System

Abstract

A system is provided.SOLUTION: This system is provided with a means for acquiring and recording the image of clothes owned by a user himself / herself, a means for acquiring the weather information of the day, the lowest temperature and the highest temperature and application information, a means for proposing the combination of proper clothes based on the information, a means for visually displaying the proposed combination and a means for presenting a purchase recommendation item.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, choosing clothes often requires a lot of time, and it is often difficult to find the right combination. Furthermore, for users who lack fashion knowledge or taste, choosing clothes that are on trend is even more difficult. These problems hinder the selection of functional and stylish clothing in daily life. The purpose of this invention is to solve these problems and help users efficiently choose attractive clothing. [Means for solving the problem]

[0005] This invention provides a system that allows users to acquire and record images of their clothing. It also provides a system that includes a means for acquiring the current day's weather information, minimum and maximum temperatures, and usage information. Based on this information, the system uses artificial intelligence to suggest appropriate clothing combinations and visually displays the suggested combinations as if they were being worn by a mannequin. Furthermore, the system provides a means for suggesting recommended items to add to the user's wardrobe based on the user's tastes and preferences, thereby reducing the time and effort required for selecting clothes and improving fashion sense.

[0006] "Clothing" refers to clothing and fashion items worn by individuals, including shirts, pants, jackets, dresses, skirts, etc.

[0007] An "image" is a recording of visual information as a single still image, and includes photographs taken with a camera or smartphone.

[0008] "Means for recording" refers to systems or devices that have the function of saving as digital data, including storage devices and cloud systems that save image files.

[0009] "Weather information" refers to information about the weather conditions on a particular day, such as sunny, cloudy, rainy, or snowy.

[0010] "Minimum and maximum temperatures" refer to the lowest and highest temperatures recorded on a particular day.

[0011] "Use information" is information about the purpose or situation when selecting clothing, and includes the activity details such as business casual, leisure, walking, etc.

[0012] "Proposal means" refers to a system or algorithm that has the ability to provide optimal solutions or options based on input information.

[0013] "Visual display means" refers to a system or device that has the functionality to visually present the suggested information to the user, including a display or smartphone screen.

[0014] "Recommended purchase items" are clothing and accessories that are recommended for new purchase based on the items the user owns and trends.

[0015] "Artificial intelligence" refers to systems and algorithms that make human-like intelligent decisions through machine learning and data analysis, including the ability to automatically generate specific problem-solving and recommendations. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0037] The present invention relates to a system that improves the efficiency of clothing selection and enables users to appropriately select trendy clothing coordinations every day. Specific embodiments of this system will be described below.

[0038] System configuration

[0039] The system uses a server, user devices, and AI (artificial intelligence). User devices are smartphones or PCs, and provide an interface for users to take and upload images of clothes and input weather, temperature, and usage information. The server stores and analyzes the image data, suggests outfits, and selects recommended items for purchase. The AI ​​is also responsible for automatically generating outfit suggestions based on the clothing data and input information.

[0040] Acquiring and saving clothing data

[0041] Users take photos of their clothes

[0042] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, they may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0043] Device-based image processing and tagging

[0044] The device receives the photographed image of the clothes and performs image processing such as background removal and cropping as necessary. It also displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt, casual." This tagging allows the data to be used efficiently in subsequent outfit suggestions.

[0045] Sending and saving data to the server

[0046] The image data processed by the terminal is sent to the server along with the tag information. The server stores the received image data in a database and manages the image file path and tag information appropriately.

[0047] Coordination suggestions

[0048] User input of conditions

[0049] The user enters the weather, minimum and maximum temperatures for the day, and the purpose of the trip (business casual, leisure, walk, etc.) through the application. For example, the user might enter "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose leisure."

[0050] Server-based information processing and coordinate generation

[0051] The server analyzes the weather, temperature, and purpose information received from the user and sets appropriate condition filters. Next, it filters appropriate clothing combinations from clothing data stored in a database and uses AI to generate optimal outfits. For example, it selects the combination of "T-shirt + jeans + jacket."

[0052] Proposal visualization and display

[0053] The server generates a visualization to display the generated outfits to the user in a visually understandable way. Specifically, it generates an image of a mannequin wearing the outfits presented, and sends that image and other information to the terminal. The terminal then displays the outfits presented to the user.

[0054] Suggested purchase items

[0055] Recommended items selected by the server

[0056] The server selects recommended items to purchase based on the user's current outfit information and item data. For example, if the server is recommending new sneakers to match an outfit, it will search for and recommend appropriate items.

[0057] Displaying recommended items to users

[0058] The server sends information about the selected recommended items to the terminal, which then displays the recommended items to the user, for example, presenting them as "new sneakers that go well with these jeans."

[0059] Specific examples

[0060] After the user takes a photo of a T-shirt and tags it, the data is sent to the server for storage. The user then inputs the day's weather information (sunny, maximum 20°C, minimum 15°C) and purpose (leisure), and the server uses AI to generate a "T-shirt + jeans + jacket" outfit based on this information. The suggested outfit is displayed to the user along with a visual image of the outfit, as if worn by a mannequin. The server also recommends new sneakers to go with the outfit and displays them to the user.

[0061] In this way, users can efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life. With this system, even those who are not good at fashion can confidently enjoy wearing clothes that are in line with the latest trends.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user takes a photo of the clothes.

[0065] Users take photos of various clothes they own (such as T-shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0066] Step 2:

[0067] The device processes the image.

[0068] The device receives the photographed image of the clothes and performs image processing such as background removal and trimming as necessary. For example, it automatically cuts out unnecessary background parts of the image.

[0069] Step 3:

[0070] The device performs the tagging.

[0071] The device will prompt the user to enter appropriate tags, such as "T-shirt, casual," and the user will select and enter the tag.

[0072] Step 4:

[0073] The terminal transmits the data to the server.

[0074] Once tagging is complete on the device, the image data is sent to the server, including the tag information.

[0075] Step 5:

[0076] The server stores the data.

[0077] The server stores the received image data in a database, along with metadata including the image file path and tag information.

[0078] Step 6:

[0079] The user inputs the weather and purpose.

[0080] The user inputs the weather, minimum and maximum temperatures for the day, and the purpose of use (e.g., "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose: leisure") through the application.

[0081] Step 7:

[0082] The terminal sends the input information to the server.

[0083] The terminal transmits the weather information, temperature information, and usage information input by the user to the server.

[0084] Step 8:

[0085] The server analyzes the information and generates coordinates.

[0086] The server analyzes the weather, temperature, and purpose information received from the user, then filters the clothing data stored in the database to find suitable clothing combinations and uses AI to generate the optimal outfit.

[0087] Step 9:

[0088] The server generates the visualization.

[0089] The server generates a visualization to display the generated outfits in a visually understandable way, specifically creating an image of a mannequin wearing the outfits presented.

[0090] Step 10:

[0091] The server sends the visualization to the device.

[0092] The server transmits the generated visualization and coordination proposal information to the terminal.

[0093] Step 11:

[0094] The device displays the suggestions to the user.

[0095] The device will then display suggested outfits to the user, such as "leisure outfits suitable for a sunny 20-degree day."

[0096] Step 12:

[0097] The server selects recommended items for purchase.

[0098] The server selects appropriate recommended items to purchase based on the user's outfit information and item data, such as new sneakers that go well with the outfit.

[0099] Step 13:

[0100] The server transmits recommended item information to the terminal.

[0101] The server transmits information about the selected recommended purchase items to the terminal.

[0102] Step 14:

[0103] The device displays the recommended items to the user.

[0104] The device will then display selected purchase recommendations to the user, such as "New sneakers to go with these jeans."

[0105] Example 1

[0106] 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."

[0107] In modern life, fashion coordination is a daily challenge for many people, and choosing appropriate clothing based on trends, seasons, and occasions can be particularly challenging. Furthermore, there is a growing demand for systems that effectively utilize existing clothing and provide comprehensive fashion advice, including the selection of new items to purchase. However, existing systems struggle to reduce the user's workload and make efficient and accurate fashion suggestions. The purpose of this invention is to provide a system that solves these problems and allows users to easily and effectively enjoy daily coordination.

[0108] 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.

[0109] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for tagging and recording the clothing images based on user input, a means for proposing appropriate clothing combinations based on the tagged image data and taking into account weather information, temperature information, and usage information, a means for visually displaying the proposed combinations, and a means for presenting recommended purchase items for the proposed combinations. This allows users to efficiently manage their clothing and automatically receive recommendations for optimal coordination based on trends and usage. Furthermore, new items to purchase are also recommended, reducing the burden of fashion in general.

[0110] "Clothing" refers to clothing to be worn, such as shirts, pants, jackets, etc.

[0111] "Means for acquiring and recording images" refers to devices and functions that allow users to digitally save and manage images of clothing taken with a smartphone or digital camera.

[0112] "Means for tagging and recording images of clothing based on user input" refers to devices or functions that allow users to add descriptions such as category and use to images of clothing and store them in a database.

[0113] "Means for proposing appropriate clothing combinations based on tagged image data, taking into consideration weather information, temperature information, and usage information" refers to devices and technologies that analyze stored tagged image data and weather information, temperature information, and usage information acquired in real time, and automatically select the optimal clothing combinations based on this information.

[0114] "Means for visually displaying suggested combinations" refers to devices or functions that use images or graphics to clearly present optimal clothing combinations to users.

[0115] The "means for presenting recommended items for purchase" refers to a device or function that can recommend new items to the user to purchase to go with the proposed outfit and display detailed information about them.

[0116] "Means of utilizing artificial intelligence" refers to systems and algorithms that use technologies such as machine learning and deep learning to suggest clothing combinations.

[0117] "Means for transmitting and displaying on a user's device" refers to the equipment or functions for transmitting the generated coordination information and information on recommended purchase items to a user's device (smartphone, tablet, PC, etc.) via the Internet or other communication means and displaying it.

[0118] This invention relates to a system that supports users in efficiently coordinating their daily fashion. The system is composed of a server, a user terminal, and artificial intelligence (AI).

[0119] System configuration

[0120] The basic components of this system are as follows:

[0121] User device (smartphone, PC)

[0122] Servers (high-performance database servers, GPU servers)

[0123] AI model (generative AI model)

[0124] Acquiring clothing data

[0125] User-generated photos of clothing

[0126] The user uses a smartphone or digital camera to take a picture of the clothes they own. For example, they perform a specific action such as "taking a picture of their favorite red T-shirt."

[0127] Uploading images using a device

[0128] Users upload the images they take to the device through an application on their smartphone or computer. Once the images are uploaded, the device prompts the user to add tags. They can enter tags such as "T-shirt, casual."

[0129] Image processing and analysis

[0130] Image processing by device

[0131] The device performs image processing on the uploaded image, such as background removal and cropping. Specifically, for example, background removal is performed using the OpenCV library. The corrected image is then displayed and the user can tag it.

[0132] Sending data to the server

[0133] The image data and tag information processed on the device are sent to the server, which stores the received data in a database and manages the image file paths and tag information.

[0134] Coordination suggestions

[0135] User input of conditions

[0136] The user enters the weather forecast for the day, the minimum and maximum temperatures, and the purpose of the trip (e.g., business casual, leisure, walk) through the application. For example, "Sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure."

[0137] Server-based information analysis and coordinate generation

[0138] The server analyzes the data by setting appropriate filters based on the input weather, temperature, and usage information. The server then uses an AI model (e.g., a generative AI model built using TensorFlow) to automatically generate optimal clothing combinations. For example, it suggests a combination such as "T-shirt + jeans + jacket."

[0139] Visualization and Display

[0140] Server-generated visuals

[0141] To visually display the generated outfit, the server uses a generative model (e.g., Generative Adversarial Networks) to generate an image of the outfit as it would appear on a mannequin.

[0142] Sending and displaying data to the device

[0143] The generated visualization is sent from the server to the terminal, which then displays the suggested outfits along with the images to the user.

[0144] Suggested purchase items

[0145] Recommended items selected by the server

[0146] The server selects new items to purchase based on the user's current outfit information and item data. For example, it searches for and selects "new sneakers that go well with these jeans."

[0147] Displaying recommended items on your device

[0148] Information on the selected recommended items is sent from the server to the device, and the device presents the information to the user, for example, displaying "new sneakers that go well with these jeans."

[0149] Prompt Sentence Examples

[0150] Here is an example of an input prompt for a generative AI model:

[0151] "Today's weather is sunny, with a high of 20 degrees and a low of 15 degrees. I'm looking for an outfit for a leisure trip. Please suggest the best outfit based on the photos and tag information of the clothes I own."

[0152] This system allows users to efficiently find attractive fashion coordinations and also provides suggestions for new items to purchase, easing the burden of fashion selection.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1:

[0155] User-generated photos of clothing

[0156] A user takes a photo of various clothes (shirts, pants, jackets, etc.) they own using a smartphone or camera. For example, they think, "I want to take a photo of this red T-shirt," and take a photo of the T-shirt with their camera. The input is the actual clothes, and the output is a digital image of the clothes. This image is used for subsequent analysis.

[0157] Step 2:

[0158] Device image upload and tagging

[0159] Users upload captured images through an application on their device (smartphone or PC). The device automatically processes the image, such as removing the background and cropping, and prompts the user to enter tags. For example, the user might tag the image as "T-shirt, casual." The input is the captured image, and the output is the processed image and its tag information. This data is sent to the server.

[0160] Step 3:

[0161] Sending data to the server

[0162] The image data and tag information sent from the device arrive at the server. The server stores this data in a database. For example, it records the image file path and associates it with the tag information. The input is the processed image and tag information, and the output is information managed in the database. By managing this data appropriately, it can be used to propose outfits later.

[0163] Step 4:

[0164] User input of conditions

[0165] Through the application, users can input the weather information for the day, the minimum and maximum temperatures, and the purpose of the trip (e.g., business casual, leisure, walk). For example, they can input "sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure." The input is the weather and purpose information, and the output is the search criteria set using that information.

[0166] Step 5:

[0167] Server-based information analysis and filter settings

[0168] The server analyzes the input information on weather, temperature, and purpose, and sets the necessary condition filters based on that information. For example, filtering clothes that match sunny weather and temperatures between 15 and 20 degrees. The input is weather, temperature, and purpose information, and the output is a list of clothes that meet the filter conditions.

[0169] Step 6:

[0170] Coordination generation using AI

[0171] The server analyzes the clothing data that matches the filter criteria using an AI model (e.g., a generative AI model using TensorFlow) and suggests optimal outfits. For example, it generates a combination of "T-shirt + jeans + jacket." The input is the filtered clothing list, and the output is the suggested outfit information.

[0172] Step 7:

[0173] Server-generated visuals

[0174] The server uses a generative model (e.g., GAN) to create an image of a mannequin wearing the generated outfit to visually display it. The input is the outfit information, and the output is the visualized image.

[0175] Step 8:

[0176] Sending and displaying data to the device

[0177] The generated visualization is sent from the server to the device, which then displays this image to the user. For example, an image of a mannequin wearing the proposed outfit is displayed on a smartphone screen. The input is the generated visual data, and the output is the visual information displayed on the user's screen.

[0178] Step 9:

[0179] Server selection of recommended items for purchase

[0180] The server analyzes the user's current outfit information and the user's owned item data to select new items to purchase. For example, it selects "new sneakers that go well with these jeans." The input is the outfit information and the owned item data, and the output is a list of recommended items to purchase.

[0181] Step 10:

[0182] Displaying recommended items on your device

[0183] The server sends the selected recommended items to the device, which then displays them to the user. For example, a suggestion such as "new sneakers that go well with these jeans" is displayed to the user. The input is the recommended item information, and the output is the recommended items displayed on the user's screen.

[0184] (Application example 1)

[0185] 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."

[0186] It is difficult for users to efficiently utilize the clothes they have on hand and choose the optimal outfit for the weather and occasion, especially when they are short on time or lack confidence in fashion. Even in brick-and-mortar stores, there is no system that can provide real-time outfit suggestions when customers are considering a purchase, which prevents them from fully motivating them to buy. Furthermore, there is a lack of efficient fashion support systems that use new interfaces such as smart glasses.

[0187] 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.

[0188] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for acquiring weather information for the day, minimum and maximum temperatures, and usage information, a means for proposing appropriate clothing combinations based on this information, a means for visually displaying the proposed combinations, a means for presenting recommended items for purchase, a means for acquiring images of clothing using a camera in the smart glasses, a means for transmitting the acquired image data and user information to the server and receiving outfit suggestions, and a means for displaying the suggested outfits on the display of the smart glasses. This allows users to efficiently select their daily fashion and receive outfit suggestions in real time, even in physical stores. Furthermore, the server can recommend new items to purchase based on the user's tastes and preferences, thereby increasing purchasing motivation.

[0189] - "Clothing" refers to clothing worn by people in daily life or for specific purposes.

[0190] "Image acquisition means" refers to a device or function that takes an image of clothing and records it as digital data.

[0191] "Weather information" refers to meteorological data such as the weather conditions and temperature of the day.

[0192] "Use information" refers to information about the purpose and scene of the clothing to be used on the day.

[0193] The "coordination suggestion means" is a function that automatically selects the optimal clothing combination based on acquired image data, weather, and purpose information.

[0194] "Visual display means" refers to a device or function for visually presenting suggested clothing combinations to a user.

[0195] The "means for presenting recommended items for purchase" is a function that provides information on recommended new products for purchase based on the clothing data and coordination held by the user.

[0196] "Smart glasses" are eyeglass-type devices that have built-in cameras and displays, allowing users to obtain visual information in real time.

[0197] A "camera" is a device that takes still images and videos and records them as digital data.

[0198] A "display" is a device for visually displaying digital data.

[0199] A "server" is a computer system that stores, analyzes, and processes data over a network.

[0200] "User Information" means personal data, settings, preferences, and other information provided by Users.

[0201] This invention relates to a system that acquires and records images of clothes that a user has on hand, and suggests optimal clothing combinations based on weather information and usage information. In particular, by using smart glasses, clothing suggestions can be realized in real time. The following describes in detail the embodiments of this invention.

[0202] System configuration

[0203] 1. Hardware

[0204] Smart glasses: Equipped with a camera and a display, they can acquire and display information according to the user's line of sight.

[0205] Server: A computer system used to store data, analyze data, and generate proposals over a network.

[0206] User device: A device used by a user to input and view information, such as a smartphone, tablet, or computer.

[0207] 2. Software

[0208] OpenCV: A library for acquiring and processing images from the smart glasses camera.

[0209] Requests: A library for exchanging data with a server via HTTP communication.

[0210] PIL (Python Imaging Library): A library for displaying suggested coordinate images.

[0211] Generative AI model: An artificial intelligence (AI) that suggests optimal outfits based on user input.

[0212] Acquiring and saving clothing data

[0213] Users take photos of their clothes

[0214] The user takes a photo of their own clothing using the camera in the smart glasses, which then acquires digital image data of the clothing. For example, they take a photo of a T-shirt and upload the image to the system.

[0215] Image processing and tagging with smart glasses

[0216] The smart glasses terminal receives the photographed image of the clothes and performs necessary image processing such as background removal and cropping. The user is then prompted to enter appropriate tags such as "T-shirt" or "casual."

[0217] Sending and saving data to the server

[0218] The image data processed by the smart glasses terminal is sent to the server along with the tag information. The server stores this data in a database and properly manages the image file path and tag information.

[0219] Coordination suggestions

[0220] User input of conditions

[0221] The user inputs the weather, minimum and maximum temperatures, and the purpose of the day (business casual or leisure) through smart glasses or a device. For example, the user can input "sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure."

[0222] Server-based information processing and coordinate generation

[0223] The server analyzes the weather, temperature, and purpose information received from the user and sets appropriate condition filters. Next, it filters appropriate clothing combinations from clothing data stored in a database and generates optimal outfits using a generative AI model. For example, it selects the combination of "T-shirt + jeans + jacket."

[0224] Displaying suggestions through smart glasses

[0225] The server generates a visualization of the generated outfit to display it to the user in a visually understandable way, and sends it to the smart glasses. The user can then check the proposed outfit through the smart glasses' display.

[0226] Suggested purchase items

[0227] Recommended items selected by the server

[0228] The server selects recommended items to purchase based on the current outfit information and the user's item data, for example, recommending new sneakers that go well with the outfit.

[0229] Displaying recommended items using smart glasses

[0230] Information on recommended items for purchase is presented to the user through the smart glasses display.

[0231] Examples of concrete examples and prompts

[0232] For example, a user takes a photo of a T-shirt, tags it, and then sends the data to a server for storage. The user then inputs the day's weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure), and the server uses a generative AI model to generate a "T-shirt + jeans + jacket" outfit. The suggested outfit is then displayed to the user through the smart glasses' display. The server then recommends new sneakers to go with the outfit and displays them to the user.

[0233] Example prompt sentence:

[0234] "Please suggest an outfit that includes the T-shirt the user photographed. The weather information is 'Sunny, maximum 20°C, minimum 15°C', and the purpose is 'Leisure.'"

[0235] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0236] Step 1:

[0237] A user puts on the smart glasses and takes an image of their clothing.

[0238] Input: Actual clothing

[0239] Data processing: Using the camera in the smart glasses, images of the clothing are captured as digital data.

[0240] Output: Digital image data of clothing

[0241] Step 2:

[0242] The device (smart glasses) processes the image data of the clothing, removes the background, crops the image, and displays a tagging prompt for the user to add tags.

[0243] Input: Digital image data of clothing

[0244] Data processing: Image processing is performed using OpenCV to remove background and crop. A tagging prompt is displayed so that the user can enter appropriate tags, such as "T-shirt" or "casual."

[0245] Output: Processed image data and tag information

[0246] Step 3:

[0247] The terminal transmits the processed image data and tag information to the server.

[0248] Input: Processed image data and tag information

[0249] Data calculation: Use the Requests library to send data to the server via HTTP.

[0250] Output: Image data and tag information are saved on the server.

[0251] Step 4:

[0252] The user uses smart glasses or other devices to input weather information, minimum and maximum temperatures, and usage information.

[0253] Input: Today's weather information, minimum and maximum temperatures, usage information

[0254] Data processing: The user manually enters the information.

[0255] Output: Weather information, minimum and maximum temperatures, usage information

[0256] Step 5:

[0257] The server analyzes the weather information, minimum and maximum temperatures, and usage information received from the user and sets appropriate condition filters.

[0258] Input: Weather information, minimum and maximum temperatures, usage information

[0259] Data calculation: The server sets condition filters and matches them with clothing data in the database.

[0260] Output: Clothing data that matches the conditions

[0261] Step 6:

[0262] The server uses a generative AI model to generate the optimal outfit based on clothing data that matches the conditions.

[0263] Input: Clothing data that matches the conditions

[0264] Data calculation: The generative AI model suggests optimal outfits based on clothing data and user input.

[0265] Output: Coordination suggestion data

[0266] Step 7:

[0267] A visualization for visually displaying the coordination proposal generated by the server is generated and transmitted to the display of the smart glasses.

[0268] Input: Coordination suggestion data

[0269] Data Computing: Generate visualizations and send data to the smart glasses using the Requests library.

[0270] Output: Coordinated image displayed on the smart glasses display

[0271] Step 8:

[0272] The server selects recommended items to purchase based on the current coordination information and item data owned by the user.

[0273] Input: Coordination suggestion data, user's item data

[0274] Data calculation: The server selects the best recommended items to purchase.

[0275] Output: Recommended purchase item data

[0276] Step 9:

[0277] Information on recommended purchase items is sent to the smart glasses display and presented to the user.

[0278] Input: Recommended purchase item data

[0279] Data calculation: Use the Requests library to send recommended purchase information to the smart glasses.

[0280] Output: Recommended purchase information displayed on the smart glasses display

[0281] 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.

[0282] This invention relates to a system that acquires and records images of clothing owned by a user, proposes optimal outfits based on the weather, temperature, and purpose information for the day, and further optimizes the proposals by recognizing the user's emotions. Specific embodiments of this system are described below.

[0283] System configuration

[0284] This system uses a server, a user device, and an emotion engine. The user device is a smartphone or PC, and provides an interface for taking and uploading images of clothing and inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions.

[0285] Acquiring and saving clothing data

[0286] The user takes a photo of the clothes

[0287] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, they may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0288] Device-based image processing and tagging

[0289] The device receives the image of the clothing, performs image processing such as background removal and cropping as needed, and displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt, casual."

[0290] Sending and saving data to the server

[0291] The image data processed by the device is sent to the server along with the tag information. The server stores the received image data in a database and manages metadata including the image file path and tag information.

[0292] Coordination suggestions

[0293] User input of conditions

[0294] The user enters the weather, minimum and maximum temperatures for the day, and the purpose of the trip (business casual, leisure, walk, etc.) through the application. For example, the user might enter "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose leisure."

[0295] Emotion engine emotion recognition

[0296] When a user launches the application, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state, for example, by using a camera to analyze whether they are smiling.

[0297] Server-based information processing and coordinate generation

[0298] The server analyzes the weather, temperature, and purpose information received from the user, as well as the emotional data obtained from the emotion engine. It then filters appropriate clothing combinations from the clothing data stored in the database and uses AI to generate optimal outfits. For example, it selects the combination of "T-shirt + jeans + jacket." If the user is expressing positive emotions such as happiness or joy, it will prioritize bright-colored clothing.

[0299] Proposal visualization and display

[0300] The server generates a visualization to display the generated outfits in a visually understandable way. Specifically, it creates an image of a mannequin wearing the outfit and sends it to the terminal. The terminal then displays the outfits presented to the user.

[0301] Suggested purchase items

[0302] Recommended items selected by the server

[0303] The server selects recommended items to purchase based on the user's current outfit information and item data. For example, when recommending new sneakers to match an outfit, it searches for and suggests appropriate items. It also takes into account emotional data and prioritizes displaying items that the user is likely to like.

[0304] Displaying recommended items to users

[0305] The server sends information about the selected recommended items to the terminal, which then displays the recommended items to the user, for example, presenting them as "new sneakers that go well with these jeans."

[0306] Specific examples

[0307] After the user takes a photo of a T-shirt and tags it, the data is sent to the server for storage. Meanwhile, when the user inputs the day's weather information (sunny, maximum 20°C, minimum 15°C) and purpose (leisure), the emotion engine analyzes the user's facial expressions and recognizes positive emotions (e.g., smiling). The server uses AI to generate outfits based on this information and emotion data, such as a "T-shirt + jeans + jacket." The suggested outfits are displayed to the user along with visual images of them being worn by a mannequin. Furthermore, the server selects new sneakers that go well with the outfit as recommended items and displays them to the user. If the user is expressing positive emotions, bright colors and fun designs are prioritized.

[0308] This system allows users to efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in a way that suits their emotions.

[0309] The processing flow will be explained below.

[0310] Step 1:

[0311] The user takes a photo of the clothes.

[0312] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user might think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0313] Step 2:

[0314] The device processes the image.

[0315] The device receives the photographed image of the clothes, performs image processing such as background removal and cropping as necessary to optimize the image, and then temporarily stores the image data.

[0316] Step 3:

[0317] The device performs the tagging.

[0318] The device will prompt the user to enter appropriate tags, such as "T-shirt, casual," and the user will select and enter the tag.

[0319] Step 4:

[0320] The terminal transmits the data to the server.

[0321] The image data for which tagging has been completed on the terminal is transmitted to the server together with the tag information.

[0322] Step 5:

[0323] The server stores the data.

[0324] The server stores the received image data in a database and manages metadata including image file paths and tag information.

[0325] Step 6:

[0326] The user inputs weather and usage information.

[0327] Through the application, users input the weather, minimum and maximum temperatures for the day, and the purpose of use (for example, "sunny, maximum 20 degrees, minimum 15 degrees, purpose: leisure").

[0328] Step 7:

[0329] The terminal sends the input information to the server.

[0330] The terminal transmits the weather information, temperature information, and usage information input by the user to the server.

[0331] Step 8:

[0332] The emotion engine analyzes the user's emotions.

[0333] The device uses facial recognition technology to capture the user's facial expression data and transmits it to the emotion engine, which then analyzes the user's emotions (e.g., joy, sadness, neutrality, etc.).

[0334] Step 9:

[0335] The server analyzes the condition information.

[0336] The server analyzes the weather, temperature, purpose and emotion data received from the user and from the emotion engine.

[0337] Step 10:

[0338] The server generates the coordinates.

[0339] The server uses AI to filter and generate optimal clothing combinations (e.g., "T-shirt + jeans + jacket") from clothing data stored in a database based on the user's emotional state and usage information.

[0340] Step 11:

[0341] The server generates the visualization.

[0342] The server generates an image of a mannequin wearing the proposed outfit in order to visually display the created outfit in an easy-to-understand manner.

[0343] Step 12:

[0344] The server sends the visualization to the device.

[0345] The server transmits the generated visualization and coordination proposal information to the terminal.

[0346] Step 13:

[0347] The device displays the suggestions to the user.

[0348] The device will then present the user with suggested outfits, such as "Leisure outfits suitable for a sunny 20-degree day."

[0349] Step 14:

[0350] The server selects recommended items for purchase.

[0351] The server selects recommended items to purchase (e.g., new sneakers) based on the user's current outfit information and item data. It also takes into account emotional data and prioritizes displaying items that the user is likely to like.

[0352] Step 15:

[0353] The server transmits recommended item information to the terminal.

[0354] The server transmits information about the selected recommended purchase items to the terminal.

[0355] Step 16:

[0356] The device displays the recommended items to the user.

[0357] The device will then display selected purchase recommendations to the user, such as "new sneakers that go well with these jeans."

[0358] This allows users to efficiently select attractive clothing and receive suggestions that are in line with their emotions, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in accordance with their mood and emotions.

[0359] Example 2

[0360] 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."

[0361] In modern life, users spend a lot of time choosing their daily clothing. It takes a lot of experience to coordinate outfits appropriately based on the weather, temperature, and occasion. It's also extremely difficult to coordinate outfits based on the user's emotional state. This often leads to users choosing inappropriate clothing, which can lead to an unpleasant day. It's also difficult to provide users with appropriate purchasing recommendations.

[0362] 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.

[0363] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for acquiring the current day's weather information, minimum and maximum temperatures, and usage information, a means for analyzing the acquired facial expressions and voice to recognize the user's emotional state, a means for using artificial intelligence to suggest appropriate clothing combinations based on this information and the user's emotional state, a means for visually displaying the suggested combinations, and a means for presenting recommended items for purchase. This allows the user to quickly and efficiently find the optimal outfit, and suggestions can be made based on the weather, usage, and emotional state. Furthermore, presenting appropriate recommended items for purchase allows the user to appropriately consider purchasing new items.

[0364] The "means for acquiring and recording images" refers to a system for taking photos of clothing owned by the user and saving the image data.

[0365] "Means for obtaining weather information" refers to a system for obtaining meteorological data such as the weather and temperature of the day.

[0366] "Means for acquiring usage information" refers to a system that allows users to input the purpose and situation for wearing the clothing and acquire that information.

[0367] "Means for recognizing emotional state" refers to technology that analyzes the user's facial expressions and voice to determine their current emotions.

[0368] "Means for suggesting using artificial intelligence" refers to a mechanism that utilizes artificial intelligence to generate optimal clothing combinations based on acquired information and perceived emotional state.

[0369] "Visual display means" refers to a technique that visually displays the suggested clothing combinations in a way that is easy for the user to understand.

[0370] "Means for suggesting recommended items for purchase" refers to a system that suggests new clothing and accessories suitable for coordinating with users.

[0371] The "means of tagging and recording" is a system that allows users to tag their clothing to indicate the type, usage situation, etc., and save that information.

[0372] "Data-based suggestion means" refers to technology that determines and suggests appropriate clothing combinations based on stored tagged data.

[0373] "Means for displaying a visual representation on a mannequin" refers to technology that displays the proposed clothing combination as if it were being worn by a virtual mannequin.

[0374] "Means for recommending based on user preferences" refers to technology that recommends new clothing items by taking into account the user's past choices and preferences.

[0375] "Means for suggesting based on user input" refers to technology that generates appropriate clothing combinations based on direct user input information.

[0376] The "means for transmitting and displaying on the user's terminal" refers to a mechanism for transmitting the generated combination information and data on recommended purchase items to the user's terminal and displaying them.

[0377] This invention relates to a system that acquires and records images of clothing owned by a user, suggests optimal outfits based on the day's weather information, temperature information, and usage information, and further optimizes suggestions by recognizing the user's emotions.

[0378] System configuration

[0379] This system uses a server, a user device, and an emotion engine. The user device is a smartphone or PC, and provides an interface for taking and uploading images of clothing and inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions.

[0380] System Operation

[0381] 1. Acquiring and saving clothing data

[0382] A user uses the smartphone camera to take pictures of various types of clothing they own. For example, if they want to upload a T-shirt, they take a picture of the T-shirt with their smartphone. The device receives the image and performs image processing such as background removal and cropping. It also prompts the user to enter tags, and the user enters appropriate tags such as "T-shirt" or "casual." The device then sends the processed image data along with the tag information to the server. The server stores the received data in a database and manages the image file path and tag information as metadata.

[0383] 2. Coordination suggestions

[0384] The user enters the day's weather (e.g., sunny, cloudy, rainy), minimum and maximum temperatures, and intended use (e.g., business casual, leisure, walk) through a smartphone app. Specifically, the user enters "sunny, maximum temperature 20°C, minimum temperature 15°C, intended use leisure." When the user launches the application, the emotion engine analyzes the user's facial expressions and voice through the camera and identifies their current emotion as "positive" or "negative." The server then performs an integrated analysis of the weather, temperature, and intended use information received from the user, along with the emotional data obtained from the emotion engine. The server then filters appropriate clothing combinations from the clothing data stored in the database and generates an optimal outfit using a generative AI model. For example, a T-shirt, jeans, and jacket outfit is selected. If the user indicates a positive emotion, bright-colored items are prioritized. The server generates an image of a mannequin wearing the proposed outfit to visually display it and sends it to the device. The device then displays the proposed outfit to the user.

[0385] 3. Recommended purchase items

[0386] The server analyzes the generated outfit and the user's item data to select the necessary items (such as new sneakers). It also takes into account emotional data and recommends items that the user is likely to like. The server then sends information about recommended items to the device, which then displays this to the user in the form of, for example, "New sneakers that go well with these jeans."

[0387] Specific examples

[0388] After the user takes a photo of a T-shirt and tags it as "T-shirt" and "casual," the device sends the image data to the server, which stores the image and tag information in a database. At the same time, the user enters weather information ("sunny, maximum 20 degrees, minimum 15 degrees") and purpose information ("leisure") into the app. The emotion engine analyzes the user's facial expression and recognizes "positive" (smiling). Based on these conditions, the server uses an AI model to generate a "T-shirt + jeans + jacket" outfit and displays it as a visualization. The server also selects recommended items, such as new sneakers, and presents them to the user. An example prompt is shown below.

[0389] "A user uploaded a T-shirt and entered 'casual' and 'T-shirt' in the tags. Then, the user entered 'sunny' for the weather on the day, a maximum temperature of '20 degrees', a minimum temperature of '15 degrees', and 'leisure' as the purpose. The user was recognized as smiling. Please suggest the best outfit based on these conditions."

[0390] This system will significantly reduce the effort required for users to choose their daily fashion and allow them to enjoy the best possible coordination that suits their emotions and the situation.

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] Step 1:

[0393] A user takes a photo of clothing they own with their smartphone camera. Specifically, the user wants to upload a T-shirt, so they take a photo of the T-shirt. The input is the image taken with the smartphone, and the output is image data of the clothing.

[0394] Step 2:

[0395] The device receives the captured image and performs image processing such as background removal and cropping. The input is the captured image, and the output is the cropped image data with the background removed. The device displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt" or "casual." The input is the user's tagging information, and the output is the tagged image data.

[0396] Step 3:

[0397] The device sends the processed image data and tag information to the server. The input is tagged image data, and the output is the completed transmission to the server. The server saves the received image data in a database and manages the image file path and tag information as metadata. The input is tagged image data, and the output is the data saved in the database.

[0398] Step 4:

[0399] The user inputs the weather, minimum and maximum temperatures, and purpose of use for the day through a smartphone app. For example, they might input "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose of use leisure." The input is information such as the weather, temperature, and purpose entered by the user, and the output is data related to these conditions.

[0400] Step 5:

[0401] When a user launches the application, the emotion engine analyzes the user's facial expressions and voice through the camera to recognize their current emotional state. For example, it recognizes a user's smile and determines that it is a positive emotion. The input is the user's facial expressions and voice, and the output is the recognized emotional data.

[0402] Step 6:

[0403] The server analyzes the weather, temperature, and purpose information received from the user, as well as the emotion data obtained from the emotion engine. The input is weather, temperature, purpose, and emotion data, and the output is the analysis result obtained by integrating this information. Then, appropriate clothing combinations are filtered from the clothing data stored in the database. The optimal outfit is generated using a generative AI model. For example, a combination of T-shirt + jeans + jacket is selected. The input is the filtering conditions (weather, temperature, purpose, emotion data), and the output is the generated outfit data.

[0404] Step 7:

[0405] The server generates a visualization to display the proposed outfits in a visually easy-to-understand manner. Specifically, it creates an image of a mannequin wearing the proposed outfit and sends it to the terminal. The input is the generated outfit data, and the output is the visualization image. The terminal displays the proposed outfit to the user. The input is the visualization image, and the output is the outfit image displayed to the user.

[0406] Step 8:

[0407] The server analyzes the generated coordination information and the user's owned item data. The input is the coordination information and owned item data, and the output is the analysis results. Based on the analysis results, the server selects the necessary items (e.g., new sneakers). The input is the analysis results, and the output is the selected recommended purchase items. It also takes into account emotional data and recommends items that the user is likely to like. The input is emotional data, and the output is recommended purchase items based on emotions.

[0408] Step 9:

[0409] The server sends information about the selected recommended purchase items to the terminal. The input is the selected recommended purchase item information, and the output is the completion of transmission to the terminal. The terminal presents the recommended purchase item to the user, such as "new sneakers that go well with these jeans." The input is the recommended purchase item information sent from the server, and the output is the recommended purchase item information displayed to the user.

[0410] (Application example 2)

[0411] 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."

[0412] Conventional outfit suggestion systems have difficulty making optimal suggestions based on the user's emotions and real-time circumstances. Furthermore, users have limited means of directly visually confirming their outfits, which means there is a lack of consistency between the suggested outfits and the actual outfits or purchases they make. Furthermore, there is also the problem that the suggested items do not adequately reflect the user's emotions, tastes, and preferences.

[0413] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing and analyzing the user's emotions, means for visualizing and displaying suggested outfits on the smart glasses, and means for displaying recommended items in cooperation with a virtual store. This allows the optimal outfit suggestions to be made based on the user's emotions and real-time situation, and the suggestions can be visually confirmed, enabling a more integrated outfit and purchasing experience.

[0414] "Clothing image acquisition means" refers to devices and software that allow users to photograph and record their own clothing and acquire image data.

[0415] "Weather information acquisition means" refers to a device or software for acquiring information about the weather and temperature of the day.

[0416] "Use information acquisition means" refers to a device or software that acquires the situation and purpose (business, leisure, etc.) in which the clothing will be used.

[0417] A "clothing combination suggestion means" is a device or software that suggests clothing combinations suitable for the weather and purpose based on clothing data held by the user.

[0418] A "visual display means" is a device or software that visually presents suggested clothing combinations to a user.

[0419] The "means for presenting recommended items for purchase" is a device or software that recommends new items that go well with the proposed outfit to the user.

[0420] "Emotion recognition means" refers to devices or software that analyze a user's facial expressions and voice and recognize their emotional state.

[0421] "Smart glasses" are wearable devices that allow users to obtain information visually by wearing them.

[0422] A "visualization means" is a device or software that displays suggested outfits in the user's field of vision in real time.

[0423] A "virtual store" is a virtual store system where you can browse and purchase products online.

[0424] "Artificial intelligence" refers to algorithms and software that can analyze data and make decisions automatically.

[0425] This invention is a system that acquires and records images of clothing owned by a user and suggests optimal outfits based on weather information, temperature information, usage information, and emotional data. Furthermore, it has the function of visually displaying the suggested outfits using smart glasses and presenting recommended items for purchase. A specific embodiment of this system will be described below.

[0426] System configuration

[0427] The system's main components are a server, a user device (smartphone or PC), smart glasses, and an emotion engine. The user device takes and uploads images of clothing and provides an interface for inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions. The smart glasses function as a device for visualizing suggested outfits in real time.

[0428] Acquiring and saving clothing data

[0429] The user takes a photo of the clothes

[0430] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user might think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0431] Device-based image processing and tagging

[0432] The device receives the image of the clothing and performs image processing such as background removal and cropping. Using OCR technology, it automatically tags the clothing and prompts the user to confirm the appropriate tag (e.g., "T-shirt, casual").

[0433] Sending and saving data to the server

[0434] The image data processed on the device is sent to the server along with the tag information. The server stores the received image data in a database and manages metadata including the image file path and tag information. MySQL or MongoDB are suitable databases.

[0435] Coordination suggestions

[0436] User input of conditions

[0437] Through the application, users input the weather for the day, the minimum and maximum temperatures, and the purpose of the outfit (business casual, leisure, walking, etc.).

[0438] Emotion engine emotion recognition

[0439] When a user launches the application, it uses an emotion engine (e.g., AWS Rekognition, IBM Watson) to analyze the user's facial expressions and voice to recognize their current emotional state. It also uses a camera to analyze facial expressions.

[0440] Server-based information processing and coordinate generation

[0441] The server analyzes the weather, temperature, and usage information received from the user, as well as the emotion data obtained from the emotion engine. It then uses an AI model (e.g., TensorFlow, PyTorch) to generate the optimal outfit. For example, it selects the combination of "T-shirt + jeans + jacket." If the user expresses positive emotions, it will prioritize bright-colored clothing.

[0442] Proposal visualization and display

[0443] The server then generates an image of the outfit being worn by a mannequin to visually display it in a way that is easy to understand. This image data is then sent to the smart glasses, which display it in real time to the user's field of vision.

[0444] Suggested purchase items

[0445] Recommended items selected by the server

[0446] The server works with the virtual store to select recommended items based on the user's current outfit information and item data. Recommended items are prioritized based on the user's emotional data and prioritize items that match the outfit.

[0447] Displaying recommended items to users

[0448] The server sends the information of the selected recommended items to the smart glasses, and displays appropriate recommended items to the user, such as "new sneakers that go well with these jeans."

[0449] Examples of concrete examples and prompts

[0450] As a concrete example, we will explain how a user takes a photo of a T-shirt, tags it, and then sends the data to a server for storage. When the user inputs the day's weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure), the emotion engine analyzes the user's facial expressions and recognizes positive emotions (e.g., smiling). Based on this information, the server generates a "T-shirt + jeans + jacket" outfit and visualizes it on the smart glasses. Furthermore, the server connects with a virtual store to recommend new sneakers that go well with this outfit and displays them to the user.

[0451] An example of a prompt is as follows:

[0452] "The system removes the background from images of clothing taken by the user and automatically tags them. Based on weather, temperature, usage information, and the user's emotional data, it generates optimal outfits and visualizes them on the smart glasses display. This uses computer vision and emotion recognition."

[0453] This system allows users to efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in a way that suits their emotions.

[0454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0455] Program processing flow

[0456] Step 1: Image capture and tagging of clothing

[0457] The device receives an image of clothing taken by the user. The image undergoes image processing such as background removal and cropping, and is automatically tagged using OCR technology. The tag information and image data are sent from the device to the server. The input is the "image of clothing," and the output is the "background-removed image and tag information."

[0458] Step 2: Obtaining weather information, temperature information, and usage information

[0459] The user enters the day's weather information, minimum and maximum temperatures, and usage information through the application. The input is "weather information, temperature information, usage information," and the output is "weather information, temperature information, usage information data." This allows the server to obtain the environmental data necessary for analysis.

[0460] Step 3: Acquire and analyze emotion data

[0461] When a user launches the application, the device (smart glasses or smartphone) captures the user's facial expressions and voice using a camera and microphone. This data is sent to the emotion engine, which analyzes the user's emotional state. The input is "facial expression and voice data," and the output is "emotion data."

[0462] Step 4: Generate coordinates

[0463] The server uses an AI model (such as TensorFlow or PyTorch) to analyze weather information, temperature information, usage information, emotional data, and clothing data stored in a database, and generates an appropriate outfit. The input is "weather information, temperature information, usage information, emotional data, clothing data," and the output is "optimal outfit information."

[0464] Step 5: Visualization and visualization

[0465] The server generates a visual representation of the outfit on a mannequin based on the coordinated outfit information. The generated image is sent to the smart glasses, allowing the user to view it in real time. The input is "coordinated outfit information," and the output is a "visualized image."

[0466] Step 6: Select recommended items to purchase

[0467] The server analyzes the user's outfit information and owned item data to select recommended items for purchase. It connects with the virtual store to obtain related product data. The input is "outfit information and owned item data," and the output is "a list of recommended items for purchase."

[0468] Step 7: View recommended items to buy

[0469] The server sends the selected recommended items to the smart glasses, and the user can purchase the items through a purchase link. The input is a "list of recommended items to purchase," and the output is a "purchase link and item information."

[0470] Specific examples and prompts

[0471] Examples:

[0472] The user takes a photo of a T-shirt through the smart glasses, and the image with the background removed is sent to the server. The user then enters weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure) into the application. At the same time, the emotion engine analyzes the user's positive emotion (smile). Based on this information, the server generates a "T-shirt + jeans + jacket" outfit and displays a visualized image on the smart glasses. In addition, recommended items for new sneakers are presented, which the user can review and purchase.

[0473] Examples of prompts:

[0474] "The system removes the background from images of clothing taken by the user and automatically tags them. Based on weather, temperature, usage information, and the user's emotional data, it generates optimal outfits and visualizes them on the smart glasses display. This uses computer vision and emotion recognition."

[0475] 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.

[0476] 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.

[0477] 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.

[0478] [Second embodiment]

[0479] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0480] 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.

[0481] 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).

[0482] 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.

[0483] 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.

[0484] 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).

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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."

[0491] The present invention relates to a system that improves the efficiency of clothing selection and enables users to appropriately select trendy clothing coordinations every day. Specific embodiments of this system will be described below.

[0492] System configuration

[0493] The system uses a server, user devices, and AI (artificial intelligence). User devices are smartphones or PCs, and provide an interface for users to take and upload images of clothes and input weather, temperature, and usage information. The server stores and analyzes the image data, suggests outfits, and selects recommended items for purchase. The AI ​​is also responsible for automatically generating outfit suggestions based on the clothing data and input information.

[0494] Acquiring and saving clothing data

[0495] Users take photos of their clothes

[0496] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, they may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0497] Device-based image processing and tagging

[0498] The device receives the photographed image of the clothes and performs image processing such as background removal and cropping as necessary. It also displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt, casual." This tagging allows the data to be used efficiently in subsequent outfit suggestions.

[0499] Sending and saving data to the server

[0500] The image data processed by the terminal is sent to the server along with the tag information. The server stores the received image data in a database and manages the image file path and tag information appropriately.

[0501] Coordination suggestions

[0502] User input of conditions

[0503] The user enters the weather, minimum and maximum temperatures for the day, and the purpose of the trip (business casual, leisure, walk, etc.) through the application. For example, the user might enter "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose leisure."

[0504] Server-based information processing and coordinate generation

[0505] The server analyzes the weather, temperature, and purpose information received from the user and sets appropriate condition filters. Next, it filters appropriate clothing combinations from clothing data stored in a database and uses AI to generate optimal outfits. For example, it selects the combination of "T-shirt + jeans + jacket."

[0506] Proposal visualization and display

[0507] The server generates a visualization to display the generated outfits to the user in a visually understandable way. Specifically, it generates an image of a mannequin wearing the outfits presented, and sends that image and other information to the terminal. The terminal then displays the outfits presented to the user.

[0508] Suggested purchase items

[0509] Recommended items selected by the server

[0510] The server selects recommended items to purchase based on the user's current outfit information and item data. For example, if the server is recommending new sneakers to match an outfit, it will search for and recommend appropriate items.

[0511] Displaying recommended items to users

[0512] The server sends information about the selected recommended items to the terminal, which then displays the recommended items to the user, for example, presenting them as "new sneakers that go well with these jeans."

[0513] Specific examples

[0514] After the user takes a photo of a T-shirt and tags it, the data is sent to the server for storage. The user then inputs the day's weather information (sunny, maximum 20°C, minimum 15°C) and purpose (leisure), and the server uses AI to generate a "T-shirt + jeans + jacket" outfit based on this information. The suggested outfit is displayed to the user along with a visual image of the outfit, as if worn by a mannequin. The server also recommends new sneakers to go with the outfit and displays them to the user.

[0515] In this way, users can efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life. With this system, even those who are not good at fashion can confidently enjoy wearing clothes that are in line with the latest trends.

[0516] The processing flow will be explained below.

[0517] Step 1:

[0518] The user takes a photo of the clothes.

[0519] Users take photos of various clothes they own (such as T-shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0520] Step 2:

[0521] The device processes the image.

[0522] The device receives the photographed image of the clothes and performs image processing such as background removal and trimming as necessary. For example, it automatically cuts out unnecessary background parts of the image.

[0523] Step 3:

[0524] The device performs the tagging.

[0525] The device will prompt the user to enter appropriate tags, such as "T-shirt, casual," and the user will select and enter the tag.

[0526] Step 4:

[0527] The terminal transmits the data to the server.

[0528] Once tagging is complete on the device, the image data is sent to the server, including the tag information.

[0529] Step 5:

[0530] The server stores the data.

[0531] The server stores the received image data in a database, along with metadata including the image file path and tag information.

[0532] Step 6:

[0533] The user inputs the weather and purpose.

[0534] The user inputs the weather, minimum and maximum temperatures for the day, and the purpose of use (e.g., "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose: leisure") through the application.

[0535] Step 7:

[0536] The terminal sends the input information to the server.

[0537] The terminal transmits the weather information, temperature information, and usage information input by the user to the server.

[0538] Step 8:

[0539] The server analyzes the information and generates coordinates.

[0540] The server analyzes the weather, temperature, and purpose information received from the user, then filters the clothing data stored in the database to find suitable clothing combinations and uses AI to generate the optimal outfit.

[0541] Step 9:

[0542] The server generates the visualization.

[0543] The server generates a visualization to display the generated outfits in a visually understandable way, specifically creating an image of a mannequin wearing the outfits presented.

[0544] Step 10:

[0545] The server sends the visualization to the device.

[0546] The server transmits the generated visualization and coordination proposal information to the terminal.

[0547] Step 11:

[0548] The device displays the suggestions to the user.

[0549] The device will then display suggested outfits to the user, such as "leisure outfits suitable for a sunny 20-degree day."

[0550] Step 12:

[0551] The server selects recommended items for purchase.

[0552] The server selects appropriate recommended items to purchase based on the user's outfit information and item data, such as new sneakers that go well with the outfit.

[0553] Step 13:

[0554] The server transmits recommended item information to the terminal.

[0555] The server transmits information about the selected recommended purchase items to the terminal.

[0556] Step 14:

[0557] The device displays the recommended items to the user.

[0558] The device will then display selected purchase recommendations to the user, such as "New sneakers to go with these jeans."

[0559] Example 1

[0560] 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."

[0561] In modern life, fashion coordination is a daily challenge for many people, and choosing appropriate clothing based on trends, seasons, and occasions can be particularly challenging. Furthermore, there is a growing demand for systems that effectively utilize existing clothing and provide comprehensive fashion advice, including the selection of new items to purchase. However, existing systems struggle to reduce the user's workload and make efficient and accurate fashion suggestions. The purpose of this invention is to provide a system that solves these problems and allows users to easily and effectively enjoy daily coordination.

[0562] 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.

[0563] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for tagging and recording the clothing images based on user input, a means for proposing appropriate clothing combinations based on the tagged image data and taking into account weather information, temperature information, and usage information, a means for visually displaying the proposed combinations, and a means for presenting recommended purchase items for the proposed combinations. This allows users to efficiently manage their clothing and automatically receive recommendations for optimal coordination based on trends and usage. Furthermore, new items to purchase are also recommended, reducing the burden of fashion in general.

[0564] "Clothing" refers to clothing to be worn, such as shirts, pants, jackets, etc.

[0565] "Means for acquiring and recording images" refers to devices and functions that allow users to digitally save and manage images of clothing taken with a smartphone or digital camera.

[0566] "Means for tagging and recording images of clothing based on user input" refers to devices or functions that allow users to add descriptions such as category and use to images of clothing and store them in a database.

[0567] "Means for proposing appropriate clothing combinations based on tagged image data, taking into consideration weather information, temperature information, and usage information" refers to devices and technologies that analyze stored tagged image data and weather information, temperature information, and usage information acquired in real time, and automatically select the optimal clothing combinations based on this information.

[0568] "Means for visually displaying suggested combinations" refers to devices or functions that use images or graphics to clearly present optimal clothing combinations to users.

[0569] The "means for presenting recommended items for purchase" refers to a device or function that can recommend new items to the user to purchase to go with the proposed outfit and display detailed information about them.

[0570] "Means of utilizing artificial intelligence" refers to systems and algorithms that use technologies such as machine learning and deep learning to suggest clothing combinations.

[0571] "Means for transmitting and displaying on a user's device" refers to the equipment or functions for transmitting the generated coordination information and information on recommended purchase items to a user's device (smartphone, tablet, PC, etc.) via the Internet or other communication means and displaying it.

[0572] This invention relates to a system that supports users in efficiently coordinating their daily fashion. The system is composed of a server, a user terminal, and artificial intelligence (AI).

[0573] System configuration

[0574] The basic components of this system are as follows:

[0575] User device (smartphone, PC)

[0576] Servers (high-performance database servers, GPU servers)

[0577] AI model (generative AI model)

[0578] Acquiring clothing data

[0579] User-generated photos of clothing

[0580] The user uses a smartphone or digital camera to take a picture of the clothes they own. For example, they perform a specific action such as "taking a picture of their favorite red T-shirt."

[0581] Uploading images using a device

[0582] Users upload the images they take to the device through an application on their smartphone or computer. Once the images are uploaded, the device prompts the user to add tags. They can enter tags such as "T-shirt, casual."

[0583] Image processing and analysis

[0584] Image processing by device

[0585] The device performs image processing on the uploaded image, such as background removal and cropping. Specifically, for example, background removal is performed using the OpenCV library. The corrected image is then displayed and the user can tag it.

[0586] Sending data to the server

[0587] The image data and tag information processed on the device are sent to the server, which stores the received data in a database and manages the image file paths and tag information.

[0588] Coordination suggestions

[0589] User input of conditions

[0590] The user enters the weather forecast for the day, the minimum and maximum temperatures, and the purpose of the trip (e.g., business casual, leisure, walk) through the application. For example, "Sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure."

[0591] Server-based information analysis and coordinate generation

[0592] The server analyzes the data by setting appropriate filters based on the input weather, temperature, and usage information. The server then uses an AI model (e.g., a generative AI model built using TensorFlow) to automatically generate optimal clothing combinations. For example, it suggests a combination such as "T-shirt + jeans + jacket."

[0593] Visualization and Display

[0594] Server-generated visuals

[0595] To visually display the generated outfit, the server uses a generative model (e.g., Generative Adversarial Networks) to generate an image of the outfit as it would appear on a mannequin.

[0596] Sending and displaying data to the device

[0597] The generated visualization is sent from the server to the terminal, which then displays the suggested outfits along with the images to the user.

[0598] Suggested purchase items

[0599] Recommended items selected by the server

[0600] The server selects new items to purchase based on the user's current outfit information and item data. For example, it searches for and selects "new sneakers that go well with these jeans."

[0601] Displaying recommended items on your device

[0602] Information on the selected recommended items is sent from the server to the device, and the device presents the information to the user, for example, displaying "new sneakers that go well with these jeans."

[0603] Prompt Sentence Examples

[0604] Here is an example of an input prompt for a generative AI model:

[0605] "Today's weather is sunny, with a high of 20 degrees and a low of 15 degrees. I'm looking for an outfit for a leisure trip. Please suggest the best outfit based on the photos and tag information of the clothes I own."

[0606] This system allows users to efficiently find attractive fashion coordinations and also provides suggestions for new items to purchase, easing the burden of fashion selection.

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

[0608] Step 1:

[0609] User-generated photos of clothing

[0610] A user takes a photo of various clothes (shirts, pants, jackets, etc.) they own using a smartphone or camera. For example, they think, "I want to take a photo of this red T-shirt," and take a photo of the T-shirt with their camera. The input is the actual clothes, and the output is a digital image of the clothes. This image is used for subsequent analysis.

[0611] Step 2:

[0612] Device image upload and tagging

[0613] Users upload captured images through an application on their device (smartphone or PC). The device automatically processes the image, such as removing the background and cropping, and prompts the user to enter tags. For example, the user might tag the image as "T-shirt, casual." The input is the captured image, and the output is the processed image and its tag information. This data is sent to the server.

[0614] Step 3:

[0615] Sending data to the server

[0616] The image data and tag information sent from the device arrive at the server. The server stores this data in a database. For example, it records the image file path and associates it with the tag information. The input is the processed image and tag information, and the output is information managed in the database. By managing this data appropriately, it can be used to propose outfits later.

[0617] Step 4:

[0618] User input of conditions

[0619] Through the application, users can input the weather information for the day, the minimum and maximum temperatures, and the purpose of the trip (e.g., business casual, leisure, walk). For example, they can input "sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure." The input is the weather and purpose information, and the output is the search criteria set using that information.

[0620] Step 5:

[0621] Server-based information analysis and filter settings

[0622] The server analyzes the input information on weather, temperature, and purpose, and sets the necessary condition filters based on that information. For example, filtering clothes that match sunny weather and temperatures between 15 and 20 degrees. The input is weather, temperature, and purpose information, and the output is a list of clothes that meet the filter conditions.

[0623] Step 6:

[0624] Coordination generation using AI

[0625] The server analyzes the clothing data that matches the filter criteria using an AI model (e.g., a generative AI model using TensorFlow) and suggests optimal outfits. For example, it generates a combination of "T-shirt + jeans + jacket." The input is the filtered clothing list, and the output is the suggested outfit information.

[0626] Step 7:

[0627] Server-generated visuals

[0628] The server uses a generative model (e.g., GAN) to create an image of a mannequin wearing the generated outfit to visually display it. The input is the outfit information, and the output is the visualized image.

[0629] Step 8:

[0630] Sending and displaying data to the device

[0631] The generated visualization is sent from the server to the device, which then displays this image to the user. For example, an image of a mannequin wearing the proposed outfit is displayed on a smartphone screen. The input is the generated visual data, and the output is the visual information displayed on the user's screen.

[0632] Step 9:

[0633] Server selection of recommended items for purchase

[0634] The server analyzes the user's current outfit information and the user's owned item data to select new items to purchase. For example, it selects "new sneakers that go well with these jeans." The input is the outfit information and the owned item data, and the output is a list of recommended items to purchase.

[0635] Step 10:

[0636] Displaying recommended items on your device

[0637] The server sends the selected recommended items to the device, which then displays them to the user. For example, a suggestion such as "new sneakers that go well with these jeans" is displayed to the user. The input is the recommended item information, and the output is the recommended items displayed on the user's screen.

[0638] (Application example 1)

[0639] 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."

[0640] It is difficult for users to efficiently utilize the clothes they have on hand and choose the optimal outfit for the weather and occasion, especially when they are short on time or lack confidence in fashion. Even in brick-and-mortar stores, there is no system that can provide real-time outfit suggestions when customers are considering a purchase, which prevents them from fully motivating them to buy. Furthermore, there is a lack of efficient fashion support systems that use new interfaces such as smart glasses.

[0641] 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.

[0642] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for acquiring weather information for the day, minimum and maximum temperatures, and usage information, a means for proposing appropriate clothing combinations based on this information, a means for visually displaying the proposed combinations, a means for presenting recommended items for purchase, a means for acquiring images of clothing using a camera in the smart glasses, a means for transmitting the acquired image data and user information to the server and receiving outfit suggestions, and a means for displaying the suggested outfits on the display of the smart glasses. This allows users to efficiently select their daily fashion and receive outfit suggestions in real time, even in physical stores. Furthermore, the server can recommend new items to purchase based on the user's tastes and preferences, thereby increasing purchasing motivation.

[0643] - "Clothing" refers to clothing worn by people in daily life or for specific purposes.

[0644] "Image acquisition means" refers to a device or function that takes an image of clothing and records it as digital data.

[0645] "Weather information" refers to meteorological data such as the weather conditions and temperature of the day.

[0646] "Use information" refers to information about the purpose and scene of the clothing to be used on the day.

[0647] The "coordination suggestion means" is a function that automatically selects the optimal clothing combination based on acquired image data, weather, and purpose information.

[0648] "Visual display means" refers to a device or function for visually presenting suggested clothing combinations to a user.

[0649] The "means for presenting recommended items for purchase" is a function that provides information on recommended new products for purchase based on the clothing data and coordination held by the user.

[0650] "Smart glasses" are eyeglass-type devices that have built-in cameras and displays, allowing users to obtain visual information in real time.

[0651] A "camera" is a device that takes still images and videos and records them as digital data.

[0652] A "display" is a device for visually displaying digital data.

[0653] A "server" is a computer system that stores, analyzes, and processes data over a network.

[0654] "User Information" means personal data, settings, preferences, and other information provided by Users.

[0655] This invention relates to a system that acquires and records images of clothes that a user has on hand, and suggests optimal clothing combinations based on weather information and usage information. In particular, by using smart glasses, clothing suggestions can be realized in real time. The following describes in detail the embodiments of this invention.

[0656] System configuration

[0657] 1. Hardware

[0658] Smart glasses: Equipped with a camera and a display, they can acquire and display information according to the user's line of sight.

[0659] Server: A computer system used to store data, analyze data, and generate proposals over a network.

[0660] User device: A device used by a user to input and view information, such as a smartphone, tablet, or computer.

[0661] 2. Software

[0662] OpenCV: A library for acquiring and processing images from the smart glasses camera.

[0663] Requests: A library for exchanging data with a server via HTTP communication.

[0664] PIL (Python Imaging Library): A library for displaying suggested coordinate images.

[0665] Generative AI model: An artificial intelligence (AI) that suggests optimal outfits based on user input.

[0666] Acquiring and saving clothing data

[0667] Users take photos of their clothes

[0668] The user takes a photo of their own clothing using the camera in the smart glasses, which then acquires digital image data of the clothing. For example, they take a photo of a T-shirt and upload the image to the system.

[0669] Image processing and tagging with smart glasses

[0670] The smart glasses terminal receives the photographed image of the clothes and performs necessary image processing such as background removal and cropping. The user is then prompted to enter appropriate tags such as "T-shirt" or "casual."

[0671] Sending and saving data to the server

[0672] The image data processed by the smart glasses terminal is sent to the server along with the tag information. The server stores this data in a database and properly manages the image file path and tag information.

[0673] Coordination suggestions

[0674] User input of conditions

[0675] The user inputs the weather, minimum and maximum temperatures, and the purpose of the day (business casual or leisure) through smart glasses or a device. For example, the user can input "sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure."

[0676] Server-based information processing and coordinate generation

[0677] The server analyzes the weather, temperature, and purpose information received from the user and sets appropriate condition filters. Next, it filters appropriate clothing combinations from clothing data stored in a database and generates optimal outfits using a generative AI model. For example, it selects the combination of "T-shirt + jeans + jacket."

[0678] Displaying suggestions through smart glasses

[0679] The server generates a visualization of the generated outfit to display it to the user in a visually understandable way, and sends it to the smart glasses. The user can then check the proposed outfit through the smart glasses' display.

[0680] Suggested purchase items

[0681] Recommended items selected by the server

[0682] The server selects recommended items to purchase based on the current outfit information and the user's item data, for example, recommending new sneakers that go well with the outfit.

[0683] Displaying recommended items using smart glasses

[0684] Information on recommended items for purchase is presented to the user through the smart glasses display.

[0685] Examples of concrete examples and prompts

[0686] For example, a user takes a photo of a T-shirt, tags it, and then sends the data to a server for storage. The user then inputs the day's weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure), and the server uses a generative AI model to generate a "T-shirt + jeans + jacket" outfit. The suggested outfit is then displayed to the user through the smart glasses' display. The server then recommends new sneakers to go with the outfit and displays them to the user.

[0687] Example prompt sentence:

[0688] "Please suggest an outfit that includes the T-shirt the user photographed. The weather information is 'Sunny, maximum 20°C, minimum 15°C', and the purpose is 'Leisure.'"

[0689] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0690] Step 1:

[0691] A user puts on the smart glasses and takes an image of their clothing.

[0692] Input: Actual clothing

[0693] Data processing: Using the camera in the smart glasses, images of the clothing are captured as digital data.

[0694] Output: Digital image data of clothing

[0695] Step 2:

[0696] The device (smart glasses) processes the image data of the clothing, removes the background, crops the image, and displays a tagging prompt for the user to add tags.

[0697] Input: Digital image data of clothing

[0698] Data processing: Image processing is performed using OpenCV to remove background and crop. A tagging prompt is displayed so that the user can enter appropriate tags, such as "T-shirt" or "casual."

[0699] Output: Processed image data and tag information

[0700] Step 3:

[0701] The terminal transmits the processed image data and tag information to the server.

[0702] Input: Processed image data and tag information

[0703] Data calculation: Use the Requests library to send data to the server via HTTP.

[0704] Output: Image data and tag information are saved on the server.

[0705] Step 4:

[0706] The user uses smart glasses or other devices to input weather information, minimum and maximum temperatures, and usage information.

[0707] Input: Today's weather information, minimum and maximum temperatures, usage information

[0708] Data processing: The user manually enters the information.

[0709] Output: Weather information, minimum and maximum temperatures, usage information

[0710] Step 5:

[0711] The server analyzes the weather information, minimum and maximum temperatures, and usage information received from the user and sets appropriate condition filters.

[0712] Input: Weather information, minimum and maximum temperatures, usage information

[0713] Data calculation: The server sets condition filters and matches them with clothing data in the database.

[0714] Output: Clothing data that matches the conditions

[0715] Step 6:

[0716] The server uses a generative AI model to generate the optimal outfit based on clothing data that matches the conditions.

[0717] Input: Clothing data that matches the conditions

[0718] Data calculation: The generative AI model suggests optimal outfits based on clothing data and user input.

[0719] Output: Coordination suggestion data

[0720] Step 7:

[0721] A visualization for visually displaying the coordination proposal generated by the server is generated and transmitted to the display of the smart glasses.

[0722] Input: Coordination suggestion data

[0723] Data Computing: Generate visualizations and send data to the smart glasses using the Requests library.

[0724] Output: Coordinated image displayed on the smart glasses display

[0725] Step 8:

[0726] The server selects recommended items to purchase based on the current coordination information and item data owned by the user.

[0727] Input: Coordination suggestion data, user's item data

[0728] Data calculation: The server selects the best recommended items to purchase.

[0729] Output: Recommended purchase item data

[0730] Step 9:

[0731] Information on recommended purchase items is sent to the smart glasses display and presented to the user.

[0732] Input: Recommended purchase item data

[0733] Data calculation: Use the Requests library to send recommended purchase information to the smart glasses.

[0734] Output: Recommended purchase information displayed on the smart glasses display

[0735] 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.

[0736] This invention relates to a system that acquires and records images of clothing owned by a user, proposes optimal outfits based on the weather, temperature, and purpose information for the day, and further optimizes the proposals by recognizing the user's emotions. Specific embodiments of this system are described below.

[0737] System configuration

[0738] This system uses a server, a user device, and an emotion engine. The user device is a smartphone or PC, and provides an interface for taking and uploading images of clothing and inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions.

[0739] Acquiring and saving clothing data

[0740] The user takes a photo of the clothes

[0741] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, they may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0742] Device-based image processing and tagging

[0743] The device receives the image of the clothing, performs image processing such as background removal and cropping as needed, and displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt, casual."

[0744] Sending and saving data to the server

[0745] The image data processed by the device is sent to the server along with the tag information. The server stores the received image data in a database and manages metadata including the image file path and tag information.

[0746] Coordination suggestions

[0747] User input of conditions

[0748] The user enters the weather, minimum and maximum temperatures for the day, and the purpose of the trip (business casual, leisure, walk, etc.) through the application. For example, the user might enter "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose leisure."

[0749] Emotion engine emotion recognition

[0750] When a user launches the application, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state, for example, by using a camera to analyze whether they are smiling.

[0751] Server-based information processing and coordinate generation

[0752] The server analyzes the weather, temperature, and purpose information received from the user, as well as the emotional data obtained from the emotion engine. It then filters appropriate clothing combinations from the clothing data stored in the database and uses AI to generate optimal outfits. For example, it selects the combination of "T-shirt + jeans + jacket." If the user is expressing positive emotions such as happiness or joy, it will prioritize bright-colored clothing.

[0753] Proposal visualization and display

[0754] The server generates a visualization to display the generated outfits in a visually understandable way. Specifically, it creates an image of a mannequin wearing the outfit and sends it to the terminal. The terminal then displays the outfits presented to the user.

[0755] Suggested purchase items

[0756] Recommended items selected by the server

[0757] The server selects recommended items to purchase based on the user's current outfit information and item data. For example, when recommending new sneakers to match an outfit, it searches for and suggests appropriate items. It also takes into account emotional data and prioritizes displaying items that the user is likely to like.

[0758] Displaying recommended items to users

[0759] The server sends information about the selected recommended items to the terminal, which then displays the recommended items to the user, for example, presenting them as "new sneakers that go well with these jeans."

[0760] Specific examples

[0761] After the user takes a photo of a T-shirt and tags it, the data is sent to the server for storage. Meanwhile, when the user inputs the day's weather information (sunny, maximum 20°C, minimum 15°C) and purpose (leisure), the emotion engine analyzes the user's facial expressions and recognizes positive emotions (e.g., smiling). The server uses AI to generate outfits based on this information and emotion data, such as a "T-shirt + jeans + jacket." The suggested outfits are displayed to the user along with visual images of them being worn by a mannequin. Furthermore, the server selects new sneakers that go well with the outfit as recommended items and displays them to the user. If the user is expressing positive emotions, bright colors and fun designs are prioritized.

[0762] This system allows users to efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in a way that suits their emotions.

[0763] The processing flow will be explained below.

[0764] Step 1:

[0765] The user takes a photo of the clothes.

[0766] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user might think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0767] Step 2:

[0768] The device processes the image.

[0769] The device receives the photographed image of the clothes, performs image processing such as background removal and cropping as necessary to optimize the image, and then temporarily stores the image data.

[0770] Step 3:

[0771] The device performs the tagging.

[0772] The device will prompt the user to enter appropriate tags, such as "T-shirt, casual," and the user will select and enter the tag.

[0773] Step 4:

[0774] The terminal transmits the data to the server.

[0775] The image data for which tagging has been completed on the terminal is transmitted to the server together with the tag information.

[0776] Step 5:

[0777] The server stores the data.

[0778] The server stores the received image data in a database and manages metadata including image file paths and tag information.

[0779] Step 6:

[0780] The user inputs weather and usage information.

[0781] Through the application, users input the weather, minimum and maximum temperatures for the day, and the purpose of use (for example, "sunny, maximum 20 degrees, minimum 15 degrees, purpose: leisure").

[0782] Step 7:

[0783] The terminal sends the input information to the server.

[0784] The terminal transmits the weather information, temperature information, and usage information input by the user to the server.

[0785] Step 8:

[0786] The emotion engine analyzes the user's emotions.

[0787] The device uses facial recognition technology to capture the user's facial expression data and transmits it to the emotion engine, which then analyzes the user's emotions (e.g., joy, sadness, neutrality, etc.).

[0788] Step 9:

[0789] The server analyzes the condition information.

[0790] The server analyzes the weather, temperature, purpose and emotion data received from the user and from the emotion engine.

[0791] Step 10:

[0792] The server generates the coordinates.

[0793] The server uses AI to filter and generate optimal clothing combinations (e.g., "T-shirt + jeans + jacket") from clothing data stored in a database based on the user's emotional state and usage information.

[0794] Step 11:

[0795] The server generates the visualization.

[0796] The server generates an image of a mannequin wearing the proposed outfit in order to visually display the created outfit in an easy-to-understand manner.

[0797] Step 12:

[0798] The server sends the visualization to the device.

[0799] The server transmits the generated visualization and coordination proposal information to the terminal.

[0800] Step 13:

[0801] The device displays the suggestions to the user.

[0802] The device will then present the user with suggested outfits, such as "Leisure outfits suitable for a sunny 20-degree day."

[0803] Step 14:

[0804] The server selects recommended items for purchase.

[0805] The server selects recommended items to purchase (e.g., new sneakers) based on the user's current outfit information and item data. It also takes into account emotional data and prioritizes displaying items that the user is likely to like.

[0806] Step 15:

[0807] The server transmits recommended item information to the terminal.

[0808] The server transmits information about the selected recommended purchase items to the terminal.

[0809] Step 16:

[0810] The device displays the recommended items to the user.

[0811] The device will then display selected purchase recommendations to the user, such as "new sneakers that go well with these jeans."

[0812] This allows users to efficiently select attractive clothing and receive suggestions that are in line with their emotions, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in accordance with their mood and emotions.

[0813] Example 2

[0814] 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."

[0815] In modern life, users spend a lot of time choosing their daily clothing. It takes a lot of experience to coordinate outfits appropriately based on the weather, temperature, and occasion. It's also extremely difficult to coordinate outfits based on the user's emotional state. This often leads to users choosing inappropriate clothing, which can lead to an unpleasant day. It's also difficult to provide users with appropriate purchasing recommendations.

[0816] 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.

[0817] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for acquiring the current day's weather information, minimum and maximum temperatures, and usage information, a means for analyzing the acquired facial expressions and voice to recognize the user's emotional state, a means for using artificial intelligence to suggest appropriate clothing combinations based on this information and the user's emotional state, a means for visually displaying the suggested combinations, and a means for presenting recommended items for purchase. This allows the user to quickly and efficiently find the optimal outfit, and suggestions can be made based on the weather, usage, and emotional state. Furthermore, presenting appropriate recommended items for purchase allows the user to appropriately consider purchasing new items.

[0818] The "means for acquiring and recording images" refers to a system for taking photos of clothing owned by the user and saving the image data.

[0819] "Means for obtaining weather information" refers to a system for obtaining meteorological data such as the weather and temperature of the day.

[0820] "Means for acquiring usage information" refers to a system that allows users to input the purpose and situation for wearing the clothing and acquire that information.

[0821] "Means for recognizing emotional state" refers to technology that analyzes the user's facial expressions and voice to determine their current emotions.

[0822] "Means for suggesting using artificial intelligence" refers to a mechanism that utilizes artificial intelligence to generate optimal clothing combinations based on acquired information and perceived emotional state.

[0823] "Visual display means" refers to a technique that visually displays the suggested clothing combinations in a way that is easy for the user to understand.

[0824] "Means for suggesting recommended items for purchase" refers to a system that suggests new clothing and accessories suitable for coordinating with users.

[0825] The "means of tagging and recording" is a system that allows users to tag their clothing to indicate the type, usage situation, etc., and save that information.

[0826] "Data-based suggestion means" refers to technology that determines and suggests appropriate clothing combinations based on stored tagged data.

[0827] "Means for displaying a visual representation on a mannequin" refers to technology that displays the proposed clothing combination as if it were being worn by a virtual mannequin.

[0828] "Means for recommending based on user preferences" refers to technology that recommends new clothing items by taking into account the user's past choices and preferences.

[0829] "Means for suggesting based on user input" refers to technology that generates appropriate clothing combinations based on direct user input information.

[0830] The "means for transmitting and displaying on the user's terminal" refers to a mechanism for transmitting the generated combination information and data on recommended purchase items to the user's terminal and displaying them.

[0831] This invention relates to a system that acquires and records images of clothing owned by a user, suggests optimal outfits based on the day's weather information, temperature information, and usage information, and further optimizes suggestions by recognizing the user's emotions.

[0832] System configuration

[0833] This system uses a server, a user device, and an emotion engine. The user device is a smartphone or PC, and provides an interface for taking and uploading images of clothing and inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions.

[0834] System Operation

[0835] 1. Acquiring and saving clothing data

[0836] A user uses the smartphone camera to take pictures of various types of clothing they own. For example, if they want to upload a T-shirt, they take a picture of the T-shirt with their smartphone. The device receives the image and performs image processing such as background removal and cropping. It also prompts the user to enter tags, and the user enters appropriate tags such as "T-shirt" or "casual." The device then sends the processed image data along with the tag information to the server. The server stores the received data in a database and manages the image file path and tag information as metadata.

[0837] 2. Coordination suggestions

[0838] The user enters the day's weather (e.g., sunny, cloudy, rainy), minimum and maximum temperatures, and intended use (e.g., business casual, leisure, walk) through a smartphone app. Specifically, the user enters "sunny, maximum temperature 20°C, minimum temperature 15°C, intended use leisure." When the user launches the application, the emotion engine analyzes the user's facial expressions and voice through the camera and identifies their current emotion as "positive" or "negative." The server then performs an integrated analysis of the weather, temperature, and intended use information received from the user, along with the emotional data obtained from the emotion engine. The server then filters appropriate clothing combinations from the clothing data stored in the database and generates an optimal outfit using a generative AI model. For example, a T-shirt, jeans, and jacket outfit is selected. If the user indicates a positive emotion, bright-colored items are prioritized. The server generates an image of a mannequin wearing the proposed outfit to visually display it and sends it to the device. The device then displays the proposed outfit to the user.

[0839] 3. Recommended purchase items

[0840] The server analyzes the generated outfit and the user's item data to select the necessary items (such as new sneakers). It also takes into account emotional data and recommends items that the user is likely to like. The server then sends information about recommended items to the device, which then displays this to the user in the form of, for example, "New sneakers that go well with these jeans."

[0841] Specific examples

[0842] After the user takes a photo of a T-shirt and tags it as "T-shirt" and "casual," the device sends the image data to the server, which stores the image and tag information in a database. At the same time, the user enters weather information ("sunny, maximum 20 degrees, minimum 15 degrees") and purpose information ("leisure") into the app. The emotion engine analyzes the user's facial expression and recognizes "positive" (smiling). Based on these conditions, the server uses an AI model to generate a "T-shirt + jeans + jacket" outfit and displays it as a visualization. The server also selects recommended items, such as new sneakers, and presents them to the user. An example prompt is shown below.

[0843] "A user uploaded a T-shirt and entered 'casual' and 'T-shirt' in the tags. Then, the user entered 'sunny' for the weather on the day, a maximum temperature of '20 degrees', a minimum temperature of '15 degrees', and 'leisure' as the purpose. The user was recognized as smiling. Please suggest the best outfit based on these conditions."

[0844] This system will significantly reduce the effort required for users to choose their daily fashion and allow them to enjoy the best possible coordination that suits their emotions and the situation.

[0845] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0846] Step 1:

[0847] A user takes a photo of clothing they own with their smartphone camera. Specifically, the user wants to upload a T-shirt, so they take a photo of the T-shirt. The input is the image taken with the smartphone, and the output is image data of the clothing.

[0848] Step 2:

[0849] The device receives the captured image and performs image processing such as background removal and cropping. The input is the captured image, and the output is the cropped image data with the background removed. The device displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt" or "casual." The input is the user's tagging information, and the output is the tagged image data.

[0850] Step 3:

[0851] The device sends the processed image data and tag information to the server. The input is tagged image data, and the output is the completed transmission to the server. The server saves the received image data in a database and manages the image file path and tag information as metadata. The input is tagged image data, and the output is the data saved in the database.

[0852] Step 4:

[0853] The user inputs the weather, minimum and maximum temperatures, and purpose of use for the day through a smartphone app. For example, they might input "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose of use leisure." The input is information such as the weather, temperature, and purpose entered by the user, and the output is data related to these conditions.

[0854] Step 5:

[0855] When a user launches the application, the emotion engine analyzes the user's facial expressions and voice through the camera to recognize their current emotional state. For example, it recognizes a user's smile and determines that it is a positive emotion. The input is the user's facial expressions and voice, and the output is the recognized emotional data.

[0856] Step 6:

[0857] The server analyzes the weather, temperature, and purpose information received from the user, as well as the emotion data obtained from the emotion engine. The input is weather, temperature, purpose, and emotion data, and the output is the analysis result obtained by integrating this information. Then, appropriate clothing combinations are filtered from the clothing data stored in the database. The optimal outfit is generated using a generative AI model. For example, a combination of T-shirt + jeans + jacket is selected. The input is the filtering conditions (weather, temperature, purpose, emotion data), and the output is the generated outfit data.

[0858] Step 7:

[0859] The server generates a visualization to display the proposed outfits in a visually easy-to-understand manner. Specifically, it creates an image of a mannequin wearing the proposed outfit and sends it to the terminal. The input is the generated outfit data, and the output is the visualization image. The terminal displays the proposed outfit to the user. The input is the visualization image, and the output is the outfit image displayed to the user.

[0860] Step 8:

[0861] The server analyzes the generated coordination information and the user's owned item data. The input is the coordination information and owned item data, and the output is the analysis results. Based on the analysis results, the server selects the necessary items (e.g., new sneakers). The input is the analysis results, and the output is the selected recommended purchase items. It also takes into account emotional data and recommends items that the user is likely to like. The input is emotional data, and the output is recommended purchase items based on emotions.

[0862] Step 9:

[0863] The server sends information about the selected recommended purchase items to the terminal. The input is the selected recommended purchase item information, and the output is the completion of transmission to the terminal. The terminal presents the recommended purchase item to the user, such as "new sneakers that go well with these jeans." The input is the recommended purchase item information sent from the server, and the output is the recommended purchase item information displayed to the user.

[0864] (Application example 2)

[0865] 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."

[0866] Conventional outfit suggestion systems have difficulty making optimal suggestions based on the user's emotions and real-time circumstances. Furthermore, users have limited means of directly visually confirming their outfits, which means there is a lack of consistency between the suggested outfits and the actual outfits or purchases they make. Furthermore, there is also the problem that the suggested items do not adequately reflect the user's emotions, tastes, and preferences.

[0867] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing and analyzing the user's emotions, means for visualizing and displaying suggested outfits on the smart glasses, and means for displaying recommended items in cooperation with a virtual store. This allows the optimal outfit suggestions to be made based on the user's emotions and real-time situation, and the suggestions can be visually confirmed, enabling a more integrated outfit and purchasing experience.

[0868] "Clothing image acquisition means" refers to devices and software that allow users to photograph and record their own clothing and acquire image data.

[0869] "Weather information acquisition means" refers to a device or software for acquiring information about the weather and temperature of the day.

[0870] "Use information acquisition means" refers to a device or software that acquires the situation and purpose (business, leisure, etc.) in which the clothing will be used.

[0871] A "clothing combination suggestion means" is a device or software that suggests clothing combinations suitable for the weather and purpose based on clothing data held by the user.

[0872] A "visual display means" is a device or software that visually presents suggested clothing combinations to a user.

[0873] The "means for presenting recommended items for purchase" is a device or software that recommends new items that go well with the proposed outfit to the user.

[0874] "Emotion recognition means" refers to devices or software that analyze a user's facial expressions and voice and recognize their emotional state.

[0875] "Smart glasses" are wearable devices that allow users to obtain information visually by wearing them.

[0876] A "visualization means" is a device or software that displays suggested outfits in the user's field of vision in real time.

[0877] A "virtual store" is a virtual store system where you can browse and purchase products online.

[0878] "Artificial intelligence" refers to algorithms and software that can analyze data and make decisions automatically.

[0879] This invention is a system that acquires and records images of clothing owned by a user and suggests optimal outfits based on weather information, temperature information, usage information, and emotional data. Furthermore, it has the function of visually displaying the suggested outfits using smart glasses and presenting recommended items for purchase. A specific embodiment of this system will be described below.

[0880] System configuration

[0881] The system's main components are a server, a user device (smartphone or PC), smart glasses, and an emotion engine. The user device takes and uploads images of clothing and provides an interface for inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions. The smart glasses function as a device for visualizing suggested outfits in real time.

[0882] Acquiring and saving clothing data

[0883] The user takes a photo of the clothes

[0884] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user might think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0885] Device-based image processing and tagging

[0886] The device receives the image of the clothing and performs image processing such as background removal and cropping. Using OCR technology, it automatically tags the clothing and prompts the user to confirm the appropriate tag (e.g., "T-shirt, casual").

[0887] Sending and saving data to the server

[0888] The image data processed on the device is sent to the server along with the tag information. The server stores the received image data in a database and manages metadata including the image file path and tag information. MySQL or MongoDB are suitable databases.

[0889] Coordination suggestions

[0890] User input of conditions

[0891] Through the application, users input the weather for the day, the minimum and maximum temperatures, and the purpose of the outfit (business casual, leisure, walking, etc.).

[0892] Emotion engine emotion recognition

[0893] When a user launches the application, it uses an emotion engine (e.g., AWS Rekognition, IBM Watson) to analyze the user's facial expressions and voice to recognize their current emotional state. It also uses a camera to analyze facial expressions.

[0894] Server-based information processing and coordinate generation

[0895] The server analyzes the weather, temperature, and usage information received from the user, as well as the emotion data obtained from the emotion engine. It then uses an AI model (e.g., TensorFlow, PyTorch) to generate the optimal outfit. For example, it selects the combination of "T-shirt + jeans + jacket." If the user expresses positive emotions, it will prioritize bright-colored clothing.

[0896] Proposal visualization and display

[0897] The server then generates an image of the outfit being worn by a mannequin to visually display it in a way that is easy to understand. This image data is then sent to the smart glasses, which display it in real time to the user's field of vision.

[0898] Suggested purchase items

[0899] Recommended items selected by the server

[0900] The server works with the virtual store to select recommended items based on the user's current outfit information and item data. Recommended items are prioritized based on the user's emotional data and prioritize items that match the outfit.

[0901] Displaying recommended items to users

[0902] The server sends the information of the selected recommended items to the smart glasses, and displays appropriate recommended items to the user, such as "new sneakers that go well with these jeans."

[0903] Examples of concrete examples and prompts

[0904] As a concrete example, we will explain how a user takes a photo of a T-shirt, tags it, and then sends the data to a server for storage. When the user inputs the day's weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure), the emotion engine analyzes the user's facial expressions and recognizes positive emotions (e.g., smiling). Based on this information, the server generates a "T-shirt + jeans + jacket" outfit and visualizes it on the smart glasses. Furthermore, the server connects with a virtual store to recommend new sneakers that go well with this outfit and displays them to the user.

[0905] An example of a prompt is as follows:

[0906] "The system removes the background from images of clothing taken by the user and automatically tags them. Based on weather, temperature, usage information, and the user's emotional data, it generates optimal outfits and visualizes them on the smart glasses display. This uses computer vision and emotion recognition."

[0907] This system allows users to efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in a way that suits their emotions.

[0908] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0909] Program processing flow

[0910] Step 1: Image capture and tagging of clothing

[0911] The device receives an image of clothing taken by the user. The image undergoes image processing such as background removal and cropping, and is automatically tagged using OCR technology. The tag information and image data are sent from the device to the server. The input is the "image of clothing," and the output is the "background-removed image and tag information."

[0912] Step 2: Obtaining weather information, temperature information, and usage information

[0913] The user enters the day's weather information, minimum and maximum temperatures, and usage information through the application. The input is "weather information, temperature information, usage information," and the output is "weather information, temperature information, usage information data." This allows the server to obtain the environmental data necessary for analysis.

[0914] Step 3: Acquire and analyze emotion data

[0915] When a user launches the application, the device (smart glasses or smartphone) captures the user's facial expressions and voice using a camera and microphone. This data is sent to the emotion engine, which analyzes the user's emotional state. The input is "facial expression and voice data," and the output is "emotion data."

[0916] Step 4: Generate coordinates

[0917] The server uses an AI model (such as TensorFlow or PyTorch) to analyze weather information, temperature information, usage information, emotional data, and clothing data stored in a database, and generates an appropriate outfit. The input is "weather information, temperature information, usage information, emotional data, clothing data," and the output is "optimal outfit information."

[0918] Step 5: Visualization and visualization

[0919] The server generates a visual representation of the outfit on a mannequin based on the coordinated outfit information. The generated image is sent to the smart glasses, allowing the user to view it in real time. The input is "coordinated outfit information," and the output is a "visualized image."

[0920] Step 6: Select recommended items to purchase

[0921] The server analyzes the user's outfit information and owned item data to select recommended items for purchase. It connects with the virtual store to obtain related product data. The input is "outfit information and owned item data," and the output is "a list of recommended items for purchase."

[0922] Step 7: View recommended items to buy

[0923] The server sends the selected recommended items to the smart glasses, and the user can purchase the items through a purchase link. The input is a "list of recommended items to purchase," and the output is a "purchase link and item information."

[0924] Specific examples and prompts

[0925] Examples:

[0926] The user takes a photo of a T-shirt through the smart glasses, and the image with the background removed is sent to the server. The user then enters weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure) into the application. At the same time, the emotion engine analyzes the user's positive emotion (smile). Based on this information, the server generates a "T-shirt + jeans + jacket" outfit and displays a visualized image on the smart glasses. In addition, recommended items for new sneakers are presented, which the user can review and purchase.

[0927] Examples of prompts:

[0928] "The system removes the background from images of clothing taken by the user and automatically tags them. Based on weather, temperature, usage information, and the user's emotional data, it generates optimal outfits and visualizes them on the smart glasses display. This uses computer vision and emotion recognition."

[0929] 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.

[0930] 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.

[0931] 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.

[0932] [Third embodiment]

[0933] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0934] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0935] 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).

[0936] 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.

[0937] 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.

[0938] 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).

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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."

[0945] The present invention relates to a system that improves the efficiency of clothing selection and enables users to appropriately select trendy clothing coordinations every day. Specific embodiments of this system will be described below.

[0946] System configuration

[0947] The system uses a server, user devices, and AI (artificial intelligence). User devices are smartphones or PCs, and provide an interface for users to take and upload images of clothes and input weather, temperature, and usage information. The server stores and analyzes the image data, suggests outfits, and selects recommended items for purchase. The AI ​​is also responsible for automatically generating outfit suggestions based on the clothing data and input information.

[0948] Acquiring and saving clothing data

[0949] Users take photos of their clothes

[0950] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, they may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0951] Device-based image processing and tagging

[0952] The device receives the photographed image of the clothes and performs image processing such as background removal and cropping as necessary. It also displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt, casual." This tagging allows the data to be used efficiently in subsequent outfit suggestions.

[0953] Sending and saving data to the server

[0954] The image data processed by the terminal is sent to the server along with the tag information. The server stores the received image data in a database and manages the image file path and tag information appropriately.

[0955] Coordination suggestions

[0956] User input of conditions

[0957] The user enters the weather, minimum and maximum temperatures for the day, and the purpose of the trip (business casual, leisure, walk, etc.) through the application. For example, the user might enter "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose leisure."

[0958] Server-based information processing and coordinate generation

[0959] The server analyzes the weather, temperature, and purpose information received from the user and sets appropriate condition filters. Next, it filters appropriate clothing combinations from clothing data stored in a database and uses AI to generate optimal outfits. For example, it selects the combination of "T-shirt + jeans + jacket."

[0960] Proposal visualization and display

[0961] The server generates a visualization to display the generated outfits to the user in a visually understandable way. Specifically, it generates an image of a mannequin wearing the outfits presented, and sends that image and other information to the terminal. The terminal then displays the outfits presented to the user.

[0962] Suggested purchase items

[0963] Recommended items selected by the server

[0964] The server selects recommended items to purchase based on the user's current outfit information and item data. For example, if the server is recommending new sneakers to match an outfit, it will search for and recommend appropriate items.

[0965] Displaying recommended items to users

[0966] The server sends information about the selected recommended items to the terminal, which then displays the recommended items to the user, for example, presenting them as "new sneakers that go well with these jeans."

[0967] Specific examples

[0968] After the user takes a photo of a T-shirt and tags it, the data is sent to the server for storage. The user then inputs the day's weather information (sunny, maximum 20°C, minimum 15°C) and purpose (leisure), and the server uses AI to generate a "T-shirt + jeans + jacket" outfit based on this information. The suggested outfit is displayed to the user along with a visual image of the outfit, as if worn by a mannequin. The server also recommends new sneakers to go with the outfit and displays them to the user.

[0969] In this way, users can efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life. With this system, even those who are not good at fashion can confidently enjoy wearing clothes that are in line with the latest trends.

[0970] The processing flow will be explained below.

[0971] Step 1:

[0972] The user takes a photo of the clothes.

[0973] Users take photos of various clothes they own (such as T-shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[0974] Step 2:

[0975] The device processes the image.

[0976] The device receives the photographed image of the clothes and performs image processing such as background removal and trimming as necessary. For example, it automatically cuts out unnecessary background parts of the image.

[0977] Step 3:

[0978] The device performs the tagging.

[0979] The device will prompt the user to enter appropriate tags, such as "T-shirt, casual," and the user will select and enter the tag.

[0980] Step 4:

[0981] The terminal transmits the data to the server.

[0982] Once tagging is complete on the device, the image data is sent to the server, including the tag information.

[0983] Step 5:

[0984] The server stores the data.

[0985] The server stores the received image data in a database, along with metadata including the image file path and tag information.

[0986] Step 6:

[0987] The user inputs the weather and purpose.

[0988] The user inputs the weather, minimum and maximum temperatures for the day, and the purpose of use (e.g., "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose: leisure") through the application.

[0989] Step 7:

[0990] The terminal sends the input information to the server.

[0991] The terminal transmits the weather information, temperature information, and usage information input by the user to the server.

[0992] Step 8:

[0993] The server analyzes the information and generates coordinates.

[0994] The server analyzes the weather, temperature, and purpose information received from the user, then filters the clothing data stored in the database to find suitable clothing combinations and uses AI to generate the optimal outfit.

[0995] Step 9:

[0996] The server generates the visualization.

[0997] The server generates a visualization to display the generated outfits in a visually understandable way, specifically creating an image of a mannequin wearing the outfits presented.

[0998] Step 10:

[0999] The server sends the visualization to the device.

[1000] The server transmits the generated visualization and coordination proposal information to the terminal.

[1001] Step 11:

[1002] The device displays the suggestions to the user.

[1003] The device will then display suggested outfits to the user, such as "leisure outfits suitable for a sunny 20-degree day."

[1004] Step 12:

[1005] The server selects recommended items for purchase.

[1006] The server selects appropriate recommended items to purchase based on the user's outfit information and item data, such as new sneakers that go well with the outfit.

[1007] Step 13:

[1008] The server transmits recommended item information to the terminal.

[1009] The server transmits information about the selected recommended purchase items to the terminal.

[1010] Step 14:

[1011] The device displays the recommended items to the user.

[1012] The device will then display selected purchase recommendations to the user, such as "New sneakers to go with these jeans."

[1013] Example 1

[1014] 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."

[1015] In modern life, fashion coordination is a daily challenge for many people, and choosing appropriate clothing based on trends, seasons, and occasions can be particularly challenging. Furthermore, there is a growing demand for systems that effectively utilize existing clothing and provide comprehensive fashion advice, including the selection of new items to purchase. However, existing systems struggle to reduce the user's workload and make efficient and accurate fashion suggestions. The purpose of this invention is to provide a system that solves these problems and allows users to easily and effectively enjoy daily coordination.

[1016] 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.

[1017] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for tagging and recording the clothing images based on user input, a means for proposing appropriate clothing combinations based on the tagged image data and taking into account weather information, temperature information, and usage information, a means for visually displaying the proposed combinations, and a means for presenting recommended purchase items for the proposed combinations. This allows users to efficiently manage their clothing and automatically receive recommendations for optimal coordination based on trends and usage. Furthermore, new items to purchase are also recommended, reducing the burden of fashion in general.

[1018] "Clothing" refers to clothing to be worn, such as shirts, pants, jackets, etc.

[1019] "Means for acquiring and recording images" refers to devices and functions that allow users to digitally save and manage images of clothing taken with a smartphone or digital camera.

[1020] "Means for tagging and recording images of clothing based on user input" refers to devices or functions that allow users to add descriptions such as category and use to images of clothing and store them in a database.

[1021] "Means for proposing appropriate clothing combinations based on tagged image data, taking into consideration weather information, temperature information, and usage information" refers to devices and technologies that analyze stored tagged image data and weather information, temperature information, and usage information acquired in real time, and automatically select the optimal clothing combinations based on this information.

[1022] "Means for visually displaying suggested combinations" refers to devices or functions that use images or graphics to clearly present optimal clothing combinations to users.

[1023] The "means for presenting recommended items for purchase" refers to a device or function that can recommend new items to the user to purchase to go with the proposed outfit and display detailed information about them.

[1024] "Means of utilizing artificial intelligence" refers to systems and algorithms that use technologies such as machine learning and deep learning to suggest clothing combinations.

[1025] "Means for transmitting and displaying on a user's device" refers to the equipment or functions for transmitting the generated coordination information and information on recommended purchase items to a user's device (smartphone, tablet, PC, etc.) via the Internet or other communication means and displaying it.

[1026] This invention relates to a system that supports users in efficiently coordinating their daily fashion. The system is composed of a server, a user terminal, and artificial intelligence (AI).

[1027] System configuration

[1028] The basic components of this system are as follows:

[1029] User device (smartphone, PC)

[1030] Servers (high-performance database servers, GPU servers)

[1031] AI model (generative AI model)

[1032] Acquiring clothing data

[1033] User-generated photos of clothing

[1034] The user uses a smartphone or digital camera to take a picture of the clothes they own. For example, they perform a specific action such as "taking a picture of their favorite red T-shirt."

[1035] Uploading images using a device

[1036] Users upload the images they take to the device through an application on their smartphone or computer. Once the images are uploaded, the device prompts the user to add tags. They can enter tags such as "T-shirt, casual."

[1037] Image processing and analysis

[1038] Image processing by device

[1039] The device performs image processing on the uploaded image, such as background removal and cropping. Specifically, for example, background removal is performed using the OpenCV library. The corrected image is then displayed and the user can tag it.

[1040] Sending data to the server

[1041] The image data and tag information processed on the device are sent to the server, which stores the received data in a database and manages the image file paths and tag information.

[1042] Coordination suggestions

[1043] User input of conditions

[1044] The user enters the weather forecast for the day, the minimum and maximum temperatures, and the purpose of the trip (e.g., business casual, leisure, walk) through the application. For example, "Sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure."

[1045] Server-based information analysis and coordinate generation

[1046] The server analyzes the data by setting appropriate filters based on the input weather, temperature, and usage information. The server then uses an AI model (e.g., a generative AI model built using TensorFlow) to automatically generate optimal clothing combinations. For example, it suggests a combination such as "T-shirt + jeans + jacket."

[1047] Visualization and Display

[1048] Server-generated visuals

[1049] To visually display the generated outfit, the server uses a generative model (e.g., Generative Adversarial Networks) to generate an image of the outfit as it would appear on a mannequin.

[1050] Sending and displaying data to the device

[1051] The generated visualization is sent from the server to the terminal, which then displays the suggested outfits along with the images to the user.

[1052] Suggested purchase items

[1053] Recommended items selected by the server

[1054] The server selects new items to purchase based on the user's current outfit information and item data. For example, it searches for and selects "new sneakers that go well with these jeans."

[1055] Displaying recommended items on your device

[1056] Information on the selected recommended items is sent from the server to the device, and the device presents the information to the user, for example, displaying "new sneakers that go well with these jeans."

[1057] Prompt Sentence Examples

[1058] Here is an example of an input prompt for a generative AI model:

[1059] "Today's weather is sunny, with a high of 20 degrees and a low of 15 degrees. I'm looking for an outfit for a leisure trip. Please suggest the best outfit based on the photos and tag information of the clothes I own."

[1060] This system allows users to efficiently find attractive fashion coordinations and also provides suggestions for new items to purchase, easing the burden of fashion selection.

[1061] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1062] Step 1:

[1063] User-generated photos of clothing

[1064] A user takes a photo of various clothes (shirts, pants, jackets, etc.) they own using a smartphone or camera. For example, they think, "I want to take a photo of this red T-shirt," and take a photo of the T-shirt with their camera. The input is the actual clothes, and the output is a digital image of the clothes. This image is used for subsequent analysis.

[1065] Step 2:

[1066] Device image upload and tagging

[1067] Users upload captured images through an application on their device (smartphone or PC). The device automatically processes the image, such as removing the background and cropping, and prompts the user to enter tags. For example, the user might tag the image as "T-shirt, casual." The input is the captured image, and the output is the processed image and its tag information. This data is sent to the server.

[1068] Step 3:

[1069] Sending data to the server

[1070] The image data and tag information sent from the device arrive at the server. The server stores this data in a database. For example, it records the image file path and associates it with the tag information. The input is the processed image and tag information, and the output is information managed in the database. By managing this data appropriately, it can be used to propose outfits later.

[1071] Step 4:

[1072] User input of conditions

[1073] Through the application, users can input the weather information for the day, the minimum and maximum temperatures, and the purpose of the trip (e.g., business casual, leisure, walk). For example, they can input "sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure." The input is the weather and purpose information, and the output is the search criteria set using that information.

[1074] Step 5:

[1075] Server-based information analysis and filter settings

[1076] The server analyzes the input information on weather, temperature, and purpose, and sets the necessary condition filters based on that information. For example, filtering clothes that match sunny weather and temperatures between 15 and 20 degrees. The input is weather, temperature, and purpose information, and the output is a list of clothes that meet the filter conditions.

[1077] Step 6:

[1078] Coordination generation using AI

[1079] The server analyzes the clothing data that matches the filter criteria using an AI model (e.g., a generative AI model using TensorFlow) and suggests optimal outfits. For example, it generates a combination of "T-shirt + jeans + jacket." The input is the filtered clothing list, and the output is the suggested outfit information.

[1080] Step 7:

[1081] Server-generated visuals

[1082] The server uses a generative model (e.g., GAN) to create an image of a mannequin wearing the generated outfit to visually display it. The input is the outfit information, and the output is the visualized image.

[1083] Step 8:

[1084] Sending and displaying data to the device

[1085] The generated visualization is sent from the server to the device, which then displays this image to the user. For example, an image of a mannequin wearing the proposed outfit is displayed on a smartphone screen. The input is the generated visual data, and the output is the visual information displayed on the user's screen.

[1086] Step 9:

[1087] Server selection of recommended items for purchase

[1088] The server analyzes the user's current outfit information and the user's owned item data to select new items to purchase. For example, it selects "new sneakers that go well with these jeans." The input is the outfit information and the owned item data, and the output is a list of recommended items to purchase.

[1089] Step 10:

[1090] Displaying recommended items on your device

[1091] The server sends the selected recommended items to the device, which then displays them to the user. For example, a suggestion such as "new sneakers that go well with these jeans" is displayed to the user. The input is the recommended item information, and the output is the recommended items displayed on the user's screen.

[1092] (Application example 1)

[1093] 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."

[1094] It is difficult for users to efficiently utilize the clothes they have on hand and choose the optimal outfit for the weather and occasion, especially when they are short on time or lack confidence in fashion. Even in brick-and-mortar stores, there is no system that can provide real-time outfit suggestions when customers are considering a purchase, which prevents them from fully motivating them to buy. Furthermore, there is a lack of efficient fashion support systems that use new interfaces such as smart glasses.

[1095] 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.

[1096] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for acquiring weather information for the day, minimum and maximum temperatures, and usage information, a means for proposing appropriate clothing combinations based on this information, a means for visually displaying the proposed combinations, a means for presenting recommended items for purchase, a means for acquiring images of clothing using a camera in the smart glasses, a means for transmitting the acquired image data and user information to the server and receiving outfit suggestions, and a means for displaying the suggested outfits on the display of the smart glasses. This allows users to efficiently select their daily fashion and receive outfit suggestions in real time, even in physical stores. Furthermore, the server can recommend new items to purchase based on the user's tastes and preferences, thereby increasing purchasing motivation.

[1097] - "Clothing" refers to clothing worn by people in daily life or for specific purposes.

[1098] "Image acquisition means" refers to a device or function that takes an image of clothing and records it as digital data.

[1099] "Weather information" refers to meteorological data such as the weather conditions and temperature of the day.

[1100] "Use information" refers to information about the purpose and scene of the clothing to be used on the day.

[1101] The "coordination suggestion means" is a function that automatically selects the optimal clothing combination based on acquired image data, weather, and purpose information.

[1102] "Visual display means" refers to a device or function for visually presenting suggested clothing combinations to a user.

[1103] The "means for presenting recommended items for purchase" is a function that provides information on recommended new products for purchase based on the clothing data and coordination held by the user.

[1104] "Smart glasses" are eyeglass-type devices that have built-in cameras and displays, allowing users to obtain visual information in real time.

[1105] A "camera" is a device that takes still images and videos and records them as digital data.

[1106] A "display" is a device for visually displaying digital data.

[1107] A "server" is a computer system that stores, analyzes, and processes data over a network.

[1108] "User Information" means personal data, settings, preferences, and other information provided by Users.

[1109] This invention relates to a system that acquires and records images of clothes that a user has on hand, and suggests optimal clothing combinations based on weather information and usage information. In particular, by using smart glasses, clothing suggestions can be realized in real time. The following describes in detail the embodiments of this invention.

[1110] System configuration

[1111] 1. Hardware

[1112] Smart glasses: Equipped with a camera and a display, they can acquire and display information according to the user's line of sight.

[1113] Server: A computer system used to store data, analyze data, and generate proposals over a network.

[1114] User device: A device used by a user to input and view information, such as a smartphone, tablet, or computer.

[1115] 2. Software

[1116] OpenCV: A library for acquiring and processing images from the smart glasses camera.

[1117] Requests: A library for exchanging data with a server via HTTP communication.

[1118] PIL (Python Imaging Library): A library for displaying suggested coordinate images.

[1119] Generative AI model: An artificial intelligence (AI) that suggests optimal outfits based on user input.

[1120] Acquiring and saving clothing data

[1121] Users take photos of their clothes

[1122] The user takes a photo of their own clothing using the camera in the smart glasses, which then acquires digital image data of the clothing. For example, they take a photo of a T-shirt and upload the image to the system.

[1123] Image processing and tagging with smart glasses

[1124] The smart glasses terminal receives the photographed image of the clothes and performs necessary image processing such as background removal and cropping. The user is then prompted to enter appropriate tags such as "T-shirt" or "casual."

[1125] Sending and saving data to the server

[1126] The image data processed by the smart glasses terminal is sent to the server along with the tag information. The server stores this data in a database and properly manages the image file path and tag information.

[1127] Coordination suggestions

[1128] User input of conditions

[1129] The user inputs the weather, minimum and maximum temperatures, and the purpose of the day (business casual or leisure) through smart glasses or a device. For example, the user can input "sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure."

[1130] Server-based information processing and coordinate generation

[1131] The server analyzes the weather, temperature, and purpose information received from the user and sets appropriate condition filters. Next, it filters appropriate clothing combinations from clothing data stored in a database and generates optimal outfits using a generative AI model. For example, it selects the combination of "T-shirt + jeans + jacket."

[1132] Displaying suggestions through smart glasses

[1133] The server generates a visualization of the generated outfit to display it to the user in a visually understandable way, and sends it to the smart glasses. The user can then check the proposed outfit through the smart glasses' display.

[1134] Suggested purchase items

[1135] Recommended items selected by the server

[1136] The server selects recommended items to purchase based on the current outfit information and the user's item data, for example, recommending new sneakers that go well with the outfit.

[1137] Displaying recommended items using smart glasses

[1138] Information on recommended items for purchase is presented to the user through the smart glasses display.

[1139] Examples of concrete examples and prompts

[1140] For example, a user takes a photo of a T-shirt, tags it, and then sends the data to a server for storage. The user then inputs the day's weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure), and the server uses a generative AI model to generate a "T-shirt + jeans + jacket" outfit. The suggested outfit is then displayed to the user through the smart glasses' display. The server then recommends new sneakers to go with the outfit and displays them to the user.

[1141] Example prompt sentence:

[1142] "Please suggest an outfit that includes the T-shirt the user photographed. The weather information is 'Sunny, maximum 20°C, minimum 15°C', and the purpose is 'Leisure.'"

[1143] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1144] Step 1:

[1145] A user puts on the smart glasses and takes an image of their clothing.

[1146] Input: Actual clothing

[1147] Data processing: Using the camera in the smart glasses, images of the clothing are captured as digital data.

[1148] Output: Digital image data of clothing

[1149] Step 2:

[1150] The device (smart glasses) processes the image data of the clothing, removes the background, crops the image, and displays a tagging prompt for the user to add tags.

[1151] Input: Digital image data of clothing

[1152] Data processing: Image processing is performed using OpenCV to remove background and crop. A tagging prompt is displayed so that the user can enter appropriate tags, such as "T-shirt" or "casual."

[1153] Output: Processed image data and tag information

[1154] Step 3:

[1155] The terminal transmits the processed image data and tag information to the server.

[1156] Input: Processed image data and tag information

[1157] Data calculation: Use the Requests library to send data to the server via HTTP.

[1158] Output: Image data and tag information are saved on the server.

[1159] Step 4:

[1160] The user uses smart glasses or other devices to input weather information, minimum and maximum temperatures, and usage information.

[1161] Input: Today's weather information, minimum and maximum temperatures, usage information

[1162] Data processing: The user manually enters the information.

[1163] Output: Weather information, minimum and maximum temperatures, usage information

[1164] Step 5:

[1165] The server analyzes the weather information, minimum and maximum temperatures, and usage information received from the user and sets appropriate condition filters.

[1166] Input: Weather information, minimum and maximum temperatures, usage information

[1167] Data calculation: The server sets condition filters and matches them with clothing data in the database.

[1168] Output: Clothing data that matches the conditions

[1169] Step 6:

[1170] The server uses a generative AI model to generate the optimal outfit based on clothing data that matches the conditions.

[1171] Input: Clothing data that matches the conditions

[1172] Data calculation: The generative AI model suggests optimal outfits based on clothing data and user input.

[1173] Output: Coordination suggestion data

[1174] Step 7:

[1175] A visualization for visually displaying the coordination proposal generated by the server is generated and transmitted to the display of the smart glasses.

[1176] Input: Coordination suggestion data

[1177] Data Computing: Generate visualizations and send data to the smart glasses using the Requests library.

[1178] Output: Coordinated image displayed on the smart glasses display

[1179] Step 8:

[1180] The server selects recommended items to purchase based on the current coordination information and item data owned by the user.

[1181] Input: Coordination suggestion data, user's item data

[1182] Data calculation: The server selects the best recommended items to purchase.

[1183] Output: Recommended purchase item data

[1184] Step 9:

[1185] Information on recommended purchase items is sent to the smart glasses display and presented to the user.

[1186] Input: Recommended purchase item data

[1187] Data calculation: Use the Requests library to send recommended purchase information to the smart glasses.

[1188] Output: Recommended purchase information displayed on the smart glasses display

[1189] 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.

[1190] This invention relates to a system that acquires and records images of clothing owned by a user, proposes optimal outfits based on the weather, temperature, and purpose information for the day, and further optimizes the proposals by recognizing the user's emotions. Specific embodiments of this system are described below.

[1191] System configuration

[1192] This system uses a server, a user device, and an emotion engine. The user device is a smartphone or PC, and provides an interface for taking and uploading images of clothing and inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions.

[1193] Acquiring and saving clothing data

[1194] The user takes a photo of the clothes

[1195] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, they may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[1196] Device-based image processing and tagging

[1197] The device receives the image of the clothing, performs image processing such as background removal and cropping as needed, and displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt, casual."

[1198] Sending and saving data to the server

[1199] The image data processed by the device is sent to the server along with the tag information. The server stores the received image data in a database and manages metadata including the image file path and tag information.

[1200] Coordination suggestions

[1201] User input of conditions

[1202] The user enters the weather, minimum and maximum temperatures for the day, and the purpose of the trip (business casual, leisure, walk, etc.) through the application. For example, the user might enter "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose leisure."

[1203] Emotion engine emotion recognition

[1204] When a user launches the application, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state, for example, by using a camera to analyze whether they are smiling.

[1205] Server-based information processing and coordinate generation

[1206] The server analyzes the weather, temperature, and purpose information received from the user, as well as the emotional data obtained from the emotion engine. It then filters appropriate clothing combinations from the clothing data stored in the database and uses AI to generate optimal outfits. For example, it selects the combination of "T-shirt + jeans + jacket." If the user is expressing positive emotions such as happiness or joy, it will prioritize bright-colored clothing.

[1207] Proposal visualization and display

[1208] The server generates a visualization to display the generated outfits in a visually understandable way. Specifically, it creates an image of a mannequin wearing the outfit and sends it to the terminal. The terminal then displays the outfits presented to the user.

[1209] Suggested purchase items

[1210] Recommended items selected by the server

[1211] The server selects recommended items to purchase based on the user's current outfit information and item data. For example, when recommending new sneakers to match an outfit, it searches for and suggests appropriate items. It also takes into account emotional data and prioritizes displaying items that the user is likely to like.

[1212] Displaying recommended items to users

[1213] The server sends information about the selected recommended items to the terminal, which then displays the recommended items to the user, for example, presenting them as "new sneakers that go well with these jeans."

[1214] Specific examples

[1215] After the user takes a photo of a T-shirt and tags it, the data is sent to the server for storage. Meanwhile, when the user inputs the day's weather information (sunny, maximum 20°C, minimum 15°C) and purpose (leisure), the emotion engine analyzes the user's facial expressions and recognizes positive emotions (e.g., smiling). The server uses AI to generate outfits based on this information and emotion data, such as a "T-shirt + jeans + jacket." The suggested outfits are displayed to the user along with visual images of them being worn by a mannequin. Furthermore, the server selects new sneakers that go well with the outfit as recommended items and displays them to the user. If the user is expressing positive emotions, bright colors and fun designs are prioritized.

[1216] This system allows users to efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in a way that suits their emotions.

[1217] The processing flow will be explained below.

[1218] Step 1:

[1219] The user takes a photo of the clothes.

[1220] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user might think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[1221] Step 2:

[1222] The device processes the image.

[1223] The device receives the photographed image of the clothes, performs image processing such as background removal and cropping as necessary to optimize the image, and then temporarily stores the image data.

[1224] Step 3:

[1225] The device performs the tagging.

[1226] The device will prompt the user to enter appropriate tags, such as "T-shirt, casual," and the user will select and enter the tag.

[1227] Step 4:

[1228] The terminal transmits the data to the server.

[1229] The image data for which tagging has been completed on the terminal is transmitted to the server together with the tag information.

[1230] Step 5:

[1231] The server stores the data.

[1232] The server stores the received image data in a database and manages metadata including image file paths and tag information.

[1233] Step 6:

[1234] The user inputs weather and usage information.

[1235] Through the application, users input the weather, minimum and maximum temperatures for the day, and the purpose of use (for example, "sunny, maximum 20 degrees, minimum 15 degrees, purpose: leisure").

[1236] Step 7:

[1237] The terminal sends the input information to the server.

[1238] The terminal transmits the weather information, temperature information, and usage information input by the user to the server.

[1239] Step 8:

[1240] The emotion engine analyzes the user's emotions.

[1241] The device uses facial recognition technology to capture the user's facial expression data and transmits it to the emotion engine, which then analyzes the user's emotions (e.g., joy, sadness, neutrality, etc.).

[1242] Step 9:

[1243] The server analyzes the condition information.

[1244] The server analyzes the weather, temperature, purpose and emotion data received from the user and from the emotion engine.

[1245] Step 10:

[1246] The server generates the coordinates.

[1247] The server uses AI to filter and generate optimal clothing combinations (e.g., "T-shirt + jeans + jacket") from clothing data stored in a database based on the user's emotional state and usage information.

[1248] Step 11:

[1249] The server generates the visualization.

[1250] The server generates an image of a mannequin wearing the proposed outfit in order to visually display the created outfit in an easy-to-understand manner.

[1251] Step 12:

[1252] The server sends the visualization to the device.

[1253] The server transmits the generated visualization and coordination proposal information to the terminal.

[1254] Step 13:

[1255] The device displays the suggestions to the user.

[1256] The device will then present the user with suggested outfits, such as "Leisure outfits suitable for a sunny 20-degree day."

[1257] Step 14:

[1258] The server selects recommended items for purchase.

[1259] The server selects recommended items to purchase (e.g., new sneakers) based on the user's current outfit information and item data. It also takes into account emotional data and prioritizes displaying items that the user is likely to like.

[1260] Step 15:

[1261] The server transmits recommended item information to the terminal.

[1262] The server transmits information about the selected recommended purchase items to the terminal.

[1263] Step 16:

[1264] The device displays the recommended items to the user.

[1265] The device will then display selected purchase recommendations to the user, such as "new sneakers that go well with these jeans."

[1266] This allows users to efficiently select attractive clothing and receive suggestions that are in line with their emotions, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in accordance with their mood and emotions.

[1267] Example 2

[1268] 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."

[1269] In modern life, users spend a lot of time choosing their daily clothing. It takes a lot of experience to coordinate outfits appropriately based on the weather, temperature, and occasion. It's also extremely difficult to coordinate outfits based on the user's emotional state. This often leads to users choosing inappropriate clothing, which can lead to an unpleasant day. It's also difficult to provide users with appropriate purchasing recommendations.

[1270] 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.

[1271] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for acquiring the current day's weather information, minimum and maximum temperatures, and usage information, a means for analyzing the acquired facial expressions and voice to recognize the user's emotional state, a means for using artificial intelligence to suggest appropriate clothing combinations based on this information and the user's emotional state, a means for visually displaying the suggested combinations, and a means for presenting recommended items for purchase. This allows the user to quickly and efficiently find the optimal outfit, and suggestions can be made based on the weather, usage, and emotional state. Furthermore, presenting appropriate recommended items for purchase allows the user to appropriately consider purchasing new items.

[1272] The "means for acquiring and recording images" refers to a system for taking photos of clothing owned by the user and saving the image data.

[1273] "Means for obtaining weather information" refers to a system for obtaining meteorological data such as the weather and temperature of the day.

[1274] "Means for acquiring usage information" refers to a system that allows users to input the purpose and situation for wearing the clothing and acquire that information.

[1275] "Means for recognizing emotional state" refers to technology that analyzes the user's facial expressions and voice to determine their current emotions.

[1276] "Means for suggesting using artificial intelligence" refers to a mechanism that utilizes artificial intelligence to generate optimal clothing combinations based on acquired information and perceived emotional state.

[1277] "Visual display means" refers to a technique that visually displays the suggested clothing combinations in a way that is easy for the user to understand.

[1278] "Means for suggesting recommended items for purchase" refers to a system that suggests new clothing and accessories suitable for coordinating with users.

[1279] The "means of tagging and recording" is a system that allows users to tag their clothing to indicate the type, usage situation, etc., and save that information.

[1280] "Data-based suggestion means" refers to technology that determines and suggests appropriate clothing combinations based on stored tagged data.

[1281] "Means for displaying a visual representation on a mannequin" refers to technology that displays the proposed clothing combination as if it were being worn by a virtual mannequin.

[1282] "Means for recommending based on user preferences" refers to technology that recommends new clothing items by taking into account the user's past choices and preferences.

[1283] "Means for suggesting based on user input" refers to technology that generates appropriate clothing combinations based on direct user input information.

[1284] The "means for transmitting and displaying on the user's terminal" refers to a mechanism for transmitting the generated combination information and data on recommended purchase items to the user's terminal and displaying them.

[1285] This invention relates to a system that acquires and records images of clothing owned by a user, suggests optimal outfits based on the day's weather information, temperature information, and usage information, and further optimizes suggestions by recognizing the user's emotions.

[1286] System configuration

[1287] This system uses a server, a user device, and an emotion engine. The user device is a smartphone or PC, and provides an interface for taking and uploading images of clothing and inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions.

[1288] System Operation

[1289] 1. Acquiring and saving clothing data

[1290] A user uses the smartphone camera to take pictures of various types of clothing they own. For example, if they want to upload a T-shirt, they take a picture of the T-shirt with their smartphone. The device receives the image and performs image processing such as background removal and cropping. It also prompts the user to enter tags, and the user enters appropriate tags such as "T-shirt" or "casual." The device then sends the processed image data along with the tag information to the server. The server stores the received data in a database and manages the image file path and tag information as metadata.

[1291] 2. Coordination suggestions

[1292] The user enters the day's weather (e.g., sunny, cloudy, rainy), minimum and maximum temperatures, and intended use (e.g., business casual, leisure, walk) through a smartphone app. Specifically, the user enters "sunny, maximum temperature 20°C, minimum temperature 15°C, intended use leisure." When the user launches the application, the emotion engine analyzes the user's facial expressions and voice through the camera and identifies their current emotion as "positive" or "negative." The server then performs an integrated analysis of the weather, temperature, and intended use information received from the user, along with the emotional data obtained from the emotion engine. The server then filters appropriate clothing combinations from the clothing data stored in the database and generates an optimal outfit using a generative AI model. For example, a T-shirt, jeans, and jacket outfit is selected. If the user indicates a positive emotion, bright-colored items are prioritized. The server generates an image of a mannequin wearing the proposed outfit to visually display it and sends it to the device. The device then displays the proposed outfit to the user.

[1293] 3. Recommended purchase items

[1294] The server analyzes the generated outfit and the user's item data to select the necessary items (such as new sneakers). It also takes into account emotional data and recommends items that the user is likely to like. The server then sends information about recommended items to the device, which then displays this to the user in the form of, for example, "New sneakers that go well with these jeans."

[1295] Specific examples

[1296] After the user takes a photo of a T-shirt and tags it as "T-shirt" and "casual," the device sends the image data to the server, which stores the image and tag information in a database. At the same time, the user enters weather information ("sunny, maximum 20 degrees, minimum 15 degrees") and purpose information ("leisure") into the app. The emotion engine analyzes the user's facial expression and recognizes "positive" (smiling). Based on these conditions, the server uses an AI model to generate a "T-shirt + jeans + jacket" outfit and displays it as a visualization. The server also selects recommended items, such as new sneakers, and presents them to the user. An example prompt is shown below.

[1297] "A user uploaded a T-shirt and entered 'casual' and 'T-shirt' in the tags. Then, the user entered 'sunny' for the weather on the day, a maximum temperature of '20 degrees', a minimum temperature of '15 degrees', and 'leisure' as the purpose. The user was recognized as smiling. Please suggest the best outfit based on these conditions."

[1298] This system will significantly reduce the effort required for users to choose their daily fashion and allow them to enjoy the best possible coordination that suits their emotions and the situation.

[1299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1300] Step 1:

[1301] A user takes a photo of clothing they own with their smartphone camera. Specifically, the user wants to upload a T-shirt, so they take a photo of the T-shirt. The input is the image taken with the smartphone, and the output is image data of the clothing.

[1302] Step 2:

[1303] The device receives the captured image and performs image processing such as background removal and cropping. The input is the captured image, and the output is the cropped image data with the background removed. The device displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt" or "casual." The input is the user's tagging information, and the output is the tagged image data.

[1304] Step 3:

[1305] The device sends the processed image data and tag information to the server. The input is tagged image data, and the output is the completed transmission to the server. The server saves the received image data in a database and manages the image file path and tag information as metadata. The input is tagged image data, and the output is the data saved in the database.

[1306] Step 4:

[1307] The user inputs the weather, minimum and maximum temperatures, and purpose of use for the day through a smartphone app. For example, they might input "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose of use leisure." The input is information such as the weather, temperature, and purpose entered by the user, and the output is data related to these conditions.

[1308] Step 5:

[1309] When a user launches the application, the emotion engine analyzes the user's facial expressions and voice through the camera to recognize their current emotional state. For example, it recognizes a user's smile and determines that it is a positive emotion. The input is the user's facial expressions and voice, and the output is the recognized emotional data.

[1310] Step 6:

[1311] The server analyzes the weather, temperature, and purpose information received from the user, as well as the emotion data obtained from the emotion engine. The input is weather, temperature, purpose, and emotion data, and the output is the analysis result obtained by integrating this information. Then, appropriate clothing combinations are filtered from the clothing data stored in the database. The optimal outfit is generated using a generative AI model. For example, a combination of T-shirt + jeans + jacket is selected. The input is the filtering conditions (weather, temperature, purpose, emotion data), and the output is the generated outfit data.

[1312] Step 7:

[1313] The server generates a visualization to display the proposed outfits in a visually easy-to-understand manner. Specifically, it creates an image of a mannequin wearing the proposed outfit and sends it to the terminal. The input is the generated outfit data, and the output is the visualization image. The terminal displays the proposed outfit to the user. The input is the visualization image, and the output is the outfit image displayed to the user.

[1314] Step 8:

[1315] The server analyzes the generated coordination information and the user's owned item data. The input is the coordination information and owned item data, and the output is the analysis results. Based on the analysis results, the server selects the necessary items (e.g., new sneakers). The input is the analysis results, and the output is the selected recommended purchase items. It also takes into account emotional data and recommends items that the user is likely to like. The input is emotional data, and the output is recommended purchase items based on emotions.

[1316] Step 9:

[1317] The server sends information about the selected recommended purchase items to the terminal. The input is the selected recommended purchase item information, and the output is the completion of transmission to the terminal. The terminal presents the recommended purchase item to the user, such as "new sneakers that go well with these jeans." The input is the recommended purchase item information sent from the server, and the output is the recommended purchase item information displayed to the user.

[1318] (Application example 2)

[1319] 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."

[1320] Conventional outfit suggestion systems have difficulty making optimal suggestions based on the user's emotions and real-time circumstances. Furthermore, users have limited means of directly visually confirming their outfits, which means there is a lack of consistency between the suggested outfits and the actual outfits or purchases they make. Furthermore, there is also the problem that the suggested items do not adequately reflect the user's emotions, tastes, and preferences.

[1321] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing and analyzing the user's emotions, means for visualizing and displaying suggested outfits on the smart glasses, and means for displaying recommended items in cooperation with a virtual store. This allows the optimal outfit suggestions to be made based on the user's emotions and real-time situation, and the suggestions can be visually confirmed, enabling a more integrated outfit and purchasing experience.

[1322] "Clothing image acquisition means" refers to devices and software that allow users to photograph and record their own clothing and acquire image data.

[1323] "Weather information acquisition means" refers to a device or software for acquiring information about the weather and temperature of the day.

[1324] "Use information acquisition means" refers to a device or software that acquires the situation and purpose (business, leisure, etc.) in which the clothing will be used.

[1325] A "clothing combination suggestion means" is a device or software that suggests clothing combinations suitable for the weather and purpose based on clothing data held by the user.

[1326] A "visual display means" is a device or software that visually presents suggested clothing combinations to a user.

[1327] The "means for presenting recommended items for purchase" is a device or software that recommends new items that go well with the proposed outfit to the user.

[1328] "Emotion recognition means" refers to devices or software that analyze a user's facial expressions and voice and recognize their emotional state.

[1329] "Smart glasses" are wearable devices that allow users to obtain information visually by wearing them.

[1330] A "visualization means" is a device or software that displays suggested outfits in the user's field of vision in real time.

[1331] A "virtual store" is a virtual store system where you can browse and purchase products online.

[1332] "Artificial intelligence" refers to algorithms and software that can analyze data and make decisions automatically.

[1333] This invention is a system that acquires and records images of clothing owned by a user and suggests optimal outfits based on weather information, temperature information, usage information, and emotional data. Furthermore, it has the function of visually displaying the suggested outfits using smart glasses and presenting recommended items for purchase. A specific embodiment of this system will be described below.

[1334] System configuration

[1335] The system's main components are a server, a user device (smartphone or PC), smart glasses, and an emotion engine. The user device takes and uploads images of clothing and provides an interface for inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions. The smart glasses function as a device for visualizing suggested outfits in real time.

[1336] Acquiring and saving clothing data

[1337] The user takes a photo of the clothes

[1338] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user might think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[1339] Device-based image processing and tagging

[1340] The device receives the image of the clothing and performs image processing such as background removal and cropping. Using OCR technology, it automatically tags the clothing and prompts the user to confirm the appropriate tag (e.g., "T-shirt, casual").

[1341] Sending and saving data to the server

[1342] The image data processed on the device is sent to the server along with the tag information. The server stores the received image data in a database and manages metadata including the image file path and tag information. MySQL or MongoDB are suitable databases.

[1343] Coordination suggestions

[1344] User input of conditions

[1345] Through the application, users input the weather for the day, the minimum and maximum temperatures, and the purpose of the outfit (business casual, leisure, walking, etc.).

[1346] Emotion engine emotion recognition

[1347] When a user launches the application, it uses an emotion engine (e.g., AWS Rekognition, IBM Watson) to analyze the user's facial expressions and voice to recognize their current emotional state. It also uses a camera to analyze facial expressions.

[1348] Server-based information processing and coordinate generation

[1349] The server analyzes the weather, temperature, and usage information received from the user, as well as the emotion data obtained from the emotion engine. It then uses an AI model (e.g., TensorFlow, PyTorch) to generate the optimal outfit. For example, it selects the combination of "T-shirt + jeans + jacket." If the user expresses positive emotions, it will prioritize bright-colored clothing.

[1350] Proposal visualization and display

[1351] The server then generates an image of the outfit being worn by a mannequin to visually display it in a way that is easy to understand. This image data is then sent to the smart glasses, which display it in real time to the user's field of vision.

[1352] Suggested purchase items

[1353] Recommended items selected by the server

[1354] The server works with the virtual store to select recommended items based on the user's current outfit information and item data. Recommended items are prioritized based on the user's emotional data and prioritize items that match the outfit.

[1355] Displaying recommended items to users

[1356] The server sends the information of the selected recommended items to the smart glasses, and displays appropriate recommended items to the user, such as "new sneakers that go well with these jeans."

[1357] Examples of concrete examples and prompts

[1358] As a concrete example, we will explain how a user takes a photo of a T-shirt, tags it, and then sends the data to a server for storage. When the user inputs the day's weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure), the emotion engine analyzes the user's facial expressions and recognizes positive emotions (e.g., smiling). Based on this information, the server generates a "T-shirt + jeans + jacket" outfit and visualizes it on the smart glasses. Furthermore, the server connects with a virtual store to recommend new sneakers that go well with this outfit and displays them to the user.

[1359] An example of a prompt is as follows:

[1360] "The system removes the background from images of clothing taken by the user and automatically tags them. Based on weather, temperature, usage information, and the user's emotional data, it generates optimal outfits and visualizes them on the smart glasses display. This uses computer vision and emotion recognition."

[1361] This system allows users to efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in a way that suits their emotions.

[1362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1363] Program processing flow

[1364] Step 1: Image capture and tagging of clothing

[1365] The device receives an image of clothing taken by the user. The image undergoes image processing such as background removal and cropping, and is automatically tagged using OCR technology. The tag information and image data are sent from the device to the server. The input is the "image of clothing," and the output is the "background-removed image and tag information."

[1366] Step 2: Obtaining weather information, temperature information, and usage information

[1367] The user enters the day's weather information, minimum and maximum temperatures, and usage information through the application. The input is "weather information, temperature information, usage information," and the output is "weather information, temperature information, usage information data." This allows the server to obtain the environmental data necessary for analysis.

[1368] Step 3: Acquire and analyze emotion data

[1369] When a user launches the application, the device (smart glasses or smartphone) captures the user's facial expressions and voice using a camera and microphone. This data is sent to the emotion engine, which analyzes the user's emotional state. The input is "facial expression and voice data," and the output is "emotion data."

[1370] Step 4: Generate coordinates

[1371] The server uses an AI model (such as TensorFlow or PyTorch) to analyze weather information, temperature information, usage information, emotional data, and clothing data stored in a database, and generates an appropriate outfit. The input is "weather information, temperature information, usage information, emotional data, clothing data," and the output is "optimal outfit information."

[1372] Step 5: Visualization and visualization

[1373] The server generates a visual representation of the outfit on a mannequin based on the coordinated outfit information. The generated image is sent to the smart glasses, allowing the user to view it in real time. The input is "coordinated outfit information," and the output is a "visualized image."

[1374] Step 6: Select recommended items to purchase

[1375] The server analyzes the user's outfit information and owned item data to select recommended items for purchase. It connects with the virtual store to obtain related product data. The input is "outfit information and owned item data," and the output is "a list of recommended items for purchase."

[1376] Step 7: View recommended items to buy

[1377] The server sends the selected recommended items to the smart glasses, and the user can purchase the items through a purchase link. The input is a "list of recommended items to purchase," and the output is a "purchase link and item information."

[1378] Specific examples and prompts

[1379] Examples:

[1380] The user takes a photo of a T-shirt through the smart glasses, and the image with the background removed is sent to the server. The user then enters weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure) into the application. At the same time, the emotion engine analyzes the user's positive emotion (smile). Based on this information, the server generates a "T-shirt + jeans + jacket" outfit and displays a visualized image on the smart glasses. In addition, recommended items for new sneakers are presented, which the user can review and purchase.

[1381] Examples of prompts:

[1382] "The system removes the background from images of clothing taken by the user and automatically tags them. Based on weather, temperature, usage information, and the user's emotional data, it generates optimal outfits and visualizes them on the smart glasses display. This uses computer vision and emotion recognition."

[1383] 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.

[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 headset type terminal 314.

[1386] [Fourth embodiment]

[1387] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1388] 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.

[1389] 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).

[1390] 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.

[1391] 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.

[1392] 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).

[1393] 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.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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."

[1400] The present invention relates to a system that improves the efficiency of clothing selection and enables users to appropriately select trendy clothing coordinations every day. Specific embodiments of this system will be described below.

[1401] System configuration

[1402] The system uses a server, user devices, and AI (artificial intelligence). User devices are smartphones or PCs, and provide an interface for users to take and upload images of clothes and input weather, temperature, and usage information. The server stores and analyzes the image data, suggests outfits, and selects recommended items for purchase. The AI ​​is also responsible for automatically generating outfit suggestions based on the clothing data and input information.

[1403] Acquiring and saving clothing data

[1404] Users take photos of their clothes

[1405] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, they may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[1406] Device-based image processing and tagging

[1407] The device receives the photographed image of the clothes and performs image processing such as background removal and cropping as necessary. It also displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt, casual." This tagging allows the data to be used efficiently in subsequent outfit suggestions.

[1408] Sending and saving data to the server

[1409] The image data processed by the terminal is sent to the server along with the tag information. The server stores the received image data in a database and manages the image file path and tag information appropriately.

[1410] Coordination suggestions

[1411] User input of conditions

[1412] The user enters the weather, minimum and maximum temperatures for the day, and the purpose of the trip (business casual, leisure, walk, etc.) through the application. For example, the user might enter "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose leisure."

[1413] Server-based information processing and coordinate generation

[1414] The server analyzes the weather, temperature, and purpose information received from the user and sets appropriate condition filters. Next, it filters appropriate clothing combinations from clothing data stored in a database and uses AI to generate optimal outfits. For example, it selects the combination of "T-shirt + jeans + jacket."

[1415] Proposal visualization and display

[1416] The server generates a visualization to display the generated outfits to the user in a visually understandable way. Specifically, it generates an image of a mannequin wearing the outfits presented, and sends that image and other information to the terminal. The terminal then displays the outfits presented to the user.

[1417] Suggested purchase items

[1418] Recommended items selected by the server

[1419] The server selects recommended items to purchase based on the user's current outfit information and item data. For example, if the server is recommending new sneakers to match an outfit, it will search for and recommend appropriate items.

[1420] Displaying recommended items to users

[1421] The server sends information about the selected recommended items to the terminal, which then displays the recommended items to the user, for example, presenting them as "new sneakers that go well with these jeans."

[1422] Specific examples

[1423] After the user takes a photo of a T-shirt and tags it, the data is sent to the server for storage. The user then inputs the day's weather information (sunny, maximum 20°C, minimum 15°C) and purpose (leisure), and the server uses AI to generate a "T-shirt + jeans + jacket" outfit based on this information. The suggested outfit is displayed to the user along with a visual image of the outfit, as if worn by a mannequin. The server also recommends new sneakers to go with the outfit and displays them to the user.

[1424] In this way, users can efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life. With this system, even those who are not good at fashion can confidently enjoy wearing clothes that are in line with the latest trends.

[1425] The processing flow will be explained below.

[1426] Step 1:

[1427] The user takes a photo of the clothes.

[1428] Users take photos of various clothes they own (such as T-shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[1429] Step 2:

[1430] The device processes the image.

[1431] The device receives the photographed image of the clothes and performs image processing such as background removal and trimming as necessary. For example, it automatically cuts out unnecessary background parts of the image.

[1432] Step 3:

[1433] The device performs the tagging.

[1434] The device will prompt the user to enter appropriate tags, such as "T-shirt, casual," and the user will select and enter the tag.

[1435] Step 4:

[1436] The terminal transmits the data to the server.

[1437] Once tagging is complete on the device, the image data is sent to the server, including the tag information.

[1438] Step 5:

[1439] The server stores the data.

[1440] The server stores the received image data in a database, along with metadata including the image file path and tag information.

[1441] Step 6:

[1442] The user inputs the weather and purpose.

[1443] The user inputs the weather, minimum and maximum temperatures for the day, and the purpose of use (e.g., "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose: leisure") through the application.

[1444] Step 7:

[1445] The terminal sends the input information to the server.

[1446] The terminal transmits the weather information, temperature information, and usage information input by the user to the server.

[1447] Step 8:

[1448] The server analyzes the information and generates coordinates.

[1449] The server analyzes the weather, temperature, and purpose information received from the user, then filters the clothing data stored in the database to find suitable clothing combinations and uses AI to generate the optimal outfit.

[1450] Step 9:

[1451] The server generates the visualization.

[1452] The server generates a visualization to display the generated outfits in a visually understandable way, specifically creating an image of a mannequin wearing the outfits presented.

[1453] Step 10:

[1454] The server sends the visualization to the device.

[1455] The server transmits the generated visualization and coordination proposal information to the terminal.

[1456] Step 11:

[1457] The device displays the suggestions to the user.

[1458] The device will then display suggested outfits to the user, such as "leisure outfits suitable for a sunny 20-degree day."

[1459] Step 12:

[1460] The server selects recommended items for purchase.

[1461] The server selects appropriate recommended items to purchase based on the user's outfit information and item data, such as new sneakers that go well with the outfit.

[1462] Step 13:

[1463] The server transmits recommended item information to the terminal.

[1464] The server transmits information about the selected recommended purchase items to the terminal.

[1465] Step 14:

[1466] The device displays the recommended items to the user.

[1467] The device will then display selected purchase recommendations to the user, such as "New sneakers to go with these jeans."

[1468] Example 1

[1469] 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."

[1470] In modern life, fashion coordination is a daily challenge for many people, and choosing appropriate clothing based on trends, seasons, and occasions can be particularly challenging. Furthermore, there is a growing demand for systems that effectively utilize existing clothing and provide comprehensive fashion advice, including the selection of new items to purchase. However, existing systems struggle to reduce the user's workload and make efficient and accurate fashion suggestions. The purpose of this invention is to provide a system that solves these problems and allows users to easily and effectively enjoy daily coordination.

[1471] 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.

[1472] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for tagging and recording the clothing images based on user input, a means for proposing appropriate clothing combinations based on the tagged image data and taking into account weather information, temperature information, and usage information, a means for visually displaying the proposed combinations, and a means for presenting recommended purchase items for the proposed combinations. This allows users to efficiently manage their clothing and automatically receive recommendations for optimal coordination based on trends and usage. Furthermore, new items to purchase are also recommended, reducing the burden of fashion in general.

[1473] "Clothing" refers to clothing to be worn, such as shirts, pants, jackets, etc.

[1474] "Means for acquiring and recording images" refers to devices and functions that allow users to digitally save and manage images of clothing taken with a smartphone or digital camera.

[1475] "Means for tagging and recording images of clothing based on user input" refers to devices or functions that allow users to add descriptions such as category and use to images of clothing and store them in a database.

[1476] "Means for proposing appropriate clothing combinations based on tagged image data, taking into consideration weather information, temperature information, and usage information" refers to devices and technologies that analyze stored tagged image data and weather information, temperature information, and usage information acquired in real time, and automatically select the optimal clothing combinations based on this information.

[1477] "Means for visually displaying suggested combinations" refers to devices or functions that use images or graphics to clearly present optimal clothing combinations to users.

[1478] The "means for presenting recommended items for purchase" refers to a device or function that can recommend new items to the user to purchase to go with the proposed outfit and display detailed information about them.

[1479] "Means of utilizing artificial intelligence" refers to systems and algorithms that use technologies such as machine learning and deep learning to suggest clothing combinations.

[1480] "Means for transmitting and displaying on a user's device" refers to the equipment or functions for transmitting the generated coordination information and information on recommended purchase items to a user's device (smartphone, tablet, PC, etc.) via the Internet or other communication means and displaying it.

[1481] This invention relates to a system that supports users in efficiently coordinating their daily fashion. The system is composed of a server, a user terminal, and artificial intelligence (AI).

[1482] System configuration

[1483] The basic components of this system are as follows:

[1484] User device (smartphone, PC)

[1485] Servers (high-performance database servers, GPU servers)

[1486] AI model (generative AI model)

[1487] Acquiring clothing data

[1488] User-generated photos of clothing

[1489] The user uses a smartphone or digital camera to take a picture of the clothes they own. For example, they perform a specific action such as "taking a picture of their favorite red T-shirt."

[1490] Uploading images using a device

[1491] Users upload the images they take to the device through an application on their smartphone or computer. Once the images are uploaded, the device prompts the user to add tags. They can enter tags such as "T-shirt, casual."

[1492] Image processing and analysis

[1493] Image processing by device

[1494] The device performs image processing on the uploaded image, such as background removal and cropping. Specifically, for example, background removal is performed using the OpenCV library. The corrected image is then displayed and the user can tag it.

[1495] Sending data to the server

[1496] The image data and tag information processed on the device are sent to the server, which stores the received data in a database and manages the image file paths and tag information.

[1497] Coordination suggestions

[1498] User input of conditions

[1499] The user enters the weather forecast for the day, the minimum and maximum temperatures, and the purpose of the trip (e.g., business casual, leisure, walk) through the application. For example, "Sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure."

[1500] Server-based information analysis and coordinate generation

[1501] The server analyzes the data by setting appropriate filters based on the input weather, temperature, and usage information. The server then uses an AI model (e.g., a generative AI model built using TensorFlow) to automatically generate optimal clothing combinations. For example, it suggests a combination such as "T-shirt + jeans + jacket."

[1502] Visualization and Display

[1503] Server-generated visuals

[1504] To visually display the generated outfit, the server uses a generative model (e.g., Generative Adversarial Networks) to generate an image of the outfit as it would appear on a mannequin.

[1505] Sending and displaying data to the device

[1506] The generated visualization is sent from the server to the terminal, which then displays the suggested outfits along with the images to the user.

[1507] Suggested purchase items

[1508] Recommended items selected by the server

[1509] The server selects new items to purchase based on the user's current outfit information and item data. For example, it searches for and selects "new sneakers that go well with these jeans."

[1510] Displaying recommended items on your device

[1511] Information on the selected recommended items is sent from the server to the device, and the device presents the information to the user, for example, displaying "new sneakers that go well with these jeans."

[1512] Prompt Sentence Examples

[1513] Here is an example of an input prompt for a generative AI model:

[1514] "Today's weather is sunny, with a high of 20 degrees and a low of 15 degrees. I'm looking for an outfit for a leisure trip. Please suggest the best outfit based on the photos and tag information of the clothes I own."

[1515] This system allows users to efficiently find attractive fashion coordinations and also provides suggestions for new items to purchase, easing the burden of fashion selection.

[1516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1517] Step 1:

[1518] User-generated photos of clothing

[1519] A user takes a photo of various clothes (shirts, pants, jackets, etc.) they own using a smartphone or camera. For example, they think, "I want to take a photo of this red T-shirt," and take a photo of the T-shirt with their camera. The input is the actual clothes, and the output is a digital image of the clothes. This image is used for subsequent analysis.

[1520] Step 2:

[1521] Device image upload and tagging

[1522] Users upload captured images through an application on their device (smartphone or PC). The device automatically processes the image, such as removing the background and cropping, and prompts the user to enter tags. For example, the user might tag the image as "T-shirt, casual." The input is the captured image, and the output is the processed image and its tag information. This data is sent to the server.

[1523] Step 3:

[1524] Sending data to the server

[1525] The image data and tag information sent from the device arrive at the server. The server stores this data in a database. For example, it records the image file path and associates it with the tag information. The input is the processed image and tag information, and the output is information managed in the database. By managing this data appropriately, it can be used to propose outfits later.

[1526] Step 4:

[1527] User input of conditions

[1528] Through the application, users can input the weather information for the day, the minimum and maximum temperatures, and the purpose of the trip (e.g., business casual, leisure, walk). For example, they can input "sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure." The input is the weather and purpose information, and the output is the search criteria set using that information.

[1529] Step 5:

[1530] Server-based information analysis and filter settings

[1531] The server analyzes the input information on weather, temperature, and purpose, and sets the necessary condition filters based on that information. For example, filtering clothes that match sunny weather and temperatures between 15 and 20 degrees. The input is weather, temperature, and purpose information, and the output is a list of clothes that meet the filter conditions.

[1532] Step 6:

[1533] Coordination generation using AI

[1534] The server analyzes the clothing data that matches the filter criteria using an AI model (e.g., a generative AI model using TensorFlow) and suggests optimal outfits. For example, it generates a combination of "T-shirt + jeans + jacket." The input is the filtered clothing list, and the output is the suggested outfit information.

[1535] Step 7:

[1536] Server-generated visuals

[1537] The server uses a generative model (e.g., GAN) to create an image of a mannequin wearing the generated outfit to visually display it. The input is the outfit information, and the output is the visualized image.

[1538] Step 8:

[1539] Sending and displaying data to the device

[1540] The generated visualization is sent from the server to the device, which then displays this image to the user. For example, an image of a mannequin wearing the proposed outfit is displayed on a smartphone screen. The input is the generated visual data, and the output is the visual information displayed on the user's screen.

[1541] Step 9:

[1542] Server selection of recommended items for purchase

[1543] The server analyzes the user's current outfit information and the user's owned item data to select new items to purchase. For example, it selects "new sneakers that go well with these jeans." The input is the outfit information and the owned item data, and the output is a list of recommended items to purchase.

[1544] Step 10:

[1545] Displaying recommended items on your device

[1546] The server sends the selected recommended items to the device, which then displays them to the user. For example, a suggestion such as "new sneakers that go well with these jeans" is displayed to the user. The input is the recommended item information, and the output is the recommended items displayed on the user's screen.

[1547] (Application example 1)

[1548] 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."

[1549] It is difficult for users to efficiently utilize the clothes they have on hand and choose the optimal outfit for the weather and occasion, especially when they are short on time or lack confidence in fashion. Even in brick-and-mortar stores, there is no system that can provide real-time outfit suggestions when customers are considering a purchase, which prevents them from fully motivating them to buy. Furthermore, there is a lack of efficient fashion support systems that use new interfaces such as smart glasses.

[1550] 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.

[1551] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for acquiring weather information for the day, minimum and maximum temperatures, and usage information, a means for proposing appropriate clothing combinations based on this information, a means for visually displaying the proposed combinations, a means for presenting recommended items for purchase, a means for acquiring images of clothing using a camera in the smart glasses, a means for transmitting the acquired image data and user information to the server and receiving outfit suggestions, and a means for displaying the suggested outfits on the display of the smart glasses. This allows users to efficiently select their daily fashion and receive outfit suggestions in real time, even in physical stores. Furthermore, the server can recommend new items to purchase based on the user's tastes and preferences, thereby increasing purchasing motivation.

[1552] - "Clothing" refers to clothing worn by people in daily life or for specific purposes.

[1553] "Image acquisition means" refers to a device or function that takes an image of clothing and records it as digital data.

[1554] "Weather information" refers to meteorological data such as the weather conditions and temperature of the day.

[1555] "Use information" refers to information about the purpose and scene of the clothing to be used on the day.

[1556] The "coordination suggestion means" is a function that automatically selects the optimal clothing combination based on acquired image data, weather, and purpose information.

[1557] "Visual display means" refers to a device or function for visually presenting suggested clothing combinations to a user.

[1558] The "means for presenting recommended items for purchase" is a function that provides information on recommended new products for purchase based on the clothing data and coordination held by the user.

[1559] "Smart glasses" are eyeglass-type devices that have built-in cameras and displays, allowing users to obtain visual information in real time.

[1560] A "camera" is a device that takes still images and videos and records them as digital data.

[1561] A "display" is a device for visually displaying digital data.

[1562] A "server" is a computer system that stores, analyzes, and processes data over a network.

[1563] "User Information" means personal data, settings, preferences, and other information provided by Users.

[1564] This invention relates to a system that acquires and records images of clothes that a user has on hand, and suggests optimal clothing combinations based on weather information and usage information. In particular, by using smart glasses, clothing suggestions can be realized in real time. The following describes in detail the embodiments of this invention.

[1565] System configuration

[1566] 1. Hardware

[1567] Smart glasses: Equipped with a camera and a display, they can acquire and display information according to the user's line of sight.

[1568] Server: A computer system used to store data, analyze data, and generate proposals over a network.

[1569] User device: A device used by a user to input and view information, such as a smartphone, tablet, or computer.

[1570] 2. Software

[1571] OpenCV: A library for acquiring and processing images from the smart glasses camera.

[1572] Requests: A library for exchanging data with a server via HTTP communication.

[1573] PIL (Python Imaging Library): A library for displaying suggested coordinate images.

[1574] Generative AI model: An artificial intelligence (AI) that suggests optimal outfits based on user input.

[1575] Acquiring and saving clothing data

[1576] Users take photos of their clothes

[1577] The user takes a photo of their own clothing using the camera in the smart glasses, which then acquires digital image data of the clothing. For example, they take a photo of a T-shirt and upload the image to the system.

[1578] Image processing and tagging with smart glasses

[1579] The smart glasses terminal receives the photographed image of the clothes and performs necessary image processing such as background removal and cropping. The user is then prompted to enter appropriate tags such as "T-shirt" or "casual."

[1580] Sending and saving data to the server

[1581] The image data processed by the smart glasses terminal is sent to the server along with the tag information. The server stores this data in a database and properly manages the image file path and tag information.

[1582] Coordination suggestions

[1583] User input of conditions

[1584] The user inputs the weather, minimum and maximum temperatures, and the purpose of the day (business casual or leisure) through smart glasses or a device. For example, the user can input "sunny, maximum 20 degrees, minimum 15 degrees, purpose is leisure."

[1585] Server-based information processing and coordinate generation

[1586] The server analyzes the weather, temperature, and purpose information received from the user and sets appropriate condition filters. Next, it filters appropriate clothing combinations from clothing data stored in a database and generates optimal outfits using a generative AI model. For example, it selects the combination of "T-shirt + jeans + jacket."

[1587] Displaying suggestions through smart glasses

[1588] The server generates a visualization of the generated outfit to display it to the user in a visually understandable way, and sends it to the smart glasses. The user can then check the proposed outfit through the smart glasses' display.

[1589] Suggested purchase items

[1590] Recommended items selected by the server

[1591] The server selects recommended items to purchase based on the current outfit information and the user's item data, for example, recommending new sneakers that go well with the outfit.

[1592] Displaying recommended items using smart glasses

[1593] Information on recommended items for purchase is presented to the user through the smart glasses display.

[1594] Examples of concrete examples and prompts

[1595] For example, a user takes a photo of a T-shirt, tags it, and then sends the data to a server for storage. The user then inputs the day's weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure), and the server uses a generative AI model to generate a "T-shirt + jeans + jacket" outfit. The suggested outfit is then displayed to the user through the smart glasses' display. The server then recommends new sneakers to go with the outfit and displays them to the user.

[1596] Example prompt sentence:

[1597] "Please suggest an outfit that includes the T-shirt the user photographed. The weather information is 'Sunny, maximum 20°C, minimum 15°C', and the purpose is 'Leisure.'"

[1598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1599] Step 1:

[1600] A user puts on the smart glasses and takes an image of their clothing.

[1601] Input: Actual clothing

[1602] Data processing: Using the camera in the smart glasses, images of the clothing are captured as digital data.

[1603] Output: Digital image data of clothing

[1604] Step 2:

[1605] The device (smart glasses) processes the image data of the clothing, removes the background, crops the image, and displays a tagging prompt for the user to add tags.

[1606] Input: Digital image data of clothing

[1607] Data processing: Image processing is performed using OpenCV to remove background and crop. A tagging prompt is displayed so that the user can enter appropriate tags, such as "T-shirt" or "casual."

[1608] Output: Processed image data and tag information

[1609] Step 3:

[1610] The terminal transmits the processed image data and tag information to the server.

[1611] Input: Processed image data and tag information

[1612] Data calculation: Use the Requests library to send data to the server via HTTP.

[1613] Output: Image data and tag information are saved on the server.

[1614] Step 4:

[1615] The user uses smart glasses or other devices to input weather information, minimum and maximum temperatures, and usage information.

[1616] Input: Today's weather information, minimum and maximum temperatures, usage information

[1617] Data processing: The user manually enters the information.

[1618] Output: Weather information, minimum and maximum temperatures, usage information

[1619] Step 5:

[1620] The server analyzes the weather information, minimum and maximum temperatures, and usage information received from the user and sets appropriate condition filters.

[1621] Input: Weather information, minimum and maximum temperatures, usage information

[1622] Data calculation: The server sets condition filters and matches them with clothing data in the database.

[1623] Output: Clothing data that matches the conditions

[1624] Step 6:

[1625] The server uses a generative AI model to generate the optimal outfit based on clothing data that matches the conditions.

[1626] Input: Clothing data that matches the conditions

[1627] Data calculation: The generative AI model suggests optimal outfits based on clothing data and user input.

[1628] Output: Coordination suggestion data

[1629] Step 7:

[1630] A visualization for visually displaying the coordination proposal generated by the server is generated and transmitted to the display of the smart glasses.

[1631] Input: Coordination suggestion data

[1632] Data Computing: Generate visualizations and send data to the smart glasses using the Requests library.

[1633] Output: Coordinated image displayed on the smart glasses display

[1634] Step 8:

[1635] The server selects recommended items to purchase based on the current coordination information and item data owned by the user.

[1636] Input: Coordination suggestion data, user's item data

[1637] Data calculation: The server selects the best recommended items to purchase.

[1638] Output: Recommended purchase item data

[1639] Step 9:

[1640] Information on recommended purchase items is sent to the smart glasses display and presented to the user.

[1641] Input: Recommended purchase item data

[1642] Data calculation: Use the Requests library to send recommended purchase information to the smart glasses.

[1643] Output: Recommended purchase information displayed on the smart glasses display

[1644] 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.

[1645] This invention relates to a system that acquires and records images of clothing owned by a user, proposes optimal outfits based on the weather, temperature, and purpose information for the day, and further optimizes the proposals by recognizing the user's emotions. Specific embodiments of this system are described below.

[1646] System configuration

[1647] This system uses a server, a user device, and an emotion engine. The user device is a smartphone or PC, and provides an interface for taking and uploading images of clothing and inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions.

[1648] Acquiring and saving clothing data

[1649] The user takes a photo of the clothes

[1650] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, they may think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[1651] Device-based image processing and tagging

[1652] The device receives the image of the clothing, performs image processing such as background removal and cropping as needed, and displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt, casual."

[1653] Sending and saving data to the server

[1654] The image data processed by the device is sent to the server along with the tag information. The server stores the received image data in a database and manages metadata including the image file path and tag information.

[1655] Coordination suggestions

[1656] User input of conditions

[1657] The user enters the weather, minimum and maximum temperatures for the day, and the purpose of the trip (business casual, leisure, walk, etc.) through the application. For example, the user might enter "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose leisure."

[1658] Emotion engine emotion recognition

[1659] When a user launches the application, the emotion engine analyzes the user's facial expressions and voice to recognize their current emotional state, for example, by using a camera to analyze whether they are smiling.

[1660] Server-based information processing and coordinate generation

[1661] The server analyzes the weather, temperature, and purpose information received from the user, as well as the emotional data obtained from the emotion engine. It then filters appropriate clothing combinations from the clothing data stored in the database and uses AI to generate optimal outfits. For example, it selects the combination of "T-shirt + jeans + jacket." If the user is expressing positive emotions such as happiness or joy, it will prioritize bright-colored clothing.

[1662] Proposal visualization and display

[1663] The server generates a visualization to display the generated outfits in a visually understandable way. Specifically, it creates an image of a mannequin wearing the outfit and sends it to the terminal. The terminal then displays the outfits presented to the user.

[1664] Suggested purchase items

[1665] Recommended items selected by the server

[1666] The server selects recommended items to purchase based on the user's current outfit information and item data. For example, when recommending new sneakers to match an outfit, it searches for and suggests appropriate items. It also takes into account emotional data and prioritizes displaying items that the user is likely to like.

[1667] Displaying recommended items to users

[1668] The server sends information about the selected recommended items to the terminal, which then displays the recommended items to the user, for example, presenting them as "new sneakers that go well with these jeans."

[1669] Specific examples

[1670] After the user takes a photo of a T-shirt and tags it, the data is sent to the server for storage. Meanwhile, when the user inputs the day's weather information (sunny, maximum 20°C, minimum 15°C) and purpose (leisure), the emotion engine analyzes the user's facial expressions and recognizes positive emotions (e.g., smiling). The server uses AI to generate outfits based on this information and emotion data, such as a "T-shirt + jeans + jacket." The suggested outfits are displayed to the user along with visual images of them being worn by a mannequin. Furthermore, the server selects new sneakers that go well with the outfit as recommended items and displays them to the user. If the user is expressing positive emotions, bright colors and fun designs are prioritized.

[1671] This system allows users to efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in a way that suits their emotions.

[1672] The processing flow will be explained below.

[1673] Step 1:

[1674] The user takes a photo of the clothes.

[1675] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user might think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[1676] Step 2:

[1677] The device processes the image.

[1678] The device receives the photographed image of the clothes, performs image processing such as background removal and cropping as necessary to optimize the image, and then temporarily stores the image data.

[1679] Step 3:

[1680] The device performs the tagging.

[1681] The device will prompt the user to enter appropriate tags, such as "T-shirt, casual," and the user will select and enter the tag.

[1682] Step 4:

[1683] The terminal transmits the data to the server.

[1684] The image data for which tagging has been completed on the terminal is transmitted to the server together with the tag information.

[1685] Step 5:

[1686] The server stores the data.

[1687] The server stores the received image data in a database and manages metadata including image file paths and tag information.

[1688] Step 6:

[1689] The user inputs weather and usage information.

[1690] Through the application, users input the weather, minimum and maximum temperatures for the day, and the purpose of use (for example, "sunny, maximum 20 degrees, minimum 15 degrees, purpose: leisure").

[1691] Step 7:

[1692] The terminal sends the input information to the server.

[1693] The terminal transmits the weather information, temperature information, and usage information input by the user to the server.

[1694] Step 8:

[1695] The emotion engine analyzes the user's emotions.

[1696] The device uses facial recognition technology to capture the user's facial expression data and transmits it to the emotion engine, which then analyzes the user's emotions (e.g., joy, sadness, neutrality, etc.).

[1697] Step 9:

[1698] The server analyzes the condition information.

[1699] The server analyzes the weather, temperature, purpose and emotion data received from the user and from the emotion engine.

[1700] Step 10:

[1701] The server generates the coordinates.

[1702] The server uses AI to filter and generate optimal clothing combinations (e.g., "T-shirt + jeans + jacket") from clothing data stored in a database based on the user's emotional state and usage information.

[1703] Step 11:

[1704] The server generates the visualization.

[1705] The server generates an image of a mannequin wearing the proposed outfit in order to visually display the created outfit in an easy-to-understand manner.

[1706] Step 12:

[1707] The server sends the visualization to the device.

[1708] The server transmits the generated visualization and coordination proposal information to the terminal.

[1709] Step 13:

[1710] The device displays the suggestions to the user.

[1711] The device will then present the user with suggested outfits, such as "Leisure outfits suitable for a sunny 20-degree day."

[1712] Step 14:

[1713] The server selects recommended items for purchase.

[1714] The server selects recommended items to purchase (e.g., new sneakers) based on the user's current outfit information and item data. It also takes into account emotional data and prioritizes displaying items that the user is likely to like.

[1715] Step 15:

[1716] The server transmits recommended item information to the terminal.

[1717] The server transmits information about the selected recommended purchase items to the terminal.

[1718] Step 16:

[1719] The device displays the recommended items to the user.

[1720] The device will then display selected purchase recommendations to the user, such as "new sneakers that go well with these jeans."

[1721] This allows users to efficiently select attractive clothing and receive suggestions that are in line with their emotions, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in accordance with their mood and emotions.

[1722] Example 2

[1723] 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."

[1724] In modern life, users spend a lot of time choosing their daily clothing. It takes a lot of experience to coordinate outfits appropriately based on the weather, temperature, and occasion. It's also extremely difficult to coordinate outfits based on the user's emotional state. This often leads to users choosing inappropriate clothing, which can lead to an unpleasant day. It's also difficult to provide users with appropriate purchasing recommendations.

[1725] 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.

[1726] In this invention, the server includes a means for acquiring and recording images of clothing owned by the user, a means for acquiring the current day's weather information, minimum and maximum temperatures, and usage information, a means for analyzing the acquired facial expressions and voice to recognize the user's emotional state, a means for using artificial intelligence to suggest appropriate clothing combinations based on this information and the user's emotional state, a means for visually displaying the suggested combinations, and a means for presenting recommended items for purchase. This allows the user to quickly and efficiently find the optimal outfit, and suggestions can be made based on the weather, usage, and emotional state. Furthermore, presenting appropriate recommended items for purchase allows the user to appropriately consider purchasing new items.

[1727] The "means for acquiring and recording images" refers to a system for taking photos of clothing owned by the user and saving the image data.

[1728] "Means for obtaining weather information" refers to a system for obtaining meteorological data such as the weather and temperature of the day.

[1729] "Means for acquiring usage information" refers to a system that allows users to input the purpose and situation for wearing the clothing and acquire that information.

[1730] "Means for recognizing emotional state" refers to technology that analyzes the user's facial expressions and voice to determine their current emotions.

[1731] "Means for suggesting using artificial intelligence" refers to a mechanism that utilizes artificial intelligence to generate optimal clothing combinations based on acquired information and perceived emotional state.

[1732] "Visual display means" refers to a technique that visually displays the suggested clothing combinations in a way that is easy for the user to understand.

[1733] "Means for suggesting recommended items for purchase" refers to a system that suggests new clothing and accessories suitable for coordinating with users.

[1734] The "means of tagging and recording" is a system that allows users to tag their clothing to indicate the type, usage situation, etc., and save that information.

[1735] "Data-based suggestion means" refers to technology that determines and suggests appropriate clothing combinations based on stored tagged data.

[1736] "Means for displaying a visual representation on a mannequin" refers to technology that displays the proposed clothing combination as if it were being worn by a virtual mannequin.

[1737] "Means for recommending based on user preferences" refers to technology that recommends new clothing items by taking into account the user's past choices and preferences.

[1738] "Means for suggesting based on user input" refers to technology that generates appropriate clothing combinations based on direct user input information.

[1739] The "means for transmitting and displaying on the user's terminal" refers to a mechanism for transmitting the generated combination information and data on recommended purchase items to the user's terminal and displaying them.

[1740] This invention relates to a system that acquires and records images of clothing owned by a user, suggests optimal outfits based on the day's weather information, temperature information, and usage information, and further optimizes suggestions by recognizing the user's emotions.

[1741] System configuration

[1742] This system uses a server, a user device, and an emotion engine. The user device is a smartphone or PC, and provides an interface for taking and uploading images of clothing and inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions.

[1743] System Operation

[1744] 1. Acquiring and saving clothing data

[1745] A user uses the smartphone camera to take pictures of various types of clothing they own. For example, if they want to upload a T-shirt, they take a picture of the T-shirt with their smartphone. The device receives the image and performs image processing such as background removal and cropping. It also prompts the user to enter tags, and the user enters appropriate tags such as "T-shirt" or "casual." The device then sends the processed image data along with the tag information to the server. The server stores the received data in a database and manages the image file path and tag information as metadata.

[1746] 2. Coordination suggestions

[1747] The user enters the day's weather (e.g., sunny, cloudy, rainy), minimum and maximum temperatures, and intended use (e.g., business casual, leisure, walk) through a smartphone app. Specifically, the user enters "sunny, maximum temperature 20°C, minimum temperature 15°C, intended use leisure." When the user launches the application, the emotion engine analyzes the user's facial expressions and voice through the camera and identifies their current emotion as "positive" or "negative." The server then performs an integrated analysis of the weather, temperature, and intended use information received from the user, along with the emotional data obtained from the emotion engine. The server then filters appropriate clothing combinations from the clothing data stored in the database and generates an optimal outfit using a generative AI model. For example, a T-shirt, jeans, and jacket outfit is selected. If the user indicates a positive emotion, bright-colored items are prioritized. The server generates an image of a mannequin wearing the proposed outfit to visually display it and sends it to the device. The device then displays the proposed outfit to the user.

[1748] 3. Recommended purchase items

[1749] The server analyzes the generated outfit and the user's item data to select the necessary items (such as new sneakers). It also takes into account emotional data and recommends items that the user is likely to like. The server then sends information about recommended items to the device, which then displays this to the user in the form of, for example, "New sneakers that go well with these jeans."

[1750] Specific examples

[1751] After the user takes a photo of a T-shirt and tags it as "T-shirt" and "casual," the device sends the image data to the server, which stores the image and tag information in a database. At the same time, the user enters weather information ("sunny, maximum 20 degrees, minimum 15 degrees") and purpose information ("leisure") into the app. The emotion engine analyzes the user's facial expression and recognizes "positive" (smiling). Based on these conditions, the server uses an AI model to generate a "T-shirt + jeans + jacket" outfit and displays it as a visualization. The server also selects recommended items, such as new sneakers, and presents them to the user. An example prompt is shown below.

[1752] "A user uploaded a T-shirt and entered 'casual' and 'T-shirt' in the tags. Then, the user entered 'sunny' for the weather on the day, a maximum temperature of '20 degrees', a minimum temperature of '15 degrees', and 'leisure' as the purpose. The user was recognized as smiling. Please suggest the best outfit based on these conditions."

[1753] This system will significantly reduce the effort required for users to choose their daily fashion and allow them to enjoy the best possible coordination that suits their emotions and the situation.

[1754] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1755] Step 1:

[1756] A user takes a photo of clothing they own with their smartphone camera. Specifically, the user wants to upload a T-shirt, so they take a photo of the T-shirt. The input is the image taken with the smartphone, and the output is image data of the clothing.

[1757] Step 2:

[1758] The device receives the captured image and performs image processing such as background removal and cropping. The input is the captured image, and the output is the cropped image data with the background removed. The device displays a tagging prompt to the user, asking them to enter appropriate tags such as "T-shirt" or "casual." The input is the user's tagging information, and the output is the tagged image data.

[1759] Step 3:

[1760] The device sends the processed image data and tag information to the server. The input is tagged image data, and the output is the completed transmission to the server. The server saves the received image data in a database and manages the image file path and tag information as metadata. The input is tagged image data, and the output is the data saved in the database.

[1761] Step 4:

[1762] The user inputs the weather, minimum and maximum temperatures, and purpose of use for the day through a smartphone app. For example, they might input "sunny, maximum temperature 20 degrees, minimum temperature 15 degrees, purpose of use leisure." The input is information such as the weather, temperature, and purpose entered by the user, and the output is data related to these conditions.

[1763] Step 5:

[1764] When a user launches the application, the emotion engine analyzes the user's facial expressions and voice through the camera to recognize their current emotional state. For example, it recognizes a user's smile and determines that it is a positive emotion. The input is the user's facial expressions and voice, and the output is the recognized emotional data.

[1765] Step 6:

[1766] The server analyzes the weather, temperature, and purpose information received from the user, as well as the emotion data obtained from the emotion engine. The input is weather, temperature, purpose, and emotion data, and the output is the analysis result obtained by integrating this information. Then, appropriate clothing combinations are filtered from the clothing data stored in the database. The optimal outfit is generated using a generative AI model. For example, a combination of T-shirt + jeans + jacket is selected. The input is the filtering conditions (weather, temperature, purpose, emotion data), and the output is the generated outfit data.

[1767] Step 7:

[1768] The server generates a visualization to display the proposed outfits in a visually easy-to-understand manner. Specifically, it creates an image of a mannequin wearing the proposed outfit and sends it to the terminal. The input is the generated outfit data, and the output is the visualization image. The terminal displays the proposed outfit to the user. The input is the visualization image, and the output is the outfit image displayed to the user.

[1769] Step 8:

[1770] The server analyzes the generated coordination information and the user's owned item data. The input is the coordination information and owned item data, and the output is the analysis results. Based on the analysis results, the server selects the necessary items (e.g., new sneakers). The input is the analysis results, and the output is the selected recommended purchase items. It also takes into account emotional data and recommends items that the user is likely to like. The input is emotional data, and the output is recommended purchase items based on emotions.

[1771] Step 9:

[1772] The server sends information about the selected recommended purchase items to the terminal. The input is the selected recommended purchase item information, and the output is the completion of transmission to the terminal. The terminal presents the recommended purchase item to the user, such as "new sneakers that go well with these jeans." The input is the recommended purchase item information sent from the server, and the output is the recommended purchase item information displayed to the user.

[1773] (Application example 2)

[1774] 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."

[1775] Conventional outfit suggestion systems have difficulty making optimal suggestions based on the user's emotions and real-time circumstances. Furthermore, users have limited means of directly visually confirming their outfits, which means there is a lack of consistency between the suggested outfits and the actual outfits or purchases they make. Furthermore, there is also the problem that the suggested items do not adequately reflect the user's emotions, tastes, and preferences.

[1776] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing and analyzing the user's emotions, means for visualizing and displaying suggested outfits on the smart glasses, and means for displaying recommended items in cooperation with a virtual store. This allows the optimal outfit suggestions to be made based on the user's emotions and real-time situation, and the suggestions can be visually confirmed, enabling a more integrated outfit and purchasing experience.

[1777] "Clothing image acquisition means" refers to devices and software that allow users to photograph and record their own clothing and acquire image data.

[1778] "Weather information acquisition means" refers to a device or software for acquiring information about the weather and temperature of the day.

[1779] "Use information acquisition means" refers to a device or software that acquires the situation and purpose (business, leisure, etc.) in which the clothing will be used.

[1780] A "clothing combination suggestion means" is a device or software that suggests clothing combinations suitable for the weather and purpose based on clothing data held by the user.

[1781] A "visual display means" is a device or software that visually presents suggested clothing combinations to a user.

[1782] The "means for presenting recommended items for purchase" is a device or software that recommends new items that go well with the proposed outfit to the user.

[1783] "Emotion recognition means" refers to devices or software that analyze a user's facial expressions and voice and recognize their emotional state.

[1784] "Smart glasses" are wearable devices that allow users to obtain information visually by wearing them.

[1785] A "visualization means" is a device or software that displays suggested outfits in the user's field of vision in real time.

[1786] A "virtual store" is a virtual store system where you can browse and purchase products online.

[1787] "Artificial intelligence" refers to algorithms and software that can analyze data and make decisions automatically.

[1788] This invention is a system that acquires and records images of clothing owned by a user and suggests optimal outfits based on weather information, temperature information, usage information, and emotional data. Furthermore, it has the function of visually displaying the suggested outfits using smart glasses and presenting recommended items for purchase. A specific embodiment of this system will be described below.

[1789] System configuration

[1790] The system's main components are a server, a user device (smartphone or PC), smart glasses, and an emotion engine. The user device takes and uploads images of clothing and provides an interface for inputting weather and usage information. The server stores and analyzes image data, processes emotion data, suggests outfits, and selects recommended items for purchase. The emotion engine is a component that recognizes and analyzes user emotions. The smart glasses function as a device for visualizing suggested outfits in real time.

[1791] Acquiring and saving clothing data

[1792] The user takes a photo of the clothes

[1793] Users take photos of various clothes they own (shirts, pants, jackets, etc.) with their smartphone or camera. For example, a user might think, "I want to upload this T-shirt," and take a photo of that T-shirt.

[1794] Device-based image processing and tagging

[1795] The device receives the image of the clothing and performs image processing such as background removal and cropping. Using OCR technology, it automatically tags the clothing and prompts the user to confirm the appropriate tag (e.g., "T-shirt, casual").

[1796] Sending and saving data to the server

[1797] The image data processed on the device is sent to the server along with the tag information. The server stores the received image data in a database and manages metadata including the image file path and tag information. MySQL or MongoDB are suitable databases.

[1798] Coordination suggestions

[1799] User input of conditions

[1800] Through the application, users input the weather for the day, the minimum and maximum temperatures, and the purpose of the outfit (business casual, leisure, walking, etc.).

[1801] Emotion engine emotion recognition

[1802] When a user launches the application, it uses an emotion engine (e.g., AWS Rekognition, IBM Watson) to analyze the user's facial expressions and voice to recognize their current emotional state. It also uses a camera to analyze facial expressions.

[1803] Server-based information processing and coordinate generation

[1804] The server analyzes the weather, temperature, and usage information received from the user, as well as the emotion data obtained from the emotion engine. It then uses an AI model (e.g., TensorFlow, PyTorch) to generate the optimal outfit. For example, it selects the combination of "T-shirt + jeans + jacket." If the user expresses positive emotions, it will prioritize bright-colored clothing.

[1805] Proposal visualization and display

[1806] The server then generates an image of the outfit being worn by a mannequin to visually display it in a way that is easy to understand. This image data is then sent to the smart glasses, which display it in real time to the user's field of vision.

[1807] Suggested purchase items

[1808] Recommended items selected by the server

[1809] The server works with the virtual store to select recommended items based on the user's current outfit information and item data. Recommended items are prioritized based on the user's emotional data and prioritize items that match the outfit.

[1810] Displaying recommended items to users

[1811] The server sends the information of the selected recommended items to the smart glasses, and displays appropriate recommended items to the user, such as "new sneakers that go well with these jeans."

[1812] Examples of concrete examples and prompts

[1813] As a concrete example, we will explain how a user takes a photo of a T-shirt, tags it, and then sends the data to a server for storage. When the user inputs the day's weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure), the emotion engine analyzes the user's facial expressions and recognizes positive emotions (e.g., smiling). Based on this information, the server generates a "T-shirt + jeans + jacket" outfit and visualizes it on the smart glasses. Furthermore, the server connects with a virtual store to recommend new sneakers that go well with this outfit and displays them to the user.

[1814] An example of a prompt is as follows:

[1815] "The system removes the background from images of clothing taken by the user and automatically tags them. Based on weather, temperature, usage information, and the user's emotional data, it generates optimal outfits and visualizes them on the smart glasses display. This uses computer vision and emotion recognition."

[1816] This system allows users to efficiently select attractive clothing, significantly reducing the effort required for fashion selection in everyday life and allowing them to enjoy dressing in a way that suits their emotions.

[1817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1818] Program processing flow

[1819] Step 1: Image capture and tagging of clothing

[1820] The device receives an image of clothing taken by the user. The image undergoes image processing such as background removal and cropping, and is automatically tagged using OCR technology. The tag information and image data are sent from the device to the server. The input is the "image of clothing," and the output is the "background-removed image and tag information."

[1821] Step 2: Obtaining weather information, temperature information, and usage information

[1822] The user enters the day's weather information, minimum and maximum temperatures, and usage information through the application. The input is "weather information, temperature information, usage information," and the output is "weather information, temperature information, usage information data." This allows the server to obtain the environmental data necessary for analysis.

[1823] Step 3: Acquire and analyze emotion data

[1824] When a user launches the application, the device (smart glasses or smartphone) captures the user's facial expressions and voice using a camera and microphone. This data is sent to the emotion engine, which analyzes the user's emotional state. The input is "facial expression and voice data," and the output is "emotion data."

[1825] Step 4: Generate coordinates

[1826] The server uses an AI model (such as TensorFlow or PyTorch) to analyze weather information, temperature information, usage information, emotional data, and clothing data stored in a database, and generates an appropriate outfit. The input is "weather information, temperature information, usage information, emotional data, clothing data," and the output is "optimal outfit information."

[1827] Step 5: Visualization and visualization

[1828] The server generates a visual representation of the outfit on a mannequin based on the coordinated outfit information. The generated image is sent to the smart glasses, allowing the user to view it in real time. The input is "coordinated outfit information," and the output is a "visualized image."

[1829] Step 6: Select recommended items to purchase

[1830] The server analyzes the user's outfit information and owned item data to select recommended items for purchase. It connects with the virtual store to obtain related product data. The input is "outfit information and owned item data," and the output is "a list of recommended items for purchase."

[1831] Step 7: View recommended items to buy

[1832] The server sends the selected recommended items to the smart glasses, and the user can purchase the items through a purchase link. The input is a "list of recommended items to purchase," and the output is a "purchase link and item information."

[1833] Specific examples and prompts

[1834] Examples:

[1835] The user takes a photo of a T-shirt through the smart glasses, and the image with the background removed is sent to the server. The user then enters weather information (sunny, maximum 20 degrees, minimum 15 degrees) and purpose (leisure) into the application. At the same time, the emotion engine analyzes the user's positive emoti...

Claims

1. A means for acquiring and recording images of clothing owned by the user; A means for acquiring the current day's weather information, minimum and maximum temperatures, and usage information; A method for proposing appropriate clothing combinations based on this information, and a means for visually displaying the proposed combinations; A system including means for presenting recommended items for purchase.

2. A means for tagging and recording images of clothing owned by a user; A means for generating a suggested clothing combination based on the tagged image data, taking into consideration weather information, temperature information, and usage information; a means for displaying the proposed combinations in a visual representation, such as on a mannequin; 10. The system of claim 1, further comprising means for recommending new clothing items to add to the user's wardrobe based on the user's tastes and preferences.

3. A means of utilizing artificial intelligence to suggest clothing combinations based on user input; The system according to claim 1, further comprising means for transmitting the generated combination information and recommended purchase items to a user terminal and displaying them.

Images