System

Abstract

A system is provided.SOLUTION: The system includes a means for registering images of clothes owned by a user, a means for collecting weather information, temperature information, and trend information of a current location, a means for selecting optimal clothes based on the collected information, and a means for proposing the selected clothes to the user.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 clothing plays an important role in an individual's daily life. However, choosing the right outfit for each day based on changes in weather, temperature, and fashion trends can be a tedious and time-consuming task. Choosing the best outfit for each day can be particularly stressful for busy people. To solve this problem, a system is needed that automatically suggests the best outfit by utilizing information such as weather, temperature, and fashion trends. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for registering images of clothes owned by a user, a means for collecting weather information, temperature information, and trend information for the user's current location, a means for selecting optimal clothing based on the collected information, and a means for suggesting the selected clothing to the user. Specifically, the system includes a means for automatically classifying clothing categories and attributes from registered clothing images using image recognition technology, and provides a means for generating and suggesting optimal clothing based on weather information, temperature information, and trend information in response to a user request. This allows users to easily decide on their daily outfits and reduces the stress of choosing their daily outfits.

[0006] "User" refers to any individual or legal entity that uses the System.

[0007] "Image recognition technology" refers to technology that includes algorithms and methods for extracting and identifying specific information from an input image.

[0008] "Weather Information" means data regarding current and forecast weather conditions.

[0009] "Temperature Information" refers to data regarding current and forecast temperatures.

[0010] "Trend information" refers to data on the latest fashions and trends.

[0011] "Means" refers to functions or devices provided to achieve a specific purpose.

[0012] "Clothing images" refer to still images taken or uploaded by a user of clothing they own.

[0013] "Clothing category" refers to the type or classification of clothing, and includes classifications such as shirts, pants, and dresses.

[0014] "Attributes" refer to characteristics of clothing such as color, design, and style.

[0015] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[0016] An "algorithm" refers to a computational procedure or processing method for solving a specific problem.

[0017] "Server" refers to a central computer system for providing a particular service.

[0018] "Terminal" refers to a device operated by a user, such as a computer, smartphone, or tablet.

[0019] A "request" refers to an instruction given by a user requesting a specific operation or information.

[0020] "Coordination" refers to the action of combining multiple pieces of clothing to select the most suitable outfit.

[0021] "Cloud storage" refers to a remote storage service for storing data over the Internet. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] ---

[0044] This invention is a system that allows users to register images of their clothing, and then selects and suggests optimal clothing to the user based on collected weather, temperature, and trend information for the user's current location. The program processing of this system is described in detail below.

[0045] Clothing registration

[0046] Taking and uploading images

[0047] Users use their smartphones or other devices to take photos of new clothes or upload existing photos to the app, which then collects image data of the clothes the user owns.

[0048] Image transmission

[0049] The device sends the uploaded image data to the server. After the sending is complete, the device notifies the user.

[0050] Image analysis

[0051] The server stores the received images in cloud storage. Using image recognition technology, the server analyzes the images of the clothes and automatically classifies them into categories (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[0052] Saving to a database

[0053] The server saves the analysis results in a database as clothing attribute data, and once the data has been saved, the server notifies the device.

[0054] Gathering external information

[0055] Obtaining weather and temperature information

[0056] The server calls the weather API at a specified time (e.g., every morning at 8:00) to obtain weather and temperature information for the user's current location. This information is then updated in the weather database.

[0057] Obtaining trend information

[0058] The server accesses the trend APIs of fashion news sites and social media sites to obtain the latest trend information, which is then updated in the trend database.

[0059] Choosing the best outfit

[0060] Sending a coordination request

[0061] The user launches the app and sends a request saying, "Suggest an outfit for today."

[0062] Obtaining information

[0063] The server retrieves the latest weather, temperature, and trend information from a database.

[0064] Coordinate generation

[0065] Based on this information, the server uses an algorithm to generate combinations of clothes from the database, which then selects the optimal outfit based on weather (e.g., light clothing suitable for a sunny day at 25°C) and trend information (e.g., navy blue is in fashion).

[0066] Suggestions for users

[0067] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[0068] Specific examples

[0069] Clothing registration

[0070] A user purchases a new navy dress and uploads a photo of it to the app.

[0071] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "dress," "navy," and "casual" and store the images in a database.

[0072] Gathering external information

[0073] The server calls the weather API every morning at 8:00, obtains information such as "sunny, 25°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "navy color is trending."

[0074] Choosing the best outfit

[0075] The user launches the app and requests, "Suggest an outfit for today."

[0076] The server selects a "navy dress," "white sneakers," and "beige cardigan" based on weather, temperature, and trend information, and sends the outfit to the device.

[0077] The terminal displays the proposal to the user, who confirms it.

[0078] This embodiment allows the user to easily select the most suitable outfit, reducing the effort and stress of everyday outfit selection.

[0079] The processing flow will be explained below.

[0080] Clothing registration

[0081] Step 1:

[0082] Users use their smartphone to take a photo of a new outfit or upload a photo of an existing outfit to the app.

[0083] Step 2:

[0084] The terminal transmits the image data received from the user to the server.

[0085] Step 3:

[0086] The server stores the received images in cloud storage.

[0087] Step 4:

[0088] The server uses image recognition technology to analyze the image and automatically classify the clothing into categories and attributes, such as dresses, shirts, pants, navy, casual, etc.

[0089] Step 5:

[0090] The server saves the analysis results in a database and notifies the user via the terminal that saving has been completed.

[0091] Gathering external information

[0092] Step 1:

[0093] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[0094] Step 2:

[0095] The server updates the weather and temperature information it has obtained into a weather database.

[0096] Step 3:

[0097] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[0098] Step 4:

[0099] The server updates the acquired trend information in the trend database.

[0100] Choosing the best outfit

[0101] Step 1:

[0102] The user launches the app and sends a request saying, "Suggest an outfit for today."

[0103] Step 2:

[0104] The server receives the request and retrieves the latest weather, temperature, and trend information from the database.

[0105] Step 3:

[0106] Based on the above information, the server generates the optimal outfit combination from a clothing database. For example, consider a light outfit suitable for a sunny day with a temperature of 25°C.

[0107] Step 4:

[0108] The server creates a list of the coordinates it has generated and sends the suggestions to the terminal.

[0109] Suggestions for users

[0110] Step 1:

[0111] The terminal displays the coordinate proposal received from the server on a user interface.

[0112] Step 2:

[0113] The device notifies the user, informing them that "Today's outfit is ready."

[0114] Step 3:

[0115] The user checks the proposed outfits and, if they like them, they adopt them. If they don't like them, they send a new request asking for other suggestions.

[0116] Step 4:

[0117] The server receives the re-request, generates another coordinate, and sends it again to the terminal.

[0118] Example 1

[0119] 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."

[0120] In today's world, choosing the right outfit for each day is a time-consuming and labor-intensive task for many people. It is particularly difficult to choose the perfect outfit that takes into account the weather, temperature, and the latest fashion trends. Furthermore, there are few effective systems that allow users to effortlessly find the perfect combination from among their existing clothes. This invention aims to solve the above-mentioned problem by allowing users to register images of their own clothes, collecting external information, and suggesting the best outfits based on that information.

[0121] 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.

[0122] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting meteorological information, temperature information, and fashion information for the current location, means for selecting optimal clothing based on the collected information, and means for suggesting the selected clothing to the user. This allows the user to easily select optimal clothing, reducing the effort and stress of choosing clothes every day.

[0123] "User" refers to an individual who uses the system to register images of clothing and use suggested outfits as a reference.

[0124] "Server" refers to a computer system that processes data sent by users, collects external information, generates optimal clothing suggestions, and provides them to users.

[0125] "Device" refers to a mobile information terminal such as a smartphone or tablet, which allows a user to take and upload images of clothes and receive and display suggestions from the server.

[0126] "Means for registering images of clothes" refers to a method or device that allows a user to take or upload images of clothes using a terminal and send them to a server.

[0127] "Weather information" refers to environmental information such as the weather, temperature, and humidity at the user's current location.

[0128] "Temperature information" refers to information about the temperature at a particular location and time.

[0129] "Trend information" refers to information about current fashion trends.

[0130] "Image analysis technology" refers to technology that uses computer vision and machine learning to recognize objects in images and automatically classify their attributes and categories.

[0131] "Machine learning algorithms" refer to computer algorithms that learn patterns and rules in new data based on past data.

[0132] "Cloud storage" refers to remote data storage services that allow you to store, manage, and access data over the Internet.

[0133] "API" stands for Application Program Interface, a set of definitions and protocols that allow software to communicate with other software.

[0134] "Suggestion" refers to the optimal outfit combination generated based on the information collected by the server and the user's data.

[0135] This invention is a system that allows a user to register images of clothes owned by the user, and selects and suggests optimal clothing to the user based on collected weather information, temperature information, and fashion information for the user's current location. The following describes in detail an embodiment of the invention.

[0136] The system consists of three main components: a server, a terminal, and a user.

[0137] Users can use their smartphones or other devices to take pictures of their clothes or upload existing photos to the app. For example, if a user purchases a new navy dress, they can take a photo of it using their smartphone's camera and upload the image through the app. This image is then sent from the device to the server.

[0138] The server saves the uploaded images in cloud storage (e.g., Amazon S3). It then analyzes the images using image analysis technology (e.g., Google® Cloud Vision API) and automatically classifies the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.). The analysis results are saved in a database (e.g., MySQL®). After saving is complete, the server sends a notification to the device to inform the user of the results.

[0139] Next, the server calls a weather API (e.g., OpenWeatherMap API) at a specified time (e.g., every morning at 8:00) to obtain weather information and temperature information for the user's current location. This information is updated in the weather database. The server also calls a trend API (e.g., Twitter Trending Topics API) to obtain the latest fashion trend information and update the trend database.

[0140] When a user launches the app and sends a request to "Suggest me today's outfit," the server retrieves the latest weather, temperature, and fashion information from the database. Based on this information, the server uses a machine learning algorithm (for example, an algorithm using Python) to generate outfit combinations from the database. In this way, the optimal outfit, such as a navy dress, white sneakers, and a beige cardigan, is selected from the collected information.

[0141] The server sends the generated outfits to the terminal, which displays the suggestions to the user. The user can then check the suggested outfits and select them as their outfit for the day.

[0142] Prompt Sentence Examples

[0143] Below is an example of an input prompt sentence to the generative AI model to explain this system.

[0144] A user takes a photo of a "navy dress" using a smartphone app and uploads the image. The device sends the image to a server, which analyzes the image using Google Cloud Vision API. The analysis results are saved in a database. Weather information is obtained using the OpenWeatherMap API every morning at 8:00, and trend information is collected using the Twitter Trending Topics API. When a user requests "Suggest an outfit for today," the server generates the optimal outfit and displays it on the device. This process is explained below.

[0145] This embodiment allows the user to easily select the most suitable outfit, reducing the effort and stress of everyday outfit selection.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1: Take and upload a photo of your outfit

[0148] Specific explanation

[0149] The user takes a photo of a new outfit using the smartphone camera or selects an existing photo from the photo library. The input is the image, and the output is image data, which is stored in the device's application.

[0150] Specific actions

[0151] A user opens the application and taps the camera button to take a photo. The captured image is displayed within the application. The user then taps the "Upload" button, and the image is ready to be sent to the server.

[0152] Step 2: Sending image data

[0153] Specific explanation

[0154] The device sends the captured or selected image to the server. The input is the registered image data, and the output is the image data sent to the server. The device sends the image data to the server using the REST API.

[0155] Specific actions

[0156] The device encodes the image data and sends it to the REST API endpoint. The server receives the image data and prepares it for storage.

[0157] Step 3: Save your images to cloud storage

[0158] Specific explanation

[0159] The server stores the received images in cloud storage. The input is the received image data, and the output is the stored image on cloud storage. The server stores the images using a cloud storage service such as Amazon S3.

[0160] Specific actions

[0161] The server uploads the received image data to an S3 bucket and obtains the destination URL after saving is complete.

[0162] Step 4: Image analysis

[0163] Specific explanation

[0164] The server analyzes images stored in cloud storage using image analysis technology. The input is the image URL in cloud storage, and the output is the analysis results of clothing categories and attributes. The server performs image analysis using the Google Cloud Vision API, etc.

[0165] Specific actions

[0166] The server sends the image URL to the Google Cloud Vision API and receives the analysis results (e.g., one-piece dress, navy, casual). The server then retrieves the analysis results in JSON format and prepares to store them in the database.

[0167] Step 5: Saving to the Database

[0168] Specific explanation

[0169] The server saves the results of image analysis in a database. The input is the analysis result data, and the output is attribute data saved in the database. The server saves the data using a database system such as MySQL.

[0170] Specific actions

[0171] The server generates an SQL query to save the analysis results to a MySQL database, executes the query, saves the results to the database, and sends a notification to the device after saving is complete.

[0172] Step 6: Gather weather and trend information

[0173] Specific explanation

[0174] The server calls the weather API and trend API at the specified time to collect the latest external information. The input is the API request, and the output is weather and trend information data. The server uses the OpenWeatherMap API and Twitter Trending Topics API, etc.

[0175] Specific actions

[0176] Every morning at 8:00, the server calls the OpenWeatherMap API to obtain weather information such as "Sunny, 25°C." It also calls the Twitter Trending Topics API to obtain trend information such as "Navy color is trending." This information is then stored in a database.

[0177] Step 7: Receiving a Coordination Request

[0178] Specific explanation

[0179] The user launches the app and sends a request saying, "Suggest an outfit for today." The input is the request data from the user, and the output is a confirmation that the request was received. The server prepares to process the request.

[0180] Specific actions

[0181] When a user taps the "Suggest an outfit" button on the app's home screen, a request is sent to the server, which receives the request and begins processing it.

[0182] Step 8: Generate coordinates

[0183] Specific explanation

[0184] The server generates optimal outfits based on weather information, fashion trends, and the user's clothing database. The input is weather information, fashion trends, and clothing database data, and the output is the generated outfit. The server generates outfits using a machine learning algorithm.

[0185] Specific actions

[0186] The server runs a machine learning algorithm implemented in Python to generate outfits such as a navy dress, white sneakers, and a beige cardigan. The generated results are obtained in JSON format and sent to the device as the final proposal data.

[0187] Step 9: Displaying suggestions to the user

[0188] Specific explanation

[0189] The terminal receives the coordinate suggestion sent from the server and displays it to the user. The input is the suggestion data from the server, and the output is the coordinate displayed to the user.

[0190] Specific actions

[0191] The device analyzes the proposal data received from the server and displays it on the user interface. The user checks the proposal and selects it as the outfit for the day.

[0192] By following the above steps, the system can use the user's clothing images to suggest optimal outfits based on weather information and fashion trends.

[0193] (Application example 1)

[0194] 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."

[0195] In modern society, it is important for users to choose appropriate clothing when going out. However, taking into account the weather, temperature, and the latest fashion trends takes a lot of time and effort. At the same time, the use of delivery services is increasing, and there is a demand for suggesting the most appropriate clothing for the delivery situation. Existing systems do not exist that can process weather, temperature, and trend information in an integrated manner and respond when ordering delivery. Therefore, the challenge is to provide a system that allows users to smoothly select appropriate clothing.

[0196] 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.

[0197] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting weather information, temperature information, and trend information for the current location, and means for suggesting optimal clothing based on the weather, temperature, and trend information at the time of ordering delivery, thereby enabling the user to easily select appropriate clothing in real time, including when ordering delivery.

[0198] "User" refers to a person who accesses the system, registers images of clothing, and receives suggestions.

[0199] "Means for registering images of clothes" refers to the function of uploading images of clothes owned by the user to the system and storing them in the database.

[0200] "Current location weather information" refers to data about the weather in the area where the user is located.

[0201] "Temperature information" refers to data relating to the temperature in the area where the user is located.

[0202] "Trend information" refers to information about the latest fashions and styles.

[0203] "Means of collection" refers to the functionality for obtaining weather information, temperature information, and trend information from external sources.

[0204] "Means for selecting optimal clothing" refers to a function that generates an appropriate outfit from the clothes the user owns based on collected information.

[0205] "Means for suggesting to the user" refers to a function that displays the selected outfit coordination on the user's device to inform the user.

[0206] "A means of suggesting the most appropriate clothing based on the weather, temperature, and trend information at the time of ordering delivery" refers to a function that suggests appropriate clothing by taking into account the weather, temperature, and trend information at the time of ordering delivery.

[0207] This invention is a system that allows users to register images of their clothing, and selects and suggests optimal clothing to users based on collected weather, temperature, and trend information for their current location. It also includes a function to suggest appropriate clothing depending on the situation at the time of delivery order.

[0208] System Program Overview

[0209] 1. Clothing registration

[0210] Users take pictures of the clothes using a device such as a smartphone and upload them to the app.

[0211] The terminal transmits the uploaded image data to the server.

[0212] The server stores the images in cloud storage and uses image recognition technology to automatically classify the clothing categories and attributes, then stores them in a database.

[0213] 2. Gathering external information

[0214] The server calls the weather API, obtains the weather and temperature information for the user's current location, and updates the weather database.

[0215] The server calls the trend API, obtains the latest trend information, and updates it in the trend database.

[0216] 3. Choosing the right outfit

[0217] The user launches the app and sends a request to "suggest an outfit for today."

[0218] The server retrieves weather information, temperature information, and trend information from a database.

[0219] Based on this information, the server uses an algorithm to generate the optimal outfit combination.

[0220] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[0221] 4. Recommended attire for delivery orders

[0222] When a user places an order for delivery, the server obtains weather information, temperature information, and trend information at the time of the order.

[0223] Based on this information, the server selects the most suitable outfit for the delivery and suggests it to the user.

[0224] Hardware and Software Configuration

[0225] Smartphone: Used as the user's device to take and upload images and display suggestions.

[0226] Cloud storage: Used as a storage location for image data.

[0227] Server: Calls various APIs (weather API, trend API) and manages the database.

[0228] Database: Stores and manages clothing attributes, weather information, and trend information.

[0229] Algorithm: Implements logic to select the best outfit based on weather, temperature, and trend information.

[0230] Specific examples

[0231] Clothing registration

[0232] A user purchases a new "black jacket" and uploads a photo to the app.

[0233] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "jacket," "black," and "formal" and store the images in a database.

[0234] Gathering external information

[0235] The server calls the weather API every morning at 8:00, obtains information such as "Cloudy, 18°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "Black is in fashion."

[0236] Choosing the best outfit

[0237] The user launches the app and requests, "Suggest an outfit for today."

[0238] The server selects a "black jacket," "white shirt," and "black pants" based on weather, temperature, and trend information, and sends the outfit to the device.

[0239] The terminal displays the proposal to the user, who confirms it.

[0240] Recommendations for the best attire when ordering delivery

[0241] A user orders delivery and checks weather forecasts and other information when it arrives.

[0242] The server suggests "waterproof coats," "casual jeans," and "waterproof shoes" based on weather information, temperature information, and trend information at the time of the user's order.

[0243] The terminal displays these suggestions to the user for confirmation.

[0244] Prompt Sentence Examples

[0245] Prompt sentence to input to the generative AI model:

[0246] text

[0247] We will develop a system that allows users to register images of their clothing and suggests optimal outfits based on external weather information, temperature information, and trend information.

[0248] Clothes are uploaded as images to the app, which then collects the latest information from weather and trend APIs and uses an algorithm to suggest outfits.

[0249] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0250] Step 1:

[0251] The user takes a photo of the clothes using a device such as a smartphone and uploads it to the app. The input is the photographed image of the clothes, and the output is the uploaded image data. The user uses the app's image upload function, selects the image file, and taps the send button.

[0252] Step 2:

[0253] The device sends the uploaded image data to the server. The input is the image data uploaded by the user, and the output is the image data sent to the server. The device compresses the image data and sends it to the specified URL on the server via the Internet.

[0254] Step 3:

[0255] The server stores the images in cloud storage and uses image recognition technology to automatically classify the clothing category and attributes, which are then stored in a database. The input is the image data sent to the server, and the output is a database entry containing the clothing category and attributes. The server runs an image recognition algorithm, analyzes the clothing features in the image, and extracts the category (e.g., jacket) and attributes (e.g., black, formal), which are then stored in the database.

[0256] Step 4:

[0257] The server calls the weather API, obtains weather and temperature information for the user's current location, and updates the weather database. The input is the user's current location, and the output is the latest weather and temperature data. The server calls the specified API endpoint and saves the obtained data in the weather database.

[0258] Step 5:

[0259] The server calls the trend API, obtains the latest trend information, and updates it in the trend database. There is no input (periodic updates), and the output is the latest trend information. The server periodically calls the trend APIs of fashion news sites and SNS, and saves the obtained trend data in the trend database.

[0260] Step 6:

[0261] The user launches the app and requests, "Suggest an outfit for today." There is no input (user operation), and the output is an outfit suggestion request. When the user opens the app, they select an outfit suggestion request using a dedicated button or menu.

[0262] Step 7:

[0263] The server retrieves the latest weather, temperature, and trend information from the database. The input is a request for outfit suggestions from the user, and the output is the latest weather, temperature, and trend information. The server executes a database query to retrieve the required data.

[0264] Step 8:

[0265] The server uses an algorithm to generate the optimal outfit combination based on this information. The input is the acquired weather, temperature, and trend information, and the output is the optimal outfit combination. The server runs the algorithm, selects the optimal outfit based on each piece of information, and generates the result.

[0266] Step 9:

[0267] The server sends the generated outfit to the terminal, which then displays the suggestions to the user. The input is the generated optimal outfit, and the output is the suggestions displayed on the user's terminal. The server sends the data again, and the terminal receives it and displays it on the user interface.

[0268] Step 10:

[0269] A user places a delivery order and sends the information to the server. The input is the delivery order information, and the output is a notification to the server. The user places an order from the delivery app, and the information is notified to the system.

[0270] Step 11:

[0271] The server retrieves weather, temperature, and trend information at the time of order. The input is the delivery order notification, and the output is the latest weather, temperature, and trend information. The server immediately calls the API to retrieve new data.

[0272] Step 12:

[0273] The server selects the best outfit for delivery arrival, and the device displays the suggestion to the user. The input is acquired weather, temperature, and trend information, and the output is the suggested outfit for the user. The server uses an algorithm to select the outfit and sends the result to the device, which displays it.

[0274] 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.

[0275] ---

[0276] This invention is a system that suggests optimal outfits by registering images of clothes owned by the user, collecting weather information, temperature information, and trend information, and combining this with an emotion engine that recognizes the user's emotions. The program processing of this system is explained in detail below.

[0277] Clothing registration

[0278] Taking and uploading images

[0279] Users use their smartphones to take photos of new clothes or upload photos of existing clothes to the app, which then collects image data of the clothes the user owns.

[0280] Image transmission

[0281] The terminal transmits the image data received from the user to the server.

[0282] Image analysis

[0283] The server stores the received images in cloud storage. Using image recognition technology, the server analyzes the images of the clothes and automatically classifies them into categories (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[0284] Saving to a database

[0285] The server stores the analysis results in a database as clothing attribute data.

[0286] Gathering external information

[0287] Obtaining weather and temperature information

[0288] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[0289] Obtaining trend information

[0290] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[0291] Recognition of emotional information

[0292] Entering emotion data

[0293] The user provides emotional information through facial expressions, voice, or input data.

[0294] Emotional Data Analysis

[0295] The device uses an emotion engine to analyze the user's facial expressions, voice, and input data to recognize emotional information. For example, if the user inputs "I'm in a good mood today," the emotion engine will recognize this as a positive emotion.

[0296] Choosing the best outfit

[0297] Sending a coordination request

[0298] The user launches the app and sends a request saying, "Suggest an outfit for today."

[0299] Obtaining information

[0300] The server receives the request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[0301] Coordinate generation

[0302] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses positive emotions on a sunny day with a temperature of 25°C, it will suggest brighter clothing.

[0303] Suggestions for users

[0304] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[0305] Specific examples

[0306] Clothing registration

[0307] A user purchases a new navy dress and uploads a photo of it to the app.

[0308] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "dress," "navy," and "casual" and store the images in a database.

[0309] Gathering external information

[0310] The server calls the weather API every morning at 8:00, obtains information such as "sunny, 25°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "navy color is trending."

[0311] Recognition of emotional information

[0312] A user types into the app, "I'm feeling good today."

[0313] The device uses an emotion engine to recognize the user's emotion as positive from this input.

[0314] Choosing the best outfit

[0315] The user launches the app and requests, "Suggest an outfit for today."

[0316] The server selects a "navy dress," "white sneakers," and "yellow cardigan" based on the weather, temperature, trends, and the user's positive emotions, and sends the outfit to the device.

[0317] The terminal displays the proposal to the user, who confirms it.

[0318] This embodiment allows the user to easily select the most suitable outfit and enjoy coordinating it to suit their mood and emotions, thereby reducing the effort and stress of everyday outfit selection.

[0319] The processing flow will be explained below.

[0320] Clothing registration

[0321] Step 1:

[0322] Users use their smartphone to take a photo of a new outfit or upload a photo of an existing outfit to the app.

[0323] Step 2:

[0324] The terminal transmits the image data received from the user to the server.

[0325] Step 3:

[0326] The server stores the received images in cloud storage.

[0327] Step 4:

[0328] The server uses image recognition technology to analyze the image and automatically classify the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[0329] Step 5:

[0330] The server saves the analysis results in a database and notifies the user via the terminal that saving has been completed.

[0331] Gathering external information

[0332] Step 1:

[0333] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[0334] Step 2:

[0335] The server updates the weather and temperature information it has obtained into a weather database.

[0336] Step 3:

[0337] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[0338] Step 4:

[0339] The server updates the acquired trend information in the trend database.

[0340] Recognition of emotional information

[0341] Step 1:

[0342] The user provides emotional information to the app through a facial recognition camera, voice input, or text input.

[0343] Step 2:

[0344] The device uses an emotion engine to analyze the user's facial expressions, voice, or input data. For example, facial recognition technology is used for facial expressions, voice analysis technology is used for voice, and natural language processing technology is used for text input to recognize the user's emotions.

[0345] Step 3:

[0346] The device transmits the recognized emotion information to the server.

[0347] Choosing the best outfit

[0348] Step 1:

[0349] A user launches the app and sends a request such as, "Suggest an outfit for today." The request also includes the user's emotional information.

[0350] Step 2:

[0351] The server receives the request and retrieves the latest weather information, temperature information, and trend information from the database, as well as the user's emotional information.

[0352] Step 3:

[0353] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses positive emotions on a sunny day with a temperature of 25°C, it will suggest brighter clothing.

[0354] Step 4:

[0355] The server creates a list of the coordinates it has generated and sends the suggestions to the terminal.

[0356] Suggestions for users

[0357] Step 1:

[0358] The terminal displays the coordinate proposal received from the server on a user interface.

[0359] Step 2:

[0360] The device notifies the user, informing them that "Today's outfit is ready."

[0361] Step 3:

[0362] The user checks the proposed outfits and, if they like them, they adopt them. If they don't like them, they send a new request asking for other suggestions.

[0363] Step 4:

[0364] The server receives the re-request, generates another coordinate, and sends it again to the terminal.

[0365] ---

[0366] These specific processing steps allow the user to easily select the most suitable outfit and enjoy coordinating it to suit their mood and emotions, thereby reducing the effort and stress of everyday outfit selection.

[0367] Example 2

[0368] 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."

[0369] Conventional clothing suggestion systems require users to register images of their clothing, and it is not easy to automatically suggest optimal clothing in conjunction with external information such as weather and fashion information. Furthermore, since suggestions do not take the user's emotions into consideration, user satisfaction is often low. In response to these issues, a system is needed that recognizes the user's emotions and combines them with external information to suggest optimal clothing.

[0370] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for registering images of clothing owned by the user, a means for collecting meteorological information, temperature information, and fashion information for the current location, a means for inputting and analyzing the user's emotions, a means for selecting optimal clothing based on the collected and analyzed information, and a means for suggesting the selected clothing to the user. This allows the user to receive suggestions for optimal clothing that match their emotions and current situation.

[0371] ---

[0372] A "user" is someone who uses this system to register images of clothing they own and receive suggestions for the best outfits.

[0373] "Clothing" refers to fashion items such as clothes and accessories that are owned by a user and are registered in this system.

[0374] "Registration" refers to the process by which a user adds an image of an item of clothing to the system and stores it on the server.

[0375] "Weather information" refers to the weather conditions and forecasts at the current location, and includes information such as sunny, rainy, and cloudy weather, for example.

[0376] "Temperature information" refers to temperature data at the current location.

[0377] "Trend information" refers to information about the latest fashion trends and tendencies, and is collected from fashion news sites, social media, etc.

[0378] "Emotion" refers to the mood or mental state that a user is experiencing.

[0379] "Analysis" refers to the process of understanding the content and intent of input data (e.g., images, emotional input text).

[0380] "Suggestion" refers to the act of recommending optimal clothing to a user based on collected and analyzed information.

[0381] ---

[0382] Above are the definitions of each important word.

[0383] This invention is a system that registers images of clothing owned by a user, collects weather information, temperature information, and fashion information, and further recognizes the user's emotions to suggest optimal outfits. Specific embodiments for implementing this system are described below.

[0384] Clothing registration

[0385] Taking and uploading images

[0386] Users can take a picture of a new item of clothing using the camera function of their smartphone, or select an existing image from the gallery, thereby registering the image data of the clothing they own in the system.

[0387] Image transmission

[0388] The device temporarily stores the uploaded image data in local storage and then transmits it to the server via the communication network. Specifically, the device sends the image file to the server's upload API endpoint using an HTTP POST request.

[0389] Image analysis

[0390] When the server receives an image file, it first stores it in cloud storage (e.g., AWS (registered trademark) S3). It then analyzes the image using image recognition technology (e.g., TENSORFLOW (registered trademark)) and automatically classifies the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.). The analysis results are stored in a data structure such as JSON format and saved in a database (e.g., MySQL).

[0391] Gathering external information

[0392] Obtaining weather and temperature information

[0393] The server automatically calls a weather API (e.g., OpenWeatherMap) at a specified time (e.g., every morning at 8:00) to obtain weather and temperature information for the current location. The obtained data is stored in a weather table.

[0394] Obtaining trend information

[0395] The server periodically calls the trend APIs of fashion news sites and social networking sites (e.g., Twitter API) to collect the latest trend information. The collected information is stored in a trend information table.

[0396] Recognition of emotional information

[0397] Entering emotion data

[0398] Users use the "Emotion Input" section within the app to enter their emotion into a text box, for example, "I'm feeling good today," and then tap the "Send" button.

[0399] Emotional Data Analysis

[0400] The device analyzes the input text or sends it to a server to analyze the user's emotions using an emotion analysis engine (e.g., Google Cloud Natural Language API). The analysis results are stored in the user's emotion database.

[0401] Choosing the best outfit

[0402] Sending a coordination request

[0403] The user starts the app and taps the button to request "Suggest an outfit for today." This request is sent to the server.

[0404] Obtaining information

[0405] When the server receives a request, it first obtains the latest emotion information from the user's emotion database, then obtains the latest weather and temperature information from the weather table, and the latest fashion information from the trend information table.

[0406] Coordinate generation

[0407] The server generates the optimal outfit combination for the target user from a clothing database based on the acquired weather, temperature, fashion, and emotion information. Specifically, it runs an algorithm that suggests lighter clothing when the weather is sunny, the temperature is 25°C, and the user expresses positive emotions. This is done using Python's Pandas library, among other things.

[0408] Suggestions for users

[0409] The server sends the generated coordinate data in JSON format to the device, which then displays the received coordinate information within the app and notifies the user.

[0410] Specific examples

[0411] Examples of clothing registration

[0412] A user purchases a new navy dress, takes a photo of it with their smartphone, and uploads it to the app.

[0413] The device sends the photo to the server, which uses image recognition technology (e.g., TensorFlow) to classify it as "dress," "navy," or "casual," and stores it in a database.

[0414] Examples of collecting external information

[0415] The server calls a weather API (e.g., OpenWeatherMap) every morning at 8:00, obtains information such as "sunny, 25°C," and updates the weather database. It also calls a trend API (e.g., Twitter API) to collect and update the latest information, such as "navy color is trending."

[0416] Specific examples of emotional information recognition

[0417] A user types "I'm feeling good today" into the app and submits it.

[0418] The device or server uses an emotion analysis engine to recognize this input as positive and store it as the user's emotion data.

[0419] Examples of choosing the best clothing

[0420] The user launches the app and requests, "Suggest an outfit for today."

[0421] The server selects a "navy dress," "white sneakers," and "yellow cardigan" based on weather, temperature, trends, and emotional information, and sends them to the device.

[0422] The terminal displays the suggestion and the user confirms it.

[0423] Examples of prompt statements

[0424] A simple prompt for image analysis

[0425] "Please summarize the process of analyzing images and automatically classifying clothing categories and attributes."

[0426] A prompt for a simple explanation of outfit suggestions

[0427] "Please summarize the operation of a system that suggests optimal clothing based on weather, temperature, fashion, and emotional information."

[0428] The above is an embodiment of the present invention.

[0429] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0430] ---

[0431] Step 1: Take and upload a photo of your outfit

[0432] The user activates the smartphone camera and takes a picture of a new clothing item, or selects an existing image from the gallery.

[0433] Input: An image file of the clothing item photographed or selected by the user.

[0434] Output: The clothing image file is saved to the device's local storage.

[0435] Step 2: Sending image data

[0436] The device sends the image file stored in local storage to the server via an HTTP POST request.

[0437] Input: Clothing image file (retrieved from local storage).

[0438] Output: The image file is transferred to the server.

[0439] Step 3: Save and analyze images

[0440] The server saves the received image files in cloud storage (e.g. AWS S3).

[0441] The server uses image recognition technology such as TensorFlow to analyze the image files and automatically classify clothing categories (e.g., dresses, shirts, etc.) and attributes (e.g., navy, stripes, etc.).

[0442] Input: The submitted image file.

[0443] Output: Clothing category and attribute information (JSON format).

[0444] Step 4: Save the analysis results to the database

[0445] The server stores the analysis results in a clothing database (e.g., MySQL).

[0446] Input: Parsed JSON data.

[0447] Output: The analysis results are saved in the corresponding tables in the database.

[0448] Step 5: Get Weather Information

[0449] The server calls a weather API (e.g., OpenWeatherMap) every morning at 8:00 to obtain weather and temperature information for the current location.

[0450] Input: API request parameters (e.g. latitude, longitude).

[0451] Output: Retrieved weather and temperature information (JSON format).

[0452] Step 6: Save the weather information

[0453] The weather and temperature information obtained by the server is stored in a weather table.

[0454] Input: Weather and temperature information (JSON format).

[0455] Output: Weather and temperature information is saved in a weather table.

[0456] Step 7: Obtaining Trend Information

[0457] The server periodically calls a trend API (e.g., Twitter API) to obtain the latest trend information.

[0458] Input: API request parameters (e.g. keyword search).

[0459] Output: Retrieved trend information (JSON format).

[0460] Step 8: Save the trend information

[0461] The trend information acquired by the server is stored in a trend information table.

[0462] Input: Trend information (JSON format).

[0463] Output: Trend information is saved in the trend information table.

[0464] Step 9: Enter emotion data

[0465] In the emotion input section of the app, the user enters an emotion into the text box and taps the "Send" button.

[0466] Input: The emotion text entered by the user.

[0467] Output: Emotion input data is temporarily saved on the device.

[0468] Step 10: Analyze the sentiment data

[0469] The device uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to analyze the input emotion text and classify emotions such as positive and negative.

[0470] Input: Emotion input data.

[0471] Output: Parsed emotion information (JSON format).

[0472] Step 11: Storing Emotion Data

[0473] The server stores the analyzed emotion information in the user's emotion database.

[0474] Input: Parsed emotion information (JSON format).

[0475] Output: The emotion information is stored in the emotion database.

[0476] Step 12: Sending a Coordination Request

[0477] The user launches the app and requests, "Suggest an outfit for today."

[0478] Input: Request information.

[0479] Output: The request information is sent to the server.

[0480] Step 13: Obtaining Information

[0481] The server obtains the latest emotion information from the user's emotion database, the latest weather and temperature information from the weather table, and the latest fashion information from the trend information table.

[0482] Input: Request information.

[0483] Output: Acquired emotion information, weather information, and trend information (JSON format).

[0484] Step 14: Generate coordinates

[0485] Based on the information obtained by the server, the optimal outfit combination is generated from the user's clothing database (e.g., by manipulating the data using Python's Pandas library).

[0486] Input: Emotion information, weather information, trend information.

[0487] Output: Optimal coordinate information (JSON format).

[0488] Step 15: User recommendations

[0489] The server sends the generated coordinate information to the device, which then displays the received coordinate information within the app and notifies the user.

[0490] Input: Optimal coordinate information (JSON format).

[0491] Output: Coordination suggestions for the user are displayed within the app.

[0492] ---

[0493] The above is the flow of processing of the program of this system.

[0494] (Application example 2)

[0495] 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."

[0496] Conventional clothing suggestion systems can suggest optimal outfits based on weather, temperature, and trend information, but they cannot make suggestions that take into account the user's emotional state, which hinders user satisfaction. Furthermore, especially in virtual stores, there is an increasing demand for users to be able to select outfits that match their emotions and mood of the day in real time under various conditions. This will improve the user experience and reduce the hassle and stress of everyday outfit selection.

[0497] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0498] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting weather information, temperature information, and trend information for the current location, means for recognizing the user's emotional information, means for selecting optimal clothing based on the collected information, and means for suggesting the selected clothing to the user. This makes it possible to suggest optimal clothing that takes the user's emotional state into consideration, improving the user experience in the virtual store and reducing the effort and stress of everyday clothing selection.

[0499] The "means for registering images of clothing" refers to a system that allows users to take photos of clothing they own, upload them to the application, and store them in a database, thereby managing the information on the registered clothing.

[0500] "Means for collecting weather information and temperature information for the current location" refers to a mechanism that uses an external weather API to obtain the weather and temperature for the user's current location at a specific time and stores this information in a database.

[0501] "Means for collecting trend information" refers to a system that uses trend APIs from fashion news sites and social networking services to obtain the latest fashion trend information and store it in a database.

[0502] The "means for recognizing user's emotional information" is a mechanism that uses an emotion engine that recognizes emotions by analyzing the user's facial expressions, voice, and input data from the user.

[0503] The "means for selecting optimal clothing" is a mechanism that includes an algorithm that generates clothing combinations from a database based on collected weather information, temperature information, trend information, and user emotional information.

[0504] The "means of suggesting to the user" is a mechanism that presents the generated optimal clothing combination to the user via a device such as a smartphone or head-mounted display.

[0505] A "virtual store" is a virtual store environment that can be accessed via the Internet and has the function of allowing users to try on and purchase items in a virtual space.

[0506] To implement this invention, the following system configuration is required: This system has the function of registering images of clothes owned by the user and suggesting optimal outfits by combining weather information, temperature information, trend information, and emotion analysis.

[0507] Clothing registration

[0508] 1. Taking and uploading images:

[0509] Users use their smartphones or head-mounted displays to take photos of new clothes or upload photos of existing clothes to the application, which then collects image data of the clothes the user owns.

[0510] 2. Sending images:

[0511] The terminal transmits the image data received from the user to the cloud server.

[0512] 3. Image Analysis:

[0513] The server stores the received images in cloud storage and automatically classifies them into clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.) using image recognition technology (e.g., general image recognition technology).

[0514] 4. Save to database:

[0515] The server stores the analysis results in a database as clothing attribute data.

[0516] Gathering external information

[0517] 1. Obtaining weather and temperature information:

[0518] The server calls a weather API (e.g., a general weather information API) at a specified time (e.g., 8:00 a.m. every morning) and obtains weather and temperature information for the current location.

[0519] 2. Obtaining trend information:

[0520] The server calls the trend API (e.g., a general trend information API) of a fashion news site or social networking service to obtain the latest trend information.

[0521] Recognition of emotional information

[0522] 1. Emotion data input:

[0523] The user provides emotional information through facial expressions, voice, or input data.

[0524] 2. Emotional Data Analysis:

[0525] The device uses an emotion engine (e.g., a general emotion analysis engine) to analyze the user's facial expressions, voice, and input data to recognize emotional information. For example, if the user inputs "I'm in a good mood today," the emotion engine will recognize this as a positive emotion.

[0526] Choosing the best outfit

[0527] 1. Send a coordination request:

[0528] The user launches the application and sends a request saying, "Suggest me today's outfit."

[0529] 2. Acquisition of information:

[0530] The server receives the request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[0531] 3. Coordinate generation:

[0532] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses a relaxed mood on a cloudy day with a temperature of 18°C, the server will suggest a relaxed style of clothing.

[0533] 4. Suggestions for users:

[0534] The server sends the generated outfits to the device, which displays the suggestions to the user. If the user likes the outfits, they can try them on virtually and decide whether to wear them in real life.

[0535] Specific examples

[0536] 1. Clothing Registration:

[0537] A user purchases a new "red cardigan" and uploads a photo of it to the application.

[0538] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "cardigan," "red," and "casual" and store the images in a database.

[0539] 2. Collection of external information:

[0540] The server calls the weather API every morning at 8:00, obtains information such as "Cloudy, 18°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "Khaki is in fashion."

[0541] 3. Recognition of emotional information:

[0542] The user types into the application, "I want to relax today."

[0543] The device uses an emotion engine to recognize the user's emotion as relaxed from this input.

[0544] 4. Choosing the right outfit:

[0545] The user launches the app and requests, "Suggest an outfit for today."

[0546] The server selects a "red cardigan," "khaki wide-leg pants," and "plain sweatshirt" based on the weather, temperature, trends, and the user's relaxed mood, and sends the outfit to the device.

[0547] The device displays suggestions to the user, who can then virtually try them on, helping them choose actual outfits.

[0548] Prompt Sentence Examples

[0549] "Use the weather API and trend API to obtain weather and trend information for the current location, analyze the user's emotions using the emotion engine, and then combine this with registered clothing data using image recognition technology to suggest the best outfit."

[0550] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0551] Step 1:

[0552] Taking and uploading images

[0553] Input: The user takes a photo of a new outfit with their smartphone or uploads a photo of an existing outfit to the application.

[0554] How it works: A user launches the smartphone application and uses the camera to take a picture of the outfit, or selects and uploads an existing photo from the gallery.

[0555] Output: Image data of the clothes is saved on the device.

[0556] Step 2:

[0557] Image transmission

[0558] Input: Image data of clothes stored on the device.

[0559] Operation: The device sends the uploaded image data to the cloud server, where it is uploaded to cloud storage via the Internet.

[0560] Output: The image data of the clothes is saved on the cloud server.

[0561] Step 3:

[0562] Image analysis

[0563] Input: Image data of clothes stored on a cloud server.

[0564] How it works: The server uses image recognition technology to automatically analyze clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.). Specifically, it uses image analysis software (e.g., general image recognition software).

[0565] Output: Parsed clothing category and attribute data.

[0566] Step 4:

[0567] Saving to a database

[0568] Input: Parsed clothing category and attribute data.

[0569] Operation: The server stores the analysis results in a database, which is a storage system for unifying the management of clothing categories and attributes.

[0570] Output: New clothing information is added to the database.

[0571] Step 5:

[0572] Obtaining weather and temperature information

[0573] Input: The server will be accessed at the specified time (e.g., every morning at 8:00).

[0574] How it works: The server calls a weather API (e.g., a general weather information API) to get weather and temperature information for the current location by sending a request to the API endpoint and parsing the response.

[0575] Output: Current location weather and temperature information is saved on the server.

[0576] Step 6:

[0577] Obtaining trend information

[0578] Input: The server is accessed periodically or upon request from the user.

[0579] How it works: The server calls a trend API (e.g., a general trend information API) to obtain the latest fashion trend information. To do this, the server sends a request to the API endpoint and analyzes the response data.

[0580] Output: The latest fashion trend information is stored on the server.

[0581] Step 7:

[0582] Entering emotion data

[0583] Input: Data that a user types into an application (e.g., facial expressions, voice, text).

[0584] How it works: The user captures their facial expressions with a camera, expresses their emotions through voice, or inputs their emotions through text through the application. The device collects the data in real time.

[0585] Output: The acquired emotion data is temporarily stored on the device.

[0586] Step 8:

[0587] Emotional Data Analysis

[0588] Input: Emotion data stored on the device.

[0589] How it works: The device uses a sentiment analysis engine (e.g., a general sentiment analysis engine) to analyze the sentiment data, for example, text analysis using natural language processing to classify positive or negative sentiment.

[0590] Output: The analyzed emotion information is saved on the device.

[0591] Step 9:

[0592] Sending a coordination request

[0593] Input: A request from the user.

[0594] Operation: The user launches the application and sends a request saying, "Suggest me an outfit for today." This request is sent from the device to the server.

[0595] Output: The request data is sent to the server.

[0596] Step 10:

[0597] Obtaining information

[0598] Input: The request data sent to the server.

[0599] How it works: The server receives a request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[0600] Output: The necessary information is consolidated on the server side.

[0601] Step 11:

[0602] Coordinate generation

[0603] Input: Integrated weather information, temperature information, trend information, and sentiment information.

[0604] How it works: The server uses this information to run an algorithm that generates optimal outfit combinations. Specifically, it filters the clothing information in the database based on the collected data and suggests outfit combinations that meet the criteria.

[0605] Output: Optimal outfit combination data.

[0606] Step 12:

[0607] Suggestions for users

[0608] Input: Generated optimal outfit combination data.

[0609] Operation: The server sends the generated coordinates to the terminal, which then displays the suggestions to the user. At this time, the terminal displays visual information to make it easier for the user to make a selection.

[0610] Output: The proposed coordinates that are displayed to the user.

[0611] 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.

[0612] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0613] 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.

[0614] [Second embodiment]

[0615] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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."

[0627] ---

[0628] This invention is a system that allows users to register images of their clothing, and then selects and suggests optimal clothing to the user based on collected weather, temperature, and trend information for the user's current location. The program processing of this system is described in detail below.

[0629] Clothing registration

[0630] Taking and uploading images

[0631] Users use their smartphones or other devices to take photos of new clothes or upload existing photos to the app, which then collects image data of the clothes the user owns.

[0632] Image transmission

[0633] The device sends the uploaded image data to the server. After the sending is complete, the device notifies the user.

[0634] Image analysis

[0635] The server stores the received images in cloud storage. Using image recognition technology, the server analyzes the images of the clothes and automatically classifies them into categories (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[0636] Saving to a database

[0637] The server saves the analysis results in a database as clothing attribute data, and once the data has been saved, the server notifies the device.

[0638] Gathering external information

[0639] Obtaining weather and temperature information

[0640] The server calls the weather API at a specified time (e.g., every morning at 8:00) to obtain weather and temperature information for the user's current location. This information is then updated in the weather database.

[0641] Obtaining trend information

[0642] The server accesses the trend APIs of fashion news sites and social media sites to obtain the latest trend information, which is then updated in the trend database.

[0643] Choosing the best outfit

[0644] Sending a coordination request

[0645] The user launches the app and sends a request saying, "Suggest an outfit for today."

[0646] Obtaining information

[0647] The server retrieves the latest weather, temperature, and trend information from a database.

[0648] Coordinate generation

[0649] Based on this information, the server uses an algorithm to generate combinations of clothes from the database, which then selects the optimal outfit based on weather (e.g., light clothing suitable for a sunny day at 25°C) and trend information (e.g., navy blue is in fashion).

[0650] Suggestions for users

[0651] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[0652] Specific examples

[0653] Clothing registration

[0654] A user purchases a new navy dress and uploads a photo of it to the app.

[0655] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "dress," "navy," and "casual" and store the images in a database.

[0656] Gathering external information

[0657] The server calls the weather API every morning at 8:00, obtains information such as "sunny, 25°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "navy color is trending."

[0658] Choosing the best outfit

[0659] The user launches the app and requests, "Suggest an outfit for today."

[0660] The server selects a "navy dress," "white sneakers," and "beige cardigan" based on weather, temperature, and trend information, and sends the outfit to the device.

[0661] The terminal displays the proposal to the user, who confirms it.

[0662] This embodiment allows the user to easily select the most suitable outfit, reducing the effort and stress of everyday outfit selection.

[0663] The processing flow will be explained below.

[0664] Clothing registration

[0665] Step 1:

[0666] Users use their smartphone to take a photo of a new outfit or upload a photo of an existing outfit to the app.

[0667] Step 2:

[0668] The terminal transmits the image data received from the user to the server.

[0669] Step 3:

[0670] The server stores the received images in cloud storage.

[0671] Step 4:

[0672] The server uses image recognition technology to analyze the image and automatically classify the clothing into categories and attributes, such as dresses, shirts, pants, navy, casual, etc.

[0673] Step 5:

[0674] The server saves the analysis results in a database and notifies the user via the terminal that saving has been completed.

[0675] Gathering external information

[0676] Step 1:

[0677] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[0678] Step 2:

[0679] The server updates the weather and temperature information it has obtained into a weather database.

[0680] Step 3:

[0681] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[0682] Step 4:

[0683] The server updates the acquired trend information in the trend database.

[0684] Choosing the best outfit

[0685] Step 1:

[0686] The user launches the app and sends a request saying, "Suggest an outfit for today."

[0687] Step 2:

[0688] The server receives the request and retrieves the latest weather, temperature, and trend information from the database.

[0689] Step 3:

[0690] Based on the above information, the server generates the optimal outfit combination from a clothing database. For example, consider a light outfit suitable for a sunny day with a temperature of 25°C.

[0691] Step 4:

[0692] The server creates a list of the coordinates it has generated and sends the suggestions to the terminal.

[0693] Suggestions for users

[0694] Step 1:

[0695] The terminal displays the coordinate proposal received from the server on a user interface.

[0696] Step 2:

[0697] The device notifies the user, informing them that "Today's outfit is ready."

[0698] Step 3:

[0699] The user checks the proposed outfits and, if they like them, they adopt them. If they don't like them, they send a new request asking for other suggestions.

[0700] Step 4:

[0701] The server receives the re-request, generates another coordinate, and sends it again to the terminal.

[0702] Example 1

[0703] 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."

[0704] In today's world, choosing the right outfit for each day is a time-consuming and labor-intensive task for many people. It is particularly difficult to choose the perfect outfit that takes into account the weather, temperature, and the latest fashion trends. Furthermore, there are few effective systems that allow users to effortlessly find the perfect combination from among their existing clothes. This invention aims to solve the above-mentioned problem by allowing users to register images of their own clothes, collecting external information, and suggesting the best outfits based on that information.

[0705] 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.

[0706] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting meteorological information, temperature information, and fashion information for the current location, means for selecting optimal clothing based on the collected information, and means for suggesting the selected clothing to the user. This allows the user to easily select optimal clothing, reducing the effort and stress of choosing clothes every day.

[0707] "User" refers to an individual who uses the system to register images of clothing and use suggested outfits as a reference.

[0708] "Server" refers to a computer system that processes data sent by users, collects external information, generates optimal clothing suggestions, and provides them to users.

[0709] "Device" refers to a mobile information terminal such as a smartphone or tablet, which allows a user to take and upload images of clothes and receive and display suggestions from the server.

[0710] "Means for registering images of clothes" refers to a method or device that allows a user to take or upload images of clothes using a terminal and send them to a server.

[0711] "Weather information" refers to environmental information such as the weather, temperature, and humidity at the user's current location.

[0712] "Temperature information" refers to information about the temperature at a particular location and time.

[0713] "Trend information" refers to information about current fashion trends.

[0714] "Image analysis technology" refers to technology that uses computer vision and machine learning to recognize objects in images and automatically classify their attributes and categories.

[0715] "Machine learning algorithms" refer to computer algorithms that learn patterns and rules in new data based on past data.

[0716] "Cloud storage" refers to remote data storage services that allow you to store, manage, and access data over the Internet.

[0717] "API" stands for Application Program Interface, a set of definitions and protocols that allow software to communicate with other software.

[0718] "Suggestion" refers to the optimal outfit combination generated based on the information collected by the server and the user's data.

[0719] This invention is a system that allows a user to register images of clothes owned by the user, and selects and suggests optimal clothing to the user based on collected weather information, temperature information, and fashion information for the user's current location. The following describes in detail an embodiment of the invention.

[0720] The system consists of three main components: a server, a terminal, and a user.

[0721] Users can use their smartphones or other devices to take pictures of their clothes or upload existing photos to the app. For example, if a user purchases a new navy dress, they can take a photo of it using their smartphone's camera and upload the image through the app. This image is then sent from the device to the server.

[0722] The server saves the uploaded images in cloud storage (e.g., Amazon S3). It then analyzes the images using image analysis technology (e.g., Google Cloud Vision API) and automatically classifies the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.). The analysis results are saved in a database (e.g., MySQL). After saving is complete, the server sends a notification to the device to inform the user of the results.

[0723] Next, the server calls a weather API (e.g., OpenWeatherMap API) at a specified time (e.g., every morning at 8:00) to obtain weather information and temperature information for the user's current location. This information is updated in the weather database. The server also calls a trend API (e.g., Twitter Trending Topics API) to obtain the latest fashion trend information and update the trend database.

[0724] When a user launches the app and sends a request to "Suggest me today's outfit," the server retrieves the latest weather, temperature, and fashion information from the database. Based on this information, the server uses a machine learning algorithm (for example, an algorithm using Python) to generate outfit combinations from the database. In this way, the optimal outfit, such as a navy dress, white sneakers, and a beige cardigan, is selected from the collected information.

[0725] The server sends the generated outfits to the terminal, which displays the suggestions to the user. The user can then check the suggested outfits and select them as their outfit for the day.

[0726] Prompt Sentence Examples

[0727] Below is an example of an input prompt sentence to the generative AI model to explain this system.

[0728] A user takes a photo of a "navy dress" using a smartphone app and uploads the image. The device sends the image to a server, which analyzes the image using Google Cloud Vision API. The analysis results are saved in a database. Weather information is obtained using the OpenWeatherMap API every morning at 8:00, and trend information is collected using the Twitter Trending Topics API. When a user requests "Suggest an outfit for today," the server generates the optimal outfit and displays it on the device. This process is explained below.

[0729] This embodiment allows the user to easily select the most suitable outfit, reducing the effort and stress of everyday outfit selection.

[0730] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0731] Step 1: Take and upload a photo of your outfit

[0732] Specific explanation

[0733] The user takes a photo of a new outfit using the smartphone camera or selects an existing photo from the photo library. The input is the image, and the output is image data, which is stored in the device's application.

[0734] Specific actions

[0735] A user opens the application and taps the camera button to take a photo. The captured image is displayed within the application. The user then taps the "Upload" button, and the image is ready to be sent to the server.

[0736] Step 2: Sending image data

[0737] Specific explanation

[0738] The device sends the captured or selected image to the server. The input is the registered image data, and the output is the image data sent to the server. The device sends the image data to the server using the REST API.

[0739] Specific actions

[0740] The device encodes the image data and sends it to the REST API endpoint. The server receives the image data and prepares it for storage.

[0741] Step 3: Save your images to cloud storage

[0742] Specific explanation

[0743] The server stores the received images in cloud storage. The input is the received image data, and the output is the stored image on cloud storage. The server stores the images using a cloud storage service such as Amazon S3.

[0744] Specific actions

[0745] The server uploads the received image data to an S3 bucket and obtains the destination URL after saving is complete.

[0746] Step 4: Image analysis

[0747] Specific explanation

[0748] The server analyzes images stored in cloud storage using image analysis technology. The input is the image URL in cloud storage, and the output is the analysis results of clothing categories and attributes. The server performs image analysis using the Google Cloud Vision API, etc.

[0749] Specific actions

[0750] The server sends the image URL to the Google Cloud Vision API and receives the analysis results (e.g., one-piece dress, navy, casual). The server then retrieves the analysis results in JSON format and prepares to store them in the database.

[0751] Step 5: Saving to the Database

[0752] Specific explanation

[0753] The server saves the results of image analysis in a database. The input is the analysis result data, and the output is attribute data saved in the database. The server saves the data using a database system such as MySQL.

[0754] Specific actions

[0755] The server generates an SQL query to save the analysis results to a MySQL database, executes the query, saves the results to the database, and sends a notification to the device after saving is complete.

[0756] Step 6: Gather weather and trend information

[0757] Specific explanation

[0758] The server calls the weather API and trend API at the specified time to collect the latest external information. The input is the API request, and the output is weather and trend information data. The server uses the OpenWeatherMap API and Twitter Trending Topics API, etc.

[0759] Specific actions

[0760] Every morning at 8:00, the server calls the OpenWeatherMap API to obtain weather information such as "Sunny, 25°C." It also calls the Twitter Trending Topics API to obtain trend information such as "Navy color is trending." This information is then stored in a database.

[0761] Step 7: Receiving a Coordination Request

[0762] Specific explanation

[0763] The user launches the app and sends a request saying, "Suggest an outfit for today." The input is the request data from the user, and the output is a confirmation that the request was received. The server prepares to process the request.

[0764] Specific actions

[0765] When a user taps the "Suggest an outfit" button on the app's home screen, a request is sent to the server, which receives the request and begins processing it.

[0766] Step 8: Generate coordinates

[0767] Specific explanation

[0768] The server generates optimal outfits based on weather information, fashion trends, and the user's clothing database. The input is weather information, fashion trends, and clothing database data, and the output is the generated outfit. The server generates outfits using a machine learning algorithm.

[0769] Specific actions

[0770] The server runs a machine learning algorithm implemented in Python to generate outfits such as a navy dress, white sneakers, and a beige cardigan. The generated results are obtained in JSON format and sent to the device as the final proposal data.

[0771] Step 9: Displaying suggestions to the user

[0772] Specific explanation

[0773] The terminal receives the coordinate suggestion sent from the server and displays it to the user. The input is the suggestion data from the server, and the output is the coordinate displayed to the user.

[0774] Specific actions

[0775] The device analyzes the proposal data received from the server and displays it on the user interface. The user checks the proposal and selects it as the outfit for the day.

[0776] By following the above steps, the system can use the user's clothing images to suggest optimal outfits based on weather information and fashion trends.

[0777] (Application example 1)

[0778] 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."

[0779] In modern society, it is important for users to choose appropriate clothing when going out. However, taking into account the weather, temperature, and the latest fashion trends takes a lot of time and effort. At the same time, the use of delivery services is increasing, and there is a demand for suggesting the most appropriate clothing for the delivery situation. Existing systems do not exist that can process weather, temperature, and trend information in an integrated manner and respond when ordering delivery. Therefore, the challenge is to provide a system that allows users to smoothly select appropriate clothing.

[0780] 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.

[0781] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting weather information, temperature information, and trend information for the current location, and means for suggesting optimal clothing based on the weather, temperature, and trend information at the time of ordering delivery, thereby enabling the user to easily select appropriate clothing in real time, including when ordering delivery.

[0782] "User" refers to a person who accesses the system, registers images of clothing, and receives suggestions.

[0783] "Means for registering images of clothes" refers to the function of uploading images of clothes owned by the user to the system and storing them in the database.

[0784] "Current location weather information" refers to data about the weather in the area where the user is located.

[0785] "Temperature information" refers to data relating to the temperature in the area where the user is located.

[0786] "Trend information" refers to information about the latest fashions and styles.

[0787] "Means of collection" refers to the functionality for obtaining weather information, temperature information, and trend information from external sources.

[0788] "Means for selecting optimal clothing" refers to a function that generates an appropriate outfit from the clothes the user owns based on collected information.

[0789] "Means for suggesting to the user" refers to a function that displays the selected outfit coordination on the user's device to inform the user.

[0790] "A means of suggesting the most appropriate clothing based on the weather, temperature, and trend information at the time of ordering delivery" refers to a function that suggests appropriate clothing by taking into account the weather, temperature, and trend information at the time of ordering delivery.

[0791] This invention is a system that allows users to register images of their clothing, and selects and suggests optimal clothing to users based on collected weather, temperature, and trend information for their current location. It also includes a function to suggest appropriate clothing depending on the situation at the time of delivery order.

[0792] System Program Overview

[0793] 1. Clothing registration

[0794] Users take pictures of the clothes using a device such as a smartphone and upload them to the app.

[0795] The terminal transmits the uploaded image data to the server.

[0796] The server stores the images in cloud storage and uses image recognition technology to automatically classify the clothing categories and attributes, then stores them in a database.

[0797] 2. Gathering external information

[0798] The server calls the weather API, obtains the weather and temperature information for the user's current location, and updates the weather database.

[0799] The server calls the trend API, obtains the latest trend information, and updates it in the trend database.

[0800] 3. Choosing the right outfit

[0801] The user launches the app and sends a request to "suggest an outfit for today."

[0802] The server retrieves weather information, temperature information, and trend information from a database.

[0803] Based on this information, the server uses an algorithm to generate the optimal outfit combination.

[0804] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[0805] 4. Recommended attire for delivery orders

[0806] When a user places an order for delivery, the server obtains weather information, temperature information, and trend information at the time of the order.

[0807] Based on this information, the server selects the most suitable outfit for the delivery and suggests it to the user.

[0808] Hardware and Software Configuration

[0809] Smartphone: Used as the user's device to take and upload images and display suggestions.

[0810] Cloud storage: Used as a storage location for image data.

[0811] Server: Calls various APIs (weather API, trend API) and manages the database.

[0812] Database: Stores and manages clothing attributes, weather information, and trend information.

[0813] Algorithm: Implements logic to select the best outfit based on weather, temperature, and trend information.

[0814] Specific examples

[0815] Clothing registration

[0816] A user purchases a new "black jacket" and uploads a photo to the app.

[0817] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "jacket," "black," and "formal" and store the images in a database.

[0818] Gathering external information

[0819] The server calls the weather API every morning at 8:00, obtains information such as "Cloudy, 18°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "Black is in fashion."

[0820] Choosing the best outfit

[0821] The user launches the app and requests, "Suggest an outfit for today."

[0822] The server selects a "black jacket," "white shirt," and "black pants" based on weather, temperature, and trend information, and sends the outfit to the device.

[0823] The terminal displays the proposal to the user, who confirms it.

[0824] Recommendations for the best attire when ordering delivery

[0825] A user orders delivery and checks weather forecasts and other information when it arrives.

[0826] The server suggests "waterproof coats," "casual jeans," and "waterproof shoes" based on weather information, temperature information, and trend information at the time of the user's order.

[0827] The terminal displays these suggestions to the user for confirmation.

[0828] Prompt Sentence Examples

[0829] Prompt sentence to input to the generative AI model:

[0830] text

[0831] We will develop a system that allows users to register images of their clothing and suggests optimal outfits based on external weather information, temperature information, and trend information.

[0832] Clothes are uploaded as images to the app, which then collects the latest information from weather and trend APIs and uses an algorithm to suggest outfits.

[0833] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0834] Step 1:

[0835] The user takes a photo of the clothes using a device such as a smartphone and uploads it to the app. The input is the photographed image of the clothes, and the output is the uploaded image data. The user uses the app's image upload function, selects the image file, and taps the send button.

[0836] Step 2:

[0837] The device sends the uploaded image data to the server. The input is the image data uploaded by the user, and the output is the image data sent to the server. The device compresses the image data and sends it to the specified URL on the server via the Internet.

[0838] Step 3:

[0839] The server stores the images in cloud storage and uses image recognition technology to automatically classify the clothing category and attributes, which are then stored in a database. The input is the image data sent to the server, and the output is a database entry containing the clothing category and attributes. The server runs an image recognition algorithm, analyzes the clothing features in the image, and extracts the category (e.g., jacket) and attributes (e.g., black, formal), which are then stored in the database.

[0840] Step 4:

[0841] The server calls the weather API, obtains weather and temperature information for the user's current location, and updates the weather database. The input is the user's current location, and the output is the latest weather and temperature data. The server calls the specified API endpoint and saves the obtained data in the weather database.

[0842] Step 5:

[0843] The server calls the trend API, obtains the latest trend information, and updates it in the trend database. There is no input (periodic updates), and the output is the latest trend information. The server periodically calls the trend APIs of fashion news sites and SNS, and saves the obtained trend data in the trend database.

[0844] Step 6:

[0845] The user launches the app and requests, "Suggest an outfit for today." There is no input (user operation), and the output is an outfit suggestion request. When the user opens the app, they select an outfit suggestion request using a dedicated button or menu.

[0846] Step 7:

[0847] The server retrieves the latest weather, temperature, and trend information from the database. The input is a request for outfit suggestions from the user, and the output is the latest weather, temperature, and trend information. The server executes a database query to retrieve the required data.

[0848] Step 8:

[0849] The server uses an algorithm to generate the optimal outfit combination based on this information. The input is the acquired weather, temperature, and trend information, and the output is the optimal outfit combination. The server runs the algorithm, selects the optimal outfit based on each piece of information, and generates the result.

[0850] Step 9:

[0851] The server sends the generated outfit to the terminal, which then displays the suggestions to the user. The input is the generated optimal outfit, and the output is the suggestions displayed on the user's terminal. The server sends the data again, and the terminal receives it and displays it on the user interface.

[0852] Step 10:

[0853] A user places a delivery order and sends the information to the server. The input is the delivery order information, and the output is a notification to the server. The user places an order from the delivery app, and the information is notified to the system.

[0854] Step 11:

[0855] The server retrieves weather, temperature, and trend information at the time of order. The input is the delivery order notification, and the output is the latest weather, temperature, and trend information. The server immediately calls the API to retrieve new data.

[0856] Step 12:

[0857] The server selects the best outfit for delivery arrival, and the device displays the suggestion to the user. The input is acquired weather, temperature, and trend information, and the output is the suggested outfit for the user. The server uses an algorithm to select the outfit and sends the result to the device, which displays it.

[0858] 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.

[0859] ---

[0860] This invention is a system that suggests optimal outfits by registering images of clothes owned by the user, collecting weather information, temperature information, and trend information, and combining this with an emotion engine that recognizes the user's emotions. The program processing of this system is explained in detail below.

[0861] Clothing registration

[0862] Taking and uploading images

[0863] Users use their smartphones to take photos of new clothes or upload photos of existing clothes to the app, which then collects image data of the clothes the user owns.

[0864] Image transmission

[0865] The terminal transmits the image data received from the user to the server.

[0866] Image analysis

[0867] The server stores the received images in cloud storage. Using image recognition technology, the server analyzes the images of the clothes and automatically classifies them into categories (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[0868] Saving to a database

[0869] The server stores the analysis results in a database as clothing attribute data.

[0870] Gathering external information

[0871] Obtaining weather and temperature information

[0872] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[0873] Obtaining trend information

[0874] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[0875] Recognition of emotional information

[0876] Entering emotion data

[0877] The user provides emotional information through facial expressions, voice, or input data.

[0878] Emotional Data Analysis

[0879] The device uses an emotion engine to analyze the user's facial expressions, voice, and input data to recognize emotional information. For example, if the user inputs "I'm in a good mood today," the emotion engine will recognize this as a positive emotion.

[0880] Choosing the best outfit

[0881] Sending a coordination request

[0882] The user launches the app and sends a request saying, "Suggest an outfit for today."

[0883] Obtaining information

[0884] The server receives the request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[0885] Coordinate generation

[0886] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses positive emotions on a sunny day with a temperature of 25°C, it will suggest brighter clothing.

[0887] Suggestions for users

[0888] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[0889] Specific examples

[0890] Clothing registration

[0891] A user purchases a new navy dress and uploads a photo of it to the app.

[0892] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "dress," "navy," and "casual" and store the images in a database.

[0893] Gathering external information

[0894] The server calls the weather API every morning at 8:00, obtains information such as "sunny, 25°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "navy color is trending."

[0895] Recognition of emotional information

[0896] A user types into the app, "I'm feeling good today."

[0897] The device uses an emotion engine to recognize the user's emotion as positive from this input.

[0898] Choosing the best outfit

[0899] The user launches the app and requests, "Suggest an outfit for today."

[0900] The server selects a "navy dress," "white sneakers," and "yellow cardigan" based on the weather, temperature, trends, and the user's positive emotions, and sends the outfit to the device.

[0901] The terminal displays the proposal to the user, who confirms it.

[0902] This embodiment allows the user to easily select the most suitable outfit and enjoy coordinating it to suit their mood and emotions, thereby reducing the effort and stress of everyday outfit selection.

[0903] The processing flow will be explained below.

[0904] Clothing registration

[0905] Step 1:

[0906] Users use their smartphone to take a photo of a new outfit or upload a photo of an existing outfit to the app.

[0907] Step 2:

[0908] The terminal transmits the image data received from the user to the server.

[0909] Step 3:

[0910] The server stores the received images in cloud storage.

[0911] Step 4:

[0912] The server uses image recognition technology to analyze the image and automatically classify the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[0913] Step 5:

[0914] The server saves the analysis results in a database and notifies the user via the terminal that saving has been completed.

[0915] Gathering external information

[0916] Step 1:

[0917] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[0918] Step 2:

[0919] The server updates the weather and temperature information it has obtained into a weather database.

[0920] Step 3:

[0921] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[0922] Step 4:

[0923] The server updates the acquired trend information in the trend database.

[0924] Recognition of emotional information

[0925] Step 1:

[0926] The user provides emotional information to the app through a facial recognition camera, voice input, or text input.

[0927] Step 2:

[0928] The device uses an emotion engine to analyze the user's facial expressions, voice, or input data. For example, facial recognition technology is used for facial expressions, voice analysis technology is used for voice, and natural language processing technology is used for text input to recognize the user's emotions.

[0929] Step 3:

[0930] The device transmits the recognized emotion information to the server.

[0931] Choosing the best outfit

[0932] Step 1:

[0933] A user launches the app and sends a request such as, "Suggest an outfit for today." The request also includes the user's emotional information.

[0934] Step 2:

[0935] The server receives the request and retrieves the latest weather information, temperature information, and trend information from the database, as well as the user's emotional information.

[0936] Step 3:

[0937] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses positive emotions on a sunny day with a temperature of 25°C, it will suggest brighter clothing.

[0938] Step 4:

[0939] The server creates a list of the coordinates it has generated and sends the suggestions to the terminal.

[0940] Suggestions for users

[0941] Step 1:

[0942] The terminal displays the coordinate proposal received from the server on a user interface.

[0943] Step 2:

[0944] The device notifies the user, informing them that "Today's outfit is ready."

[0945] Step 3:

[0946] The user checks the proposed outfits and, if they like them, they adopt them. If they don't like them, they send a new request asking for other suggestions.

[0947] Step 4:

[0948] The server receives the re-request, generates another coordinate, and sends it again to the terminal.

[0949] ---

[0950] These specific processing steps allow the user to easily select the most suitable outfit and enjoy coordinating it to suit their mood and emotions, thereby reducing the effort and stress of everyday outfit selection.

[0951] Example 2

[0952] 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."

[0953] Conventional clothing suggestion systems require users to register images of their clothing, and it is not easy to automatically suggest optimal clothing in conjunction with external information such as weather and fashion information. Furthermore, since suggestions do not take the user's emotions into consideration, user satisfaction is often low. In response to these issues, a system is needed that recognizes the user's emotions and combines them with external information to suggest optimal clothing.

[0954] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for registering images of clothing owned by the user, a means for collecting meteorological information, temperature information, and fashion information for the current location, a means for inputting and analyzing the user's emotions, a means for selecting optimal clothing based on the collected and analyzed information, and a means for suggesting the selected clothing to the user. This allows the user to receive suggestions for optimal clothing that match their emotions and current situation.

[0955] ---

[0956] A "user" is someone who uses this system to register images of clothing they own and receive suggestions for the best outfits.

[0957] "Clothing" refers to fashion items such as clothes and accessories that are owned by a user and are registered in this system.

[0958] "Registration" refers to the process by which a user adds an image of an item of clothing to the system and stores it on the server.

[0959] "Weather information" refers to the weather conditions and forecasts at the current location, and includes information such as sunny, rainy, and cloudy weather, for example.

[0960] "Temperature information" refers to temperature data at the current location.

[0961] "Trend information" refers to information about the latest fashion trends and tendencies, and is collected from fashion news sites, social media, etc.

[0962] "Emotion" refers to the mood or mental state that a user is experiencing.

[0963] "Analysis" refers to the process of understanding the content and intent of input data (e.g., images, emotional input text).

[0964] "Suggestion" refers to the act of recommending optimal clothing to a user based on collected and analyzed information.

[0965] ---

[0966] Above are the definitions of each important word.

[0967] This invention is a system that registers images of clothing owned by a user, collects weather information, temperature information, and fashion information, and further recognizes the user's emotions to suggest optimal outfits. Specific embodiments for implementing this system are described below.

[0968] Clothing registration

[0969] Taking and uploading images

[0970] Users can take a picture of a new item of clothing using the camera function of their smartphone, or select an existing image from the gallery, thereby registering the image data of the clothing they own in the system.

[0971] Image transmission

[0972] The device temporarily stores the uploaded image data in local storage and then transmits it to the server via the communication network. Specifically, the device sends the image file to the server's upload API endpoint using an HTTP POST request.

[0973] Image analysis

[0974] When the server receives an image file, it first saves it in cloud storage (e.g., AWS S3). It then analyzes the image using image recognition technology (e.g., TensorFlow) and automatically classifies it into clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.). The analysis results are stored in a data structure such as JSON format and saved in a database (e.g., MySQL).

[0975] Gathering external information

[0976] Obtaining weather and temperature information

[0977] The server automatically calls a weather API (e.g., OpenWeatherMap) at a specified time (e.g., every morning at 8:00) to obtain weather and temperature information for the current location. The obtained data is stored in a weather table.

[0978] Obtaining trend information

[0979] The server periodically calls the trend APIs of fashion news sites and social networking sites (e.g., Twitter API) to collect the latest trend information. The collected information is stored in a trend information table.

[0980] Recognition of emotional information

[0981] Entering emotion data

[0982] Users use the "Emotion Input" section within the app to enter their emotion into a text box, for example, "I'm feeling good today," and then tap the "Send" button.

[0983] Emotional Data Analysis

[0984] The device analyzes the input text or sends it to a server to analyze the user's emotions using an emotion analysis engine (e.g., Google Cloud Natural Language API). The analysis results are stored in the user's emotion database.

[0985] Choosing the best outfit

[0986] Sending a coordination request

[0987] The user starts the app and taps the button to request "Suggest an outfit for today." This request is sent to the server.

[0988] Obtaining information

[0989] When the server receives a request, it first obtains the latest emotion information from the user's emotion database, then obtains the latest weather and temperature information from the weather table, and the latest fashion information from the trend information table.

[0990] Coordinate generation

[0991] The server generates the optimal outfit combination for the target user from a clothing database based on the acquired weather, temperature, fashion, and emotion information. Specifically, it runs an algorithm that suggests lighter clothing when the weather is sunny, the temperature is 25°C, and the user expresses positive emotions. This is done using Python's Pandas library, among other things.

[0992] Suggestions for users

[0993] The server sends the generated coordinate data in JSON format to the device, which then displays the received coordinate information within the app and notifies the user.

[0994] Specific examples

[0995] Examples of clothing registration

[0996] A user purchases a new navy dress, takes a photo of it with their smartphone, and uploads it to the app.

[0997] The device sends the photo to the server, which uses image recognition technology (e.g., TensorFlow) to classify it as "dress," "navy," or "casual," and stores it in a database.

[0998] Examples of collecting external information

[0999] The server calls a weather API (e.g., OpenWeatherMap) every morning at 8:00, obtains information such as "sunny, 25°C," and updates the weather database. It also calls a trend API (e.g., Twitter API) to collect and update the latest information, such as "navy color is trending."

[1000] Specific examples of emotional information recognition

[1001] A user types "I'm feeling good today" into the app and submits it.

[1002] The device or server uses an emotion analysis engine to recognize this input as positive and store it as the user's emotion data.

[1003] Examples of choosing the best clothing

[1004] The user launches the app and requests, "Suggest an outfit for today."

[1005] The server selects a "navy dress," "white sneakers," and "yellow cardigan" based on weather, temperature, trends, and emotional information, and sends them to the device.

[1006] The terminal displays the suggestion and the user confirms it.

[1007] Examples of prompt statements

[1008] A simple prompt for image analysis

[1009] "Please summarize the process of analyzing images and automatically classifying clothing categories and attributes."

[1010] A prompt for a simple explanation of outfit suggestions

[1011] "Please summarize the operation of a system that suggests optimal clothing based on weather, temperature, fashion, and emotional information."

[1012] The above is an embodiment of the present invention.

[1013] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1014] ---

[1015] Step 1: Take and upload a photo of your outfit

[1016] The user activates the smartphone camera and takes a picture of a new clothing item, or selects an existing image from the gallery.

[1017] Input: An image file of the clothing item photographed or selected by the user.

[1018] Output: The clothing image file is saved to the device's local storage.

[1019] Step 2: Sending image data

[1020] The device sends the image file stored in local storage to the server via an HTTP POST request.

[1021] Input: Clothing image file (retrieved from local storage).

[1022] Output: The image file is transferred to the server.

[1023] Step 3: Save and analyze images

[1024] The server saves the received image files in cloud storage (e.g. AWS S3).

[1025] The server uses image recognition technology such as TensorFlow to analyze the image files and automatically classify clothing categories (e.g., dresses, shirts, etc.) and attributes (e.g., navy, stripes, etc.).

[1026] Input: The submitted image file.

[1027] Output: Clothing category and attribute information (JSON format).

[1028] Step 4: Save the analysis results to the database

[1029] The server stores the analysis results in a clothing database (e.g., MySQL).

[1030] Input: Parsed JSON data.

[1031] Output: The analysis results are saved in the corresponding tables in the database.

[1032] Step 5: Get Weather Information

[1033] The server calls a weather API (e.g., OpenWeatherMap) every morning at 8:00 to obtain weather and temperature information for the current location.

[1034] Input: API request parameters (e.g. latitude, longitude).

[1035] Output: Retrieved weather and temperature information (JSON format).

[1036] Step 6: Save the weather information

[1037] The weather and temperature information obtained by the server is stored in a weather table.

[1038] Input: Weather and temperature information (JSON format).

[1039] Output: Weather and temperature information is saved in a weather table.

[1040] Step 7: Obtaining Trend Information

[1041] The server periodically calls a trend API (e.g., Twitter API) to obtain the latest trend information.

[1042] Input: API request parameters (e.g. keyword search).

[1043] Output: Retrieved trend information (JSON format).

[1044] Step 8: Save the trend information

[1045] The trend information acquired by the server is stored in a trend information table.

[1046] Input: Trend information (JSON format).

[1047] Output: Trend information is saved in the trend information table.

[1048] Step 9: Enter emotion data

[1049] In the emotion input section of the app, the user enters an emotion into the text box and taps the "Send" button.

[1050] Input: The emotion text entered by the user.

[1051] Output: Emotion input data is temporarily saved on the device.

[1052] Step 10: Analyze the sentiment data

[1053] The device uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to analyze the input emotion text and classify emotions such as positive and negative.

[1054] Input: Emotion input data.

[1055] Output: Parsed emotion information (JSON format).

[1056] Step 11: Storing Emotion Data

[1057] The server stores the analyzed emotion information in the user's emotion database.

[1058] Input: Parsed emotion information (JSON format).

[1059] Output: The emotion information is stored in the emotion database.

[1060] Step 12: Sending a Coordination Request

[1061] The user launches the app and requests, "Suggest an outfit for today."

[1062] Input: Request information.

[1063] Output: The request information is sent to the server.

[1064] Step 13: Obtaining Information

[1065] The server obtains the latest emotion information from the user's emotion database, the latest weather and temperature information from the weather table, and the latest fashion information from the trend information table.

[1066] Input: Request information.

[1067] Output: Acquired emotion information, weather information, and trend information (JSON format).

[1068] Step 14: Generate coordinates

[1069] Based on the information obtained by the server, the optimal outfit combination is generated from the user's clothing database (e.g., by manipulating the data using Python's Pandas library).

[1070] Input: Emotion information, weather information, trend information.

[1071] Output: Optimal coordinate information (JSON format).

[1072] Step 15: User recommendations

[1073] The server sends the generated coordinate information to the device, which then displays the received coordinate information within the app and notifies the user.

[1074] Input: Optimal coordinate information (JSON format).

[1075] Output: Coordination suggestions for the user are displayed within the app.

[1076] ---

[1077] The above is the flow of processing of the program of this system.

[1078] (Application example 2)

[1079] 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."

[1080] Conventional clothing suggestion systems can suggest optimal outfits based on weather, temperature, and trend information, but they cannot make suggestions that take into account the user's emotional state, which hinders user satisfaction. Furthermore, especially in virtual stores, there is an increasing demand for users to be able to select outfits that match their emotions and mood of the day in real time under various conditions. This will improve the user experience and reduce the hassle and stress of everyday outfit selection.

[1081] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1082] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting weather information, temperature information, and trend information for the current location, means for recognizing the user's emotional information, means for selecting optimal clothing based on the collected information, and means for suggesting the selected clothing to the user. This makes it possible to suggest optimal clothing that takes the user's emotional state into consideration, improving the user experience in the virtual store and reducing the effort and stress of everyday clothing selection.

[1083] The "means for registering images of clothing" refers to a system that allows users to take photos of clothing they own, upload them to the application, and store them in a database, thereby managing the information on the registered clothing.

[1084] "Means for collecting weather information and temperature information for the current location" refers to a mechanism that uses an external weather API to obtain the weather and temperature for the user's current location at a specific time and stores this information in a database.

[1085] "Means for collecting trend information" refers to a system that uses trend APIs from fashion news sites and social networking services to obtain the latest fashion trend information and store it in a database.

[1086] The "means for recognizing user's emotional information" is a mechanism that uses an emotion engine that recognizes emotions by analyzing the user's facial expressions, voice, and input data from the user.

[1087] The "means for selecting optimal clothing" is a mechanism that includes an algorithm that generates clothing combinations from a database based on collected weather information, temperature information, trend information, and user emotional information.

[1088] The "means of suggesting to the user" is a mechanism that presents the generated optimal clothing combination to the user via a device such as a smartphone or head-mounted display.

[1089] A "virtual store" is a virtual store environment that can be accessed via the Internet and has the function of allowing users to try on and purchase items in a virtual space.

[1090] To implement this invention, the following system configuration is required: This system has the function of registering images of clothes owned by the user and suggesting optimal outfits by combining weather information, temperature information, trend information, and emotion analysis.

[1091] Clothing registration

[1092] 1. Taking and uploading images:

[1093] Users use their smartphones or head-mounted displays to take photos of new clothes or upload photos of existing clothes to the application, which then collects image data of the clothes the user owns.

[1094] 2. Sending images:

[1095] The terminal transmits the image data received from the user to the cloud server.

[1096] 3. Image Analysis:

[1097] The server stores the received images in cloud storage and automatically classifies them into clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.) using image recognition technology (e.g., general image recognition technology).

[1098] 4. Save to database:

[1099] The server stores the analysis results in a database as clothing attribute data.

[1100] Gathering external information

[1101] 1. Obtaining weather and temperature information:

[1102] The server calls a weather API (e.g., a general weather information API) at a specified time (e.g., 8:00 a.m. every morning) and obtains weather and temperature information for the current location.

[1103] 2. Obtaining trend information:

[1104] The server calls the trend API (e.g., a general trend information API) of a fashion news site or social networking service to obtain the latest trend information.

[1105] Recognition of emotional information

[1106] 1. Emotion data input:

[1107] The user provides emotional information through facial expressions, voice, or input data.

[1108] 2. Emotional Data Analysis:

[1109] The device uses an emotion engine (e.g., a general emotion analysis engine) to analyze the user's facial expressions, voice, and input data to recognize emotional information. For example, if the user inputs "I'm in a good mood today," the emotion engine will recognize this as a positive emotion.

[1110] Choosing the best outfit

[1111] 1. Send a coordination request:

[1112] The user launches the application and sends a request saying, "Suggest me today's outfit."

[1113] 2. Acquisition of information:

[1114] The server receives the request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[1115] 3. Coordinate generation:

[1116] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses a relaxed mood on a cloudy day with a temperature of 18°C, the server will suggest a relaxed style of clothing.

[1117] 4. Suggestions for users:

[1118] The server sends the generated outfits to the device, which displays the suggestions to the user. If the user likes the outfits, they can try them on virtually and decide whether to wear them in real life.

[1119] Specific examples

[1120] 1. Clothing Registration:

[1121] A user purchases a new "red cardigan" and uploads a photo of it to the application.

[1122] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "cardigan," "red," and "casual" and store the images in a database.

[1123] 2. Collection of external information:

[1124] The server calls the weather API every morning at 8:00, obtains information such as "Cloudy, 18°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "Khaki is in fashion."

[1125] 3. Recognition of emotional information:

[1126] The user types into the application, "I want to relax today."

[1127] The device uses an emotion engine to recognize the user's emotion as relaxed from this input.

[1128] 4. Choosing the right outfit:

[1129] The user launches the app and requests, "Suggest an outfit for today."

[1130] The server selects a "red cardigan," "khaki wide-leg pants," and "plain sweatshirt" based on the weather, temperature, trends, and the user's relaxed mood, and sends the outfit to the device.

[1131] The device displays suggestions to the user, who can then virtually try them on, helping them choose actual outfits.

[1132] Prompt Sentence Examples

[1133] "Use the weather API and trend API to obtain weather and trend information for the current location, analyze the user's emotions using the emotion engine, and then combine this with registered clothing data using image recognition technology to suggest the best outfit."

[1134] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1135] Step 1:

[1136] Taking and uploading images

[1137] Input: The user takes a photo of a new outfit with their smartphone or uploads a photo of an existing outfit to the application.

[1138] How it works: A user launches the smartphone application and uses the camera to take a picture of the outfit, or selects and uploads an existing photo from the gallery.

[1139] Output: Image data of the clothes is saved on the device.

[1140] Step 2:

[1141] Image transmission

[1142] Input: Image data of clothes stored on the device.

[1143] Operation: The device sends the uploaded image data to the cloud server, where it is uploaded to cloud storage via the Internet.

[1144] Output: The image data of the clothes is saved on the cloud server.

[1145] Step 3:

[1146] Image analysis

[1147] Input: Image data of clothes stored on a cloud server.

[1148] How it works: The server uses image recognition technology to automatically analyze clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.). Specifically, it uses image analysis software (e.g., general image recognition software).

[1149] Output: Parsed clothing category and attribute data.

[1150] Step 4:

[1151] Saving to a database

[1152] Input: Parsed clothing category and attribute data.

[1153] Operation: The server stores the analysis results in a database, which is a storage system for unifying the management of clothing categories and attributes.

[1154] Output: New clothing information is added to the database.

[1155] Step 5:

[1156] Obtaining weather and temperature information

[1157] Input: The server will be accessed at the specified time (e.g., every morning at 8:00).

[1158] How it works: The server calls a weather API (e.g., a general weather information API) to get weather and temperature information for the current location by sending a request to the API endpoint and parsing the response.

[1159] Output: Current location weather and temperature information is saved on the server.

[1160] Step 6:

[1161] Obtaining trend information

[1162] Input: The server is accessed periodically or upon request from the user.

[1163] How it works: The server calls a trend API (e.g., a general trend information API) to obtain the latest fashion trend information. To do this, the server sends a request to the API endpoint and analyzes the response data.

[1164] Output: The latest fashion trend information is stored on the server.

[1165] Step 7:

[1166] Entering emotion data

[1167] Input: Data that a user types into an application (e.g., facial expressions, voice, text).

[1168] How it works: The user captures their facial expressions with a camera, expresses their emotions through voice, or inputs their emotions through text through the application. The device collects the data in real time.

[1169] Output: The acquired emotion data is temporarily stored on the device.

[1170] Step 8:

[1171] Emotional Data Analysis

[1172] Input: Emotion data stored on the device.

[1173] How it works: The device uses a sentiment analysis engine (e.g., a general sentiment analysis engine) to analyze the sentiment data, for example, text analysis using natural language processing to classify positive or negative sentiment.

[1174] Output: The analyzed emotion information is saved on the device.

[1175] Step 9:

[1176] Sending a coordination request

[1177] Input: A request from the user.

[1178] Operation: The user launches the application and sends a request saying, "Suggest me an outfit for today." This request is sent from the device to the server.

[1179] Output: The request data is sent to the server.

[1180] Step 10:

[1181] Obtaining information

[1182] Input: The request data sent to the server.

[1183] How it works: The server receives a request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[1184] Output: The necessary information is consolidated on the server side.

[1185] Step 11:

[1186] Coordinate generation

[1187] Input: Integrated weather information, temperature information, trend information, and sentiment information.

[1188] How it works: The server uses this information to run an algorithm that generates optimal outfit combinations. Specifically, it filters the clothing information in the database based on the collected data and suggests outfit combinations that meet the criteria.

[1189] Output: Optimal outfit combination data.

[1190] Step 12:

[1191] Suggestions for users

[1192] Input: Generated optimal outfit combination data.

[1193] Operation: The server sends the generated coordinates to the terminal, which then displays the suggestions to the user. At this time, the terminal displays visual information to make it easier for the user to make a selection.

[1194] Output: The proposed coordinates that are displayed to the user.

[1195] 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.

[1196] 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.

[1197] 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.

[1198] [Third embodiment]

[1199] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1200] 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.

[1201] 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).

[1202] 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.

[1203] 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.

[1204] 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).

[1205] 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.

[1206] 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.

[1207] 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.

[1208] 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.

[1209] 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.

[1210] 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."

[1211] ---

[1212] This invention is a system that allows users to register images of their clothing, and then selects and suggests optimal clothing to the user based on collected weather, temperature, and trend information for the user's current location. The program processing of this system is described in detail below.

[1213] Clothing registration

[1214] Taking and uploading images

[1215] Users use their smartphones or other devices to take photos of new clothes or upload existing photos to the app, which then collects image data of the clothes the user owns.

[1216] Image transmission

[1217] The device sends the uploaded image data to the server. After the sending is complete, the device notifies the user.

[1218] Image analysis

[1219] The server stores the received images in cloud storage. Using image recognition technology, the server analyzes the images of the clothes and automatically classifies them into categories (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[1220] Saving to a database

[1221] The server saves the analysis results in a database as clothing attribute data, and once the data has been saved, the server notifies the device.

[1222] Gathering external information

[1223] Obtaining weather and temperature information

[1224] The server calls the weather API at a specified time (e.g., every morning at 8:00) to obtain weather and temperature information for the user's current location. This information is then updated in the weather database.

[1225] Obtaining trend information

[1226] The server accesses the trend APIs of fashion news sites and social media sites to obtain the latest trend information, which is then updated in the trend database.

[1227] Choosing the best outfit

[1228] Sending a coordination request

[1229] The user launches the app and sends a request saying, "Suggest an outfit for today."

[1230] Obtaining information

[1231] The server retrieves the latest weather, temperature, and trend information from a database.

[1232] Coordinate generation

[1233] Based on this information, the server uses an algorithm to generate combinations of clothes from the database, which then selects the optimal outfit based on weather (e.g., light clothing suitable for a sunny day at 25°C) and trend information (e.g., navy blue is in fashion).

[1234] Suggestions for users

[1235] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[1236] Specific examples

[1237] Clothing registration

[1238] A user purchases a new navy dress and uploads a photo of it to the app.

[1239] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "dress," "navy," and "casual" and store the images in a database.

[1240] Gathering external information

[1241] The server calls the weather API every morning at 8:00, obtains information such as "sunny, 25°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "navy color is trending."

[1242] Choosing the best outfit

[1243] The user launches the app and requests, "Suggest an outfit for today."

[1244] The server selects a "navy dress," "white sneakers," and "beige cardigan" based on weather, temperature, and trend information, and sends the outfit to the device.

[1245] The terminal displays the proposal to the user, who confirms it.

[1246] This embodiment allows the user to easily select the most suitable outfit, reducing the effort and stress of everyday outfit selection.

[1247] The processing flow will be explained below.

[1248] Clothing registration

[1249] Step 1:

[1250] Users use their smartphone to take a photo of a new outfit or upload a photo of an existing outfit to the app.

[1251] Step 2:

[1252] The terminal transmits the image data received from the user to the server.

[1253] Step 3:

[1254] The server stores the received images in cloud storage.

[1255] Step 4:

[1256] The server uses image recognition technology to analyze the image and automatically classify the clothing into categories and attributes, such as dresses, shirts, pants, navy, casual, etc.

[1257] Step 5:

[1258] The server saves the analysis results in a database and notifies the user via the terminal that saving has been completed.

[1259] Gathering external information

[1260] Step 1:

[1261] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[1262] Step 2:

[1263] The server updates the weather and temperature information it has obtained into a weather database.

[1264] Step 3:

[1265] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[1266] Step 4:

[1267] The server updates the acquired trend information in the trend database.

[1268] Choosing the best outfit

[1269] Step 1:

[1270] The user launches the app and sends a request saying, "Suggest an outfit for today."

[1271] Step 2:

[1272] The server receives the request and retrieves the latest weather, temperature, and trend information from the database.

[1273] Step 3:

[1274] Based on the above information, the server generates the optimal outfit combination from a clothing database. For example, consider a light outfit suitable for a sunny day with a temperature of 25°C.

[1275] Step 4:

[1276] The server creates a list of the coordinates it has generated and sends the suggestions to the terminal.

[1277] Suggestions for users

[1278] Step 1:

[1279] The terminal displays the coordinate proposal received from the server on a user interface.

[1280] Step 2:

[1281] The device notifies the user, informing them that "Today's outfit is ready."

[1282] Step 3:

[1283] The user checks the proposed outfits and, if they like them, they adopt them. If they don't like them, they send a new request asking for other suggestions.

[1284] Step 4:

[1285] The server receives the re-request, generates another coordinate, and sends it again to the terminal.

[1286] Example 1

[1287] 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."

[1288] In today's world, choosing the right outfit for each day is a time-consuming and labor-intensive task for many people. It is particularly difficult to choose the perfect outfit that takes into account the weather, temperature, and the latest fashion trends. Furthermore, there are few effective systems that allow users to effortlessly find the perfect combination from among their existing clothes. This invention aims to solve the above-mentioned problem by allowing users to register images of their own clothes, collecting external information, and suggesting the best outfits based on that information.

[1289] 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.

[1290] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting meteorological information, temperature information, and fashion information for the current location, means for selecting optimal clothing based on the collected information, and means for suggesting the selected clothing to the user. This allows the user to easily select optimal clothing, reducing the effort and stress of choosing clothes every day.

[1291] "User" refers to an individual who uses the system to register images of clothing and use suggested outfits as a reference.

[1292] "Server" refers to a computer system that processes data sent by users, collects external information, generates optimal clothing suggestions, and provides them to users.

[1293] "Device" refers to a mobile information terminal such as a smartphone or tablet, which allows a user to take and upload images of clothes and receive and display suggestions from the server.

[1294] "Means for registering images of clothes" refers to a method or device that allows a user to take or upload images of clothes using a terminal and send them to a server.

[1295] "Weather information" refers to environmental information such as the weather, temperature, and humidity at the user's current location.

[1296] "Temperature information" refers to information about the temperature at a particular location and time.

[1297] "Trend information" refers to information about current fashion trends.

[1298] "Image analysis technology" refers to technology that uses computer vision and machine learning to recognize objects in images and automatically classify their attributes and categories.

[1299] "Machine learning algorithms" refer to computer algorithms that learn patterns and rules in new data based on past data.

[1300] "Cloud storage" refers to remote data storage services that allow you to store, manage, and access data over the Internet.

[1301] "API" stands for Application Program Interface, a set of definitions and protocols that allow software to communicate with other software.

[1302] "Suggestion" refers to the optimal outfit combination generated based on the information collected by the server and the user's data.

[1303] This invention is a system that allows a user to register images of clothes owned by the user, and selects and suggests optimal clothing to the user based on collected weather information, temperature information, and fashion information for the user's current location. The following describes in detail an embodiment of the invention.

[1304] The system consists of three main components: a server, a terminal, and a user.

[1305] Users can use their smartphones or other devices to take pictures of their clothes or upload existing photos to the app. For example, if a user purchases a new navy dress, they can take a photo of it using their smartphone's camera and upload the image through the app. This image is then sent from the device to the server.

[1306] The server saves the uploaded images in cloud storage (e.g., Amazon S3). It then analyzes the images using image analysis technology (e.g., Google Cloud Vision API) and automatically classifies the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.). The analysis results are saved in a database (e.g., MySQL). After saving is complete, the server sends a notification to the device to inform the user of the results.

[1307] Next, the server calls a weather API (e.g., OpenWeatherMap API) at a specified time (e.g., every morning at 8:00) to obtain weather information and temperature information for the user's current location. This information is updated in the weather database. The server also calls a trend API (e.g., Twitter Trending Topics API) to obtain the latest fashion trend information and update the trend database.

[1308] When a user launches the app and sends a request to "Suggest me today's outfit," the server retrieves the latest weather, temperature, and fashion information from the database. Based on this information, the server uses a machine learning algorithm (for example, an algorithm using Python) to generate outfit combinations from the database. In this way, the optimal outfit, such as a navy dress, white sneakers, and a beige cardigan, is selected from the collected information.

[1309] The server sends the generated outfits to the terminal, which displays the suggestions to the user. The user can then check the suggested outfits and select them as their outfit for the day.

[1310] Prompt Sentence Examples

[1311] Below is an example of an input prompt sentence to the generative AI model to explain this system.

[1312] A user takes a photo of a "navy dress" using a smartphone app and uploads the image. The device sends the image to a server, which analyzes the image using Google Cloud Vision API. The analysis results are saved in a database. Weather information is obtained using the OpenWeatherMap API every morning at 8:00, and trend information is collected using the Twitter Trending Topics API. When a user requests "Suggest an outfit for today," the server generates the optimal outfit and displays it on the device. This process is explained below.

[1313] This embodiment allows the user to easily select the most suitable outfit, reducing the effort and stress of everyday outfit selection.

[1314] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1315] Step 1: Take and upload a photo of your outfit

[1316] Specific explanation

[1317] The user takes a photo of a new outfit using the smartphone camera or selects an existing photo from the photo library. The input is the image, and the output is image data, which is stored in the device's application.

[1318] Specific actions

[1319] A user opens the application and taps the camera button to take a photo. The captured image is displayed within the application. The user then taps the "Upload" button, and the image is ready to be sent to the server.

[1320] Step 2: Sending image data

[1321] Specific explanation

[1322] The device sends the captured or selected image to the server. The input is the registered image data, and the output is the image data sent to the server. The device sends the image data to the server using the REST API.

[1323] Specific actions

[1324] The device encodes the image data and sends it to the REST API endpoint. The server receives the image data and prepares it for storage.

[1325] Step 3: Save your images to cloud storage

[1326] Specific explanation

[1327] The server stores the received images in cloud storage. The input is the received image data, and the output is the stored image on cloud storage. The server stores the images using a cloud storage service such as Amazon S3.

[1328] Specific actions

[1329] The server uploads the received image data to an S3 bucket and obtains the destination URL after saving is complete.

[1330] Step 4: Image analysis

[1331] Specific explanation

[1332] The server analyzes images stored in cloud storage using image analysis technology. The input is the image URL in cloud storage, and the output is the analysis results of clothing categories and attributes. The server performs image analysis using the Google Cloud Vision API, etc.

[1333] Specific actions

[1334] The server sends the image URL to the Google Cloud Vision API and receives the analysis results (e.g., one-piece dress, navy, casual). The server then retrieves the analysis results in JSON format and prepares to store them in the database.

[1335] Step 5: Saving to the Database

[1336] Specific explanation

[1337] The server saves the results of image analysis in a database. The input is the analysis result data, and the output is attribute data saved in the database. The server saves the data using a database system such as MySQL.

[1338] Specific actions

[1339] The server generates an SQL query to save the analysis results to a MySQL database, executes the query, saves the results to the database, and sends a notification to the device after saving is complete.

[1340] Step 6: Gather weather and trend information

[1341] Specific explanation

[1342] The server calls the weather API and trend API at the specified time to collect the latest external information. The input is the API request, and the output is weather and trend information data. The server uses the OpenWeatherMap API and Twitter Trending Topics API, etc.

[1343] Specific actions

[1344] Every morning at 8:00, the server calls the OpenWeatherMap API to obtain weather information such as "Sunny, 25°C." It also calls the Twitter Trending Topics API to obtain trend information such as "Navy color is trending." This information is then stored in a database.

[1345] Step 7: Receiving a Coordination Request

[1346] Specific explanation

[1347] The user launches the app and sends a request saying, "Suggest an outfit for today." The input is the request data from the user, and the output is a confirmation that the request was received. The server prepares to process the request.

[1348] Specific actions

[1349] When a user taps the "Suggest an outfit" button on the app's home screen, a request is sent to the server, which receives the request and begins processing it.

[1350] Step 8: Generate coordinates

[1351] Specific explanation

[1352] The server generates optimal outfits based on weather information, fashion trends, and the user's clothing database. The input is weather information, fashion trends, and clothing database data, and the output is the generated outfit. The server generates outfits using a machine learning algorithm.

[1353] Specific actions

[1354] The server runs a machine learning algorithm implemented in Python to generate outfits such as a navy dress, white sneakers, and a beige cardigan. The generated results are obtained in JSON format and sent to the device as the final proposal data.

[1355] Step 9: Displaying suggestions to the user

[1356] Specific explanation

[1357] The terminal receives the coordinate suggestion sent from the server and displays it to the user. The input is the suggestion data from the server, and the output is the coordinate displayed to the user.

[1358] Specific actions

[1359] The device analyzes the proposal data received from the server and displays it on the user interface. The user checks the proposal and selects it as the outfit for the day.

[1360] By following the above steps, the system can use the user's clothing images to suggest optimal outfits based on weather information and fashion trends.

[1361] (Application example 1)

[1362] 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."

[1363] In modern society, it is important for users to choose appropriate clothing when going out. However, taking into account the weather, temperature, and the latest fashion trends takes a lot of time and effort. At the same time, the use of delivery services is increasing, and there is a demand for suggesting the most appropriate clothing for the delivery situation. Existing systems do not exist that can process weather, temperature, and trend information in an integrated manner and respond when ordering delivery. Therefore, the challenge is to provide a system that allows users to smoothly select appropriate clothing.

[1364] 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.

[1365] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting weather information, temperature information, and trend information for the current location, and means for suggesting optimal clothing based on the weather, temperature, and trend information at the time of ordering delivery, thereby enabling the user to easily select appropriate clothing in real time, including when ordering delivery.

[1366] "User" refers to a person who accesses the system, registers images of clothing, and receives suggestions.

[1367] "Means for registering images of clothes" refers to the function of uploading images of clothes owned by the user to the system and storing them in the database.

[1368] "Current location weather information" refers to data about the weather in the area where the user is located.

[1369] "Temperature information" refers to data relating to the temperature in the area where the user is located.

[1370] "Trend information" refers to information about the latest fashions and styles.

[1371] "Means of collection" refers to the functionality for obtaining weather information, temperature information, and trend information from external sources.

[1372] "Means for selecting optimal clothing" refers to a function that generates an appropriate outfit from the clothes the user owns based on collected information.

[1373] "Means for suggesting to the user" refers to a function that displays the selected outfit coordination on the user's device to inform the user.

[1374] "A means of suggesting the most appropriate clothing based on the weather, temperature, and trend information at the time of ordering delivery" refers to a function that suggests appropriate clothing by taking into account the weather, temperature, and trend information at the time of ordering delivery.

[1375] This invention is a system that allows users to register images of their clothing, and selects and suggests optimal clothing to users based on collected weather, temperature, and trend information for their current location. It also includes a function to suggest appropriate clothing depending on the situation at the time of delivery order.

[1376] System Program Overview

[1377] 1. Clothing registration

[1378] Users take pictures of the clothes using a device such as a smartphone and upload them to the app.

[1379] The terminal transmits the uploaded image data to the server.

[1380] The server stores the images in cloud storage and uses image recognition technology to automatically classify the clothing categories and attributes, then stores them in a database.

[1381] 2. Gathering external information

[1382] The server calls the weather API, obtains the weather and temperature information for the user's current location, and updates the weather database.

[1383] The server calls the trend API, obtains the latest trend information, and updates it in the trend database.

[1384] 3. Choosing the right outfit

[1385] The user launches the app and sends a request to "suggest an outfit for today."

[1386] The server retrieves weather information, temperature information, and trend information from a database.

[1387] Based on this information, the server uses an algorithm to generate the optimal outfit combination.

[1388] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[1389] 4. Recommended attire for delivery orders

[1390] When a user places an order for delivery, the server obtains weather information, temperature information, and trend information at the time of the order.

[1391] Based on this information, the server selects the most suitable outfit for the delivery and suggests it to the user.

[1392] Hardware and Software Configuration

[1393] Smartphone: Used as the user's device to take and upload images and display suggestions.

[1394] Cloud storage: Used as a storage location for image data.

[1395] Server: Calls various APIs (weather API, trend API) and manages the database.

[1396] Database: Stores and manages clothing attributes, weather information, and trend information.

[1397] Algorithm: Implements logic to select the best outfit based on weather, temperature, and trend information.

[1398] Specific examples

[1399] Clothing registration

[1400] A user purchases a new "black jacket" and uploads a photo to the app.

[1401] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "jacket," "black," and "formal" and store the images in a database.

[1402] Gathering external information

[1403] The server calls the weather API every morning at 8:00, obtains information such as "Cloudy, 18°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "Black is in fashion."

[1404] Choosing the best outfit

[1405] The user launches the app and requests, "Suggest an outfit for today."

[1406] The server selects a "black jacket," "white shirt," and "black pants" based on weather, temperature, and trend information, and sends the outfit to the device.

[1407] The terminal displays the proposal to the user, who confirms it.

[1408] Recommendations for the best attire when ordering delivery

[1409] A user orders delivery and checks weather forecasts and other information when it arrives.

[1410] The server suggests "waterproof coats," "casual jeans," and "waterproof shoes" based on weather information, temperature information, and trend information at the time of the user's order.

[1411] The terminal displays these suggestions to the user for confirmation.

[1412] Prompt Sentence Examples

[1413] Prompt sentence to input to the generative AI model:

[1414] text

[1415] We will develop a system that allows users to register images of their clothing and suggests optimal outfits based on external weather information, temperature information, and trend information.

[1416] Clothes are uploaded as images to the app, which then collects the latest information from weather and trend APIs and uses an algorithm to suggest outfits.

[1417] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1418] Step 1:

[1419] The user takes a photo of the clothes using a device such as a smartphone and uploads it to the app. The input is the photographed image of the clothes, and the output is the uploaded image data. The user uses the app's image upload function, selects the image file, and taps the send button.

[1420] Step 2:

[1421] The device sends the uploaded image data to the server. The input is the image data uploaded by the user, and the output is the image data sent to the server. The device compresses the image data and sends it to the specified URL on the server via the Internet.

[1422] Step 3:

[1423] The server stores the images in cloud storage and uses image recognition technology to automatically classify the clothing category and attributes, which are then stored in a database. The input is the image data sent to the server, and the output is a database entry containing the clothing category and attributes. The server runs an image recognition algorithm, analyzes the clothing features in the image, and extracts the category (e.g., jacket) and attributes (e.g., black, formal), which are then stored in the database.

[1424] Step 4:

[1425] The server calls the weather API, obtains weather and temperature information for the user's current location, and updates the weather database. The input is the user's current location, and the output is the latest weather and temperature data. The server calls the specified API endpoint and saves the obtained data in the weather database.

[1426] Step 5:

[1427] The server calls the trend API, obtains the latest trend information, and updates it in the trend database. There is no input (periodic updates), and the output is the latest trend information. The server periodically calls the trend APIs of fashion news sites and SNS, and saves the obtained trend data in the trend database.

[1428] Step 6:

[1429] The user launches the app and requests, "Suggest an outfit for today." There is no input (user operation), and the output is an outfit suggestion request. When the user opens the app, they select an outfit suggestion request using a dedicated button or menu.

[1430] Step 7:

[1431] The server retrieves the latest weather, temperature, and trend information from the database. The input is a request for outfit suggestions from the user, and the output is the latest weather, temperature, and trend information. The server executes a database query to retrieve the required data.

[1432] Step 8:

[1433] The server uses an algorithm to generate the optimal outfit combination based on this information. The input is the acquired weather, temperature, and trend information, and the output is the optimal outfit combination. The server runs the algorithm, selects the optimal outfit based on each piece of information, and generates the result.

[1434] Step 9:

[1435] The server sends the generated outfit to the terminal, which then displays the suggestions to the user. The input is the generated optimal outfit, and the output is the suggestions displayed on the user's terminal. The server sends the data again, and the terminal receives it and displays it on the user interface.

[1436] Step 10:

[1437] A user places a delivery order and sends the information to the server. The input is the delivery order information, and the output is a notification to the server. The user places an order from the delivery app, and the information is notified to the system.

[1438] Step 11:

[1439] The server retrieves weather, temperature, and trend information at the time of order. The input is the delivery order notification, and the output is the latest weather, temperature, and trend information. The server immediately calls the API to retrieve new data.

[1440] Step 12:

[1441] The server selects the best outfit for delivery arrival, and the device displays the suggestion to the user. The input is acquired weather, temperature, and trend information, and the output is the suggested outfit for the user. The server uses an algorithm to select the outfit and sends the result to the device, which displays it.

[1442] 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.

[1443] ---

[1444] This invention is a system that suggests optimal outfits by registering images of clothes owned by the user, collecting weather information, temperature information, and trend information, and combining this with an emotion engine that recognizes the user's emotions. The program processing of this system is explained in detail below.

[1445] Clothing registration

[1446] Taking and uploading images

[1447] Users use their smartphones to take photos of new clothes or upload photos of existing clothes to the app, which then collects image data of the clothes the user owns.

[1448] Image transmission

[1449] The terminal transmits the image data received from the user to the server.

[1450] Image analysis

[1451] The server stores the received images in cloud storage. Using image recognition technology, the server analyzes the images of the clothes and automatically classifies them into categories (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[1452] Saving to a database

[1453] The server stores the analysis results in a database as clothing attribute data.

[1454] Gathering external information

[1455] Obtaining weather and temperature information

[1456] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[1457] Obtaining trend information

[1458] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[1459] Recognition of emotional information

[1460] Entering emotion data

[1461] The user provides emotional information through facial expressions, voice, or input data.

[1462] Emotional Data Analysis

[1463] The device uses an emotion engine to analyze the user's facial expressions, voice, and input data to recognize emotional information. For example, if the user inputs "I'm in a good mood today," the emotion engine will recognize this as a positive emotion.

[1464] Choosing the best outfit

[1465] Sending a coordination request

[1466] The user launches the app and sends a request saying, "Suggest an outfit for today."

[1467] Obtaining information

[1468] The server receives the request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[1469] Coordinate generation

[1470] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses positive emotions on a sunny day with a temperature of 25°C, it will suggest brighter clothing.

[1471] Suggestions for users

[1472] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[1473] Specific examples

[1474] Clothing registration

[1475] A user purchases a new navy dress and uploads a photo of it to the app.

[1476] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "dress," "navy," and "casual" and store the images in a database.

[1477] Gathering external information

[1478] The server calls the weather API every morning at 8:00, obtains information such as "sunny, 25°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "navy color is trending."

[1479] Recognition of emotional information

[1480] A user types into the app, "I'm feeling good today."

[1481] The device uses an emotion engine to recognize the user's emotion as positive from this input.

[1482] Choosing the best outfit

[1483] The user launches the app and requests, "Suggest an outfit for today."

[1484] The server selects a "navy dress," "white sneakers," and "yellow cardigan" based on the weather, temperature, trends, and the user's positive emotions, and sends the outfit to the device.

[1485] The terminal displays the proposal to the user, who confirms it.

[1486] This embodiment allows the user to easily select the most suitable outfit and enjoy coordinating it to suit their mood and emotions, thereby reducing the effort and stress of everyday outfit selection.

[1487] The processing flow will be explained below.

[1488] Clothing registration

[1489] Step 1:

[1490] Users use their smartphone to take a photo of a new outfit or upload a photo of an existing outfit to the app.

[1491] Step 2:

[1492] The terminal transmits the image data received from the user to the server.

[1493] Step 3:

[1494] The server stores the received images in cloud storage.

[1495] Step 4:

[1496] The server uses image recognition technology to analyze the image and automatically classify the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[1497] Step 5:

[1498] The server saves the analysis results in a database and notifies the user via the terminal that saving has been completed.

[1499] Gathering external information

[1500] Step 1:

[1501] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[1502] Step 2:

[1503] The server updates the weather and temperature information it has obtained into a weather database.

[1504] Step 3:

[1505] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[1506] Step 4:

[1507] The server updates the acquired trend information in the trend database.

[1508] Recognition of emotional information

[1509] Step 1:

[1510] The user provides emotional information to the app through a facial recognition camera, voice input, or text input.

[1511] Step 2:

[1512] The device uses an emotion engine to analyze the user's facial expressions, voice, or input data. For example, facial recognition technology is used for facial expressions, voice analysis technology is used for voice, and natural language processing technology is used for text input to recognize the user's emotions.

[1513] Step 3:

[1514] The device transmits the recognized emotion information to the server.

[1515] Choosing the best outfit

[1516] Step 1:

[1517] A user launches the app and sends a request such as, "Suggest an outfit for today." The request also includes the user's emotional information.

[1518] Step 2:

[1519] The server receives the request and retrieves the latest weather information, temperature information, and trend information from the database, as well as the user's emotional information.

[1520] Step 3:

[1521] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses positive emotions on a sunny day with a temperature of 25°C, it will suggest brighter clothing.

[1522] Step 4:

[1523] The server creates a list of the coordinates it has generated and sends the suggestions to the terminal.

[1524] Suggestions for users

[1525] Step 1:

[1526] The terminal displays the coordinate proposal received from the server on a user interface.

[1527] Step 2:

[1528] The device notifies the user, informing them that "Today's outfit is ready."

[1529] Step 3:

[1530] The user checks the proposed outfits and, if they like them, they adopt them. If they don't like them, they send a new request asking for other suggestions.

[1531] Step 4:

[1532] The server receives the re-request, generates another coordinate, and sends it again to the terminal.

[1533] ---

[1534] These specific processing steps allow the user to easily select the most suitable outfit and enjoy coordinating it to suit their mood and emotions, thereby reducing the effort and stress of everyday outfit selection.

[1535] Example 2

[1536] 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."

[1537] Conventional clothing suggestion systems require users to register images of their clothing, and it is not easy to automatically suggest optimal clothing in conjunction with external information such as weather and fashion information. Furthermore, since suggestions do not take the user's emotions into consideration, user satisfaction is often low. In response to these issues, a system is needed that recognizes the user's emotions and combines them with external information to suggest optimal clothing.

[1538] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for registering images of clothing owned by the user, a means for collecting meteorological information, temperature information, and fashion information for the current location, a means for inputting and analyzing the user's emotions, a means for selecting optimal clothing based on the collected and analyzed information, and a means for suggesting the selected clothing to the user. This allows the user to receive suggestions for optimal clothing that match their emotions and current situation.

[1539] ---

[1540] A "user" is someone who uses this system to register images of clothing they own and receive suggestions for the best outfits.

[1541] "Clothing" refers to fashion items such as clothes and accessories that are owned by a user and are registered in this system.

[1542] "Registration" refers to the process by which a user adds an image of an item of clothing to the system and stores it on the server.

[1543] "Weather information" refers to the weather conditions and forecasts at the current location, and includes information such as sunny, rainy, and cloudy weather, for example.

[1544] "Temperature information" refers to temperature data at the current location.

[1545] "Trend information" refers to information about the latest fashion trends and tendencies, and is collected from fashion news sites, social media, etc.

[1546] "Emotion" refers to the mood or mental state that a user is experiencing.

[1547] "Analysis" refers to the process of understanding the content and intent of input data (e.g., images, emotional input text).

[1548] "Suggestion" refers to the act of recommending optimal clothing to a user based on collected and analyzed information.

[1549] ---

[1550] Above are the definitions of each important word.

[1551] This invention is a system that registers images of clothing owned by a user, collects weather information, temperature information, and fashion information, and further recognizes the user's emotions to suggest optimal outfits. Specific embodiments for implementing this system are described below.

[1552] Clothing registration

[1553] Taking and uploading images

[1554] Users can take a picture of a new item of clothing using the camera function of their smartphone, or select an existing image from the gallery, thereby registering the image data of the clothing they own in the system.

[1555] Image transmission

[1556] The device temporarily stores the uploaded image data in local storage and then transmits it to the server via the communication network. Specifically, the device sends the image file to the server's upload API endpoint using an HTTP POST request.

[1557] Image analysis

[1558] When the server receives an image file, it first saves it in cloud storage (e.g., AWS S3). It then analyzes the image using image recognition technology (e.g., TensorFlow) and automatically classifies it into clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.). The analysis results are stored in a data structure such as JSON format and saved in a database (e.g., MySQL).

[1559] Gathering external information

[1560] Obtaining weather and temperature information

[1561] The server automatically calls a weather API (e.g., OpenWeatherMap) at a specified time (e.g., every morning at 8:00) to obtain weather and temperature information for the current location. The obtained data is stored in a weather table.

[1562] Obtaining trend information

[1563] The server periodically calls the trend APIs of fashion news sites and social networking sites (e.g., Twitter API) to collect the latest trend information. The collected information is stored in a trend information table.

[1564] Recognition of emotional information

[1565] Entering emotion data

[1566] Users use the "Emotion Input" section within the app to enter their emotion into a text box, for example, "I'm feeling good today," and then tap the "Send" button.

[1567] Emotional Data Analysis

[1568] The device analyzes the input text or sends it to a server to analyze the user's emotions using an emotion analysis engine (e.g., Google Cloud Natural Language API). The analysis results are stored in the user's emotion database.

[1569] Choosing the best outfit

[1570] Sending a coordination request

[1571] The user starts the app and taps the button to request "Suggest an outfit for today." This request is sent to the server.

[1572] Obtaining information

[1573] When the server receives a request, it first obtains the latest emotion information from the user's emotion database, then obtains the latest weather and temperature information from the weather table, and the latest fashion information from the trend information table.

[1574] Coordinate generation

[1575] The server generates the optimal outfit combination for the target user from a clothing database based on the acquired weather, temperature, fashion, and emotion information. Specifically, it runs an algorithm that suggests lighter clothing when the weather is sunny, the temperature is 25°C, and the user expresses positive emotions. This is done using Python's Pandas library, among other things.

[1576] Suggestions for users

[1577] The server sends the generated coordinate data in JSON format to the device, which then displays the received coordinate information within the app and notifies the user.

[1578] Specific examples

[1579] Examples of clothing registration

[1580] A user purchases a new navy dress, takes a photo of it with their smartphone, and uploads it to the app.

[1581] The device sends the photo to the server, which uses image recognition technology (e.g., TensorFlow) to classify it as "dress," "navy," or "casual," and stores it in a database.

[1582] Examples of collecting external information

[1583] The server calls a weather API (e.g., OpenWeatherMap) every morning at 8:00, obtains information such as "sunny, 25°C," and updates the weather database. It also calls a trend API (e.g., Twitter API) to collect and update the latest information, such as "navy color is trending."

[1584] Specific examples of emotional information recognition

[1585] A user types "I'm feeling good today" into the app and submits it.

[1586] The device or server uses an emotion analysis engine to recognize this input as positive and store it as the user's emotion data.

[1587] Examples of choosing the best clothing

[1588] The user launches the app and requests, "Suggest an outfit for today."

[1589] The server selects a "navy dress," "white sneakers," and "yellow cardigan" based on weather, temperature, trends, and emotional information, and sends them to the device.

[1590] The terminal displays the suggestion and the user confirms it.

[1591] Examples of prompt statements

[1592] A simple prompt for image analysis

[1593] "Please summarize the process of analyzing images and automatically classifying clothing categories and attributes."

[1594] A prompt for a simple explanation of outfit suggestions

[1595] "Please summarize the operation of a system that suggests optimal clothing based on weather, temperature, fashion, and emotional information."

[1596] The above is an embodiment of the present invention.

[1597] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1598] ---

[1599] Step 1: Take and upload a photo of your outfit

[1600] The user activates the smartphone camera and takes a picture of a new clothing item, or selects an existing image from the gallery.

[1601] Input: An image file of the clothing item photographed or selected by the user.

[1602] Output: The clothing image file is saved to the device's local storage.

[1603] Step 2: Sending image data

[1604] The device sends the image file stored in local storage to the server via an HTTP POST request.

[1605] Input: Clothing image file (retrieved from local storage).

[1606] Output: The image file is transferred to the server.

[1607] Step 3: Save and analyze images

[1608] The server saves the received image files in cloud storage (e.g. AWS S3).

[1609] The server uses image recognition technology such as TensorFlow to analyze the image files and automatically classify clothing categories (e.g., dresses, shirts, etc.) and attributes (e.g., navy, stripes, etc.).

[1610] Input: The submitted image file.

[1611] Output: Clothing category and attribute information (JSON format).

[1612] Step 4: Save the analysis results to the database

[1613] The server stores the analysis results in a clothing database (e.g., MySQL).

[1614] Input: Parsed JSON data.

[1615] Output: The analysis results are saved in the corresponding tables in the database.

[1616] Step 5: Get Weather Information

[1617] The server calls a weather API (e.g., OpenWeatherMap) every morning at 8:00 to obtain weather and temperature information for the current location.

[1618] Input: API request parameters (e.g. latitude, longitude).

[1619] Output: Retrieved weather and temperature information (JSON format).

[1620] Step 6: Save the weather information

[1621] The weather and temperature information obtained by the server is stored in a weather table.

[1622] Input: Weather and temperature information (JSON format).

[1623] Output: Weather and temperature information is saved in a weather table.

[1624] Step 7: Obtaining Trend Information

[1625] The server periodically calls a trend API (e.g., Twitter API) to obtain the latest trend information.

[1626] Input: API request parameters (e.g. keyword search).

[1627] Output: Retrieved trend information (JSON format).

[1628] Step 8: Save the trend information

[1629] The trend information acquired by the server is stored in a trend information table.

[1630] Input: Trend information (JSON format).

[1631] Output: Trend information is saved in the trend information table.

[1632] Step 9: Enter emotion data

[1633] In the emotion input section of the app, the user enters an emotion into the text box and taps the "Send" button.

[1634] Input: The emotion text entered by the user.

[1635] Output: Emotion input data is temporarily saved on the device.

[1636] Step 10: Analyze the sentiment data

[1637] The device uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to analyze the input emotion text and classify emotions such as positive and negative.

[1638] Input: Emotion input data.

[1639] Output: Parsed emotion information (JSON format).

[1640] Step 11: Storing Emotion Data

[1641] The server stores the analyzed emotion information in the user's emotion database.

[1642] Input: Parsed emotion information (JSON format).

[1643] Output: The emotion information is stored in the emotion database.

[1644] Step 12: Sending a Coordination Request

[1645] The user launches the app and requests, "Suggest an outfit for today."

[1646] Input: Request information.

[1647] Output: The request information is sent to the server.

[1648] Step 13: Obtaining Information

[1649] The server obtains the latest emotion information from the user's emotion database, the latest weather and temperature information from the weather table, and the latest fashion information from the trend information table.

[1650] Input: Request information.

[1651] Output: Acquired emotion information, weather information, and trend information (JSON format).

[1652] Step 14: Generate coordinates

[1653] Based on the information obtained by the server, the optimal outfit combination is generated from the user's clothing database (e.g., by manipulating the data using Python's Pandas library).

[1654] Input: Emotion information, weather information, trend information.

[1655] Output: Optimal coordinate information (JSON format).

[1656] Step 15: User recommendations

[1657] The server sends the generated coordinate information to the device, which then displays the received coordinate information within the app and notifies the user.

[1658] Input: Optimal coordinate information (JSON format).

[1659] Output: Coordination suggestions for the user are displayed within the app.

[1660] ---

[1661] The above is the flow of processing of the program of this system.

[1662] (Application example 2)

[1663] 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."

[1664] Conventional clothing suggestion systems can suggest optimal outfits based on weather, temperature, and trend information, but they cannot make suggestions that take into account the user's emotional state, which hinders user satisfaction. Furthermore, especially in virtual stores, there is an increasing demand for users to be able to select outfits that match their emotions and mood of the day in real time under various conditions. This will improve the user experience and reduce the hassle and stress of everyday outfit selection.

[1665] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1666] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting weather information, temperature information, and trend information for the current location, means for recognizing the user's emotional information, means for selecting optimal clothing based on the collected information, and means for suggesting the selected clothing to the user. This makes it possible to suggest optimal clothing that takes the user's emotional state into consideration, improving the user experience in the virtual store and reducing the effort and stress of everyday clothing selection.

[1667] The "means for registering images of clothing" refers to a system that allows users to take photos of clothing they own, upload them to the application, and store them in a database, thereby managing the information on the registered clothing.

[1668] "Means for collecting weather information and temperature information for the current location" refers to a mechanism that uses an external weather API to obtain the weather and temperature for the user's current location at a specific time and stores this information in a database.

[1669] "Means for collecting trend information" refers to a system that uses trend APIs from fashion news sites and social networking services to obtain the latest fashion trend information and store it in a database.

[1670] The "means for recognizing user's emotional information" is a mechanism that uses an emotion engine that recognizes emotions by analyzing the user's facial expressions, voice, and input data from the user.

[1671] The "means for selecting optimal clothing" is a mechanism that includes an algorithm that generates clothing combinations from a database based on collected weather information, temperature information, trend information, and user emotional information.

[1672] The "means of suggesting to the user" is a mechanism that presents the generated optimal clothing combination to the user via a device such as a smartphone or head-mounted display.

[1673] A "virtual store" is a virtual store environment that can be accessed via the Internet and has the function of allowing users to try on and purchase items in a virtual space.

[1674] To implement this invention, the following system configuration is required: This system has the function of registering images of clothes owned by the user and suggesting optimal outfits by combining weather information, temperature information, trend information, and emotion analysis.

[1675] Clothing registration

[1676] 1. Taking and uploading images:

[1677] Users use their smartphones or head-mounted displays to take photos of new clothes or upload photos of existing clothes to the application, which then collects image data of the clothes the user owns.

[1678] 2. Sending images:

[1679] The terminal transmits the image data received from the user to the cloud server.

[1680] 3. Image Analysis:

[1681] The server stores the received images in cloud storage and automatically classifies them into clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.) using image recognition technology (e.g., general image recognition technology).

[1682] 4. Save to database:

[1683] The server stores the analysis results in a database as clothing attribute data.

[1684] Gathering external information

[1685] 1. Obtaining weather and temperature information:

[1686] The server calls a weather API (e.g., a general weather information API) at a specified time (e.g., 8:00 a.m. every morning) and obtains weather and temperature information for the current location.

[1687] 2. Obtaining trend information:

[1688] The server calls the trend API (e.g., a general trend information API) of a fashion news site or social networking service to obtain the latest trend information.

[1689] Recognition of emotional information

[1690] 1. Emotion data input:

[1691] The user provides emotional information through facial expressions, voice, or input data.

[1692] 2. Emotional Data Analysis:

[1693] The device uses an emotion engine (e.g., a general emotion analysis engine) to analyze the user's facial expressions, voice, and input data to recognize emotional information. For example, if the user inputs "I'm in a good mood today," the emotion engine will recognize this as a positive emotion.

[1694] Choosing the best outfit

[1695] 1. Send a coordination request:

[1696] The user launches the application and sends a request saying, "Suggest me today's outfit."

[1697] 2. Acquisition of information:

[1698] The server receives the request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[1699] 3. Coordinate generation:

[1700] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses a relaxed mood on a cloudy day with a temperature of 18°C, the server will suggest a relaxed style of clothing.

[1701] 4. Suggestions for users:

[1702] The server sends the generated outfits to the device, which displays the suggestions to the user. If the user likes the outfits, they can try them on virtually and decide whether to wear them in real life.

[1703] Specific examples

[1704] 1. Clothing Registration:

[1705] A user purchases a new "red cardigan" and uploads a photo of it to the application.

[1706] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "cardigan," "red," and "casual" and store the images in a database.

[1707] 2. Collection of external information:

[1708] The server calls the weather API every morning at 8:00, obtains information such as "Cloudy, 18°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "Khaki is in fashion."

[1709] 3. Recognition of emotional information:

[1710] The user types into the application, "I want to relax today."

[1711] The device uses an emotion engine to recognize the user's emotion as relaxed from this input.

[1712] 4. Choosing the right outfit:

[1713] The user launches the app and requests, "Suggest an outfit for today."

[1714] The server selects a "red cardigan," "khaki wide-leg pants," and "plain sweatshirt" based on the weather, temperature, trends, and the user's relaxed mood, and sends the outfit to the device.

[1715] The device displays suggestions to the user, who can then virtually try them on, helping them choose actual outfits.

[1716] Prompt Sentence Examples

[1717] "Use the weather API and trend API to obtain weather and trend information for the current location, analyze the user's emotions using the emotion engine, and then combine this with registered clothing data using image recognition technology to suggest the best outfit."

[1718] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1719] Step 1:

[1720] Taking and uploading images

[1721] Input: The user takes a photo of a new outfit with their smartphone or uploads a photo of an existing outfit to the application.

[1722] How it works: A user launches the smartphone application and uses the camera to take a picture of the outfit, or selects and uploads an existing photo from the gallery.

[1723] Output: Image data of the clothes is saved on the device.

[1724] Step 2:

[1725] Image transmission

[1726] Input: Image data of clothes stored on the device.

[1727] Operation: The device sends the uploaded image data to the cloud server, where it is uploaded to cloud storage via the Internet.

[1728] Output: The image data of the clothes is saved on the cloud server.

[1729] Step 3:

[1730] Image analysis

[1731] Input: Image data of clothes stored on a cloud server.

[1732] How it works: The server uses image recognition technology to automatically analyze clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.). Specifically, it uses image analysis software (e.g., general image recognition software).

[1733] Output: Parsed clothing category and attribute data.

[1734] Step 4:

[1735] Saving to a database

[1736] Input: Parsed clothing category and attribute data.

[1737] Operation: The server stores the analysis results in a database, which is a storage system for unifying the management of clothing categories and attributes.

[1738] Output: New clothing information is added to the database.

[1739] Step 5:

[1740] Obtaining weather and temperature information

[1741] Input: The server will be accessed at the specified time (e.g., every morning at 8:00).

[1742] How it works: The server calls a weather API (e.g., a general weather information API) to get weather and temperature information for the current location by sending a request to the API endpoint and parsing the response.

[1743] Output: Current location weather and temperature information is saved on the server.

[1744] Step 6:

[1745] Obtaining trend information

[1746] Input: The server is accessed periodically or upon request from the user.

[1747] How it works: The server calls a trend API (e.g., a general trend information API) to obtain the latest fashion trend information. To do this, the server sends a request to the API endpoint and analyzes the response data.

[1748] Output: The latest fashion trend information is stored on the server.

[1749] Step 7:

[1750] Entering emotion data

[1751] Input: Data that a user types into an application (e.g., facial expressions, voice, text).

[1752] How it works: The user captures their facial expressions with a camera, expresses their emotions through voice, or inputs their emotions through text through the application. The device collects the data in real time.

[1753] Output: The acquired emotion data is temporarily stored on the device.

[1754] Step 8:

[1755] Emotional Data Analysis

[1756] Input: Emotion data stored on the device.

[1757] How it works: The device uses a sentiment analysis engine (e.g., a general sentiment analysis engine) to analyze the sentiment data, for example, text analysis using natural language processing to classify positive or negative sentiment.

[1758] Output: The analyzed emotion information is saved on the device.

[1759] Step 9:

[1760] Sending a coordination request

[1761] Input: A request from the user.

[1762] Operation: The user launches the application and sends a request saying, "Suggest me an outfit for today." This request is sent from the device to the server.

[1763] Output: The request data is sent to the server.

[1764] Step 10:

[1765] Obtaining information

[1766] Input: The request data sent to the server.

[1767] How it works: The server receives a request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[1768] Output: The necessary information is consolidated on the server side.

[1769] Step 11:

[1770] Coordinate generation

[1771] Input: Integrated weather information, temperature information, trend information, and sentiment information.

[1772] How it works: The server uses this information to run an algorithm that generates optimal outfit combinations. Specifically, it filters the clothing information in the database based on the collected data and suggests outfit combinations that meet the criteria.

[1773] Output: Optimal outfit combination data.

[1774] Step 12:

[1775] Suggestions for users

[1776] Input: Generated optimal outfit combination data.

[1777] Operation: The server sends the generated coordinates to the terminal, which then displays the suggestions to the user. At this time, the terminal displays visual information to make it easier for the user to make a selection.

[1778] Output: The proposed coordinates that are displayed to the user.

[1779] 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.

[1780] 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.

[1781] 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.

[1782] [Fourth embodiment]

[1783] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1784] 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.

[1785] 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).

[1786] 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.

[1787] 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.

[1788] 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).

[1789] 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.

[1790] 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.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] 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.

[1795] 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."

[1796] ---

[1797] This invention is a system that allows users to register images of their clothing, and then selects and suggests optimal clothing to the user based on collected weather, temperature, and trend information for the user's current location. The program processing of this system is described in detail below.

[1798] Clothing registration

[1799] Taking and uploading images

[1800] Users use their smartphones or other devices to take photos of new clothes or upload existing photos to the app, which then collects image data of the clothes the user owns.

[1801] Image transmission

[1802] The device sends the uploaded image data to the server. After the sending is complete, the device notifies the user.

[1803] Image analysis

[1804] The server stores the received images in cloud storage. Using image recognition technology, the server analyzes the images of the clothes and automatically classifies them into categories (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[1805] Saving to a database

[1806] The server saves the analysis results in a database as clothing attribute data, and once the data has been saved, the server notifies the device.

[1807] Gathering external information

[1808] Obtaining weather and temperature information

[1809] The server calls the weather API at a specified time (e.g., every morning at 8:00) to obtain weather and temperature information for the user's current location. This information is then updated in the weather database.

[1810] Obtaining trend information

[1811] The server accesses the trend APIs of fashion news sites and social media sites to obtain the latest trend information, which is then updated in the trend database.

[1812] Choosing the best outfit

[1813] Sending a coordination request

[1814] The user launches the app and sends a request saying, "Suggest an outfit for today."

[1815] Obtaining information

[1816] The server retrieves the latest weather, temperature, and trend information from a database.

[1817] Coordinate generation

[1818] Based on this information, the server uses an algorithm to generate combinations of clothes from the database, which then selects the optimal outfit based on weather (e.g., light clothing suitable for a sunny day at 25°C) and trend information (e.g., navy blue is in fashion).

[1819] Suggestions for users

[1820] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[1821] Specific examples

[1822] Clothing registration

[1823] A user purchases a new navy dress and uploads a photo of it to the app.

[1824] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "dress," "navy," and "casual" and store the images in a database.

[1825] Gathering external information

[1826] The server calls the weather API every morning at 8:00, obtains information such as "sunny, 25°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "navy color is trending."

[1827] Choosing the best outfit

[1828] The user launches the app and requests, "Suggest an outfit for today."

[1829] The server selects a "navy dress," "white sneakers," and "beige cardigan" based on weather, temperature, and trend information, and sends the outfit to the device.

[1830] The terminal displays the proposal to the user, who confirms it.

[1831] This embodiment allows the user to easily select the most suitable outfit, reducing the effort and stress of everyday outfit selection.

[1832] The processing flow will be explained below.

[1833] Clothing registration

[1834] Step 1:

[1835] Users use their smartphone to take a photo of a new outfit or upload a photo of an existing outfit to the app.

[1836] Step 2:

[1837] The terminal transmits the image data received from the user to the server.

[1838] Step 3:

[1839] The server stores the received images in cloud storage.

[1840] Step 4:

[1841] The server uses image recognition technology to analyze the image and automatically classify the clothing into categories and attributes, such as dresses, shirts, pants, navy, casual, etc.

[1842] Step 5:

[1843] The server saves the analysis results in a database and notifies the user via the terminal that saving has been completed.

[1844] Gathering external information

[1845] Step 1:

[1846] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[1847] Step 2:

[1848] The server updates the weather and temperature information it has obtained into a weather database.

[1849] Step 3:

[1850] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[1851] Step 4:

[1852] The server updates the acquired trend information in the trend database.

[1853] Choosing the best outfit

[1854] Step 1:

[1855] The user launches the app and sends a request saying, "Suggest an outfit for today."

[1856] Step 2:

[1857] The server receives the request and retrieves the latest weather, temperature, and trend information from the database.

[1858] Step 3:

[1859] Based on the above information, the server generates the optimal outfit combination from a clothing database. For example, consider a light outfit suitable for a sunny day with a temperature of 25°C.

[1860] Step 4:

[1861] The server creates a list of the coordinates it has generated and sends the suggestions to the terminal.

[1862] Suggestions for users

[1863] Step 1:

[1864] The terminal displays the coordinate proposal received from the server on a user interface.

[1865] Step 2:

[1866] The device notifies the user, informing them that "Today's outfit is ready."

[1867] Step 3:

[1868] The user checks the proposed outfits and, if they like them, they adopt them. If they don't like them, they send a new request asking for other suggestions.

[1869] Step 4:

[1870] The server receives the re-request, generates another coordinate, and sends it again to the terminal.

[1871] Example 1

[1872] 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."

[1873] In today's world, choosing the right outfit for each day is a time-consuming and labor-intensive task for many people. It is particularly difficult to choose the perfect outfit that takes into account the weather, temperature, and the latest fashion trends. Furthermore, there are few effective systems that allow users to effortlessly find the perfect combination from among their existing clothes. This invention aims to solve the above-mentioned problem by allowing users to register images of their own clothes, collecting external information, and suggesting the best outfits based on that information.

[1874] 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.

[1875] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting meteorological information, temperature information, and fashion information for the current location, means for selecting optimal clothing based on the collected information, and means for suggesting the selected clothing to the user. This allows the user to easily select optimal clothing, reducing the effort and stress of choosing clothes every day.

[1876] "User" refers to an individual who uses the system to register images of clothing and use suggested outfits as a reference.

[1877] "Server" refers to a computer system that processes data sent by users, collects external information, generates optimal clothing suggestions, and provides them to users.

[1878] "Device" refers to a mobile information terminal such as a smartphone or tablet, which allows a user to take and upload images of clothes and receive and display suggestions from the server.

[1879] "Means for registering images of clothes" refers to a method or device that allows a user to take or upload images of clothes using a terminal and send them to a server.

[1880] "Weather information" refers to environmental information such as the weather, temperature, and humidity at the user's current location.

[1881] "Temperature information" refers to information about the temperature at a particular location and time.

[1882] "Trend information" refers to information about current fashion trends.

[1883] "Image analysis technology" refers to technology that uses computer vision and machine learning to recognize objects in images and automatically classify their attributes and categories.

[1884] "Machine learning algorithms" refer to computer algorithms that learn patterns and rules in new data based on past data.

[1885] "Cloud storage" refers to remote data storage services that allow you to store, manage, and access data over the Internet.

[1886] "API" stands for Application Program Interface, a set of definitions and protocols that allow software to communicate with other software.

[1887] "Suggestion" refers to the optimal outfit combination generated based on the information collected by the server and the user's data.

[1888] This invention is a system that allows a user to register images of clothes owned by the user, and selects and suggests optimal clothing to the user based on collected weather information, temperature information, and fashion information for the user's current location. The following describes in detail an embodiment of the invention.

[1889] The system consists of three main components: a server, a terminal, and a user.

[1890] Users can use their smartphones or other devices to take pictures of their clothes or upload existing photos to the app. For example, if a user purchases a new navy dress, they can take a photo of it using their smartphone's camera and upload the image through the app. This image is then sent from the device to the server.

[1891] The server saves the uploaded images in cloud storage (e.g., Amazon S3). It then analyzes the images using image analysis technology (e.g., Google Cloud Vision API) and automatically classifies the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.). The analysis results are saved in a database (e.g., MySQL). After saving is complete, the server sends a notification to the device to inform the user of the results.

[1892] Next, the server calls a weather API (e.g., OpenWeatherMap API) at a specified time (e.g., every morning at 8:00) to obtain weather information and temperature information for the user's current location. This information is updated in the weather database. The server also calls a trend API (e.g., Twitter Trending Topics API) to obtain the latest fashion trend information and update the trend database.

[1893] When a user launches the app and sends a request to "Suggest me today's outfit," the server retrieves the latest weather, temperature, and fashion information from the database. Based on this information, the server uses a machine learning algorithm (for example, an algorithm using Python) to generate outfit combinations from the database. In this way, the optimal outfit, such as a navy dress, white sneakers, and a beige cardigan, is selected from the collected information.

[1894] The server sends the generated outfits to the terminal, which displays the suggestions to the user. The user can then check the suggested outfits and select them as their outfit for the day.

[1895] Prompt Sentence Examples

[1896] Below is an example of an input prompt sentence to the generative AI model to explain this system.

[1897] A user takes a photo of a "navy dress" using a smartphone app and uploads the image. The device sends the image to a server, which analyzes the image using Google Cloud Vision API. The analysis results are saved in a database. Weather information is obtained using the OpenWeatherMap API every morning at 8:00, and trend information is collected using the Twitter Trending Topics API. When a user requests "Suggest an outfit for today," the server generates the optimal outfit and displays it on the device. This process is explained below.

[1898] This embodiment allows the user to easily select the most suitable outfit, reducing the effort and stress of everyday outfit selection.

[1899] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1900] Step 1: Take and upload a photo of your outfit

[1901] Specific explanation

[1902] The user takes a photo of a new outfit using the smartphone camera or selects an existing photo from the photo library. The input is the image, and the output is image data, which is stored in the device's application.

[1903] Specific actions

[1904] A user opens the application and taps the camera button to take a photo. The captured image is displayed within the application. The user then taps the "Upload" button, and the image is ready to be sent to the server.

[1905] Step 2: Sending image data

[1906] Specific explanation

[1907] The device sends the captured or selected image to the server. The input is the registered image data, and the output is the image data sent to the server. The device sends the image data to the server using the REST API.

[1908] Specific actions

[1909] The device encodes the image data and sends it to the REST API endpoint. The server receives the image data and prepares it for storage.

[1910] Step 3: Save your images to cloud storage

[1911] Specific explanation

[1912] The server stores the received images in cloud storage. The input is the received image data, and the output is the stored image on cloud storage. The server stores the images using a cloud storage service such as Amazon S3.

[1913] Specific actions

[1914] The server uploads the received image data to an S3 bucket and obtains the destination URL after saving is complete.

[1915] Step 4: Image analysis

[1916] Specific explanation

[1917] The server analyzes images stored in cloud storage using image analysis technology. The input is the image URL in cloud storage, and the output is the analysis results of clothing categories and attributes. The server performs image analysis using the Google Cloud Vision API, etc.

[1918] Specific actions

[1919] The server sends the image URL to the Google Cloud Vision API and receives the analysis results (e.g., one-piece dress, navy, casual). The server then retrieves the analysis results in JSON format and prepares to store them in the database.

[1920] Step 5: Saving to the Database

[1921] Specific explanation

[1922] The server saves the results of image analysis in a database. The input is the analysis result data, and the output is attribute data saved in the database. The server saves the data using a database system such as MySQL.

[1923] Specific actions

[1924] The server generates an SQL query to save the analysis results to a MySQL database, executes the query, saves the results to the database, and sends a notification to the device after saving is complete.

[1925] Step 6: Gather weather and trend information

[1926] Specific explanation

[1927] The server calls the weather API and trend API at the specified time to collect the latest external information. The input is the API request, and the output is weather and trend information data. The server uses the OpenWeatherMap API and Twitter Trending Topics API, etc.

[1928] Specific actions

[1929] Every morning at 8:00, the server calls the OpenWeatherMap API to obtain weather information such as "Sunny, 25°C." It also calls the Twitter Trending Topics API to obtain trend information such as "Navy color is trending." This information is then stored in a database.

[1930] Step 7: Receiving a Coordination Request

[1931] Specific explanation

[1932] The user launches the app and sends a request saying, "Suggest an outfit for today." The input is the request data from the user, and the output is a confirmation that the request was received. The server prepares to process the request.

[1933] Specific actions

[1934] When a user taps the "Suggest an outfit" button on the app's home screen, a request is sent to the server, which receives the request and begins processing it.

[1935] Step 8: Generate coordinates

[1936] Specific explanation

[1937] The server generates optimal outfits based on weather information, fashion trends, and the user's clothing database. The input is weather information, fashion trends, and clothing database data, and the output is the generated outfit. The server generates outfits using a machine learning algorithm.

[1938] Specific actions

[1939] The server runs a machine learning algorithm implemented in Python to generate outfits such as a navy dress, white sneakers, and a beige cardigan. The generated results are obtained in JSON format and sent to the device as the final proposal data.

[1940] Step 9: Displaying suggestions to the user

[1941] Specific explanation

[1942] The terminal receives the coordinate suggestion sent from the server and displays it to the user. The input is the suggestion data from the server, and the output is the coordinate displayed to the user.

[1943] Specific actions

[1944] The device analyzes the proposal data received from the server and displays it on the user interface. The user checks the proposal and selects it as the outfit for the day.

[1945] By following the above steps, the system can use the user's clothing images to suggest optimal outfits based on weather information and fashion trends.

[1946] (Application example 1)

[1947] 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."

[1948] In modern society, it is important for users to choose appropriate clothing when going out. However, taking into account the weather, temperature, and the latest fashion trends takes a lot of time and effort. At the same time, the use of delivery services is increasing, and there is a demand for suggesting the most appropriate clothing for the delivery situation. Existing systems do not exist that can process weather, temperature, and trend information in an integrated manner and respond when ordering delivery. Therefore, the challenge is to provide a system that allows users to smoothly select appropriate clothing.

[1949] 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.

[1950] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting weather information, temperature information, and trend information for the current location, and means for suggesting optimal clothing based on the weather, temperature, and trend information at the time of ordering delivery, thereby enabling the user to easily select appropriate clothing in real time, including when ordering delivery.

[1951] "User" refers to a person who accesses the system, registers images of clothing, and receives suggestions.

[1952] "Means for registering images of clothes" refers to the function of uploading images of clothes owned by the user to the system and storing them in the database.

[1953] "Current location weather information" refers to data about the weather in the area where the user is located.

[1954] "Temperature information" refers to data relating to the temperature in the area where the user is located.

[1955] "Trend information" refers to information about the latest fashions and styles.

[1956] "Means of collection" refers to the functionality for obtaining weather information, temperature information, and trend information from external sources.

[1957] "Means for selecting optimal clothing" refers to a function that generates an appropriate outfit from the clothes the user owns based on collected information.

[1958] "Means for suggesting to the user" refers to a function that displays the selected outfit coordination on the user's device to inform the user.

[1959] "A means of suggesting the most appropriate clothing based on the weather, temperature, and trend information at the time of ordering delivery" refers to a function that suggests appropriate clothing by taking into account the weather, temperature, and trend information at the time of ordering delivery.

[1960] This invention is a system that allows users to register images of their clothing, and selects and suggests optimal clothing to users based on collected weather, temperature, and trend information for their current location. It also includes a function to suggest appropriate clothing depending on the situation at the time of delivery order.

[1961] System Program Overview

[1962] 1. Clothing registration

[1963] Users take pictures of the clothes using a device such as a smartphone and upload them to the app.

[1964] The terminal transmits the uploaded image data to the server.

[1965] The server stores the images in cloud storage and uses image recognition technology to automatically classify the clothing categories and attributes, then stores them in a database.

[1966] 2. Gathering external information

[1967] The server calls the weather API, obtains the weather and temperature information for the user's current location, and updates the weather database.

[1968] The server calls the trend API, obtains the latest trend information, and updates it in the trend database.

[1969] 3. Choosing the right outfit

[1970] The user launches the app and sends a request to "suggest an outfit for today."

[1971] The server retrieves weather information, temperature information, and trend information from a database.

[1972] Based on this information, the server uses an algorithm to generate the optimal outfit combination.

[1973] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[1974] 4. Recommended attire for delivery orders

[1975] When a user places an order for delivery, the server obtains weather information, temperature information, and trend information at the time of the order.

[1976] Based on this information, the server selects the most suitable outfit for the delivery and suggests it to the user.

[1977] Hardware and Software Configuration

[1978] Smartphone: Used as the user's device to take and upload images and display suggestions.

[1979] Cloud storage: Used as a storage location for image data.

[1980] Server: Calls various APIs (weather API, trend API) and manages the database.

[1981] Database: Stores and manages clothing attributes, weather information, and trend information.

[1982] Algorithm: Implements logic to select the best outfit based on weather, temperature, and trend information.

[1983] Specific examples

[1984] Clothing registration

[1985] A user purchases a new "black jacket" and uploads a photo to the app.

[1986] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "jacket," "black," and "formal" and store the images in a database.

[1987] Gathering external information

[1988] The server calls the weather API every morning at 8:00, obtains information such as "Cloudy, 18°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "Black is in fashion."

[1989] Choosing the best outfit

[1990] The user launches the app and requests, "Suggest an outfit for today."

[1991] The server selects a "black jacket," "white shirt," and "black pants" based on weather, temperature, and trend information, and sends the outfit to the device.

[1992] The terminal displays the proposal to the user, who confirms it.

[1993] Recommendations for the best attire when ordering delivery

[1994] A user orders delivery and checks weather forecasts and other information when it arrives.

[1995] The server suggests "waterproof coats," "casual jeans," and "waterproof shoes" based on weather information, temperature information, and trend information at the time of the user's order.

[1996] The terminal displays these suggestions to the user for confirmation.

[1997] Prompt Sentence Examples

[1998] Prompt sentence to input to the generative AI model:

[1999] text

[2000] We will develop a system that allows users to register images of their clothing and suggests optimal outfits based on external weather information, temperature information, and trend information.

[2001] Clothes are uploaded as images to the app, which then collects the latest information from weather and trend APIs and uses an algorithm to suggest outfits.

[2002] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2003] Step 1:

[2004] The user takes a photo of the clothes using a device such as a smartphone and uploads it to the app. The input is the photographed image of the clothes, and the output is the uploaded image data. The user uses the app's image upload function, selects the image file, and taps the send button.

[2005] Step 2:

[2006] The device sends the uploaded image data to the server. The input is the image data uploaded by the user, and the output is the image data sent to the server. The device compresses the image data and sends it to the specified URL on the server via the Internet.

[2007] Step 3:

[2008] The server stores the images in cloud storage and uses image recognition technology to automatically classify the clothing category and attributes, which are then stored in a database. The input is the image data sent to the server, and the output is a database entry containing the clothing category and attributes. The server runs an image recognition algorithm, analyzes the clothing features in the image, and extracts the category (e.g., jacket) and attributes (e.g., black, formal), which are then stored in the database.

[2009] Step 4:

[2010] The server calls the weather API, obtains weather and temperature information for the user's current location, and updates the weather database. The input is the user's current location, and the output is the latest weather and temperature data. The server calls the specified API endpoint and saves the obtained data in the weather database.

[2011] Step 5:

[2012] The server calls the trend API, obtains the latest trend information, and updates it in the trend database. There is no input (periodic updates), and the output is the latest trend information. The server periodically calls the trend APIs of fashion news sites and SNS, and saves the obtained trend data in the trend database.

[2013] Step 6:

[2014] The user launches the app and requests, "Suggest an outfit for today." There is no input (user operation), and the output is an outfit suggestion request. When the user opens the app, they select an outfit suggestion request using a dedicated button or menu.

[2015] Step 7:

[2016] The server retrieves the latest weather, temperature, and trend information from the database. The input is a request for outfit suggestions from the user, and the output is the latest weather, temperature, and trend information. The server executes a database query to retrieve the required data.

[2017] Step 8:

[2018] The server uses an algorithm to generate the optimal outfit combination based on this information. The input is the acquired weather, temperature, and trend information, and the output is the optimal outfit combination. The server runs the algorithm, selects the optimal outfit based on each piece of information, and generates the result.

[2019] Step 9:

[2020] The server sends the generated outfit to the terminal, which then displays the suggestions to the user. The input is the generated optimal outfit, and the output is the suggestions displayed on the user's terminal. The server sends the data again, and the terminal receives it and displays it on the user interface.

[2021] Step 10:

[2022] A user places a delivery order and sends the information to the server. The input is the delivery order information, and the output is a notification to the server. The user places an order from the delivery app, and the information is notified to the system.

[2023] Step 11:

[2024] The server retrieves weather, temperature, and trend information at the time of order. The input is the delivery order notification, and the output is the latest weather, temperature, and trend information. The server immediately calls the API to retrieve new data.

[2025] Step 12:

[2026] The server selects the best outfit for delivery arrival, and the device displays the suggestion to the user. The input is acquired weather, temperature, and trend information, and the output is the suggested outfit for the user. The server uses an algorithm to select the outfit and sends the result to the device, which displays it.

[2027] 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.

[2028] ---

[2029] This invention is a system that suggests optimal outfits by registering images of clothes owned by the user, collecting weather information, temperature information, and trend information, and combining this with an emotion engine that recognizes the user's emotions. The program processing of this system is explained in detail below.

[2030] Clothing registration

[2031] Taking and uploading images

[2032] Users use their smartphones to take photos of new clothes or upload photos of existing clothes to the app, which then collects image data of the clothes the user owns.

[2033] Image transmission

[2034] The terminal transmits the image data received from the user to the server.

[2035] Image analysis

[2036] The server stores the received images in cloud storage. Using image recognition technology, the server analyzes the images of the clothes and automatically classifies them into categories (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[2037] Saving to a database

[2038] The server stores the analysis results in a database as clothing attribute data.

[2039] Gathering external information

[2040] Obtaining weather and temperature information

[2041] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[2042] Obtaining trend information

[2043] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[2044] Recognition of emotional information

[2045] Entering emotion data

[2046] The user provides emotional information through facial expressions, voice, or input data.

[2047] Emotional Data Analysis

[2048] The device uses an emotion engine to analyze the user's facial expressions, voice, and input data to recognize emotional information. For example, if the user inputs "I'm in a good mood today," the emotion engine will recognize this as a positive emotion.

[2049] Choosing the best outfit

[2050] Sending a coordination request

[2051] The user launches the app and sends a request saying, "Suggest an outfit for today."

[2052] Obtaining information

[2053] The server receives the request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[2054] Coordinate generation

[2055] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses positive emotions on a sunny day with a temperature of 25°C, it will suggest brighter clothing.

[2056] Suggestions for users

[2057] The server sends the generated coordinates to the terminal, which displays the suggestions to the user.

[2058] Specific examples

[2059] Clothing registration

[2060] A user purchases a new navy dress and uploads a photo of it to the app.

[2061] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "dress," "navy," and "casual" and store the images in a database.

[2062] Gathering external information

[2063] The server calls the weather API every morning at 8:00, obtains information such as "sunny, 25°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "navy color is trending."

[2064] Recognition of emotional information

[2065] A user types into the app, "I'm feeling good today."

[2066] The device uses an emotion engine to recognize the user's emotion as positive from this input.

[2067] Choosing the best outfit

[2068] The user launches the app and requests, "Suggest an outfit for today."

[2069] The server selects a "navy dress," "white sneakers," and "yellow cardigan" based on the weather, temperature, trends, and the user's positive emotions, and sends the outfit to the device.

[2070] The terminal displays the proposal to the user, who confirms it.

[2071] This embodiment allows the user to easily select the most suitable outfit and enjoy coordinating it to suit their mood and emotions, thereby reducing the effort and stress of everyday outfit selection.

[2072] The processing flow will be explained below.

[2073] Clothing registration

[2074] Step 1:

[2075] Users use their smartphone to take a photo of a new outfit or upload a photo of an existing outfit to the app.

[2076] Step 2:

[2077] The terminal transmits the image data received from the user to the server.

[2078] Step 3:

[2079] The server stores the received images in cloud storage.

[2080] Step 4:

[2081] The server uses image recognition technology to analyze the image and automatically classify the clothing category (e.g., dress, shirt, pants) and attributes (e.g., navy, stripes, etc.).

[2082] Step 5:

[2083] The server saves the analysis results in a database and notifies the user via the terminal that saving has been completed.

[2084] Gathering external information

[2085] Step 1:

[2086] The server calls the weather API at a specified time (e.g., 8:00 a.m. every morning) to obtain weather and temperature information for the current location.

[2087] Step 2:

[2088] The server updates the weather and temperature information it has obtained into a weather database.

[2089] Step 3:

[2090] The server calls the trend APIs of fashion news sites and social media sites to obtain the latest trend information.

[2091] Step 4:

[2092] The server updates the acquired trend information in the trend database.

[2093] Recognition of emotional information

[2094] Step 1:

[2095] The user provides emotional information to the app through a facial recognition camera, voice input, or text input.

[2096] Step 2:

[2097] The device uses an emotion engine to analyze the user's facial expressions, voice, or input data. For example, facial recognition technology is used for facial expressions, voice analysis technology is used for voice, and natural language processing technology is used for text input to recognize the user's emotions.

[2098] Step 3:

[2099] The device transmits the recognized emotion information to the server.

[2100] Choosing the best outfit

[2101] Step 1:

[2102] A user launches the app and sends a request such as, "Suggest an outfit for today." The request also includes the user's emotional information.

[2103] Step 2:

[2104] The server receives the request and retrieves the latest weather information, temperature information, and trend information from the database, as well as the user's emotional information.

[2105] Step 3:

[2106] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses positive emotions on a sunny day with a temperature of 25°C, it will suggest brighter clothing.

[2107] Step 4:

[2108] The server creates a list of the coordinates it has generated and sends the suggestions to the terminal.

[2109] Suggestions for users

[2110] Step 1:

[2111] The terminal displays the coordinate proposal received from the server on a user interface.

[2112] Step 2:

[2113] The device notifies the user, informing them that "Today's outfit is ready."

[2114] Step 3:

[2115] The user checks the proposed outfits and, if they like them, they adopt them. If they don't like them, they send a new request asking for other suggestions.

[2116] Step 4:

[2117] The server receives the re-request, generates another coordinate, and sends it again to the terminal.

[2118] ---

[2119] These specific processing steps allow the user to easily select the most suitable outfit and enjoy coordinating it to suit their mood and emotions, thereby reducing the effort and stress of everyday outfit selection.

[2120] Example 2

[2121] 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."

[2122] Conventional clothing suggestion systems require users to register images of their clothing, and it is not easy to automatically suggest optimal clothing in conjunction with external information such as weather and fashion information. Furthermore, since suggestions do not take the user's emotions into consideration, user satisfaction is often low. In response to these issues, a system is needed that recognizes the user's emotions and combines them with external information to suggest optimal clothing.

[2123] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for registering images of clothing owned by the user, a means for collecting meteorological information, temperature information, and fashion information for the current location, a means for inputting and analyzing the user's emotions, a means for selecting optimal clothing based on the collected and analyzed information, and a means for suggesting the selected clothing to the user. This allows the user to receive suggestions for optimal clothing that match their emotions and current situation.

[2124] ---

[2125] A "user" is someone who uses this system to register images of clothing they own and receive suggestions for the best outfits.

[2126] "Clothing" refers to fashion items such as clothes and accessories that are owned by a user and are registered in this system.

[2127] "Registration" refers to the process by which a user adds an image of an item of clothing to the system and stores it on the server.

[2128] "Weather information" refers to the weather conditions and forecasts at the current location, and includes information such as sunny, rainy, and cloudy weather, for example.

[2129] "Temperature information" refers to temperature data at the current location.

[2130] "Trend information" refers to information about the latest fashion trends and tendencies, and is collected from fashion news sites, social media, etc.

[2131] "Emotion" refers to the mood or mental state that a user is experiencing.

[2132] "Analysis" refers to the process of understanding the content and intent of input data (e.g., images, emotional input text).

[2133] "Suggestion" refers to the act of recommending optimal clothing to a user based on collected and analyzed information.

[2134] ---

[2135] Above are the definitions of each important word.

[2136] This invention is a system that registers images of clothing owned by a user, collects weather information, temperature information, and fashion information, and further recognizes the user's emotions to suggest optimal outfits. Specific embodiments for implementing this system are described below.

[2137] Clothing registration

[2138] Taking and uploading images

[2139] Users can take a picture of a new item of clothing using the camera function of their smartphone, or select an existing image from the gallery, thereby registering the image data of the clothing they own in the system.

[2140] Image transmission

[2141] The device temporarily stores the uploaded image data in local storage and then transmits it to the server via the communication network. Specifically, the device sends the image file to the server's upload API endpoint using an HTTP POST request.

[2142] Image analysis

[2143] When the server receives an image file, it first saves it in cloud storage (e.g., AWS S3). It then analyzes the image using image recognition technology (e.g., TensorFlow) and automatically classifies it into clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.). The analysis results are stored in a data structure such as JSON format and saved in a database (e.g., MySQL).

[2144] Gathering external information

[2145] Obtaining weather and temperature information

[2146] The server automatically calls a weather API (e.g., OpenWeatherMap) at a specified time (e.g., every morning at 8:00) to obtain weather and temperature information for the current location. The obtained data is stored in a weather table.

[2147] Obtaining trend information

[2148] The server periodically calls the trend APIs of fashion news sites and social networking sites (e.g., Twitter API) to collect the latest trend information. The collected information is stored in a trend information table.

[2149] Recognition of emotional information

[2150] Entering emotion data

[2151] Users use the "Emotion Input" section within the app to enter their emotion into a text box, for example, "I'm feeling good today," and then tap the "Send" button.

[2152] Emotional Data Analysis

[2153] The device analyzes the input text or sends it to a server to analyze the user's emotions using an emotion analysis engine (e.g., Google Cloud Natural Language API). The analysis results are stored in the user's emotion database.

[2154] Choosing the best outfit

[2155] Sending a coordination request

[2156] The user starts the app and taps the button to request "Suggest an outfit for today." This request is sent to the server.

[2157] Obtaining information

[2158] When the server receives a request, it first obtains the latest emotion information from the user's emotion database, then obtains the latest weather and temperature information from the weather table, and the latest fashion information from the trend information table.

[2159] Coordinate generation

[2160] The server generates the optimal outfit combination for the target user from a clothing database based on the acquired weather, temperature, fashion, and emotion information. Specifically, it runs an algorithm that suggests lighter clothing when the weather is sunny, the temperature is 25°C, and the user expresses positive emotions. This is done using Python's Pandas library, among other things.

[2161] Suggestions for users

[2162] The server sends the generated coordinate data in JSON format to the device, which then displays the received coordinate information within the app and notifies the user.

[2163] Specific examples

[2164] Examples of clothing registration

[2165] A user purchases a new navy dress, takes a photo of it with their smartphone, and uploads it to the app.

[2166] The device sends the photo to the server, which uses image recognition technology (e.g., TensorFlow) to classify it as "dress," "navy," or "casual," and stores it in a database.

[2167] Examples of collecting external information

[2168] The server calls a weather API (e.g., OpenWeatherMap) every morning at 8:00, obtains information such as "sunny, 25°C," and updates the weather database. It also calls a trend API (e.g., Twitter API) to collect and update the latest information, such as "navy color is trending."

[2169] Specific examples of emotional information recognition

[2170] A user types "I'm feeling good today" into the app and submits it.

[2171] The device or server uses an emotion analysis engine to recognize this input as positive and store it as the user's emotion data.

[2172] Examples of choosing the best clothing

[2173] The user launches the app and requests, "Suggest an outfit for today."

[2174] The server selects a "navy dress," "white sneakers," and "yellow cardigan" based on weather, temperature, trends, and emotional information, and sends them to the device.

[2175] The terminal displays the suggestion and the user confirms it.

[2176] Examples of prompt statements

[2177] A simple prompt for image analysis

[2178] "Please summarize the process of analyzing images and automatically classifying clothing categories and attributes."

[2179] A prompt for a simple explanation of outfit suggestions

[2180] "Please summarize the operation of a system that suggests optimal clothing based on weather, temperature, fashion, and emotional information."

[2181] The above is an embodiment of the present invention.

[2182] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2183] ---

[2184] Step 1: Take and upload a photo of your outfit

[2185] The user activates the smartphone camera and takes a picture of a new clothing item, or selects an existing image from the gallery.

[2186] Input: An image file of the clothing item photographed or selected by the user.

[2187] Output: The clothing image file is saved to the device's local storage.

[2188] Step 2: Sending image data

[2189] The device sends the image file stored in local storage to the server via an HTTP POST request.

[2190] Input: Clothing image file (retrieved from local storage).

[2191] Output: The image file is transferred to the server.

[2192] Step 3: Save and analyze images

[2193] The server saves the received image files in cloud storage (e.g. AWS S3).

[2194] The server uses image recognition technology such as TensorFlow to analyze the image files and automatically classify clothing categories (e.g., dresses, shirts, etc.) and attributes (e.g., navy, stripes, etc.).

[2195] Input: The submitted image file.

[2196] Output: Clothing category and attribute information (JSON format).

[2197] Step 4: Save the analysis results to the database

[2198] The server stores the analysis results in a clothing database (e.g., MySQL).

[2199] Input: Parsed JSON data.

[2200] Output: The analysis results are saved in the corresponding tables in the database.

[2201] Step 5: Get Weather Information

[2202] The server calls a weather API (e.g., OpenWeatherMap) every morning at 8:00 to obtain weather and temperature information for the current location.

[2203] Input: API request parameters (e.g. latitude, longitude).

[2204] Output: Retrieved weather and temperature information (JSON format).

[2205] Step 6: Save the weather information

[2206] The weather and temperature information obtained by the server is stored in a weather table.

[2207] Input: Weather and temperature information (JSON format).

[2208] Output: Weather and temperature information is saved in a weather table.

[2209] Step 7: Obtaining Trend Information

[2210] The server periodically calls a trend API (e.g., Twitter API) to obtain the latest trend information.

[2211] Input: API request parameters (e.g. keyword search).

[2212] Output: Retrieved trend information (JSON format).

[2213] Step 8: Save the trend information

[2214] The trend information acquired by the server is stored in a trend information table.

[2215] Input: Trend information (JSON format).

[2216] Output: Trend information is saved in the trend information table.

[2217] Step 9: Enter emotion data

[2218] In the emotion input section of the app, the user enters an emotion into the text box and taps the "Send" button.

[2219] Input: The emotion text entered by the user.

[2220] Output: Emotion input data is temporarily saved on the device.

[2221] Step 10: Analyze the sentiment data

[2222] The device uses an emotion analysis engine (e.g., Google Cloud Natural Language API) to analyze the input emotion text and classify emotions such as positive and negative.

[2223] Input: Emotion input data.

[2224] Output: Parsed emotion information (JSON format).

[2225] Step 11: Storing Emotion Data

[2226] The server stores the analyzed emotion information in the user's emotion database.

[2227] Input: Parsed emotion information (JSON format).

[2228] Output: The emotion information is stored in the emotion database.

[2229] Step 12: Sending a Coordination Request

[2230] The user launches the app and requests, "Suggest an outfit for today."

[2231] Input: Request information.

[2232] Output: The request information is sent to the server.

[2233] Step 13: Obtaining Information

[2234] The server obtains the latest emotion information from the user's emotion database, the latest weather and temperature information from the weather table, and the latest fashion information from the trend information table.

[2235] Input: Request information.

[2236] Output: Acquired emotion information, weather information, and trend information (JSON format).

[2237] Step 14: Generate coordinates

[2238] Based on the information obtained by the server, the optimal outfit combination is generated from the user's clothing database (e.g., by manipulating the data using Python's Pandas library).

[2239] Input: Emotion information, weather information, trend information.

[2240] Output: Optimal coordinate information (JSON format).

[2241] Step 15: User recommendations

[2242] The server sends the generated coordinate information to the device, which then displays the received coordinate information within the app and notifies the user.

[2243] Input: Optimal coordinate information (JSON format).

[2244] Output: Coordination suggestions for the user are displayed within the app.

[2245] ---

[2246] The above is the flow of processing of the program of this system.

[2247] (Application example 2)

[2248] 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."

[2249] Conventional clothing suggestion systems can suggest optimal outfits based on weather, temperature, and trend information, but they cannot make suggestions that take into account the user's emotional state, which hinders user satisfaction. Furthermore, especially in virtual stores, there is an increasing demand for users to be able to select outfits that match their emotions and mood of the day in real time under various conditions. This will improve the user experience and reduce the hassle and stress of everyday outfit selection.

[2250] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2251] In this invention, the server includes means for registering images of clothes owned by the user, means for collecting weather information, temperature information, and trend information for the current location, means for recognizing the user's emotional information, means for selecting optimal clothing based on the collected information, and means for suggesting the selected clothing to the user. This makes it possible to suggest optimal clothing that takes the user's emotional state into consideration, improving the user experience in the virtual store and reducing the effort and stress of everyday clothing selection.

[2252] The "means for registering images of clothing" refers to a system that allows users to take photos of clothing they own, upload them to the application, and store them in a database, thereby managing the information on the registered clothing.

[2253] "Means for collecting weather information and temperature information for the current location" refers to a mechanism that uses an external weather API to obtain the weather and temperature for the user's current location at a specific time and stores this information in a database.

[2254] "Means for collecting trend information" refers to a system that uses trend APIs from fashion news sites and social networking services to obtain the latest fashion trend information and store it in a database.

[2255] The "means for recognizing user's emotional information" is a mechanism that uses an emotion engine that recognizes emotions by analyzing the user's facial expressions, voice, and input data from the user.

[2256] The "means for selecting optimal clothing" is a mechanism that includes an algorithm that generates clothing combinations from a database based on collected weather information, temperature information, trend information, and user emotional information.

[2257] The "means of suggesting to the user" is a mechanism that presents the generated optimal clothing combination to the user via a device such as a smartphone or head-mounted display.

[2258] A "virtual store" is a virtual store environment that can be accessed via the Internet and has the function of allowing users to try on and purchase items in a virtual space.

[2259] To implement this invention, the following system configuration is required: This system has the function of registering images of clothes owned by the user and suggesting optimal outfits by combining weather information, temperature information, trend information, and emotion analysis.

[2260] Clothing registration

[2261] 1. Taking and uploading images:

[2262] Users use their smartphones or head-mounted displays to take photos of new clothes or upload photos of existing clothes to the application, which then collects image data of the clothes the user owns.

[2263] 2. Sending images:

[2264] The terminal transmits the image data received from the user to the cloud server.

[2265] 3. Image Analysis:

[2266] The server stores the received images in cloud storage and automatically classifies them into clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.) using image recognition technology (e.g., general image recognition technology).

[2267] 4. Save to database:

[2268] The server stores the analysis results in a database as clothing attribute data.

[2269] Gathering external information

[2270] 1. Obtaining weather and temperature information:

[2271] The server calls a weather API (e.g., a general weather information API) at a specified time (e.g., 8:00 a.m. every morning) and obtains weather and temperature information for the current location.

[2272] 2. Obtaining trend information:

[2273] The server calls the trend API (e.g., a general trend information API) of a fashion news site or social networking service to obtain the latest trend information.

[2274] Recognition of emotional information

[2275] 1. Emotion data input:

[2276] The user provides emotional information through facial expressions, voice, or input data.

[2277] 2. Emotional Data Analysis:

[2278] The device uses an emotion engine (e.g., a general emotion analysis engine) to analyze the user's facial expressions, voice, and input data to recognize emotional information. For example, if the user inputs "I'm in a good mood today," the emotion engine will recognize this as a positive emotion.

[2279] Choosing the best outfit

[2280] 1. Send a coordination request:

[2281] The user launches the application and sends a request saying, "Suggest me today's outfit."

[2282] 2. Acquisition of information:

[2283] The server receives the request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[2284] 3. Coordinate generation:

[2285] The server then uses this information to generate optimal outfit combinations from a clothing database. For example, if the user expresses a relaxed mood on a cloudy day with a temperature of 18°C, the server will suggest a relaxed style of clothing.

[2286] 4. Suggestions for users:

[2287] The server sends the generated outfits to the device, which displays the suggestions to the user. If the user likes the outfits, they can try them on virtually and decide whether to wear them in real life.

[2288] Specific examples

[2289] 1. Clothing Registration:

[2290] A user purchases a new "red cardigan" and uploads a photo of it to the application.

[2291] The device sends the image to the server, which then uses image recognition technology to automatically classify attributes such as "cardigan," "red," and "casual" and store the images in a database.

[2292] 2. Collection of external information:

[2293] The server calls the weather API every morning at 8:00, obtains information such as "Cloudy, 18°C" and updates the weather database. It also calls the trend API to collect and update the latest information such as "Khaki is in fashion."

[2294] 3. Recognition of emotional information:

[2295] The user types into the application, "I want to relax today."

[2296] The device uses an emotion engine to recognize the user's emotion as relaxed from this input.

[2297] 4. Choosing the right outfit:

[2298] The user launches the app and requests, "Suggest an outfit for today."

[2299] The server selects a "red cardigan," "khaki wide-leg pants," and "plain sweatshirt" based on the weather, temperature, trends, and the user's relaxed mood, and sends the outfit to the device.

[2300] The device displays suggestions to the user, who can then virtually try them on, helping them choose actual outfits.

[2301] Prompt Sentence Examples

[2302] "Use the weather API and trend API to obtain weather and trend information for the current location, analyze the user's emotions using the emotion engine, and then combine this with registered clothing data using image recognition technology to suggest the best outfit."

[2303] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2304] Step 1:

[2305] Taking and uploading images

[2306] Input: The user takes a photo of a new outfit with their smartphone or uploads a photo of an existing outfit to the application.

[2307] How it works: A user launches the smartphone application and uses the camera to take a picture of the outfit, or selects and uploads an existing photo from the gallery.

[2308] Output: Image data of the clothes is saved on the device.

[2309] Step 2:

[2310] Image transmission

[2311] Input: Image data of clothes stored on the device.

[2312] Operation: The device sends the uploaded image data to the cloud server, where it is uploaded to cloud storage via the Internet.

[2313] Output: The image data of the clothes is saved on the cloud server.

[2314] Step 3:

[2315] Image analysis

[2316] Input: Image data of clothes stored on a cloud server.

[2317] How it works: The server uses image recognition technology to automatically analyze clothing categories (e.g., dresses, shirts, pants) and attributes (e.g., navy, stripes, etc.). Specifically, it uses image analysis software (e.g., general image recognition software).

[2318] Output: Parsed clothing category and attribute data.

[2319] Step 4:

[2320] Saving to a database

[2321] Input: Parsed clothing category and attribute data.

[2322] Operation: The server stores the analysis results in a database, which is a storage system for unifying the management of clothing categories and attributes.

[2323] Output: New clothing information is added to the database.

[2324] Step 5:

[2325] Obtaining weather and temperature information

[2326] Input: The server will be accessed at the specified time (e.g., every morning at 8:00).

[2327] How it works: The server calls a weather API (e.g., a general weather information API) to get weather and temperature information for the current location by sending a request to the API endpoint and parsing the response.

[2328] Output: Current location weather and temperature information is saved on the server.

[2329] Step 6:

[2330] Obtaining trend information

[2331] Input: The server is accessed periodically or upon request from the user.

[2332] How it works: The server calls a trend API (e.g., a general trend information API) to obtain the latest fashion trend information. To do this, the server sends a request to the API endpoint and analyzes the response data.

[2333] Output: The latest fashion trend information is stored on the server.

[2334] Step 7:

[2335] Entering emotion data

[2336] Input: Data that a user types into an application (e.g., facial expressions, voice, text).

[2337] How it works: The user captures their facial expressions with a camera, expresses their emotions through voice, or inputs their emotions through text through the application. The device collects the data in real time.

[2338] Output: The acquired emotion data is temporarily stored on the device.

[2339] Step 8:

[2340] Emotional Data Analysis

[2341] Input: Emotion data stored on the device.

[2342] How it works: The device uses a sentiment analysis engine (e.g., a general sentiment analysis engine) to analyze the sentiment data, for example, text analysis using natural language processing to classify positive or negative sentiment.

[2343] Output: The analyzed emotion information is saved on the device.

[2344] Step 9:

[2345] Sending a coordination request

[2346] Input: A request from the user.

[2347] Operation: The user launches the application and sends a request saying, "Suggest me an outfit for today." This request is sent from the device to the server.

[2348] Output: The request data is sent to the server.

[2349] Step 10:

[2350] Obtaining information

[2351] Input: The request data sent to the server.

[2352] How it works: The server receives a request and retrieves the latest weather, temperature, and trend information from the database, as well as the recognized emotion information from the emotion engine.

[2353] Output: The necessary information is consolidated on the server side.

[2354] Step 11:

[2355] Coordinate generation

[2356] Input: Integrated weather information, temperature information, trend information, and sentiment information.

[2357] How it works: The server uses this information to run an algorithm that generates optimal outfit combinations. Specifically, it filters the clothing information in the database based on the collected data and suggests outfit combinations that meet the criteria.

[2358] Output: Optimal outfit combination data.

[2359] Step 12:

[2360] Suggestions for users

[2361] Input: Generated optimal outfit combination data.

[2362] Operation: The server sends the generated coordinates to the terminal, which then displays the suggestions to the user. At this time, the terminal displays visual information to make it easier for the user to make a selection.

[2363] Output: The proposed coordinates that are displayed to the user.

[2364] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2365] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation m...

Claims

1. A means for registering images of clothes owned by a user; a means for collecting weather information, temperature information, and trend information for a current location; A means of selecting the most suitable outfit based on the collected information; A means for suggesting the selected outfit to the user; A system including:

2. Using image recognition technology, it automatically classifies clothing categories and attributes from registered clothing images. The system of claim 1 .

3. Generate optimal outfit suggestions based on weather, temperature, and trend information in response to user requests. The system of claim 1 .

Images