System

Abstract

A system is provided.SOLUTION: A system including means for a user to upload a portrait photograph of the user, means for analyzing a skin color, an eye color, and a hair color from the uploaded photograph using an image recognition technology, means for diagnosing a personal color of the user on the basis of an analysis result using generative artificial intelligence, means for proposing an optimal fashion and color coordination on the basis of the diagnosed personal color, means for uploading an image again when the user tries the proposed fashion, and means for analyzing the uploaded image again and evaluating fitness for the proposed fashion.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] The problem that the present invention aims to solve is the difficulty that it is currently facing for users to accurately grasp their own personal color and easily select optimal fashion and color coordination based on that color. In particular, individual personal color diagnosis requires specialized knowledge and skills, and many people are unable to make accurate judgments. Another problem is the difficulty of evaluating the suitability of suggested fashion and creating a style that maximizes one's own attractiveness. Therefore, there is a need for a system that allows users to enjoy fashion with confidence and is useful for everyday styling. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: a means for a user to upload a portrait photograph of themselves; a means for analyzing skin color, eye color, and hair color from the uploaded photograph using image recognition technology; a means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence; a means for suggesting optimal fashion and color coordination based on the diagnosed personal color; a means for the user to re-upload an image when trying on the suggested fashion; and a means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion. Furthermore, by including a means for saving information about the diagnosed personal color and the suggested fashion in a user profile and a means for individually calculating the degree of match of skin color, eye color, and hair color when evaluating suitability and then calculating an evaluation score based on the average of these, a more accurate evaluation can be performed.

[0006] A "user" is an individual who uses this system to receive their own personal color diagnosis and fashion advice.

[0007] A "portrait" is an image that a user takes of their face and appearance and uploads to the system.

[0008] "Image recognition technology" is a technology that uses computer vision algorithms to analyze features in an image and identify skin color, eye color, hair color, etc.

[0009] "Generative AI" is an AI that uses machine learning and deep learning to analyze data and generate personal colors and fashion suggestions for users.

[0010] "Analysis results" are data such as skin color, eye color, and hair color obtained using image recognition technology.

[0011] "Personal colors" are categories of colors that best suit a user, and are often classified into four seasons: spring, summer, autumn, and winter.

[0012] "Fashion and color coordination" refers to suggested combinations of clothing and accessories based on personal colors.

[0013] "Suggestions" refer to the optimal fashion and color coordination content that the artificial intelligence generates based on the user's personal color.

[0014] "Suitability" is an evaluation criterion that indicates how well the proposed fashion or color coordination matches the user's personal color.

[0015] The "evaluation score" is a numerical value that quantitatively expresses the fitness. [Brief explanation of the drawings]

[0016] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] 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), Bluetooth (registered trademark), etc.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system that allows users to understand their own personal color and select optimal fashion and color coordination based on that information. The system allows users to upload their own portrait photos and diagnoses their personal color using image recognition technology and generative artificial intelligence. Furthermore, the system suggests optimal fashion and evaluates suitability based on the diagnosed personal color.

[0038] Specifically, the process begins when a user uploads a photo of their face to the system. The device sends the user's photo to the server, which stores the received image. The server then uses image recognition technology to analyze the skin tone, eye color, and hair color from the uploaded photo. The results of this analysis are organized as numerical data.

[0039] The server uses the generative AI to diagnose the user's personal color based on the analysis results. This diagnosis result is saved in the user's profile, and the generative AI suggests optimal fashion and color coordination based on the diagnosed personal color. The suggestions are organized and provided to the user as display data.

[0040] For example, if a user is diagnosed with a "spring" personal color, the system will suggest fashion items that suit "spring" (e.g., pastel or warm-toned clothing). The user tries out the suggested fashions and outfits and uploads the results to the system as a new photo. The server receives this new image and analyzes it again using image recognition technology.

[0041] Finally, the server evaluates the suitability of the proposed fashion. The suitability evaluation calculates the degree of match for skin tone, eye color, and hair color individually, and calculates an evaluation score based on the average of these. This score is saved in the user profile and notified to the user. In this way, users can find the fashion and coordination that best suits their personal color, and feel confident in their everyday styling.

[0042] The features of the present invention are as follows:

[0043] 1. A system that uses image recognition technology to analyze skin color, eye color, and hair color based on portrait photos uploaded by users, and then uses generative artificial intelligence to diagnose personal color based on the analysis results.

[0044] 2. A system that uses generative AI to suggest optimal fashion and color coordination for users based on their diagnosed personal colors.

[0045] 3. A system in which users try out the suggested fashion and upload the image again, and the new image is analyzed to evaluate its suitability.

[0046] This makes it easier for users to find fashion that suits them even without specialized knowledge, improving the quality of styling in their daily lives.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] Users use their terminal to take a photo of themselves and upload the image to the system.

[0050] Step 2:

[0051] The terminal transmits the image data uploaded by the user to the server via an HTTP request.

[0052] Step 3:

[0053] The server saves the received image data in the specified directory, assigning a unique file name to the saved image data to prevent files with the same name from being overwritten.

[0054] Step 4:

[0055] The server analyzes the stored images using image recognition technology. Specifically, it uses open source libraries (e.g., OpenCV) to perform facial recognition and extract skin color, eye color, and hair color from the images.

[0056] Step 5:

[0057] The server organizes the extracted color information (skin color, eye color, hair color) as numerical data, which allows the analysis results to be saved as numerical data for later processing.

[0058] Step 6:

[0059] The server inputs the organized numerical data into a generative artificial intelligence model (e.g., a machine learning algorithm) to diagnose the user's personal color.

[0060] Step 7:

[0061] The server saves the diagnosis results (personal color) in the user profile, allowing the user to check their personal color information at any time.

[0062] Step 8:

[0063] The server uses AI to generate optimal fashion and color coordination based on the diagnosed personal color. Specifically, it uses a model to generate appropriate coordination suggestions.

[0064] Step 9:

[0065] The server organizes the generated suggestions and sends them to the user interface as display data, allowing the user to select fashion based on these suggestions.

[0066] Step 10:

[0067] The user tries out the suggested fashion and color coordination, then takes another photo of themselves and uploads it to the system.

[0068] Step 11:

[0069] The terminal again transmits the newly captured image data to the server.

[0070] Step 12:

[0071] The server then analyzes the newly received image using image recognition technology and extracts skin color, eye color, and hair color in the same way as in the pre-processing.

[0072] Step 13:

[0073] The server compares the extracted new color information with the original personal color and evaluates the suitability of the new color. Specifically, it calculates the degree of match for each color individually and averages them to calculate an evaluation score.

[0074] Step 14:

[0075] The server stores the evaluation scores in the user's profile and notifies the user of the results using a notification system, allowing the user to see the fitness of their fashion choices.

[0076] Example 1

[0077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0078] Conventional personal color diagnostic systems have made it difficult for users to accurately grasp their own personal colors and select optimal fashion and color coordination based on the results. Furthermore, the system lacks the functionality to evaluate the suitability of suggested fashions and provide feedback to the user. This has meant that users have to spend a lot of time and effort to find the perfect fashion for them.

[0079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0080] In this invention, the server includes means for users to upload their portrait photos, means for analyzing skin color, eye color, and hair color from the uploaded photos using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for proposing optimal fashion and color coordination based on the diagnosed personal color, means for users to re-upload images when they try on the suggested fashion, means for analyzing the re-uploaded images and evaluating the suitability of the suggested fashion, means for saving the results of the suitability evaluation in a user profile, and means for notifying the user of the saved results. This allows users to easily select optimal fashion based on their personal color and find highly suitable coordination.

[0081] "User" means an individual who uses the system to upload a portrait photo of themselves and receive a personal color diagnosis and fashion suggestions.

[0082] "Device" means an electronic device used by a user to upload portrait photographs and re-upload photographs for suggested fashions and review.

[0083] "Server" refers to a central computer system that receives, stores, and analyzes image data sent from terminals and provides personal color diagnosis and fashion suggestions.

[0084] "Image recognition technology" is a technology that analyzes skin color, eye color, and hair color from uploaded portrait photos and extracts them as numerical data.

[0085] "Generative AI" is an AI technology that diagnoses a user's personal color based on the analysis results, and generates and suggests appropriate fashion and color coordination based on the diagnosis results.

[0086] "Personal colors" refers to a set of colors that best suit a user based on their skin tone, eye color, and hair color.

[0087] "Fashion and color coordination" refers to the suggestion of a combination of clothing and accessories that suits the user based on the diagnosed personal color.

[0088] "Suitability" is an index that evaluates how well the proposed fashion matches the user's personal color.

[0089] A "user profile" is a database that stores user-related identification information, diagnostic results, proposal results, fitness evaluation results, and the like.

[0090] "Notification" refers to the act of electronically sending information such as diagnostic results, evaluation results, and proposals from the server to the user.

[0091] This is a system that allows users to understand their own personal color and select the most suitable fashion and color coordination based on that. Users upload their portrait photos using a terminal, and the server uses image recognition technology and generative AI to diagnose their personal color and make fashion suggestions.

[0092] First, a user uploads their portrait photo to the system from a terminal. This terminal can be an electronic device such as a PC, smartphone, or tablet. At this stage, the terminal sends the uploaded image data to the server.

[0093] The server stores the received images in dedicated storage and then analyzes them using image recognition technology. Image recognition libraries such as OpenCV and TensorFlow are used. This analysis extracts the user's skin color, eye color, and hair color as numerical data. For example, skin color is quantified as RGB(231, 192, 146), eye color as RGB(89, 60, 31), and hair color as RGB(45, 35, 25).

[0094] The server then uses the extracted numerical data to diagnose the user's personal color using a generative AI (e.g., GPT-3.5). Specifically, the server inputs the following prompt into the generative AI:

[0095] "What are the personal colors of users with these skin tones, eye colors, and hair colors?"

[0096] In response to this prompt, the AI ​​generator will respond with either spring, summer, fall, or winter. This diagnosis is saved in the user's profile.

[0097] The server then uses generative AI to suggest appropriate fashion and color coordination based on the user's personal color, using prompts like the following:

[0098] "Please suggest fashion items suitable for users with spring personal colors."

[0099] The generative AI generates specific fashion items and outfit suggestions (e.g., a pastel-colored top and warm-colored bottoms), and these suggestions are notified to the user.

[0100] The user tries on the proposed fashion, takes a new photo of the results, and uploads it back to the system. The server then performs a new analysis and evaluates the user's suitability for the proposed fashion. This evaluation involves calculating the degree of match for skin tone, eye color, and hair color individually, and calculating an average score. The evaluation results are saved in the user's profile and notified to the user.

[0101] For example, if a user's personal color is diagnosed as "spring" and the suggested fashion items are pastel or warm-toned clothing, the suitability evaluation allows the user to confirm numerically how well these items suit the user. In this way, users can easily select the optimal fashion based on their personal color and find highly suitable outfits.

[0102] As described above, the present invention provides a system that enables users to efficiently search for the fashion that best suits them, thereby increasing users' confidence in their styling.

[0103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0104] Step 1:

[0105] A user accesses the system's web interface from a terminal, selects and uploads their own portrait. The input is the user's portrait, and the output is an image transfer request to the server. At this stage, the terminal sends the image file to the server as an HTTP request.

[0106] Step 2:

[0107] The device receives an upload request from the user and sends the portrait photo to the server. The server saves the received image data in dedicated storage. The input is the image data sent from the device, and the output is the image file saved in the storage.

[0108] Step 3:

[0109] The server reads the saved images from storage and analyzes them using image recognition technology. Libraries such as OpenCV and TensorFlow are used here. The input is the saved image data, and the output is analyzed numerical data (skin color, eye color, hair color). Specifically, it analyzes pixel values ​​and extracts the color of each element in RGB format.

[0110] Step 4:

[0111] The server passes the analyzed numerical data to a generative artificial intelligence (generative AI) that diagnoses the user's personal color. The input is the numerical data of the analysis results, and the output is the personal color diagnosis result. For example, GPT-3.5 is used as the generative AI, and the prompt sentence "What is the personal color of a user with these skin tones, eye colors, and hair colors?" is input.

[0112] Step 5:

[0113] Based on the diagnosed personal color, the server uses a generation AI to suggest optimal fashion and color coordination to the user. The input is the personal color diagnosis result, and the output is specific fashion suggestions. The generation AI is input with a prompt statement such as "Please suggest fashion items that are suitable for the user's spring personal color," and the AI ​​generates the suggestions.

[0114] Step 6:

[0115] The server notifies the user of the generated fashion suggestions. The input is the fashion suggestions from the generation AI, and the output is the fashion suggestion data displayed on the user's device.

[0116] Step 7:

[0117] The user tries out the suggested fashions and uploads the resulting photo to the system again. The input is the newly taken user photo, and the output is an image transmission request to the server. At this stage, the device again sends the image to the server as an HTTP request.

[0118] Step 8:

[0119] The server then analyzes the received image again using image recognition technology and evaluates its suitability for the proposed fashion. The input is the newly uploaded image data, and the output is a numerical evaluation of suitability. Again, the color of each element is extracted in RGB format, and the analysis is performed in the same way as the first time.

[0120] Step 9:

[0121] The server saves the results of the fitness evaluation in the user profile and notifies the user. The input is the fitness evaluation result, and the output is the data saved in the user profile and the notification data to the user. Specific operations include calculating the degree of match individually and calculating their average score.

[0122] In this way, the present system operates through processing steps that allow the user to easily select and evaluate fashion based on personal color.

[0123] (Application example 1)

[0124] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0125] Conventional personal color diagnostic systems only perform a diagnosis based on a still image of the user, and are limited to fashion suggestions and fitness evaluations based on the diagnosis results. However, no systems exist that can be effectively utilized in brick-and-mortar shopping or real-time fashion consulting environments. This has resulted in a lack of support for users when selecting optimal fashion items on the spot. Therefore, there is a need for a system that allows users to use a smart device in a brick-and-mortar store to receive real-time suggestions for fashion items based on their personal color.

[0126] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0127] In this invention, the server includes: means for a user to upload a portrait photograph of themselves; means for analyzing skin color, eye color, and hair color from the uploaded image using image recognition technology; means for diagnosing the user's personal color based on the analysis result using generative artificial intelligence; means for suggesting optimal fashion and color coordination based on the diagnosed personal color; means for the user to re-upload an image when trying on the suggested fashion; means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion; and means for the user to use their own smart device to be suggested fashion items based on their own personal color in real time at a physical store. This allows the user to use their smart device in a physical store to have fashion items that are optimal for their personal color suggested in real time.

[0128] "Means for users to upload their portraits" refers to the functionality of the device or software that allows users to submit images of themselves to the system.

[0129] "Image recognition technology" is a technology that uses computer vision to analyze images and extract specific attributes and information.

[0130] "Skin color" refers to the skin tone seen on the user's face, hands, etc.

[0131] "Eye color" is the color of the user's iris.

[0132] "Hair color" refers to the color of the user's hair.

[0133] "Generative artificial intelligence" is a technology that uses machine learning and deep learning to analyze data and generate new information.

[0134] "Means for diagnosing personal color" refers to the function of a device or software that identifies the user's optimal color based on analyzed data.

[0135] The "means for proposing optimal fashion and color coordination" refers to a device or software function that suggests appropriate clothing and color combinations to the user based on the diagnostic results.

[0136] "Means for re-uploading images" refers to the functionality of the device or software that allows a user to submit new images to the system.

[0137] The "means for evaluating the suitability of the proposed fashion" is a function of a device or software that evaluates how suitable the fashion tried by the user is.

[0138] "Smart devices" are mobile information terminals that can connect to the Internet, such as smartphones and smart glasses.

[0139] A "physical store" is a store in a physical location where users can purchase products in person.

[0140] "Means of making real-time suggestions" refers to the functionality of devices or software that instantly present appropriate information and suggestions to users on the spot.

[0141] This invention is a system that allows users to understand their own personal color and select optimal fashion and color coordination based on that information. The system allows users to upload their own portrait photos and diagnoses their personal color using image recognition technology and generative artificial intelligence. Furthermore, the system makes optimal fashion suggestions and evaluates suitability based on the diagnosed personal color. The same process can be provided in real time in physical stores.

[0142] Specifically, it operates as follows.

[0143] Hardware, software, and data processing used

[0144] The device (smartphone or smart glasses) is used by the user to take and upload a portrait of themselves, which is then sent to a server, where image processing libraries such as OpenCV are used to analyze the image and extract skin, eye, and hair color.

[0145] The server then uses a generative AI model such as TensorFlow to diagnose the user's personal color based on the extracted data. The diagnosis results are classified into one of three color categories: spring, summer, autumn, or winter. Once the diagnosis results are obtained, the server uses generative AI to suggest fashion items and color coordination that best fit the user's personal color.

[0146] Furthermore, the user tries on the proposed fashion and re-uploads the photo to evaluate its suitability. The re-uploaded image is analyzed again, and the suitability of the proposed fashion is calculated based on how well it suits the user. Suitability is calculated by calculating the match of skin color, eye color, and hair color individually, and calculating an evaluation score based on the average of these.

[0147] In physical stores, users can use their smart devices to receive a real-time personal color diagnosis and fashion item suggestions, allowing them to instantly find the perfect items for themselves in-store.

[0148] Specific examples

[0149] Suppose a user is shopping in a physical store. The user takes out their smartphone, launches an application, takes a photo, and uploads it to the system. The server receives the photo, analyzes it, and determines that the user's personal color is "autumn." The system then suggests fashion items that are suitable for "autumn" (for example, a bronze skirt or an olive green top). The user can then accept the suggestions, browse the products in the store, and choose the fashion items that best suit them.

[0150] Prompt Sentence Examples

[0151] Here are some examples of prompts for generative AI models:

[0152] Analyze portrait photos uploaded by users and diagnose their personal color based on their skin tone, eye color, and hair color. The result of the diagnosis should be either "spring, summer, autumn, or winter," and suggest appropriate fashion items based on that result.

[0153] The embodiments of the present invention allow users to easily find fashion that suits them even without specialized knowledge, improving the quality of their styling in everyday life. Furthermore, even when shopping in a physical store, users can receive appropriate advice in real time, allowing them to enjoy a more fulfilling shopping experience.

[0154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0155] Step 1:

[0156] Users take a portrait of themselves with their smartphone or smart glasses and upload it to a server through the application.

[0157] Input: A portrait of the user (image file).

[0158] Output: Image file saved on the server.

[0159] Specific behavior: A user launches the application, takes a portrait of themselves using the camera function, and uploads the image to the server using the application's send function.

[0160] Step 2:

[0161] The server analyzes the images it receives using image processing libraries such as OpenCV to extract skin color, eye color, and hair color.

[0162] Input: A portrait of the user stored on the server.

[0163] Output: Extracted image data feature values ​​(skin color, eye color, hair color).

[0164] Specific operation: The server program uses OpenCV to read the image and executes a color analysis algorithm to extract skin color, eye color, and hair color as numerical data.

[0165] Step 3:

[0166] The server uses generative artificial intelligence (generative AI model) to diagnose the user's personal color based on the extracted data.

[0167] Input: Extracted image data feature values.

[0168] Output: Personal color analysis result (spring, summer, autumn, or winter).

[0169] Specific operation: The server program inputs the extracted feature values ​​into the generative AI model, performs a personal color diagnosis, and obtains the diagnosis results using a prompt sentence for the generative AI model.

[0170] Step 4:

[0171] The server uses generative AI based on the diagnostic results to suggest fashion and color coordination that best suits the user's personal color.

[0172] Input: Personal color analysis results.

[0173] Output: Suggested fashion items and color coordination.

[0174] Specific operation: Based on the diagnosis results, the server program inputs a list of appropriate fashion items into the generation AI, and suggests items that suit each personal color.

[0175] Step 5:

[0176] The user tries on the suggested fashion and uploads the photo to the server again through the application.

[0177] Input: A portrait of the user wearing the fashion they're trying on.

[0178] Output: A new image file saved on the server.

[0179] Specific operation: The user tries on the suggested fashion, takes another photo using their smart device, and uploads it to the server via the application.

[0180] Step 6:

[0181] The server analyzes the re-uploaded image and evaluates its suitability for the proposed fashion.

[0182] Input: A re-uploaded portrait of the user.

[0183] Output: Fitness evaluation score.

[0184] How it works: The server analyzes the newly uploaded image again using OpenCV to extract skin, eye, and hair color, and evaluates its suitability using a generative AI model. The evaluation results are calculated as a score and notified to the user. This score is also saved in the user's profile.

[0185] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0186] This invention is a system that allows users to understand their own personal colors and select optimal fashion and color coordination based on those colors, and also incorporates an emotion engine that recognizes the user's emotions to provide more personalized fashion suggestions. This system allows users to upload their own portrait photos, and uses image recognition technology and generative AI to diagnose their personal colors, and further uses the emotion engine to recognize the user's emotions.

[0187] Specifically, the process begins when a user uploads a photo of their face to the system. The device sends the user's photo to the server, which stores the received image. The server then uses image recognition technology to analyze the skin tone, eye color, and hair color from the uploaded photo. The results of this analysis are organized as numerical data.

[0188] The server uses the generative AI to diagnose the user's personal color based on the analysis results. This diagnosis result is saved in the user's profile, and the generative AI suggests optimal fashion and color coordination based on the diagnosed personal color. The suggestions are organized and provided to the user as display data.

[0189] In addition, this invention incorporates an emotion engine to recognize emotions from a user's facial photograph. The server recognizes the user's emotional state and reflects this information, along with the user's personal color diagnosis results, in the suggested fashion and color coordination. For example, if the user's personal color is "spring" and the emotion engine recognizes the user's emotion as "joy," it can suggest fashion items in relatively bright colors that enhance the user's energy.

[0190] The detailed operation of the system will be described below.

[0191] First, the user takes a photo of their face and uploads it to the system. The device then sends the image to a server, which stores it and analyzes it using image recognition technology. The analysis identifies the user's skin color, eye color, and hair color, and organizes this color information into numerical data.

[0192] The server then uses generative artificial intelligence to analyze the color information and diagnose the user's personal color. The diagnosed personal color is saved in the user's profile. At the same time, the server uses an emotion engine to recognize the user's emotional state. This emotional information is also saved in the user's profile.

[0193] The server uses generative AI to suggest optimal fashion and color coordination based on the diagnosis results and the user's emotional information. These suggestions are adjusted to match the user's emotions, so they suggest fashion that matches the user's feelings. The user can also try on the suggested fashion and upload the photo back to the system, where the server analyzes the new image and evaluates its suitability. The suitability evaluation calculates the degree of match for skin tone, eye color, and hair color individually, and calculates an evaluation score based on the average of these.

[0194] The evaluation results are saved in the user's profile and notified to the user. This allows the user to check whether their fashion choices were appropriate and use the information to make future choices. The introduction of an emotion engine allows suggestions to be made that are also based on the user's emotional state, providing a more satisfying fashion experience.

[0195] As described above, the present invention is a system that combines a user's personal color diagnosis with emotion recognition, thereby enabling optimal fashion suggestions and fitness evaluation for the user.

[0196] The processing flow will be explained below.

[0197] Step 1:

[0198] Users take a photo of themselves and upload the image to the system.

[0199] Step 2:

[0200] The terminal transmits the image data uploaded by the user to the server via an HTTP request.

[0201] Step 3:

[0202] The server saves the received image data in the specified directory, assigning a unique file name to the file to prevent it from being overwritten.

[0203] Step 4:

[0204] The server analyzes the stored images using image recognition technology, specifically, by using open source libraries (e.g., OpenCV) to perform facial recognition and extract skin color, eye color, and hair color from the images.

[0205] Step 5:

[0206] The server organizes the extracted color information (skin color, eye color, hair color) as numerical data, which allows the analysis results to be saved as numerical data for later processing.

[0207] Step 6:

[0208] The server inputs the organized numerical data into a generative artificial intelligence model (e.g., a machine learning algorithm) to diagnose the user's personal color.

[0209] Step 7:

[0210] The server saves the diagnosis results (personal color) in the user profile, allowing the user to check their personal color information at any time.

[0211] Step 8:

[0212] The server again analyzes the user's portrait and recognizes the user's emotional state using an emotion engine that utilizes facial recognition technology to analyze the user's facial expressions and identify their emotional state.

[0213] Step 9:

[0214] The server stores the emotion information recognized by the emotion engine in the user profile, which is then used for future fashion suggestions.

[0215] Step 10:

[0216] The server uses AI to generate optimal fashion and color coordination based on the diagnosed personal color and emotional information. For example, if a user's personal color is "spring" and their emotional state is "joy," the server will suggest a bright color coordination.

[0217] Step 11:

[0218] The server organizes the generated suggestions and sends them to the user interface as display data, allowing the user to select fashion based on these suggestions.

[0219] Step 12:

[0220] The user tries out the suggested fashion and color coordination, then takes another photo of themselves and uploads it to the system.

[0221] Step 13:

[0222] The terminal again transmits the newly captured image data to the server.

[0223] Step 14:

[0224] The server then analyzes the newly received image using image recognition technology, extracting skin, eye, and hair color in the same way as in the preprocessing. It then uses an emotion engine to re-recognize the user's emotions from the new facial expressions.

[0225] Step 15:

[0226] The server evaluates the suitability of the proposed fashion based on the extracted new color and emotion information. Specifically, it calculates the degree of match for skin color, eye color, and hair color separately and averages them to calculate an evaluation score.

[0227] Step 16:

[0228] The server saves the evaluation score and the new emotional evaluation in the user's profile and notifies the user of the results using a notification system, allowing the user to check the fitness of their fashion choices and their emotional state and use this information to make future choices.

[0229] Example 2

[0230] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0231] Conventional fashion suggestion systems may make suggestions based on a user's personal color, but they do not provide personalized suggestions that take into account the user's emotional state. This can result in a failure to suggest fashion that matches the user's momentary emotions or daily mood, potentially reducing user satisfaction. Furthermore, systems lack a mechanism for evaluating how well suggested fashions suit the user's actual appearance. As a result, even if a user tries on suggested fashion items, it is difficult to objectively evaluate their suitability.

[0232] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0233] In this invention, the server includes means for a user to take and upload a portrait photo of themselves, means for analyzing skin color, eye color, and hair color from the uploaded photo using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for recognizing the user's emotional state using an emotion engine, means for suggesting optimal fashion and color coordination based on the diagnosed personal color and the recognized emotional state, means for the user to re-upload an image when trying on the suggested fashion, and means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion. This enables fashion suggestions that take into account both the user's personal color and emotional state, and enables objective evaluation of the suitability of suggested fashion items.

[0234] "User" refers to a person who uses the system to diagnose their own personal color and receive optimal fashion suggestions.

[0235] A "portrait" refers to a photograph that includes the user's face and is image data that is uploaded to the system.

[0236] "Image recognition technology" refers to a collection of algorithms and software used to analyze certain features in uploaded photos, including, for example, face detection and color analysis.

[0237] "Generative AI" refers to a group of algorithms and models that generate new information or suggestions based on given data, including natural language processing models and image generation models.

[0238] "Personal color" refers to the color tone that best suits a user, as diagnosed based on the user's skin color, eye color, hair color, etc. For example, it is classified into categories such as "spring" and "autumn."

[0239] An "emotion engine" is an algorithm or software that can recognize a user's emotional state from a photograph of their face. For example, it can identify emotions such as "joy," "sadness," and "anger."

[0240] "Fashion and color coordination" refers to suggested combinations of clothing and accessories based on the user's personal color and emotional state.

[0241] "Suitability" is an index that evaluates how well the proposed fashion matches the user's appearance. Specifically, it is a score calculated based on the degree of match of skin color, eye color, and hair color.

[0242] "User profile" refers to a database or file that stores information such as a user's personal colors, suggested fashions, emotional state, etc.

[0243] "Analysis" refers to the process of using image recognition technology to identify colors and features from uploaded photos and organize them as numerical data.

[0244] "Diagnosis" refers to the process of identifying a user's personal color based on the analysis results.

[0245] "Suggestion" refers to the act of providing the user with optimal fashion and color coordination based on the diagnosed personal color and emotional state.

[0246] The present invention is a system that allows users to understand their own personal colors and select optimal fashion and color coordination based on those colors. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the system can provide more personalized fashion suggestions. The following specific procedures and techniques are used to implement this system.

[0247] First, a user takes a photo of their face using a device (e.g., a smartphone or PC) and uploads it to the system. The device then sends the uploaded image to the server.

[0248] The server saves the received image data in a temporary directory, for example, the / tmp / uploads / directory, and returns a response to the terminal notifying that the image was successfully saved.

[0249] Next, the server uses an image processing library (e.g., OpenCV or Dlib) to detect faces in the uploaded image and analyze skin, eye, and hair colors. Specifically, it uses a face detection algorithm to identify the face area and obtain pixel color information within the face area. This color information is organized as numerical data, expressed as, for example, HSV (hue, saturation, brightness) values.

[0250] Based on the analysis results, a generative artificial intelligence (generative AI) model is used to diagnose the user's personal color. GPT-3 or a similar model is used as the generative AI. Specifically, data on the user's skin color, eye color, and hair color is input into the generative AI model, and a diagnosis of the personal color (such as "Spring," "Summer," "Autumn," or "Winter") is output. The diagnosed personal color is saved in the user's profile.

[0251] Furthermore, the server uses an emotion engine (e.g., emotion recognition API) to recognize emotions from the user's facial photograph. For example, by using Microsoft's Face API or Google's Cloud Vision API, emotions such as "happiness," "sadness," and "anger" can be identified. The recognized emotion information is then added to and saved in the user profile.

[0252] Next, the generation AI will make optimal fashion suggestions based on the diagnosed personal color and recognized emotional information. Specifically, the generation AI is input with the following prompt: "The user's personal color is ____, and their current emotional state is ____. Please suggest the optimal fashion coordination." Based on this prompt, the generation AI will suggest fashion items and coordinations.

[0253] The user tries on the proposed fashion and uploads the photo back to the system. The server analyzes the re-uploaded image and evaluates the suitability of the proposed fashion. Specifically, it evaluates the degree of match between the original skin color, eye color, and hair color and the new image, and calculates the average of these as a suitability score. The evaluation results are saved in the user profile and notified to the user.

[0254] With the above system, users can receive optimal fashion suggestions that match their personal color and emotional state, and can also check the suitability of the suggestions.

[0255] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0256] Step 1:

[0257] Users take a photo of their face using a device (such as a smartphone or PC) and upload it to the system.

[0258] Input: A portrait taken by the user

[0259] Output: Image data transmission request from the device to the server

[0260] Specifically, the user takes a photo of their face using the device's camera app, selects the image file on the system's upload screen, and clicks the upload button to send the image data to the server.

[0261] Step 2:

[0262] The server stores the received image data in a temporary directory, for example, the / tmp / uploads / directory.

[0263] Input: Image data sent from the device

[0264] Output: Notification that the image file has been saved to the storage location

[0265] Specifically, the server receives the HTTP request, writes the image data to the save directory, and if the save is successful, returns a save completion response to the device.

[0266] Step 3:

[0267] The server uses image recognition technology (e.g., OpenCV or Dlib) to detect faces from uploaded images and analyze skin tone, eye color, and hair color.

[0268] Input: Saved image data

[0269] Output: Numerical data of skin color, eye color, and hair color as analysis results

[0270] Specifically, a face detection algorithm is used to identify the face area, and pixel color information within the face area is collected and converted into HSV (hue, saturation, brightness) values.These analysis results are then stored on the server as numerical data.

[0271] Step 4:

[0272] The server uses a generative artificial intelligence (generative AI) model to diagnose the user's personal color based on the analysis results.

[0273] Input: Numerical data for skin color, eye color, and hair color

[0274] Output: Diagnosed personal color

[0275] Specifically, data on skin color, eye color, and hair color is input into the generative AI model, which outputs a personal color (e.g., "Spring," "Summer," "Autumn," or "Winter"). The generated personal color information is saved in the user profile.

[0276] Step 5:

[0277] The server uses an emotion engine (e.g., emotion recognition API) to recognize emotions from the user's facial photograph.

[0278] Input: Uploaded face photo

[0279] Output: Recognized emotion information

[0280] Specifically, facial images are sent to an emotion recognition API to obtain emotional states such as "happiness," "sadness," and "anger." This emotional information is then added to and saved in the user profile.

[0281] Step 6:

[0282] The server uses generative AI to suggest optimal fashion and color coordination based on the diagnosed personal color and recognized emotional information.

[0283] Input: User's personal color, emotional state

[0284] Output: Suggested fashion and color coordination

[0285] Specifically, the AI ​​generates a prompt message such as, "The user's personal color is ____, and their current emotional state is ____. Please suggest the best fashion coordination for them." The AI ​​then outputs the generated fashion suggestions. These suggestions are then sent back to the user.

[0286] Step 7:

[0287] The user tries on the suggested fashions and uploads the photos back to the system.

[0288] Input: A photo of you trying out the suggested fashion

[0289] Output: A request to send new image data from the device to the server

[0290] Specifically, the user tries on the proposed fashion, takes a photo of the outfit, and clicks the upload button. The image data is then sent to the server.

[0291] Step 8:

[0292] The server analyzes newly uploaded images and evaluates the suitability of the proposed fashions.

[0293] Input: Newly uploaded image, original skin color, eye color, and hair color data

[0294] Output: Evaluation score

[0295] Specifically, the system evaluates the degree of match between the original skin color, eye color, and hair color and the new image, and calculates the average of these as a fitness score. The evaluation results are saved in the user profile and notified to the user.

[0296] This allows users to receive fashion suggestions that are best suited to their personal color and emotional state, and also to check the suitability of those suggestions.

[0297] (Application example 2)

[0298] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0299] In the modern fashion industry, suggestions based on personal color diagnosis are becoming commonplace, but there is still no system that can provide personalized fashion suggestions that take the user's emotions into account. Furthermore, there are limited ways to receive these suggestions in real time while shopping in a physical store. Therefore, there is a need for a system that can take the user's emotions into account and instantly suggest the most suitable fashion items in a physical store.

[0300] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload a portrait photo of themselves, means for analyzing skin color, eye color, and hair color from the uploaded photo using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for proposing optimal fashion and color coordination based on the diagnosed personal color, emotion recognition means for recognizing the user's emotional state, means for adjusting the proposed fashion and color coordination based on the emotional state, means for displaying the proposal through a smart wearable device, means for re-uploading an image when the user tries on the proposed fashion, and means for analyzing the re-uploaded image and evaluating the suitability of the proposed fashion. This enables personalized fashion proposals that take the user's emotions into account in real time.

[0301] "User" refers to an individual who uses the system.

[0302] A "portrait" refers to an image file that shows the user's face.

[0303] "Image recognition technology" refers to the technology that allows computers to extract and analyze specific information from image data.

[0304] "Generative AI" refers to AI that generates new data and information using technologies such as machine learning and deep learning.

[0305] "Personal color" refers to a color palette classified based on an individual's skin tone, eye color, hair color, etc.

[0306] "Color coordination" refers to the way colors of fashion items are combined.

[0307] "Emotion recognizer" refers to technology for identifying a user's emotional state.

[0308] The term "smart wearable device" refers to a computer worn by a user, including, for example, smart glasses.

[0309] "Means for displaying suggestions" refers to the technology or device used to show the system-generated suggestions to the user.

[0310] "Suitability" refers to the degree to which the suggested fashion item suits the user.

[0311] The "evaluation score" is a numerical representation of the suitability, and is an indicator of the effectiveness of the proposal.

[0312] The present invention provides a system that diagnoses a user's personal color and emotional state and suggests optimal fashion and color coordination based on the results. Furthermore, this system has the feature of displaying suggestions in real time in a physical store using a smart wearable device and instantly reflecting the user's evaluation.

[0313] First, the user takes a photo of their face using the camera on their smart glasses and uploads it to the system. This photo is then sent to a server, where image recognition technology such as OpenCV is used to analyze the user's skin tone, eye color, and hair color from the photo. The analyzed data is then organized into numerical values.

[0314] The server then uses generative artificial intelligence to further analyze this color information and diagnose the user's personal color. The generative artificial intelligence models used include machine learning and deep learning algorithms. The results of this diagnosis are saved in the user's profile. Furthermore, emotion recognition technology is used to recognize the user's emotional state. One technology used for this is EmotionRecognizer.

[0315] Based on the diagnosed personal color and emotional information, the server uses a generative AI model to suggest optimal fashion and color coordination. This includes suggesting fashion items with colors and styles that are best suited to the user's emotional state. The suggestions are displayed in real time on the smart glasses.

[0316] For example, if the user's personal color is "spring" and the emotion is recognized as "joy," items that enhance brightness and vitality, such as a light green skirt, are suggested.

[0317] An example prompt is:

[0318] "The user's personal color is 'spring' and their emotion is 'joy.' If you were to make fashion suggestions based on these conditions, what items would be best? Please focus on items with bright colors that bring out their energy."

[0319] When a user tries on a suggested fashion and uploads the photo back to the system, the server analyzes the new image and evaluates its suitability. This suitability is calculated based on the average of the individual skin, eye, and hair color matches. This evaluation result is also saved in the user's profile and will be used for future suggestions.

[0320] As described above, this system can provide real-time fashion suggestions in real stores that take into account the user's personal color and emotional information, significantly improving the user experience, allowing users to make practical fashion choices with high satisfaction.

[0321] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0322] Step 1:

[0323] A user takes a portrait of themselves using the camera on their smart glasses and uploads the portrait to the system. The input is the user's portrait, and the output is image data sent to the server. Specifically, after taking a picture with the camera on the smart glasses, the saved image file is uploaded to the server.

[0324] Step 2:

[0325] The server analyzes the uploaded facial photo using image recognition technology. The input is the uploaded facial photo, and the output is the analysis results of skin color, eye color, and hair color. Specifically, OpenCV is used to identify areas of each color from the facial photo and organize them as numerical data.

[0326] Step 3:

[0327] The server then uses artificial intelligence to analyze the analyzed color information and diagnose the user's personal color. The input is numerical data for skin color, eye color, and hair color, and the output is the user's personal color. Specifically, a machine learning algorithm is used to determine the personal color through a predictive model.

[0328] Step 4:

[0329] The server uses emotion recognition technology to recognize the user's emotional state. The input is a photo of the user's face, and the output is the user's emotional information. Specifically, it uses EmotionRecognizer to classify emotions from facial expressions.

[0330] Step 5:

[0331] The server inputs personal color and emotional information into a generative AI model to suggest optimal fashion and color coordination. The input is the user's personal color and emotional information, and the output is suggested fashion items. Specifically, the generative AI model generates optimal fashion based on the prompt text and displays it on the smart glasses' display.

[0332] Step 6:

[0333] The user tries on the suggested fashion and uploads the result as a new face photo to the system. The input is the new face photo, and the output is the transmission of image data to the server. Specifically, the user takes a photo again with the smart glasses camera, saves the image, and sends it to the server.

[0334] Step 7:

[0335] The server analyzes the newly uploaded image and evaluates its suitability for the proposed fashion. The input is the newly uploaded face photo, and the output is a suitability evaluation score. Specifically, the server calculates the degree of match for skin color, eye color, and hair color, and calculates the evaluation score from the average.

[0336] Step 8:

[0337] The server saves the evaluation results in the user's profile and reflects them in future fashion suggestions. The input is the fitness evaluation score, and the output is an updated user profile. Specifically, the evaluation scores are saved in a database and used as training data for the generative AI model.

[0338] 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.

[0339] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0340] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0341] [Second embodiment]

[0342] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0343] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0344] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0345] 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.

[0346] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0347] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0348] 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.

[0349] 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.

[0350] 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 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.

[0351] 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.

[0352] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0353] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0354] The present invention is a system that allows users to understand their own personal color and select optimal fashion and color coordination based on that information. The system allows users to upload their own portrait photos and diagnoses their personal color using image recognition technology and generative artificial intelligence. Furthermore, the system suggests optimal fashion and evaluates suitability based on the diagnosed personal color.

[0355] Specifically, the process begins when a user uploads a photo of their face to the system. The device sends the user's photo to the server, which stores the received image. The server then uses image recognition technology to analyze the skin tone, eye color, and hair color from the uploaded photo. The results of this analysis are organized as numerical data.

[0356] The server uses the generative AI to diagnose the user's personal color based on the analysis results. This diagnosis result is saved in the user's profile, and the generative AI suggests optimal fashion and color coordination based on the diagnosed personal color. The suggestions are organized and provided to the user as display data.

[0357] For example, if a user is diagnosed with a "spring" personal color, the system will suggest fashion items that suit "spring" (e.g., pastel or warm-toned clothing). The user tries out the suggested fashions and outfits and uploads the results to the system as a new photo. The server receives this new image and analyzes it again using image recognition technology.

[0358] Finally, the server evaluates the suitability of the proposed fashion. The suitability evaluation calculates the degree of match for skin tone, eye color, and hair color individually, and calculates an evaluation score based on the average of these. This score is saved in the user profile and notified to the user. In this way, users can find the fashion and coordination that best suits their personal color, and feel confident in their everyday styling.

[0359] The features of the present invention are as follows:

[0360] 1. A system that uses image recognition technology to analyze skin color, eye color, and hair color based on portrait photos uploaded by users, and then uses generative artificial intelligence to diagnose personal color based on the analysis results.

[0361] 2. A system that uses generative AI to suggest optimal fashion and color coordination for users based on their diagnosed personal colors.

[0362] 3. A system in which users try out the suggested fashion and upload the image again, and the new image is analyzed to evaluate its suitability.

[0363] This makes it easier for users to find fashion that suits them even without specialized knowledge, improving the quality of styling in their daily lives.

[0364] The processing flow will be explained below.

[0365] Step 1:

[0366] Users use their terminal to take a photo of themselves and upload the image to the system.

[0367] Step 2:

[0368] The terminal transmits the image data uploaded by the user to the server via an HTTP request.

[0369] Step 3:

[0370] The server saves the received image data in the specified directory, assigning a unique file name to the saved image data to prevent files with the same name from being overwritten.

[0371] Step 4:

[0372] The server analyzes the stored images using image recognition technology. Specifically, it uses open source libraries (e.g., OpenCV) to perform facial recognition and extract skin color, eye color, and hair color from the images.

[0373] Step 5:

[0374] The server organizes the extracted color information (skin color, eye color, hair color) as numerical data, which allows the analysis results to be saved as numerical data for later processing.

[0375] Step 6:

[0376] The server inputs the organized numerical data into a generative artificial intelligence model (e.g., a machine learning algorithm) to diagnose the user's personal color.

[0377] Step 7:

[0378] The server saves the diagnosis results (personal color) in the user profile, allowing the user to check their personal color information at any time.

[0379] Step 8:

[0380] The server uses AI to generate optimal fashion and color coordination based on the diagnosed personal color. Specifically, it uses a model to generate appropriate coordination suggestions.

[0381] Step 9:

[0382] The server organizes the generated suggestions and sends them to the user interface as display data, allowing the user to select fashion based on these suggestions.

[0383] Step 10:

[0384] The user tries out the suggested fashion and color coordination, then takes another photo of themselves and uploads it to the system.

[0385] Step 11:

[0386] The terminal again transmits the newly captured image data to the server.

[0387] Step 12:

[0388] The server then analyzes the newly received image using image recognition technology and extracts skin color, eye color, and hair color in the same way as in the pre-processing.

[0389] Step 13:

[0390] The server compares the extracted new color information with the original personal color and evaluates the suitability of the new color. Specifically, it calculates the degree of match for each color individually and averages them to calculate an evaluation score.

[0391] Step 14:

[0392] The server stores the evaluation scores in the user's profile and notifies the user of the results using a notification system, allowing the user to see the fitness of their fashion choices.

[0393] Example 1

[0394] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0395] Conventional personal color diagnostic systems have made it difficult for users to accurately grasp their own personal colors and select optimal fashion and color coordination based on the results. Furthermore, the system lacks the functionality to evaluate the suitability of suggested fashions and provide feedback to the user. This has meant that users have to spend a lot of time and effort to find the perfect fashion for them.

[0396] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0397] In this invention, the server includes means for users to upload their portrait photos, means for analyzing skin color, eye color, and hair color from the uploaded photos using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for proposing optimal fashion and color coordination based on the diagnosed personal color, means for users to re-upload images when they try on the suggested fashion, means for analyzing the re-uploaded images and evaluating the suitability of the suggested fashion, means for saving the results of the suitability evaluation in a user profile, and means for notifying the user of the saved results. This allows users to easily select optimal fashion based on their personal color and find highly suitable coordination.

[0398] "User" means an individual who uses the system to upload a portrait photo of themselves and receive a personal color diagnosis and fashion suggestions.

[0399] "Device" means an electronic device used by a user to upload portrait photographs and re-upload photographs for suggested fashions and review.

[0400] "Server" refers to a central computer system that receives, stores, and analyzes image data sent from terminals and provides personal color diagnosis and fashion suggestions.

[0401] "Image recognition technology" is a technology that analyzes skin color, eye color, and hair color from uploaded portrait photos and extracts them as numerical data.

[0402] "Generative AI" is an AI technology that diagnoses a user's personal color based on the analysis results, and generates and suggests appropriate fashion and color coordination based on the diagnosis results.

[0403] "Personal colors" refers to a set of colors that best suit a user based on their skin tone, eye color, and hair color.

[0404] "Fashion and color coordination" refers to the suggestion of a combination of clothing and accessories that suits the user based on the diagnosed personal color.

[0405] "Suitability" is an index that evaluates how well the proposed fashion matches the user's personal color.

[0406] A "user profile" is a database that stores user-related identification information, diagnostic results, proposal results, fitness evaluation results, and the like.

[0407] "Notification" refers to the act of electronically sending information such as diagnostic results, evaluation results, and proposals from the server to the user.

[0408] This is a system that allows users to understand their own personal color and select the most suitable fashion and color coordination based on that. Users upload their portrait photos using a terminal, and the server uses image recognition technology and generative AI to diagnose their personal color and make fashion suggestions.

[0409] First, a user uploads their portrait photo to the system from a terminal. This terminal can be an electronic device such as a PC, smartphone, or tablet. At this stage, the terminal sends the uploaded image data to the server.

[0410] The server stores the received images in dedicated storage and then analyzes them using image recognition technology. Image recognition libraries such as OpenCV and TensorFlow are used. This analysis extracts the user's skin color, eye color, and hair color as numerical data. For example, skin color is quantified as RGB(231, 192, 146), eye color as RGB(89, 60, 31), and hair color as RGB(45, 35, 25).

[0411] The server then uses the extracted numerical data to diagnose the user's personal color using a generative AI (e.g., GPT-3.5). Specifically, the server inputs the following prompt into the generative AI:

[0412] "What are the personal colors of users with these skin tones, eye colors, and hair colors?"

[0413] In response to this prompt, the AI ​​generator will respond with either spring, summer, fall, or winter. This diagnosis is saved in the user's profile.

[0414] The server then uses generative AI to suggest appropriate fashion and color coordination based on the user's personal color, using prompts like the following:

[0415] "Please suggest fashion items suitable for users with spring personal colors."

[0416] The generative AI generates specific fashion items and outfit suggestions (e.g., a pastel-colored top and warm-colored bottoms), and these suggestions are notified to the user.

[0417] The user tries on the proposed fashion, takes a new photo of the results, and uploads it back to the system. The server then performs a new analysis and evaluates the user's suitability for the proposed fashion. This evaluation involves calculating the degree of match for skin tone, eye color, and hair color individually, and calculating an average score. The evaluation results are saved in the user's profile and notified to the user.

[0418] For example, if a user's personal color is diagnosed as "spring" and the suggested fashion items are pastel or warm-toned clothing, the suitability evaluation allows the user to confirm numerically how well these items suit the user. In this way, users can easily select the optimal fashion based on their personal color and find highly suitable outfits.

[0419] As described above, the present invention provides a system that enables users to efficiently search for the fashion that best suits them, thereby increasing users' confidence in their styling.

[0420] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0421] Step 1:

[0422] A user accesses the system's web interface from a terminal, selects and uploads their own portrait. The input is the user's portrait, and the output is an image transfer request to the server. At this stage, the terminal sends the image file to the server as an HTTP request.

[0423] Step 2:

[0424] The device receives an upload request from the user and sends the portrait photo to the server. The server saves the received image data in dedicated storage. The input is the image data sent from the device, and the output is the image file saved in the storage.

[0425] Step 3:

[0426] The server reads the saved images from storage and analyzes them using image recognition technology. Libraries such as OpenCV and TensorFlow are used here. The input is the saved image data, and the output is analyzed numerical data (skin color, eye color, hair color). Specifically, it analyzes pixel values ​​and extracts the color of each element in RGB format.

[0427] Step 4:

[0428] The server passes the analyzed numerical data to a generative artificial intelligence (generative AI) that diagnoses the user's personal color. The input is the numerical data of the analysis results, and the output is the personal color diagnosis result. For example, GPT-3.5 is used as the generative AI, and the prompt sentence "What is the personal color of a user with these skin tones, eye colors, and hair colors?" is input.

[0429] Step 5:

[0430] Based on the diagnosed personal color, the server uses a generation AI to suggest optimal fashion and color coordination to the user. The input is the personal color diagnosis result, and the output is specific fashion suggestions. The generation AI is input with a prompt statement such as "Please suggest fashion items that are suitable for the user's spring personal color," and the AI ​​generates the suggestions.

[0431] Step 6:

[0432] The server notifies the user of the generated fashion suggestions. The input is the fashion suggestions from the generation AI, and the output is the fashion suggestion data displayed on the user's device.

[0433] Step 7:

[0434] The user tries out the suggested fashions and uploads the resulting photo to the system again. The input is the newly taken user photo, and the output is an image transmission request to the server. At this stage, the device again sends the image to the server as an HTTP request.

[0435] Step 8:

[0436] The server then analyzes the received image again using image recognition technology and evaluates its suitability for the proposed fashion. The input is the newly uploaded image data, and the output is a numerical evaluation of suitability. Again, the color of each element is extracted in RGB format, and the analysis is performed in the same way as the first time.

[0437] Step 9:

[0438] The server saves the results of the fitness evaluation in the user profile and notifies the user. The input is the fitness evaluation result, and the output is the data saved in the user profile and the notification data to the user. Specific operations include calculating the degree of match individually and calculating their average score.

[0439] In this way, the present system operates through processing steps that allow the user to easily select and evaluate fashion based on personal color.

[0440] (Application example 1)

[0441] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0442] Conventional personal color diagnostic systems only perform a diagnosis based on a still image of the user, and are limited to fashion suggestions and fitness evaluations based on the diagnosis results. However, no systems exist that can be effectively utilized in brick-and-mortar shopping or real-time fashion consulting environments. This has resulted in a lack of support for users when selecting optimal fashion items on the spot. Therefore, there is a need for a system that allows users to use a smart device in a brick-and-mortar store to receive real-time suggestions for fashion items based on their personal color.

[0443] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0444] In this invention, the server includes: means for a user to upload a portrait photograph of themselves; means for analyzing skin color, eye color, and hair color from the uploaded image using image recognition technology; means for diagnosing the user's personal color based on the analysis result using generative artificial intelligence; means for suggesting optimal fashion and color coordination based on the diagnosed personal color; means for the user to re-upload an image when trying on the suggested fashion; means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion; and means for the user to use their own smart device to be suggested fashion items based on their own personal color in real time at a physical store. This allows the user to use their smart device in a physical store to have fashion items that are optimal for their personal color suggested in real time.

[0445] "Means for users to upload their portraits" refers to the functionality of the device or software that allows users to submit images of themselves to the system.

[0446] "Image recognition technology" is a technology that uses computer vision to analyze images and extract specific attributes and information.

[0447] "Skin color" refers to the skin tone seen on the user's face, hands, etc.

[0448] "Eye color" is the color of the user's iris.

[0449] "Hair color" refers to the color of the user's hair.

[0450] "Generative artificial intelligence" is a technology that uses machine learning and deep learning to analyze data and generate new information.

[0451] "Means for diagnosing personal color" refers to the function of a device or software that identifies the user's optimal color based on analyzed data.

[0452] The "means for proposing optimal fashion and color coordination" refers to a device or software function that suggests appropriate clothing and color combinations to the user based on the diagnostic results.

[0453] "Means for re-uploading images" refers to the functionality of the device or software that allows a user to submit new images to the system.

[0454] The "means for evaluating the suitability of the proposed fashion" is a function of a device or software that evaluates how suitable the fashion tried by the user is.

[0455] "Smart devices" are mobile information terminals that can connect to the Internet, such as smartphones and smart glasses.

[0456] A "physical store" is a store in a physical location where users can purchase products in person.

[0457] "Means of making real-time suggestions" refers to the functionality of devices or software that instantly present appropriate information and suggestions to users on the spot.

[0458] This invention is a system that allows users to understand their own personal color and select optimal fashion and color coordination based on that information. The system allows users to upload their own portrait photos and diagnoses their personal color using image recognition technology and generative artificial intelligence. Furthermore, the system makes optimal fashion suggestions and evaluates suitability based on the diagnosed personal color. The same process can be provided in real time in physical stores.

[0459] Specifically, it operates as follows.

[0460] Hardware, software, and data processing used

[0461] The device (smartphone or smart glasses) is used by the user to take and upload a portrait of themselves, which is then sent to a server, where image processing libraries such as OpenCV are used to analyze the image and extract skin, eye, and hair color.

[0462] The server then uses a generative AI model such as TensorFlow to diagnose the user's personal color based on the extracted data. The diagnosis results are classified into one of three color categories: spring, summer, autumn, or winter. Once the diagnosis results are obtained, the server uses generative AI to suggest fashion items and color coordination that best fit the user's personal color.

[0463] Furthermore, the user tries on the proposed fashion and re-uploads the photo to evaluate its suitability. The re-uploaded image is analyzed again, and the suitability of the proposed fashion is calculated based on how well it suits the user. Suitability is calculated by calculating the match of skin color, eye color, and hair color individually, and calculating an evaluation score based on the average of these.

[0464] In physical stores, users can use their smart devices to receive a real-time personal color diagnosis and fashion item suggestions, allowing them to instantly find the perfect items for themselves in-store.

[0465] Specific examples

[0466] Suppose a user is shopping in a physical store. The user takes out their smartphone, launches an application, takes a photo, and uploads it to the system. The server receives the photo, analyzes it, and determines that the user's personal color is "autumn." The system then suggests fashion items that are suitable for "autumn" (for example, a bronze skirt or an olive green top). The user can then accept the suggestions, browse the products in the store, and choose the fashion items that best suit them.

[0467] Prompt Sentence Examples

[0468] Here are some examples of prompts for generative AI models:

[0469] Analyze portrait photos uploaded by users and diagnose their personal color based on their skin tone, eye color, and hair color. The result of the diagnosis should be either "spring, summer, autumn, or winter," and suggest appropriate fashion items based on that result.

[0470] The embodiments of the present invention allow users to easily find fashion that suits them even without specialized knowledge, improving the quality of their styling in everyday life. Furthermore, even when shopping in a physical store, users can receive appropriate advice in real time, allowing them to enjoy a more fulfilling shopping experience.

[0471] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0472] Step 1:

[0473] Users take a portrait of themselves with their smartphone or smart glasses and upload it to a server through the application.

[0474] Input: A portrait of the user (image file).

[0475] Output: Image file saved on the server.

[0476] Specific behavior: A user launches the application, takes a portrait of themselves using the camera function, and uploads the image to the server using the application's send function.

[0477] Step 2:

[0478] The server analyzes the images it receives using image processing libraries such as OpenCV to extract skin color, eye color, and hair color.

[0479] Input: A portrait of the user stored on the server.

[0480] Output: Extracted image data feature values ​​(skin color, eye color, hair color).

[0481] Specific operation: The server program uses OpenCV to read the image and executes a color analysis algorithm to extract skin color, eye color, and hair color as numerical data.

[0482] Step 3:

[0483] The server uses generative artificial intelligence (generative AI model) to diagnose the user's personal color based on the extracted data.

[0484] Input: Extracted image data feature values.

[0485] Output: Personal color analysis result (spring, summer, autumn, or winter).

[0486] Specific operation: The server program inputs the extracted feature values ​​into the generative AI model, performs a personal color diagnosis, and obtains the diagnosis results using a prompt sentence for the generative AI model.

[0487] Step 4:

[0488] The server uses generative AI based on the diagnostic results to suggest fashion and color coordination that best suits the user's personal color.

[0489] Input: Personal color analysis results.

[0490] Output: Suggested fashion items and color coordination.

[0491] Specific operation: Based on the diagnosis results, the server program inputs a list of appropriate fashion items into the generation AI, and suggests items that suit each personal color.

[0492] Step 5:

[0493] The user tries on the suggested fashion and uploads the photo to the server again through the application.

[0494] Input: A portrait of the user wearing the fashion they're trying on.

[0495] Output: A new image file saved on the server.

[0496] Specific operation: The user tries on the suggested fashion, takes another photo using their smart device, and uploads it to the server via the application.

[0497] Step 6:

[0498] The server analyzes the re-uploaded image and evaluates its suitability for the proposed fashion.

[0499] Input: A re-uploaded portrait of the user.

[0500] Output: Fitness evaluation score.

[0501] How it works: The server analyzes the newly uploaded image again using OpenCV to extract skin, eye, and hair color, and evaluates its suitability using a generative AI model. The evaluation results are calculated as a score and notified to the user. This score is also saved in the user's profile.

[0502] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0503] This invention is a system that allows users to understand their own personal colors and select optimal fashion and color coordination based on those colors, and also incorporates an emotion engine that recognizes the user's emotions to provide more personalized fashion suggestions. This system allows users to upload their own portrait photos, and uses image recognition technology and generative AI to diagnose their personal colors, and further uses the emotion engine to recognize the user's emotions.

[0504] Specifically, the process begins when a user uploads a photo of their face to the system. The device sends the user's photo to the server, which stores the received image. The server then uses image recognition technology to analyze the skin tone, eye color, and hair color from the uploaded photo. The results of this analysis are organized as numerical data.

[0505] The server uses the generative AI to diagnose the user's personal color based on the analysis results. This diagnosis result is saved in the user's profile, and the generative AI suggests optimal fashion and color coordination based on the diagnosed personal color. The suggestions are organized and provided to the user as display data.

[0506] In addition, this invention incorporates an emotion engine to recognize emotions from a user's facial photograph. The server recognizes the user's emotional state and reflects this information, along with the user's personal color diagnosis results, in the suggested fashion and color coordination. For example, if the user's personal color is "spring" and the emotion engine recognizes the user's emotion as "joy," it can suggest fashion items in relatively bright colors that enhance the user's energy.

[0507] The detailed operation of the system will be described below.

[0508] First, the user takes a photo of their face and uploads it to the system. The device then sends the image to a server, which stores it and analyzes it using image recognition technology. The analysis identifies the user's skin color, eye color, and hair color, and organizes this color information into numerical data.

[0509] The server then uses generative artificial intelligence to analyze the color information and diagnose the user's personal color. The diagnosed personal color is saved in the user's profile. At the same time, the server uses an emotion engine to recognize the user's emotional state. This emotional information is also saved in the user's profile.

[0510] The server uses generative AI to suggest optimal fashion and color coordination based on the diagnosis results and the user's emotional information. These suggestions are adjusted to match the user's emotions, so they suggest fashion that matches the user's feelings. The user can also try on the suggested fashion and upload the photo back to the system, where the server analyzes the new image and evaluates its suitability. The suitability evaluation calculates the degree of match for skin tone, eye color, and hair color individually, and calculates an evaluation score based on the average of these.

[0511] The evaluation results are saved in the user's profile and notified to the user. This allows the user to check whether their fashion choices were appropriate and use the information to make future choices. The introduction of an emotion engine allows suggestions to be made that are also based on the user's emotional state, providing a more satisfying fashion experience.

[0512] As described above, the present invention is a system that combines a user's personal color diagnosis with emotion recognition, thereby enabling optimal fashion suggestions and fitness evaluation for the user.

[0513] The processing flow will be explained below.

[0514] Step 1:

[0515] Users take a photo of themselves and upload the image to the system.

[0516] Step 2:

[0517] The terminal transmits the image data uploaded by the user to the server via an HTTP request.

[0518] Step 3:

[0519] The server saves the received image data in the specified directory, assigning a unique file name to the file to prevent it from being overwritten.

[0520] Step 4:

[0521] The server analyzes the stored images using image recognition technology, specifically, by using open source libraries (e.g., OpenCV) to perform facial recognition and extract skin color, eye color, and hair color from the images.

[0522] Step 5:

[0523] The server organizes the extracted color information (skin color, eye color, hair color) as numerical data, which allows the analysis results to be saved as numerical data for later processing.

[0524] Step 6:

[0525] The server inputs the organized numerical data into a generative artificial intelligence model (e.g., a machine learning algorithm) to diagnose the user's personal color.

[0526] Step 7:

[0527] The server saves the diagnosis results (personal color) in the user profile, allowing the user to check their personal color information at any time.

[0528] Step 8:

[0529] The server again analyzes the user's portrait and recognizes the user's emotional state using an emotion engine that utilizes facial recognition technology to analyze the user's facial expressions and identify their emotional state.

[0530] Step 9:

[0531] The server stores the emotion information recognized by the emotion engine in the user profile, which is then used for future fashion suggestions.

[0532] Step 10:

[0533] The server uses AI to generate optimal fashion and color coordination based on the diagnosed personal color and emotional information. For example, if a user's personal color is "spring" and their emotional state is "joy," the server will suggest a bright color coordination.

[0534] Step 11:

[0535] The server organizes the generated suggestions and sends them to the user interface as display data, allowing the user to select fashion based on these suggestions.

[0536] Step 12:

[0537] The user tries out the suggested fashion and color coordination, then takes another photo of themselves and uploads it to the system.

[0538] Step 13:

[0539] The terminal again transmits the newly captured image data to the server.

[0540] Step 14:

[0541] The server then analyzes the newly received image using image recognition technology, extracting skin, eye, and hair color in the same way as in the preprocessing. It then uses an emotion engine to re-recognize the user's emotions from the new facial expressions.

[0542] Step 15:

[0543] The server evaluates the suitability of the proposed fashion based on the extracted new color and emotion information. Specifically, it calculates the degree of match for skin color, eye color, and hair color separately and averages them to calculate an evaluation score.

[0544] Step 16:

[0545] The server saves the evaluation score and the new emotional evaluation in the user's profile and notifies the user of the results using a notification system, allowing the user to check the fitness of their fashion choices and their emotional state and use this information to make future choices.

[0546] Example 2

[0547] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0548] Conventional fashion suggestion systems may make suggestions based on a user's personal color, but they do not provide personalized suggestions that take into account the user's emotional state. This can result in a failure to suggest fashion that matches the user's momentary emotions or daily mood, potentially reducing user satisfaction. Furthermore, systems lack a mechanism for evaluating how well suggested fashions suit the user's actual appearance. As a result, even if a user tries on suggested fashion items, it is difficult to objectively evaluate their suitability.

[0549] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0550] In this invention, the server includes means for a user to take and upload a portrait photo of themselves, means for analyzing skin color, eye color, and hair color from the uploaded photo using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for recognizing the user's emotional state using an emotion engine, means for suggesting optimal fashion and color coordination based on the diagnosed personal color and the recognized emotional state, means for the user to re-upload an image when trying on the suggested fashion, and means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion. This enables fashion suggestions that take into account both the user's personal color and emotional state, and enables objective evaluation of the suitability of suggested fashion items.

[0551] "User" refers to a person who uses the system to diagnose their own personal color and receive optimal fashion suggestions.

[0552] A "portrait" refers to a photograph that includes the user's face and is image data that is uploaded to the system.

[0553] "Image recognition technology" refers to a collection of algorithms and software used to analyze certain features in uploaded photos, including, for example, face detection and color analysis.

[0554] "Generative AI" refers to a group of algorithms and models that generate new information or suggestions based on given data, including natural language processing models and image generation models.

[0555] "Personal color" refers to the color tone that best suits a user, as diagnosed based on the user's skin color, eye color, hair color, etc. For example, it is classified into categories such as "spring" and "autumn."

[0556] An "emotion engine" is an algorithm or software that can recognize a user's emotional state from a photograph of their face. For example, it can identify emotions such as "joy," "sadness," and "anger."

[0557] "Fashion and color coordination" refers to suggested combinations of clothing and accessories based on the user's personal color and emotional state.

[0558] "Suitability" is an index that evaluates how well the proposed fashion matches the user's appearance. Specifically, it is a score calculated based on the degree of match of skin color, eye color, and hair color.

[0559] "User profile" refers to a database or file that stores information such as a user's personal colors, suggested fashions, emotional state, etc.

[0560] "Analysis" refers to the process of using image recognition technology to identify colors and features from uploaded photos and organize them as numerical data.

[0561] "Diagnosis" refers to the process of identifying a user's personal color based on the analysis results.

[0562] "Suggestion" refers to the act of providing the user with optimal fashion and color coordination based on the diagnosed personal color and emotional state.

[0563] The present invention is a system that allows users to understand their own personal colors and select optimal fashion and color coordination based on those colors. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the system can provide more personalized fashion suggestions. The following specific procedures and techniques are used to implement this system.

[0564] First, a user takes a photo of their face using a device (e.g., a smartphone or PC) and uploads it to the system. The device then sends the uploaded image to the server.

[0565] The server saves the received image data in a temporary directory, for example, the / tmp / uploads / directory, and returns a response to the terminal notifying that the image was successfully saved.

[0566] Next, the server uses an image processing library (e.g., OpenCV or Dlib) to detect faces in the uploaded image and analyze skin, eye, and hair colors. Specifically, it uses a face detection algorithm to identify the face area and obtain pixel color information within the face area. This color information is organized as numerical data, expressed as, for example, HSV (hue, saturation, brightness) values.

[0567] Based on the analysis results, a generative artificial intelligence (generative AI) model is used to diagnose the user's personal color. GPT-3 or a similar model is used as the generative AI. Specifically, data on the user's skin color, eye color, and hair color is input into the generative AI model, and a diagnosis of the personal color (such as "Spring," "Summer," "Autumn," or "Winter") is output. The diagnosed personal color is saved in the user's profile.

[0568] Furthermore, the server uses an emotion engine (e.g., emotion recognition API) to recognize emotions from the user's facial photograph. For example, by using Microsoft's Face API or Google's Cloud Vision API, emotions such as "happiness," "sadness," and "anger" can be identified. The recognized emotion information is then added to and saved in the user profile.

[0569] Next, the generation AI will make optimal fashion suggestions based on the diagnosed personal color and recognized emotional information. Specifically, the generation AI is input with the following prompt: "The user's personal color is ____, and their current emotional state is ____. Please suggest the optimal fashion coordination." Based on this prompt, the generation AI will suggest fashion items and coordinations.

[0570] The user tries on the proposed fashion and uploads the photo back to the system. The server analyzes the re-uploaded image and evaluates the suitability of the proposed fashion. Specifically, it evaluates the degree of match between the original skin color, eye color, and hair color and the new image, and calculates the average of these as a suitability score. The evaluation results are saved in the user profile and notified to the user.

[0571] With the above system, users can receive optimal fashion suggestions that match their personal color and emotional state, and can also check the suitability of the suggestions.

[0572] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0573] Step 1:

[0574] Users take a photo of their face using a device (such as a smartphone or PC) and upload it to the system.

[0575] Input: A portrait taken by the user

[0576] Output: Image data transmission request from the device to the server

[0577] Specifically, the user takes a photo of their face using the device's camera app, selects the image file on the system's upload screen, and clicks the upload button to send the image data to the server.

[0578] Step 2:

[0579] The server stores the received image data in a temporary directory, for example, the / tmp / uploads / directory.

[0580] Input: Image data sent from the device

[0581] Output: Notification that the image file has been saved to the storage location

[0582] Specifically, the server receives the HTTP request, writes the image data to the save directory, and if the save is successful, returns a save completion response to the device.

[0583] Step 3:

[0584] The server uses image recognition technology (e.g., OpenCV or Dlib) to detect faces from uploaded images and analyze skin tone, eye color, and hair color.

[0585] Input: Saved image data

[0586] Output: Numerical data of skin color, eye color, and hair color as analysis results

[0587] Specifically, a face detection algorithm is used to identify the face area, and pixel color information within the face area is collected and converted into HSV (hue, saturation, brightness) values.These analysis results are then stored on the server as numerical data.

[0588] Step 4:

[0589] The server uses a generative artificial intelligence (generative AI) model to diagnose the user's personal color based on the analysis results.

[0590] Input: Numerical data for skin color, eye color, and hair color

[0591] Output: Diagnosed personal color

[0592] Specifically, data on skin color, eye color, and hair color is input into the generative AI model, which outputs a personal color (e.g., "Spring," "Summer," "Autumn," or "Winter"). The generated personal color information is saved in the user profile.

[0593] Step 5:

[0594] The server uses an emotion engine (e.g., emotion recognition API) to recognize emotions from the user's facial photograph.

[0595] Input: Uploaded face photo

[0596] Output: Recognized emotion information

[0597] Specifically, facial images are sent to an emotion recognition API to obtain emotional states such as "happiness," "sadness," and "anger." This emotional information is then added to and saved in the user profile.

[0598] Step 6:

[0599] The server uses generative AI to suggest optimal fashion and color coordination based on the diagnosed personal color and recognized emotional information.

[0600] Input: User's personal color, emotional state

[0601] Output: Suggested fashion and color coordination

[0602] Specifically, the AI ​​generates a prompt message such as, "The user's personal color is ____, and their current emotional state is ____. Please suggest the best fashion coordination for them." The AI ​​then outputs the generated fashion suggestions. These suggestions are then sent back to the user.

[0603] Step 7:

[0604] The user tries on the suggested fashions and uploads the photos back to the system.

[0605] Input: A photo of you trying out the suggested fashion

[0606] Output: A request to send new image data from the device to the server

[0607] Specifically, the user tries on the proposed fashion, takes a photo of the outfit, and clicks the upload button. The image data is then sent to the server.

[0608] Step 8:

[0609] The server analyzes newly uploaded images and evaluates the suitability of the proposed fashions.

[0610] Input: Newly uploaded image, original skin color, eye color, and hair color data

[0611] Output: Evaluation score

[0612] Specifically, the system evaluates the degree of match between the original skin color, eye color, and hair color and the new image, and calculates the average of these as a fitness score. The evaluation results are saved in the user profile and notified to the user.

[0613] This allows users to receive fashion suggestions that are best suited to their personal color and emotional state, and also to check the suitability of those suggestions.

[0614] (Application example 2)

[0615] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0616] In the modern fashion industry, suggestions based on personal color diagnosis are becoming commonplace, but there is still no system that can provide personalized fashion suggestions that take the user's emotions into account. Furthermore, there are limited ways to receive these suggestions in real time while shopping in a physical store. Therefore, there is a need for a system that can take the user's emotions into account and instantly suggest the most suitable fashion items in a physical store.

[0617] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload a portrait photo of themselves, means for analyzing skin color, eye color, and hair color from the uploaded photo using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for proposing optimal fashion and color coordination based on the diagnosed personal color, emotion recognition means for recognizing the user's emotional state, means for adjusting the proposed fashion and color coordination based on the emotional state, means for displaying the proposal through a smart wearable device, means for re-uploading an image when the user tries on the proposed fashion, and means for analyzing the re-uploaded image and evaluating the suitability of the proposed fashion. This enables personalized fashion proposals that take the user's emotions into account in real time.

[0618] "User" refers to an individual who uses the system.

[0619] A "portrait" refers to an image file that shows the user's face.

[0620] "Image recognition technology" refers to the technology that allows computers to extract and analyze specific information from image data.

[0621] "Generative AI" refers to AI that generates new data and information using technologies such as machine learning and deep learning.

[0622] "Personal color" refers to a color palette classified based on an individual's skin tone, eye color, hair color, etc.

[0623] "Color coordination" refers to the way colors of fashion items are combined.

[0624] "Emotion recognizer" refers to technology for identifying a user's emotional state.

[0625] The term "smart wearable device" refers to a computer worn by a user, including, for example, smart glasses.

[0626] "Means for displaying suggestions" refers to the technology or device used to show the system-generated suggestions to the user.

[0627] "Suitability" refers to the degree to which the suggested fashion item suits the user.

[0628] The "evaluation score" is a numerical representation of the suitability, and is an indicator of the effectiveness of the proposal.

[0629] The present invention provides a system that diagnoses a user's personal color and emotional state and suggests optimal fashion and color coordination based on the results. Furthermore, this system has the feature of displaying suggestions in real time in a physical store using a smart wearable device and instantly reflecting the user's evaluation.

[0630] First, the user takes a photo of their face using the camera on their smart glasses and uploads it to the system. This photo is then sent to a server, where image recognition technology such as OpenCV is used to analyze the user's skin tone, eye color, and hair color from the photo. The analyzed data is then organized into numerical values.

[0631] The server then uses generative artificial intelligence to further analyze this color information and diagnose the user's personal color. The generative artificial intelligence models used include machine learning and deep learning algorithms. The results of this diagnosis are saved in the user's profile. Furthermore, emotion recognition technology is used to recognize the user's emotional state. One technology used for this is EmotionRecognizer.

[0632] Based on the diagnosed personal color and emotional information, the server uses a generative AI model to suggest optimal fashion and color coordination. This includes suggesting fashion items with colors and styles that are best suited to the user's emotional state. The suggestions are displayed in real time on the smart glasses.

[0633] For example, if the user's personal color is "spring" and the emotion is recognized as "joy," items that enhance brightness and vitality, such as a light green skirt, are suggested.

[0634] An example prompt is:

[0635] "The user's personal color is 'spring' and their emotion is 'joy.' If you were to make fashion suggestions based on these conditions, what items would be best? Please focus on items with bright colors that bring out their energy."

[0636] When a user tries on a suggested fashion and uploads the photo back to the system, the server analyzes the new image and evaluates its suitability. This suitability is calculated based on the average of the individual skin, eye, and hair color matches. This evaluation result is also saved in the user's profile and will be used for future suggestions.

[0637] As described above, this system can provide real-time fashion suggestions in real stores that take into account the user's personal color and emotional information, significantly improving the user experience, allowing users to make practical fashion choices with high satisfaction.

[0638] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0639] Step 1:

[0640] A user takes a portrait of themselves using the camera on their smart glasses and uploads the portrait to the system. The input is the user's portrait, and the output is image data sent to the server. Specifically, after taking a picture with the camera on the smart glasses, the saved image file is uploaded to the server.

[0641] Step 2:

[0642] The server analyzes the uploaded facial photo using image recognition technology. The input is the uploaded facial photo, and the output is the analysis results of skin color, eye color, and hair color. Specifically, OpenCV is used to identify areas of each color from the facial photo and organize them as numerical data.

[0643] Step 3:

[0644] The server then uses artificial intelligence to analyze the analyzed color information and diagnose the user's personal color. The input is numerical data for skin color, eye color, and hair color, and the output is the user's personal color. Specifically, a machine learning algorithm is used to determine the personal color through a predictive model.

[0645] Step 4:

[0646] The server uses emotion recognition technology to recognize the user's emotional state. The input is a photo of the user's face, and the output is the user's emotional information. Specifically, it uses EmotionRecognizer to classify emotions from facial expressions.

[0647] Step 5:

[0648] The server inputs personal color and emotional information into a generative AI model to suggest optimal fashion and color coordination. The input is the user's personal color and emotional information, and the output is suggested fashion items. Specifically, the generative AI model generates optimal fashion based on the prompt text and displays it on the smart glasses' display.

[0649] Step 6:

[0650] The user tries on the suggested fashion and uploads the result as a new face photo to the system. The input is the new face photo, and the output is the transmission of image data to the server. Specifically, the user takes a photo again with the smart glasses camera, saves the image, and sends it to the server.

[0651] Step 7:

[0652] The server analyzes the newly uploaded image and evaluates its suitability for the proposed fashion. The input is the newly uploaded face photo, and the output is a suitability evaluation score. Specifically, the server calculates the degree of match for skin color, eye color, and hair color, and calculates the evaluation score from the average.

[0653] Step 8:

[0654] The server saves the evaluation results in the user's profile and reflects them in future fashion suggestions. The input is the fitness evaluation score, and the output is an updated user profile. Specifically, the evaluation scores are saved in a database and used as training data for the generative AI model.

[0655] 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.

[0656] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0657] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0658] [Third embodiment]

[0659] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0660] 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.

[0661] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0662] 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.

[0663] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0664] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0665] 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.

[0666] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0667] 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 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.

[0668] 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.

[0669] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0670] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0671] The present invention is a system that allows users to understand their own personal color and select optimal fashion and color coordination based on that information. The system allows users to upload their own portrait photos and diagnoses their personal color using image recognition technology and generative artificial intelligence. Furthermore, the system suggests optimal fashion and evaluates suitability based on the diagnosed personal color.

[0672] Specifically, the process begins when a user uploads a photo of their face to the system. The device sends the user's photo to the server, which stores the received image. The server then uses image recognition technology to analyze the skin tone, eye color, and hair color from the uploaded photo. The results of this analysis are organized as numerical data.

[0673] The server uses the generative AI to diagnose the user's personal color based on the analysis results. This diagnosis result is saved in the user's profile, and the generative AI suggests optimal fashion and color coordination based on the diagnosed personal color. The suggestions are organized and provided to the user as display data.

[0674] For example, if a user is diagnosed with a "spring" personal color, the system will suggest fashion items that suit "spring" (e.g., pastel or warm-toned clothing). The user tries out the suggested fashions and outfits and uploads the results to the system as a new photo. The server receives this new image and analyzes it again using image recognition technology.

[0675] Finally, the server evaluates the suitability of the proposed fashion. The suitability evaluation calculates the degree of match for skin tone, eye color, and hair color individually, and calculates an evaluation score based on the average of these. This score is saved in the user profile and notified to the user. In this way, users can find the fashion and coordination that best suits their personal color, and feel confident in their everyday styling.

[0676] The features of the present invention are as follows:

[0677] 1. A system that uses image recognition technology to analyze skin color, eye color, and hair color based on portrait photos uploaded by users, and then uses generative artificial intelligence to diagnose personal color based on the analysis results.

[0678] 2. A system that uses generative AI to suggest optimal fashion and color coordination for users based on their diagnosed personal colors.

[0679] 3. A system in which users try out the suggested fashion and upload the image again, and the new image is analyzed to evaluate its suitability.

[0680] This makes it easier for users to find fashion that suits them even without specialized knowledge, improving the quality of styling in their daily lives.

[0681] The processing flow will be explained below.

[0682] Step 1:

[0683] Users use their terminal to take a photo of themselves and upload the image to the system.

[0684] Step 2:

[0685] The terminal transmits the image data uploaded by the user to the server via an HTTP request.

[0686] Step 3:

[0687] The server saves the received image data in the specified directory, assigning a unique file name to the saved image data to prevent files with the same name from being overwritten.

[0688] Step 4:

[0689] The server analyzes the stored images using image recognition technology. Specifically, it uses open source libraries (e.g., OpenCV) to perform facial recognition and extract skin color, eye color, and hair color from the images.

[0690] Step 5:

[0691] The server organizes the extracted color information (skin color, eye color, hair color) as numerical data, which allows the analysis results to be saved as numerical data for later processing.

[0692] Step 6:

[0693] The server inputs the organized numerical data into a generative artificial intelligence model (e.g., a machine learning algorithm) to diagnose the user's personal color.

[0694] Step 7:

[0695] The server saves the diagnosis results (personal color) in the user profile, allowing the user to check their personal color information at any time.

[0696] Step 8:

[0697] The server uses AI to generate optimal fashion and color coordination based on the diagnosed personal color. Specifically, it uses a model to generate appropriate coordination suggestions.

[0698] Step 9:

[0699] The server organizes the generated suggestions and sends them to the user interface as display data, allowing the user to select fashion based on these suggestions.

[0700] Step 10:

[0701] The user tries out the suggested fashion and color coordination, then takes another photo of themselves and uploads it to the system.

[0702] Step 11:

[0703] The terminal again transmits the newly captured image data to the server.

[0704] Step 12:

[0705] The server then analyzes the newly received image using image recognition technology and extracts skin color, eye color, and hair color in the same way as in the pre-processing.

[0706] Step 13:

[0707] The server compares the extracted new color information with the original personal color and evaluates the suitability of the new color. Specifically, it calculates the degree of match for each color individually and averages them to calculate an evaluation score.

[0708] Step 14:

[0709] The server stores the evaluation scores in the user's profile and notifies the user of the results using a notification system, allowing the user to see the fitness of their fashion choices.

[0710] Example 1

[0711] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0712] Conventional personal color diagnostic systems have made it difficult for users to accurately grasp their own personal colors and select optimal fashion and color coordination based on the results. Furthermore, the system lacks the functionality to evaluate the suitability of suggested fashions and provide feedback to the user. This has meant that users have to spend a lot of time and effort to find the perfect fashion for them.

[0713] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0714] In this invention, the server includes means for users to upload their portrait photos, means for analyzing skin color, eye color, and hair color from the uploaded photos using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for proposing optimal fashion and color coordination based on the diagnosed personal color, means for users to re-upload images when they try on the suggested fashion, means for analyzing the re-uploaded images and evaluating the suitability of the suggested fashion, means for saving the results of the suitability evaluation in a user profile, and means for notifying the user of the saved results. This allows users to easily select optimal fashion based on their personal color and find highly suitable coordination.

[0715] "User" means an individual who uses the system to upload a portrait photo of themselves and receive a personal color diagnosis and fashion suggestions.

[0716] "Device" means an electronic device used by a user to upload portrait photographs and re-upload photographs for suggested fashions and review.

[0717] "Server" refers to a central computer system that receives, stores, and analyzes image data sent from terminals and provides personal color diagnosis and fashion suggestions.

[0718] "Image recognition technology" is a technology that analyzes skin color, eye color, and hair color from uploaded portrait photos and extracts them as numerical data.

[0719] "Generative AI" is an AI technology that diagnoses a user's personal color based on the analysis results, and generates and suggests appropriate fashion and color coordination based on the diagnosis results.

[0720] "Personal colors" refers to a set of colors that best suit a user based on their skin tone, eye color, and hair color.

[0721] "Fashion and color coordination" refers to the suggestion of a combination of clothing and accessories that suits the user based on the diagnosed personal color.

[0722] "Suitability" is an index that evaluates how well the proposed fashion matches the user's personal color.

[0723] A "user profile" is a database that stores user-related identification information, diagnostic results, proposal results, fitness evaluation results, and the like.

[0724] "Notification" refers to the act of electronically sending information such as diagnostic results, evaluation results, and proposals from the server to the user.

[0725] This is a system that allows users to understand their own personal color and select the most suitable fashion and color coordination based on that. Users upload their portrait photos using a terminal, and the server uses image recognition technology and generative AI to diagnose their personal color and make fashion suggestions.

[0726] First, a user uploads their portrait photo to the system from a terminal. This terminal can be an electronic device such as a PC, smartphone, or tablet. At this stage, the terminal sends the uploaded image data to the server.

[0727] The server stores the received images in dedicated storage and then analyzes them using image recognition technology. Image recognition libraries such as OpenCV and TensorFlow are used. This analysis extracts the user's skin color, eye color, and hair color as numerical data. For example, skin color is quantified as RGB(231, 192, 146), eye color as RGB(89, 60, 31), and hair color as RGB(45, 35, 25).

[0728] The server then uses the extracted numerical data to diagnose the user's personal color using a generative AI (e.g., GPT-3.5). Specifically, the server inputs the following prompt into the generative AI:

[0729] "What are the personal colors of users with these skin tones, eye colors, and hair colors?"

[0730] In response to this prompt, the AI ​​generator will respond with either spring, summer, fall, or winter. This diagnosis is saved in the user's profile.

[0731] The server then uses generative AI to suggest appropriate fashion and color coordination based on the user's personal color, using prompts like the following:

[0732] "Please suggest fashion items suitable for users with spring personal colors."

[0733] The generative AI generates specific fashion items and outfit suggestions (e.g., a pastel-colored top and warm-colored bottoms), and these suggestions are notified to the user.

[0734] The user tries on the proposed fashion, takes a new photo of the results, and uploads it back to the system. The server then performs a new analysis and evaluates the user's suitability for the proposed fashion. This evaluation involves calculating the degree of match for skin tone, eye color, and hair color individually, and calculating an average score. The evaluation results are saved in the user's profile and notified to the user.

[0735] For example, if a user's personal color is diagnosed as "spring" and the suggested fashion items are pastel or warm-toned clothing, the suitability evaluation allows the user to confirm numerically how well these items suit the user. In this way, users can easily select the optimal fashion based on their personal color and find highly suitable outfits.

[0736] As described above, the present invention provides a system that enables users to efficiently search for the fashion that best suits them, thereby increasing users' confidence in their styling.

[0737] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0738] Step 1:

[0739] A user accesses the system's web interface from a terminal, selects and uploads their own portrait. The input is the user's portrait, and the output is an image transfer request to the server. At this stage, the terminal sends the image file to the server as an HTTP request.

[0740] Step 2:

[0741] The device receives an upload request from the user and sends the portrait photo to the server. The server saves the received image data in dedicated storage. The input is the image data sent from the device, and the output is the image file saved in the storage.

[0742] Step 3:

[0743] The server reads the saved images from storage and analyzes them using image recognition technology. Libraries such as OpenCV and TensorFlow are used here. The input is the saved image data, and the output is analyzed numerical data (skin color, eye color, hair color). Specifically, it analyzes pixel values ​​and extracts the color of each element in RGB format.

[0744] Step 4:

[0745] The server passes the analyzed numerical data to a generative artificial intelligence (generative AI) that diagnoses the user's personal color. The input is the numerical data of the analysis results, and the output is the personal color diagnosis result. For example, GPT-3.5 is used as the generative AI, and the prompt sentence "What is the personal color of a user with these skin tones, eye colors, and hair colors?" is input.

[0746] Step 5:

[0747] Based on the diagnosed personal color, the server uses a generation AI to suggest optimal fashion and color coordination to the user. The input is the personal color diagnosis result, and the output is specific fashion suggestions. The generation AI is input with a prompt statement such as "Please suggest fashion items that are suitable for the user's spring personal color," and the AI ​​generates the suggestions.

[0748] Step 6:

[0749] The server notifies the user of the generated fashion suggestions. The input is the fashion suggestions from the generation AI, and the output is the fashion suggestion data displayed on the user's device.

[0750] Step 7:

[0751] The user tries out the suggested fashions and uploads the resulting photo to the system again. The input is the newly taken user photo, and the output is an image transmission request to the server. At this stage, the device again sends the image to the server as an HTTP request.

[0752] Step 8:

[0753] The server then analyzes the received image again using image recognition technology and evaluates its suitability for the proposed fashion. The input is the newly uploaded image data, and the output is a numerical evaluation of suitability. Again, the color of each element is extracted in RGB format, and the analysis is performed in the same way as the first time.

[0754] Step 9:

[0755] The server saves the results of the fitness evaluation in the user profile and notifies the user. The input is the fitness evaluation result, and the output is the data saved in the user profile and the notification data to the user. Specific operations include calculating the degree of match individually and calculating their average score.

[0756] In this way, the present system operates through processing steps that allow the user to easily select and evaluate fashion based on personal color.

[0757] (Application example 1)

[0758] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0759] Conventional personal color diagnostic systems only perform a diagnosis based on a still image of the user, and are limited to fashion suggestions and fitness evaluations based on the diagnosis results. However, no systems exist that can be effectively utilized in brick-and-mortar shopping or real-time fashion consulting environments. This has resulted in a lack of support for users when selecting optimal fashion items on the spot. Therefore, there is a need for a system that allows users to use a smart device in a brick-and-mortar store to receive real-time suggestions for fashion items based on their personal color.

[0760] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0761] In this invention, the server includes: means for a user to upload a portrait photograph of themselves; means for analyzing skin color, eye color, and hair color from the uploaded image using image recognition technology; means for diagnosing the user's personal color based on the analysis result using generative artificial intelligence; means for suggesting optimal fashion and color coordination based on the diagnosed personal color; means for the user to re-upload an image when trying on the suggested fashion; means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion; and means for the user to use their own smart device to be suggested fashion items based on their own personal color in real time at a physical store. This allows the user to use their smart device in a physical store to have fashion items that are optimal for their personal color suggested in real time.

[0762] "Means for users to upload their portraits" refers to the functionality of the device or software that allows users to submit images of themselves to the system.

[0763] "Image recognition technology" is a technology that uses computer vision to analyze images and extract specific attributes and information.

[0764] "Skin color" refers to the skin tone seen on the user's face, hands, etc.

[0765] "Eye color" is the color of the user's iris.

[0766] "Hair color" refers to the color of the user's hair.

[0767] "Generative artificial intelligence" is a technology that uses machine learning and deep learning to analyze data and generate new information.

[0768] "Means for diagnosing personal color" refers to the function of a device or software that identifies the user's optimal color based on analyzed data.

[0769] The "means for proposing optimal fashion and color coordination" refers to a device or software function that suggests appropriate clothing and color combinations to the user based on the diagnostic results.

[0770] "Means for re-uploading images" refers to the functionality of the device or software that allows a user to submit new images to the system.

[0771] The "means for evaluating the suitability of the proposed fashion" is a function of a device or software that evaluates how suitable the fashion tried by the user is.

[0772] "Smart devices" are mobile information terminals that can connect to the Internet, such as smartphones and smart glasses.

[0773] A "physical store" is a store in a physical location where users can purchase products in person.

[0774] "Means of making real-time suggestions" refers to the functionality of devices or software that instantly present appropriate information and suggestions to users on the spot.

[0775] This invention is a system that allows users to understand their own personal color and select optimal fashion and color coordination based on that information. The system allows users to upload their own portrait photos and diagnoses their personal color using image recognition technology and generative artificial intelligence. Furthermore, the system makes optimal fashion suggestions and evaluates suitability based on the diagnosed personal color. The same process can be provided in real time in physical stores.

[0776] Specifically, it operates as follows.

[0777] Hardware, software, and data processing used

[0778] The device (smartphone or smart glasses) is used by the user to take and upload a portrait of themselves, which is then sent to a server, where image processing libraries such as OpenCV are used to analyze the image and extract skin, eye, and hair color.

[0779] The server then uses a generative AI model such as TensorFlow to diagnose the user's personal color based on the extracted data. The diagnosis results are classified into one of three color categories: spring, summer, autumn, or winter. Once the diagnosis results are obtained, the server uses generative AI to suggest fashion items and color coordination that best fit the user's personal color.

[0780] Furthermore, the user tries on the proposed fashion and re-uploads the photo to evaluate its suitability. The re-uploaded image is analyzed again, and the suitability of the proposed fashion is calculated based on how well it suits the user. Suitability is calculated by calculating the match of skin color, eye color, and hair color individually, and calculating an evaluation score based on the average of these.

[0781] In physical stores, users can use their smart devices to receive a real-time personal color diagnosis and fashion item suggestions, allowing them to instantly find the perfect items for themselves in-store.

[0782] Specific examples

[0783] Suppose a user is shopping in a physical store. The user takes out their smartphone, launches an application, takes a photo, and uploads it to the system. The server receives the photo, analyzes it, and determines that the user's personal color is "autumn." The system then suggests fashion items that are suitable for "autumn" (for example, a bronze skirt or an olive green top). The user can then accept the suggestions, browse the products in the store, and choose the fashion items that best suit them.

[0784] Prompt Sentence Examples

[0785] Here are some examples of prompts for generative AI models:

[0786] Analyze portrait photos uploaded by users and diagnose their personal color based on their skin tone, eye color, and hair color. The result of the diagnosis should be either "spring, summer, autumn, or winter," and suggest appropriate fashion items based on that result.

[0787] The embodiments of the present invention allow users to easily find fashion that suits them even without specialized knowledge, improving the quality of their styling in everyday life. Furthermore, even when shopping in a physical store, users can receive appropriate advice in real time, allowing them to enjoy a more fulfilling shopping experience.

[0788] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0789] Step 1:

[0790] Users take a portrait of themselves with their smartphone or smart glasses and upload it to a server through the application.

[0791] Input: A portrait of the user (image file).

[0792] Output: Image file saved on the server.

[0793] Specific behavior: A user launches the application, takes a portrait of themselves using the camera function, and uploads the image to the server using the application's send function.

[0794] Step 2:

[0795] The server analyzes the images it receives using image processing libraries such as OpenCV to extract skin color, eye color, and hair color.

[0796] Input: A portrait of the user stored on the server.

[0797] Output: Extracted image data feature values ​​(skin color, eye color, hair color).

[0798] Specific operation: The server program uses OpenCV to read the image and executes a color analysis algorithm to extract skin color, eye color, and hair color as numerical data.

[0799] Step 3:

[0800] The server uses generative artificial intelligence (generative AI model) to diagnose the user's personal color based on the extracted data.

[0801] Input: Extracted image data feature values.

[0802] Output: Personal color analysis result (spring, summer, autumn, or winter).

[0803] Specific operation: The server program inputs the extracted feature values ​​into the generative AI model, performs a personal color diagnosis, and obtains the diagnosis results using a prompt sentence for the generative AI model.

[0804] Step 4:

[0805] The server uses generative AI based on the diagnostic results to suggest fashion and color coordination that best suits the user's personal color.

[0806] Input: Personal color analysis results.

[0807] Output: Suggested fashion items and color coordination.

[0808] Specific operation: Based on the diagnosis results, the server program inputs a list of appropriate fashion items into the generation AI, and suggests items that suit each personal color.

[0809] Step 5:

[0810] The user tries on the suggested fashion and uploads the photo to the server again through the application.

[0811] Input: A portrait of the user wearing the fashion they're trying on.

[0812] Output: A new image file saved on the server.

[0813] Specific operation: The user tries on the suggested fashion, takes another photo using their smart device, and uploads it to the server via the application.

[0814] Step 6:

[0815] The server analyzes the re-uploaded image and evaluates its suitability for the proposed fashion.

[0816] Input: A re-uploaded portrait of the user.

[0817] Output: Fitness evaluation score.

[0818] How it works: The server analyzes the newly uploaded image again using OpenCV to extract skin, eye, and hair color, and evaluates its suitability using a generative AI model. The evaluation results are calculated as a score and notified to the user. This score is also saved in the user's profile.

[0819] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0820] This invention is a system that allows users to understand their own personal colors and select optimal fashion and color coordination based on those colors, and also incorporates an emotion engine that recognizes the user's emotions to provide more personalized fashion suggestions. This system allows users to upload their own portrait photos, and uses image recognition technology and generative AI to diagnose their personal colors, and further uses the emotion engine to recognize the user's emotions.

[0821] Specifically, the process begins when a user uploads a photo of their face to the system. The device sends the user's photo to the server, which stores the received image. The server then uses image recognition technology to analyze the skin tone, eye color, and hair color from the uploaded photo. The results of this analysis are organized as numerical data.

[0822] The server uses the generative AI to diagnose the user's personal color based on the analysis results. This diagnosis result is saved in the user's profile, and the generative AI suggests optimal fashion and color coordination based on the diagnosed personal color. The suggestions are organized and provided to the user as display data.

[0823] In addition, this invention incorporates an emotion engine to recognize emotions from a user's facial photograph. The server recognizes the user's emotional state and reflects this information, along with the user's personal color diagnosis results, in the suggested fashion and color coordination. For example, if the user's personal color is "spring" and the emotion engine recognizes the user's emotion as "joy," it can suggest fashion items in relatively bright colors that enhance the user's energy.

[0824] The detailed operation of the system will be described below.

[0825] First, the user takes a photo of their face and uploads it to the system. The device then sends the image to a server, which stores it and analyzes it using image recognition technology. The analysis identifies the user's skin color, eye color, and hair color, and organizes this color information into numerical data.

[0826] The server then uses generative artificial intelligence to analyze the color information and diagnose the user's personal color. The diagnosed personal color is saved in the user's profile. At the same time, the server uses an emotion engine to recognize the user's emotional state. This emotional information is also saved in the user's profile.

[0827] The server uses generative AI to suggest optimal fashion and color coordination based on the diagnosis results and the user's emotional information. These suggestions are adjusted to match the user's emotions, so they suggest fashion that matches the user's feelings. The user can also try on the suggested fashion and upload the photo back to the system, where the server analyzes the new image and evaluates its suitability. The suitability evaluation calculates the degree of match for skin tone, eye color, and hair color individually, and calculates an evaluation score based on the average of these.

[0828] The evaluation results are saved in the user's profile and notified to the user. This allows the user to check whether their fashion choices were appropriate and use the information to make future choices. The introduction of an emotion engine allows suggestions to be made that are also based on the user's emotional state, providing a more satisfying fashion experience.

[0829] As described above, the present invention is a system that combines a user's personal color diagnosis with emotion recognition, thereby enabling optimal fashion suggestions and fitness evaluation for the user.

[0830] The processing flow will be explained below.

[0831] Step 1:

[0832] Users take a photo of themselves and upload the image to the system.

[0833] Step 2:

[0834] The terminal transmits the image data uploaded by the user to the server via an HTTP request.

[0835] Step 3:

[0836] The server saves the received image data in the specified directory, assigning a unique file name to the file to prevent it from being overwritten.

[0837] Step 4:

[0838] The server analyzes the stored images using image recognition technology, specifically, by using open source libraries (e.g., OpenCV) to perform facial recognition and extract skin color, eye color, and hair color from the images.

[0839] Step 5:

[0840] The server organizes the extracted color information (skin color, eye color, hair color) as numerical data, which allows the analysis results to be saved as numerical data for later processing.

[0841] Step 6:

[0842] The server inputs the organized numerical data into a generative artificial intelligence model (e.g., a machine learning algorithm) to diagnose the user's personal color.

[0843] Step 7:

[0844] The server saves the diagnosis results (personal color) in the user profile, allowing the user to check their personal color information at any time.

[0845] Step 8:

[0846] The server again analyzes the user's portrait and recognizes the user's emotional state using an emotion engine that utilizes facial recognition technology to analyze the user's facial expressions and identify their emotional state.

[0847] Step 9:

[0848] The server stores the emotion information recognized by the emotion engine in the user profile, which is then used for future fashion suggestions.

[0849] Step 10:

[0850] The server uses AI to generate optimal fashion and color coordination based on the diagnosed personal color and emotional information. For example, if a user's personal color is "spring" and their emotional state is "joy," the server will suggest a bright color coordination.

[0851] Step 11:

[0852] The server organizes the generated suggestions and sends them to the user interface as display data, allowing the user to select fashion based on these suggestions.

[0853] Step 12:

[0854] The user tries out the suggested fashion and color coordination, then takes another photo of themselves and uploads it to the system.

[0855] Step 13:

[0856] The terminal again transmits the newly captured image data to the server.

[0857] Step 14:

[0858] The server then analyzes the newly received image using image recognition technology, extracting skin, eye, and hair color in the same way as in the preprocessing. It then uses an emotion engine to re-recognize the user's emotions from the new facial expressions.

[0859] Step 15:

[0860] The server evaluates the suitability of the proposed fashion based on the extracted new color and emotion information. Specifically, it calculates the degree of match for skin color, eye color, and hair color separately and averages them to calculate an evaluation score.

[0861] Step 16:

[0862] The server saves the evaluation score and the new emotional evaluation in the user's profile and notifies the user of the results using a notification system, allowing the user to check the fitness of their fashion choices and their emotional state and use this information to make future choices.

[0863] Example 2

[0864] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0865] Conventional fashion suggestion systems may make suggestions based on a user's personal color, but they do not provide personalized suggestions that take into account the user's emotional state. This can result in a failure to suggest fashion that matches the user's momentary emotions or daily mood, potentially reducing user satisfaction. Furthermore, systems lack a mechanism for evaluating how well suggested fashions suit the user's actual appearance. As a result, even if a user tries on suggested fashion items, it is difficult to objectively evaluate their suitability.

[0866] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0867] In this invention, the server includes means for a user to take and upload a portrait photo of themselves, means for analyzing skin color, eye color, and hair color from the uploaded photo using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for recognizing the user's emotional state using an emotion engine, means for suggesting optimal fashion and color coordination based on the diagnosed personal color and the recognized emotional state, means for the user to re-upload an image when trying on the suggested fashion, and means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion. This enables fashion suggestions that take into account both the user's personal color and emotional state, and enables objective evaluation of the suitability of suggested fashion items.

[0868] "User" refers to a person who uses the system to diagnose their own personal color and receive optimal fashion suggestions.

[0869] A "portrait" refers to a photograph that includes the user's face and is image data that is uploaded to the system.

[0870] "Image recognition technology" refers to a collection of algorithms and software used to analyze certain features in uploaded photos, including, for example, face detection and color analysis.

[0871] "Generative AI" refers to a group of algorithms and models that generate new information or suggestions based on given data, including natural language processing models and image generation models.

[0872] "Personal color" refers to the color tone that best suits a user, as diagnosed based on the user's skin color, eye color, hair color, etc. For example, it is classified into categories such as "spring" and "autumn."

[0873] An "emotion engine" is an algorithm or software that can recognize a user's emotional state from a photograph of their face. For example, it can identify emotions such as "joy," "sadness," and "anger."

[0874] "Fashion and color coordination" refers to suggested combinations of clothing and accessories based on the user's personal color and emotional state.

[0875] "Suitability" is an index that evaluates how well the proposed fashion matches the user's appearance. Specifically, it is a score calculated based on the degree of match of skin color, eye color, and hair color.

[0876] "User profile" refers to a database or file that stores information such as a user's personal colors, suggested fashions, emotional state, etc.

[0877] "Analysis" refers to the process of using image recognition technology to identify colors and features from uploaded photos and organize them as numerical data.

[0878] "Diagnosis" refers to the process of identifying a user's personal color based on the analysis results.

[0879] "Suggestion" refers to the act of providing the user with optimal fashion and color coordination based on the diagnosed personal color and emotional state.

[0880] The present invention is a system that allows users to understand their own personal colors and select optimal fashion and color coordination based on those colors. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the system can provide more personalized fashion suggestions. The following specific procedures and techniques are used to implement this system.

[0881] First, a user takes a photo of their face using a device (e.g., a smartphone or PC) and uploads it to the system. The device then sends the uploaded image to the server.

[0882] The server saves the received image data in a temporary directory, for example, the / tmp / uploads / directory, and returns a response to the terminal notifying that the image was successfully saved.

[0883] Next, the server uses an image processing library (e.g., OpenCV or Dlib) to detect faces in the uploaded image and analyze skin, eye, and hair colors. Specifically, it uses a face detection algorithm to identify the face area and obtain pixel color information within the face area. This color information is organized as numerical data, expressed as, for example, HSV (hue, saturation, brightness) values.

[0884] Based on the analysis results, a generative artificial intelligence (generative AI) model is used to diagnose the user's personal color. GPT-3 or a similar model is used as the generative AI. Specifically, data on the user's skin color, eye color, and hair color is input into the generative AI model, and a diagnosis of the personal color (such as "Spring," "Summer," "Autumn," or "Winter") is output. The diagnosed personal color is saved in the user's profile.

[0885] Furthermore, the server uses an emotion engine (e.g., emotion recognition API) to recognize emotions from the user's facial photograph. For example, by using Microsoft's Face API or Google's Cloud Vision API, emotions such as "happiness," "sadness," and "anger" can be identified. The recognized emotion information is then added to and saved in the user profile.

[0886] Next, the generation AI will make optimal fashion suggestions based on the diagnosed personal color and recognized emotional information. Specifically, the generation AI is input with the following prompt: "The user's personal color is ____, and their current emotional state is ____. Please suggest the optimal fashion coordination." Based on this prompt, the generation AI will suggest fashion items and coordinations.

[0887] The user tries on the proposed fashion and uploads the photo back to the system. The server analyzes the re-uploaded image and evaluates the suitability of the proposed fashion. Specifically, it evaluates the degree of match between the original skin color, eye color, and hair color and the new image, and calculates the average of these as a suitability score. The evaluation results are saved in the user profile and notified to the user.

[0888] With the above system, users can receive optimal fashion suggestions that match their personal color and emotional state, and can also check the suitability of the suggestions.

[0889] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0890] Step 1:

[0891] Users take a photo of their face using a device (such as a smartphone or PC) and upload it to the system.

[0892] Input: A portrait taken by the user

[0893] Output: Image data transmission request from the device to the server

[0894] Specifically, the user takes a photo of their face using the device's camera app, selects the image file on the system's upload screen, and clicks the upload button to send the image data to the server.

[0895] Step 2:

[0896] The server stores the received image data in a temporary directory, for example, the / tmp / uploads / directory.

[0897] Input: Image data sent from the device

[0898] Output: Notification that the image file has been saved to the storage location

[0899] Specifically, the server receives the HTTP request, writes the image data to the save directory, and if the save is successful, returns a save completion response to the device.

[0900] Step 3:

[0901] The server uses image recognition technology (e.g., OpenCV or Dlib) to detect faces from uploaded images and analyze skin tone, eye color, and hair color.

[0902] Input: Saved image data

[0903] Output: Numerical data of skin color, eye color, and hair color as analysis results

[0904] Specifically, a face detection algorithm is used to identify the face area, and pixel color information within the face area is collected and converted into HSV (hue, saturation, brightness) values.These analysis results are then stored on the server as numerical data.

[0905] Step 4:

[0906] The server uses a generative artificial intelligence (generative AI) model to diagnose the user's personal color based on the analysis results.

[0907] Input: Numerical data for skin color, eye color, and hair color

[0908] Output: Diagnosed personal color

[0909] Specifically, data on skin color, eye color, and hair color is input into the generative AI model, which outputs a personal color (e.g., "Spring," "Summer," "Autumn," or "Winter"). The generated personal color information is saved in the user profile.

[0910] Step 5:

[0911] The server uses an emotion engine (e.g., emotion recognition API) to recognize emotions from the user's facial photograph.

[0912] Input: Uploaded face photo

[0913] Output: Recognized emotion information

[0914] Specifically, facial images are sent to an emotion recognition API to obtain emotional states such as "happiness," "sadness," and "anger." This emotional information is then added to and saved in the user profile.

[0915] Step 6:

[0916] The server uses generative AI to suggest optimal fashion and color coordination based on the diagnosed personal color and recognized emotional information.

[0917] Input: User's personal color, emotional state

[0918] Output: Suggested fashion and color coordination

[0919] Specifically, the AI ​​generates a prompt message such as, "The user's personal color is ____, and their current emotional state is ____. Please suggest the best fashion coordination for them." The AI ​​then outputs the generated fashion suggestions. These suggestions are then sent back to the user.

[0920] Step 7:

[0921] The user tries on the suggested fashions and uploads the photos back to the system.

[0922] Input: A photo of you trying out the suggested fashion

[0923] Output: A request to send new image data from the device to the server

[0924] Specifically, the user tries on the proposed fashion, takes a photo of the outfit, and clicks the upload button. The image data is then sent to the server.

[0925] Step 8:

[0926] The server analyzes newly uploaded images and evaluates the suitability of the proposed fashions.

[0927] Input: Newly uploaded image, original skin color, eye color, and hair color data

[0928] Output: Evaluation score

[0929] Specifically, the system evaluates the degree of match between the original skin color, eye color, and hair color and the new image, and calculates the average of these as a fitness score. The evaluation results are saved in the user profile and notified to the user.

[0930] This allows users to receive fashion suggestions that are best suited to their personal color and emotional state, and also to check the suitability of those suggestions.

[0931] (Application example 2)

[0932] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0933] In the modern fashion industry, suggestions based on personal color diagnosis are becoming commonplace, but there is still no system that can provide personalized fashion suggestions that take the user's emotions into account. Furthermore, there are limited ways to receive these suggestions in real time while shopping in a physical store. Therefore, there is a need for a system that can take the user's emotions into account and instantly suggest the most suitable fashion items in a physical store.

[0934] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload a portrait photo of themselves, means for analyzing skin color, eye color, and hair color from the uploaded photo using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for proposing optimal fashion and color coordination based on the diagnosed personal color, emotion recognition means for recognizing the user's emotional state, means for adjusting the proposed fashion and color coordination based on the emotional state, means for displaying the proposal through a smart wearable device, means for re-uploading an image when the user tries on the proposed fashion, and means for analyzing the re-uploaded image and evaluating the suitability of the proposed fashion. This enables personalized fashion proposals that take the user's emotions into account in real time.

[0935] "User" refers to an individual who uses the system.

[0936] A "portrait" refers to an image file that shows the user's face.

[0937] "Image recognition technology" refers to the technology that allows computers to extract and analyze specific information from image data.

[0938] "Generative AI" refers to AI that generates new data and information using technologies such as machine learning and deep learning.

[0939] "Personal color" refers to a color palette classified based on an individual's skin tone, eye color, hair color, etc.

[0940] "Color coordination" refers to the way colors of fashion items are combined.

[0941] "Emotion recognizer" refers to technology for identifying a user's emotional state.

[0942] The term "smart wearable device" refers to a computer worn by a user, including, for example, smart glasses.

[0943] "Means for displaying suggestions" refers to the technology or device used to show the system-generated suggestions to the user.

[0944] "Suitability" refers to the degree to which the suggested fashion item suits the user.

[0945] The "evaluation score" is a numerical representation of the suitability, and is an indicator of the effectiveness of the proposal.

[0946] The present invention provides a system that diagnoses a user's personal color and emotional state and suggests optimal fashion and color coordination based on the results. Furthermore, this system has the feature of displaying suggestions in real time in a physical store using a smart wearable device and instantly reflecting the user's evaluation.

[0947] First, the user takes a photo of their face using the camera on their smart glasses and uploads it to the system. This photo is then sent to a server, where image recognition technology such as OpenCV is used to analyze the user's skin tone, eye color, and hair color from the photo. The analyzed data is then organized into numerical values.

[0948] The server then uses generative artificial intelligence to further analyze this color information and diagnose the user's personal color. The generative artificial intelligence models used include machine learning and deep learning algorithms. The results of this diagnosis are saved in the user's profile. Furthermore, emotion recognition technology is used to recognize the user's emotional state. One technology used for this is EmotionRecognizer.

[0949] Based on the diagnosed personal color and emotional information, the server uses a generative AI model to suggest optimal fashion and color coordination. This includes suggesting fashion items with colors and styles that are best suited to the user's emotional state. The suggestions are displayed in real time on the smart glasses.

[0950] For example, if the user's personal color is "spring" and the emotion is recognized as "joy," items that enhance brightness and vitality, such as a light green skirt, are suggested.

[0951] An example prompt is:

[0952] "The user's personal color is 'spring' and their emotion is 'joy.' If you were to make fashion suggestions based on these conditions, what items would be best? Please focus on items with bright colors that bring out their energy."

[0953] When a user tries on a suggested fashion and uploads the photo back to the system, the server analyzes the new image and evaluates its suitability. This suitability is calculated based on the average of the individual skin, eye, and hair color matches. This evaluation result is also saved in the user's profile and will be used for future suggestions.

[0954] As described above, this system can provide real-time fashion suggestions in real stores that take into account the user's personal color and emotional information, significantly improving the user experience, allowing users to make practical fashion choices with high satisfaction.

[0955] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0956] Step 1:

[0957] A user takes a portrait of themselves using the camera on their smart glasses and uploads the portrait to the system. The input is the user's portrait, and the output is image data sent to the server. Specifically, after taking a picture with the camera on the smart glasses, the saved image file is uploaded to the server.

[0958] Step 2:

[0959] The server analyzes the uploaded facial photo using image recognition technology. The input is the uploaded facial photo, and the output is the analysis results of skin color, eye color, and hair color. Specifically, OpenCV is used to identify areas of each color from the facial photo and organize them as numerical data.

[0960] Step 3:

[0961] The server then uses artificial intelligence to analyze the analyzed color information and diagnose the user's personal color. The input is numerical data for skin color, eye color, and hair color, and the output is the user's personal color. Specifically, a machine learning algorithm is used to determine the personal color through a predictive model.

[0962] Step 4:

[0963] The server uses emotion recognition technology to recognize the user's emotional state. The input is a photo of the user's face, and the output is the user's emotional information. Specifically, it uses EmotionRecognizer to classify emotions from facial expressions.

[0964] Step 5:

[0965] The server inputs personal color and emotional information into a generative AI model to suggest optimal fashion and color coordination. The input is the user's personal color and emotional information, and the output is suggested fashion items. Specifically, the generative AI model generates optimal fashion based on the prompt text and displays it on the smart glasses' display.

[0966] Step 6:

[0967] The user tries on the suggested fashion and uploads the result as a new face photo to the system. The input is the new face photo, and the output is the transmission of image data to the server. Specifically, the user takes a photo again with the smart glasses camera, saves the image, and sends it to the server.

[0968] Step 7:

[0969] The server analyzes the newly uploaded image and evaluates its suitability for the proposed fashion. The input is the newly uploaded face photo, and the output is a suitability evaluation score. Specifically, the server calculates the degree of match for skin color, eye color, and hair color, and calculates the evaluation score from the average.

[0970] Step 8:

[0971] The server saves the evaluation results in the user's profile and reflects them in future fashion suggestions. The input is the fitness evaluation score, and the output is an updated user profile. Specifically, the evaluation scores are saved in a database and used as training data for the generative AI model.

[0972] 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.

[0973] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0974] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0975] [Fourth embodiment]

[0976] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0977] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0978] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0979] 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.

[0980] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0981] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0982] 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.

[0983] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[0984] 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.

[0985] 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 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.

[0986] 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.

[0987] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0988] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0989] The present invention is a system that allows users to understand their own personal color and select optimal fashion and color coordination based on that information. The system allows users to upload their own portrait photos and diagnoses their personal color using image recognition technology and generative artificial intelligence. Furthermore, the system suggests optimal fashion and evaluates suitability based on the diagnosed personal color.

[0990] Specifically, the process begins when a user uploads a photo of their face to the system. The device sends the user's photo to the server, which stores the received image. The server then uses image recognition technology to analyze the skin tone, eye color, and hair color from the uploaded photo. The results of this analysis are organized as numerical data.

[0991] The server uses the generative AI to diagnose the user's personal color based on the analysis results. This diagnosis result is saved in the user's profile, and the generative AI suggests optimal fashion and color coordination based on the diagnosed personal color. The suggestions are organized and provided to the user as display data.

[0992] For example, if a user is diagnosed with a "spring" personal color, the system will suggest fashion items that suit "spring" (e.g., pastel or warm-toned clothing). The user tries out the suggested fashions and outfits and uploads the results to the system as a new photo. The server receives this new image and analyzes it again using image recognition technology.

[0993] Finally, the server evaluates the suitability of the proposed fashion. The suitability evaluation calculates the degree of match for skin tone, eye color, and hair color individually, and calculates an evaluation score based on the average of these. This score is saved in the user profile and notified to the user. In this way, users can find the fashion and coordination that best suits their personal color, and feel confident in their everyday styling.

[0994] The features of the present invention are as follows:

[0995] 1. A system that uses image recognition technology to analyze skin color, eye color, and hair color based on portrait photos uploaded by users, and then uses generative artificial intelligence to diagnose personal color based on the analysis results.

[0996] 2. A system that uses generative AI to suggest optimal fashion and color coordination for users based on their diagnosed personal colors.

[0997] 3. A system in which users try out the suggested fashion and upload the image again, and the new image is analyzed to evaluate its suitability.

[0998] This makes it easier for users to find fashion that suits them even without specialized knowledge, improving the quality of styling in their daily lives.

[0999] The processing flow will be explained below.

[1000] Step 1:

[1001] Users use their terminal to take a photo of themselves and upload the image to the system.

[1002] Step 2:

[1003] The terminal transmits the image data uploaded by the user to the server via an HTTP request.

[1004] Step 3:

[1005] The server saves the received image data in the specified directory, assigning a unique file name to the saved image data to prevent files with the same name from being overwritten.

[1006] Step 4:

[1007] The server analyzes the stored images using image recognition technology. Specifically, it uses open source libraries (e.g., OpenCV) to perform facial recognition and extract skin color, eye color, and hair color from the images.

[1008] Step 5:

[1009] The server organizes the extracted color information (skin color, eye color, hair color) as numerical data, which allows the analysis results to be saved as numerical data for later processing.

[1010] Step 6:

[1011] The server inputs the organized numerical data into a generative artificial intelligence model (e.g., a machine learning algorithm) to diagnose the user's personal color.

[1012] Step 7:

[1013] The server saves the diagnosis results (personal color) in the user profile, allowing the user to check their personal color information at any time.

[1014] Step 8:

[1015] The server uses AI to generate optimal fashion and color coordination based on the diagnosed personal color. Specifically, it uses a model to generate appropriate coordination suggestions.

[1016] Step 9:

[1017] The server organizes the generated suggestions and sends them to the user interface as display data, allowing the user to select fashion based on these suggestions.

[1018] Step 10:

[1019] The user tries out the suggested fashion and color coordination, then takes another photo of themselves and uploads it to the system.

[1020] Step 11:

[1021] The terminal again transmits the newly captured image data to the server.

[1022] Step 12:

[1023] The server then analyzes the newly received image using image recognition technology and extracts skin color, eye color, and hair color in the same way as in the pre-processing.

[1024] Step 13:

[1025] The server compares the extracted new color information with the original personal color and evaluates the suitability of the new color. Specifically, it calculates the degree of match for each color individually and averages them to calculate an evaluation score.

[1026] Step 14:

[1027] The server stores the evaluation scores in the user's profile and notifies the user of the results using a notification system, allowing the user to see the fitness of their fashion choices.

[1028] Example 1

[1029] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1030] Conventional personal color diagnostic systems have made it difficult for users to accurately grasp their own personal colors and select optimal fashion and color coordination based on the results. Furthermore, the system lacks the functionality to evaluate the suitability of suggested fashions and provide feedback to the user. This has meant that users have to spend a lot of time and effort to find the perfect fashion for them.

[1031] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1032] In this invention, the server includes means for users to upload their portrait photos, means for analyzing skin color, eye color, and hair color from the uploaded photos using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for proposing optimal fashion and color coordination based on the diagnosed personal color, means for users to re-upload images when they try on the suggested fashion, means for analyzing the re-uploaded images and evaluating the suitability of the suggested fashion, means for saving the results of the suitability evaluation in a user profile, and means for notifying the user of the saved results. This allows users to easily select optimal fashion based on their personal color and find highly suitable coordination.

[1033] "User" means an individual who uses the system to upload a portrait photo of themselves and receive a personal color diagnosis and fashion suggestions.

[1034] "Device" means an electronic device used by a user to upload portrait photographs and re-upload photographs for suggested fashions and review.

[1035] "Server" refers to a central computer system that receives, stores, and analyzes image data sent from terminals and provides personal color diagnosis and fashion suggestions.

[1036] "Image recognition technology" is a technology that analyzes skin color, eye color, and hair color from uploaded portrait photos and extracts them as numerical data.

[1037] "Generative AI" is an AI technology that diagnoses a user's personal color based on the analysis results, and generates and suggests appropriate fashion and color coordination based on the diagnosis results.

[1038] "Personal colors" refers to a set of colors that best suit a user based on their skin tone, eye color, and hair color.

[1039] "Fashion and color coordination" refers to the suggestion of a combination of clothing and accessories that suits the user based on the diagnosed personal color.

[1040] "Suitability" is an index that evaluates how well the proposed fashion matches the user's personal color.

[1041] A "user profile" is a database that stores user-related identification information, diagnostic results, proposal results, fitness evaluation results, and the like.

[1042] "Notification" refers to the act of electronically sending information such as diagnostic results, evaluation results, and proposals from the server to the user.

[1043] This is a system that allows users to understand their own personal color and select the most suitable fashion and color coordination based on that. Users upload their portrait photos using a terminal, and the server uses image recognition technology and generative AI to diagnose their personal color and make fashion suggestions.

[1044] First, a user uploads their portrait photo to the system from a terminal. This terminal can be an electronic device such as a PC, smartphone, or tablet. At this stage, the terminal sends the uploaded image data to the server.

[1045] The server stores the received images in dedicated storage and then analyzes them using image recognition technology. Image recognition libraries such as OpenCV and TensorFlow are used. This analysis extracts the user's skin color, eye color, and hair color as numerical data. For example, skin color is quantified as RGB(231, 192, 146), eye color as RGB(89, 60, 31), and hair color as RGB(45, 35, 25).

[1046] The server then uses the extracted numerical data to diagnose the user's personal color using a generative AI (e.g., GPT-3.5). Specifically, the server inputs the following prompt into the generative AI:

[1047] "What are the personal colors of users with these skin tones, eye colors, and hair colors?"

[1048] In response to this prompt, the AI ​​generator will respond with either spring, summer, fall, or winter. This diagnosis is saved in the user's profile.

[1049] The server then uses generative AI to suggest appropriate fashion and color coordination based on the user's personal color, using prompts like the following:

[1050] "Please suggest fashion items suitable for users with spring personal colors."

[1051] The generative AI generates specific fashion items and outfit suggestions (e.g., a pastel-colored top and warm-colored bottoms), and these suggestions are notified to the user.

[1052] The user tries on the proposed fashion, takes a new photo of the results, and uploads it back to the system. The server then performs a new analysis and evaluates the user's suitability for the proposed fashion. This evaluation involves calculating the degree of match for skin tone, eye color, and hair color individually, and calculating an average score. The evaluation results are saved in the user's profile and notified to the user.

[1053] For example, if a user's personal color is diagnosed as "spring" and the suggested fashion items are pastel or warm-toned clothing, the suitability evaluation allows the user to confirm numerically how well these items suit the user. In this way, users can easily select the optimal fashion based on their personal color and find highly suitable outfits.

[1054] As described above, the present invention provides a system that enables users to efficiently search for the fashion that best suits them, thereby increasing users' confidence in their styling.

[1055] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1056] Step 1:

[1057] A user accesses the system's web interface from a terminal, selects and uploads their own portrait. The input is the user's portrait, and the output is an image transfer request to the server. At this stage, the terminal sends the image file to the server as an HTTP request.

[1058] Step 2:

[1059] The device receives an upload request from the user and sends the portrait photo to the server. The server saves the received image data in dedicated storage. The input is the image data sent from the device, and the output is the image file saved in the storage.

[1060] Step 3:

[1061] The server reads the saved images from storage and analyzes them using image recognition technology. Libraries such as OpenCV and TensorFlow are used here. The input is the saved image data, and the output is analyzed numerical data (skin color, eye color, hair color). Specifically, it analyzes pixel values ​​and extracts the color of each element in RGB format.

[1062] Step 4:

[1063] The server passes the analyzed numerical data to a generative artificial intelligence (generative AI) that diagnoses the user's personal color. The input is the numerical data of the analysis results, and the output is the personal color diagnosis result. For example, GPT-3.5 is used as the generative AI, and the prompt sentence "What is the personal color of a user with these skin tones, eye colors, and hair colors?" is input.

[1064] Step 5:

[1065] Based on the diagnosed personal color, the server uses a generation AI to suggest optimal fashion and color coordination to the user. The input is the personal color diagnosis result, and the output is specific fashion suggestions. The generation AI is input with a prompt statement such as "Please suggest fashion items that are suitable for the user's spring personal color," and the AI ​​generates the suggestions.

[1066] Step 6:

[1067] The server notifies the user of the generated fashion suggestions. The input is the fashion suggestions from the generation AI, and the output is the fashion suggestion data displayed on the user's device.

[1068] Step 7:

[1069] The user tries out the suggested fashions and uploads the resulting photo to the system again. The input is the newly taken user photo, and the output is an image transmission request to the server. At this stage, the device again sends the image to the server as an HTTP request.

[1070] Step 8:

[1071] The server then analyzes the received image again using image recognition technology and evaluates its suitability for the proposed fashion. The input is the newly uploaded image data, and the output is a numerical evaluation of suitability. Again, the color of each element is extracted in RGB format, and the analysis is performed in the same way as the first time.

[1072] Step 9:

[1073] The server saves the results of the fitness evaluation in the user profile and notifies the user. The input is the fitness evaluation result, and the output is the data saved in the user profile and the notification data to the user. Specific operations include calculating the degree of match individually and calculating their average score.

[1074] In this way, the present system operates through processing steps that allow the user to easily select and evaluate fashion based on personal color.

[1075] (Application example 1)

[1076] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1077] Conventional personal color diagnostic systems only perform a diagnosis based on a still image of the user, and are limited to fashion suggestions and fitness evaluations based on the diagnosis results. However, no systems exist that can be effectively utilized in brick-and-mortar shopping or real-time fashion consulting environments. This has resulted in a lack of support for users when selecting optimal fashion items on the spot. Therefore, there is a need for a system that allows users to use a smart device in a brick-and-mortar store to receive real-time suggestions for fashion items based on their personal color.

[1078] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1079] In this invention, the server includes: means for a user to upload a portrait photograph of themselves; means for analyzing skin color, eye color, and hair color from the uploaded image using image recognition technology; means for diagnosing the user's personal color based on the analysis result using generative artificial intelligence; means for suggesting optimal fashion and color coordination based on the diagnosed personal color; means for the user to re-upload an image when trying on the suggested fashion; means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion; and means for the user to use their own smart device to be suggested fashion items based on their own personal color in real time at a physical store. This allows the user to use their smart device in a physical store to have fashion items that are optimal for their personal color suggested in real time.

[1080] "Means for users to upload their portraits" refers to the functionality of the device or software that allows users to submit images of themselves to the system.

[1081] "Image recognition technology" is a technology that uses computer vision to analyze images and extract specific attributes and information.

[1082] "Skin color" refers to the skin tone seen on the user's face, hands, etc.

[1083] "Eye color" is the color of the user's iris.

[1084] "Hair color" refers to the color of the user's hair.

[1085] "Generative artificial intelligence" is a technology that uses machine learning and deep learning to analyze data and generate new information.

[1086] "Means for diagnosing personal color" refers to the function of a device or software that identifies the user's optimal color based on analyzed data.

[1087] The "means for proposing optimal fashion and color coordination" refers to a device or software function that suggests appropriate clothing and color combinations to the user based on the diagnostic results.

[1088] "Means for re-uploading images" refers to the functionality of the device or software that allows a user to submit new images to the system.

[1089] The "means for evaluating the suitability of the proposed fashion" is a function of a device or software that evaluates how suitable the fashion tried by the user is.

[1090] "Smart devices" are mobile information terminals that can connect to the Internet, such as smartphones and smart glasses.

[1091] A "physical store" is a store in a physical location where users can purchase products in person.

[1092] "Means of making real-time suggestions" refers to the functionality of devices or software that instantly present appropriate information and suggestions to users on the spot.

[1093] This invention is a system that allows users to understand their own personal color and select optimal fashion and color coordination based on that information. The system allows users to upload their own portrait photos and diagnoses their personal color using image recognition technology and generative artificial intelligence. Furthermore, the system makes optimal fashion suggestions and evaluates suitability based on the diagnosed personal color. The same process can be provided in real time in physical stores.

[1094] Specifically, it operates as follows.

[1095] Hardware, software, and data processing used

[1096] The device (smartphone or smart glasses) is used by the user to take and upload a portrait of themselves, which is then sent to a server, where image processing libraries such as OpenCV are used to analyze the image and extract skin, eye, and hair color.

[1097] The server then uses a generative AI model such as TensorFlow to diagnose the user's personal color based on the extracted data. The diagnosis results are classified into one of three color categories: spring, summer, autumn, or winter. Once the diagnosis results are obtained, the server uses generative AI to suggest fashion items and color coordination that best fit the user's personal color.

[1098] Furthermore, the user tries on the proposed fashion and re-uploads the photo to evaluate its suitability. The re-uploaded image is analyzed again, and the suitability of the proposed fashion is calculated based on how well it suits the user. Suitability is calculated by calculating the match of skin color, eye color, and hair color individually, and calculating an evaluation score based on the average of these.

[1099] In physical stores, users can use their smart devices to receive a real-time personal color diagnosis and fashion item suggestions, allowing them to instantly find the perfect items for themselves in-store.

[1100] Specific examples

[1101] Suppose a user is shopping in a physical store. The user takes out their smartphone, launches an application, takes a photo, and uploads it to the system. The server receives the photo, analyzes it, and determines that the user's personal color is "autumn." The system then suggests fashion items that are suitable for "autumn" (for example, a bronze skirt or an olive green top). The user can then accept the suggestions, browse the products in the store, and choose the fashion items that best suit them.

[1102] Prompt Sentence Examples

[1103] Here are some examples of prompts for generative AI models:

[1104] Analyze portrait photos uploaded by users and diagnose their personal color based on their skin tone, eye color, and hair color. The result of the diagnosis should be either "spring, summer, autumn, or winter," and suggest appropriate fashion items based on that result.

[1105] The embodiments of the present invention allow users to easily find fashion that suits them even without specialized knowledge, improving the quality of their styling in everyday life. Furthermore, even when shopping in a physical store, users can receive appropriate advice in real time, allowing them to enjoy a more fulfilling shopping experience.

[1106] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1107] Step 1:

[1108] Users take a portrait of themselves with their smartphone or smart glasses and upload it to a server through the application.

[1109] Input: A portrait of the user (image file).

[1110] Output: Image file saved on the server.

[1111] Specific behavior: A user launches the application, takes a portrait of themselves using the camera function, and uploads the image to the server using the application's send function.

[1112] Step 2:

[1113] The server analyzes the images it receives using image processing libraries such as OpenCV to extract skin color, eye color, and hair color.

[1114] Input: A portrait of the user stored on the server.

[1115] Output: Extracted image data feature values ​​(skin color, eye color, hair color).

[1116] Specific operation: The server program uses OpenCV to read the image and executes a color analysis algorithm to extract skin color, eye color, and hair color as numerical data.

[1117] Step 3:

[1118] The server uses generative artificial intelligence (generative AI model) to diagnose the user's personal color based on the extracted data.

[1119] Input: Extracted image data feature values.

[1120] Output: Personal color analysis result (spring, summer, autumn, or winter).

[1121] Specific operation: The server program inputs the extracted feature values ​​into the generative AI model, performs a personal color diagnosis, and obtains the diagnosis results using a prompt sentence for the generative AI model.

[1122] Step 4:

[1123] The server uses generative AI based on the diagnostic results to suggest fashion and color coordination that best suits the user's personal color.

[1124] Input: Personal color analysis results.

[1125] Output: Suggested fashion items and color coordination.

[1126] Specific operation: Based on the diagnosis results, the server program inputs a list of appropriate fashion items into the generation AI, and suggests items that suit each personal color.

[1127] Step 5:

[1128] The user tries on the suggested fashion and uploads the photo to the server again through the application.

[1129] Input: A portrait of the user wearing the fashion they're trying on.

[1130] Output: A new image file saved on the server.

[1131] Specific operation: The user tries on the suggested fashion, takes another photo using their smart device, and uploads it to the server via the application.

[1132] Step 6:

[1133] The server analyzes the re-uploaded image and evaluates its suitability for the proposed fashion.

[1134] Input: A re-uploaded portrait of the user.

[1135] Output: Fitness evaluation score.

[1136] How it works: The server analyzes the newly uploaded image again using OpenCV to extract skin, eye, and hair color, and evaluates its suitability using a generative AI model. The evaluation results are calculated as a score and notified to the user. This score is also saved in the user's profile.

[1137] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1138] This invention is a system that allows users to understand their own personal colors and select optimal fashion and color coordination based on those colors, and also incorporates an emotion engine that recognizes the user's emotions to provide more personalized fashion suggestions. This system allows users to upload their own portrait photos, and uses image recognition technology and generative AI to diagnose their personal colors, and further uses the emotion engine to recognize the user's emotions.

[1139] Specifically, the process begins when a user uploads a photo of their face to the system. The device sends the user's photo to the server, which stores the received image. The server then uses image recognition technology to analyze the skin tone, eye color, and hair color from the uploaded photo. The results of this analysis are organized as numerical data.

[1140] The server uses the generative AI to diagnose the user's personal color based on the analysis results. This diagnosis result is saved in the user's profile, and the generative AI suggests optimal fashion and color coordination based on the diagnosed personal color. The suggestions are organized and provided to the user as display data.

[1141] In addition, this invention incorporates an emotion engine to recognize emotions from a user's facial photograph. The server recognizes the user's emotional state and reflects this information, along with the user's personal color diagnosis results, in the suggested fashion and color coordination. For example, if the user's personal color is "spring" and the emotion engine recognizes the user's emotion as "joy," it can suggest fashion items in relatively bright colors that enhance the user's energy.

[1142] The detailed operation of the system will be described below.

[1143] First, the user takes a photo of their face and uploads it to the system. The device then sends the image to a server, which stores it and analyzes it using image recognition technology. The analysis identifies the user's skin color, eye color, and hair color, and organizes this color information into numerical data.

[1144] The server then uses generative artificial intelligence to analyze the color information and diagnose the user's personal color. The diagnosed personal color is saved in the user's profile. At the same time, the server uses an emotion engine to recognize the user's emotional state. This emotional information is also saved in the user's profile.

[1145] The server uses generative AI to suggest optimal fashion and color coordination based on the diagnosis results and the user's emotional information. These suggestions are adjusted to match the user's emotions, so they suggest fashion that matches the user's feelings. The user can also try on the suggested fashion and upload the photo back to the system, where the server analyzes the new image and evaluates its suitability. The suitability evaluation calculates the degree of match for skin tone, eye color, and hair color individually, and calculates an evaluation score based on the average of these.

[1146] The evaluation results are saved in the user's profile and notified to the user. This allows the user to check whether their fashion choices were appropriate and use the information to make future choices. The introduction of an emotion engine allows suggestions to be made that are also based on the user's emotional state, providing a more satisfying fashion experience.

[1147] As described above, the present invention is a system that combines a user's personal color diagnosis with emotion recognition, thereby enabling optimal fashion suggestions and fitness evaluation for the user.

[1148] The processing flow will be explained below.

[1149] Step 1:

[1150] Users take a photo of themselves and upload the image to the system.

[1151] Step 2:

[1152] The terminal transmits the image data uploaded by the user to the server via an HTTP request.

[1153] Step 3:

[1154] The server saves the received image data in the specified directory, assigning a unique file name to the file to prevent it from being overwritten.

[1155] Step 4:

[1156] The server analyzes the stored images using image recognition technology, specifically, by using open source libraries (e.g., OpenCV) to perform facial recognition and extract skin color, eye color, and hair color from the images.

[1157] Step 5:

[1158] The server organizes the extracted color information (skin color, eye color, hair color) as numerical data, which allows the analysis results to be saved as numerical data for later processing.

[1159] Step 6:

[1160] The server inputs the organized numerical data into a generative artificial intelligence model (e.g., a machine learning algorithm) to diagnose the user's personal color.

[1161] Step 7:

[1162] The server saves the diagnosis results (personal color) in the user profile, allowing the user to check their personal color information at any time.

[1163] Step 8:

[1164] The server again analyzes the user's portrait and recognizes the user's emotional state using an emotion engine that utilizes facial recognition technology to analyze the user's facial expressions and identify their emotional state.

[1165] Step 9:

[1166] The server stores the emotion information recognized by the emotion engine in the user profile, which is then used for future fashion suggestions.

[1167] Step 10:

[1168] The server uses AI to generate optimal fashion and color coordination based on the diagnosed personal color and emotional information. For example, if a user's personal color is "spring" and their emotional state is "joy," the server will suggest a bright color coordination.

[1169] Step 11:

[1170] The server organizes the generated suggestions and sends them to the user interface as display data, allowing the user to select fashion based on these suggestions.

[1171] Step 12:

[1172] The user tries out the suggested fashion and color coordination, then takes another photo of themselves and uploads it to the system.

[1173] Step 13:

[1174] The terminal again transmits the newly captured image data to the server.

[1175] Step 14:

[1176] The server then analyzes the newly received image using image recognition technology, extracting skin, eye, and hair color in the same way as in the preprocessing. It then uses an emotion engine to re-recognize the user's emotions from the new facial expressions.

[1177] Step 15:

[1178] The server evaluates the suitability of the proposed fashion based on the extracted new color and emotion information. Specifically, it calculates the degree of match for skin color, eye color, and hair color separately and averages them to calculate an evaluation score.

[1179] Step 16:

[1180] The server saves the evaluation score and the new emotional evaluation in the user's profile and notifies the user of the results using a notification system, allowing the user to check the fitness of their fashion choices and their emotional state and use this information to make future choices.

[1181] Example 2

[1182] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1183] Conventional fashion suggestion systems may make suggestions based on a user's personal color, but they do not provide personalized suggestions that take into account the user's emotional state. This can result in a failure to suggest fashion that matches the user's momentary emotions or daily mood, potentially reducing user satisfaction. Furthermore, systems lack a mechanism for evaluating how well suggested fashions suit the user's actual appearance. As a result, even if a user tries on suggested fashion items, it is difficult to objectively evaluate their suitability.

[1184] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1185] In this invention, the server includes means for a user to take and upload a portrait photo of themselves, means for analyzing skin color, eye color, and hair color from the uploaded photo using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for recognizing the user's emotional state using an emotion engine, means for suggesting optimal fashion and color coordination based on the diagnosed personal color and the recognized emotional state, means for the user to re-upload an image when trying on the suggested fashion, and means for analyzing the re-uploaded image and evaluating the suitability of the suggested fashion. This enables fashion suggestions that take into account both the user's personal color and emotional state, and enables objective evaluation of the suitability of suggested fashion items.

[1186] "User" refers to a person who uses the system to diagnose their own personal color and receive optimal fashion suggestions.

[1187] A "portrait" refers to a photograph that includes the user's face and is image data that is uploaded to the system.

[1188] "Image recognition technology" refers to a collection of algorithms and software used to analyze certain features in uploaded photos, including, for example, face detection and color analysis.

[1189] "Generative AI" refers to a group of algorithms and models that generate new information or suggestions based on given data, including natural language processing models and image generation models.

[1190] "Personal color" refers to the color tone that best suits a user, as diagnosed based on the user's skin color, eye color, hair color, etc. For example, it is classified into categories such as "spring" and "autumn."

[1191] An "emotion engine" is an algorithm or software that can recognize a user's emotional state from a photograph of their face. For example, it can identify emotions such as "joy," "sadness," and "anger."

[1192] "Fashion and color coordination" refers to suggested combinations of clothing and accessories based on the user's personal color and emotional state.

[1193] "Suitability" is an index that evaluates how well the proposed fashion matches the user's appearance. Specifically, it is a score calculated based on the degree of match of skin color, eye color, and hair color.

[1194] "User profile" refers to a database or file that stores information such as a user's personal colors, suggested fashions, emotional state, etc.

[1195] "Analysis" refers to the process of using image recognition technology to identify colors and features from uploaded photos and organize them as numerical data.

[1196] "Diagnosis" refers to the process of identifying a user's personal color based on the analysis results.

[1197] "Suggestion" refers to the act of providing the user with optimal fashion and color coordination based on the diagnosed personal color and emotional state.

[1198] The present invention is a system that allows users to understand their own personal colors and select optimal fashion and color coordination based on those colors. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the system can provide more personalized fashion suggestions. The following specific procedures and techniques are used to implement this system.

[1199] First, a user takes a photo of their face using a device (e.g., a smartphone or PC) and uploads it to the system. The device then sends the uploaded image to the server.

[1200] The server saves the received image data in a temporary directory, for example, the / tmp / uploads / directory, and returns a response to the terminal notifying that the image was successfully saved.

[1201] Next, the server uses an image processing library (e.g., OpenCV or Dlib) to detect faces in the uploaded image and analyze skin, eye, and hair colors. Specifically, it uses a face detection algorithm to identify the face area and obtain pixel color information within the face area. This color information is organized as numerical data, expressed as, for example, HSV (hue, saturation, brightness) values.

[1202] Based on the analysis results, a generative artificial intelligence (generative AI) model is used to diagnose the user's personal color. GPT-3 or a similar model is used as the generative AI. Specifically, data on the user's skin color, eye color, and hair color is input into the generative AI model, and a diagnosis of the personal color (such as "Spring," "Summer," "Autumn," or "Winter") is output. The diagnosed personal color is saved in the user's profile.

[1203] Furthermore, the server uses an emotion engine (e.g., emotion recognition API) to recognize emotions from the user's facial photograph. For example, by using Microsoft's Face API or Google's Cloud Vision API, emotions such as "happiness," "sadness," and "anger" can be identified. The recognized emotion information is then added to and saved in the user profile.

[1204] Next, the generation AI will make optimal fashion suggestions based on the diagnosed personal color and recognized emotional information. Specifically, the generation AI is input with the following prompt: "The user's personal color is ____, and their current emotional state is ____. Please suggest the optimal fashion coordination." Based on this prompt, the generation AI will suggest fashion items and coordinations.

[1205] The user tries on the proposed fashion and uploads the photo back to the system. The server analyzes the re-uploaded image and evaluates the suitability of the proposed fashion. Specifically, it evaluates the degree of match between the original skin color, eye color, and hair color and the new image, and calculates the average of these as a suitability score. The evaluation results are saved in the user profile and notified to the user.

[1206] With the above system, users can receive optimal fashion suggestions that match their personal color and emotional state, and can also check the suitability of the suggestions.

[1207] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1208] Step 1:

[1209] Users take a photo of their face using a device (such as a smartphone or PC) and upload it to the system.

[1210] Input: A portrait taken by the user

[1211] Output: Image data transmission request from the device to the server

[1212] Specifically, the user takes a photo of their face using the device's camera app, selects the image file on the system's upload screen, and clicks the upload button to send the image data to the server.

[1213] Step 2:

[1214] The server stores the received image data in a temporary directory, for example, the / tmp / uploads / directory.

[1215] Input: Image data sent from the device

[1216] Output: Notification that the image file has been saved to the storage location

[1217] Specifically, the server receives the HTTP request, writes the image data to the save directory, and if the save is successful, returns a save completion response to the device.

[1218] Step 3:

[1219] The server uses image recognition technology (e.g., OpenCV or Dlib) to detect faces from uploaded images and analyze skin tone, eye color, and hair color.

[1220] Input: Saved image data

[1221] Output: Numerical data of skin color, eye color, and hair color as analysis results

[1222] Specifically, a face detection algorithm is used to identify the face area, and pixel color information within the face area is collected and converted into HSV (hue, saturation, brightness) values.These analysis results are then stored on the server as numerical data.

[1223] Step 4:

[1224] The server uses a generative artificial intelligence (generative AI) model to diagnose the user's personal color based on the analysis results.

[1225] Input: Numerical data for skin color, eye color, and hair color

[1226] Output: Diagnosed personal color

[1227] Specifically, data on skin color, eye color, and hair color is input into the generative AI model, which outputs a personal color (e.g., "Spring," "Summer," "Autumn," or "Winter"). The generated personal color information is saved in the user profile.

[1228] Step 5:

[1229] The server uses an emotion engine (e.g., emotion recognition API) to recognize emotions from the user's facial photograph.

[1230] Input: Uploaded face photo

[1231] Output: Recognized emotion information

[1232] Specifically, facial images are sent to an emotion recognition API to obtain emotional states such as "happiness," "sadness," and "anger." This emotional information is then added to and saved in the user profile.

[1233] Step 6:

[1234] The server uses generative AI to suggest optimal fashion and color coordination based on the diagnosed personal color and recognized emotional information.

[1235] Input: User's personal color, emotional state

[1236] Output: Suggested fashion and color coordination

[1237] Specifically, the AI ​​generates a prompt message such as, "The user's personal color is ____, and their current emotional state is ____. Please suggest the best fashion coordination for them." The AI ​​then outputs the generated fashion suggestions. These suggestions are then sent back to the user.

[1238] Step 7:

[1239] The user tries on the suggested fashions and uploads the photos back to the system.

[1240] Input: A photo of you trying out the suggested fashion

[1241] Output: A request to send new image data from the device to the server

[1242] Specifically, the user tries on the proposed fashion, takes a photo of the outfit, and clicks the upload button. The image data is then sent to the server.

[1243] Step 8:

[1244] The server analyzes newly uploaded images and evaluates the suitability of the proposed fashions.

[1245] Input: Newly uploaded image, original skin color, eye color, and hair color data

[1246] Output: Evaluation score

[1247] Specifically, the system evaluates the degree of match between the original skin color, eye color, and hair color and the new image, and calculates the average of these as a fitness score. The evaluation results are saved in the user profile and notified to the user.

[1248] This allows users to receive fashion suggestions that are best suited to their personal color and emotional state, and also to check the suitability of those suggestions.

[1249] (Application example 2)

[1250] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1251] In the modern fashion industry, suggestions based on personal color diagnosis are becoming commonplace, but there is still no system that can provide personalized fashion suggestions that take the user's emotions into account. Furthermore, there are limited ways to receive these suggestions in real time while shopping in a physical store. Therefore, there is a need for a system that can take the user's emotions into account and instantly suggest the most suitable fashion items in a physical store.

[1252] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to upload a portrait photo of themselves, means for analyzing skin color, eye color, and hair color from the uploaded photo using image recognition technology, means for diagnosing the user's personal color based on the analysis results using generative artificial intelligence, means for proposing optimal fashion and color coordination based on the diagnosed personal color, emotion recognition means for recognizing the user's emotional state, means for adjusting the proposed fashion and color coordination based on the emotional state, means for displaying the proposal through a smart wearable device, means for re-uploading an image when the user tries on the proposed fashion, and means for analyzing the re-uploaded image and evaluating the suitability of the proposed fashion. This enables personalized fashion proposals that take the user's emotions into account in real time.

[1253] "User" refers to an individual who uses the system.

[1254] A "portrait" refers to an image file that shows the user's face.

[1255] "Image recognition technology" refers to the technology that allows computers to extract and analyze specific information from image data.

[1256] "Generative AI" refers to AI that generates new data and information using technologies such as machine learning and deep learning.

[1257] "Personal color" refers to a color palette classified based on an individual's skin tone, eye color, hair color, etc.

[1258] "Color coordination" refers to the way colors of fashion items are combined.

[1259] "Emotion recognizer" refers to technology for identifying a user's emotional state.

[1260] The term "smart wearable device" refers to a computer worn by a user, including, for example, smart glasses.

[1261] "Means for displaying suggestions" refers to the technology or device used to show the system-generated suggestions to the user.

[1262] "Suitability" refers to the degree to which the suggested fashion item suits the user.

[1263] The "evaluation score" is a numerical representation of the suitability, and is an indicator of the effectiveness of the proposal.

[1264] The present invention provides a system that diagnoses a user's personal color and emotional state and suggests optimal fashion and color coordination based on the results. Furthermore, this system has the feature of displaying suggestions in real time in a physical store using a smart wearable device and instantly reflecting the user's evaluation.

[1265] First, the user takes a photo of their face using the camera on their smart glasses and uploads it to the system. This photo is then sent to a server, where image recognition technology such as OpenCV is used to analyze the user's skin tone, eye color, and hair color from the photo. The analyzed data is then organized into numerical values.

[1266] The server then uses generative artificial intelligence to further analyze this color information and diagnose the user's personal color. The generative artificial intelligence models used include machine learning and deep learning algorithms. The results of this diagnosis are saved in the user's profile. Furthermore, emotion recognition technology is used to recognize the user's emotional state. One technology used for this is EmotionRecognizer.

[1267] Based on the diagnosed personal color and emotional information, the server uses a generative AI model to suggest optimal fashion and color coordination. This includes suggesting fashion items with colors and styles that are best suited to the user's emotional state. The suggestions are displayed in real time on the smart glasses.

[1268] For example, if the user's personal color is "spring" and the emotion is recognized as "joy," items that enhance brightness and vitality, such as a light green skirt, are suggested.

[1269] An example prompt is:

[1270] "The user's personal color is 'spring' and their emotion is 'joy.' If you were to make fashion suggestions based on these conditions, what items would be best? Please focus on items with bright colors that bring out their energy."

[1271] When a user tries on a suggested fashion and uploads the photo back to the system, the server analyzes the new image and evaluates its suitability. This suitability is calculated based on the average of the individual skin, eye, and hair color matches. This evaluation result is also saved in the user's profile and will be used for future suggestions.

[1272] As described above, this system can provide real-time fashion suggestions in real stores that take into account the user's personal color and emotional information, significantly improving the user experience, allowing users to make practical fashion choices with high satisfaction.

[1273] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1274] Step 1:

[1275] A user takes a portrait of themselves using the camera on their smart glasses and uploads the portrait to the system. The input is the user's portrait, and the output is image data sent to the server. Specifically, after taking a picture with the camera on the smart glasses, the saved image file is uploaded to the server.

[1276] Step 2:

[1277] The server analyzes the uploaded facial photo using image recognition technology. The input is the uploaded facial photo, and the output is the analysis results of skin color, eye color, and hair color. Specifically, OpenCV is used to identify areas of each color from the facial photo and organize them as numerical data.

[1278] Step 3:

[1279] The server then uses artificial intelligence to analyze the analyzed color information and diagnose the user's personal color. The input is numerical data for skin color, eye color, and hair color, and the output is the user's personal color. Specifically, a machine learning algorithm is used to determine the personal color through a predictive model.

[1280] Step 4:

[1281] The server uses emotion recognition technology to recognize the user's emotional state. The input is a photo of the user's face, and the output is the user's emotional information. Specifically, it uses EmotionRecognizer to classify emotions from facial expressions.

[1282] Step 5:

[1283] The server inputs personal color and emotional information into a generative AI model to suggest optimal fashion and color coordination. The input is the user's personal color and emotional information, and the output is suggested fashion items. Specifically, the generative AI model generates optimal fashion based on the prompt text and displays it on the smart glasses' display.

[1284] Step 6:

[1285] The user tries on the suggested fashion and uploads the result as a new face photo to the system. The input is the new face photo, and the output is the transmission of image data to the server. Specifically, the user takes a photo again with the smart glasses camera, saves the image, and sends it to the server.

[1286] Step 7:

[1287] The server analyzes the newly uploaded image and evaluates its suitability for the proposed fashion. The input is the newly uploaded face photo, and the output is a suitability evaluation score. Specifically, the server calculates the degree of match for skin color, eye color, and hair color, and calculates the evaluation score from the average.

[1288] Step 8:

[1289] The server saves the evaluation results in the user's profile and reflects them in future fashion suggestions. The input is the fitness evaluation score, and the output is an updated user profile. Specifically, the evaluation scores are saved in a database and used as training data for the generative AI model.

[1290] 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.

[1291] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1292] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1293] 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.

[1294] 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 includes both affect 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.

[1295] 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.

[1296] 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).

[1297] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1298] 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."

[1299] 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.

[1300] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1301] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1302] 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.

[1303] 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.

[1304] 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.

[1305] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1306] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1307] 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.

[1308] 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.

[1309] 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.

[1310] 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.

[1311] The following is further disclosed regarding the above embodiment.

[1312] (Claim 1)

[1313] a means for users to upload a portrait of themselves;

[1314] A means for analyzing skin color, eye color, and hair color from uploaded photos using image recognition technology;

[1315] A means for diagnosing the user's personal color based on the analysis results using a generative artificial intelligence;

[1316] A means for proposing optimal fashion and color coordination based on the diagnosed personal color;

[1317] A means for users to re-upload images when they try on the suggested fashion;

[1318] A means for analyzing the uploaded image again and evaluating its suitability for the proposed fashion;

[1319] A system including:

[1320] (Claim 2)

[1321] 10. The system of claim 1, further comprising means for storing information about the diagnosed personal color and suggested fashion in a user profile.

[1322] (Claim 3)

[1323] 2. The system according to claim 1, further comprising means for calculating the degree of match of skin color, eye color, and hair color separately when evaluating fitness, and calculating an evaluation score based on an average value of these.

[1324] "Example 1"

[1325] (Claim 1)

[1326] a means for users to upload a portrait of themselves;

[1327] A means for analyzing skin color, eye color, and hair color from uploaded photos using image recognition technology;

[1328] A means for diagnosing the user's personal color based on the analysis results using a generative artificial intelligence;

[1329] A means for proposing optimal fashion and color coordination based on the diagnosed personal color;

[1330] A means for users to re-upload images when they try on the suggested fashion;

[1331] A means for analyzing the uploaded image again and evaluating its suitability for the proposed fashion;

[1332] means for storing the results of the fitness assessment in a user profile;

[1333] means for notifying a user of the stored results;

[1334] A system including:

[1335] (Claim 2)

[1336] 10. The system of claim 1, further comprising means for storing information about the diagnosed personal color and suggested fashion in a user profile.

[1337] (Claim 3)

[1338] 2. The system according to claim 1, further comprising means for calculating the degree of match of skin color, eye color, and hair color separately when evaluating fitness, and calculating an evaluation score based on an average value of these.

[1339] "Application Example 1"

[1340] (Claim 1)

[1341] a means for users to upload a portrait of themselves;

[1342] means for analyzing skin color, eye color, and hair color from uploaded images using image recognition technology;

[1343] A means for diagnosing the user's personal color based on the analysis results using a generative artificial intelligence;

[1344] A means for proposing optimal fashion and color coordination based on the diagnosed personal color;

[1345] A means for users to re-upload images when they try on the suggested fashion;

[1346] A means for analyzing the uploaded image again and evaluating its suitability for the proposed fashion;

[1347] A means for users to use their own smart devices to receive real-time recommendations for fashion items based on their personal colors in a physical store;

[1348] A system including:

[1349] (Claim 2)

[1350] 10. The system of claim 1, further comprising means for storing information about the diagnosed personal color and suggested fashion in a user profile.

[1351] (Claim 3)

[1352] 2. The system according to claim 1, further comprising means for calculating the degree of match of skin color, eye color, and hair color separately when evaluating fitness, and calculating an evaluation score based on an average value of these.

[1353] "Example 2: Combining Emotion Engines"

[1354] (Claim 1)

[1355] a means for users to take and upload portrait photographs of themselves;

[1356] A means for analyzing skin color, eye color, and hair color from uploaded photos using image recognition technology;

[1357] A means for diagnosing the user's personal color based on the analysis results using a generative artificial intelligence;

[1358] means for recognizing an emotional state of a user using an emotion engine;

[1359] A means for suggesting optimal fashion and color coordination based on the diagnosed personal color and the recognized emotional state;

[1360] A means for users to re-upload images when they try on the suggested fashion;

[1361] A means for analyzing the uploaded image again and evaluating its suitability for the proposed fashion;

[1362] A system including:

[1363] (Claim 2)

[1364] 10. The system of claim 1, further comprising means for storing information about the diagnosed personal color and suggested fashion, as well as the recognized emotional state, in a user profile.

[1365] (Claim 3)

[1366] 2. The system according to claim 1, further comprising means for calculating the degree of match of skin color, eye color, and hair color separately when evaluating fitness, and calculating an evaluation score based on an average value of these.

[1367] "Application example 2 when combining emotion engines"

[1368] (Claim 1)

[1369] a means for users to upload a portrait of themselves;

[1370] A means for analyzing skin color, eye color, and hair color from uploaded photos using image recognition technology;

[1371] A means for diagnosing the user's personal color based on the analysis results using a generative artificial intelligence;

[1372] A means for proposing optimal fashion and color coordination based on the diagnosed personal color;

[1373] emotion recognition means for recognizing an emotional state of a user;

[1374] means for tailoring fashion and color coordination suggestions based on emotional state;

[1375] means for displaying the suggestions through a smart wearable device;

[1376] A means for users to re-upload images when they try on the suggested fashion;

[1377] A means for analyzing the uploaded image again and evaluating its suitability for the proposed fashion;

[1378] A system including:

[1379] (Claim 2)

[1380] 10. The system of claim 1, further comprising means for storing information about the diagnosed personal color and suggested fashion in a user profile.

[1381] (Claim 3)

[1382] 2. The system according to claim 1, further comprising means for calculating the degree of match of skin color, eye color, and hair color separately when evaluating fitness, and calculating an evaluation score based on an average value of these. [Explanation of symbols]

[1383] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for users to upload a portrait of themselves; A means for analyzing skin color, eye color, and hair color from uploaded photos using image recognition technology; A means for diagnosing the user's personal color based on the analysis results using a generative artificial intelligence; A means for proposing optimal fashion and color coordination based on the diagnosed personal color; A means for users to re-upload images when they try on the suggested fashion; A means for analyzing the uploaded image again and evaluating its suitability for the proposed fashion; A system including:

2. 10. The system of claim 1, further comprising means for storing information about the diagnosed personal color and suggested fashion in a user profile.

3. 2. The system according to claim 1, further comprising means for calculating the degree of match of skin color, eye color, and hair color separately when evaluating the fitness, and calculating the evaluation score based on the average value of these.

Images