hairstyle in detail can be created. This allows the stylist to attach the 3D model of the proposed style to enable the stylist to visually understand it.

[0049] The item suggestion unit can filter optimal products based on reviews and ratings of the suggested items. For example, the item suggestion unit analyzes reviews and ratings of items suggested by a generation AI and filters optimal products. For example, it prioritizes the suggestion of products with high user ratings. The item suggestion unit also takes into account the content of reviews. For example, it selects products based on user feedback. This makes it possible to filter optimal products based on reviews and ratings of suggested items.

[0050] The item suggestion unit can provide detailed instructions on how to use and maintain the suggested item. For example, the item suggestion unit provides detailed instructions on how to use the item suggested by the generation AI. For example, it describes how to use a specific makeup product or how to coordinate fashion items. The item suggestion unit also provides instructions on maintenance methods. For example, it describes how to wash clothes or how to care for accessories. This allows for detailed instructions on how to use and maintain the suggested item.

[0051] The item suggestion unit can add a function to customize suggested items to suit the user's budget and preferences. For example, the item suggestion unit develops a function to customize items suggested by the generation AI to suit the user's budget. For example, it prioritizes suggesting items that can be purchased within the budget. The item suggestion unit also selects items to suit the user's preferences. For example, it makes suggestions based on the user's favorite brands and styles. This allows suggested items to be customized to suit the user's budget and preferences.

[0052] The item suggestion unit can add a function that allows the user to try on suggested items in virtual reality (VR). The item suggestion unit, for example, develops a function that allows the user to try on suggested items in VR. For example, the user can wear a VR headset and try on new items in a virtual space. The item suggestion unit can also save the results of trying on items in VR. For example, the history of items tried on by the user can be saved so that the history can be checked later. This allows the user to try on suggested items in virtual reality.

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

[0054] The image change suggestion system can further customize suggestions based on the user's lifestyle and occupation. For example, for a user with an active lifestyle, it can suggest hairstyles that are easy to maintain and fashion that is easy to move in. It can also suggest makeup and fashion that is suitable for business situations. This makes it possible to suggest the most suitable image change to suit the user's lifestyle and occupation.

[0055] The photo and video acquisition unit tracks the user's movements and can suggest dynamic poses and actions. For example, when a user takes a video, the generation AI tracks the user's movements and suggests optimal dynamic poses. For example, it may give instructions such as "Wave at the camera." The photo and video acquisition unit also suggests dynamic actions. For example, it may give instructions such as "Try jumping." This allows it to suggest dynamic poses and actions.

[0056] The hairstyle suggestion unit can analyze the user's past hairstyle history and make new suggestions based on the most popular styles. For example, the generation AI analyzes the user's past hairstyle history and identifies the most popular style. For example, new suggestions can be made based on hairstyles that have received many likes on social media in the past. The hairstyle suggestion unit also takes user feedback into consideration. For example, suggestions can be made based on hairstyles that have been well-received by the user at a hair salon in the past. This allows new suggestions to be made based on the user's past hairstyle history.

[0057] The fashion suggestion unit can analyze a user's past fashion history and make new suggestions based on the most popular styles. For example, the generation AI analyzes a user's past fashion history and identifies the most popular styles. For example, it makes new suggestions based on fashions that have received many likes on social media in the past. The fashion suggestion unit also takes user feedback into consideration. For example, it makes suggestions based on the fashions that the user was most satisfied with in the past. This makes it possible to make new suggestions based on past fashion history.

[0058] The makeup suggestion unit can suggest appropriate makeup products by taking into account the user's skin condition and allergy information. For example, the generative AI analyzes the user's skin condition and suggests the most suitable makeup products. For example, it might suggest a moisturizing foundation for dry skin. The makeup suggestion unit also takes into account the user's allergy information. For example, it might suggest makeup products that contain ingredients that do not cause allergies. This makes it possible to suggest appropriate makeup products based on the user's skin condition and allergy information.

[0059] The order sheet generation unit can attach a 3D model of the proposed style to the order sheet to allow the stylist to visually understand it. For example, the 3D model of the proposed style can be attached to the order sheet to allow the stylist to visually understand it. For example, the order sheet generation unit can create a 3D model of the hairstyle desired by the user and add it to the order sheet. The order sheet generation unit can also adjust the level of detail of the 3D model. For example, a high-precision 3D model that reproduces the hairstyle in detail can be created. This allows the 3D model of the proposed style to be attached to allow the stylist to visually understand it.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The photo / video acquisition unit acquires photos or videos from the user. For example, it can use a smartphone camera to take photos or videos from the front. It can also analyze photos and videos uploaded by the user. Step 2: The image input section inputs the image the user wants to achieve. For example, the user can input "a casual and stylish look" or "a style that looks mature." Step 3: The hairstyle suggestion unit suggests hairstyles based on the photos or videos acquired by the photo / video acquisition unit and the images input by the image input unit. For example, it analyzes the user's face shape and hair type and suggests multiple hairstyles that suit the user. Step 4: The fashion suggestion unit suggests fashion based on the photos or videos acquired by the photo / video acquisition unit and the images input by the image input unit. For example, it analyzes the user's body type and suggests casual yet stylish clothing or sophisticated clothing. Step 5: The makeup suggestion unit suggests makeup based on the photo or video acquired by the photo / video acquisition unit and the image input by the image input unit. For example, it analyzes the user's skin color and facial features and suggests the optimal makeup method. Step 6: The order sheet generation unit generates an order sheet for the beauty salon based on the hairstyle and makeup suggested by the hairstyle suggestion unit and makeup suggestion unit. For example, an order sheet is generated that describes details of the hairstyle, coloring agents to be used, makeup procedures, etc. Step 7: The item suggestion unit suggests items to be used in the fashion and makeup suggested by the fashion suggestion unit and makeup suggestion unit, and where to buy them. For example, it lists specific brands and places to buy the suggested clothes, accessories, and makeup products.

[0062] (Example 2) The image change suggestion system according to an embodiment of the present invention allows users to take photos or videos of themselves, and the AI ​​generator proposes ways to change their entire image, including hairstyle, fashion, and makeup, and also provides an order form for a beauty salon, the items to be used, and where to purchase them. This allows the image change suggestion system to easily enable users to achieve a complete image change.

[0063] The image change suggestion system according to the embodiment includes a photo / video acquisition unit, an image input unit, a hairstyle suggestion unit, a fashion suggestion unit, a makeup suggestion unit, an order sheet generation unit, and an item suggestion unit. The photo / video acquisition unit acquires photos or videos of the user. For example, it takes frontal photos or videos using a smartphone camera. The photo / video acquisition unit can also analyze photos and videos uploaded by the user. For example, the user uploads photos and videos taken in the past to the system and analyzes them. The image input unit inputs the user's desired image. For example, the input is in the form of "casual and stylish" or "adult-looking style." The hairstyle suggestion unit suggests a hairstyle based on the photos or videos acquired by the photo / video acquisition unit and the image input by the image input unit. For example, it analyzes the user's face shape and hair type and suggests multiple hairstyles that suit the user. The fashion suggestion unit suggests fashion based on the photos or videos acquired by the photo / video acquisition unit and the image input by the image input unit. For example, it analyzes the user's body type and suggests casual and stylish clothing or adult-looking clothing. The makeup suggestion unit suggests makeup based on the photo or video acquired by the photo / video acquisition unit and the image input by the image input unit. For example, it analyzes the user's skin color and facial features and suggests the optimal makeup method. The order sheet generation unit generates an order sheet for a beauty salon based on the hairstyle and makeup suggested by the hairstyle suggestion unit and the makeup suggestion unit. For example, it generates an order sheet that describes details of the hairstyle, the coloring agent to be used, and the makeup procedure. The item suggestion unit suggests items to be used in the fashion and makeup suggested by the fashion suggestion unit and the makeup suggestion unit, and where to purchase them. For example, it lists specific brands and places to purchase the suggested clothes, accessories, and makeup products. This allows the makeover suggestion system according to the embodiment to easily achieve a full-body makeover. For example, this is useful when you want to try a new style for a special event or when you want to refresh your everyday style.In addition, users can smoothly place orders and purchase items at beauty salons, improving convenience for them.

[0064] The photo and video acquisition unit can analyze the user's facial expression and posture and automatically suggest the optimal shooting angle and lighting. For example, when a user takes a photo or video, the photo and video acquisition unit uses the generation AI to analyze the user's facial expression and posture in real time and suggest the optimal shooting angle. For example, it may instruct the user to adjust the direction of the face or the angle of the body. The photo and video acquisition unit also suggests lighting adjustments. For example, it may instruct the user to adjust the direction or intensity of the light. This allows the system to automatically suggest the optimal shooting angle and lighting.

[0065] The photo and video acquisition unit provides real-time feedback when taking photos, guiding the user until optimal photos and videos are taken. For example, when a user takes a photo, the generation AI in the photo and video acquisition unit provides real-time feedback, guiding the user until the optimal expression and pose are captured. For example, it may issue instructions such as "Please smile a little more." The photo and video acquisition unit also provides real-time feedback when taking videos. For example, it may issue instructions such as "Please wave at the camera." This allows the user to be guided until the optimal photos and videos are taken.

[0066] The photo / video acquisition unit can use the emotion estimation function to analyze the user's emotional state and suggest music and environmental sounds to help them take photos in a relaxed state. For example, before the user starts taking photos, the photo / video acquisition unit uses the emotion estimation function to analyze the user's emotional state and suggest relaxing music. For example, if the user is nervous, the unit plays music with a relaxing effect. The photo / video acquisition unit also suggests environmental sounds. For example, the unit plays relaxing environmental sounds such as the sounds of nature or the sounds of a cafe. This allows the unit to suggest music and environmental sounds to help you take photos in a relaxed state.

[0067] The photo and video acquisition unit can automatically combine photos and videos taken with backgrounds to generate images in different situations. For example, the photo and video acquisition unit uses a generation AI to analyze photos taken by the user and automatically combine backgrounds. For example, it can add a background that makes it look like the user is at a beach. The photo and video acquisition unit also combines backgrounds when shooting videos. For example, it can add a background that makes it look like the user is at a party. This makes it possible to generate images in different situations.

[0068] The photo and video acquisition unit tracks the user's movements when taking photos and can suggest dynamic poses and actions. For example, when a user takes a video, the photo and video acquisition unit's generation AI tracks the user's movements and suggests optimal dynamic poses. For example, it issues instructions such as "Wave your hand at the camera." The photo and video acquisition unit also suggests dynamic actions. For example, it issues instructions such as "Try jumping." This makes it possible to suggest dynamic poses and actions.

[0069] The photo and video acquisition unit can use the emotion estimation function to suggest facial expressions and poses that will make the user feel most confident in real time. For example, when a user takes a photo, the generation AI in the photo and video acquisition unit uses the emotion estimation function to analyze the user's facial expression and suggest the facial expression that will make the user feel most confident. For example, it may give instructions such as "Smile a little more." The photo and video acquisition unit also suggests poses. For example, it may give instructions such as "Stand with your chest out." This allows the system to suggest facial expressions and poses that will make the user feel most confident in real time.

[0070] The hairstyle suggestion unit can analyze the user's past hairstyle history and make new suggestions based on the most popular style. For example, the hairstyle suggestion unit uses a generation AI to analyze the user's past hairstyle history and identify the most popular style. For example, it makes new suggestions based on hairstyles that have received many likes on social media in the past. The hairstyle suggestion unit also takes user feedback into consideration. For example, it makes suggestions based on hairstyles that have been well-received by the user at a hair salon in the past. This allows it to make new suggestions based on the user's past hairstyle history.

[0071] The hairstyle suggestion unit can take into account the season and trends and suggest hairstyles that match the latest fashions. For example, the generation AI analyzes the season and trends and suggests hairstyles that match the latest fashions. For example, it suggests light short hair in the summer and voluminous long hair in the winter. The hairstyle suggestion unit also takes into account the user's preferences. For example, it makes suggestions based on the user's favorite styles. This makes it possible to suggest hairstyles that match the latest fashions.

[0072] The hairstyle suggestion unit can use the emotion estimation function to identify the hairstyle that evokes the most positive emotions in the user and suggest that style. For example, the generation AI uses the emotion estimation function to identify the hairstyle that evokes the most positive emotions in the user. For example, it analyzes past photos and suggests the hairstyle that most often makes the user smile. The hairstyle suggestion unit also takes user feedback into consideration. For example, it makes suggestions based on the hairstyle that the user was most satisfied with in the past. This makes it possible to suggest the hairstyle that evokes the most positive emotions in the user.

[0073] The hairstyle suggestion unit can suggest an appropriate hairstyle by taking into consideration the user's lifestyle and occupation. For example, the generation AI analyzes the user's lifestyle and suggests the most suitable hairstyle. For example, it suggests short hair that is easy to maintain for a user with an active lifestyle. The hairstyle suggestion unit also takes into consideration the user's occupation. For example, it suggests a hairstyle that is suitable for business situations. This makes it possible to suggest a hairstyle that suits the user's lifestyle and occupation.

[0074] The hairstyle suggestion unit can add a function that allows the user to try out the proposed hairstyle in virtual reality (VR). The hairstyle suggestion unit may develop a function that allows the user to try out the proposed hairstyle in VR. For example, the user can wear a VR headset and try out a new hairstyle in a virtual space. The hairstyle suggestion unit can also save the results of trying on hairstyles in VR. For example, the history of hairstyles tried by the user can be saved so that they can be checked later. This allows the user to try out the proposed hairstyle in virtual reality.

[0075] The fashion suggestion unit can analyze the user's past fashion history and make new suggestions based on the most popular styles. For example, the fashion suggestion unit uses a generation AI to analyze the user's past fashion history and identify the most popular styles. For example, new suggestions are made based on fashions that have received many likes on social media in the past. The fashion suggestion unit also takes user feedback into consideration. For example, suggestions are made based on the fashions that the user was most satisfied with in the past. This makes it possible to make new suggestions based on past fashion history.

[0076] The fashion suggestion unit can analyze the user's past fashion history and make new suggestions based on the most popular styles. For example, the fashion suggestion unit uses a generation AI to analyze the user's past fashion history and identify the most popular styles. For example, new suggestions are made based on fashions that have received many likes on social media in the past. The fashion suggestion unit also takes user feedback into consideration. For example, suggestions are made based on the fashions that the user was most satisfied with in the past. This makes it possible to make new suggestions based on past fashion history.

[0077] The fashion suggestion unit can suggest the most suitable fashion style by taking into account the season and weather. For example, the fashion suggestion unit uses a generative AI to analyze the season and weather and suggest the most suitable fashion style. For example, it suggests clothes made of light materials in summer and clothes made of warm materials in winter. The fashion suggestion unit also takes into account the user's preferences. For example, it makes suggestions based on the user's favorite styles. This makes it possible to suggest the most suitable fashion style to suit the season and weather.

[0078] The fashion suggestion unit can use the emotion estimation function to identify the fashion style that evokes the most positive emotions in the user and suggest that style. For example, the generation AI uses the emotion estimation function to identify the fashion style that evokes the most positive emotions in the user. For example, it analyzes past photos and suggests fashion that has the most smiles on the user's face. The fashion suggestion unit also takes user feedback into consideration. For example, it makes suggestions based on the fashion that the user was most satisfied with in the past. This makes it possible to suggest fashion styles that evoke the most positive emotions in the user.

[0079] The fashion suggestion unit can take into account the user's planned activities and suggest an appropriate fashion style. For example, the generation AI analyzes the user's planned activities and suggests the most suitable fashion style. For example, it suggests business casual for work and casual elegant for dates. The fashion suggestion unit also takes into account the user's preferences. For example, it makes suggestions based on the user's favorite styles. This makes it possible to suggest an appropriate fashion style that matches the user's planned activities.

[0080] The fashion suggestion unit can add a function that allows the user to try on suggested fashions in virtual reality (VR). The fashion suggestion unit, for example, develops a function that allows the user to try on suggested fashions in VR. For example, the user can wear a VR headset and try on new fashions in a virtual space. The fashion suggestion unit can also save the results of trying on clothes in VR. For example, the history of fashions that the user has tried on can be saved so that they can be checked later. This allows the user to try on suggested fashions in virtual reality.

[0081] The fashion suggestion unit can use the emotion estimation function to suggest a fashion style that will make the user feel most confident. For example, the generation AI uses the emotion estimation function to identify a fashion style that will make the user feel most confident. For example, it analyzes past photos and suggests fashion that brings out the most confident expression. The fashion suggestion unit also takes user feedback into consideration. For example, it makes suggestions based on the fashion that the user was most satisfied with in the past. This makes it possible to suggest a fashion style that will make the user feel most confident.

[0082] The makeup suggestion unit takes into account the season and trends and can suggest makeup that matches the latest trends. For example, the makeup suggestion unit uses a generative AI to analyze the season and trends and suggest makeup that matches the latest trends. For example, it suggests light makeup in summer and heavy makeup in winter. The makeup suggestion unit also takes into account the user's preferences. For example, it makes suggestions based on the user's favorite makeup style. This makes it possible to suggest makeup that matches the latest trends.

[0083] The makeup suggestion unit can use the emotion estimation function to identify the makeup style that evokes the most positive emotions in the user and suggest that style. For example, the makeup suggestion unit uses the emotion estimation function to identify the makeup style that evokes the most positive emotions in the user using a generation AI. For example, it analyzes past photos and suggests makeup that brings out the most smiles. The makeup suggestion unit also takes user feedback into consideration. For example, it makes suggestions based on the makeup that the user was most satisfied with in the past. This makes it possible to suggest a makeup style that evokes the most positive emotions in the user.

[0084] The makeup suggestion unit can suggest appropriate makeup products by taking into account the user's skin condition and allergy information. For example, the makeup suggestion unit uses a generative AI to analyze the user's skin condition and suggest the most suitable makeup products. For example, it might suggest a foundation with moisturizing effects for dry skin. The makeup suggestion unit also takes into account the user's allergy information. For example, it might suggest makeup products that contain ingredients that do not cause allergies. This makes it possible to suggest appropriate makeup products based on the user's skin condition and allergy information.

[0085] The makeup suggestion unit can add a function that allows the user to try out the suggested makeup in virtual reality (VR). The makeup suggestion unit may develop a function that allows the user to try out the suggested makeup in VR. For example, the user can wear a VR headset and try out new makeup in a virtual space. The makeup suggestion unit can also save the results of the try-on in VR. For example, the history of the makeup the user has tried can be saved so that it can be checked later. This allows the user to try out the suggested makeup in virtual reality.

[0086] The makeup suggestion unit uses the emotion estimation function to suggest the most relaxing makeup style for the user, thereby reducing stress. For example, the generation AI uses the emotion estimation function to identify the most relaxing makeup style for the user and suggests that style. For example, suggestions are made based on the makeup that the user wears when they are relaxed. The makeup suggestion unit also takes user feedback into consideration. For example, suggestions are made based on the makeup that the user found most relaxing in the past. This allows the makeup suggestion unit to suggest the most relaxing makeup style for the user, thereby reducing stress.

[0087] The order sheet generation unit can reflect the user's past salon history and stylist evaluations. For example, the order sheet generation unit uses a generation AI to analyze the user's past salon history and reflect this in the order sheet. For example, the order sheet generation unit can suggest the optimal style based on the evaluations of salons and stylists visited in the past. The order sheet generation unit also takes user feedback into consideration. For example, it can make suggestions based on the style that the user was most satisfied with in the past. This makes it possible to reflect the user's past salon history and stylist evaluations.

[0088] The order sheet generation unit can use the emotion estimation function to identify the style that the user is most satisfied with and record the details on the order sheet. For example, the order sheet generation unit uses the emotion estimation function to identify the style that the user is most satisfied with and record the details on the order sheet. For example, it analyzes past photos and makes suggestions based on the style that elicited the most positive emotions. The order sheet generation unit also takes user feedback into consideration. For example, it makes suggestions based on the style that the user was most satisfied with in the past. This allows the style that the user is most satisfied with to be identified and the details on the order sheet.

[0089] The order sheet generation unit can add a function that digitizes order sheets and shares them with beauty salons in real time. The order sheet generation unit, for example, develops a system that can digitize order sheets and share them with beauty salons in real time. For example, a user creates an order sheet on a smartphone and sends it to the beauty salon. The order sheet generation unit can also use a sharing platform. For example, the order sheet can be shared using cloud storage. This allows the order sheet to be digitized and shared with the beauty salon in real time.

[0090] The order sheet generation unit can attach a 3D model of the proposed style to the order sheet to enable the stylist to visually understand it. For example, the order sheet generation unit can create a 3D model of the hairstyle desired by the user and add it to the order sheet. The order sheet generation unit can also adjust the level of detail of the 3D model. For example, a high-precision 3D model that reproduces the hairstyle in detail can be created. This allows the stylist to attach the 3D model of the proposed style to enable the stylist to visually understand it.

[0091] The order sheet generation unit can use the emotion estimation function to identify the style that makes the user most relaxed and record the details on the order sheet. For example, the order sheet generation unit uses the emotion estimation function to identify the style that makes the user most relaxed and record the details on the order sheet. For example, it analyzes past photos and makes suggestions based on the style that brings out the most relaxed expression. The order sheet generation unit also takes user feedback into consideration. For example, it makes suggestions based on the style that the user found most relaxing in the past. This allows the style that makes the user most relaxed to be identified and the details to be recorded on the order sheet.

[0092] The item suggestion unit can filter optimal products based on reviews and ratings of the suggested items. For example, the item suggestion unit analyzes reviews and ratings of items suggested by a generation AI and filters optimal products. For example, it prioritizes the suggestion of products with high user ratings. The item suggestion unit also takes into account the content of reviews. For example, it selects products based on user feedback. This makes it possible to filter optimal products based on reviews and ratings of suggested items.

[0093] The item suggestion unit can provide detailed instructions on how to use and maintain the suggested item. For example, the item suggestion unit provides detailed instructions on how to use the item suggested by the generation AI. For example, it describes how to use a specific makeup product or how to coordinate fashion items. The item suggestion unit also provides instructions on maintenance methods. For example, it describes how to wash clothes or how to care for accessories. This allows for detailed instructions on how to use and maintain the suggested item.

[0094] The item suggestion unit can add a function to customize suggested items to suit the user's budget and preferences. For example, the item suggestion unit develops a function to customize items suggested by the generation AI to suit the user's budget. For example, it prioritizes suggesting items that can be purchased within the budget. The item suggestion unit also selects items to suit the user's preferences. For example, it makes suggestions based on the user's favorite brands and styles. This allows suggested items to be customized to suit the user's budget and preferences.

[0095] The item suggestion unit can add a function that allows the user to try on suggested items in virtual reality (VR). The item suggestion unit, for example, develops a function that allows the user to try on suggested items in VR. For example, the user can wear a VR headset and try on new items in a virtual space. The item suggestion unit can also save the results of trying on items in VR. For example, the history of items tried on by the user can be saved so that the history can be checked later. This allows the user to try on suggested items in virtual reality.

[0096] The item suggestion unit can use the emotion estimation function to identify the item that makes the user most relaxed and suggest where to purchase it. For example, the generation AI can use the emotion estimation function to identify the item that makes the user most relaxed and suggest where to purchase it. For example, it can analyze past purchase history and make suggestions based on the item that elicited the most relaxing emotion. The item suggestion unit also takes user feedback into consideration. For example, it can make suggestions based on the item that the user was most satisfied with in the past. This makes it possible to identify the item that makes the user most relaxed and suggest where to purchase it.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The image change suggestion system can further customize suggestions based on the user's lifestyle and occupation. For example, for a user with an active lifestyle, it can suggest hairstyles that are easy to maintain and fashion that is easy to move in. It can also suggest makeup and fashion that is suitable for business situations. This makes it possible to suggest the most suitable image change to suit the user's lifestyle and occupation.

[0099] The photo and video acquisition unit tracks the user's movements and can suggest dynamic poses and actions. For example, when a user takes a video, the generation AI tracks the user's movements and suggests optimal dynamic poses. For example, it may give instructions such as "Wave at the camera." The photo and video acquisition unit also suggests dynamic actions. For example, it may give instructions such as "Try jumping." This allows it to suggest dynamic poses and actions.

[0100] The photo and video acquisition unit can use the emotion estimation function to analyze the user's emotional state and suggest music and environmental sounds to help them take photos in a relaxed state. For example, before the user starts taking photos, the generation AI can use the emotion estimation function to analyze the user's emotional state and suggest relaxing music. For example, if the user is nervous, it can play music with a relaxing effect. The photo and video acquisition unit also suggests environmental sounds. For example, it can play relaxing environmental sounds such as the sounds of nature or the sounds of a cafe. This allows it to suggest music and environmental sounds to help you take photos in a relaxed state.

[0101] The hairstyle suggestion unit can analyze the user's past hairstyle history and make new suggestions based on the most popular styles. For example, the generation AI analyzes the user's past hairstyle history and identifies the most popular style. For example, new suggestions can be made based on hairstyles that have received many likes on social media in the past. The hairstyle suggestion unit also takes user feedback into consideration. For example, suggestions can be made based on hairstyles that have been well-received by the user at a hair salon in the past. This allows new suggestions to be made based on the user's past hairstyle history.

[0102] The hairstyle suggestion unit can use the emotion estimation function to identify the hairstyle that evokes the most positive emotions in the user and suggest that style. For example, the generation AI can use the emotion estimation function to identify the hairstyle that evokes the most positive emotions in the user. For example, it can analyze past photos and suggest the hairstyle that most often evokes smiles. The hairstyle suggestion unit also takes user feedback into consideration. For example, it can make suggestions based on the hairstyle that the user was most satisfied with in the past. This makes it possible to suggest the hairstyle that evokes the most positive emotions in the user.

[0103] The fashion suggestion unit can analyze a user's past fashion history and make new suggestions based on the most popular styles. For example, the generation AI analyzes a user's past fashion history and identifies the most popular styles. For example, it makes new suggestions based on fashions that have received many likes on social media in the past. The fashion suggestion unit also takes user feedback into consideration. For example, it makes suggestions based on the fashions that the user was most satisfied with in the past. This makes it possible to make new suggestions based on past fashion history.

[0104] The fashion suggestion unit can use the emotion estimation function to identify the fashion style that evokes the most positive emotions in the user and suggest that style. For example, the generation AI can use the emotion estimation function to identify the fashion style that evokes the most positive emotions in the user. For example, it can analyze past photos and suggest fashion that shows the most smiles. The fashion suggestion unit also takes user feedback into consideration. For example, it can make suggestions based on the fashion that the user was most satisfied with in the past. This makes it possible to suggest fashion styles that evoke the most positive emotions in the user.

[0105] The makeup suggestion unit can suggest appropriate makeup products by taking into account the user's skin condition and allergy information. For example, the generative AI analyzes the user's skin condition and suggests the most suitable makeup products. For example, it might suggest a moisturizing foundation for dry skin. The makeup suggestion unit also takes into account the user's allergy information. For example, it might suggest makeup products that contain ingredients that do not cause allergies. This makes it possible to suggest appropriate makeup products based on the user's skin condition and allergy information.

[0106] The makeup suggestion unit can use the emotion estimation function to identify the makeup style that evokes the most positive emotions in the user and suggest that style. For example, the generation AI can use the emotion estimation function to identify the makeup style that evokes the most positive emotions in the user. For example, it can analyze past photos and suggest makeup that brings out the most smiles. The makeup suggestion unit also takes user feedback into consideration. For example, it can make suggestions based on the makeup that the user was most satisfied with in the past. This makes it possible to suggest a makeup style that evokes the most positive emotions in the user.

[0107] The order sheet generation unit can attach a 3D model of the proposed style to the order sheet to allow the stylist to visually understand it. For example, the 3D model of the proposed style can be attached to the order sheet to allow the stylist to visually understand it. For example, the order sheet generation unit can create a 3D model of the hairstyle desired by the user and add it to the order sheet. The order sheet generation unit can also adjust the level of detail of the 3D model. For example, a high-precision 3D model that reproduces the hairstyle in detail can be created. This allows the 3D model of the proposed style to be attached to allow the stylist to visually understand it.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The photo / video acquisition unit acquires photos or videos from the user. For example, it can use a smartphone camera to take photos or videos from the front. It can also analyze photos and videos uploaded by the user. Step 2: The image input section inputs the image the user wants to achieve. For example, the user can input "a casual and stylish look" or "a style that looks mature." Step 3: The hairstyle suggestion unit suggests hairstyles based on the photos or videos acquired by the photo / video acquisition unit and the images input by the image input unit. For example, it analyzes the user's face shape and hair type and suggests multiple hairstyles that suit the user. Step 4: The fashion suggestion unit suggests fashion based on the photos or videos acquired by the photo / video acquisition unit and the images input by the image input unit. For example, it analyzes the user's body type and suggests casual yet stylish clothing or sophisticated clothing. Step 5: The makeup suggestion unit suggests makeup based on the photo or video acquired by the photo / video acquisition unit and the image input by the image input unit. For example, it analyzes the user's skin color and facial features and suggests the optimal makeup method. Step 6: The order sheet generation unit generates an order sheet for the beauty salon based on the hairstyle and makeup suggested by the hairstyle suggestion unit and makeup suggestion unit. For example, an order sheet is generated that describes details of the hairstyle, coloring agents to be used, makeup procedures, etc. Step 7: The item suggestion unit suggests items to be used in the fashion and makeup suggested by the fashion suggestion unit and makeup suggestion unit, and where to buy them. For example, it lists specific brands and places to buy the suggested clothes, accessories, and makeup products.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

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

[0144] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0157] 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.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo / video acquisition unit that acquires a user's photo or video; an image input unit for inputting an image the user wants to have; a hairstyle suggestion unit that suggests a hairstyle based on the photo or the video acquired by the photo / video acquisition unit and the image input by the image input unit; a fashion suggestion unit that suggests fashion based on the photo or video acquired by the photo / video acquisition unit and the image input by the image input unit; a makeup suggestion unit that suggests makeup based on the photo or video acquired by the photo / video acquisition unit and the image input by the image input unit; an order sheet generation unit that generates an order sheet for a beauty salon based on the hairstyle and makeup suggested by the hairstyle suggestion unit and the makeup suggestion unit; an item suggestion unit that suggests items to be used in the fashion and makeup suggested by the fashion suggestion unit and the makeup suggestion unit, and where to purchase the items; A system characterized by:

2. The photo / video acquisition unit The emotion estimation function analyzes the user's emotional state and suggests music and environmental sounds to help them take photos in a relaxed state.

2. The system of claim 1.

3. The hairstyle suggestion unit Analyze the user's past hairstyle history and make new suggestions based on the most popular styles.

2. The system of claim 1.

4. The fashion suggestion department Using an emotion estimation function, the fashion style that the user feels the most positive about is identified and that style is suggested.

2. The system of claim 1.

5. The makeup suggestion unit Using an emotion estimation function, the makeup style that evokes the most positive emotions from the user is identified and that style is suggested.

2. The system of claim 1.

6. The order sheet generation unit Using an emotion estimation function, the style in which the user feels most relaxed is identified, and the details thereof are entered in the order sheet.

2. The system of claim 1.

7. The item suggestion unit Using an emotion estimation function, the item that most relaxes the user is identified, and the place of purchase of the item is suggested.

2. The system of claim 1.

Images