System

Abstract

A system is provided.SOLUTION: A system, comprising: means for capturing a picture of clothing owned by a user; means for sending the captured picture of the clothing to a server; means for the server to analyze the sent picture and extract information of the clothing; means for storing the extracted information of the clothing in a database; means for the user to input style information of going out; means for generating optimal coordination based on the input style information; means for sending the generated coordination to a terminal of the user; and means for the terminal to display the sent coordination.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] In today's busy lifestyles, it is not easy for users to choose the perfect outfit from the many clothes they own. Choosing the right outfit can take a lot of time and effort, especially for special events such as work, dates, or important meetings. Furthermore, without the knowledge of appropriate outfits, users may be confused about what to choose and end up choosing inappropriate clothing. Another problem is the lack of support for efficiently using the clothes they own. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system including a means for capturing photos of clothing owned by a user, a means for transmitting the captured photos of the clothing to a server, a means for the server to analyze the transmitted photos and extract clothing information, a means for storing the extracted clothing information in a database, a means for the user to input style information for going out, a means for generating an optimal coordination based on the input style information, a means for transmitting the generated coordination to the user's terminal, and a means for the terminal to display the transmitted coordination.

[0006] Specifically, when analyzing the captured photos of clothing, the system identifies the type, color, material, and pattern of each garment and stores this information in a database. When the user inputs their style information for going out, the system uses the information in the database to generate and suggest optimal outfits. This allows users to easily choose appropriate outfits and efficiently use their clothing even in busy lives.

[0007] A "user" is an individual who uses this system to receive suggestions for coordinating clothing that they own.

[0008] "Clothing" is a general term for clothes and accessories that users own and for which they can receive coordination suggestions through this system.

[0009] "Means for capturing photos" refers to a function or device that allows a user to take photos of clothing they own and capture the photos in the system as digital data.

[0010] The "server" is a central computer system that analyzes the transmitted digital data, stores the clothing information in a database, generates outfits, and transmits them to the terminal.

[0011] "Means for analyzing images" refers to algorithms or programs that extract information such as type, color, material, and pattern from the captured photograph of clothing.

[0012] A "database" is a collection of information that stores analyzed clothing information and is managed in a searchable state for suggesting outfits.

[0013] "Outing style information" is information input by the user regarding the clothing style according to the purpose of going out that day or the event (for example, work, a date, an important meeting, etc.).

[0014] The "means for generating coordination" refers to an algorithm or program that suggests optimal outfit combinations based on clothing information stored in the database and outdoor style information entered by the user.

[0015] A "terminal" is a device such as a smartphone or computer that a user uses to operate the system.

[0016] The "means for displaying coordination" refers to a screen or function for visually presenting the coordination proposals generated on the terminal to the user. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system that suggests outfit coordination for a user's clothing. The system aims to automatically suggest optimal outfit coordination by having the user import a photo of their own clothing and input style information for that day's outing.

[0039] 1. Importing photos

[0040] Users use devices such as smartphones or PCs to take photos of their clothing, including tops, bottoms, outerwear, and accessories. A dedicated application is installed on the device, and the photos are imported through this application.

[0041] 2. Upload an image

[0042] The captured photo is sent from the device to the server, where it is temporarily stored and awaits the next analysis process.

[0043] 3. Image Analysis

[0044] The server applies image analysis algorithms to the received images, which identify the type of clothing (e.g., shirt, pants, skirt, jacket, etc.), color, material, and pattern (solid, striped, checked, etc.). This information is stored in a database for later use in searches and suggestions.

[0045] 4. Enter style information

[0046] Through the application, the user inputs style information for the day's outing, including, for example, "work," "date," "important meeting," etc. The input information is sent from the terminal to the server.

[0047] 5. Generating Coordination Proposals

[0048] The server compares the clothing information stored in the database with the style information sent by the user to generate optimal outfit suggestions, which include combinations of tops, bottoms, outerwear, and accessories.

[0049] 6. Submitting and Viewing Proposals

[0050] The generated coordination suggestions are sent from the server to the user's device, which receives them and displays them visually in the application. The user can then review the suggestions and choose the style they like best.

[0051] Specific examples

[0052] For example, if a user enters style information for an "important meeting," the server generates a formal outfit. Specifically, the server selects a black suit, white shirt, tie, and leather shoes from the database and proposes this as one outfit. The user can then confirm this proposal in the application and actually attend the meeting in that style.

[0053] In this way, the system supports busy users in efficiently selecting the most suitable clothing.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user takes a photo of their own clothing using a smartphone or PC, then launches a dedicated application and imports the photo of the clothing.

[0057] Step 2:

[0058] The photos captured by the device are sent to the server via the application. The data sent includes the photos as well as identification information such as the user ID.

[0059] Step 3:

[0060] The server receives the transmitted photo data, temporarily stores it, and prepares it for the image analysis process.

[0061] Step 4:

[0062] The server applies image analysis algorithms to extract information such as the type of clothing, color, material, and pattern, using machine learning models and image recognition techniques.

[0063] Step 5:

[0064] The server stores the analysis results in a database, which contains information such as the type of clothing, color, material, pattern, image file name, and user ID.

[0065] Step 6:

[0066] Through the application, the user inputs style information for the day's outing, such as "work," "date," or "important meeting."

[0067] Step 7:

[0068] The terminal transmits the input style information to the server. The transmitted data includes the style information and the user ID.

[0069] Step 8:

[0070] The server searches for clothing information in a database based on the received style information, and executes an algorithm to generate the optimal outfit.

[0071] Step 9:

[0072] The server generates outfit suggestions, which include tops, bottoms, outerwear, and accessories.

[0073] Step 10:

[0074] The server transmits the generated coordination proposal to the user's terminal.

[0075] Step 11:

[0076] The application displays the coordinated outfit suggestions received by the device, allowing the user to review the suggested outfits and select from them.

[0077] Example 1

[0078] 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."

[0079] Existing clothing coordination systems require users to manually select clothing and consider how to coordinate their outfits to match their style, which is time-consuming and labor-intensive. Furthermore, many systems lack the functionality to provide users with optimal suggestions based on their style for the day. Furthermore, because they do not utilize advanced image analysis or generative AI models, they often fail to provide highly accurate outfit suggestions.

[0080] 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.

[0081] In this invention, the server includes means for capturing photos of clothes owned by the user, means for transmitting the captured photos of the clothes to an information processing device, means for the information processing device to analyze the transmitted photos and extract clothing information, means for storing the extracted clothing information in a recording device, means for the user to input style information for going out, means for generating an optimal outfit based on the input style information, means for transmitting the generated outfit to the user's terminal, means for the terminal to display the transmitted outfit, means for generating outfit suggestions according to the style information using a generative AI model, and means for inputting a prompt sentence into the generative AI model to obtain outfit suggestions. This allows the user to receive outfit suggestions that are optimal for the day's outing style without any hassle.

[0082] Below are definitions of important terms included in the claims.

[0083] "User" refers to a person who uses this system to receive clothing coordination suggestions.

[0084] "Clothing" refers to all clothing items owned by the user, including tops, bottoms, outerwear, accessories, etc.

[0085] "Photo" refers to image data of clothing taken by the user.

[0086] "Means of capture" refers to the method of taking or scanning a photo of clothing using a user's device and inputting it into the system.

[0087] "Terminal" refers to an electronic device, such as a smartphone or computer, that a user uses to access the system.

[0088] "Information processing device" refers to a computing device such as a server that processes data, analyzes, and generates proposals for the system.

[0089] "Transmission means" refers to the communication technology and protocol for transferring data from the terminal to the information processing device.

[0090] "Means for analyzing" refers to a method for extracting clothing information from the submitted photograph using an image analysis algorithm.

[0091] "Clothing information" is data about analyzed clothing, including characteristics such as type, color, material, and pattern.

[0092] "Recording device" refers to a storage device such as a database for storing data.

[0093] "Outing style information" refers to information that the user inputs according to the purpose and situation of the day, and examples include "work," "date," and "important meeting."

[0094] The "means for generating" refers to a method for automatically creating an optimal coordination based on input style information.

[0095] "Coordination suggestions" refer to clothing combination suggestions generated based on analyzed clothing information and style information.

[0096] A "generative AI model" refers to an artificial intelligence model that creates appropriate coordination suggestions based on input data.

[0097] "Prompt" refers to a text-based instruction that is input into a generative AI model and used to generate outfit suggestions.

[0098] "Display means" refers to the method or technology for visually displaying coordination suggestions on a terminal.

[0099] The present invention relates to a system that suggests outfit coordination for a user's clothing. The system aims to automatically suggest optimal outfit coordination by having the user import a photo of their own clothing and input style information for that day's outing.

[0100] Importing photos

[0101] Users use devices such as smartphones or PCs to take photos of their clothing, including tops, bottoms, outerwear, and accessories. Users install a dedicated application on their devices and import photos through this application.

[0102] Uploading an image

[0103] The captured photos are sent from the device to an information processing device, which temporarily stores the received image files and waits for the next analysis process. For example, a cloud service such as Amazon Web Services (AWS) can be used.

[0104] Image analysis

[0105] The information processing device applies an image analysis algorithm to the received image. This analysis uses image analysis libraries such as TensorFlow and OpenCV. The algorithm identifies the type of clothing (e.g., shirt, pants, skirt, jacket, etc.), color, material, and pattern (solid, striped, checked, etc.). This information is output in JSON format and stored in a recording device (database).

[0106] Entering style information

[0107] The user inputs style information for the day's outing through the application. This style information includes "work," "date," "important meeting," etc. The input information is sent from the terminal to the information processing device.

[0108] Generating outfit suggestions

[0109] The information processing device compares the clothing information stored in the recording device with the style information sent by the user to generate optimal outfits. In this process, a generative AI model (e.g., GPT-4) is used, and appropriate prompts are input into the model to generate optimal outfit suggestions.

[0110] Prompt Sentence Examples

[0111] Below are some example prompts to input to a generative AI model:

[0112] When a user enters style information for an "important meeting," please suggest the best outfit. The database contains the following clothing information:

[0113] black suit

[0114] White shirt

[0115] tie

[0116] leather shoes

[0117] Aim to suggest a formal style.

[0118] Submitting and Viewing Proposals

[0119] The generated outfit suggestions are sent from the information processing device to the user's device. The device receives the suggestions and displays them visually in the application. The user can then review the suggestions and select a style they like.

[0120] For example, if a user inputs style information for an "important meeting," the information processing device generates a formal outfit. Specifically, the information processing device selects a black suit, a white shirt, a tie, and leather shoes from the storage device and proposes this as one outfit. The user can then check this proposal in the application and actually attend the meeting in that style.

[0121] As described above, this system supports busy users in efficiently selecting the most suitable clothing.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1: Take and import photos

[0124] Users take photos of their own clothing using the camera on their smartphone or computer. Specifically, users launch a dedicated application, which activates the device's camera. By tapping the "Take Photo" button, the camera activates and users can take photos of the clothing. The photos are saved in the application.

[0125] Input: Clothes before shooting

[0126] Output: Photos of clothing stored on the device

[0127] Step 2: Upload an image

[0128] The device selects the photo in the application and sends it to the server. When the user taps the "Upload" button, the device sends the photo to the server over a secure HTTP connection. A progress bar is displayed during the transfer.

[0129] Input: Photos of clothing stored on the device

[0130] Output: Image file sent to the server

[0131] Step 3: Image analysis

[0132] The server inputs the received photo into an image analysis algorithm, which runs a Python script to load a TensorFlow model and input the photo into the model, identifying the type, color, material, and pattern of the clothing, and outputs the results in JSON format.

[0133] Input: Image file sent to the server

[0134] Output: Clothing information in JSON format (type, color, material, pattern)

[0135] Step 4: Saving to the Database

[0136] The server saves the analyzed clothing information in a database. It receives the analysis results in JSON format and saves them in the "clothing information" table in the database.

[0137] Input: Clothing information in JSON format

[0138] Output: Clothing information stored in the database

[0139] Step 5: Entering style information

[0140] The user enters style information for the day's outing through the application. The user selects the appropriate option (e.g., "Work," "Date," or "Important Meeting") on the style information input screen and taps the "Send" button. The entered information is sent from the device to the server.

[0141] Input: User's outing style information

[0142] Output: Style information sent to the server

[0143] Step 6: Generate outfit suggestions

[0144] The server compares the clothing information stored in the database with the style information sent by the user to generate the optimal outfit. The server obtains the style information, creates a corresponding prompt sentence, and inputs it into a generative AI model (e.g., GPT-4). The model then provides outfit suggestions, which are then stored in the database.

[0145] Input: Clothing information and style information from the database, prompt text

[0146] Output: Generated outfit suggestions

[0147] Step 7: Submit and view your proposal

[0148] The server sends the generated outfit suggestions to the user's device, which then displays the received suggestions on the application's "suggestion display screen." The user can review the suggestions and use options such as "favorite," "share," and "purchase link."

[0149] Input: Generated outfit suggestions

[0150] Output: Coordination suggestions displayed on the device

[0151] The above are the specific processing steps of the system program.

[0152] (Application example 1)

[0153] 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."

[0154] In today's world, consumers want to shop efficiently within limited time. However, in many stores, the time required to try on clothes and the time spent thinking about how to combine them are a significant burden. Furthermore, it is difficult to consider how to best coordinate new purchases with existing clothing. This creates the problem of being unable to coordinate appropriate outfits.

[0155] 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.

[0156] In this invention, the server includes means for importing photos of clothing owned by a user, means for transmitting the imported photos of the clothing to the server, means for the server to analyze the transmitted photos and extract information about the clothing, means for automatically generating outfit suggestions based on prompt sentences using a generative AI model, means for simulating trying on new products in the store using augmented reality technology, means for transmitting the generated outfit suggestions to the user's device, and means for the device to display the transmitted outfit suggestions. This allows consumers to efficiently check the optimal outfits between newly purchased items and their own clothing, even in a physical store, eliminating the need to try on items and enabling an optimal shopping experience while saving time.

[0157] The "means for capturing photos of clothing owned by the user" refers to a means for the user to take pictures of clothing owned by the user and save them in a digital device.

[0158] The "means for transmitting the captured photograph of the clothing to the server" is a means for transmitting the image of the clothing stored in the user's digital device to the server via data communication.

[0159] "Means for the server to analyze the transmitted photograph and extract information about the clothing" refers to means for the server to analyze the received image and identify information such as the type, color, material, and pattern of the clothing.

[0160] The "means for automatically generating coordination suggestions based on prompt sentences using a generative AI model" is a system that uses an artificial intelligence model to generate optimal coordination in response to style requests from users.

[0161] "Means for simulating trying on new products in a store using augmented reality technology" refers to a means for using augmented reality technology to allow a user to virtually try on a product that they are considering purchasing in a physical store.

[0162] The "means for transmitting the generated coordinate information to the user's terminal" refers to a means for transmitting the automatically generated coordinate information to the user's digital device.

[0163] The "means for displaying the coordinates sent by the terminal" refers to a means for visually displaying the coordinate information received on the user's digital device.

[0164] The present invention provides the following specific method for implementing a system that suggests outfit coordination for clothing owned by a user. This system is configured using a user terminal, a server, a generative AI model, and augmented reality technology.

[0165] First, users take photos of their clothing using a smartphone or tablet. These photos are then imported into the device using a dedicated application and sent to a server via the Internet. The server receives the photos and uses image analysis algorithms (e.g., PyTorch or TensorFlow) to identify the type, color, material, and pattern of the clothing.

[0166] The extracted information is then stored in a database, and an interface is provided for users to input style information for outings (e.g., work, date, important meeting). The style information entered by the user is sent to the server, which then generates an appropriate outfit based on it.

[0167] Here, a generative AI model (e.g., GPT-4 or BERT) is used to automatically generate optimal outfits based on the input style information and clothing information stored in the database. The generative AI model uses prompt sentences to generate suggestions. For example, the following prompt sentences are used:

[0168] "As a casual style suggestion, please suggest the best outfit to wear with a white T-shirt and denim pants that I own, using a new item (shirt)."

[0169] Furthermore, in physical stores, augmented reality technology is available for try-on simulations. Users can use smart glasses or smartphones to view products in the store in AR and try them on virtually. This augmented reality technology (e.g., ARKit and ARCore) allows users to see new products in the store in real time and visualize how they will be combined with existing clothing.

[0170] The generated outfit suggestions are sent from the server to the user's device, and the user can check the outfit through the application. For example, if the user inputs style information for an "important meeting," a black suit, white shirt, tie, and leather shoes will be suggested.

[0171] The hardware used to realize this system includes smartphones, tablets, and smart glasses (e.g., Vuzix and Google Glass), allowing users to efficiently and effectively receive outfit suggestions and enjoy a smooth shopping experience even in physical stores.

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1:

[0174] The user takes a photo of the clothing they own using a smartphone or tablet.

[0175] The input is image data of the clothing. The user uses a dedicated application to take these images and save them on the device. The output of this process is image data of the clothing stored on the device.

[0176] Step 2:

[0177] The terminal transmits the captured photograph of the clothing to the server.

[0178] The input is image data of the clothing stored in the terminal. The image data is sent to the server using data communication. The output of this process is the image data imported into the server.

[0179] Step 3:

[0180] The server analyzes the sent photo and extracts information about the clothing.

[0181] The input is image data of clothing stored on a server. Image analysis algorithms (e.g., PyTorch or TensorFlow) are used to identify the type, color, material, and pattern of the clothing. The output of this process is extracted clothing information (e.g., tops-white-cotton-plain).

[0182] Step 4:

[0183] The server stores the extracted clothing information in a database.

[0184] The input is the clothing information extracted by the analysis. The clothing information is saved in a database (e.g., Firebase or MongoDB). The output of this process is the clothing information saved in the database.

[0185] Step 5:

[0186] The user inputs style information for going out.

[0187] The input is the purpose and situation of the outing (e.g., work, date, important meeting). The user inputs this style information through a dedicated application. The output of this process is the user's style request data.

[0188] Step 6:

[0189] The server generates the optimal coordinates based on the input style information.

[0190] The input is the user's style information and clothing information stored in a database. A generative AI model (e.g., GPT-4 or BERT) is used to generate the optimal outfit. The output of this process is an automatically generated outfit suggestion based on the prompt sentence.

[0191] Step 7:

[0192] Users can simulate trying on new products in a physical store using augmented reality technology.

[0193] The input is product data from a physical store. The user uses smart glasses or a smartphone to virtually try on new products using AR technology (e.g., ARKit or ARCore). The output of this process is visual feedback of the AR virtual try-on experience.

[0194] Step 8:

[0195] The server transmits the generated coordinates to the user's terminal.

[0196] The input is automatically generated coordinate suggestion data. The suggestion is transmitted to the user's terminal via data communication. The output of this process is the coordinate suggestion data received by the user's terminal.

[0197] Step 9:

[0198] The terminal displays the transmitted coordinates.

[0199] The input is the outfit suggestion data received from the server. A dedicated application is used to visually display the outfit suggestion to the user. The output of this process is the outfit suggestion visually displayed to the user.

[0200] 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.

[0201] This invention combines an emotion engine with a system that suggests outfits for users' clothing. The system aims to automatically suggest optimal outfits based on the user's mood and emotions on that day by having the user take a photo of their own clothing and recognize their emotions.

[0202] 1. Importing photos

[0203] Users can take photos of their clothing using a smartphone or PC. They then use a dedicated application to import the photos of their clothing. These photos include tops, bottoms, outerwear, accessories, etc.

[0204] 2. Upload an image

[0205] The captured photo is sent from the device to the server, which receives the image file and temporarily stores it.

[0206] 3. Image Analysis

[0207] The server analyzes the received image data. This analysis process identifies the type of clothing, color, material, and pattern (e.g., solid, striped, checked, etc.). This information is stored in a database and used to suggest outfits later.

[0208] 4. Enter style information

[0209] The user inputs style information for going out through the application, such as "work," "date," "important meeting," etc. The input information is sent from the device to the server.

[0210] 5. Emotion recognition

[0211] When a user inputs voice or a photo via an application, the device sends this to the emotion engine, which then uses voice recognition or image analysis technology to identify the user's emotion. For example, if a user inputs a voice message saying "I'm happy today" or a photo of a smiling face, the emotion engine will recognize the emotion as "joy."

[0212] 6. Coordination Proposal Generation

[0213] The server generates optimal outfits based on clothing information stored in a database, input style information, and recognized emotions. For example, if the user feels "happy," it can suggest bright colors and casual styles.

[0214] 7. Submitting and Viewing Proposals

[0215] The generated outfit suggestions are sent from the server to the user's device, where they are visually displayed. The user can then review the suggested outfits and select from them.

[0216] Specific examples

[0217] For example, if a user inputs style information for a "date" and the emotion engine recognizes the emotion "nervous," the server will select clothes in muted colors and relaxed materials from the database and suggest them as an outfit. The user can then check this suggestion in the application and actually go out on a date in that style.

[0218] In this way, the system can provide a more personalized service by proposing optimal outfits that take the user's emotions into consideration.

[0219] The processing flow will be explained below.

[0220] Step 1:

[0221] The user takes a photo of the clothing they own using a smartphone or computer, then launches a dedicated application and imports the photo of the clothing.

[0222] Step 2:

[0223] The device sends the captured photo to the server. The data sent includes the photo as well as identification information such as the user ID.

[0224] Step 3:

[0225] The server receives the transmitted photo data, temporarily stores it, and prepares it for the image analysis process.

[0226] Step 4:

[0227] The server applies image analysis algorithms to extract information such as the type of clothing, color, material, and pattern, using machine learning models and image recognition techniques.

[0228] Step 5:

[0229] The server stores the analysis results in a database, which contains information such as the type of clothing, color, material, pattern, image file name, and user ID.

[0230] Step 6:

[0231] The user inputs style information for the day's outing through the application, including, for example, "work," "date," "important meeting," and the like.

[0232] Step 7:

[0233] The terminal transmits the input style information to the server. The transmitted data includes the user ID in addition to the style information.

[0234] Step 8:

[0235] The user inputs voice or a photo via the application, which is then analyzed by the emotion engine. The emotion engine uses voice recognition or image analysis technology to identify the user's emotion. For example, if the user inputs a voice message saying "I'm happy today" or a photo of a smiling face, the emotion engine will recognize the emotion as "joy."

[0236] Step 9:

[0237] The device sends the recognized emotion information to the server. The sent data includes style information, emotion information, and user ID.

[0238] Step 10:

[0239] The server generates optimal outfits based on clothing information stored in a database, input style information, and recognized emotions. For example, if the user is feeling "happy," it can suggest bright colors and casual styles.

[0240] Step 11:

[0241] The server transmits the generated coordination proposal to the user's terminal.

[0242] Step 12:

[0243] The application displays the coordinated outfit suggestions received by the device, allowing the user to review the suggested outfits and select from them.

[0244] Example 2

[0245] 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."

[0246] The present invention aims to provide a system for suggesting clothing coordination for users that automatically generates optimal coordinations taking into account the user's emotions and provides personalized suggestions to each user. Conventional simple clothing combination suggestion systems are unable to reflect the user's emotions or mood of the day, making it difficult to obtain suggestions that satisfy the user. Another problem is that users themselves have to spend time trying out various combinations through trial and error.

[0247] 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.

[0248] In this invention, the server includes means for capturing images of clothing owned by the user, means for transmitting the captured images of the clothing to the server, means for the server to analyze the transmitted images and extract characteristic information about the clothing, means for storing the extracted characteristic information about the clothing in a database, means for the user to input information about the type of outing, means for generating an optimal clothing combination based on the input type information and emotion information, means for transmitting the generated clothing combination to the user's terminal, and means for the terminal to display the transmitted clothing combination, thereby enabling personalized coordination suggestions that take the user's emotions into consideration.

[0249] "User" refers to a person who uses the system to receive clothing coordination suggestions.

[0250] "Clothing images" refers to photographs of clothing taken by users and their digital data.

[0251] "Server" refers to a central system that receives, stores, processes, and transmits data over the Internet.

[0252] "Feature information" refers to attribute data such as the type, color, material, and pattern of clothing.

[0253] "Database" refers to a collection of information that has been structured and stored to allow easy search and retrieval as needed.

[0254] "Type information" refers to information entered by the user regarding the purpose and circumstances of the outing.

[0255] "Emotional information" refers to information that indicates the user's current emotions and moods.

[0256] "Clothing combination" refers to a coordination suggested based on feature information, type information, and emotion information.

[0257] "Terminal" refers to a device such as a smartphone or computer that a user uses to access the system.

[0258] This invention combines an emotion engine with a system that suggests outfits for users' clothing. The system aims to automatically suggest optimal outfits based on the user's mood and emotions on that day, after the user inputs an image of their own clothing.

[0259] First, the user takes a photo of their clothing using a smartphone or computer. The photo includes tops, bottoms, outerwear, accessories, etc. The user then uses a dedicated application to import the image of the clothing into their device.

[0260] The device then sends the captured image to a server, which temporarily stores the image and analyzes it using image recognition algorithms (e.g., OpenCV or TensorFlow). This analysis process identifies the type of clothing, color, material, and pattern (e.g., solid, striped, checked, etc.). This feature information is stored in a database and used to suggest outfits later.

[0261] The user then inputs the type of outing through the application, which may include "work," "date," "important meeting," etc. The input type information is sent from the terminal to the server.

[0262] Furthermore, users can input information about their emotions and moods through the application. They can input emotions through voice (e.g., "I'm happy today") or images (e.g., a photo of a smiling face). The device sends this input data to the emotion engine, which then identifies the user's emotions using voice recognition or image analysis technology (e.g., voice recognition service or image analysis service).

[0263] The server uses a generative AI model to generate optimal outfits based on clothing feature information stored in the database and category and emotion information entered by the user. For example, if a user enters category information of "date" and emotion information of "happy," the server can suggest outfits in bright colors and casual styles.

[0264] The generated outfit suggestions are sent from the server to the user's device. The device visually displays the suggested outfits, allowing the user to select their favorite outfit from among them. As a specific example, if a user inputs style information for a "date" and the emotion engine recognizes the emotion "nervous," the server will select clothes in muted colors and relaxed materials and suggest these as outfits. The user can check these suggestions in the application and actually go out on a date in that style.

[0265] Example prompt sentence:

[0266] "Design a system that uses an emotion engine to recognize the user's emotions and suggests clothing coordination that matches the user's mood that day. Please include detailed instructions on how to input images of the user's clothing, type information, and emotional information, and generate and suggest optimal coordination."

[0267] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0268] Step 1:

[0269] Users use their smartphones or computers to take pictures of their clothing.

[0270] Input: Images of clothing taken by the user

[0271] How it works: The user launches the dedicated application and uses the camera function to take a picture of the clothing.

[0272] Output: Image file of the clothing saved on the user's device

[0273] Step 2:

[0274] The device sends the captured image of the clothing to the server.

[0275] Input: Image file of clothing stored on the user's device

[0276] How it works: The device uploads image data to a server via an internet connection.

[0277] Output: Image data of clothing stored on the server

[0278] Step 3:

[0279] The server analyzes the received image data and extracts characteristic information about the clothing.

[0280] Input: Image data of clothing stored on the server

[0281] How it works: The server uses image recognition algorithms (e.g. OpenCV or TensorFlow) to identify the type, color, material, and pattern of the clothing.

[0282] Output: Extracted clothing feature information (type, color, material, pattern)

[0283] Step 4:

[0284] The server stores the extracted clothing feature information in a database.

[0285] Input: Extracted clothing feature information

[0286] How it works: The server structures the feature information and stores it in a database.

[0287] Output: Clothing feature information stored in a database

[0288] Step 5:

[0289] The user uses the application to input information about the type of trip.

[0290] Input: Type of outing information entered by the user (e.g., work, date, meeting)

[0291] How it works: The user enters category information into the application's input field and presses the submit button.

[0292] Output: Type information sent from the user terminal to the server

[0293] Step 6:

[0294] The user inputs emotion information through the application.

[0295] Input: Emotional information entered by the user (e.g., voice "I'm happy today," photo of a smiling face)

[0296] How it works: The user records audio or takes a photo and sends emotional information through the application.

[0297] Output: Emotion information sent from the user device to the server

[0298] Step 7:

[0299] The server receives the emotion information and identifies the emotion using an emotion engine.

[0300] Input: Emotion information stored on the server

[0301] How it works: The server uses voice recognition or image analysis technology (e.g., voice recognition service or image analysis service) to identify emotions.

[0302] Output: Identified emotion information (e.g., "joy," "tension")

[0303] Step 8:

[0304] The server generates a coordinate based on the clothing feature information stored in the database, the type information input by the user, and the identified emotion information.

[0305] Input: Stored clothing feature information, type information, emotion information

[0306] How it works: The server uses a generative AI model to generate the optimal outfit. For example, it suggests bright colors and casual styles based on the type of "date" and the emotion of "joy."

[0307] Output: Generated outfit suggestions

[0308] Step 9:

[0309] The server transmits the generated coordination proposal to the user's terminal.

[0310] Input: Generated outfit suggestions

[0311] Operation: The server sends the generated coordination proposal to the user's terminal.

[0312] Output: Coordination proposals sent to the user's device

[0313] Step 10:

[0314] The terminal visually displays the proposed coordination.

[0315] Input: Coordination proposal sent to the user's device

[0316] Operation: The device displays an image and description of the outfit on the application screen.

[0317] Output: Coordination suggestions displayed to the user

[0318] (Application example 2)

[0319] 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."

[0320] Many current coordination suggestion systems suggest outfits based on the user's clothing and style information, but they do not take into account the user's emotions or moods, making it difficult to suggest personalized outfits. Furthermore, there is a lack of suggestions for purchasing new items in collaboration with online shopping sites, which prevents the system from further enriching the user's fashion experience.

[0321] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0322] In this invention, the server includes means for capturing photos of clothing owned by the user, means for transmitting the captured photos of the clothing to the server, means for the server to analyze the transmitted photos and extract clothing information, means for storing the extracted clothing information in a database, means for the user to input style information for going out, means for recognizing the user's emotions, means for linking the input style information based on the recognized emotions to generate an optimal outfit, means for transmitting the generated outfit to the user's terminal, and means for the terminal to display the transmitted outfit. This makes it possible to propose personalized outfits that take the user's emotions and moods into consideration, and further enriches the user's fashion experience by adding purchasing suggestions for new items in collaboration with mail order sites.

[0323] The "means for capturing photos of clothing" refers to a device or method for taking photos of clothing owned by a user and acquiring them as digital data.

[0324] The "means for transmitting the photograph of the clothes to the server" is a network communication device or protocol for transmitting the captured photograph data of the clothes to the server via the Internet.

[0325] The "means for extracting clothing information" refers to image analysis technology and algorithms that analyze the photographic data of clothing sent to the server and identify the type, color, material, pattern, etc. of each piece of clothing.

[0326] The "means for storing in a database" refers to a storage device or system for organizing and centrally managing the extracted clothing information.

[0327] The "means for inputting outing style information" is an interface that allows the user to input the day's plans and desired style (e.g., work, date, important meeting, etc.).

[0328] "Means for recognizing emotions" refers to voice recognition technology and image analysis technology for identifying emotions from the user's voice, facial expressions, etc.

[0329] The "means for generating optimal outfits" refers to an algorithm or system that automatically generates optimal outfit combinations based on clothing information stored in a database, input style information, and recognized emotions.

[0330] The "means for transmitting the coordinates to the user's terminal" refers to a communication device or protocol for transmitting the information about the generated coordinates to the user's terminal such as a smartphone or computer.

[0331] The "means for displaying coordinated outfits sent to the terminal" refers to a display device or application for visually displaying coordinated outfits sent to the user's terminal.

[0332] "Recommended items on the online shopping site" are items that users can purchase to go with the suggested outfits, and are products offered on the online shopping site.

[0333] The present invention provides a system for suggesting clothing coordination for a user, and provides personalized suggestions by recognizing the user's emotions. This system includes the following components:

[0334] First, a user uses a device such as a smartphone or PC to take a photo of their clothing, including tops, bottoms, outerwear, and accessories, and imports it into the device. The device then sends the photo data to the server.

[0335] The server analyzes the received photo data and identifies the type, color, material, and pattern of each garment using image analysis techniques such as OpenCV and Keras. The analysis results are stored in a database and used to suggest outfits later.

[0336] Through the application, users input their outing style information, including "work," "date," "important meeting," etc. The input information is sent to the server and stored in a database.

[0337] Furthermore, the device provides a means for inputting voice and facial photographs to recognize the user's emotions. The device sends this data to an emotion recognition engine to identify the emotion. A pre-trained neural network model is used for emotion recognition. For example, if a user inputs the voice "I'm happy today" or a photograph of a smiling face, the emotion recognition engine will recognize the emotion "joy."

[0338] The server generates an optimal outfit based on the clothing information stored in the database, the input style information, and the recognized emotion. The generated outfit is sent from the server to the user's device and displayed on the device.

[0339] Furthermore, the system is linked to online shopping sites and can also suggest new items to purchase based on the suggested outfits. For example, it can recommend new bottoms or accessories to go with a top the user already owns, and the user can then purchase them on the online shopping site.

[0340] For example, if a user inputs their style information for a date and the emotion recognition engine recognizes the emotion "happy," the server will select clothes in bright colors and light materials from the database and suggest them as an outfit. This suggestion will be displayed on the user's device, and new items that go well with it will be recommended by the online shopping site.

[0341] Example prompts for generative AI models:

[0342] plaintext

[0343] Suggest an outfit that will make the user feel happy on a date. Based on the following clothing information the user owns: tops, bottoms, outerwear, and accessories. Please refer to the provided photos.

[0344] This allows users to enjoy creating the perfect outfit to suit their mood, and also makes purchasing new items a breeze.

[0345] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0346] Step 1:

[0347] Users use their smartphones or computers to take photos of their clothing and import them into the device. The imported photo data is categorized into tops, bottoms, outerwear, accessories, etc. This becomes the input data for the system.

[0348] Step 2:

[0349] The device sends the captured photo data of the clothes to the server. The transmission is performed using network communication, and the data is uploaded to the server via the Internet. The output of this step is the image data transferred to the server.

[0350] Step 3:

[0351] The server analyzes the received photo data. It uses image analysis techniques such as OpenCV and Keras to identify the type, color, material, and pattern of each garment. This analysis results in extracted clothing information, which is then stored in a database.

[0352] Step 4:

[0353] The user uses the application to input style information for going out. For example, they can choose from options such as "Work," "Date," and "Important Meeting." The input style information is sent from the device to the server and stored in a database. The output of this step is the style information input by the user.

[0354] Step 5:

[0355] Users input their voice or a photo of their face into the application, which recognizes their emotions. The emotion recognition engine processes this data and identifies the user's emotions using voice recognition or image analysis technology. For example, if a user inputs the voice "I'm happy today," the emotion recognition engine will recognize the emotion "joy."

[0356] Step 6:

[0357] The server generates optimal outfits based on clothing information, style information, and recognized emotions stored in the database. The algorithm uses a generative AI model to create outfits. This outfit suggestion is output.

[0358] Step 7:

[0359] The server sends the generated coordinates to the user's terminal, which visually displays the coordinates, which becomes the output for the user.

[0360] Step 8:

[0361] Based on the suggested outfit, the device interacts with the online shopping site to suggest new items for purchase. These suggestions may include new bottoms or accessories. The user can review the suggested items and complete the purchase on the online shopping site. The output of this step is the suggested items.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] [Second embodiment]

[0366] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0367] 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.

[0368] 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).

[0369] 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.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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."

[0378] The present invention relates to a system that suggests outfit coordination for a user's clothing. The system aims to automatically suggest optimal outfit coordination by having the user import a photo of their own clothing and input style information for that day's outing.

[0379] 1. Importing photos

[0380] Users use devices such as smartphones or PCs to take photos of their clothing, including tops, bottoms, outerwear, and accessories. A dedicated application is installed on the device, and the photos are imported through this application.

[0381] 2. Upload an image

[0382] The captured photo is sent from the device to the server, where it is temporarily stored and awaits the next analysis process.

[0383] 3. Image Analysis

[0384] The server applies image analysis algorithms to the received images, which identify the type of clothing (e.g., shirt, pants, skirt, jacket, etc.), color, material, and pattern (solid, striped, checked, etc.). This information is stored in a database for later use in searches and suggestions.

[0385] 4. Enter style information

[0386] Through the application, the user inputs style information for the day's outing, including, for example, "work," "date," "important meeting," etc. The input information is sent from the terminal to the server.

[0387] 5. Generating Coordination Proposals

[0388] The server compares the clothing information stored in the database with the style information sent by the user to generate optimal outfit suggestions, which include combinations of tops, bottoms, outerwear, and accessories.

[0389] 6. Submitting and Viewing Proposals

[0390] The generated coordination suggestions are sent from the server to the user's device, which receives them and displays them visually in the application. The user can then review the suggestions and choose the style they like best.

[0391] Specific examples

[0392] For example, if a user enters style information for an "important meeting," the server generates a formal outfit. Specifically, the server selects a black suit, white shirt, tie, and leather shoes from the database and proposes this as one outfit. The user can then confirm this proposal in the application and actually attend the meeting in that style.

[0393] In this way, the system supports busy users in efficiently selecting the most suitable clothing.

[0394] The processing flow will be explained below.

[0395] Step 1:

[0396] The user takes a photo of their own clothing using a smartphone or PC, then launches a dedicated application and imports the photo of the clothing.

[0397] Step 2:

[0398] The photos captured by the device are sent to the server via the application. The data sent includes the photos as well as identification information such as the user ID.

[0399] Step 3:

[0400] The server receives the transmitted photo data, temporarily stores it, and prepares it for the image analysis process.

[0401] Step 4:

[0402] The server applies image analysis algorithms to extract information such as the type of clothing, color, material, and pattern, using machine learning models and image recognition techniques.

[0403] Step 5:

[0404] The server stores the analysis results in a database, which contains information such as the type of clothing, color, material, pattern, image file name, and user ID.

[0405] Step 6:

[0406] Through the application, the user inputs style information for the day's outing, such as "work," "date," or "important meeting."

[0407] Step 7:

[0408] The terminal transmits the input style information to the server. The transmitted data includes the style information and the user ID.

[0409] Step 8:

[0410] The server searches for clothing information in a database based on the received style information, and executes an algorithm to generate the optimal outfit.

[0411] Step 9:

[0412] The server generates outfit suggestions, which include tops, bottoms, outerwear, and accessories.

[0413] Step 10:

[0414] The server transmits the generated coordination proposal to the user's terminal.

[0415] Step 11:

[0416] The application displays the coordinated outfit suggestions received by the device, allowing the user to review the suggested outfits and select from them.

[0417] Example 1

[0418] 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."

[0419] Existing clothing coordination systems require users to manually select clothing and consider how to coordinate their outfits to match their style, which is time-consuming and labor-intensive. Furthermore, many systems lack the functionality to provide users with optimal suggestions based on their style for the day. Furthermore, because they do not utilize advanced image analysis or generative AI models, they often fail to provide highly accurate outfit suggestions.

[0420] 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.

[0421] In this invention, the server includes means for capturing photos of clothes owned by the user, means for transmitting the captured photos of the clothes to an information processing device, means for the information processing device to analyze the transmitted photos and extract clothing information, means for storing the extracted clothing information in a recording device, means for the user to input style information for going out, means for generating an optimal outfit based on the input style information, means for transmitting the generated outfit to the user's terminal, means for the terminal to display the transmitted outfit, means for generating outfit suggestions according to the style information using a generative AI model, and means for inputting a prompt sentence into the generative AI model to obtain outfit suggestions. This allows the user to receive outfit suggestions that are optimal for the day's outing style without any hassle.

[0422] Below are definitions of important terms included in the claims.

[0423] "User" refers to a person who uses this system to receive clothing coordination suggestions.

[0424] "Clothing" refers to all clothing items owned by the user, including tops, bottoms, outerwear, accessories, etc.

[0425] "Photo" refers to image data of clothing taken by the user.

[0426] "Means of capture" refers to the method of taking or scanning a photo of clothing using a user's device and inputting it into the system.

[0427] "Terminal" refers to an electronic device, such as a smartphone or computer, that a user uses to access the system.

[0428] "Information processing device" refers to a computing device such as a server that processes data, analyzes, and generates proposals for the system.

[0429] "Transmission means" refers to the communication technology and protocol for transferring data from the terminal to the information processing device.

[0430] "Means for analyzing" refers to a method for extracting clothing information from the submitted photograph using an image analysis algorithm.

[0431] "Clothing information" is data about analyzed clothing, including characteristics such as type, color, material, and pattern.

[0432] "Recording device" refers to a storage device such as a database for storing data.

[0433] "Outing style information" refers to information that the user inputs according to the purpose and situation of the day, and examples include "work," "date," and "important meeting."

[0434] The "means for generating" refers to a method for automatically creating an optimal coordination based on input style information.

[0435] "Coordination suggestions" refer to clothing combination suggestions generated based on analyzed clothing information and style information.

[0436] A "generative AI model" refers to an artificial intelligence model that creates appropriate coordination suggestions based on input data.

[0437] "Prompt" refers to a text-based instruction that is input into a generative AI model and used to generate outfit suggestions.

[0438] "Display means" refers to the method or technology for visually displaying coordination suggestions on a terminal.

[0439] The present invention relates to a system that suggests outfit coordination for a user's clothing. The system aims to automatically suggest optimal outfit coordination by having the user import a photo of their own clothing and input style information for that day's outing.

[0440] Importing photos

[0441] Users use devices such as smartphones or PCs to take photos of their clothing, including tops, bottoms, outerwear, and accessories. Users install a dedicated application on their devices and import photos through this application.

[0442] Uploading an image

[0443] The captured photos are sent from the device to an information processing device, which temporarily stores the received image files and waits for the next analysis process. For example, a cloud service such as Amazon Web Services (AWS) can be used.

[0444] Image analysis

[0445] The information processing device applies an image analysis algorithm to the received image. This analysis uses image analysis libraries such as TensorFlow and OpenCV. The algorithm identifies the type of clothing (e.g., shirt, pants, skirt, jacket, etc.), color, material, and pattern (solid, striped, checked, etc.). This information is output in JSON format and stored in a recording device (database).

[0446] Entering style information

[0447] The user inputs style information for the day's outing through the application. This style information includes "work," "date," "important meeting," etc. The input information is sent from the terminal to the information processing device.

[0448] Generating outfit suggestions

[0449] The information processing device compares the clothing information stored in the recording device with the style information sent by the user to generate optimal outfits. In this process, a generative AI model (e.g., GPT-4) is used, and appropriate prompts are input into the model to generate optimal outfit suggestions.

[0450] Prompt Sentence Examples

[0451] Below are some example prompts to input to a generative AI model:

[0452] When a user enters style information for an "important meeting," please suggest the best outfit. The database contains the following clothing information:

[0453] black suit

[0454] White shirt

[0455] tie

[0456] leather shoes

[0457] Aim to suggest a formal style.

[0458] Submitting and Viewing Proposals

[0459] The generated outfit suggestions are sent from the information processing device to the user's device. The device receives the suggestions and displays them visually in the application. The user can then review the suggestions and select a style they like.

[0460] For example, if a user inputs style information for an "important meeting," the information processing device generates a formal outfit. Specifically, the information processing device selects a black suit, a white shirt, a tie, and leather shoes from the storage device and proposes this as one outfit. The user can then check this proposal in the application and actually attend the meeting in that style.

[0461] As described above, this system supports busy users in efficiently selecting the most suitable clothing.

[0462] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0463] Step 1: Take and import photos

[0464] Users take photos of their own clothing using the camera on their smartphone or computer. Specifically, users launch a dedicated application, which activates the device's camera. By tapping the "Take Photo" button, the camera activates and users can take photos of the clothing. The photos are saved in the application.

[0465] Input: Clothes before shooting

[0466] Output: Photos of clothing stored on the device

[0467] Step 2: Upload an image

[0468] The device selects the photo in the application and sends it to the server. When the user taps the "Upload" button, the device sends the photo to the server over a secure HTTP connection. A progress bar is displayed during the transfer.

[0469] Input: Photos of clothing stored on the device

[0470] Output: Image file sent to the server

[0471] Step 3: Image analysis

[0472] The server inputs the received photo into an image analysis algorithm, which runs a Python script to load a TensorFlow model and input the photo into the model, identifying the type, color, material, and pattern of the clothing, and outputs the results in JSON format.

[0473] Input: Image file sent to the server

[0474] Output: Clothing information in JSON format (type, color, material, pattern)

[0475] Step 4: Saving to the Database

[0476] The server saves the analyzed clothing information in a database. It receives the analysis results in JSON format and saves them in the "clothing information" table in the database.

[0477] Input: Clothing information in JSON format

[0478] Output: Clothing information stored in the database

[0479] Step 5: Entering style information

[0480] The user enters style information for the day's outing through the application. The user selects the appropriate option (e.g., "Work," "Date," or "Important Meeting") on the style information input screen and taps the "Send" button. The entered information is sent from the device to the server.

[0481] Input: User's outing style information

[0482] Output: Style information sent to the server

[0483] Step 6: Generate outfit suggestions

[0484] The server compares the clothing information stored in the database with the style information sent by the user to generate the optimal outfit. The server obtains the style information, creates a corresponding prompt sentence, and inputs it into a generative AI model (e.g., GPT-4). The model then provides outfit suggestions, which are then stored in the database.

[0485] Input: Clothing information and style information from the database, prompt text

[0486] Output: Generated outfit suggestions

[0487] Step 7: Submit and view your proposal

[0488] The server sends the generated outfit suggestions to the user's device, which then displays the received suggestions on the application's "suggestion display screen." The user can review the suggestions and use options such as "favorite," "share," and "purchase link."

[0489] Input: Generated outfit suggestions

[0490] Output: Coordination suggestions displayed on the device

[0491] The above are the specific processing steps of the system program.

[0492] (Application example 1)

[0493] 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."

[0494] In today's world, consumers want to shop efficiently within limited time. However, in many stores, the time required to try on clothes and the time spent thinking about how to combine them are a significant burden. Furthermore, it is difficult to consider how to best coordinate new purchases with existing clothing. This creates the problem of being unable to coordinate appropriate outfits.

[0495] 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.

[0496] In this invention, the server includes means for importing photos of clothing owned by a user, means for transmitting the imported photos of the clothing to the server, means for the server to analyze the transmitted photos and extract information about the clothing, means for automatically generating outfit suggestions based on prompt sentences using a generative AI model, means for simulating trying on new products in the store using augmented reality technology, means for transmitting the generated outfit suggestions to the user's device, and means for the device to display the transmitted outfit suggestions. This allows consumers to efficiently check the optimal outfits between newly purchased items and their own clothing, even in a physical store, eliminating the need to try on items and enabling an optimal shopping experience while saving time.

[0497] The "means for capturing photos of clothing owned by the user" refers to a means for the user to take pictures of clothing owned by the user and save them in a digital device.

[0498] The "means for transmitting the captured photograph of the clothing to the server" is a means for transmitting the image of the clothing stored in the user's digital device to the server via data communication.

[0499] "Means for the server to analyze the transmitted photograph and extract information about the clothing" refers to means for the server to analyze the received image and identify information such as the type, color, material, and pattern of the clothing.

[0500] The "means for automatically generating coordination suggestions based on prompt sentences using a generative AI model" is a system that uses an artificial intelligence model to generate optimal coordination in response to style requests from users.

[0501] "Means for simulating trying on new products in a store using augmented reality technology" refers to a means for using augmented reality technology to allow a user to virtually try on a product that they are considering purchasing in a physical store.

[0502] The "means for transmitting the generated coordinate information to the user's terminal" refers to a means for transmitting the automatically generated coordinate information to the user's digital device.

[0503] The "means for displaying the coordinates sent by the terminal" refers to a means for visually displaying the coordinate information received on the user's digital device.

[0504] The present invention provides the following specific method for implementing a system that suggests outfit coordination for clothing owned by a user. This system is configured using a user terminal, a server, a generative AI model, and augmented reality technology.

[0505] First, users take photos of their clothing using a smartphone or tablet. These photos are then imported into the device using a dedicated application and sent to a server via the Internet. The server receives the photos and uses image analysis algorithms (e.g., PyTorch or TensorFlow) to identify the type, color, material, and pattern of the clothing.

[0506] The extracted information is then stored in a database, and an interface is provided for users to input style information for outings (e.g., work, date, important meeting). The style information entered by the user is sent to the server, which then generates an appropriate outfit based on it.

[0507] Here, a generative AI model (e.g., GPT-4 or BERT) is used to automatically generate optimal outfits based on the input style information and clothing information stored in the database. The generative AI model uses prompt sentences to generate suggestions. For example, the following prompt sentences are used:

[0508] "As a casual style suggestion, please suggest the best outfit to wear with a white T-shirt and denim pants that I own, using a new item (shirt)."

[0509] Furthermore, in physical stores, augmented reality technology is available for try-on simulations. Users can use smart glasses or smartphones to view products in the store in AR and try them on virtually. This augmented reality technology (e.g., ARKit and ARCore) allows users to see new products in the store in real time and visualize how they will be combined with existing clothing.

[0510] The generated outfit suggestions are sent from the server to the user's device, and the user can check the outfit through the application. For example, if the user inputs style information for an "important meeting," a black suit, white shirt, tie, and leather shoes will be suggested.

[0511] The hardware used to realize this system includes smartphones, tablets, and smart glasses (e.g., Vuzix and Google Glass), allowing users to efficiently and effectively receive outfit suggestions and enjoy a smooth shopping experience even in physical stores.

[0512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0513] Step 1:

[0514] The user takes a photo of the clothing they own using a smartphone or tablet.

[0515] The input is image data of the clothing. The user uses a dedicated application to take these images and save them on the device. The output of this process is image data of the clothing stored on the device.

[0516] Step 2:

[0517] The terminal transmits the captured photograph of the clothing to the server.

[0518] The input is image data of the clothing stored in the terminal. The image data is sent to the server using data communication. The output of this process is the image data imported into the server.

[0519] Step 3:

[0520] The server analyzes the sent photo and extracts information about the clothing.

[0521] The input is image data of clothing stored on a server. Image analysis algorithms (e.g., PyTorch or TensorFlow) are used to identify the type, color, material, and pattern of the clothing. The output of this process is extracted clothing information (e.g., tops-white-cotton-plain).

[0522] Step 4:

[0523] The server stores the extracted clothing information in a database.

[0524] The input is the clothing information extracted by the analysis. The clothing information is saved in a database (e.g., Firebase or MongoDB). The output of this process is the clothing information saved in the database.

[0525] Step 5:

[0526] The user inputs style information for going out.

[0527] The input is the purpose and situation of the outing (e.g., work, date, important meeting). The user inputs this style information through a dedicated application. The output of this process is the user's style request data.

[0528] Step 6:

[0529] The server generates the optimal coordinates based on the input style information.

[0530] The input is the user's style information and clothing information stored in a database. A generative AI model (e.g., GPT-4 or BERT) is used to generate the optimal outfit. The output of this process is an automatically generated outfit suggestion based on the prompt sentence.

[0531] Step 7:

[0532] Users can simulate trying on new products in a physical store using augmented reality technology.

[0533] The input is product data from a physical store. The user uses smart glasses or a smartphone to virtually try on new products using AR technology (e.g., ARKit or ARCore). The output of this process is visual feedback of the AR virtual try-on experience.

[0534] Step 8:

[0535] The server transmits the generated coordinates to the user's terminal.

[0536] The input is automatically generated coordinate suggestion data. The suggestion is transmitted to the user's terminal via data communication. The output of this process is the coordinate suggestion data received by the user's terminal.

[0537] Step 9:

[0538] The terminal displays the transmitted coordinates.

[0539] The input is the outfit suggestion data received from the server. A dedicated application is used to visually display the outfit suggestion to the user. The output of this process is the outfit suggestion visually displayed to the user.

[0540] 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.

[0541] This invention combines an emotion engine with a system that suggests outfits for users' clothing. The system aims to automatically suggest optimal outfits based on the user's mood and emotions on that day by having the user take a photo of their own clothing and recognize their emotions.

[0542] 1. Importing photos

[0543] Users can take photos of their clothing using a smartphone or PC. They then use a dedicated application to import the photos of their clothing. These photos include tops, bottoms, outerwear, accessories, etc.

[0544] 2. Upload an image

[0545] The captured photo is sent from the device to the server, which receives the image file and temporarily stores it.

[0546] 3. Image Analysis

[0547] The server analyzes the received image data. This analysis process identifies the type of clothing, color, material, and pattern (e.g., solid, striped, checked, etc.). This information is stored in a database and used to suggest outfits later.

[0548] 4. Enter style information

[0549] The user inputs style information for going out through the application, such as "work," "date," "important meeting," etc. The input information is sent from the device to the server.

[0550] 5. Emotion recognition

[0551] When a user inputs voice or a photo via an application, the device sends this to the emotion engine, which then uses voice recognition or image analysis technology to identify the user's emotion. For example, if a user inputs a voice message saying "I'm happy today" or a photo of a smiling face, the emotion engine will recognize the emotion as "joy."

[0552] 6. Coordination Proposal Generation

[0553] The server generates optimal outfits based on clothing information stored in a database, input style information, and recognized emotions. For example, if the user feels "happy," it can suggest bright colors and casual styles.

[0554] 7. Submitting and Viewing Proposals

[0555] The generated outfit suggestions are sent from the server to the user's device, where they are visually displayed. The user can then review the suggested outfits and select from them.

[0556] Specific examples

[0557] For example, if a user inputs style information for a "date" and the emotion engine recognizes the emotion "nervous," the server will select clothes in muted colors and relaxed materials from the database and suggest them as an outfit. The user can then check this suggestion in the application and actually go out on a date in that style.

[0558] In this way, the system can provide a more personalized service by proposing optimal outfits that take the user's emotions into consideration.

[0559] The processing flow will be explained below.

[0560] Step 1:

[0561] The user takes a photo of the clothing they own using a smartphone or computer, then launches a dedicated application and imports the photo of the clothing.

[0562] Step 2:

[0563] The device sends the captured photo to the server. The data sent includes the photo as well as identification information such as the user ID.

[0564] Step 3:

[0565] The server receives the transmitted photo data, temporarily stores it, and prepares it for the image analysis process.

[0566] Step 4:

[0567] The server applies image analysis algorithms to extract information such as the type of clothing, color, material, and pattern, using machine learning models and image recognition techniques.

[0568] Step 5:

[0569] The server stores the analysis results in a database, which contains information such as the type of clothing, color, material, pattern, image file name, and user ID.

[0570] Step 6:

[0571] The user inputs style information for the day's outing through the application, including, for example, "work," "date," "important meeting," and the like.

[0572] Step 7:

[0573] The terminal transmits the input style information to the server. The transmitted data includes the user ID in addition to the style information.

[0574] Step 8:

[0575] The user inputs voice or a photo via the application, which is then analyzed by the emotion engine. The emotion engine uses voice recognition or image analysis technology to identify the user's emotion. For example, if the user inputs a voice message saying "I'm happy today" or a photo of a smiling face, the emotion engine will recognize the emotion as "joy."

[0576] Step 9:

[0577] The device sends the recognized emotion information to the server. The sent data includes style information, emotion information, and user ID.

[0578] Step 10:

[0579] The server generates optimal outfits based on clothing information stored in a database, input style information, and recognized emotions. For example, if the user is feeling "happy," it can suggest bright colors and casual styles.

[0580] Step 11:

[0581] The server transmits the generated coordination proposal to the user's terminal.

[0582] Step 12:

[0583] The application displays the coordinated outfit suggestions received by the device, allowing the user to review the suggested outfits and select from them.

[0584] Example 2

[0585] 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."

[0586] The present invention aims to provide a system for suggesting clothing coordination for users that automatically generates optimal coordinations taking into account the user's emotions and provides personalized suggestions to each user. Conventional simple clothing combination suggestion systems are unable to reflect the user's emotions or mood of the day, making it difficult to obtain suggestions that satisfy the user. Another problem is that users themselves have to spend time trying out various combinations through trial and error.

[0587] 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.

[0588] In this invention, the server includes means for capturing images of clothing owned by the user, means for transmitting the captured images of the clothing to the server, means for the server to analyze the transmitted images and extract characteristic information about the clothing, means for storing the extracted characteristic information about the clothing in a database, means for the user to input information about the type of outing, means for generating an optimal clothing combination based on the input type information and emotion information, means for transmitting the generated clothing combination to the user's terminal, and means for the terminal to display the transmitted clothing combination, thereby enabling personalized coordination suggestions that take the user's emotions into consideration.

[0589] "User" refers to a person who uses the system to receive clothing coordination suggestions.

[0590] "Clothing images" refers to photographs of clothing taken by users and their digital data.

[0591] "Server" refers to a central system that receives, stores, processes, and transmits data over the Internet.

[0592] "Feature information" refers to attribute data such as the type, color, material, and pattern of clothing.

[0593] "Database" refers to a collection of information that has been structured and stored to allow easy search and retrieval as needed.

[0594] "Type information" refers to information entered by the user regarding the purpose and circumstances of the outing.

[0595] "Emotional information" refers to information that indicates the user's current emotions and moods.

[0596] "Clothing combination" refers to a coordination suggested based on feature information, type information, and emotion information.

[0597] "Terminal" refers to a device such as a smartphone or computer that a user uses to access the system.

[0598] This invention combines an emotion engine with a system that suggests outfits for users' clothing. The system aims to automatically suggest optimal outfits based on the user's mood and emotions on that day, after the user inputs an image of their own clothing.

[0599] First, the user takes a photo of their clothing using a smartphone or computer. The photo includes tops, bottoms, outerwear, accessories, etc. The user then uses a dedicated application to import the image of the clothing into their device.

[0600] The device then sends the captured image to a server, which temporarily stores the image and analyzes it using image recognition algorithms (e.g., OpenCV or TensorFlow). This analysis process identifies the type of clothing, color, material, and pattern (e.g., solid, striped, checked, etc.). This feature information is stored in a database and used to suggest outfits later.

[0601] The user then inputs the type of outing through the application, which may include "work," "date," "important meeting," etc. The input type information is sent from the terminal to the server.

[0602] Furthermore, users can input information about their emotions and moods through the application. They can input emotions through voice (e.g., "I'm happy today") or images (e.g., a photo of a smiling face). The device sends this input data to the emotion engine, which then identifies the user's emotions using voice recognition or image analysis technology (e.g., voice recognition service or image analysis service).

[0603] The server uses a generative AI model to generate optimal outfits based on clothing feature information stored in the database and category and emotion information entered by the user. For example, if a user enters category information of "date" and emotion information of "happy," the server can suggest outfits in bright colors and casual styles.

[0604] The generated outfit suggestions are sent from the server to the user's device. The device visually displays the suggested outfits, allowing the user to select their favorite outfit from among them. As a specific example, if a user inputs style information for a "date" and the emotion engine recognizes the emotion "nervous," the server will select clothes in muted colors and relaxed materials and suggest these as outfits. The user can check these suggestions in the application and actually go out on a date in that style.

[0605] Example prompt sentence:

[0606] "Design a system that uses an emotion engine to recognize the user's emotions and suggests clothing coordination that matches the user's mood that day. Please include detailed instructions on how to input images of the user's clothing, type information, and emotional information, and generate and suggest optimal coordination."

[0607] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0608] Step 1:

[0609] Users use their smartphones or computers to take pictures of their clothing.

[0610] Input: Images of clothing taken by the user

[0611] How it works: The user launches the dedicated application and uses the camera function to take a picture of the clothing.

[0612] Output: Image file of the clothing saved on the user's device

[0613] Step 2:

[0614] The device sends the captured image of the clothing to the server.

[0615] Input: Image file of clothing stored on the user's device

[0616] How it works: The device uploads image data to a server via an internet connection.

[0617] Output: Image data of clothing stored on the server

[0618] Step 3:

[0619] The server analyzes the received image data and extracts characteristic information about the clothing.

[0620] Input: Image data of clothing stored on the server

[0621] How it works: The server uses image recognition algorithms (e.g. OpenCV or TensorFlow) to identify the type, color, material, and pattern of the clothing.

[0622] Output: Extracted clothing feature information (type, color, material, pattern)

[0623] Step 4:

[0624] The server stores the extracted clothing feature information in a database.

[0625] Input: Extracted clothing feature information

[0626] How it works: The server structures the feature information and stores it in a database.

[0627] Output: Clothing feature information stored in a database

[0628] Step 5:

[0629] The user uses the application to input information about the type of trip.

[0630] Input: Type of outing information entered by the user (e.g., work, date, meeting)

[0631] How it works: The user enters category information into the application's input field and presses the submit button.

[0632] Output: Type information sent from the user terminal to the server

[0633] Step 6:

[0634] The user inputs emotion information through the application.

[0635] Input: Emotional information entered by the user (e.g., voice "I'm happy today," photo of a smiling face)

[0636] How it works: The user records audio or takes a photo and sends emotional information through the application.

[0637] Output: Emotion information sent from the user device to the server

[0638] Step 7:

[0639] The server receives the emotion information and identifies the emotion using an emotion engine.

[0640] Input: Emotion information stored on the server

[0641] How it works: The server uses voice recognition or image analysis technology (e.g., voice recognition service or image analysis service) to identify emotions.

[0642] Output: Identified emotion information (e.g., "joy," "tension")

[0643] Step 8:

[0644] The server generates a coordinate based on the clothing feature information stored in the database, the type information input by the user, and the identified emotion information.

[0645] Input: Stored clothing feature information, type information, emotion information

[0646] How it works: The server uses a generative AI model to generate the optimal outfit. For example, it suggests bright colors and casual styles based on the type of "date" and the emotion of "joy."

[0647] Output: Generated outfit suggestions

[0648] Step 9:

[0649] The server transmits the generated coordination proposal to the user's terminal.

[0650] Input: Generated outfit suggestions

[0651] Operation: The server sends the generated coordination proposal to the user's terminal.

[0652] Output: Coordination proposals sent to the user's device

[0653] Step 10:

[0654] The terminal visually displays the proposed coordination.

[0655] Input: Coordination proposal sent to the user's device

[0656] Operation: The device displays an image and description of the outfit on the application screen.

[0657] Output: Coordination suggestions displayed to the user

[0658] (Application example 2)

[0659] 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."

[0660] Many current coordination suggestion systems suggest outfits based on the user's clothing and style information, but they do not take into account the user's emotions or moods, making it difficult to suggest personalized outfits. Furthermore, there is a lack of suggestions for purchasing new items in collaboration with online shopping sites, which prevents the system from further enriching the user's fashion experience.

[0661] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0662] In this invention, the server includes means for capturing photos of clothing owned by the user, means for transmitting the captured photos of the clothing to the server, means for the server to analyze the transmitted photos and extract clothing information, means for storing the extracted clothing information in a database, means for the user to input style information for going out, means for recognizing the user's emotions, means for linking the input style information based on the recognized emotions to generate an optimal outfit, means for transmitting the generated outfit to the user's terminal, and means for the terminal to display the transmitted outfit. This makes it possible to propose personalized outfits that take the user's emotions and moods into consideration, and further enriches the user's fashion experience by adding purchasing suggestions for new items in collaboration with mail order sites.

[0663] The "means for capturing photos of clothing" refers to a device or method for taking photos of clothing owned by a user and acquiring them as digital data.

[0664] The "means for transmitting the photograph of the clothes to the server" is a network communication device or protocol for transmitting the captured photograph data of the clothes to the server via the Internet.

[0665] The "means for extracting clothing information" refers to image analysis technology and algorithms that analyze the photographic data of clothing sent to the server and identify the type, color, material, pattern, etc. of each piece of clothing.

[0666] The "means for storing in a database" refers to a storage device or system for organizing and centrally managing the extracted clothing information.

[0667] The "means for inputting outing style information" is an interface that allows the user to input the day's plans and desired style (e.g., work, date, important meeting, etc.).

[0668] "Means for recognizing emotions" refers to voice recognition technology and image analysis technology for identifying emotions from the user's voice, facial expressions, etc.

[0669] The "means for generating optimal outfits" refers to an algorithm or system that automatically generates optimal outfit combinations based on clothing information stored in a database, input style information, and recognized emotions.

[0670] The "means for transmitting the coordinates to the user's terminal" refers to a communication device or protocol for transmitting the information about the generated coordinates to the user's terminal such as a smartphone or computer.

[0671] The "means for displaying coordinated outfits sent to the terminal" refers to a display device or application for visually displaying coordinated outfits sent to the user's terminal.

[0672] "Recommended items on the online shopping site" are items that users can purchase to go with the suggested outfits, and are products offered on the online shopping site.

[0673] The present invention provides a system for suggesting clothing coordination for a user, and provides personalized suggestions by recognizing the user's emotions. This system includes the following components:

[0674] First, a user uses a device such as a smartphone or PC to take a photo of their clothing, including tops, bottoms, outerwear, and accessories, and imports it into the device. The device then sends the photo data to the server.

[0675] The server analyzes the received photo data and identifies the type, color, material, and pattern of each garment using image analysis techniques such as OpenCV and Keras. The analysis results are stored in a database and used to suggest outfits later.

[0676] Through the application, users input their outing style information, including "work," "date," "important meeting," etc. The input information is sent to the server and stored in a database.

[0677] Furthermore, the device provides a means for inputting voice and facial photographs to recognize the user's emotions. The device sends this data to an emotion recognition engine to identify the emotion. A pre-trained neural network model is used for emotion recognition. For example, if a user inputs the voice "I'm happy today" or a photograph of a smiling face, the emotion recognition engine will recognize the emotion "joy."

[0678] The server generates an optimal outfit based on the clothing information stored in the database, the input style information, and the recognized emotion. The generated outfit is sent from the server to the user's device and displayed on the device.

[0679] Furthermore, the system is linked to online shopping sites and can also suggest new items to purchase based on the suggested outfits. For example, it can recommend new bottoms or accessories to go with a top the user already owns, and the user can then purchase them on the online shopping site.

[0680] For example, if a user inputs their style information for a date and the emotion recognition engine recognizes the emotion "happy," the server will select clothes in bright colors and light materials from the database and suggest them as an outfit. This suggestion will be displayed on the user's device, and new items that go well with it will be recommended by the online shopping site.

[0681] Example prompts for generative AI models:

[0682] plaintext

[0683] Suggest an outfit that will make the user feel happy on a date. Based on the following clothing information the user owns: tops, bottoms, outerwear, and accessories. Please refer to the provided photos.

[0684] This allows users to enjoy creating the perfect outfit to suit their mood, and also makes purchasing new items a breeze.

[0685] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0686] Step 1:

[0687] Users use their smartphones or computers to take photos of their clothing and import them into the device. The imported photo data is categorized into tops, bottoms, outerwear, accessories, etc. This becomes the input data for the system.

[0688] Step 2:

[0689] The device sends the captured photo data of the clothes to the server. The transmission is performed using network communication, and the data is uploaded to the server via the Internet. The output of this step is the image data transferred to the server.

[0690] Step 3:

[0691] The server analyzes the received photo data. It uses image analysis techniques such as OpenCV and Keras to identify the type, color, material, and pattern of each garment. This analysis results in extracted clothing information, which is then stored in a database.

[0692] Step 4:

[0693] The user uses the application to input style information for going out. For example, they can choose from options such as "Work," "Date," and "Important Meeting." The input style information is sent from the device to the server and stored in a database. The output of this step is the style information input by the user.

[0694] Step 5:

[0695] Users input their voice or a photo of their face into the application, which recognizes their emotions. The emotion recognition engine processes this data and identifies the user's emotions using voice recognition or image analysis technology. For example, if a user inputs the voice "I'm happy today," the emotion recognition engine will recognize the emotion "joy."

[0696] Step 6:

[0697] The server generates optimal outfits based on clothing information, style information, and recognized emotions stored in the database. The algorithm uses a generative AI model to create outfits. This outfit suggestion is output.

[0698] Step 7:

[0699] The server sends the generated coordinates to the user's terminal, which visually displays the coordinates, which becomes the output for the user.

[0700] Step 8:

[0701] Based on the suggested outfit, the device interacts with the online shopping site to suggest new items for purchase. These suggestions may include new bottoms or accessories. The user can review the suggested items and complete the purchase on the online shopping site. The output of this step is the suggested items.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] [Third embodiment]

[0706] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0707] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0708] 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).

[0709] 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.

[0710] 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.

[0711] 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).

[0712] 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.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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."

[0718] The present invention relates to a system that suggests outfit coordination for a user's clothing. The system aims to automatically suggest optimal outfit coordination by having the user import a photo of their own clothing and input style information for that day's outing.

[0719] 1. Importing photos

[0720] Users use devices such as smartphones or PCs to take photos of their clothing, including tops, bottoms, outerwear, and accessories. A dedicated application is installed on the device, and the photos are imported through this application.

[0721] 2. Upload an image

[0722] The captured photo is sent from the device to the server, where it is temporarily stored and awaits the next analysis process.

[0723] 3. Image Analysis

[0724] The server applies image analysis algorithms to the received images, which identify the type of clothing (e.g., shirt, pants, skirt, jacket, etc.), color, material, and pattern (solid, striped, checked, etc.). This information is stored in a database for later use in searches and suggestions.

[0725] 4. Enter style information

[0726] Through the application, the user inputs style information for the day's outing, including, for example, "work," "date," "important meeting," etc. The input information is sent from the terminal to the server.

[0727] 5. Generating Coordination Proposals

[0728] The server compares the clothing information stored in the database with the style information sent by the user to generate optimal outfit suggestions, which include combinations of tops, bottoms, outerwear, and accessories.

[0729] 6. Submitting and Viewing Proposals

[0730] The generated coordination suggestions are sent from the server to the user's device, which receives them and displays them visually in the application. The user can then review the suggestions and choose the style they like best.

[0731] Specific examples

[0732] For example, if a user enters style information for an "important meeting," the server generates a formal outfit. Specifically, the server selects a black suit, white shirt, tie, and leather shoes from the database and proposes this as one outfit. The user can then confirm this proposal in the application and actually attend the meeting in that style.

[0733] In this way, the system supports busy users in efficiently selecting the most suitable clothing.

[0734] The processing flow will be explained below.

[0735] Step 1:

[0736] The user takes a photo of their own clothing using a smartphone or PC, then launches a dedicated application and imports the photo of the clothing.

[0737] Step 2:

[0738] The photos captured by the device are sent to the server via the application. The data sent includes the photos as well as identification information such as the user ID.

[0739] Step 3:

[0740] The server receives the transmitted photo data, temporarily stores it, and prepares it for the image analysis process.

[0741] Step 4:

[0742] The server applies image analysis algorithms to extract information such as the type of clothing, color, material, and pattern, using machine learning models and image recognition techniques.

[0743] Step 5:

[0744] The server stores the analysis results in a database, which contains information such as the type of clothing, color, material, pattern, image file name, and user ID.

[0745] Step 6:

[0746] Through the application, the user inputs style information for the day's outing, such as "work," "date," or "important meeting."

[0747] Step 7:

[0748] The terminal transmits the input style information to the server. The transmitted data includes the style information and the user ID.

[0749] Step 8:

[0750] The server searches for clothing information in a database based on the received style information, and executes an algorithm to generate the optimal outfit.

[0751] Step 9:

[0752] The server generates outfit suggestions, which include tops, bottoms, outerwear, and accessories.

[0753] Step 10:

[0754] The server transmits the generated coordination proposal to the user's terminal.

[0755] Step 11:

[0756] The application displays the coordinated outfit suggestions received by the device, allowing the user to review the suggested outfits and select from them.

[0757] Example 1

[0758] 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."

[0759] Existing clothing coordination systems require users to manually select clothing and consider how to coordinate their outfits to match their style, which is time-consuming and labor-intensive. Furthermore, many systems lack the functionality to provide users with optimal suggestions based on their style for the day. Furthermore, because they do not utilize advanced image analysis or generative AI models, they often fail to provide highly accurate outfit suggestions.

[0760] 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.

[0761] In this invention, the server includes means for capturing photos of clothes owned by the user, means for transmitting the captured photos of the clothes to an information processing device, means for the information processing device to analyze the transmitted photos and extract clothing information, means for storing the extracted clothing information in a recording device, means for the user to input style information for going out, means for generating an optimal outfit based on the input style information, means for transmitting the generated outfit to the user's terminal, means for the terminal to display the transmitted outfit, means for generating outfit suggestions according to the style information using a generative AI model, and means for inputting a prompt sentence into the generative AI model to obtain outfit suggestions. This allows the user to receive outfit suggestions that are optimal for the day's outing style without any hassle.

[0762] Below are definitions of important terms included in the claims.

[0763] "User" refers to a person who uses this system to receive clothing coordination suggestions.

[0764] "Clothing" refers to all clothing items owned by the user, including tops, bottoms, outerwear, accessories, etc.

[0765] "Photo" refers to image data of clothing taken by the user.

[0766] "Means of capture" refers to the method of taking or scanning a photo of clothing using a user's device and inputting it into the system.

[0767] "Terminal" refers to an electronic device, such as a smartphone or computer, that a user uses to access the system.

[0768] "Information processing device" refers to a computing device such as a server that processes data, analyzes, and generates proposals for the system.

[0769] "Transmission means" refers to the communication technology and protocol for transferring data from the terminal to the information processing device.

[0770] "Means for analyzing" refers to a method for extracting clothing information from the submitted photograph using an image analysis algorithm.

[0771] "Clothing information" is data about analyzed clothing, including characteristics such as type, color, material, and pattern.

[0772] "Recording device" refers to a storage device such as a database for storing data.

[0773] "Outing style information" refers to information that the user inputs according to the purpose and situation of the day, and examples include "work," "date," and "important meeting."

[0774] The "means for generating" refers to a method for automatically creating an optimal coordination based on input style information.

[0775] "Coordination suggestions" refer to clothing combination suggestions generated based on analyzed clothing information and style information.

[0776] A "generative AI model" refers to an artificial intelligence model that creates appropriate coordination suggestions based on input data.

[0777] "Prompt" refers to a text-based instruction that is input into a generative AI model and used to generate outfit suggestions.

[0778] "Display means" refers to the method or technology for visually displaying coordination suggestions on a terminal.

[0779] The present invention relates to a system that suggests outfit coordination for a user's clothing. The system aims to automatically suggest optimal outfit coordination by having the user import a photo of their own clothing and input style information for that day's outing.

[0780] Importing photos

[0781] Users use devices such as smartphones or PCs to take photos of their clothing, including tops, bottoms, outerwear, and accessories. Users install a dedicated application on their devices and import photos through this application.

[0782] Uploading an image

[0783] The captured photos are sent from the device to an information processing device, which temporarily stores the received image files and waits for the next analysis process. For example, a cloud service such as Amazon Web Services (AWS) can be used.

[0784] Image analysis

[0785] The information processing device applies an image analysis algorithm to the received image. This analysis uses image analysis libraries such as TensorFlow and OpenCV. The algorithm identifies the type of clothing (e.g., shirt, pants, skirt, jacket, etc.), color, material, and pattern (solid, striped, checked, etc.). This information is output in JSON format and stored in a recording device (database).

[0786] Entering style information

[0787] The user inputs style information for the day's outing through the application. This style information includes "work," "date," "important meeting," etc. The input information is sent from the terminal to the information processing device.

[0788] Generating outfit suggestions

[0789] The information processing device compares the clothing information stored in the recording device with the style information sent by the user to generate optimal outfits. In this process, a generative AI model (e.g., GPT-4) is used, and appropriate prompts are input into the model to generate optimal outfit suggestions.

[0790] Prompt Sentence Examples

[0791] Below are some example prompts to input to a generative AI model:

[0792] When a user enters style information for an "important meeting," please suggest the best outfit. The database contains the following clothing information:

[0793] black suit

[0794] White shirt

[0795] tie

[0796] leather shoes

[0797] Aim to suggest a formal style.

[0798] Submitting and Viewing Proposals

[0799] The generated outfit suggestions are sent from the information processing device to the user's device. The device receives the suggestions and displays them visually in the application. The user can then review the suggestions and select a style they like.

[0800] For example, if a user inputs style information for an "important meeting," the information processing device generates a formal outfit. Specifically, the information processing device selects a black suit, a white shirt, a tie, and leather shoes from the storage device and proposes this as one outfit. The user can then check this proposal in the application and actually attend the meeting in that style.

[0801] As described above, this system supports busy users in efficiently selecting the most suitable clothing.

[0802] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0803] Step 1: Take and import photos

[0804] Users take photos of their own clothing using the camera on their smartphone or computer. Specifically, users launch a dedicated application, which activates the device's camera. By tapping the "Take Photo" button, the camera activates and users can take photos of the clothing. The photos are saved in the application.

[0805] Input: Clothes before shooting

[0806] Output: Photos of clothing stored on the device

[0807] Step 2: Upload an image

[0808] The device selects the photo in the application and sends it to the server. When the user taps the "Upload" button, the device sends the photo to the server over a secure HTTP connection. A progress bar is displayed during the transfer.

[0809] Input: Photos of clothing stored on the device

[0810] Output: Image file sent to the server

[0811] Step 3: Image analysis

[0812] The server inputs the received photo into an image analysis algorithm, which runs a Python script to load a TensorFlow model and input the photo into the model, identifying the type, color, material, and pattern of the clothing, and outputs the results in JSON format.

[0813] Input: Image file sent to the server

[0814] Output: Clothing information in JSON format (type, color, material, pattern)

[0815] Step 4: Saving to the Database

[0816] The server saves the analyzed clothing information in a database. It receives the analysis results in JSON format and saves them in the "clothing information" table in the database.

[0817] Input: Clothing information in JSON format

[0818] Output: Clothing information stored in the database

[0819] Step 5: Entering style information

[0820] The user enters style information for the day's outing through the application. The user selects the appropriate option (e.g., "Work," "Date," or "Important Meeting") on the style information input screen and taps the "Send" button. The entered information is sent from the device to the server.

[0821] Input: User's outing style information

[0822] Output: Style information sent to the server

[0823] Step 6: Generate outfit suggestions

[0824] The server compares the clothing information stored in the database with the style information sent by the user to generate the optimal outfit. The server obtains the style information, creates a corresponding prompt sentence, and inputs it into a generative AI model (e.g., GPT-4). The model then provides outfit suggestions, which are then stored in the database.

[0825] Input: Clothing information and style information from the database, prompt text

[0826] Output: Generated outfit suggestions

[0827] Step 7: Submit and view your proposal

[0828] The server sends the generated outfit suggestions to the user's device, which then displays the received suggestions on the application's "suggestion display screen." The user can review the suggestions and use options such as "favorite," "share," and "purchase link."

[0829] Input: Generated outfit suggestions

[0830] Output: Coordination suggestions displayed on the device

[0831] The above are the specific processing steps of the system program.

[0832] (Application example 1)

[0833] 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."

[0834] In today's world, consumers want to shop efficiently within limited time. However, in many stores, the time required to try on clothes and the time spent thinking about how to combine them are a significant burden. Furthermore, it is difficult to consider how to best coordinate new purchases with existing clothing. This creates the problem of being unable to coordinate appropriate outfits.

[0835] 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.

[0836] In this invention, the server includes means for importing photos of clothing owned by a user, means for transmitting the imported photos of the clothing to the server, means for the server to analyze the transmitted photos and extract information about the clothing, means for automatically generating outfit suggestions based on prompt sentences using a generative AI model, means for simulating trying on new products in the store using augmented reality technology, means for transmitting the generated outfit suggestions to the user's device, and means for the device to display the transmitted outfit suggestions. This allows consumers to efficiently check the optimal outfits between newly purchased items and their own clothing, even in a physical store, eliminating the need to try on items and enabling an optimal shopping experience while saving time.

[0837] The "means for capturing photos of clothing owned by the user" refers to a means for the user to take pictures of clothing owned by the user and save them in a digital device.

[0838] The "means for transmitting the captured photograph of the clothing to the server" is a means for transmitting the image of the clothing stored in the user's digital device to the server via data communication.

[0839] "Means for the server to analyze the transmitted photograph and extract information about the clothing" refers to means for the server to analyze the received image and identify information such as the type, color, material, and pattern of the clothing.

[0840] The "means for automatically generating coordination suggestions based on prompt sentences using a generative AI model" is a system that uses an artificial intelligence model to generate optimal coordination in response to style requests from users.

[0841] "Means for simulating trying on new products in a store using augmented reality technology" refers to a means for using augmented reality technology to allow a user to virtually try on a product that they are considering purchasing in a physical store.

[0842] The "means for transmitting the generated coordinate information to the user's terminal" refers to a means for transmitting the automatically generated coordinate information to the user's digital device.

[0843] The "means for displaying the coordinates sent by the terminal" refers to a means for visually displaying the coordinate information received on the user's digital device.

[0844] The present invention provides the following specific method for implementing a system that suggests outfit coordination for clothing owned by a user. This system is configured using a user terminal, a server, a generative AI model, and augmented reality technology.

[0845] First, users take photos of their clothing using a smartphone or tablet. These photos are then imported into the device using a dedicated application and sent to a server via the Internet. The server receives the photos and uses image analysis algorithms (e.g., PyTorch or TensorFlow) to identify the type, color, material, and pattern of the clothing.

[0846] The extracted information is then stored in a database, and an interface is provided for users to input style information for outings (e.g., work, date, important meeting). The style information entered by the user is sent to the server, which then generates an appropriate outfit based on it.

[0847] Here, a generative AI model (e.g., GPT-4 or BERT) is used to automatically generate optimal outfits based on the input style information and clothing information stored in the database. The generative AI model uses prompt sentences to generate suggestions. For example, the following prompt sentences are used:

[0848] "As a casual style suggestion, please suggest the best outfit to wear with a white T-shirt and denim pants that I own, using a new item (shirt)."

[0849] Furthermore, in physical stores, augmented reality technology is available for try-on simulations. Users can use smart glasses or smartphones to view products in the store in AR and try them on virtually. This augmented reality technology (e.g., ARKit and ARCore) allows users to see new products in the store in real time and visualize how they will be combined with existing clothing.

[0850] The generated outfit suggestions are sent from the server to the user's device, and the user can check the outfit through the application. For example, if the user inputs style information for an "important meeting," a black suit, white shirt, tie, and leather shoes will be suggested.

[0851] The hardware used to realize this system includes smartphones, tablets, and smart glasses (e.g., Vuzix and Google Glass), allowing users to efficiently and effectively receive outfit suggestions and enjoy a smooth shopping experience even in physical stores.

[0852] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0853] Step 1:

[0854] The user takes a photo of the clothing they own using a smartphone or tablet.

[0855] The input is image data of the clothing. The user uses a dedicated application to take these images and save them on the device. The output of this process is image data of the clothing stored on the device.

[0856] Step 2:

[0857] The terminal transmits the captured photograph of the clothing to the server.

[0858] The input is image data of the clothing stored in the terminal. The image data is sent to the server using data communication. The output of this process is the image data imported into the server.

[0859] Step 3:

[0860] The server analyzes the sent photo and extracts information about the clothing.

[0861] The input is image data of clothing stored on a server. Image analysis algorithms (e.g., PyTorch or TensorFlow) are used to identify the type, color, material, and pattern of the clothing. The output of this process is extracted clothing information (e.g., tops-white-cotton-plain).

[0862] Step 4:

[0863] The server stores the extracted clothing information in a database.

[0864] The input is the clothing information extracted by the analysis. The clothing information is saved in a database (e.g., Firebase or MongoDB). The output of this process is the clothing information saved in the database.

[0865] Step 5:

[0866] The user inputs style information for going out.

[0867] The input is the purpose and situation of the outing (e.g., work, date, important meeting). The user inputs this style information through a dedicated application. The output of this process is the user's style request data.

[0868] Step 6:

[0869] The server generates the optimal coordinates based on the input style information.

[0870] The input is the user's style information and clothing information stored in a database. A generative AI model (e.g., GPT-4 or BERT) is used to generate the optimal outfit. The output of this process is an automatically generated outfit suggestion based on the prompt sentence.

[0871] Step 7:

[0872] Users can simulate trying on new products in a physical store using augmented reality technology.

[0873] The input is product data from a physical store. The user uses smart glasses or a smartphone to virtually try on new products using AR technology (e.g., ARKit or ARCore). The output of this process is visual feedback of the AR virtual try-on experience.

[0874] Step 8:

[0875] The server transmits the generated coordinates to the user's terminal.

[0876] The input is automatically generated coordinate suggestion data. The suggestion is transmitted to the user's terminal via data communication. The output of this process is the coordinate suggestion data received by the user's terminal.

[0877] Step 9:

[0878] The terminal displays the transmitted coordinates.

[0879] The input is the outfit suggestion data received from the server. A dedicated application is used to visually display the outfit suggestion to the user. The output of this process is the outfit suggestion visually displayed to the user.

[0880] 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.

[0881] This invention combines an emotion engine with a system that suggests outfits for users' clothing. The system aims to automatically suggest optimal outfits based on the user's mood and emotions on that day by having the user take a photo of their own clothing and recognize their emotions.

[0882] 1. Importing photos

[0883] Users can take photos of their clothing using a smartphone or PC. They then use a dedicated application to import the photos of their clothing. These photos include tops, bottoms, outerwear, accessories, etc.

[0884] 2. Upload an image

[0885] The captured photo is sent from the device to the server, which receives the image file and temporarily stores it.

[0886] 3. Image Analysis

[0887] The server analyzes the received image data. This analysis process identifies the type of clothing, color, material, and pattern (e.g., solid, striped, checked, etc.). This information is stored in a database and used to suggest outfits later.

[0888] 4. Enter style information

[0889] The user inputs style information for going out through the application, such as "work," "date," "important meeting," etc. The input information is sent from the device to the server.

[0890] 5. Emotion recognition

[0891] When a user inputs voice or a photo via an application, the device sends this to the emotion engine, which then uses voice recognition or image analysis technology to identify the user's emotion. For example, if a user inputs a voice message saying "I'm happy today" or a photo of a smiling face, the emotion engine will recognize the emotion as "joy."

[0892] 6. Coordination Proposal Generation

[0893] The server generates optimal outfits based on clothing information stored in a database, input style information, and recognized emotions. For example, if the user feels "happy," it can suggest bright colors and casual styles.

[0894] 7. Submitting and Viewing Proposals

[0895] The generated outfit suggestions are sent from the server to the user's device, where they are visually displayed. The user can then review the suggested outfits and select from them.

[0896] Specific examples

[0897] For example, if a user inputs style information for a "date" and the emotion engine recognizes the emotion "nervous," the server will select clothes in muted colors and relaxed materials from the database and suggest them as an outfit. The user can then check this suggestion in the application and actually go out on a date in that style.

[0898] In this way, the system can provide a more personalized service by proposing optimal outfits that take the user's emotions into consideration.

[0899] The processing flow will be explained below.

[0900] Step 1:

[0901] The user takes a photo of the clothing they own using a smartphone or computer, then launches a dedicated application and imports the photo of the clothing.

[0902] Step 2:

[0903] The device sends the captured photo to the server. The data sent includes the photo as well as identification information such as the user ID.

[0904] Step 3:

[0905] The server receives the transmitted photo data, temporarily stores it, and prepares it for the image analysis process.

[0906] Step 4:

[0907] The server applies image analysis algorithms to extract information such as the type of clothing, color, material, and pattern, using machine learning models and image recognition techniques.

[0908] Step 5:

[0909] The server stores the analysis results in a database, which contains information such as the type of clothing, color, material, pattern, image file name, and user ID.

[0910] Step 6:

[0911] The user inputs style information for the day's outing through the application, including, for example, "work," "date," "important meeting," and the like.

[0912] Step 7:

[0913] The terminal transmits the input style information to the server. The transmitted data includes the user ID in addition to the style information.

[0914] Step 8:

[0915] The user inputs voice or a photo via the application, which is then analyzed by the emotion engine. The emotion engine uses voice recognition or image analysis technology to identify the user's emotion. For example, if the user inputs a voice message saying "I'm happy today" or a photo of a smiling face, the emotion engine will recognize the emotion as "joy."

[0916] Step 9:

[0917] The device sends the recognized emotion information to the server. The sent data includes style information, emotion information, and user ID.

[0918] Step 10:

[0919] The server generates optimal outfits based on clothing information stored in a database, input style information, and recognized emotions. For example, if the user is feeling "happy," it can suggest bright colors and casual styles.

[0920] Step 11:

[0921] The server transmits the generated coordination proposal to the user's terminal.

[0922] Step 12:

[0923] The application displays the coordinated outfit suggestions received by the device, allowing the user to review the suggested outfits and select from them.

[0924] Example 2

[0925] 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."

[0926] The present invention aims to provide a system for suggesting clothing coordination for users that automatically generates optimal coordinations taking into account the user's emotions and provides personalized suggestions to each user. Conventional simple clothing combination suggestion systems are unable to reflect the user's emotions or mood of the day, making it difficult to obtain suggestions that satisfy the user. Another problem is that users themselves have to spend time trying out various combinations through trial and error.

[0927] 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.

[0928] In this invention, the server includes means for capturing images of clothing owned by the user, means for transmitting the captured images of the clothing to the server, means for the server to analyze the transmitted images and extract characteristic information about the clothing, means for storing the extracted characteristic information about the clothing in a database, means for the user to input information about the type of outing, means for generating an optimal clothing combination based on the input type information and emotion information, means for transmitting the generated clothing combination to the user's terminal, and means for the terminal to display the transmitted clothing combination, thereby enabling personalized coordination suggestions that take the user's emotions into consideration.

[0929] "User" refers to a person who uses the system to receive clothing coordination suggestions.

[0930] "Clothing images" refers to photographs of clothing taken by users and their digital data.

[0931] "Server" refers to a central system that receives, stores, processes, and transmits data over the Internet.

[0932] "Feature information" refers to attribute data such as the type, color, material, and pattern of clothing.

[0933] "Database" refers to a collection of information that has been structured and stored to allow easy search and retrieval as needed.

[0934] "Type information" refers to information entered by the user regarding the purpose and circumstances of the outing.

[0935] "Emotional information" refers to information that indicates the user's current emotions and moods.

[0936] "Clothing combination" refers to a coordination suggested based on feature information, type information, and emotion information.

[0937] "Terminal" refers to a device such as a smartphone or computer that a user uses to access the system.

[0938] This invention combines an emotion engine with a system that suggests outfits for users' clothing. The system aims to automatically suggest optimal outfits based on the user's mood and emotions on that day, after the user inputs an image of their own clothing.

[0939] First, the user takes a photo of their clothing using a smartphone or computer. The photo includes tops, bottoms, outerwear, accessories, etc. The user then uses a dedicated application to import the image of the clothing into their device.

[0940] The device then sends the captured image to a server, which temporarily stores the image and analyzes it using image recognition algorithms (e.g., OpenCV or TensorFlow). This analysis process identifies the type of clothing, color, material, and pattern (e.g., solid, striped, checked, etc.). This feature information is stored in a database and used to suggest outfits later.

[0941] The user then inputs the type of outing through the application, which may include "work," "date," "important meeting," etc. The input type information is sent from the terminal to the server.

[0942] Furthermore, users can input information about their emotions and moods through the application. They can input emotions through voice (e.g., "I'm happy today") or images (e.g., a photo of a smiling face). The device sends this input data to the emotion engine, which then identifies the user's emotions using voice recognition or image analysis technology (e.g., voice recognition service or image analysis service).

[0943] The server uses a generative AI model to generate optimal outfits based on clothing feature information stored in the database and category and emotion information entered by the user. For example, if a user enters category information of "date" and emotion information of "happy," the server can suggest outfits in bright colors and casual styles.

[0944] The generated outfit suggestions are sent from the server to the user's device. The device visually displays the suggested outfits, allowing the user to select their favorite outfit from among them. As a specific example, if a user inputs style information for a "date" and the emotion engine recognizes the emotion "nervous," the server will select clothes in muted colors and relaxed materials and suggest these as outfits. The user can check these suggestions in the application and actually go out on a date in that style.

[0945] Example prompt sentence:

[0946] "Design a system that uses an emotion engine to recognize the user's emotions and suggests clothing coordination that matches the user's mood that day. Please include detailed instructions on how to input images of the user's clothing, type information, and emotional information, and generate and suggest optimal coordination."

[0947] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0948] Step 1:

[0949] Users use their smartphones or computers to take pictures of their clothing.

[0950] Input: Images of clothing taken by the user

[0951] How it works: The user launches the dedicated application and uses the camera function to take a picture of the clothing.

[0952] Output: Image file of the clothing saved on the user's device

[0953] Step 2:

[0954] The device sends the captured image of the clothing to the server.

[0955] Input: Image file of clothing stored on the user's device

[0956] How it works: The device uploads image data to a server via an internet connection.

[0957] Output: Image data of clothing stored on the server

[0958] Step 3:

[0959] The server analyzes the received image data and extracts characteristic information about the clothing.

[0960] Input: Image data of clothing stored on the server

[0961] How it works: The server uses image recognition algorithms (e.g. OpenCV or TensorFlow) to identify the type, color, material, and pattern of the clothing.

[0962] Output: Extracted clothing feature information (type, color, material, pattern)

[0963] Step 4:

[0964] The server stores the extracted clothing feature information in a database.

[0965] Input: Extracted clothing feature information

[0966] How it works: The server structures the feature information and stores it in a database.

[0967] Output: Clothing feature information stored in a database

[0968] Step 5:

[0969] The user uses the application to input information about the type of trip.

[0970] Input: Type of outing information entered by the user (e.g., work, date, meeting)

[0971] How it works: The user enters category information into the application's input field and presses the submit button.

[0972] Output: Type information sent from the user terminal to the server

[0973] Step 6:

[0974] The user inputs emotion information through the application.

[0975] Input: Emotional information entered by the user (e.g., voice "I'm happy today," photo of a smiling face)

[0976] How it works: The user records audio or takes a photo and sends emotional information through the application.

[0977] Output: Emotion information sent from the user device to the server

[0978] Step 7:

[0979] The server receives the emotion information and identifies the emotion using an emotion engine.

[0980] Input: Emotion information stored on the server

[0981] How it works: The server uses voice recognition or image analysis technology (e.g., voice recognition service or image analysis service) to identify emotions.

[0982] Output: Identified emotion information (e.g., "joy," "tension")

[0983] Step 8:

[0984] The server generates a coordinate based on the clothing feature information stored in the database, the type information input by the user, and the identified emotion information.

[0985] Input: Stored clothing feature information, type information, emotion information

[0986] How it works: The server uses a generative AI model to generate the optimal outfit. For example, it suggests bright colors and casual styles based on the type of "date" and the emotion of "joy."

[0987] Output: Generated outfit suggestions

[0988] Step 9:

[0989] The server transmits the generated coordination proposal to the user's terminal.

[0990] Input: Generated outfit suggestions

[0991] Operation: The server sends the generated coordination proposal to the user's terminal.

[0992] Output: Coordination proposals sent to the user's device

[0993] Step 10:

[0994] The terminal visually displays the proposed coordination.

[0995] Input: Coordination proposal sent to the user's device

[0996] Operation: The device displays an image and description of the outfit on the application screen.

[0997] Output: Coordination suggestions displayed to the user

[0998] (Application example 2)

[0999] 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."

[1000] Many current coordination suggestion systems suggest outfits based on the user's clothing and style information, but they do not take into account the user's emotions or moods, making it difficult to suggest personalized outfits. Furthermore, there is a lack of suggestions for purchasing new items in collaboration with online shopping sites, which prevents the system from further enriching the user's fashion experience.

[1001] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1002] In this invention, the server includes means for capturing photos of clothing owned by the user, means for transmitting the captured photos of the clothing to the server, means for the server to analyze the transmitted photos and extract clothing information, means for storing the extracted clothing information in a database, means for the user to input style information for going out, means for recognizing the user's emotions, means for linking the input style information based on the recognized emotions to generate an optimal outfit, means for transmitting the generated outfit to the user's terminal, and means for the terminal to display the transmitted outfit. This makes it possible to propose personalized outfits that take the user's emotions and moods into consideration, and further enriches the user's fashion experience by adding purchasing suggestions for new items in collaboration with mail order sites.

[1003] The "means for capturing photos of clothing" refers to a device or method for taking photos of clothing owned by a user and acquiring them as digital data.

[1004] The "means for transmitting the photograph of the clothes to the server" is a network communication device or protocol for transmitting the captured photograph data of the clothes to the server via the Internet.

[1005] The "means for extracting clothing information" refers to image analysis technology and algorithms that analyze the photographic data of clothing sent to the server and identify the type, color, material, pattern, etc. of each piece of clothing.

[1006] The "means for storing in a database" refers to a storage device or system for organizing and centrally managing the extracted clothing information.

[1007] The "means for inputting outing style information" is an interface that allows the user to input the day's plans and desired style (e.g., work, date, important meeting, etc.).

[1008] "Means for recognizing emotions" refers to voice recognition technology and image analysis technology for identifying emotions from the user's voice, facial expressions, etc.

[1009] The "means for generating optimal outfits" refers to an algorithm or system that automatically generates optimal outfit combinations based on clothing information stored in a database, input style information, and recognized emotions.

[1010] The "means for transmitting the coordinates to the user's terminal" refers to a communication device or protocol for transmitting the information about the generated coordinates to the user's terminal such as a smartphone or computer.

[1011] The "means for displaying coordinated outfits sent to the terminal" refers to a display device or application for visually displaying coordinated outfits sent to the user's terminal.

[1012] "Recommended items on the online shopping site" are items that users can purchase to go with the suggested outfits, and are products offered on the online shopping site.

[1013] The present invention provides a system for suggesting clothing coordination for a user, and provides personalized suggestions by recognizing the user's emotions. This system includes the following components:

[1014] First, a user uses a device such as a smartphone or PC to take a photo of their clothing, including tops, bottoms, outerwear, and accessories, and imports it into the device. The device then sends the photo data to the server.

[1015] The server analyzes the received photo data and identifies the type, color, material, and pattern of each garment using image analysis techniques such as OpenCV and Keras. The analysis results are stored in a database and used to suggest outfits later.

[1016] Through the application, users input their outing style information, including "work," "date," "important meeting," etc. The input information is sent to the server and stored in a database.

[1017] Furthermore, the device provides a means for inputting voice and facial photographs to recognize the user's emotions. The device sends this data to an emotion recognition engine to identify the emotion. A pre-trained neural network model is used for emotion recognition. For example, if a user inputs the voice "I'm happy today" or a photograph of a smiling face, the emotion recognition engine will recognize the emotion "joy."

[1018] The server generates an optimal outfit based on the clothing information stored in the database, the input style information, and the recognized emotion. The generated outfit is sent from the server to the user's device and displayed on the device.

[1019] Furthermore, the system is linked to online shopping sites and can also suggest new items to purchase based on the suggested outfits. For example, it can recommend new bottoms or accessories to go with a top the user already owns, and the user can then purchase them on the online shopping site.

[1020] For example, if a user inputs their style information for a date and the emotion recognition engine recognizes the emotion "happy," the server will select clothes in bright colors and light materials from the database and suggest them as an outfit. This suggestion will be displayed on the user's device, and new items that go well with it will be recommended by the online shopping site.

[1021] Example prompts for generative AI models:

[1022] plaintext

[1023] Suggest an outfit that will make the user feel happy on a date. Based on the following clothing information the user owns: tops, bottoms, outerwear, and accessories. Please refer to the provided photos.

[1024] This allows users to enjoy creating the perfect outfit to suit their mood, and also makes purchasing new items a breeze.

[1025] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1026] Step 1:

[1027] Users use their smartphones or computers to take photos of their clothing and import them into the device. The imported photo data is categorized into tops, bottoms, outerwear, accessories, etc. This becomes the input data for the system.

[1028] Step 2:

[1029] The device sends the captured photo data of the clothes to the server. The transmission is performed using network communication, and the data is uploaded to the server via the Internet. The output of this step is the image data transferred to the server.

[1030] Step 3:

[1031] The server analyzes the received photo data. It uses image analysis techniques such as OpenCV and Keras to identify the type, color, material, and pattern of each garment. This analysis results in extracted clothing information, which is then stored in a database.

[1032] Step 4:

[1033] The user uses the application to input style information for going out. For example, they can choose from options such as "Work," "Date," and "Important Meeting." The input style information is sent from the device to the server and stored in a database. The output of this step is the style information input by the user.

[1034] Step 5:

[1035] Users input their voice or a photo of their face into the application, which recognizes their emotions. The emotion recognition engine processes this data and identifies the user's emotions using voice recognition or image analysis technology. For example, if a user inputs the voice "I'm happy today," the emotion recognition engine will recognize the emotion "joy."

[1036] Step 6:

[1037] The server generates optimal outfits based on clothing information, style information, and recognized emotions stored in the database. The algorithm uses a generative AI model to create outfits. This outfit suggestion is output.

[1038] Step 7:

[1039] The server sends the generated coordinates to the user's terminal, which visually displays the coordinates, which becomes the output for the user.

[1040] Step 8:

[1041] Based on the suggested outfit, the device interacts with the online shopping site to suggest new items for purchase. These suggestions may include new bottoms or accessories. The user can review the suggested items and complete the purchase on the online shopping site. The output of this step is the suggested items.

[1042] 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.

[1043] 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.

[1044] 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.

[1045] [Fourth embodiment]

[1046] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1047] 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.

[1048] 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).

[1049] 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.

[1050] 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.

[1051] 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).

[1052] 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.

[1053] 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.

[1054] 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.

[1055] 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.

[1056] 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.

[1057] 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.

[1058] 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."

[1059] The present invention relates to a system that suggests outfit coordination for a user's clothing. The system aims to automatically suggest optimal outfit coordination by having the user import a photo of their own clothing and input style information for that day's outing.

[1060] 1. Importing photos

[1061] Users use devices such as smartphones or PCs to take photos of their clothing, including tops, bottoms, outerwear, and accessories. A dedicated application is installed on the device, and the photos are imported through this application.

[1062] 2. Upload an image

[1063] The captured photo is sent from the device to the server, where it is temporarily stored and awaits the next analysis process.

[1064] 3. Image Analysis

[1065] The server applies image analysis algorithms to the received images, which identify the type of clothing (e.g., shirt, pants, skirt, jacket, etc.), color, material, and pattern (solid, striped, checked, etc.). This information is stored in a database for later use in searches and suggestions.

[1066] 4. Enter style information

[1067] Through the application, the user inputs style information for the day's outing, including, for example, "work," "date," "important meeting," etc. The input information is sent from the terminal to the server.

[1068] 5. Generating Coordination Proposals

[1069] The server compares the clothing information stored in the database with the style information sent by the user to generate optimal outfit suggestions, which include combinations of tops, bottoms, outerwear, and accessories.

[1070] 6. Submitting and Viewing Proposals

[1071] The generated coordination suggestions are sent from the server to the user's device, which receives them and displays them visually in the application. The user can then review the suggestions and choose the style they like best.

[1072] Specific examples

[1073] For example, if a user enters style information for an "important meeting," the server generates a formal outfit. Specifically, the server selects a black suit, white shirt, tie, and leather shoes from the database and proposes this as one outfit. The user can then confirm this proposal in the application and actually attend the meeting in that style.

[1074] In this way, the system supports busy users in efficiently selecting the most suitable clothing.

[1075] The processing flow will be explained below.

[1076] Step 1:

[1077] The user takes a photo of their own clothing using a smartphone or PC, then launches a dedicated application and imports the photo of the clothing.

[1078] Step 2:

[1079] The photos captured by the device are sent to the server via the application. The data sent includes the photos as well as identification information such as the user ID.

[1080] Step 3:

[1081] The server receives the transmitted photo data, temporarily stores it, and prepares it for the image analysis process.

[1082] Step 4:

[1083] The server applies image analysis algorithms to extract information such as the type of clothing, color, material, and pattern, using machine learning models and image recognition techniques.

[1084] Step 5:

[1085] The server stores the analysis results in a database, which contains information such as the type of clothing, color, material, pattern, image file name, and user ID.

[1086] Step 6:

[1087] Through the application, the user inputs style information for the day's outing, such as "work," "date," or "important meeting."

[1088] Step 7:

[1089] The terminal transmits the input style information to the server. The transmitted data includes the style information and the user ID.

[1090] Step 8:

[1091] The server searches for clothing information in a database based on the received style information, and executes an algorithm to generate the optimal outfit.

[1092] Step 9:

[1093] The server generates outfit suggestions, which include tops, bottoms, outerwear, and accessories.

[1094] Step 10:

[1095] The server transmits the generated coordination proposal to the user's terminal.

[1096] Step 11:

[1097] The application displays the coordinated outfit suggestions received by the device, allowing the user to review the suggested outfits and select from them.

[1098] Example 1

[1099] 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."

[1100] Existing clothing coordination systems require users to manually select clothing and consider how to coordinate their outfits to match their style, which is time-consuming and labor-intensive. Furthermore, many systems lack the functionality to provide users with optimal suggestions based on their style for the day. Furthermore, because they do not utilize advanced image analysis or generative AI models, they often fail to provide highly accurate outfit suggestions.

[1101] 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.

[1102] In this invention, the server includes means for capturing photos of clothes owned by the user, means for transmitting the captured photos of the clothes to an information processing device, means for the information processing device to analyze the transmitted photos and extract clothing information, means for storing the extracted clothing information in a recording device, means for the user to input style information for going out, means for generating an optimal outfit based on the input style information, means for transmitting the generated outfit to the user's terminal, means for the terminal to display the transmitted outfit, means for generating outfit suggestions according to the style information using a generative AI model, and means for inputting a prompt sentence into the generative AI model to obtain outfit suggestions. This allows the user to receive outfit suggestions that are optimal for the day's outing style without any hassle.

[1103] Below are definitions of important terms included in the claims.

[1104] "User" refers to a person who uses this system to receive clothing coordination suggestions.

[1105] "Clothing" refers to all clothing items owned by the user, including tops, bottoms, outerwear, accessories, etc.

[1106] "Photo" refers to image data of clothing taken by the user.

[1107] "Means of capture" refers to the method of taking or scanning a photo of clothing using a user's device and inputting it into the system.

[1108] "Terminal" refers to an electronic device, such as a smartphone or computer, that a user uses to access the system.

[1109] "Information processing device" refers to a computing device such as a server that processes data, analyzes, and generates proposals for the system.

[1110] "Transmission means" refers to the communication technology and protocol for transferring data from the terminal to the information processing device.

[1111] "Means for analyzing" refers to a method for extracting clothing information from the submitted photograph using an image analysis algorithm.

[1112] "Clothing information" is data about analyzed clothing, including characteristics such as type, color, material, and pattern.

[1113] "Recording device" refers to a storage device such as a database for storing data.

[1114] "Outing style information" refers to information that the user inputs according to the purpose and situation of the day, and examples include "work," "date," and "important meeting."

[1115] The "means for generating" refers to a method for automatically creating an optimal coordination based on input style information.

[1116] "Coordination suggestions" refer to clothing combination suggestions generated based on analyzed clothing information and style information.

[1117] A "generative AI model" refers to an artificial intelligence model that creates appropriate coordination suggestions based on input data.

[1118] "Prompt" refers to a text-based instruction that is input into a generative AI model and used to generate outfit suggestions.

[1119] "Display means" refers to the method or technology for visually displaying coordination suggestions on a terminal.

[1120] The present invention relates to a system that suggests outfit coordination for a user's clothing. The system aims to automatically suggest optimal outfit coordination by having the user import a photo of their own clothing and input style information for that day's outing.

[1121] Importing photos

[1122] Users use devices such as smartphones or PCs to take photos of their clothing, including tops, bottoms, outerwear, and accessories. Users install a dedicated application on their devices and import photos through this application.

[1123] Uploading an image

[1124] The captured photos are sent from the device to an information processing device, which temporarily stores the received image files and waits for the next analysis process. For example, a cloud service such as Amazon Web Services (AWS) can be used.

[1125] Image analysis

[1126] The information processing device applies an image analysis algorithm to the received image. This analysis uses image analysis libraries such as TensorFlow and OpenCV. The algorithm identifies the type of clothing (e.g., shirt, pants, skirt, jacket, etc.), color, material, and pattern (solid, striped, checked, etc.). This information is output in JSON format and stored in a recording device (database).

[1127] Entering style information

[1128] The user inputs style information for the day's outing through the application. This style information includes "work," "date," "important meeting," etc. The input information is sent from the terminal to the information processing device.

[1129] Generating outfit suggestions

[1130] The information processing device compares the clothing information stored in the recording device with the style information sent by the user to generate optimal outfits. In this process, a generative AI model (e.g., GPT-4) is used, and appropriate prompts are input into the model to generate optimal outfit suggestions.

[1131] Prompt Sentence Examples

[1132] Below are some example prompts to input to a generative AI model:

[1133] When a user enters style information for an "important meeting," please suggest the best outfit. The database contains the following clothing information:

[1134] black suit

[1135] White shirt

[1136] tie

[1137] leather shoes

[1138] Aim to suggest a formal style.

[1139] Submitting and Viewing Proposals

[1140] The generated outfit suggestions are sent from the information processing device to the user's device. The device receives the suggestions and displays them visually in the application. The user can then review the suggestions and select a style they like.

[1141] For example, if a user inputs style information for an "important meeting," the information processing device generates a formal outfit. Specifically, the information processing device selects a black suit, a white shirt, a tie, and leather shoes from the storage device and proposes this as one outfit. The user can then check this proposal in the application and actually attend the meeting in that style.

[1142] As described above, this system supports busy users in efficiently selecting the most suitable clothing.

[1143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1144] Step 1: Take and import photos

[1145] Users take photos of their own clothing using the camera on their smartphone or computer. Specifically, users launch a dedicated application, which activates the device's camera. By tapping the "Take Photo" button, the camera activates and users can take photos of the clothing. The photos are saved in the application.

[1146] Input: Clothes before shooting

[1147] Output: Photos of clothing stored on the device

[1148] Step 2: Upload an image

[1149] The device selects the photo in the application and sends it to the server. When the user taps the "Upload" button, the device sends the photo to the server over a secure HTTP connection. A progress bar is displayed during the transfer.

[1150] Input: Photos of clothing stored on the device

[1151] Output: Image file sent to the server

[1152] Step 3: Image analysis

[1153] The server inputs the received photo into an image analysis algorithm, which runs a Python script to load a TensorFlow model and input the photo into the model, identifying the type, color, material, and pattern of the clothing, and outputs the results in JSON format.

[1154] Input: Image file sent to the server

[1155] Output: Clothing information in JSON format (type, color, material, pattern)

[1156] Step 4: Saving to the Database

[1157] The server saves the analyzed clothing information in a database. It receives the analysis results in JSON format and saves them in the "clothing information" table in the database.

[1158] Input: Clothing information in JSON format

[1159] Output: Clothing information stored in the database

[1160] Step 5: Entering style information

[1161] The user enters style information for the day's outing through the application. The user selects the appropriate option (e.g., "Work," "Date," or "Important Meeting") on the style information input screen and taps the "Send" button. The entered information is sent from the device to the server.

[1162] Input: User's outing style information

[1163] Output: Style information sent to the server

[1164] Step 6: Generate outfit suggestions

[1165] The server compares the clothing information stored in the database with the style information sent by the user to generate the optimal outfit. The server obtains the style information, creates a corresponding prompt sentence, and inputs it into a generative AI model (e.g., GPT-4). The model then provides outfit suggestions, which are then stored in the database.

[1166] Input: Clothing information and style information from the database, prompt text

[1167] Output: Generated outfit suggestions

[1168] Step 7: Submit and view your proposal

[1169] The server sends the generated outfit suggestions to the user's device, which then displays the received suggestions on the application's "suggestion display screen." The user can review the suggestions and use options such as "favorite," "share," and "purchase link."

[1170] Input: Generated outfit suggestions

[1171] Output: Coordination suggestions displayed on the device

[1172] The above are the specific processing steps of the system program.

[1173] (Application example 1)

[1174] 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."

[1175] In today's world, consumers want to shop efficiently within limited time. However, in many stores, the time required to try on clothes and the time spent thinking about how to combine them are a significant burden. Furthermore, it is difficult to consider how to best coordinate new purchases with existing clothing. This creates the problem of being unable to coordinate appropriate outfits.

[1176] 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.

[1177] In this invention, the server includes means for importing photos of clothing owned by a user, means for transmitting the imported photos of the clothing to the server, means for the server to analyze the transmitted photos and extract information about the clothing, means for automatically generating outfit suggestions based on prompt sentences using a generative AI model, means for simulating trying on new products in the store using augmented reality technology, means for transmitting the generated outfit suggestions to the user's device, and means for the device to display the transmitted outfit suggestions. This allows consumers to efficiently check the optimal outfits between newly purchased items and their own clothing, even in a physical store, eliminating the need to try on items and enabling an optimal shopping experience while saving time.

[1178] The "means for capturing photos of clothing owned by the user" refers to a means for the user to take pictures of clothing owned by the user and save them in a digital device.

[1179] The "means for transmitting the captured photograph of the clothing to the server" is a means for transmitting the image of the clothing stored in the user's digital device to the server via data communication.

[1180] "Means for the server to analyze the transmitted photograph and extract information about the clothing" refers to means for the server to analyze the received image and identify information such as the type, color, material, and pattern of the clothing.

[1181] The "means for automatically generating coordination suggestions based on prompt sentences using a generative AI model" is a system that uses an artificial intelligence model to generate optimal coordination in response to style requests from users.

[1182] "Means for simulating trying on new products in a store using augmented reality technology" refers to a means for using augmented reality technology to allow a user to virtually try on a product that they are considering purchasing in a physical store.

[1183] The "means for transmitting the generated coordinate information to the user's terminal" refers to a means for transmitting the automatically generated coordinate information to the user's digital device.

[1184] The "means for displaying the coordinates sent by the terminal" refers to a means for visually displaying the coordinate information received on the user's digital device.

[1185] The present invention provides the following specific method for implementing a system that suggests outfit coordination for clothing owned by a user. This system is configured using a user terminal, a server, a generative AI model, and augmented reality technology.

[1186] First, users take photos of their clothing using a smartphone or tablet. These photos are then imported into the device using a dedicated application and sent to a server via the Internet. The server receives the photos and uses image analysis algorithms (e.g., PyTorch or TensorFlow) to identify the type, color, material, and pattern of the clothing.

[1187] The extracted information is then stored in a database, and an interface is provided for users to input style information for outings (e.g., work, date, important meeting). The style information entered by the user is sent to the server, which then generates an appropriate outfit based on it.

[1188] Here, a generative AI model (e.g., GPT-4 or BERT) is used to automatically generate optimal outfits based on the input style information and clothing information stored in the database. The generative AI model uses prompt sentences to generate suggestions. For example, the following prompt sentences are used:

[1189] "As a casual style suggestion, please suggest the best outfit to wear with a white T-shirt and denim pants that I own, using a new item (shirt)."

[1190] Furthermore, in physical stores, augmented reality technology is available for try-on simulations. Users can use smart glasses or smartphones to view products in the store in AR and try them on virtually. This augmented reality technology (e.g., ARKit and ARCore) allows users to see new products in the store in real time and visualize how they will be combined with existing clothing.

[1191] The generated outfit suggestions are sent from the server to the user's device, and the user can check the outfit through the application. For example, if the user inputs style information for an "important meeting," a black suit, white shirt, tie, and leather shoes will be suggested.

[1192] The hardware used to realize this system includes smartphones, tablets, and smart glasses (e.g., Vuzix and Google Glass), allowing users to efficiently and effectively receive outfit suggestions and enjoy a smooth shopping experience even in physical stores.

[1193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1194] Step 1:

[1195] The user takes a photo of the clothing they own using a smartphone or tablet.

[1196] The input is image data of the clothing. The user uses a dedicated application to take these images and save them on the device. The output of this process is image data of the clothing stored on the device.

[1197] Step 2:

[1198] The terminal transmits the captured photograph of the clothing to the server.

[1199] The input is image data of the clothing stored in the terminal. The image data is sent to the server using data communication. The output of this process is the image data imported into the server.

[1200] Step 3:

[1201] The server analyzes the sent photo and extracts information about the clothing.

[1202] The input is image data of clothing stored on a server. Image analysis algorithms (e.g., PyTorch or TensorFlow) are used to identify the type, color, material, and pattern of the clothing. The output of this process is extracted clothing information (e.g., tops-white-cotton-plain).

[1203] Step 4:

[1204] The server stores the extracted clothing information in a database.

[1205] The input is the clothing information extracted by the analysis. The clothing information is saved in a database (e.g., Firebase or MongoDB). The output of this process is the clothing information saved in the database.

[1206] Step 5:

[1207] The user inputs style information for going out.

[1208] The input is the purpose and situation of the outing (e.g., work, date, important meeting). The user inputs this style information through a dedicated application. The output of this process is the user's style request data.

[1209] Step 6:

[1210] The server generates the optimal coordinates based on the input style information.

[1211] The input is the user's style information and clothing information stored in a database. A generative AI model (e.g., GPT-4 or BERT) is used to generate the optimal outfit. The output of this process is an automatically generated outfit suggestion based on the prompt sentence.

[1212] Step 7:

[1213] Users can simulate trying on new products in a physical store using augmented reality technology.

[1214] The input is product data from a physical store. The user uses smart glasses or a smartphone to virtually try on new products using AR technology (e.g., ARKit or ARCore). The output of this process is visual feedback of the AR virtual try-on experience.

[1215] Step 8:

[1216] The server transmits the generated coordinates to the user's terminal.

[1217] The input is automatically generated coordinate suggestion data. The suggestion is transmitted to the user's terminal via data communication. The output of this process is the coordinate suggestion data received by the user's terminal.

[1218] Step 9:

[1219] The terminal displays the transmitted coordinates.

[1220] The input is the outfit suggestion data received from the server. A dedicated application is used to visually display the outfit suggestion to the user. The output of this process is the outfit suggestion visually displayed to the user.

[1221] 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.

[1222] This invention combines an emotion engine with a system that suggests outfits for users' clothing. The system aims to automatically suggest optimal outfits based on the user's mood and emotions on that day by having the user take a photo of their own clothing and recognize their emotions.

[1223] 1. Importing photos

[1224] Users can take photos of their clothing using a smartphone or PC. They then use a dedicated application to import the photos of their clothing. These photos include tops, bottoms, outerwear, accessories, etc.

[1225] 2. Upload an image

[1226] The captured photo is sent from the device to the server, which receives the image file and temporarily stores it.

[1227] 3. Image Analysis

[1228] The server analyzes the received image data. This analysis process identifies the type of clothing, color, material, and pattern (e.g., solid, striped, checked, etc.). This information is stored in a database and used to suggest outfits later.

[1229] 4. Enter style information

[1230] The user inputs style information for going out through the application, such as "work," "date," "important meeting," etc. The input information is sent from the device to the server.

[1231] 5. Emotion recognition

[1232] When a user inputs voice or a photo via an application, the device sends this to the emotion engine, which then uses voice recognition or image analysis technology to identify the user's emotion. For example, if a user inputs a voice message saying "I'm happy today" or a photo of a smiling face, the emotion engine will recognize the emotion as "joy."

[1233] 6. Coordination Proposal Generation

[1234] The server generates optimal outfits based on clothing information stored in a database, input style information, and recognized emotions. For example, if the user feels "happy," it can suggest bright colors and casual styles.

[1235] 7. Submitting and Viewing Proposals

[1236] The generated outfit suggestions are sent from the server to the user's device, where they are visually displayed. The user can then review the suggested outfits and select from them.

[1237] Specific examples

[1238] For example, if a user inputs style information for a "date" and the emotion engine recognizes the emotion "nervous," the server will select clothes in muted colors and relaxed materials from the database and suggest them as an outfit. The user can then check this suggestion in the application and actually go out on a date in that style.

[1239] In this way, the system can provide a more personalized service by proposing optimal outfits that take the user's emotions into consideration.

[1240] The processing flow will be explained below.

[1241] Step 1:

[1242] The user takes a photo of the clothing they own using a smartphone or computer, then launches a dedicated application and imports the photo of the clothing.

[1243] Step 2:

[1244] The device sends the captured photo to the server. The data sent includes the photo as well as identification information such as the user ID.

[1245] Step 3:

[1246] The server receives the transmitted photo data, temporarily stores it, and prepares it for the image analysis process.

[1247] Step 4:

[1248] The server applies image analysis algorithms to extract information such as the type of clothing, color, material, and pattern, using machine learning models and image recognition techniques.

[1249] Step 5:

[1250] The server stores the analysis results in a database, which contains information such as the type of clothing, color, material, pattern, image file name, and user ID.

[1251] Step 6:

[1252] The user inputs style information for the day's outing through the application, including, for example, "work," "date," "important meeting," and the like.

[1253] Step 7:

[1254] The terminal transmits the input style information to the server. The transmitted data includes the user ID in addition to the style information.

[1255] Step 8:

[1256] The user inputs voice or a photo via the application, which is then analyzed by the emotion engine. The emotion engine uses voice recognition or image analysis technology to identify the user's emotion. For example, if the user inputs a voice message saying "I'm happy today" or a photo of a smiling face, the emotion engine will recognize the emotion as "joy."

[1257] Step 9:

[1258] The device sends the recognized emotion information to the server. The sent data includes style information, emotion information, and user ID.

[1259] Step 10:

[1260] The server generates optimal outfits based on clothing information stored in a database, input style information, and recognized emotions. For example, if the user is feeling "happy," it can suggest bright colors and casual styles.

[1261] Step 11:

[1262] The server transmits the generated coordination proposal to the user's terminal.

[1263] Step 12:

[1264] The application displays the coordinated outfit suggestions received by the device, allowing the user to review the suggested outfits and select from them.

[1265] Example 2

[1266] 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."

[1267] The present invention aims to provide a system for suggesting clothing coordination for users that automatically generates optimal coordinations taking into account the user's emotions and provides personalized suggestions to each user. Conventional simple clothing combination suggestion systems are unable to reflect the user's emotions or mood of the day, making it difficult to obtain suggestions that satisfy the user. Another problem is that users themselves have to spend time trying out various combinations through trial and error.

[1268] 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.

[1269] In this invention, the server includes means for capturing images of clothing owned by the user, means for transmitting the captured images of the clothing to the server, means for the server to analyze the transmitted images and extract characteristic information about the clothing, means for storing the extracted characteristic information about the clothing in a database, means for the user to input information about the type of outing, means for generating an optimal clothing combination based on the input type information and emotion information, means for transmitting the generated clothing combination to the user's terminal, and means for the terminal to display the transmitted clothing combination, thereby enabling personalized coordination suggestions that take the user's emotions into consideration.

[1270] "User" refers to a person who uses the system to receive clothing coordination suggestions.

[1271] "Clothing images" refers to photographs of clothing taken by users and their digital data.

[1272] "Server" refers to a central system that receives, stores, processes, and transmits data over the Internet.

[1273] "Feature information" refers to attribute data such as the type, color, material, and pattern of clothing.

[1274] "Database" refers to a collection of information that has been structured and stored to allow easy search and retrieval as needed.

[1275] "Type information" refers to information entered by the user regarding the purpose and circumstances of the outing.

[1276] "Emotional information" refers to information that indicates the user's current emotions and moods.

[1277] "Clothing combination" refers to a coordination suggested based on feature information, type information, and emotion information.

[1278] "Terminal" refers to a device such as a smartphone or computer that a user uses to access the system.

[1279] This invention combines an emotion engine with a system that suggests outfits for users' clothing. The system aims to automatically suggest optimal outfits based on the user's mood and emotions on that day, after the user inputs an image of their own clothing.

[1280] First, the user takes a photo of their clothing using a smartphone or computer. The photo includes tops, bottoms, outerwear, accessories, etc. The user then uses a dedicated application to import the image of the clothing into their device.

[1281] The device then sends the captured image to a server, which temporarily stores the image and analyzes it using image recognition algorithms (e.g., OpenCV or TensorFlow). This analysis process identifies the type of clothing, color, material, and pattern (e.g., solid, striped, checked, etc.). This feature information is stored in a database and used to suggest outfits later.

[1282] The user then inputs the type of outing through the application, which may include "work," "date," "important meeting," etc. The input type information is sent from the terminal to the server.

[1283] Furthermore, users can input information about their emotions and moods through the application. They can input emotions through voice (e.g., "I'm happy today") or images (e.g., a photo of a smiling face). The device sends this input data to the emotion engine, which then identifies the user's emotions using voice recognition or image analysis technology (e.g., voice recognition service or image analysis service).

[1284] The server uses a generative AI model to generate optimal outfits based on clothing feature information stored in the database and category and emotion information entered by the user. For example, if a user enters category information of "date" and emotion information of "happy," the server can suggest outfits in bright colors and casual styles.

[1285] The generated outfit suggestions are sent from the server to the user's device. The device visually displays the suggested outfits, allowing the user to select their favorite outfit from among them. As a specific example, if a user inputs style information for a "date" and the emotion engine recognizes the emotion "nervous," the server will select clothes in muted colors and relaxed materials and suggest these as outfits. The user can check these suggestions in the application and actually go out on a date in that style.

[1286] Example prompt sentence:

[1287] "Design a system that uses an emotion engine to recognize the user's emotions and suggests clothing coordination that matches the user's mood that day. Please include detailed instructions on how to input images of the user's clothing, type information, and emotional information, and generate and suggest optimal coordination."

[1288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1289] Step 1:

[1290] Users use their smartphones or computers to take pictures of their clothing.

[1291] Input: Images of clothing taken by the user

[1292] How it works: The user launches the dedicated application and uses the camera function to take a picture of the clothing.

[1293] Output: Image file of the clothing saved on the user's device

[1294] Step 2:

[1295] The device sends the captured image of the clothing to the server.

[1296] Input: Image file of clothing stored on the user's device

[1297] How it works: The device uploads image data to a server via an internet connection.

[1298] Output: Image data of clothing stored on the server

[1299] Step 3:

[1300] The server analyzes the received image data and extracts characteristic information about the clothing.

[1301] Input: Image data of clothing stored on the server

[1302] How it works: The server uses image recognition algorithms (e.g. OpenCV or TensorFlow) to identify the type, color, material, and pattern of the clothing.

[1303] Output: Extracted clothing feature information (type, color, material, pattern)

[1304] Step 4:

[1305] The server stores the extracted clothing feature information in a database.

[1306] Input: Extracted clothing feature information

[1307] How it works: The server structures the feature information and stores it in a database.

[1308] Output: Clothing feature information stored in a database

[1309] Step 5:

[1310] The user uses the application to input information about the type of trip.

[1311] Input: Type of outing information entered by the user (e.g., work, date, meeting)

[1312] How it works: The user enters category information into the application's input field and presses the submit button.

[1313] Output: Type information sent from the user terminal to the server

[1314] Step 6:

[1315] The user inputs emotion information through the application.

[1316] Input: Emotional information entered by the user (e.g., voice "I'm happy today," photo of a smiling face)

[1317] How it works: The user records audio or takes a photo and sends emotional information through the application.

[1318] Output: Emotion information sent from the user device to the server

[1319] Step 7:

[1320] The server receives the emotion information and identifies the emotion using an emotion engine.

[1321] Input: Emotion information stored on the server

[1322] How it works: The server uses voice recognition or image analysis technology (e.g., voice recognition service or image analysis service) to identify emotions.

[1323] Output: Identified emotion information (e.g., "joy," "tension")

[1324] Step 8:

[1325] The server generates a coordinate based on the clothing feature information stored in the database, the type information input by the user, and the identified emotion information.

[1326] Input: Stored clothing feature information, type information, emotion information

[1327] How it works: The server uses a generative AI model to generate the optimal outfit. For example, it suggests bright colors and casual styles based on the type of "date" and the emotion of "joy."

[1328] Output: Generated outfit suggestions

[1329] Step 9:

[1330] The server transmits the generated coordination proposal to the user's terminal.

[1331] Input: Generated outfit suggestions

[1332] Operation: The server sends the generated coordination proposal to the user's terminal.

[1333] Output: Coordination proposals sent to the user's device

[1334] Step 10:

[1335] The terminal visually displays the proposed coordination.

[1336] Input: Coordination proposal sent to the user's device

[1337] Operation: The device displays an image and description of the outfit on the application screen.

[1338] Output: Coordination suggestions displayed to the user

[1339] (Application example 2)

[1340] 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."

[1341] Many current coordination suggestion systems suggest outfits based on the user's clothing and style information, but they do not take into account the user's emotions or moods, making it difficult to suggest personalized outfits. Furthermore, there is a lack of suggestions for purchasing new items in collaboration with online shopping sites, which prevents the system from further enriching the user's fashion experience.

[1342] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1343] In this invention, the server includes means for capturing photos of clothing owned by the user, means for transmitting the captured photos of the clothing to the server, means for the server to analyze the transmitted photos and extract clothing information, means for storing the extracted clothing information in a database, means for the user to input style information for going out, means for recognizing the user's emotions, means for linking the input style information based on the recognized emotions to generate an optimal outfit, means for transmitting the generated outfit to the user's terminal, and means for the terminal to display the transmitted outfit. This makes it possible to propose personalized outfits that take the user's emotions and moods into consideration, and further enriches the user's fashion experience by adding purchasing suggestions for new items in collaboration with mail order sites.

[1344] The "means for capturing photos of clothing" refers to a device or method for taking photos of clothing owned by a user and acquiring them as digital data.

[1345] The "means for transmitting the photograph of the clothes to the server" is a network communication device or protocol for transmitting the captured photograph data of the clothes to the server via the Internet.

[1346] The "means for extracting clothing information" refers to image analysis technology and algorithms that analyze the photographic data of clothing sent to the server and identify the type, color, material, pattern, etc. of each piece of clothing.

[1347] The "means for storing in a database" refers to a storage device or system for organizing and centrally managing the extracted clothing information.

[1348] The "means for inputting outing style information" is an interface that allows the user to input the day's plans and desired style (e.g., work, date, important meeting, etc.).

[1349] "Means for recognizing emotions" refers to voice recognition technology and image analysis technology for identifying emotions from the user's voice, facial expressions, etc.

[1350] The "means for generating optimal outfits" refers to an algorithm or system that automatically generates optimal outfit combinations based on clothing information stored in a database, input style information, and recognized emotions.

[1351] The "means for transmitting the coordinates to the user's terminal" refers to a communication device or protocol for transmitting the information about the generated coordinates to the user's terminal such as a smartphone or computer.

[1352] The "means for displaying coordinated outfits sent to the terminal" refers to a display device or application for visually displaying coordinated outfits sent to the user's terminal.

[1353] "Recommended items on the online shopping site" are items that users can purchase to go with the suggested outfits, and are products offered on the online shopping site.

[1354] The present invention provides a system for suggesting clothing coordination for a user, and provides personalized suggestions by recognizing the user's emotions. This system includes the following components:

[1355] First, a user uses a device such as a smartphone or PC to take a photo of their clothing, including tops, bottoms, outerwear, and accessories, and imports it into the device. The device then sends the photo data to the server.

[1356] The server analyzes the received photo data and identifies the type, color, material, and pattern of each garment using image analysis techniques such as OpenCV and Keras. The analysis results are stored in a database and used to suggest outfits later.

[1357] Through the application, users input their outing style information, including "work," "date," "important meeting," etc. The input information is sent to the server and stored in a database.

[1358] Furthermore, the device provides a means for inputting voice and facial photographs to recognize the user's emotions. The device sends this data to an emotion recognition engine to identify the emotion. A pre-trained neural network model is used for emotion recognition. For example, if a user inputs the voice "I'm happy today" or a photograph of a smiling face, the emotion recognition engine will recognize the emotion "joy."

[1359] The server generates an optimal outfit based on the clothing information stored in the database, the input style information, and the recognized emotion. The generated outfit is sent from the server to the user's device and displayed on the device.

[1360] Furthermore, the system is linked to online shopping sites and can also suggest new items to purchase based on the suggested outfits. For example, it can recommend new bottoms or accessories to go with a top the user already owns, and the user can then purchase them on the online shopping site.

[1361] For example, if a user inputs their style information for a date and the emotion recognition engine recognizes the emotion "happy," the server will select clothes in bright colors and light materials from the database and suggest them as an outfit. This suggestion will be displayed on the user's device, and new items that go well with it will be recommended by the online shopping site.

[1362] Example prompts for generative AI models:

[1363] plaintext

[1364] Suggest an outfit that will make the user feel happy on a date. Based on the following clothing information the user owns: tops, bottoms, outerwear, and accessories. Please refer to the provided photos.

[1365] This allows users to enjoy creating the perfect outfit to suit their mood, and also makes purchasing new items a breeze.

[1366] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1367] Step 1:

[1368] Users use their smartphones or computers to take photos of their clothing and import them into the device. The imported photo data is categorized into tops, bottoms, outerwear, accessories, etc. This becomes the input data for the system.

[1369] Step 2:

[1370] The device sends the captured photo data of the clothes to the server. The transmission is performed using network communication, and the data is uploaded to the server via the Internet. The output of this step is the image data transferred to the server.

[1371] Step 3:

[1372] The server analyzes the received photo data. It uses image analysis techniques such as OpenCV and Keras to identify the type, color, material, and pattern of each garment. This analysis results in extracted clothing information, which is then stored in a database.

[1373] Step 4:

[1374] The user uses the application to input style information for going out. For example, they can choose from options such as "Work," "Date," and "Important Meeting." The input style information is sent from the device to the server and stored in a database. The output of this step is the style information input by the user.

[1375] Step 5:

[1376] Users input their voice or a photo of their face into the application, which recognizes their emotions. The emotion recognition engine processes this data and identifies the user's emotions using voice recognition or image analysis technology. For example, if a user inputs the voice "I'm happy today," the emotion recognition engine will recognize the emotion "joy."

[1377] Step 6:

[1378] The server generates optimal outfits based on clothing information, style information, and recognized emotions stored in the database. The algorithm uses a generative AI model to create outfits. This outfit suggestion is output.

[1379] Step 7:

[1380] The server sends the generated coordinates to the user's terminal, which visually displays the coordinates, which becomes the output for the user.

[1381] Step 8:

[1382] Based on the suggested outfit, the device interacts with the online shopping site to suggest new items for purchase. These suggestions may include new bottoms or accessories. The user can review the suggested items and complete the purchase on the online shopping site. The output of this step is the suggested items.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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).

[1390] 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.

[1391] 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."

[1392] 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.

[1393] 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).

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] The following is further disclosed regarding the above embodiment.

[1405] (Claim 1)

[1406] A means for capturing a photograph of clothing owned by the user;

[1407] means for transmitting the captured photograph of the clothing to a server;

[1408] A means for the server to analyze the transmitted photograph and extract information about the clothing;

[1409] A means for storing the extracted clothing information in a database;

[1410] A means for a user to input outing style information;

[1411] means for generating an optimal coordination based on input style information;

[1412] means for transmitting the generated coordinates to a user's terminal;

[1413] The system includes means for the terminal to display the transmitted coordinates.

[1414] (Claim 2)

[1415] 2. The system of claim 1, wherein the means for analyzing the captured photograph of clothing identifies the type, color, material, and pattern of each garment.

[1416] (Claim 3)

[1417] 2. The system of claim 1, wherein the outing style information is categorized into work, date, and important meeting.

[1418] "Example 1"

[1419] (Claim 1)

[1420] A means for capturing a photograph of clothing owned by the user;

[1421] means for transmitting the captured photograph of the clothing to an information processing device;

[1422] A means for the information processing device to analyze the transmitted photograph and extract information about the clothing;

[1423] A means for storing the extracted clothing information in a recording device;

[1424] A means for a user to input outing style information;

[1425] means for generating an optimal coordination based on input style information;

[1426] means for transmitting the generated coordinates to a user's terminal;

[1427] A means for displaying the coordinates transmitted by the terminal;

[1428] A means for generating coordination suggestions according to style information using a generative AI model;

[1429] The system includes a means for inputting a prompt sentence into a generative AI model to obtain coordination suggestions.

[1430] (Claim 2)

[1431] 2. The system of claim 1, wherein the means for analyzing the captured photograph of clothing identifies the type, color, material, and pattern of each garment.

[1432] (Claim 3)

[1433] 2. The system of claim 1, wherein the outing style information is categorized into work, date, and important meeting.

[1434] "Application Example 1"

[1435] (Claim 1)

[1436] A means for capturing a photograph of clothing owned by the user;

[1437] means for transmitting the captured photograph of the clothing to a server;

[1438] A means for the server to analyze the transmitted photograph and extract information about the clothing;

[1439] A means for storing the extracted clothing information in a database;

[1440] A means for a user to input outing style information;

[1441] means for generating an optimal coordination based on input style information;

[1442] A means for automatically generating coordination suggestions based on prompt sentences using a generative AI model;

[1443] A means to simulate trying on new products in stores using augmented reality technology,

[1444] means for transmitting the generated coordinates to a user's terminal;

[1445] The system includes means for the terminal to display the transmitted coordinates.

[1446] (Claim 2)

[1447] 2. The system of claim 1, wherein the means for analyzing the captured photograph of clothing identifies the type, color, material, and pattern of each garment.

[1448] (Claim 3)

[1449] 2. The system of claim 1, wherein the outing style information is categorized into work, date, and important meeting.

[1450] "Example 2: Combining Emotion Engines"

[1451] (Claim 1)

[1452] A means for capturing images of clothing owned by a user;

[1453] means for transmitting the captured image of the clothing to a server;

[1454] A means for the server to analyze the transmitted image and extract feature information of the clothing;

[1455] A means for storing the extracted clothing feature information in a database;

[1456] A means for a user to input information on the type of outing;

[1457] A means for generating an optimal clothing combination based on input category information and emotion information;

[1458] A means for transmitting the generated clothing combination to a user's terminal;

[1459] The system includes a means for the terminal to display the transmitted clothing combination.

[1460] (Claim 2)

[1461] 2. The system of claim 1, wherein the means for analyzing the captured clothing images identifies the type, color, material, and pattern of each garment.

[1462] (Claim 3)

[1463] 2. The system according to claim 1, wherein the outing type information is classified into work, date, and meeting.

[1464] "Application example 2 when combining emotion engines"

[1465] (Claim 1)

[1466] A means for capturing a photograph of clothing owned by the user;

[1467] means for transmitting the captured photograph of the clothing to a server;

[1468] A means for the server to analyze the transmitted photograph and extract information about the clothing;

[1469] A means for storing the extracted clothing information in a database;

[1470] A means for a user to input outing style information;

[1471] a means for recognizing a user's emotion;

[1472] A means for generating an optimal outfit based on the recognized emotion and linking it with input style information;

[1473] means for transmitting the generated coordinates to a user's terminal;

[1474] The system includes means for the terminal to display the transmitted coordinates.

[1475] (Claim 2)

[1476] 2. The system of claim 1, wherein the means for analyzing the captured photograph of clothing identifies the type, color, material, and pattern of each garment.

[1477] (Claim 3)

[1478] The system according to claim 1, wherein the outing style information is classified into work, date, and important meeting, and includes coordination suggestions based on emotion recognition and recommended items from online shopping sites. [Explanation of symbols]

[1479] 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 capturing a photograph of clothing owned by the user; means for transmitting the captured photograph of the clothing to a server; A means for the server to analyze the transmitted photograph and extract information about the clothing; A means for storing the extracted clothing information in a database; A means for a user to input outing style information; means for generating an optimal coordination based on input style information; means for transmitting the generated coordinates to a user's terminal; The system includes means for the terminal to display the transmitted coordinates.

2. 2. The system of claim 1, wherein the means for analyzing the captured photograph of clothing identifies the type, color, material, and pattern of each garment.

3. The system of claim 1, wherein the outing style information is categorized into work, date, and important meeting.

Images