system

Abstract

We provide the system. [Solution] A means of uploading images of clothing, A means for analyzing uploaded images to recognize the material, color, and care label of clothing, A means of searching for an appropriate washing method from a database based on recognized information, A means of sending the searched laundry method to the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, there are a variety of clothes and fabric products, and different washing methods are recommended for each of them. However, it is difficult for ordinary users to master all of them. In particular, when the washing instructions label is detailed or complex symbols are used, there is a possibility that the clothes may deteriorate or washing accidents may occur by using an incorrect washing method. Also, there is a problem that it takes time and effort to check the appropriate washing method through the Internet or a manual. The present invention aims to solve these problems and provide a system that allows users to easily know the optimal washing method.

Means for Solving the Problems

[0005] The present invention is a system comprising means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, and means for transmitting the retrieved washing method to a user terminal. This allows users to instantly find out the best washing method for their clothing simply by taking a picture of it and uploading it. Furthermore, by providing means for searching the database using material information, color information, and care label information as keys when searching for a washing method based on the recognized information, a more accurate washing method can be provided. In addition, by providing means for converting the search results for washing methods into a user-friendly format when transmitting them to the user terminal, it is possible to provide results in a form that is easy for the user to intuitively understand.

[0006] "Clothing images" are digital image files that users provide to the system, which are photographs of the appearance of clothing or fabric products.

[0007] "Methods for uploading" refers to the operations and functions that allow users to send and save images of clothing within the system.

[0008] "Means of image analysis" refers to algorithms and technologies used to recognize and extract specific information (such as material, color, and laundry care label) from uploaded images.

[0009] "Material" refers to the main raw materials used in clothing and textile products (e.g., cotton, polyester, wool, etc.).

[0010] "Color" refers to the visible colors seen in clothing and textile products.

[0011] A "laundry care label" refers to a label attached to clothing or fabric products that contains symbols and text indicating the appropriate washing method.

[0012] A "database" refers to a system or storage device that stores and manages information and rules about laundry methods in a searchable format.

[0013] "Search methods" refer to functions and algorithms used to retrieve information from a database using specific keys or conditions.

[0014] A "user terminal" refers to a device used by a user to interact with the system (e.g., a smartphone, tablet, or personal computer).

[0015] "Means of transmission" refers to the functions and communication protocols used to send data (e.g., laundry instructions) from the server to the user's terminal.

[0016] A "user-friendly format" refers to a method of presenting information in a way that is intuitively easy for users to understand and visually clear. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0020] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

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

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0034] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. The embodiments for carrying out this invention will be described in detail below.

[0039] System Overview

[0040] This system includes means for uploading images of clothing, means for analyzing uploaded images, means for searching a database for an appropriate washing method, and means for transmitting the retrieved washing method to the user terminal. The system provides the user with the optimal washing method by communicating between the server and the user terminal.

[0041] Program Processing Overview

[0042] Image upload (device)

[0043] First, the user takes a picture of their clothes using their smartphone or computer, or selects an image from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system.

[0044] Sending images (from the device)

[0045] The user's terminal sends the image provided by the user to the server as an HTTP POST request. At this time, request data containing the image file is generated.

[0046] Image reception and storage (server)

[0047] The server receives an HTTP POST request sent from the user's terminal. The received image is temporarily stored in the server's storage.

[0048] Image analysis (server)

[0049] The server analyzes the stored images using image analysis algorithms. For example, it uses machine learning models and image recognition tools (e.g., OpenCV, TENSORFLOW®) to extract information about clothing material, color, and care labels from the images.

[0050] Search for laundry methods (server)

[0051] The server uses the information obtained from image analysis to search a database and extract the appropriate washing method. The information used as search keys includes material, color, and the contents of the care label.

[0052] For example, if an image of a "cotton shirt" is uploaded, the server will analyze the results to obtain information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry," and then use this information as a key to search the database.

[0053] Data formatting and transmission (server)

[0054] The server formats the retrieved laundry information into a user-friendly format. This is done, for example, by converting it to JSON format. Then, it sends that data to the user's terminal as an HTTP response.

[0055] Washing instructions (on the device)

[0056] The user terminal receives laundry information sent from the server. This information is displayed within the application in a user-friendly format. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0057] In this way, users can easily find out how to wash their clothes from their smartphones or computers. The system of the present invention reduces the risk of users choosing the wrong washing method and makes it possible to extend the life of their clothes.

[0058] The following describes the processing flow.

[0059] Step 1:

[0060] User: Take a photo of the clothing with your device or select an image of clothing from your existing image gallery. Then, click the upload button in the application to upload the image of the clothing to the system.

[0061] Step 2:

[0062] Terminal: When an image is selected or captured, an HTTP POST request is generated. This request includes the image file provided by the user.

[0063] Step 3:

[0064] Terminal: Sends the generated HTTP POST request to the server. The request has an image file attached.

[0065] Step 4:

[0066] Server: Receives HTTP POST requests sent from the terminal. Saves the received image files to temporary storage.

[0067] Step 5:

[0068] Server: Reads image files stored in storage. Applies image analysis algorithms to analyze the read images. This includes using machine learning models and image recognition tools.

[0069] Step 6:

[0070] Server: The image analysis algorithm extracts the material, color, and care label information of clothing from an image. For example, a machine learning model might determine it's a "cotton shirt" and recognize from the care label that it should be washed at 30°C or below, use a laundry net, and do not tumble dry.

[0071] Step 7:

[0072] Server: Searches the database based on information obtained from image analysis. Material, color, and laundry care label information are used as search keys.

[0073] Step 8:

[0074] Server: Retrieves the optimal washing method from the database. For example, for a "cotton shirt," it might retrieve washing instructions such as "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0075] Step 9:

[0076] Server: Formats the retrieved laundry method information into a user-friendly format. For example, converts it to JSON format.

[0077] Step 10:

[0078] Server: Sends formatted laundry instruction data to the user's terminal as an HTTP response.

[0079] Step 11:

[0080] Terminal: Receives HTTP responses sent from the server. Displays the received laundry method information within the application.

[0081] Step 12:

[0082] User: Check the washing instructions displayed in the application on the device. For example, instructions such as "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer" may be displayed.

[0083] In this way, users can quickly find out the best washing method simply by uploading an image.

[0084] (Example 1)

[0085] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] In conventional laundry instruction systems, users must interpret laundry care labels on their own, which can result in selecting the wrong washing method. This problem is particularly pronounced when it is difficult to accurately determine the appropriate washing method for the material and color of the clothing. Furthermore, information on washing methods is not centrally managed and is not provided in a way that is easily accessible to users, which is another issue. The present invention aims to solve these problems and provide a system that allows users to easily obtain accurate washing instructions.

[0087] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0088] In this invention, the server includes means for the user to upload an image of clothing, means for analyzing the uploaded image to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for transmitting the retrieved washing method to the user terminal, and means for displaying the transmitted washing method in a user-friendly format. As a result, the user can simply upload an image of their clothing and be automatically provided with the optimal washing method, reducing the risk of selecting an incorrect washing method and extending the life of their clothes.

[0089] A "user terminal" refers to a computing device operated by a user, and includes smartphones, personal computers, tablets, and other similar devices.

[0090] "Image analysis" is the process of analyzing uploaded images and automatically recognizing their contents (e.g., material, color, laundry care label), using machine learning models and image recognition tools.

[0091] "Material information" refers to information about the fibers and materials that make up clothing, such as cotton, polyester, and wool.

[0092] "Color information" refers to information about the colors of clothing, and includes major color classifications such as black, white, red, and blue.

[0093] A "laundry care label" is a tag or sticker attached to clothing that contains symbols and instructions indicating the proper washing, drying, and bleaching methods.

[0094] A "database" is a collection of data designed to effectively search, access, and manage information, and is used to store information on proper washing methods.

[0095] "Washing instructions" refer to information that shows the correct procedure and precautions for washing clothes, and include water temperature, detergent to use, and drying method.

[0096] A "user-friendly format" refers to a format that is easy for users to understand and use, and specifically includes formats such as JSON and concise text displays.

[0097] A "machine learning model" refers to a mathematical model trained to perform a specific task based on data, and is used in the image analysis process.

[0098] "Image recognition tools" refer to software or libraries that automatically recognize specific objects or features within an image, and OpenCV and TensorFlow are examples of this.

[0099] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Specific embodiments for carrying out this invention are described in detail below.

[0100] This system functions by communicating between a server and a user terminal. It includes means for users to upload images of clothing, means for analyzing uploaded images, means for searching a database for appropriate washing methods based on the recognized information, and means for transmitting and displaying the retrieved washing method on the user terminal.

[0101] First, the user takes a picture of the clothing with their smartphone or computer, or selects an image from their existing image gallery. This imports the image to the user's device. The user then sends the image to the system by clicking the "Upload" button within the application.

[0102] The submitted image is sent to the server as an HTTP POST request. The server receives this request and temporarily stores the image file in its storage. The server analyzes the stored image using image recognition tools such as OpenCV or TensorFlow to extract information about the clothing's material, color, and care label.

[0103] After analysis, the server uses the recognized information as a key to search the database and identify the appropriate washing method. Specifically, it sends material information, color information, and laundry care label information as queries to the database and retrieves the resulting washing method.

[0104] The retrieved washing instructions are formatted into a user-friendly format, such as JSON. The server sends this formatted data to the user's terminal as an HTTP response. The user's terminal parses the received data and displays a message within the application such as, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0105] This system allows users to easily obtain accurate washing instructions, reducing the risk of choosing the wrong method. Furthermore, because it provides the optimal washing method suited to the material and color of the clothing, users can extend the life of their clothes.

[0106] Next, let's consider a specific example: a user uploads an image of a "black cotton shirt." The user first takes a picture of the shirt with their smartphone and clicks the "Upload" button in the application. The image is sent to the server, which analyzes it using TensorFlow and extracts information such as the material being cotton, the color being black, and the care label stating that it should be washed at 30°C or below, bleach not allowed, and tumble drying not allowed. The server sends this information as a query to the database and retrieves the information that the appropriate washing method is "Wash at 30°C or below, do not use bleach, and avoid tumble drying." This information is converted to JSON format and sent to the user's terminal as an HTTP response. The user's terminal analyzes this information and displays "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying."

[0107] Examples of specific prompt messages are as follows:

[0108] "How do I wash a black cotton shirt? The care label says to wash at 30°C or below, do not bleach, and do not tumble dry."

[0109] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0110] Step 1:

[0111] The user takes a picture of clothing or selects one from their existing image gallery using a smartphone or computer. Specifically, they open a camera or gallery app, select an image, and click the "Upload" button within the application. The input is an image of clothing, and the output is the image file imported into the application.

[0112] Step 2:

[0113] The user terminal sends the captured image file to the server as an HTTP POST request. In this process, an HTTP library is used to generate the request, and the request data, including the image file and metadata, is sent to the server. The input is the image file captured by the application, and the output is the HTTP request sent to the server.

[0114] Step 3:

[0115] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the image file in storage. Specifically, the server's endpoint handles the request and stores the received image file in the file system or database. The input is the HTTP request, and the output is the image file stored in storage.

[0116] Step 4:

[0117] The server analyzes stored images using image analysis algorithms. Specifically, it reads image files and launches machine learning models such as OpenCV or TensorFlow. Using these tools, it extracts information about the material, color, and care label of clothing from the images. The input is an image file stored in storage, and the output is the material information, color information, and care label information as analysis results.

[0118] Step 5:

[0119] The server uses information obtained through image analysis to search the database and extract the appropriate washing method. Specifically, the server sends material, color, and care label information as a query to the database. It searches this data and retrieves the record for the corresponding washing method. The input is the material information, color information, and care label information obtained as analysis results, and the output is the washing method information obtained from the database.

[0120] Step 6:

[0121] The server formats the retrieved laundry method information into a user-friendly format. Specifically, it converts the data into JSON format to make it easy for the user to understand. The formatted data is prepared as an HTTP response and sent to the user's terminal. The input is laundry method information retrieved from the database, and the output is formatted JSON data.

[0122] Step 7:

[0123] The user terminal receives laundry information in JSON data format from the server. Specifically, it parses the received response data and displays it in a user-friendly format within the application. The input is the JSON data sent from the server, and the output is the laundry instructions displayed to the user. For example, it might display, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0124] (Application Example 1)

[0125] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0126] The problems that this invention aims to solve are to enable users to easily understand how to wash their clothes and to improve the user's purchasing experience by providing real-time washing instructions in physical stores. Conventional systems had the problem that users had a high risk of choosing the wrong washing method, resulting in a shorter lifespan for their clothes. Furthermore, because real-time information was not provided in physical stores, users could not immediately find out the appropriate washing method.

[0127] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0128] In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for transmitting the retrieved washing method to a user terminal or wearable device, and communication means for providing washing instructions in real time while the user is on the move. This allows the user to receive appropriate washing instructions for their clothing in real time even while on the go, reducing the risk of choosing the wrong washing method and extending the life of the clothing. Furthermore, even in physical stores, users can check product care instructions on the spot, which can support their purchasing decisions.

[0129] "Means for uploading images of clothing" refers to a device or interface for a user to send images of clothing they have taken to a server.

[0130] "Means for analyzing uploaded images" refers to algorithms or software for analyzing the data of submitted images and recognizing the material, color, and care label of clothing in the images.

[0131] "A means of searching a database for an appropriate laundry method based on recognized information" refers to a search engine or search algorithm that searches a database based on analysis results to identify the optimal laundry method.

[0132] "Means for transmitting the retrieved laundry method to a user terminal or wearable device" refers to a communication protocol or interface for transmitting data of the identified laundry method to the user's device.

[0133] "Communication means for providing laundry instructions in real time while the user is on the move" refers to mobile networks or Wi-Fi connections that allow users to receive information in real time even while they are on the move.

[0134] "Means of converting to a user-friendly format" refers to formatting algorithms that present search results to users in an easily understandable format (such as JSON).

[0135] This invention relates to a system that uploads images of clothing, analyzes those images, and provides appropriate washing instructions. The system consists of a user terminal, a server, an image analysis algorithm, a database, and a communication interface.

[0136] 1. User terminal

[0137] Users take or select images of clothing using a device such as a smartphone, head-mounted display, or smart glasses. The device has an interface for uploading images, and users send the images to the server by pressing the upload button.

[0138] 2. Server

[0139] The server receives images sent by users and stores them in temporary storage. The server performs image analysis using the following software and hardware.

[0140] Image analysis libraries: OpenCV, TensorFlow

[0141] Databases: MySQL (registered trademark), MongoDB

[0142] Communication protocol: HTTP / HTTPS

[0143] 3. Image Analysis

[0144] The saved images are analyzed by an image analysis algorithm on the server. Using OpenCV and TensorFlow, the material, color, and care label of the clothing are recognized from the images. This analyzed data is used as key information to determine the washing method.

[0145] 4. Database Search

[0146] Based on the information obtained from image analysis, the server searches the database and extracts the appropriate washing method. The information used as search keys includes material, color, and the contents of the care label. For example, if an image of a "cotton shirt" is uploaded, the search will be performed based on the recognition results, which include information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0147] 5. Data Formatting and Transmission

[0148] The server formats the retrieved laundry information into a user-friendly format. Specifically, it converts it to JSON format and then sends it as an HTTP response to the user's terminal or wearable device.

[0149] 6. Washing instructions

[0150] The user terminal analyzes the received laundry information and displays it to the user in an appropriate format. For example, the smart glasses display might show, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0151] Examples of specific cases and prompt statements

[0152] When users are browsing products in a physical store, they can use smart glasses to obtain information on the spot.

[0153] Example of a prompt:

[0154] "How should I wash this garment?"

[0155] "How do I care for this garment?"

[0156] "Please tell me the proper way to wash this material."

[0157] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0158] Step 1:

[0159] Upload image

[0160] Users take or select images of clothing using a smartphone, head-mounted display, or smart glasses.

[0161] Input: Image of clothing

[0162] Operation: The user clicks the upload button within the application.

[0163] Output: Uploaded image file

[0164] Step 2:

[0165] Sending images

[0166] The device sends the image provided by the user to the server as an HTTP POST request.

[0167] Input: Uploaded image file

[0168] Operation: Generate an HTTP POST request and send it to the server, including image data.

[0169] Output: Request data sent to the server

[0170] Step 3:

[0171] Receiving and saving images

[0172] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the image.

[0173] Input: HTTP request with image data sent from the terminal

[0174] Operation: Receives request data and saves image data to storage.

[0175] Output: Saved image file

[0176] Step 4:

[0177] Image analysis

[0178] The server analyzes the stored images using OpenCV and TensorFlow to recognize the material, color, and care label of the clothing.

[0179] Input: Saved image file

[0180] Operation: Perform image recognition processing using OpenCV or TensorFlow. Specifically, analyze the texture, color, and text within the image.

[0181] Output: Data on recognized material, color, and care label.

[0182] Step 5:

[0183] Search for laundry methods

[0184] The server searches the database based on the recognized information and extracts the appropriate washing method.

[0185] Input: Data on recognized material, color, and care label.

[0186] Operation: Perform a database search, using the recognition information as the search key.

[0187] Output: Data on searched laundry methods

[0188] Step 6:

[0189] Data formatting and transmission

[0190] The server formats the searched laundry method information into a user-friendly format and sends it to the user's terminal or wearable device.

[0191] Input: Data on the searched laundry methods

[0192] Operation: Format the data into JSON format and send it as an HTTP response.

[0193] Output: HTTP response of formatted laundry method data

[0194] Step 7:

[0195] Washing instructions

[0196] The user terminal receives laundry instructions from the server and displays them in a user-friendly format.

[0197] Input: HTTP response of laundry method data sent from the server

[0198] Operation: Analyze data and display it within the application in a user-friendly format.

[0199] Output: Illustrated instructions for washing

[0200] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0201] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system includes a function that suggests an appropriate washing method according to the user's situation and mood by incorporating an emotion engine that recognizes the user's emotions. The embodiments for carrying out this invention will be described in detail below.

[0202] System Overview

[0203] This system includes means for uploading images of clothing, means for analyzing uploaded images, means for searching a database for appropriate washing methods, means for sending washing methods to the user terminal, and an emotion engine. The system communicates between the server and the user terminal to provide the user with the optimal washing method and to offer customized suggestions that take the user's emotions into consideration.

[0204] Program Processing Overview

[0205] Image upload (device)

[0206] First, the user takes a picture of their clothes using their smartphone or computer, or selects an image of their clothes from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system.

[0207] Sending images (from the device)

[0208] The user's terminal sends the image provided by the user to the server as an HTTP POST request. At this time, request data containing the image file is generated.

[0209] Image reception and storage (server)

[0210] The server receives an HTTP POST request sent from the user's terminal. The received image is temporarily stored in the server's storage.

[0211] Image analysis (server)

[0212] The server analyzes the stored images using image analysis algorithms. For example, it uses machine learning models and image recognition tools (e.g., OpenCV, TensorFlow) to extract information about the clothing's material, color, and care label from the images.

[0213] Emotion recognition (server)

[0214] The emotion engine recognizes the user's current emotions through analysis of their voice and images. For example, it analyzes the user's emotions from their facial expressions captured on camera or from their voice input. If the user is feeling stressed, it prioritizes suggesting simple and quick laundry methods.

[0215] Search for laundry methods (server)

[0216] The server searches a database to extract the appropriate washing method based on information obtained from image analysis and sentiment analysis results from the sentiment engine. The information used as search keys includes material, color, contents of the care label, and sentiment information.

[0217] For example, if an image of a "cotton shirt" is uploaded and the emotion engine recognizes that the user is feeling stressed, the server retrieves information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry," and uses this information as a key to search the database. Taking the user's emotions into consideration, it prioritizes suggesting simpler washing methods (e.g., avoiding recommending hand washing and encouraging the use of a washing machine).

[0218] Data formatting and transmission (server)

[0219] The server formats the retrieved laundry information into a user-friendly format. This is done, for example, by converting it to JSON format. Then, it sends that data to the user's terminal as an HTTP response.

[0220] Washing instructions (on the device)

[0221] The user terminal receives laundry information sent from the server. This information is displayed within the application in a user-friendly format. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0222] In this way, users can quickly find the optimal washing method that takes their emotions into consideration simply by uploading an image. The system of this invention reduces the risk of users choosing the wrong washing method, making it possible to extend the life of their clothes. Furthermore, by providing customized suggestions that take the user's emotions into account, the user experience is improved.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] User: Take a picture of the clothing with your device's camera, or select an image of the clothing from your existing image gallery. Then, click the upload button in the application to upload the image of the clothing to the system.

[0226] Step 2:

[0227] Terminal: When an image is selected or captured, an HTTP POST request is generated. This request includes the image file provided by the user.

[0228] Step 3:

[0229] Terminal: Sends the generated HTTP POST request to the server. The request has an image file attached.

[0230] Step 4:

[0231] Server: Receives HTTP POST requests sent from the terminal. Saves the received image files to temporary storage.

[0232] Step 5:

[0233] Server: Reads image files stored in storage and applies image analysis algorithms. For example, it uses OpenCV or TensorFlow to extract clothing material, color, and care label information from images.

[0234] Step 6:

[0235] Server: Based on the image analysis, the material of the clothing is determined to be "cotton," and the care label is recognized as "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0236] Step 7:

[0237] Server: Uses an emotion engine to recognize the user's emotions. It analyzes the user's facial expressions captured by the camera and voice input to determine if they are "feeling stressed."

[0238] Step 8:

[0239] Server: Searches the database based on image analysis results and sentiment analysis results. The search uses material, color, care label information, and sentiment information as search keys.

[0240] Step 9:

[0241] Server: Retrieves the optimal washing method from the database. For example, if the user is stressed, it prioritizes suggesting a simple and easy washing method (e.g., "Use a short cycle at 30°C or below in the washing machine").

[0242] Step 10:

[0243] Server: Formats the retrieved laundry method information into a user-friendly format. For example, converts and formats it into JSON format.

[0244] Step 11:

[0245] Server: Sends formatted laundry instruction data to the user's terminal as an HTTP response.

[0246] Step 12:

[0247] Terminal: Receives HTTP responses sent from the server. Displays the received laundry method information within the application.

[0248] Step 13:

[0249] User: Check the washing instructions displayed in the application on the device. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying." If the user is feeling stressed, additional suggestions might be displayed, such as, "Use the short cycle on your washing machine for easy washing."

[0250] In this way, users can quickly find out the best, emotionally-conscious way to do their laundry simply by uploading an image.

[0251] (Example 2)

[0252] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0253] Traditional laundry instruction systems simply provided appropriate washing instructions based on the material, color, and care label of the clothing, without considering the user's feelings. As a result, users were sometimes presented with complicated washing instructions when they were stressed or time-constrained, highlighting the need for improved user experience. Furthermore, the lack of user-friendly information presentation made it difficult for users to accurately understand and implement the provided information.

[0254] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and laundry care label of the clothing, means for using an emotion engine to recognize the user's emotions, means for searching a database for an appropriate laundry method based on the recognized information and the user's emotions, and means for converting the searched laundry method into a user-friendly format and transmitting it to the user terminal. This makes it possible for the user to quickly find the optimal laundry method that suits their emotional state, thereby improving the user experience.

[0255] The "means of uploading images of clothing" refers to a function that allows users to select images of clothing using their smartphones, computers, or other user devices and send them to the system.

[0256] "Means for analyzing uploaded images to recognize the material, color, and care label of clothing" refers to a function that analyzes images stored on the server using machine learning and image recognition tools to extract the main characteristics of the clothing.

[0257] "Means of using an emotion engine to recognize user emotions" refers to algorithms and technologies that analyze a user's facial expressions and voice to identify their current emotional state.

[0258] "Means for searching for an appropriate washing method from a database based on recognized information and user sentiment" refers to a function that searches for the optimal washing method from a database based on analyzed clothing characteristics and user sentiment information.

[0259] "A means of converting searched laundry methods into a user-friendly format and sending it to the user's terminal" refers to a function that converts search results into a format that is easy for the user to understand (e.g., JSON format) and outputs that information to the user's terminal.

[0260] This invention is a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, and includes a function to suggest an appropriate washing method according to the user's situation and mood. Specific embodiments for carrying out the invention will be described in detail below.

[0261] System Configuration

[0262] This system consists of the following main elements:

[0263] 1. How to upload images of clothing

[0264] The user uses a user device (smartphone or computer). They either take a picture of the clothing using the device's camera or select an image of the clothing from their existing image gallery. The user then uploads the image by clicking the upload button within the application.

[0265] 2. Means for analyzing uploaded images

[0266] The server uses image analysis algorithms (e.g., OpenCV, TensorFlow) to extract the material, color, and care label information of clothing from the image.

[0267] 3. Means of using an emotion engine that recognizes user emotions

[0268] An emotion engine within the server analyzes the user's voice and images to recognize their current emotional state. For example, if the user is feeling stressed, it prioritizes suggesting simple and quick laundry methods.

[0269] 4. Means for searching a database for an appropriate laundry method based on recognized information and user sentiment.

[0270] The server searches the database based on information obtained from image analysis and the sentiment engine. The information used as search keys includes material, color, laundry care label content, and sentiment information.

[0271] 5. A means of converting the searched laundry method into a user-friendly format and sending it to the user's terminal.

[0272] The server formats the search results into a user-friendly format, such as JSON, and sends it to the user's terminal as an HTTP response.

[0273] Hardware and software to be used

[0274] User devices: Smartphones, personal computers

[0275] Server: Cloud server or on-premises server

[0276] Image analysis tools: OpenCV, TensorFlow

[0277] Emotion engine: Voice analysis tool, face recognition algorithm

[0278] Specific examples

[0279] Here, specific examples of the embodiments of the present invention are shown.

[0280] 1. Specific example of a user uploading an image:

[0281] The user launches the app on the smartphone, takes a picture of the clothes or selects from the gallery, and clicks the upload button.

[0282] 2. Specific example of image analysis:

[0283] The server analyzes the received image using OpenCV and extracts the material of the clothes (e.g., cotton), color (e.g., white), and washing label (e.g., "Wash at 30°C or below").

[0284] 3. Specific example of emotion recognition:

[0285] The emotion engine analyzes the user's voice input "tired" and recognizes that the user "feels stressed".

[0286] 4. Specific example of search results:

[0287] Search the database and extract a simple washing method using "cotton shirt", "wash at 30°C or below", "use a washing net", and "do not use a dryer" as keys. The server recommends using a washing machine instead of hand washing.

[0288] 5. Example of prompt text:

[0289] The user submitted an image of a cotton shirt, and the sentiment engine determined that the user was tired. As a simple and quick washing method, it suggested, "Wash in a washing machine at 30°C or below, use a laundry net, and avoid using a dryer."

[0290] As described above, this invention allows users to quickly find the optimal, emotionally-conscious washing method simply by uploading an image. This improves the user experience and reduces the risk of selecting the wrong washing method.

[0291] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0292] Step 1: Uploading an image (User)

[0293] Users take pictures of their clothes using a smartphone or computer application, or select images from their existing gallery. Next, they click the upload button within the application to upload the clothing images to the system. This selects the image file and initiates the upload process.

[0294] Input: Images of clothing selected or photographed by the user.

[0295] Output: Image files uploaded to the system

[0296] Step 2: Sending the image (from your device)

[0297] The device sends the selected image to the server as an HTTP POST request. This request contains the embedded image file. The device generates the request data and sends it to the server over the internet.

[0298] Input: Uploaded image file

[0299] Output: Image data to be sent to the server as an HTTP POST request

[0300] Step 3: Image reception and storage (server)

[0301] The server receives the HTTP POST request sent from the terminal. The server extracts the image data from the request and temporarily stores it in the server's storage. This prepares the input data for image analysis.

[0302] Input: Image data of the HTTP POST request

[0303] Output: Image data stored in the server's storage

[0304] Step 4: Image analysis (server)

[0305] The server analyzes the saved image using image analysis algorithms (e.g., OpenCV, TensorFlow). It extracts the material, color, and washing label of the clothing from the image. It executes code written in programming languages such as Python or C++ for image analysis.

[0306] Input: Image data stored in the server's storage

[0307] Output: Information on the material, color, and washing label of the clothing extracted by the analysis

[0308] Step 5: Emotion recognition (server)

[0309] The server's emotion engine analyzes the user's voice input and image data to recognize the current emotional state. There may be voice inputs such as the user approaching the microphone and saying "tired". The emotion analysis algorithm analyzes the voice and facial expression data to identify the user's emotional state.

[0310] Input: User's voice input, facial expression data

[0311] Output: User's emotional state identified through analysis

[0312] Step 6: Search for washing instructions (server)

[0313] The server searches the database to extract appropriate washing instructions based on information obtained from image analysis and sentiment analysis results from the sentiment engine. The information used as search keys includes material, color, contents of the care label, and sentiment information. The server executes SQL queries to retrieve search results from the database.

[0314] Input: Information on clothing material, color, care label, and user sentiment.

[0315] Output: Information on appropriate washing methods extracted from the database

[0316] Step 7: Formatting and sending data (to the server)

[0317] The server formats the extracted laundry method information into a user-friendly format. For example, it formats it into JSON format to organize the necessary information. Then, it sends the formatted data to the user's terminal as an HTTP response.

[0318] Input: Laundry method information extracted from a database

[0319] Output: Formatted data and sent to the user's terminal as an HTTP response.

[0320] Step 8: Washing instructions displayed (on the device)

[0321] The user terminal receives an HTTP response sent from the server. The received data is analyzed, and laundry instructions are displayed in a user-friendly format within the application. For example, a message such as "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer" might be displayed.

[0322] Input: Formatted data sent from the server

[0323] Output: Laundry instructions displayed in a user-friendly format

[0324] The above outlines the processing steps of this system's program. This allows users to easily and quickly discover the optimal, emotionally-conscious laundry method.

[0325] (Application Example 2)

[0326] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0327] Conventional laundry method suggestion systems provided general washing instructions based on the material, color, and care label of the clothing, but they were unable to offer customized suggestions based on the user's emotions or circumstances. As a result, even when users were feeling stressed or fatigued, they were not offered appropriate care methods, leading to inconvenience. Furthermore, the information was not provided in a user-friendly format, making it difficult to understand. Therefore, a system is needed that can provide flexible suggestions in response to the user's emotions and deliver information in an easy-to-use format.

[0328] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0329] In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for recognizing the user's emotions using an emotion engine and customizing the washing method according to those emotions, and means for transmitting the searched and customized washing method to the user terminal. This makes it possible to customize and provide the optimal washing method according to the user's emotions and circumstances, thereby improving the user experience.

[0330] The "means of uploading clothing images" refer to a function that allows users to send images of clothing they have taken to the system using their smartphone or other devices.

[0331] "Methods for analyzing uploaded images" refers to algorithms and tools used to extract information such as the material, color, and care label of clothing based on images submitted by users.

[0332] "A means of searching for appropriate washing methods from a database" refers to an implementation function that searches the database for methods of properly washing clothes based on the extracted information, and obtains the necessary information.

[0333] "A means of recognizing the user's emotions using an emotion engine and customizing the laundry method according to those emotions" refers to a function that analyzes the user's current emotional state based on input such as facial expressions and voice, and adjusts and suggests the laundry method according to the user's emotions.

[0334] "Means for sending search and customized laundry methods to the user terminal" refers to an implementation function for sending the information generated by the above process to the user terminal in a format that is easy for the user to understand.

[0335] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system includes a function that incorporates an emotion engine to recognize the user's emotions and suggest an appropriate washing method according to the user's situation and mood. The embodiments for carrying out this invention will be described in detail below.

[0336] System Overview

[0337] This system includes means for uploading images of clothing, means for analyzing the uploaded images, means for searching a database for an appropriate washing method based on the recognized information, means for recognizing the user's emotions using an emotion engine and customizing the washing method according to those emotions, and means for transmitting the searched and customized washing method to the user's terminal.

[0338] Image upload (device)

[0339] First, the user takes a picture of their clothes using their smartphone or other device, or selects an image of their clothes from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system. This entire process is carried out using an HTTP POST request.

[0340] Image transmission and storage (server)

[0341] The server receives images sent from the user's terminal and temporarily stores them in the server's storage. The stored images are then used in subsequent image analysis processes.

[0342] Image analysis (server)

[0343] The server uses image recognition tools (e.g., OpenCV, TensorFlow) to analyze the stored images. The analysis extracts the clothing material, color, and care label information.

[0344] Emotion recognition (server)

[0345] The emotion engine is used to recognize the user's emotions in real time. This system determines emotions by analyzing the user's voice input and facial expressions captured by the camera. For example, if the user is feeling stressed, the system will suggest simplifying the laundry process.

[0346] Laundry method search and customization (server)

[0347] The server searches the database for an appropriate washing method based on the results of image analysis and emotion recognition by the emotion engine. This search uses material information, color information, and laundry care label information as keys. Furthermore, it proposes a customized washing method based on the user's emotional state.

[0348] Data formatting and transmission (server)

[0349] The server formats the retrieved laundry information into a user-friendly format. The information is converted to JSON format and sent to the user's terminal as an HTTP response.

[0350] Washing instructions (on the device)

[0351] The user terminal receives laundry information sent from the server and displays it in a user-friendly format within the application. For example, it might display, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0352] Usage example

[0353] For example, if a user uploads an image of a "cotton shirt" purchased from an online shopping site, and the emotion engine recognizes the user's stress level, the server searches the database based on information such as "wash at 30°C or below," "use a laundry net," and "do not tumble dry." At the same time, it takes the user's emotional state into consideration and makes customized suggestions, such as recommending the use of a washing machine. A message like, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying," is displayed on the user's terminal.

[0354] Examples of prompts for generative AI models

[0355] "Analyze images of clothing uploaded by users and provide the optimal washing method. Also, customize the suggestions based on the user's mood."

[0356] In this way, the system helps users quickly obtain optimal information about laundry, allowing them to extend the life of their clothes. Furthermore, it enhances the user experience through emotionally sensitive and customized suggestions.

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] Upload an image of clothing.

[0360] The user first selects an image of clothing from their smartphone's camera app or gallery. Next, they click the upload button within the application to upload the image of clothing to the system. The image file, as input, is sent to the server via an HTTP POST request. This causes the server to temporarily store the image file in its storage.

[0361] Step 2:

[0362] Receiving and saving images

[0363] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the included image file in the server's storage. The input is the HTTP POST request, and the output is the stored image file. This process prepares the server for the data necessary for the next image analysis step.

[0364] Step 3:

[0365] Image analysis

[0366] The server analyzes the stored images. It primarily uses image recognition tools such as OpenCV and TensorFlow to extract information about clothing material, color, and care labels from the images. It uses the stored image files as input and outputs this detailed information. This analysis result is then used to search for laundry instructions.

[0367] Step 4:

[0368] Recognition of emotions

[0369] The server uses an emotion engine to recognize the user's current emotional state. Specifically, it analyzes the user's facial expression data captured by the camera and voice input to determine the user's emotions (e.g., stress, relief, etc.). The input is the user's facial expression data and voice data, and the output is the recognized emotional information. This provides data to customize the laundry method based on the user's current emotional state.

[0370] Step 5:

[0371] Search for laundry methods

[0372] The server searches the database for the appropriate washing method based on the results of image analysis and emotion recognition. This process queries the database using material information, color information, and the contents of the care label as keys to extract the optimal washing method. The inputs are image analysis results and emotion recognition results, and the output is information on the appropriate washing method.

[0373] Step 6:

[0374] Customized laundry method shaping

[0375] The server combines the searched laundry method with the user's emotional information to customize the optimal laundry method. For example, if the user is feeling stressed, it will prioritize suggesting a simpler laundry method. The input consists of the searched laundry method information and the user's emotional information, and the output is customized laundry method information.

[0376] Step 7:

[0377] Data formatting and transmission

[0378] The server formats the customized laundry instructions into a user-friendly format. Typically, the formatted data is converted to JSON format. Next, the formatted data is sent to the user's terminal as an HTTP response. The input is customized laundry instructions, and the output is the formatted data sent to the user's terminal.

[0379] Step 8:

[0380] Washing instructions

[0381] The user terminal receives laundry instructions in JSON format from the server and displays them in a user-friendly format within the application. For example, it might display specific instructions such as, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer." The input is the JSON response from the server, and the output is the laundry instructions displayed to the user. This step allows the user to easily understand the optimal laundry method.

[0382] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0383] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0384] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0385] [Second Embodiment]

[0386] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0387] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0388] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0389] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0390] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0391] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0392] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0393] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0394] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0395] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0396] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0397] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0398] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. The embodiments for carrying out this invention will be described in detail below.

[0399] System Overview

[0400] This system includes means for uploading images of clothing, means for analyzing uploaded images, means for searching a database for an appropriate washing method, and means for transmitting the retrieved washing method to the user terminal. The system provides the user with the optimal washing method by communicating between the server and the user terminal.

[0401] Program Processing Overview

[0402] Image upload (device)

[0403] First, the user takes a picture of their clothes using their smartphone or computer, or selects an image from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system.

[0404] Sending images (from the device)

[0405] The user's terminal sends the image provided by the user to the server as an HTTP POST request. At this time, request data containing the image file is generated.

[0406] Image reception and storage (server)

[0407] The server receives an HTTP POST request sent from the user's terminal. The received image is temporarily stored in the server's storage.

[0408] Image analysis (server)

[0409] The server analyzes the stored images using image analysis algorithms. For example, it uses machine learning models and image recognition tools (e.g., OpenCV, TensorFlow) to extract information about the clothing's material, color, and care label from the images.

[0410] Search for laundry methods (server)

[0411] The server uses the information obtained from image analysis to search a database and extract the appropriate washing method. The information used as search keys includes material, color, and the contents of the care label.

[0412] For example, if an image of a "cotton shirt" is uploaded, the server will analyze the results to obtain information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry," and then use this information as a key to search the database.

[0413] Data formatting and transmission (server)

[0414] The server formats the retrieved laundry information into a user-friendly format. This is done, for example, by converting it to JSON format. Then, it sends that data to the user's terminal as an HTTP response.

[0415] Washing instructions (on the device)

[0416] The user terminal receives laundry information sent from the server. This information is displayed within the application in a user-friendly format. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0417] In this way, users can easily find out how to wash their clothes from their smartphones or computers. The system of the present invention reduces the risk of users choosing the wrong washing method and makes it possible to extend the life of their clothes.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] User: Take a photo of the clothing with your device or select an image of clothing from your existing image gallery. Then, click the upload button in the application to upload the image of the clothing to the system.

[0421] Step 2:

[0422] Terminal: When an image is selected or captured, an HTTP POST request is generated. This request includes the image file provided by the user.

[0423] Step 3:

[0424] Terminal: Sends the generated HTTP POST request to the server. The request has an image file attached.

[0425] Step 4:

[0426] Server: Receives HTTP POST requests sent from the terminal. Saves the received image files to temporary storage.

[0427] Step 5:

[0428] Server: Reads image files stored in storage. Applies image analysis algorithms to analyze the read images. This includes using machine learning models and image recognition tools.

[0429] Step 6:

[0430] Server: The image analysis algorithm extracts the material, color, and care label information of clothing from an image. For example, a machine learning model might determine it's a "cotton shirt" and recognize from the care label that it should be washed at 30°C or below, use a laundry net, and do not tumble dry.

[0431] Step 7:

[0432] Server: Searches the database based on information obtained from image analysis. Material, color, and laundry care label information are used as search keys.

[0433] Step 8:

[0434] Server: Retrieves the optimal washing method from the database. For example, for a "cotton shirt," it might retrieve washing instructions such as "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0435] Step 9:

[0436] Server: Formats the retrieved laundry method information into a user-friendly format. For example, converts it to JSON format.

[0437] Step 10:

[0438] Server: Sends formatted laundry instruction data to the user's terminal as an HTTP response.

[0439] Step 11:

[0440] Terminal: Receives HTTP responses sent from the server. Displays the received laundry method information within the application.

[0441] Step 12:

[0442] User: Check the washing instructions displayed in the application on the device. For example, instructions such as "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer" may be displayed.

[0443] In this way, users can quickly find out the best washing method simply by uploading an image.

[0444] (Example 1)

[0445] Next, we will describe Example 1. 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".

[0446] In conventional laundry instruction systems, users must interpret laundry care labels on their own, which can result in selecting the wrong washing method. This problem is particularly pronounced when it is difficult to accurately determine the appropriate washing method for the material and color of the clothing. Furthermore, information on washing methods is not centrally managed and is not provided in a way that is easily accessible to users, which is another issue. The present invention aims to solve these problems and provide a system that allows users to easily obtain accurate washing instructions.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes means for the user to upload an image of clothing, means for analyzing the uploaded image to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for transmitting the retrieved washing method to the user terminal, and means for displaying the transmitted washing method in a user-friendly format. As a result, the user can simply upload an image of their clothing and be automatically provided with the optimal washing method, reducing the risk of selecting an incorrect washing method and extending the life of their clothes.

[0449] A "user terminal" refers to a computing device operated by a user, and includes smartphones, personal computers, tablets, and other similar devices.

[0450] "Image analysis" is the process of analyzing uploaded images and automatically recognizing their contents (e.g., material, color, laundry care label), using machine learning models and image recognition tools.

[0451] "Material information" refers to information about the fibers and materials that make up clothing, such as cotton, polyester, and wool.

[0452] "Color information" refers to information about the colors of clothing, and includes major color classifications such as black, white, red, and blue.

[0453] A "laundry care label" is a tag or sticker attached to clothing that contains symbols and instructions indicating the proper washing, drying, and bleaching methods.

[0454] A "database" is a collection of data designed to effectively search, access, and manage information, and is used to store information on proper washing methods.

[0455] "Washing instructions" refer to information that shows the correct procedure and precautions for washing clothes, and include water temperature, detergent to use, and drying method.

[0456] A "user-friendly format" refers to a format that is easy for users to understand and use, and specifically includes formats such as JSON and concise text displays.

[0457] A "machine learning model" refers to a mathematical model trained to perform a specific task based on data, and is used in the image analysis process.

[0458] "Image recognition tools" refer to software or libraries that automatically recognize specific objects or features within an image, and OpenCV and TensorFlow are examples of this.

[0459] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Specific embodiments for carrying out this invention are described in detail below.

[0460] This system functions by communicating between a server and a user terminal. It includes means for users to upload images of clothing, means for analyzing uploaded images, means for searching a database for appropriate washing methods based on the recognized information, and means for transmitting and displaying the retrieved washing method on the user terminal.

[0461] First, the user takes a picture of the clothing with their smartphone or computer, or selects an image from their existing image gallery. This imports the image to the user's device. The user then sends the image to the system by clicking the "Upload" button within the application.

[0462] The submitted image is sent to the server as an HTTP POST request. The server receives this request and temporarily stores the image file in its storage. The server analyzes the stored image using image recognition tools such as OpenCV or TensorFlow to extract information about the clothing's material, color, and care label.

[0463] After analysis, the server uses the recognized information as a key to search the database and identify the appropriate washing method. Specifically, it sends material information, color information, and laundry care label information as queries to the database and retrieves the resulting washing method.

[0464] The retrieved washing instructions are formatted into a user-friendly format, such as JSON. The server sends this formatted data to the user's terminal as an HTTP response. The user's terminal parses the received data and displays a message within the application such as, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0465] This system allows users to easily obtain accurate washing instructions, reducing the risk of choosing the wrong method. Furthermore, because it provides the optimal washing method suited to the material and color of the clothing, users can extend the life of their clothes.

[0466] Next, let's consider a specific example: a user uploads an image of a "black cotton shirt." The user first takes a picture of the shirt with their smartphone and clicks the "Upload" button in the application. The image is sent to the server, which analyzes it using TensorFlow and extracts information such as the material being cotton, the color being black, and the care label stating that it should be washed at 30°C or below, bleach not allowed, and tumble drying not allowed. The server sends this information as a query to the database and retrieves the information that the appropriate washing method is "Wash at 30°C or below, do not use bleach, and avoid tumble drying." This information is converted to JSON format and sent to the user's terminal as an HTTP response. The user's terminal analyzes this information and displays "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying."

[0467] Examples of specific prompt messages are as follows:

[0468] "How do I wash a black cotton shirt? The care label says to wash at 30°C or below, do not bleach, and do not tumble dry."

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user takes a picture of clothing or selects one from their existing image gallery using a smartphone or computer. Specifically, they open a camera or gallery app, select an image, and click the "Upload" button within the application. The input is an image of clothing, and the output is the image file imported into the application.

[0472] Step 2:

[0473] The user terminal sends the captured image file to the server as an HTTP POST request. In this process, an HTTP library is used to generate the request, and the request data, including the image file and metadata, is sent to the server. The input is the image file captured by the application, and the output is the HTTP request sent to the server.

[0474] Step 3:

[0475] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the image file in storage. Specifically, the server's endpoint handles the request and stores the received image file in the file system or database. The input is the HTTP request, and the output is the image file stored in storage.

[0476] Step 4:

[0477] The server analyzes stored images using image analysis algorithms. Specifically, it reads image files and launches machine learning models such as OpenCV or TensorFlow. Using these tools, it extracts information about the material, color, and care label of clothing from the images. The input is an image file stored in storage, and the output is the material information, color information, and care label information as analysis results.

[0478] Step 5:

[0479] The server uses information obtained through image analysis to search the database and extract the appropriate washing method. Specifically, the server sends material, color, and care label information as a query to the database. It searches this data and retrieves the record for the corresponding washing method. The input is the material information, color information, and care label information obtained as analysis results, and the output is the washing method information obtained from the database.

[0480] Step 6:

[0481] The server formats the retrieved laundry method information into a user-friendly format. Specifically, it converts the data into JSON format to make it easy for the user to understand. The formatted data is prepared as an HTTP response and sent to the user's terminal. The input is laundry method information retrieved from the database, and the output is formatted JSON data.

[0482] Step 7:

[0483] The user terminal receives laundry information in JSON data format from the server. Specifically, it parses the received response data and displays it in a user-friendly format within the application. The input is the JSON data sent from the server, and the output is the laundry instructions displayed to the user. For example, it might display, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0484] (Application Example 1)

[0485] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0486] The problems that this invention aims to solve are to enable users to easily understand how to wash their clothes and to improve the user's purchasing experience by providing real-time washing instructions in physical stores. Conventional systems had the problem that users had a high risk of choosing the wrong washing method, resulting in a shorter lifespan for their clothes. Furthermore, because real-time information was not provided in physical stores, users could not immediately find out the appropriate washing method.

[0487] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0488] In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for transmitting the retrieved washing method to a user terminal or wearable device, and communication means for providing washing instructions in real time while the user is on the move. This allows the user to receive appropriate washing instructions for their clothing in real time even while on the go, reducing the risk of choosing the wrong washing method and extending the life of the clothing. Furthermore, even in physical stores, users can check product care instructions on the spot, which can support their purchasing decisions.

[0489] "Means for uploading images of clothing" refers to a device or interface for a user to send images of clothing they have taken to a server.

[0490] "Means for analyzing uploaded images" refers to algorithms or software for analyzing the data of submitted images and recognizing the material, color, and care label of clothing in the images.

[0491] "A means of searching a database for an appropriate laundry method based on recognized information" refers to a search engine or search algorithm that searches a database based on analysis results to identify the optimal laundry method.

[0492] "Means for transmitting the retrieved laundry method to a user terminal or wearable device" refers to a communication protocol or interface for transmitting data of the identified laundry method to the user's device.

[0493] "Communication means for providing laundry instructions in real time while the user is on the move" refers to mobile networks or Wi-Fi connections that allow users to receive information in real time even while they are on the move.

[0494] "Means of converting to a user-friendly format" refers to formatting algorithms that present search results to users in an easily understandable format (such as JSON).

[0495] This invention relates to a system that uploads images of clothing, analyzes those images, and provides appropriate washing instructions. The system consists of a user terminal, a server, an image analysis algorithm, a database, and a communication interface.

[0496] 1. User terminal

[0497] Users take or select images of clothing using a device such as a smartphone, head-mounted display, or smart glasses. The device has an interface for uploading images, and users send the images to the server by pressing the upload button.

[0498] 2. Server

[0499] The server receives images sent by users and stores them in temporary storage. The server performs image analysis using the following software and hardware.

[0500] Image analysis libraries: OpenCV, TensorFlow

[0501] Database: MySQL, MongoDB

[0502] Communication protocol: HTTP / HTTPS

[0503] 3. Image Analysis

[0504] The saved images are analyzed by an image analysis algorithm on the server. Using OpenCV and TensorFlow, the material, color, and care label of the clothing are recognized from the images. This analyzed data is used as key information to determine the washing method.

[0505] 4. Database Search

[0506] Based on the information obtained from image analysis, the server searches the database and extracts the appropriate washing method. The information used as search keys includes material, color, and the contents of the care label. For example, if an image of a "cotton shirt" is uploaded, the search will be performed based on the recognition results, which include information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0507] 5. Data Formatting and Transmission

[0508] The server formats the retrieved laundry information into a user-friendly format. Specifically, it converts it to JSON format and then sends it as an HTTP response to the user's terminal or wearable device.

[0509] 6. Washing instructions

[0510] The user terminal analyzes the received laundry information and displays it to the user in an appropriate format. For example, the smart glasses display might show, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0511] Examples of specific cases and prompt statements

[0512] When users are browsing products in a physical store, they can use smart glasses to obtain information on the spot.

[0513] Example of a prompt:

[0514] "How should I wash this garment?"

[0515] "How do I care for this garment?"

[0516] "Please tell me the proper way to wash this material."

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] Upload image

[0520] Users take or select images of clothing using a smartphone, head-mounted display, or smart glasses.

[0521] Input: Image of clothing

[0522] Operation: The user clicks the upload button within the application.

[0523] Output: Uploaded image file

[0524] Step 2:

[0525] Sending images

[0526] The device sends the image provided by the user to the server as an HTTP POST request.

[0527] Input: Uploaded image file

[0528] Operation: Generate an HTTP POST request and send it to the server, including image data.

[0529] Output: Request data sent to the server

[0530] Step 3:

[0531] Receiving and saving images

[0532] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the image.

[0533] Input: HTTP request with image data sent from the terminal

[0534] Operation: Receives request data and saves image data to storage.

[0535] Output: Saved image file

[0536] Step 4:

[0537] Image analysis

[0538] The server analyzes the stored images using OpenCV and TensorFlow to recognize the material, color, and care label of the clothing.

[0539] Input: Saved image file

[0540] Operation: Perform image recognition processing using OpenCV or TensorFlow. Specifically, analyze the texture, color, and text within the image.

[0541] Output: Data on recognized material, color, and care label.

[0542] Step 5:

[0543] Search for laundry methods

[0544] The server searches the database based on the recognized information and extracts the appropriate washing method.

[0545] Input: Data on recognized material, color, and care label.

[0546] Operation: Perform a database search, using the recognition information as the search key.

[0547] Output: Data on searched laundry methods

[0548] Step 6:

[0549] Data formatting and transmission

[0550] The server formats the searched laundry method information into a user-friendly format and sends it to the user's terminal or wearable device.

[0551] Input: Data on the searched laundry methods

[0552] Operation: Format the data into JSON format and send it as an HTTP response.

[0553] Output: HTTP response of formatted laundry method data

[0554] Step 7:

[0555] Washing instructions

[0556] The user terminal receives laundry instructions from the server and displays them in a user-friendly format.

[0557] Input: HTTP response of laundry method data sent from the server

[0558] Operation: Analyze data and display it within the application in a user-friendly format.

[0559] Output: Illustrated instructions for washing

[0560] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0561] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system includes a function that suggests an appropriate washing method according to the user's situation and mood by incorporating an emotion engine that recognizes the user's emotions. The embodiments for carrying out this invention will be described in detail below.

[0562] System Overview

[0563] This system includes means for uploading images of clothing, means for analyzing uploaded images, means for searching a database for appropriate washing methods, means for sending washing methods to the user terminal, and an emotion engine. The system communicates between the server and the user terminal to provide the user with the optimal washing method and to offer customized suggestions that take the user's emotions into consideration.

[0564] Program Processing Overview

[0565] Image upload (device)

[0566] First, the user takes a picture of their clothes using their smartphone or computer, or selects an image of their clothes from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system.

[0567] Sending images (from the device)

[0568] The user's terminal sends the image provided by the user to the server as an HTTP POST request. At this time, request data containing the image file is generated.

[0569] Image reception and storage (server)

[0570] The server receives an HTTP POST request sent from the user's terminal. The received image is temporarily stored in the server's storage.

[0571] Image analysis (server)

[0572] The server analyzes the stored images using image analysis algorithms. For example, it uses machine learning models and image recognition tools (e.g., OpenCV, TensorFlow) to extract information about the clothing's material, color, and care label from the images.

[0573] Emotion recognition (server)

[0574] The emotion engine recognizes the user's current emotions through analysis of their voice and images. For example, it analyzes the user's emotions from their facial expressions captured on camera or from their voice input. If the user is feeling stressed, it prioritizes suggesting simple and quick laundry methods.

[0575] Search for laundry methods (server)

[0576] The server searches a database to extract the appropriate washing method based on information obtained from image analysis and sentiment analysis results from the sentiment engine. The information used as search keys includes material, color, contents of the care label, and sentiment information.

[0577] For example, if an image of a "cotton shirt" is uploaded and the emotion engine recognizes that the user is feeling stressed, the server retrieves information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry," and uses this information as a key to search the database. Taking the user's emotions into consideration, it prioritizes suggesting simpler washing methods (e.g., avoiding recommending hand washing and encouraging the use of a washing machine).

[0578] Data formatting and transmission (server)

[0579] The server formats the retrieved laundry information into a user-friendly format. This is done, for example, by converting it to JSON format. Then, it sends that data to the user's terminal as an HTTP response.

[0580] Washing instructions (on the device)

[0581] The user terminal receives laundry information sent from the server. This information is displayed within the application in a user-friendly format. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0582] In this way, users can quickly find the optimal washing method that takes their emotions into consideration simply by uploading an image. The system of this invention reduces the risk of users choosing the wrong washing method, making it possible to extend the life of their clothes. Furthermore, by providing customized suggestions that take the user's emotions into account, the user experience is improved.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] User: Take a picture of the clothing with your device's camera, or select an image of the clothing from your existing image gallery. Then, click the upload button in the application to upload the image of the clothing to the system.

[0586] Step 2:

[0587] Terminal: When an image is selected or captured, an HTTP POST request is generated. This request includes the image file provided by the user.

[0588] Step 3:

[0589] Terminal: Sends the generated HTTP POST request to the server. The request has an image file attached.

[0590] Step 4:

[0591] Server: Receives HTTP POST requests sent from the terminal. Saves the received image files to temporary storage.

[0592] Step 5:

[0593] Server: Reads image files stored in storage and applies image analysis algorithms. For example, it uses OpenCV or TensorFlow to extract clothing material, color, and care label information from images.

[0594] Step 6:

[0595] Server: Based on the image analysis, the material of the clothing is determined to be "cotton," and the care label is recognized as "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0596] Step 7:

[0597] Server: Uses an emotion engine to recognize the user's emotions. It analyzes the user's facial expressions captured by the camera and voice input to determine if they are "feeling stressed."

[0598] Step 8:

[0599] Server: Searches the database based on image analysis results and sentiment analysis results. The search uses material, color, care label information, and sentiment information as search keys.

[0600] Step 9:

[0601] Server: Retrieves the optimal washing method from the database. For example, if the user is stressed, it prioritizes suggesting a simple and easy washing method (e.g., "Use a short cycle at 30°C or below in the washing machine").

[0602] Step 10:

[0603] Server: Formats the retrieved laundry method information into a user-friendly format. For example, converts and formats it into JSON format.

[0604] Step 11:

[0605] Server: Sends formatted laundry instruction data to the user's terminal as an HTTP response.

[0606] Step 12:

[0607] Terminal: Receives HTTP responses sent from the server. Displays the received laundry method information within the application.

[0608] Step 13:

[0609] User: Check the washing instructions displayed in the application on the device. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying." If the user is feeling stressed, additional suggestions might be displayed, such as, "Use the short cycle on your washing machine for easy washing."

[0610] In this way, users can quickly find out the best, emotionally-conscious way to do their laundry simply by uploading an image.

[0611] (Example 2)

[0612] Next, we will describe Example 2. 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".

[0613] Traditional laundry instruction systems simply provided appropriate washing instructions based on the material, color, and care label of the clothing, without considering the user's feelings. As a result, users were sometimes presented with complicated washing instructions when they were stressed or time-constrained, highlighting the need for improved user experience. Furthermore, the lack of user-friendly information presentation made it difficult for users to accurately understand and implement the provided information.

[0614] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and laundry care label of the clothing, means for using an emotion engine to recognize the user's emotions, means for searching a database for an appropriate laundry method based on the recognized information and the user's emotions, and means for converting the searched laundry method into a user-friendly format and transmitting it to the user terminal. This makes it possible for the user to quickly find the optimal laundry method that suits their emotional state, thereby improving the user experience.

[0615] The "means of uploading images of clothing" refers to a function that allows users to select images of clothing using their smartphones, computers, or other user devices and send them to the system.

[0616] "Means for analyzing uploaded images to recognize the material, color, and care label of clothing" refers to a function that analyzes images stored on the server using machine learning and image recognition tools to extract the main characteristics of the clothing.

[0617] "Means of using an emotion engine to recognize user emotions" refers to algorithms and technologies that analyze a user's facial expressions and voice to identify their current emotional state.

[0618] "Means for searching for an appropriate washing method from a database based on recognized information and user sentiment" refers to a function that searches for the optimal washing method from a database based on analyzed clothing characteristics and user sentiment information.

[0619] "A means of converting searched laundry methods into a user-friendly format and sending it to the user's terminal" refers to a function that converts search results into a format that is easy for the user to understand (e.g., JSON format) and outputs that information to the user's terminal.

[0620] This invention is a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, and includes a function to suggest an appropriate washing method according to the user's situation and mood. Specific embodiments for carrying out the invention will be described in detail below.

[0621] System Configuration

[0622] This system consists of the following main elements:

[0623] 1. How to upload images of clothing

[0624] The user uses a user device (smartphone or computer). They either take a picture of the clothing using the device's camera or select an image of the clothing from their existing image gallery. The user then uploads the image by clicking the upload button within the application.

[0625] 2. Means for analyzing uploaded images

[0626] The server uses image analysis algorithms (e.g., OpenCV, TensorFlow) to extract the material, color, and care label information of clothing from the image.

[0627] 3. Means of using an emotion engine that recognizes user emotions

[0628] An emotion engine within the server analyzes the user's voice and images to recognize their current emotional state. For example, if the user is feeling stressed, it prioritizes suggesting simple and quick laundry methods.

[0629] 4. Means for searching a database for an appropriate laundry method based on recognized information and user sentiment.

[0630] The server searches the database based on information obtained from image analysis and the sentiment engine. The information used as search keys includes material, color, laundry care label content, and sentiment information.

[0631] 5. A means of converting the searched laundry method into a user-friendly format and sending it to the user's terminal.

[0632] The server formats the search results into a user-friendly format, such as JSON, and sends it to the user's terminal as an HTTP response.

[0633] Hardware and software to be used

[0634] User devices: Smartphones, personal computers

[0635] Servers: Cloud servers and on-premises servers

[0636] Image analysis tools: OpenCV, TensorFlow

[0637] Emotion engine: Voice analysis tools, facial recognition algorithms

[0638] Specific example

[0639] Herein, we will specifically describe embodiments of the present invention.

[0640] 1. A concrete example of a user uploading an image:

[0641] The user launches the app on their smartphone, takes a picture of the clothing or selects one from their gallery, and clicks the upload button.

[0642] 2. Specific examples of image analysis:

[0643] The server analyzes the received images using OpenCV to extract the clothing material (e.g., cotton), color (e.g., white), and care label information (e.g., "Wash at 30°C or below").

[0644] 3. Specific examples of emotion recognition:

[0645] The emotion engine analyzes the user's voice input, "I'm tired," and recognizes that they are "feeling stressed."

[0646] 4. Specific examples of search results:

[0647] The system searches the database and extracts simple washing instructions using the keywords "cotton shirt," "wash at 30°C or below," "use a laundry net," and "do not tumble dry." The server then suggests avoiding hand washing and using a washing machine.

[0648] 5. Example of a prompt:

[0649] The user submitted an image of a cotton shirt, and the sentiment engine determined that the user was tired. As a simple and quick washing method, it suggested, "Wash in a washing machine at 30°C or below, use a laundry net, and avoid using a dryer."

[0650] As described above, this invention allows users to quickly find the optimal, emotionally-conscious washing method simply by uploading an image. This improves the user experience and reduces the risk of selecting the wrong washing method.

[0651] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0652] Step 1: Uploading an image (User)

[0653] Users take pictures of their clothes using a smartphone or computer application, or select images from their existing gallery. Next, they click the upload button within the application to upload the clothing images to the system. This selects the image file and initiates the upload process.

[0654] Input: Images of clothing selected or photographed by the user.

[0655] Output: Image files uploaded to the system

[0656] Step 2: Sending the image (from your device)

[0657] The device sends the selected image to the server as an HTTP POST request. This request contains the embedded image file. The device generates the request data and sends it to the server over the internet.

[0658] Input: Uploaded image file

[0659] Output: Image data sent to the server as an HTTP POST request

[0660] Step 3: Receiving and saving images (server)

[0661] The server receives an HTTP POST request sent from the terminal. The server extracts image data from the request and temporarily stores it in its storage. This prepares the input data for image analysis.

[0662] Input: Image data for an HTTP POST request

[0663] Output: Image data stored on the server's storage.

[0664] Step 4: Image analysis (server)

[0665] The server analyzes the stored images using image analysis algorithms (e.g., OpenCV, TensorFlow). It extracts the material, color, and care label information of the clothing from the images. It executes code written in programming languages ​​such as Python or C++ for image analysis.

[0666] Input: Image data stored on the server's storage.

[0667] Output: Information on the material, color, and care label of the clothing extracted through analysis.

[0668] Step 5: Emotion Recognition (Server)

[0669] The server's emotion engine analyzes the user's voice input and image data to recognize their current emotional state. For example, the user might approach the microphone and say something like, "I'm tired." The emotion analysis algorithm then analyzes the voice and facial expression data to determine the user's emotional state.

[0670] Input: User voice input, facial expression data

[0671] Output: User's emotional state identified through analysis

[0672] Step 6: Search for washing instructions (server)

[0673] The server searches the database to extract appropriate washing instructions based on information obtained from image analysis and sentiment analysis results from the sentiment engine. The information used as search keys includes material, color, contents of the care label, and sentiment information. The server executes SQL queries to retrieve search results from the database.

[0674] Input: Information on clothing material, color, care label, and user sentiment.

[0675] Output: Information on appropriate washing methods extracted from the database

[0676] Step 7: Formatting and sending data (to the server)

[0677] The server formats the extracted laundry method information into a user-friendly format. For example, it formats it into JSON format to organize the necessary information. Then, it sends the formatted data to the user's terminal as an HTTP response.

[0678] Input: Laundry method information extracted from a database

[0679] Output: Formatted data and sent to the user's terminal as an HTTP response.

[0680] Step 8: Washing instructions displayed (on the device)

[0681] The user terminal receives an HTTP response sent from the server. The received data is analyzed, and laundry instructions are displayed in a user-friendly format within the application. For example, a message such as "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer" might be displayed.

[0682] Input: Formatted data sent from the server

[0683] Output: Laundry instructions displayed in a user-friendly format

[0684] The above outlines the processing steps of this system's program. This allows users to easily and quickly discover the optimal, emotionally-conscious laundry method.

[0685] (Application Example 2)

[0686] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0687] Conventional laundry method suggestion systems provided general washing instructions based on the material, color, and care label of the clothing, but they were unable to offer customized suggestions based on the user's emotions or circumstances. As a result, even when users were feeling stressed or fatigued, they were not offered appropriate care methods, leading to inconvenience. Furthermore, the information was not provided in a user-friendly format, making it difficult to understand. Therefore, a system is needed that can provide flexible suggestions in response to the user's emotions and deliver information in an easy-to-use format.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0689] In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for recognizing the user's emotions using an emotion engine and customizing the washing method according to those emotions, and means for transmitting the searched and customized washing method to the user terminal. This makes it possible to customize and provide the optimal washing method according to the user's emotions and circumstances, thereby improving the user experience.

[0690] The "means of uploading clothing images" refer to a function that allows users to send images of clothing they have taken to the system using their smartphone or other devices.

[0691] "Methods for analyzing uploaded images" refers to algorithms and tools used to extract information such as the material, color, and care label of clothing based on images submitted by users.

[0692] "A means of searching for appropriate washing methods from a database" refers to an implementation function that searches the database for methods of properly washing clothes based on the extracted information, and obtains the necessary information.

[0693] "A means of recognizing the user's emotions using an emotion engine and customizing the laundry method according to those emotions" refers to a function that analyzes the user's current emotional state based on input such as facial expressions and voice, and adjusts and suggests the laundry method according to the user's emotions.

[0694] "Means for sending search and customized laundry methods to the user terminal" refers to an implementation function for sending the information generated by the above process to the user terminal in a format that is easy for the user to understand.

[0695] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system includes a function that incorporates an emotion engine to recognize the user's emotions and suggest an appropriate washing method according to the user's situation and mood. The embodiments for carrying out this invention will be described in detail below.

[0696] System Overview

[0697] This system includes means for uploading images of clothing, means for analyzing the uploaded images, means for searching a database for an appropriate washing method based on the recognized information, means for recognizing the user's emotions using an emotion engine and customizing the washing method according to those emotions, and means for transmitting the searched and customized washing method to the user's terminal.

[0698] Image upload (device)

[0699] First, the user takes a picture of their clothes using their smartphone or other device, or selects an image of their clothes from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system. This entire process is carried out using an HTTP POST request.

[0700] Image transmission and storage (server)

[0701] The server receives images sent from the user's terminal and temporarily stores them in the server's storage. The stored images are then used in subsequent image analysis processes.

[0702] Image analysis (server)

[0703] The server uses image recognition tools (e.g., OpenCV, TensorFlow) to analyze the stored images. The analysis extracts the clothing material, color, and care label information.

[0704] Emotion recognition (server)

[0705] The emotion engine is used to recognize the user's emotions in real time. This system determines emotions by analyzing the user's voice input and facial expressions captured by the camera. For example, if the user is feeling stressed, the system will suggest simplifying the laundry process.

[0706] Laundry method search and customization (server)

[0707] The server searches the database for an appropriate washing method based on the results of image analysis and emotion recognition by the emotion engine. This search uses material information, color information, and laundry care label information as keys. Furthermore, it proposes a customized washing method based on the user's emotional state.

[0708] Data formatting and transmission (server)

[0709] The server formats the retrieved laundry information into a user-friendly format. The information is converted to JSON format and sent to the user's terminal as an HTTP response.

[0710] Washing instructions (on the device)

[0711] The user terminal receives laundry information sent from the server and displays it in a user-friendly format within the application. For example, it might display, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0712] Usage example

[0713] For example, if a user uploads an image of a "cotton shirt" purchased from an online shopping site, and the emotion engine recognizes the user's stress level, the server searches the database based on information such as "wash at 30°C or below," "use a laundry net," and "do not tumble dry." At the same time, it takes the user's emotional state into consideration and makes customized suggestions, such as recommending the use of a washing machine. A message like, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying," is displayed on the user's terminal.

[0714] Examples of prompts for generative AI models

[0715] "Analyze images of clothing uploaded by users and provide the optimal washing method. Also, customize the suggestions based on the user's mood."

[0716] In this way, the system helps users quickly obtain optimal information about laundry, allowing them to extend the life of their clothes. Furthermore, it enhances the user experience through emotionally sensitive and customized suggestions.

[0717] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0718] Step 1:

[0719] Upload an image of clothing.

[0720] The user first selects an image of clothing from their smartphone's camera app or gallery. Next, they click the upload button within the application to upload the image of clothing to the system. The image file, as input, is sent to the server via an HTTP POST request. This causes the server to temporarily store the image file in its storage.

[0721] Step 2:

[0722] Receiving and saving images

[0723] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the included image file in the server's storage. The input is the HTTP POST request, and the output is the stored image file. This process prepares the server for the data necessary for the next image analysis step.

[0724] Step 3:

[0725] Image analysis

[0726] The server analyzes the stored images. It primarily uses image recognition tools such as OpenCV and TensorFlow to extract information about clothing material, color, and care labels from the images. It uses the stored image files as input and outputs this detailed information. This analysis result is then used to search for laundry instructions.

[0727] Step 4:

[0728] Recognition of emotions

[0729] The server uses an emotion engine to recognize the user's current emotional state. Specifically, it analyzes the user's facial expression data captured by the camera and voice input to determine the user's emotions (e.g., stress, relief, etc.). The input is the user's facial expression data and voice data, and the output is the recognized emotional information. This provides data to customize the laundry method based on the user's current emotional state.

[0730] Step 5:

[0731] Search for laundry methods

[0732] The server searches the database for the appropriate washing method based on the results of image analysis and emotion recognition. This process queries the database using material information, color information, and the contents of the care label as keys to extract the optimal washing method. The inputs are image analysis results and emotion recognition results, and the output is information on the appropriate washing method.

[0733] Step 6:

[0734] Customized laundry method shaping

[0735] The server combines the searched laundry method with the user's emotional information to customize the optimal laundry method. For example, if the user is feeling stressed, it will prioritize suggesting a simpler laundry method. The input consists of the searched laundry method information and the user's emotional information, and the output is customized laundry method information.

[0736] Step 7:

[0737] Data formatting and transmission

[0738] The server formats the customized laundry instructions into a user-friendly format. Typically, the formatted data is converted to JSON format. Next, the formatted data is sent to the user's terminal as an HTTP response. The input is customized laundry instructions, and the output is the formatted data sent to the user's terminal.

[0739] Step 8:

[0740] Washing instructions

[0741] The user terminal receives laundry instructions in JSON format from the server and displays them in a user-friendly format within the application. For example, it might display specific instructions such as, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer." The input is the JSON response from the server, and the output is the laundry instructions displayed to the user. This step allows the user to easily understand the optimal laundry method.

[0742] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0745] [Third Embodiment]

[0746] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0747] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0748] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0749] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0750] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0751] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0752] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0753] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0754] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0755] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0756] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0757] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0758] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. The embodiments for carrying out this invention will be described in detail below.

[0759] System Overview

[0760] This system includes means for uploading images of clothing, means for analyzing uploaded images, means for searching a database for an appropriate washing method, and means for transmitting the retrieved washing method to the user terminal. The system provides the user with the optimal washing method by communicating between the server and the user terminal.

[0761] Program Processing Overview

[0762] Image upload (device)

[0763] First, the user takes a picture of their clothes using their smartphone or computer, or selects an image from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system.

[0764] Sending images (from the device)

[0765] The user's terminal sends the image provided by the user to the server as an HTTP POST request. At this time, request data containing the image file is generated.

[0766] Image reception and storage (server)

[0767] The server receives an HTTP POST request sent from the user's terminal. The received image is temporarily stored in the server's storage.

[0768] Image analysis (server)

[0769] The server analyzes the stored images using image analysis algorithms. For example, it uses machine learning models and image recognition tools (e.g., OpenCV, TensorFlow) to extract information about the clothing's material, color, and care label from the images.

[0770] Search for laundry methods (server)

[0771] The server uses the information obtained from image analysis to search a database and extract the appropriate washing method. The information used as search keys includes material, color, and the contents of the care label.

[0772] For example, if an image of a "cotton shirt" is uploaded, the server will analyze the results to obtain information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry," and then use this information as a key to search the database.

[0773] Data formatting and transmission (server)

[0774] The server formats the retrieved laundry information into a user-friendly format. This is done, for example, by converting it to JSON format. Then, it sends that data to the user's terminal as an HTTP response.

[0775] Washing instructions (on the device)

[0776] The user terminal receives laundry information sent from the server. This information is displayed within the application in a user-friendly format. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0777] In this way, users can easily find out how to wash their clothes from their smartphones or computers. The system of the present invention reduces the risk of users choosing the wrong washing method and makes it possible to extend the life of their clothes.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] User: Take a photo of the clothing with your device or select an image of clothing from your existing image gallery. Then, click the upload button in the application to upload the image of the clothing to the system.

[0781] Step 2:

[0782] Terminal: When an image is selected or captured, an HTTP POST request is generated. This request includes the image file provided by the user.

[0783] Step 3:

[0784] Terminal: Sends the generated HTTP POST request to the server. The request has an image file attached.

[0785] Step 4:

[0786] Server: Receives HTTP POST requests sent from the terminal. Saves the received image files to temporary storage.

[0787] Step 5:

[0788] Server: Reads image files stored in storage. Applies image analysis algorithms to analyze the read images. This includes using machine learning models and image recognition tools.

[0789] Step 6:

[0790] Server: The image analysis algorithm extracts the material, color, and care label information of clothing from an image. For example, a machine learning model might determine it's a "cotton shirt" and recognize from the care label that it should be washed at 30°C or below, use a laundry net, and do not tumble dry.

[0791] Step 7:

[0792] Server: Searches the database based on information obtained from image analysis. Material, color, and laundry care label information are used as search keys.

[0793] Step 8:

[0794] Server: Retrieves the optimal washing method from the database. For example, for a "cotton shirt," it might retrieve washing instructions such as "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0795] Step 9:

[0796] Server: Formats the retrieved laundry method information into a user-friendly format. For example, converts it to JSON format.

[0797] Step 10:

[0798] Server: Sends formatted laundry instruction data to the user's terminal as an HTTP response.

[0799] Step 11:

[0800] Terminal: Receives HTTP responses sent from the server. Displays the received laundry method information within the application.

[0801] Step 12:

[0802] User: Check the washing instructions displayed in the application on the device. For example, instructions such as "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer" may be displayed.

[0803] In this way, users can quickly find out the best washing method simply by uploading an image.

[0804] (Example 1)

[0805] Next, we will describe Example 1. 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."

[0806] In conventional laundry instruction systems, users must interpret laundry care labels on their own, which can result in selecting the wrong washing method. This problem is particularly pronounced when it is difficult to accurately determine the appropriate washing method for the material and color of the clothing. Furthermore, information on washing methods is not centrally managed and is not provided in a way that is easily accessible to users, which is another issue. The present invention aims to solve these problems and provide a system that allows users to easily obtain accurate washing instructions.

[0807] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0808] In this invention, the server includes means for the user to upload an image of clothing, means for analyzing the uploaded image to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for transmitting the retrieved washing method to the user terminal, and means for displaying the transmitted washing method in a user-friendly format. As a result, the user can simply upload an image of their clothing and be automatically provided with the optimal washing method, reducing the risk of selecting an incorrect washing method and extending the life of their clothes.

[0809] A "user terminal" refers to a computing device operated by a user, and includes smartphones, personal computers, tablets, and other similar devices.

[0810] "Image analysis" is the process of analyzing uploaded images and automatically recognizing their contents (e.g., material, color, laundry care label), using machine learning models and image recognition tools.

[0811] "Material information" refers to information about the fibers and materials that make up clothing, such as cotton, polyester, and wool.

[0812] "Color information" refers to information about the colors of clothing, and includes major color classifications such as black, white, red, and blue.

[0813] A "laundry care label" is a tag or sticker attached to clothing that contains symbols and instructions indicating the proper washing, drying, and bleaching methods.

[0814] A "database" is a collection of data designed to effectively search, access, and manage information, and is used to store information on proper washing methods.

[0815] "Washing instructions" refer to information that shows the correct procedure and precautions for washing clothes, and include water temperature, detergent to use, and drying method.

[0816] A "user-friendly format" refers to a format that is easy for users to understand and use, and specifically includes formats such as JSON and concise text displays.

[0817] A "machine learning model" refers to a mathematical model trained to perform a specific task based on data, and is used in the image analysis process.

[0818] "Image recognition tools" refer to software or libraries that automatically recognize specific objects or features within an image, and OpenCV and TensorFlow are examples of this.

[0819] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Specific embodiments for carrying out this invention are described in detail below.

[0820] This system functions by communicating between a server and a user terminal. It includes means for users to upload images of clothing, means for analyzing uploaded images, means for searching a database for appropriate washing methods based on the recognized information, and means for transmitting and displaying the retrieved washing method on the user terminal.

[0821] First, the user takes a picture of the clothing with their smartphone or computer, or selects an image from their existing image gallery. This imports the image to the user's device. The user then sends the image to the system by clicking the "Upload" button within the application.

[0822] The submitted image is sent to the server as an HTTP POST request. The server receives this request and temporarily stores the image file in its storage. The server analyzes the stored image using image recognition tools such as OpenCV or TensorFlow to extract information about the clothing's material, color, and care label.

[0823] After analysis, the server uses the recognized information as a key to search the database and identify the appropriate washing method. Specifically, it sends material information, color information, and laundry care label information as queries to the database and retrieves the resulting washing method.

[0824] The retrieved washing instructions are formatted into a user-friendly format, such as JSON. The server sends this formatted data to the user's terminal as an HTTP response. The user's terminal parses the received data and displays a message within the application such as, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0825] This system allows users to easily obtain accurate washing instructions, reducing the risk of choosing the wrong method. Furthermore, because it provides the optimal washing method suited to the material and color of the clothing, users can extend the life of their clothes.

[0826] Next, let's consider a specific example: a user uploads an image of a "black cotton shirt." The user first takes a picture of the shirt with their smartphone and clicks the "Upload" button in the application. The image is sent to the server, which analyzes it using TensorFlow and extracts information such as the material being cotton, the color being black, and the care label stating that it should be washed at 30°C or below, bleach not allowed, and tumble drying not allowed. The server sends this information as a query to the database and retrieves the information that the appropriate washing method is "Wash at 30°C or below, do not use bleach, and avoid tumble drying." This information is converted to JSON format and sent to the user's terminal as an HTTP response. The user's terminal analyzes this information and displays "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying."

[0827] Examples of specific prompt messages are as follows:

[0828] "How do I wash a black cotton shirt? The care label says to wash at 30°C or below, do not bleach, and do not tumble dry."

[0829] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0830] Step 1:

[0831] The user takes a picture of clothing or selects one from their existing image gallery using a smartphone or computer. Specifically, they open a camera or gallery app, select an image, and click the "Upload" button within the application. The input is an image of clothing, and the output is the image file imported into the application.

[0832] Step 2:

[0833] The user terminal sends the captured image file to the server as an HTTP POST request. In this process, an HTTP library is used to generate the request, and the request data, including the image file and metadata, is sent to the server. The input is the image file captured by the application, and the output is the HTTP request sent to the server.

[0834] Step 3:

[0835] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the image file in storage. Specifically, the server's endpoint handles the request and stores the received image file in the file system or database. The input is the HTTP request, and the output is the image file stored in storage.

[0836] Step 4:

[0837] The server analyzes stored images using image analysis algorithms. Specifically, it reads image files and launches machine learning models such as OpenCV or TensorFlow. Using these tools, it extracts information about the material, color, and care label of clothing from the images. The input is an image file stored in storage, and the output is the material information, color information, and care label information as analysis results.

[0838] Step 5:

[0839] The server uses information obtained through image analysis to search the database and extract the appropriate washing method. Specifically, the server sends material, color, and care label information as a query to the database. It searches this data and retrieves the record for the corresponding washing method. The input is the material information, color information, and care label information obtained as analysis results, and the output is the washing method information obtained from the database.

[0840] Step 6:

[0841] The server formats the retrieved laundry method information into a user-friendly format. Specifically, it converts the data into JSON format to make it easy for the user to understand. The formatted data is prepared as an HTTP response and sent to the user's terminal. The input is laundry method information retrieved from the database, and the output is formatted JSON data.

[0842] Step 7:

[0843] The user terminal receives laundry information in JSON data format from the server. Specifically, it parses the received response data and displays it in a user-friendly format within the application. The input is the JSON data sent from the server, and the output is the laundry instructions displayed to the user. For example, it might display, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0844] (Application Example 1)

[0845] Next, we will explain Application Example 1. In the following explanation, 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."

[0846] The problems that this invention aims to solve are to enable users to easily understand how to wash their clothes and to improve the user's purchasing experience by providing real-time washing instructions in physical stores. Conventional systems had the problem that users had a high risk of choosing the wrong washing method, resulting in a shorter lifespan for their clothes. Furthermore, because real-time information was not provided in physical stores, users could not immediately find out the appropriate washing method.

[0847] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0848] In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for transmitting the retrieved washing method to a user terminal or wearable device, and communication means for providing washing instructions in real time while the user is on the move. This allows the user to receive appropriate washing instructions for their clothing in real time even while on the go, reducing the risk of choosing the wrong washing method and extending the life of the clothing. Furthermore, even in physical stores, users can check product care instructions on the spot, which can support their purchasing decisions.

[0849] "Means for uploading images of clothing" refers to a device or interface for a user to send images of clothing they have taken to a server.

[0850] "Means for analyzing uploaded images" refers to algorithms or software for analyzing the data of submitted images and recognizing the material, color, and care label of clothing in the images.

[0851] "A means of searching a database for an appropriate laundry method based on recognized information" refers to a search engine or search algorithm that searches a database based on analysis results to identify the optimal laundry method.

[0852] "Means for transmitting the retrieved laundry method to a user terminal or wearable device" refers to a communication protocol or interface for transmitting data of the identified laundry method to the user's device.

[0853] "Communication means for providing laundry instructions in real time while the user is on the move" refers to mobile networks or Wi-Fi connections that allow users to receive information in real time even while they are on the move.

[0854] "Means of converting to a user-friendly format" refers to formatting algorithms that present search results to users in an easily understandable format (such as JSON).

[0855] This invention relates to a system that uploads images of clothing, analyzes those images, and provides appropriate washing instructions. The system consists of a user terminal, a server, an image analysis algorithm, a database, and a communication interface.

[0856] 1. User terminal

[0857] Users take or select images of clothing using a device such as a smartphone, head-mounted display, or smart glasses. The device has an interface for uploading images, and users send the images to the server by pressing the upload button.

[0858] 2. Server

[0859] The server receives images sent by users and stores them in temporary storage. The server performs image analysis using the following software and hardware.

[0860] Image analysis libraries: OpenCV, TensorFlow

[0861] Database: MySQL, MongoDB

[0862] Communication protocol: HTTP / HTTPS

[0863] 3. Image Analysis

[0864] The saved images are analyzed by an image analysis algorithm on the server. Using OpenCV and TensorFlow, the material, color, and care label of the clothing are recognized from the images. This analyzed data is used as key information to determine the washing method.

[0865] 4. Database Search

[0866] Based on the information obtained from image analysis, the server searches the database and extracts the appropriate washing method. The information used as search keys includes material, color, and the contents of the care label. For example, if an image of a "cotton shirt" is uploaded, the search will be performed based on the recognition results, which include information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0867] 5. Data Formatting and Transmission

[0868] The server formats the retrieved laundry information into a user-friendly format. Specifically, it converts it to JSON format and then sends it as an HTTP response to the user's terminal or wearable device.

[0869] 6. Washing instructions

[0870] The user terminal analyzes the received laundry information and displays it to the user in an appropriate format. For example, the smart glasses display might show, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0871] Examples of specific cases and prompt statements

[0872] When users are browsing products in a physical store, they can use smart glasses to obtain information on the spot.

[0873] Example of a prompt:

[0874] "How should I wash this garment?"

[0875] "How do I care for this garment?"

[0876] "Please tell me the proper way to wash this material."

[0877] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0878] Step 1:

[0879] Upload image

[0880] Users take or select images of clothing using a smartphone, head-mounted display, or smart glasses.

[0881] Input: Image of clothing

[0882] Operation: The user clicks the upload button within the application.

[0883] Output: Uploaded image file

[0884] Step 2:

[0885] Sending images

[0886] The device sends the image provided by the user to the server as an HTTP POST request.

[0887] Input: Uploaded image file

[0888] Operation: Generate an HTTP POST request and send it to the server, including image data.

[0889] Output: Request data sent to the server

[0890] Step 3:

[0891] Receiving and saving images

[0892] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the image.

[0893] Input: HTTP request with image data sent from the terminal

[0894] Operation: Receives request data and saves image data to storage.

[0895] Output: Saved image file

[0896] Step 4:

[0897] Image analysis

[0898] The server analyzes the stored images using OpenCV and TensorFlow to recognize the material, color, and care label of the clothing.

[0899] Input: Saved image file

[0900] Operation: Perform image recognition processing using OpenCV or TensorFlow. Specifically, analyze the texture, color, and text within the image.

[0901] Output: Data on recognized material, color, and care label.

[0902] Step 5:

[0903] Search for laundry methods

[0904] The server searches the database based on the recognized information and extracts the appropriate washing method.

[0905] Input: Data on recognized material, color, and care label.

[0906] Operation: Perform a database search, using the recognition information as the search key.

[0907] Output: Data on searched laundry methods

[0908] Step 6:

[0909] Data formatting and transmission

[0910] The server formats the searched laundry method information into a user-friendly format and sends it to the user's terminal or wearable device.

[0911] Input: Data on the searched laundry methods

[0912] Operation: Format the data into JSON format and send it as an HTTP response.

[0913] Output: HTTP response of formatted laundry method data

[0914] Step 7:

[0915] Washing instructions

[0916] The user terminal receives laundry instructions from the server and displays them in a user-friendly format.

[0917] Input: HTTP response of laundry method data sent from the server

[0918] Operation: Analyze data and display it within the application in a user-friendly format.

[0919] Output: Illustrated instructions for washing

[0920] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0921] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system includes a function that suggests an appropriate washing method according to the user's situation and mood by incorporating an emotion engine that recognizes the user's emotions. The embodiments for carrying out this invention will be described in detail below.

[0922] System Overview

[0923] This system includes means for uploading images of clothing, means for analyzing uploaded images, means for searching a database for appropriate washing methods, means for sending washing methods to the user terminal, and an emotion engine. The system communicates between the server and the user terminal to provide the user with the optimal washing method and to offer customized suggestions that take the user's emotions into consideration.

[0924] Program Processing Overview

[0925] Image upload (device)

[0926] First, the user takes a picture of their clothes using their smartphone or computer, or selects an image of their clothes from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system.

[0927] Sending images (from the device)

[0928] The user's terminal sends the image provided by the user to the server as an HTTP POST request. At this time, request data containing the image file is generated.

[0929] Image reception and storage (server)

[0930] The server receives an HTTP POST request sent from the user's terminal. The received image is temporarily stored in the server's storage.

[0931] Image analysis (server)

[0932] The server analyzes the stored images using image analysis algorithms. For example, it uses machine learning models and image recognition tools (e.g., OpenCV, TensorFlow) to extract information about the clothing's material, color, and care label from the images.

[0933] Emotion recognition (server)

[0934] The emotion engine recognizes the user's current emotions through analysis of their voice and images. For example, it analyzes the user's emotions from their facial expressions captured on camera or from their voice input. If the user is feeling stressed, it prioritizes suggesting simple and quick laundry methods.

[0935] Search for laundry methods (server)

[0936] The server searches a database to extract the appropriate washing method based on information obtained from image analysis and sentiment analysis results from the sentiment engine. The information used as search keys includes material, color, contents of the care label, and sentiment information.

[0937] For example, if an image of a "cotton shirt" is uploaded and the emotion engine recognizes that the user is feeling stressed, the server retrieves information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry," and uses this information as a key to search the database. Taking the user's emotions into consideration, it prioritizes suggesting simpler washing methods (e.g., avoiding recommending hand washing and encouraging the use of a washing machine).

[0938] Data formatting and transmission (server)

[0939] The server formats the retrieved laundry information into a user-friendly format. This is done, for example, by converting it to JSON format. Then, it sends that data to the user's terminal as an HTTP response.

[0940] Washing instructions (on the device)

[0941] The user terminal receives laundry information sent from the server. This information is displayed within the application in a user-friendly format. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[0942] In this way, users can quickly find the optimal washing method that takes their emotions into consideration simply by uploading an image. The system of this invention reduces the risk of users choosing the wrong washing method, making it possible to extend the life of their clothes. Furthermore, by providing customized suggestions that take the user's emotions into account, the user experience is improved.

[0943] The following describes the processing flow.

[0944] Step 1:

[0945] User: Take a picture of the clothing with your device's camera, or select an image of the clothing from your existing image gallery. Then, click the upload button in the application to upload the image of the clothing to the system.

[0946] Step 2:

[0947] Terminal: When an image is selected or captured, an HTTP POST request is generated. This request includes the image file provided by the user.

[0948] Step 3:

[0949] Terminal: Sends the generated HTTP POST request to the server. The request has an image file attached.

[0950] Step 4:

[0951] Server: Receives HTTP POST requests sent from the terminal. Saves the received image files to temporary storage.

[0952] Step 5:

[0953] Server: Reads image files stored in storage and applies image analysis algorithms. For example, it uses OpenCV or TensorFlow to extract clothing material, color, and care label information from images.

[0954] Step 6:

[0955] Server: Based on the image analysis, the material of the clothing is determined to be "cotton," and the care label is recognized as "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[0956] Step 7:

[0957] Server: Uses an emotion engine to recognize the user's emotions. It analyzes the user's facial expressions captured by the camera and voice input to determine if they are "feeling stressed."

[0958] Step 8:

[0959] Server: Searches the database based on image analysis results and sentiment analysis results. The search uses material, color, care label information, and sentiment information as search keys.

[0960] Step 9:

[0961] Server: Retrieves the optimal washing method from the database. For example, if the user is stressed, it prioritizes suggesting a simple and easy washing method (e.g., "Use a short cycle at 30°C or below in the washing machine").

[0962] Step 10:

[0963] Server: Formats the retrieved laundry method information into a user-friendly format. For example, converts and formats it into JSON format.

[0964] Step 11:

[0965] Server: Sends formatted laundry instruction data to the user's terminal as an HTTP response.

[0966] Step 12:

[0967] Terminal: Receives HTTP responses sent from the server. Displays the received laundry method information within the application.

[0968] Step 13:

[0969] User: Check the washing instructions displayed in the application on the device. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying." If the user is feeling stressed, additional suggestions might be displayed, such as, "Use the short cycle on your washing machine for easy washing."

[0970] In this way, users can quickly find out the best, emotionally-conscious way to do their laundry simply by uploading an image.

[0971] (Example 2)

[0972] Next, we will describe Example 2. 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."

[0973] Traditional laundry instruction systems simply provided appropriate washing instructions based on the material, color, and care label of the clothing, without considering the user's feelings. As a result, users were sometimes presented with complicated washing instructions when they were stressed or time-constrained, highlighting the need for improved user experience. Furthermore, the lack of user-friendly information presentation made it difficult for users to accurately understand and implement the provided information.

[0974] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and laundry care label of the clothing, means for using an emotion engine to recognize the user's emotions, means for searching a database for an appropriate laundry method based on the recognized information and the user's emotions, and means for converting the searched laundry method into a user-friendly format and transmitting it to the user terminal. This makes it possible for the user to quickly find the optimal laundry method that suits their emotional state, thereby improving the user experience.

[0975] The "means of uploading images of clothing" refers to a function that allows users to select images of clothing using their smartphones, computers, or other user devices and send them to the system.

[0976] "Means for analyzing uploaded images to recognize the material, color, and care label of clothing" refers to a function that analyzes images stored on the server using machine learning and image recognition tools to extract the main characteristics of the clothing.

[0977] "Means of using an emotion engine to recognize user emotions" refers to algorithms and technologies that analyze a user's facial expressions and voice to identify their current emotional state.

[0978] "Means for searching for an appropriate washing method from a database based on recognized information and user sentiment" refers to a function that searches for the optimal washing method from a database based on analyzed clothing characteristics and user sentiment information.

[0979] "A means of converting searched laundry methods into a user-friendly format and sending it to the user's terminal" refers to a function that converts search results into a format that is easy for the user to understand (e.g., JSON format) and outputs that information to the user's terminal.

[0980] This invention is a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, and includes a function to suggest an appropriate washing method according to the user's situation and mood. Specific embodiments for carrying out the invention will be described in detail below.

[0981] System Configuration

[0982] This system consists of the following main elements:

[0983] 1. How to upload images of clothing

[0984] The user uses a user device (smartphone or computer). They either take a picture of the clothing using the device's camera or select an image of the clothing from their existing image gallery. The user then uploads the image by clicking the upload button within the application.

[0985] 2. Means for analyzing uploaded images

[0986] The server uses image analysis algorithms (e.g., OpenCV, TensorFlow) to extract the material, color, and care label information of clothing from the image.

[0987] 3. Means of using an emotion engine that recognizes user emotions

[0988] An emotion engine within the server analyzes the user's voice and images to recognize their current emotional state. For example, if the user is feeling stressed, it prioritizes suggesting simple and quick laundry methods.

[0989] 4. Means for searching a database for an appropriate laundry method based on recognized information and user sentiment.

[0990] The server searches the database based on information obtained from image analysis and the sentiment engine. The information used as search keys includes material, color, laundry care label content, and sentiment information.

[0991] 5. A means of converting the searched laundry method into a user-friendly format and sending it to the user's terminal.

[0992] The server formats the search results into a user-friendly format, such as JSON, and sends it to the user's terminal as an HTTP response.

[0993] Hardware and software to be used

[0994] User devices: Smartphones, personal computers

[0995] Servers: Cloud servers and on-premises servers

[0996] Image analysis tools: OpenCV, TensorFlow

[0997] Emotion engine: Voice analysis tools, facial recognition algorithms

[0998] Specific example

[0999] Herein, we will specifically describe embodiments of the present invention.

[1000] 1. A concrete example of a user uploading an image:

[1001] The user launches the app on their smartphone, takes a picture of the clothing or selects one from their gallery, and clicks the upload button.

[1002] 2. Specific examples of image analysis:

[1003] The server analyzes the received images using OpenCV to extract the clothing material (e.g., cotton), color (e.g., white), and care label information (e.g., "Wash at 30°C or below").

[1004] 3. Specific examples of emotion recognition:

[1005] The emotion engine analyzes the user's voice input, "I'm tired," and recognizes that they are "feeling stressed."

[1006] 4. Specific examples of search results:

[1007] The system searches the database and extracts simple washing instructions using the keywords "cotton shirt," "wash at 30°C or below," "use a laundry net," and "do not tumble dry." The server then suggests avoiding hand washing and using a washing machine.

[1008] 5. Example of a prompt:

[1009] The user submitted an image of a cotton shirt, and the sentiment engine determined that the user was tired. As a simple and quick washing method, it suggested, "Wash in a washing machine at 30°C or below, use a laundry net, and avoid using a dryer."

[1010] As described above, this invention allows users to quickly find the optimal, emotionally-conscious washing method simply by uploading an image. This improves the user experience and reduces the risk of selecting the wrong washing method.

[1011] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1012] Step 1: Uploading an image (User)

[1013] Users take pictures of their clothes using a smartphone or computer application, or select images from their existing gallery. Next, they click the upload button within the application to upload the clothing images to the system. This selects the image file and initiates the upload process.

[1014] Input: Images of clothing selected or photographed by the user.

[1015] Output: Image files uploaded to the system

[1016] Step 2: Sending the image (from your device)

[1017] The device sends the selected image to the server as an HTTP POST request. This request contains the embedded image file. The device generates the request data and sends it to the server over the internet.

[1018] Input: Uploaded image file

[1019] Output: Image data sent to the server as an HTTP POST request

[1020] Step 3: Receiving and saving images (server)

[1021] The server receives an HTTP POST request sent from the terminal. The server extracts image data from the request and temporarily stores it in its storage. This prepares the input data for image analysis.

[1022] Input: Image data for an HTTP POST request

[1023] Output: Image data stored on the server's storage.

[1024] Step 4: Image analysis (server)

[1025] The server analyzes the stored images using image analysis algorithms (e.g., OpenCV, TensorFlow). It extracts the material, color, and care label information of the clothing from the images. It executes code written in programming languages ​​such as Python or C++ for image analysis.

[1026] Input: Image data stored on the server's storage.

[1027] Output: Information on the material, color, and care label of the clothing extracted by the analysis.

[1028] Step 5: Emotion Recognition (Server)

[1029] The server's emotion engine analyzes the user's voice input and image data to recognize their current emotional state. For example, the user might approach the microphone and say something like, "I'm tired." The emotion analysis algorithm then analyzes the voice and facial expression data to determine the user's emotional state.

[1030] Input: User voice input, facial expression data

[1031] Output: User's emotional state identified through analysis

[1032] Step 6: Search for washing instructions (server)

[1033] The server searches the database to extract appropriate washing instructions based on information obtained from image analysis and sentiment analysis results from the sentiment engine. The information used as search keys includes material, color, contents of the care label, and sentiment information. The server executes SQL queries to retrieve search results from the database.

[1034] Input: Information on clothing material, color, care label, and user sentiment.

[1035] Output: Information on appropriate washing methods extracted from the database

[1036] Step 7: Formatting and sending data (to the server)

[1037] The server formats the extracted laundry method information into a user-friendly format. For example, it formats it into JSON format to organize the necessary information. Then, it sends the formatted data to the user's terminal as an HTTP response.

[1038] Input: Laundry method information extracted from a database

[1039] Output: Formatted data and sent to the user's terminal as an HTTP response.

[1040] Step 8: Washing instructions displayed (on the device)

[1041] The user terminal receives an HTTP response sent from the server. The received data is analyzed, and laundry instructions are displayed in a user-friendly format within the application. For example, a message such as "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer" might be displayed.

[1042] Input: Formatted data sent from the server

[1043] Output: Laundry instructions displayed in a user-friendly format

[1044] The above outlines the processing steps of this system's program. This allows users to easily and quickly discover the optimal, emotionally-conscious laundry method.

[1045] (Application Example 2)

[1046] Next, we will explain application example 2. In the following explanation, 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."

[1047] Conventional laundry method suggestion systems provided general washing instructions based on the material, color, and care label of the clothing, but they were unable to offer customized suggestions based on the user's emotions or circumstances. As a result, even when users were feeling stressed or fatigued, they were not offered appropriate care methods, leading to inconvenience. Furthermore, the information was not provided in a user-friendly format, making it difficult to understand. Therefore, a system is needed that can provide flexible suggestions in response to the user's emotions and deliver information in an easy-to-use format.

[1048] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1049] In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for recognizing the user's emotions using an emotion engine and customizing the washing method according to those emotions, and means for transmitting the searched and customized washing method to the user terminal. This makes it possible to customize and provide the optimal washing method according to the user's emotions and circumstances, thereby improving the user experience.

[1050] The "means of uploading clothing images" refer to a function that allows users to send images of clothing they have taken to the system using their smartphone or other devices.

[1051] "Methods for analyzing uploaded images" refers to algorithms and tools used to extract information such as the material, color, and care label of clothing based on images submitted by users.

[1052] "A means of searching for appropriate washing methods from a database" refers to an implementation function that searches the database for methods of properly washing clothes based on the extracted information, and obtains the necessary information.

[1053] "A means of recognizing the user's emotions using an emotion engine and customizing the laundry method according to those emotions" refers to a function that analyzes the user's current emotional state based on input such as facial expressions and voice, and adjusts and suggests the laundry method according to the user's emotions.

[1054] "Means for sending search and customized laundry methods to the user terminal" refers to an implementation function for sending the information generated by the above process to the user terminal in a format that is easy for the user to understand.

[1055] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system includes a function that incorporates an emotion engine to recognize the user's emotions and suggest an appropriate washing method according to the user's situation and mood. The embodiments for carrying out this invention will be described in detail below.

[1056] System Overview

[1057] This system includes means for uploading images of clothing, means for analyzing the uploaded images, means for searching a database for an appropriate washing method based on the recognized information, means for recognizing the user's emotions using an emotion engine and customizing the washing method according to those emotions, and means for transmitting the searched and customized washing method to the user's terminal.

[1058] Image upload (device)

[1059] First, the user takes a picture of their clothes using their smartphone or other device, or selects an image of their clothes from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system. This entire process is carried out using an HTTP POST request.

[1060] Image transmission and storage (server)

[1061] The server receives images sent from the user's terminal and temporarily stores them in the server's storage. The stored images are then used in subsequent image analysis processes.

[1062] Image analysis (server)

[1063] The server uses image recognition tools (e.g., OpenCV, TensorFlow) to analyze the stored images. The analysis extracts the clothing material, color, and care label information.

[1064] Emotion recognition (server)

[1065] The emotion engine is used to recognize the user's emotions in real time. This system determines emotions by analyzing the user's voice input and facial expressions captured by the camera. For example, if the user is feeling stressed, the system will suggest simplifying the laundry process.

[1066] Laundry method search and customization (server)

[1067] The server searches the database for an appropriate washing method based on the results of image analysis and emotion recognition by the emotion engine. This search uses material information, color information, and laundry care label information as keys. Furthermore, it proposes a customized washing method based on the user's emotional state.

[1068] Data formatting and transmission (server)

[1069] The server formats the retrieved laundry information into a user-friendly format. The information is converted to JSON format and sent to the user's terminal as an HTTP response.

[1070] Washing instructions (on the device)

[1071] The user terminal receives laundry information sent from the server and displays it in a user-friendly format within the application. For example, it might display, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[1072] Usage example

[1073] For example, if a user uploads an image of a "cotton shirt" purchased from an online shopping site, and the emotion engine recognizes the user's stress level, the server searches the database based on information such as "wash at 30°C or below," "use a laundry net," and "do not tumble dry." At the same time, it takes the user's emotional state into consideration and makes customized suggestions, such as recommending the use of a washing machine. A message like, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying," is displayed on the user's terminal.

[1074] Examples of prompts for generative AI models

[1075] "Analyze images of clothing uploaded by users and provide the optimal washing method. Also, customize the suggestions based on the user's mood."

[1076] In this way, the system helps users quickly obtain optimal information about laundry, allowing them to extend the life of their clothes. Furthermore, it enhances the user experience through emotionally sensitive and customized suggestions.

[1077] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1078] Step 1:

[1079] Upload an image of clothing.

[1080] The user first selects an image of clothing from their smartphone's camera app or gallery. Next, they click the upload button within the application to upload the image of clothing to the system. The image file, as input, is sent to the server via an HTTP POST request. This causes the server to temporarily store the image file in its storage.

[1081] Step 2:

[1082] Receiving and saving images

[1083] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the included image file in the server's storage. The input is the HTTP POST request, and the output is the stored image file. This process prepares the server for the data necessary for the next image analysis step.

[1084] Step 3:

[1085] Image analysis

[1086] The server analyzes the stored images. It primarily uses image recognition tools such as OpenCV and TensorFlow to extract information about clothing material, color, and care labels from the images. It uses the stored image files as input and outputs this detailed information. This analysis result is then used to search for laundry instructions.

[1087] Step 4:

[1088] Recognition of emotions

[1089] The server uses an emotion engine to recognize the user's current emotional state. Specifically, it analyzes the user's facial expression data captured by the camera and voice input to determine the user's emotions (e.g., stress, relief, etc.). The input is the user's facial expression data and voice data, and the output is the recognized emotional information. This provides data to customize the laundry method based on the user's current emotional state.

[1090] Step 5:

[1091] Search for laundry methods

[1092] The server searches the database for the appropriate washing method based on the results of image analysis and emotion recognition. This process queries the database using material information, color information, and the contents of the care label as keys to extract the optimal washing method. The inputs are image analysis results and emotion recognition results, and the output is information on the appropriate washing method.

[1093] Step 6:

[1094] Customized laundry method shaping

[1095] The server combines the searched laundry method with the user's emotional information to customize the optimal laundry method. For example, if the user is feeling stressed, it will prioritize suggesting a simpler laundry method. The input consists of the searched laundry method information and the user's emotional information, and the output is customized laundry method information.

[1096] Step 7:

[1097] Data formatting and transmission

[1098] The server formats the customized laundry instructions into a user-friendly format. Typically, the formatted data is converted to JSON format. Next, the formatted data is sent to the user's terminal as an HTTP response. The input is customized laundry instructions, and the output is the formatted data sent to the user's terminal.

[1099] Step 8:

[1100] Washing instructions

[1101] The user terminal receives laundry instructions in JSON format from the server and displays them in a user-friendly format within the application. For example, it might display specific instructions such as, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer." The input is the JSON response from the server, and the output is the laundry instructions displayed to the user. This step allows the user to easily understand the optimal laundry method.

[1102] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1103] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1104] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1105] [Fourth Embodiment]

[1106] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1107] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1109] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1110] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1113] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1114] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1115] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1116] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1117] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1118] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1119] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. The embodiments for carrying out this invention will be described in detail below.

[1120] System Overview

[1121] This system includes means for uploading images of clothing, means for analyzing uploaded images, means for searching a database for an appropriate washing method, and means for transmitting the retrieved washing method to the user terminal. The system provides the user with the optimal washing method by communicating between the server and the user terminal.

[1122] Program Processing Overview

[1123] Image upload (device)

[1124] First, the user takes a picture of their clothes using their smartphone or computer, or selects an image from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system.

[1125] Sending images (from the device)

[1126] The user's terminal sends the image provided by the user to the server as an HTTP POST request. At this time, request data containing the image file is generated.

[1127] Image reception and storage (server)

[1128] The server receives an HTTP POST request sent from the user's terminal. The received image is temporarily stored in the server's storage.

[1129] Image analysis (server)

[1130] The server analyzes the stored images using image analysis algorithms. For example, it uses machine learning models and image recognition tools (e.g., OpenCV, TensorFlow) to extract information about the clothing's material, color, and care label from the images.

[1131] Search for laundry methods (server)

[1132] The server uses the information obtained from image analysis to search a database and extract the appropriate washing method. The information used as search keys includes material, color, and the contents of the care label.

[1133] For example, if an image of a "cotton shirt" is uploaded, the server will analyze the results to obtain information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry," and then use this information as a key to search the database.

[1134] Data formatting and transmission (server)

[1135] The server formats the retrieved laundry information into a user-friendly format. This is done, for example, by converting it to JSON format. Then, it sends that data to the user's terminal as an HTTP response.

[1136] Washing instructions (on the device)

[1137] The user terminal receives laundry information sent from the server. This information is displayed within the application in a user-friendly format. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[1138] In this way, users can easily find out how to wash their clothes from their smartphones or computers. The system of the present invention reduces the risk of users choosing the wrong washing method and makes it possible to extend the life of their clothes.

[1139] The following describes the processing flow.

[1140] Step 1:

[1141] User: Take a photo of the clothing with your device or select an image of clothing from your existing image gallery. Then, click the upload button in the application to upload the image of the clothing to the system.

[1142] Step 2:

[1143] Terminal: When an image is selected or captured, an HTTP POST request is generated. This request includes the image file provided by the user.

[1144] Step 3:

[1145] Terminal: Sends the generated HTTP POST request to the server. The request has an image file attached.

[1146] Step 4:

[1147] Server: Receives HTTP POST requests sent from the terminal. Saves the received image files to temporary storage.

[1148] Step 5:

[1149] Server: Reads image files stored in storage. Applies image analysis algorithms to analyze the read images. This includes using machine learning models and image recognition tools.

[1150] Step 6:

[1151] Server: The image analysis algorithm extracts the material, color, and care label information of clothing from an image. For example, a machine learning model might determine it's a "cotton shirt" and recognize from the care label that it should be washed at 30°C or below, use a laundry net, and do not tumble dry.

[1152] Step 7:

[1153] Server: Searches the database based on information obtained from image analysis. Material, color, and laundry care label information are used as search keys.

[1154] Step 8:

[1155] Server: Retrieves the optimal washing method from the database. For example, for a "cotton shirt," it might retrieve washing instructions such as "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[1156] Step 9:

[1157] Server: Formats the retrieved laundry method information into a user-friendly format. For example, converts it to JSON format.

[1158] Step 10:

[1159] Server: Sends formatted laundry instruction data to the user's terminal as an HTTP response.

[1160] Step 11:

[1161] Terminal: Receives HTTP responses sent from the server. Displays the received laundry method information within the application.

[1162] Step 12:

[1163] User: Check the washing instructions displayed in the application on the device. For example, instructions such as "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer" may be displayed.

[1164] In this way, users can quickly find out the best washing method simply by uploading an image.

[1165] (Example 1)

[1166] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1167] In conventional laundry instruction systems, users must interpret laundry care labels on their own, which can result in selecting the wrong washing method. This problem is particularly pronounced when it is difficult to accurately determine the appropriate washing method for the material and color of the clothing. Furthermore, information on washing methods is not centrally managed and is not provided in a way that is easily accessible to users, which is another issue. The present invention aims to solve these problems and provide a system that allows users to easily obtain accurate washing instructions.

[1168] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1169] In this invention, the server includes means for the user to upload an image of clothing, means for analyzing the uploaded image to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for transmitting the retrieved washing method to the user terminal, and means for displaying the transmitted washing method in a user-friendly format. As a result, the user can simply upload an image of their clothing and be automatically provided with the optimal washing method, reducing the risk of selecting an incorrect washing method and extending the life of their clothes.

[1170] A "user terminal" refers to a computing device operated by a user, and includes smartphones, personal computers, tablets, and other similar devices.

[1171] "Image analysis" is the process of analyzing uploaded images and automatically recognizing their contents (e.g., material, color, laundry care label), using machine learning models and image recognition tools.

[1172] "Material information" refers to information about the fibers and materials that make up clothing, such as cotton, polyester, and wool.

[1173] "Color information" refers to information about the colors of clothing, and includes major color classifications such as black, white, red, and blue.

[1174] A "laundry care label" is a tag or sticker attached to clothing that contains symbols and instructions indicating the proper washing, drying, and bleaching methods.

[1175] A "database" is a collection of data designed to effectively search, access, and manage information, and is used to store information on proper washing methods.

[1176] "Washing instructions" refer to information that shows the correct procedure and precautions for washing clothes, and include water temperature, detergent to use, and drying method.

[1177] A "user-friendly format" refers to a format that is easy for users to understand and use, and specifically includes formats such as JSON and concise text displays.

[1178] A "machine learning model" refers to a mathematical model trained to perform a specific task based on data, and is used in the image analysis process.

[1179] "Image recognition tools" refer to software or libraries that automatically recognize specific objects or features within an image, and OpenCV and TensorFlow are examples of this.

[1180] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Specific embodiments for carrying out this invention are described in detail below.

[1181] This system functions by communicating between a server and a user terminal. It includes means for users to upload images of clothing, means for analyzing uploaded images, means for searching a database for appropriate washing methods based on the recognized information, and means for transmitting and displaying the retrieved washing method on the user terminal.

[1182] First, the user takes a picture of the clothing with their smartphone or computer, or selects an image from their existing image gallery. This imports the image to the user's device. The user then sends the image to the system by clicking the "Upload" button within the application.

[1183] The submitted image is sent to the server as an HTTP POST request. The server receives this request and temporarily stores the image file in its storage. The server analyzes the stored image using image recognition tools such as OpenCV or TensorFlow to extract information about the clothing's material, color, and care label.

[1184] After analysis, the server uses the recognized information as a key to search the database and identify the appropriate washing method. Specifically, it sends material information, color information, and laundry care label information as queries to the database and retrieves the resulting washing method.

[1185] The retrieved washing instructions are formatted into a user-friendly format, such as JSON. The server sends this formatted data to the user's terminal as an HTTP response. The user's terminal parses the received data and displays a message within the application such as, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[1186] This system allows users to easily obtain accurate washing instructions, reducing the risk of choosing the wrong method. Furthermore, because it provides the optimal washing method suited to the material and color of the clothing, users can extend the life of their clothes.

[1187] Next, let's consider a specific example: a user uploads an image of a "black cotton shirt." The user first takes a picture of the shirt with their smartphone and clicks the "Upload" button in the application. The image is sent to the server, which analyzes it using TensorFlow and extracts information such as the material being cotton, the color being black, and the care label stating that it should be washed at 30°C or below, bleach not allowed, and tumble drying not allowed. The server sends this information as a query to the database and retrieves the information that the appropriate washing method is "Wash at 30°C or below, do not use bleach, and avoid tumble drying." This information is converted to JSON format and sent to the user's terminal as an HTTP response. The user's terminal analyzes this information and displays "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying."

[1188] Examples of specific prompt messages are as follows:

[1189] "How do I wash a black cotton shirt? The care label says to wash at 30°C or below, do not bleach, and do not tumble dry."

[1190] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1191] Step 1:

[1192] The user takes a picture of clothing or selects one from their existing image gallery using a smartphone or computer. Specifically, they open a camera or gallery app, select an image, and click the "Upload" button within the application. The input is an image of clothing, and the output is the image file imported into the application.

[1193] Step 2:

[1194] The user terminal sends the captured image file to the server as an HTTP POST request. In this process, an HTTP library is used to generate the request, and the request data, including the image file and metadata, is sent to the server. The input is the image file captured by the application, and the output is the HTTP request sent to the server.

[1195] Step 3:

[1196] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the image file in storage. Specifically, the server's endpoint handles the request and stores the received image file in the file system or database. The input is the HTTP request, and the output is the image file stored in storage.

[1197] Step 4:

[1198] The server analyzes stored images using image analysis algorithms. Specifically, it reads image files and launches machine learning models such as OpenCV or TensorFlow. Using these tools, it extracts information about the material, color, and care label of clothing from the images. The input is an image file stored in storage, and the output is the material information, color information, and care label information as analysis results.

[1199] Step 5:

[1200] The server uses information obtained through image analysis to search the database and extract the appropriate washing method. Specifically, the server sends material, color, and care label information as a query to the database. It searches this data and retrieves the record for the corresponding washing method. The input is the material information, color information, and care label information obtained as analysis results, and the output is the washing method information obtained from the database.

[1201] Step 6:

[1202] The server formats the retrieved laundry method information into a user-friendly format. Specifically, it converts the data into JSON format to make it easy for the user to understand. The formatted data is prepared as an HTTP response and sent to the user's terminal. The input is laundry method information retrieved from the database, and the output is formatted JSON data.

[1203] Step 7:

[1204] The user terminal receives laundry information in JSON data format from the server. Specifically, it parses the received response data and displays it in a user-friendly format within the application. The input is the JSON data sent from the server, and the output is the laundry instructions displayed to the user. For example, it might display, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[1205] (Application Example 1)

[1206] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1207] The problems that this invention aims to solve are to enable users to easily understand how to wash their clothes and to improve the user's purchasing experience by providing real-time washing instructions in physical stores. Conventional systems had the problem that users had a high risk of choosing the wrong washing method, resulting in a shorter lifespan for their clothes. Furthermore, because real-time information was not provided in physical stores, users could not immediately find out the appropriate washing method.

[1208] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1209] In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for transmitting the retrieved washing method to a user terminal or wearable device, and communication means for providing washing instructions in real time while the user is on the move. This allows the user to receive appropriate washing instructions for their clothing in real time even while on the go, reducing the risk of choosing the wrong washing method and extending the life of the clothing. Furthermore, even in physical stores, users can check product care instructions on the spot, which can support their purchasing decisions.

[1210] "Means for uploading images of clothing" refers to a device or interface for a user to send images of clothing they have taken to a server.

[1211] "Means for analyzing uploaded images" refers to algorithms or software for analyzing the data of submitted images and recognizing the material, color, and care label of clothing in the images.

[1212] "A means of searching a database for an appropriate laundry method based on recognized information" refers to a search engine or search algorithm that searches a database based on analysis results to identify the optimal laundry method.

[1213] "Means for transmitting the retrieved laundry method to a user terminal or wearable device" refers to a communication protocol or interface for transmitting data of the identified laundry method to the user's device.

[1214] "Communication means for providing laundry instructions in real time while the user is on the move" refers to mobile networks or Wi-Fi connections that allow users to receive information in real time even while they are on the move.

[1215] "Means of converting to a user-friendly format" refers to formatting algorithms that present search results to users in an easily understandable format (such as JSON).

[1216] This invention relates to a system that uploads images of clothing, analyzes those images, and provides appropriate washing instructions. The system consists of a user terminal, a server, an image analysis algorithm, a database, and a communication interface.

[1217] 1. User terminal

[1218] Users take or select images of clothing using a device such as a smartphone, head-mounted display, or smart glasses. The device has an interface for uploading images, and users send the images to the server by pressing the upload button.

[1219] 2. Server

[1220] The server receives images sent by users and stores them in temporary storage. The server performs image analysis using the following software and hardware.

[1221] Image analysis libraries: OpenCV, TensorFlow

[1222] Database: MySQL, MongoDB

[1223] Communication protocol: HTTP / HTTPS

[1224] 3. Image Analysis

[1225] The saved images are analyzed by an image analysis algorithm on the server. Using OpenCV and TensorFlow, the material, color, and care label of the clothing are recognized from the images. This analyzed data is used as key information to determine the washing method.

[1226] 4. Database Search

[1227] Based on the information obtained from image analysis, the server searches the database and extracts the appropriate washing method. The information used as search keys includes material, color, and the contents of the care label. For example, if an image of a "cotton shirt" is uploaded, the search will be performed based on the recognition results, which include information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[1228] 5. Data Formatting and Transmission

[1229] The server formats the retrieved laundry information into a user-friendly format. Specifically, it converts it to JSON format and then sends it as an HTTP response to the user's terminal or wearable device.

[1230] 6. Washing instructions

[1231] The user terminal analyzes the received laundry information and displays it to the user in an appropriate format. For example, the smart glasses display might show, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[1232] Examples of specific cases and prompt statements

[1233] When users are browsing products in a physical store, they can use smart glasses to obtain information on the spot.

[1234] Example of a prompt:

[1235] "How should I wash this garment?"

[1236] "How do I care for this garment?"

[1237] "Please tell me the proper way to wash this material."

[1238] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1239] Step 1:

[1240] Upload image

[1241] Users take or select images of clothing using a smartphone, head-mounted display, or smart glasses.

[1242] Input: Image of clothing

[1243] Operation: The user clicks the upload button within the application.

[1244] Output: Uploaded image file

[1245] Step 2:

[1246] Sending images

[1247] The device sends the image provided by the user to the server as an HTTP POST request.

[1248] Input: Uploaded image file

[1249] Operation: Generate an HTTP POST request and send it to the server, including image data.

[1250] Output: Request data sent to the server

[1251] Step 3:

[1252] Receiving and saving images

[1253] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the image.

[1254] Input: HTTP request with image data sent from the terminal

[1255] Operation: Receives request data and saves image data to storage.

[1256] Output: Saved image file

[1257] Step 4:

[1258] Image analysis

[1259] The server analyzes the stored images using OpenCV and TensorFlow to recognize the material, color, and care label of the clothing.

[1260] Input: Saved image file

[1261] Operation: Perform image recognition processing using OpenCV or TensorFlow. Specifically, analyze the texture, color, and text within the image.

[1262] Output: Data on recognized material, color, and care label.

[1263] Step 5:

[1264] Search for laundry methods

[1265] The server searches the database based on the recognized information and extracts the appropriate washing method.

[1266] Input: Data on recognized material, color, and care label.

[1267] Operation: Perform a database search, using the recognition information as the search key.

[1268] Output: Data on searched laundry methods

[1269] Step 6:

[1270] Data formatting and transmission

[1271] The server formats the searched laundry method information into a user-friendly format and sends it to the user's terminal or wearable device.

[1272] Input: Data on the searched laundry methods

[1273] Operation: Format the data into JSON format and send it as an HTTP response.

[1274] Output: HTTP response of formatted laundry method data

[1275] Step 7:

[1276] Washing instructions

[1277] The user terminal receives laundry instructions from the server and displays them in a user-friendly format.

[1278] Input: HTTP response of laundry method data sent from the server

[1279] Operation: Analyze data and display it within the application in a user-friendly format.

[1280] Output: Illustrated instructions for washing

[1281] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1282] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system includes a function that suggests an appropriate washing method according to the user's situation and mood by incorporating an emotion engine that recognizes the user's emotions. The embodiments for carrying out this invention will be described in detail below.

[1283] System Overview

[1284] This system includes means for uploading images of clothing, means for analyzing uploaded images, means for searching a database for appropriate washing methods, means for sending washing methods to the user terminal, and an emotion engine. The system communicates between the server and the user terminal to provide the user with the optimal washing method and to offer customized suggestions that take the user's emotions into consideration.

[1285] Program Processing Overview

[1286] Image upload (device)

[1287] First, the user takes a picture of their clothes using their smartphone or computer, or selects an image of their clothes from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system.

[1288] Sending images (from the device)

[1289] The user's terminal sends the image provided by the user to the server as an HTTP POST request. At this time, request data containing the image file is generated.

[1290] Image reception and storage (server)

[1291] The server receives an HTTP POST request sent from the user's terminal. The received image is temporarily stored in the server's storage.

[1292] Image analysis (server)

[1293] The server analyzes the stored images using image analysis algorithms. For example, it uses machine learning models and image recognition tools (e.g., OpenCV, TensorFlow) to extract information about the clothing's material, color, and care label from the images.

[1294] Emotion recognition (server)

[1295] The emotion engine recognizes the user's current emotions through analysis of their voice and images. For example, it analyzes the user's emotions from their facial expressions captured on camera or from their voice input. If the user is feeling stressed, it prioritizes suggesting simple and quick laundry methods.

[1296] Search for laundry methods (server)

[1297] The server searches a database to extract the appropriate washing method based on information obtained from image analysis and sentiment analysis results from the sentiment engine. The information used as search keys includes material, color, contents of the care label, and sentiment information.

[1298] For example, if an image of a "cotton shirt" is uploaded and the emotion engine recognizes that the user is feeling stressed, the server retrieves information such as "cotton," "wash at 30°C or below," "use a laundry net," and "do not tumble dry," and uses this information as a key to search the database. Taking the user's emotions into consideration, it prioritizes suggesting simpler washing methods (e.g., avoiding recommending hand washing and encouraging the use of a washing machine).

[1299] Data formatting and transmission (server)

[1300] The server formats the retrieved laundry information into a user-friendly format. This is done, for example, by converting it to JSON format. Then, it sends that data to the user's terminal as an HTTP response.

[1301] Washing instructions (on the device)

[1302] The user terminal receives laundry information sent from the server. This information is displayed within the application in a user-friendly format. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[1303] In this way, users can quickly find the optimal washing method that takes their emotions into consideration simply by uploading an image. The system of this invention reduces the risk of users choosing the wrong washing method, making it possible to extend the life of their clothes. Furthermore, by providing customized suggestions that take the user's emotions into account, the user experience is improved.

[1304] The following describes the processing flow.

[1305] Step 1:

[1306] User: Take a picture of the clothing with your device's camera, or select an image of the clothing from your existing image gallery. Then, click the upload button in the application to upload the image of the clothing to the system.

[1307] Step 2:

[1308] Terminal: When an image is selected or captured, an HTTP POST request is generated. This request includes the image file provided by the user.

[1309] Step 3:

[1310] Terminal: Sends the generated HTTP POST request to the server. The request has an image file attached.

[1311] Step 4:

[1312] Server: Receives HTTP POST requests sent from the terminal. Saves the received image files to temporary storage.

[1313] Step 5:

[1314] Server: Reads image files stored in storage and applies image analysis algorithms. For example, it uses OpenCV or TensorFlow to extract clothing material, color, and care label information from images.

[1315] Step 6:

[1316] Server: Based on the image analysis, the material of the clothing is determined to be "cotton," and the care label is recognized as "wash at 30°C or below," "use a laundry net," and "do not tumble dry."

[1317] Step 7:

[1318] Server: Uses an emotion engine to recognize the user's emotions. It analyzes the user's facial expressions captured by the camera and voice input to determine if they are "feeling stressed."

[1319] Step 8:

[1320] Server: Searches the database based on image analysis results and sentiment analysis results. The search uses material, color, care label information, and sentiment information as search keys.

[1321] Step 9:

[1322] Server: Retrieves the optimal washing method from the database. For example, if the user is stressed, it prioritizes suggesting a simple and easy washing method (e.g., "Use a short cycle at 30°C or below in the washing machine").

[1323] Step 10:

[1324] Server: Formats the retrieved laundry method information into a user-friendly format. For example, converts and formats it into JSON format.

[1325] Step 11:

[1326] Server: Sends formatted laundry instruction data to the user's terminal as an HTTP response.

[1327] Step 12:

[1328] Terminal: Receives HTTP responses sent from the server. Displays the received laundry method information within the application.

[1329] Step 13:

[1330] User: Check the washing instructions displayed in the application on the device. For example, it might say, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying." If the user is feeling stressed, additional suggestions might be displayed, such as, "Use the short cycle on your washing machine for easy washing."

[1331] In this way, users can quickly find out the best, emotionally-conscious way to do their laundry simply by uploading an image.

[1332] (Example 2)

[1333] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1334] Traditional laundry instruction systems simply provided appropriate washing instructions based on the material, color, and care label of the clothing, without considering the user's feelings. As a result, users were sometimes presented with complicated washing instructions when they were stressed or time-constrained, highlighting the need for improved user experience. Furthermore, the lack of user-friendly information presentation made it difficult for users to accurately understand and implement the provided information.

[1335] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and laundry care label of the clothing, means for using an emotion engine to recognize the user's emotions, means for searching a database for an appropriate laundry method based on the recognized information and the user's emotions, and means for converting the searched laundry method into a user-friendly format and transmitting it to the user terminal. This makes it possible for the user to quickly find the optimal laundry method that suits their emotional state, thereby improving the user experience.

[1336] The "means of uploading images of clothing" refers to a function that allows users to select images of clothing using their smartphones, computers, or other user devices and send them to the system.

[1337] "Means for analyzing uploaded images to recognize the material, color, and care label of clothing" refers to a function that analyzes images stored on the server using machine learning and image recognition tools to extract the main characteristics of the clothing.

[1338] "Means of using an emotion engine to recognize user emotions" refers to algorithms and technologies that analyze a user's facial expressions and voice to identify their current emotional state.

[1339] "Means for searching for an appropriate washing method from a database based on recognized information and user sentiment" refers to a function that searches for the optimal washing method from a database based on analyzed clothing characteristics and user sentiment information.

[1340] "A means of converting searched laundry methods into a user-friendly format and sending it to the user's terminal" refers to a function that converts search results into a format that is easy for the user to understand (e.g., JSON format) and outputs that information to the user's terminal.

[1341] This invention is a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, and includes a function to suggest an appropriate washing method according to the user's situation and mood. Specific embodiments for carrying out the invention will be described in detail below.

[1342] System Configuration

[1343] This system consists of the following main elements:

[1344] 1. How to upload images of clothing

[1345] The user uses a user device (smartphone or computer). They either take a picture of the clothing using the device's camera or select an image of the clothing from their existing image gallery. The user then uploads the image by clicking the upload button within the application.

[1346] 2. Means for analyzing uploaded images

[1347] The server uses image analysis algorithms (e.g., OpenCV, TensorFlow) to extract the material, color, and care label information of clothing from the image.

[1348] 3. Means of using an emotion engine that recognizes user emotions

[1349] An emotion engine within the server analyzes the user's voice and images to recognize their current emotional state. For example, if the user is feeling stressed, it prioritizes suggesting simple and quick laundry methods.

[1350] 4. Means for searching a database for an appropriate laundry method based on recognized information and user sentiment.

[1351] The server searches the database based on information obtained from image analysis and the sentiment engine. The information used as search keys includes material, color, laundry care label content, and sentiment information.

[1352] 5. A means of converting the searched laundry method into a user-friendly format and sending it to the user's terminal.

[1353] The server formats the search results into a user-friendly format, such as JSON, and sends it to the user's terminal as an HTTP response.

[1354] Hardware and software to be used

[1355] User devices: Smartphones, personal computers

[1356] Servers: Cloud servers and on-premises servers

[1357] Image analysis tools: OpenCV, TensorFlow

[1358] Emotion engine: Voice analysis tools, facial recognition algorithms

[1359] Specific example

[1360] Herein, we will specifically describe embodiments of the present invention.

[1361] 1. A concrete example of a user uploading an image:

[1362] The user launches the app on their smartphone, takes a picture of the clothing or selects one from their gallery, and clicks the upload button.

[1363] 2. Specific examples of image analysis:

[1364] The server analyzes the received images using OpenCV to extract the clothing material (e.g., cotton), color (e.g., white), and care label information (e.g., "Wash at 30°C or below").

[1365] 3. Specific examples of emotion recognition:

[1366] The emotion engine analyzes the user's voice input, "I'm tired," and recognizes that they are "feeling stressed."

[1367] 4. Specific examples of search results:

[1368] The system searches the database and extracts simple washing instructions using the keywords "cotton shirt," "wash at 30°C or below," "use a laundry net," and "do not tumble dry." The server then suggests avoiding hand washing and using a washing machine.

[1369] 5. Example of a prompt:

[1370] The user submitted an image of a cotton shirt, and the sentiment engine determined that the user was tired. As a simple and quick washing method, it suggested, "Wash in a washing machine at 30°C or below, use a laundry net, and avoid using a dryer."

[1371] As described above, this invention allows users to quickly find the optimal, emotionally-conscious washing method simply by uploading an image. This improves the user experience and reduces the risk of selecting the wrong washing method.

[1372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1373] Step 1: Uploading an image (User)

[1374] Users take pictures of their clothes using a smartphone or computer application, or select images from their existing gallery. Next, they click the upload button within the application to upload the clothing images to the system. This selects the image file and initiates the upload process.

[1375] Input: Images of clothing selected or photographed by the user.

[1376] Output: Image files uploaded to the system

[1377] Step 2: Sending the image (from your device)

[1378] The device sends the selected image to the server as an HTTP POST request. This request contains the embedded image file. The device generates the request data and sends it to the server over the internet.

[1379] Input: Uploaded image file

[1380] Output: Image data sent to the server as an HTTP POST request

[1381] Step 3: Receiving and saving images (server)

[1382] The server receives an HTTP POST request sent from the terminal. The server extracts image data from the request and temporarily stores it in its storage. This prepares the input data for image analysis.

[1383] Input: Image data for an HTTP POST request

[1384] Output: Image data stored on the server's storage.

[1385] Step 4: Image analysis (server)

[1386] The server analyzes the stored images using image analysis algorithms (e.g., OpenCV, TensorFlow). It extracts the material, color, and care label information of the clothing from the images. It executes code written in programming languages ​​such as Python or C++ for image analysis.

[1387] Input: Image data stored on the server's storage.

[1388] Output: Information on the material, color, and care label of the clothing extracted by the analysis.

[1389] Step 5: Emotion Recognition (Server)

[1390] The server's emotion engine analyzes the user's voice input and image data to recognize their current emotional state. For example, the user might approach the microphone and say something like, "I'm tired." The emotion analysis algorithm then analyzes the voice and facial expression data to determine the user's emotional state.

[1391] Input: User voice input, facial expression data

[1392] Output: User's emotional state identified through analysis

[1393] Step 6: Search for washing instructions (server)

[1394] The server searches the database to extract appropriate washing instructions based on information obtained from image analysis and sentiment analysis results from the sentiment engine. The information used as search keys includes material, color, contents of the care label, and sentiment information. The server executes SQL queries to retrieve search results from the database.

[1395] Input: Information on clothing material, color, care label, and user sentiment.

[1396] Output: Information on appropriate washing methods extracted from the database

[1397] Step 7: Formatting and sending data (to the server)

[1398] The server formats the extracted laundry method information into a user-friendly format. For example, it formats it into JSON format to organize the necessary information. Then, it sends the formatted data to the user's terminal as an HTTP response.

[1399] Input: Laundry method information extracted from a database

[1400] Output: Formatted data and sent to the user's terminal as an HTTP response.

[1401] Step 8: Washing instructions displayed (on the device)

[1402] The user terminal receives an HTTP response sent from the server. The received data is analyzed, and laundry instructions are displayed in a user-friendly format within the application. For example, a message such as "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer" might be displayed.

[1403] Input: Formatted data sent from the server

[1404] Output: Laundry instructions displayed in a user-friendly format

[1405] The above outlines the processing steps of this system's program. This allows users to easily and quickly discover the optimal, emotionally-conscious laundry method.

[1406] (Application Example 2)

[1407] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1408] Conventional laundry method suggestion systems provided general washing instructions based on the material, color, and care label of the clothing, but they were unable to offer customized suggestions based on the user's emotions or circumstances. As a result, even when users were feeling stressed or fatigued, they were not offered appropriate care methods, leading to inconvenience. Furthermore, the information was not provided in a user-friendly format, making it difficult to understand. Therefore, a system is needed that can provide flexible suggestions in response to the user's emotions and deliver information in an easy-to-use format.

[1409] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1410] In this invention, the server includes means for uploading images of clothing, means for analyzing the uploaded images to recognize the material, color, and care label of the clothing, means for searching a database for an appropriate washing method based on the recognized information, means for recognizing the user's emotions using an emotion engine and customizing the washing method according to those emotions, and means for transmitting the searched and customized washing method to the user terminal. This makes it possible to customize and provide the optimal washing method according to the user's emotions and circumstances, thereby improving the user experience.

[1411] The "means of uploading clothing images" refer to a function that allows users to send images of clothing they have taken to the system using their smartphone or other devices.

[1412] "Methods for analyzing uploaded images" refers to algorithms and tools used to extract information such as the material, color, and care label of clothing based on images submitted by users.

[1413] "A means of searching for appropriate washing methods from a database" refers to an implementation function that searches the database for methods of properly washing clothes based on the extracted information, and obtains the necessary information.

[1414] "A means of recognizing the user's emotions using an emotion engine and customizing the laundry method according to those emotions" refers to a function that analyzes the user's current emotional state based on input such as facial expressions and voice, and adjusts and suggests the laundry method according to the user's emotions.

[1415] "Means for sending search and customized laundry methods to the user terminal" refers to an implementation function for sending the information generated by the above process to the user terminal in a format that is easy for the user to understand.

[1416] This invention relates to a system that allows users to upload images of clothing and provides a suitable washing method for those clothes. Furthermore, this system includes a function that incorporates an emotion engine to recognize the user's emotions and suggest an appropriate washing method according to the user's situation and mood. The embodiments for carrying out this invention will be described in detail below.

[1417] System Overview

[1418] This system includes means for uploading images of clothing, means for analyzing the uploaded images, means for searching a database for an appropriate washing method based on the recognized information, means for recognizing the user's emotions using an emotion engine and customizing the washing method according to those emotions, and means for transmitting the searched and customized washing method to the user's terminal.

[1419] Image upload (device)

[1420] First, the user takes a picture of their clothes using their smartphone or other device, or selects an image of their clothes from their existing image gallery. Next, they click the upload button within the application to upload the image of their clothes to the system. This entire process is carried out using an HTTP POST request.

[1421] Image transmission and storage (server)

[1422] The server receives images sent from the user's terminal and temporarily stores them in the server's storage. The stored images are then used in subsequent image analysis processes.

[1423] Image analysis (server)

[1424] The server uses image recognition tools (e.g., OpenCV, TensorFlow) to analyze the stored images. The analysis extracts the clothing material, color, and care label information.

[1425] Emotion recognition (server)

[1426] The emotion engine is used to recognize the user's emotions in real time. This system determines emotions by analyzing the user's voice input and facial expressions captured by the camera. For example, if the user is feeling stressed, the system will suggest simplifying the laundry process.

[1427] Laundry method search and customization (server)

[1428] The server searches the database for an appropriate washing method based on the results of image analysis and emotion recognition by the emotion engine. This search uses material information, color information, and laundry care label information as keys. Furthermore, it proposes a customized washing method based on the user's emotional state.

[1429] Data formatting and transmission (server)

[1430] The server formats the retrieved laundry information into a user-friendly format. The information is converted to JSON format and sent to the user's terminal as an HTTP response.

[1431] Washing instructions (on the device)

[1432] The user terminal receives laundry information sent from the server and displays it in a user-friendly format within the application. For example, it might display, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer."

[1433] Usage example

[1434] For example, if a user uploads an image of a "cotton shirt" purchased from an online shopping site, and the emotion engine recognizes the user's stress level, the server searches the database based on information such as "wash at 30°C or below," "use a laundry net," and "do not tumble dry." At the same time, it takes the user's emotional state into consideration and makes customized suggestions, such as recommending the use of a washing machine. A message like, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid tumble drying," is displayed on the user's terminal.

[1435] Examples of prompts for generative AI models

[1436] "Analyze images of clothing uploaded by users and provide the optimal washing method. Also, customize the suggestions based on the user's mood."

[1437] In this way, the system helps users quickly obtain optimal information about laundry, allowing them to extend the life of their clothes. Furthermore, it enhances the user experience through emotionally sensitive and customized suggestions.

[1438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1439] Step 1:

[1440] Upload an image of clothing.

[1441] The user first selects an image of clothing from their smartphone's camera app or gallery. Next, they click the upload button within the application to upload the image of clothing to the system. The image file, as input, is sent to the server via an HTTP POST request. This causes the server to temporarily store the image file in its storage.

[1442] Step 2:

[1443] Receiving and saving images

[1444] The server receives an HTTP POST request sent from the user's terminal and temporarily stores the included image file in the server's storage. The input is the HTTP POST request, and the output is the stored image file. This process prepares the server for the data necessary for the next image analysis step.

[1445] Step 3:

[1446] Image analysis

[1447] The server analyzes the stored images. It primarily uses image recognition tools such as OpenCV and TensorFlow to extract information about clothing material, color, and care labels from the images. It uses the stored image files as input and outputs this detailed information. This analysis result is then used to search for laundry instructions.

[1448] Step 4:

[1449] Recognition of emotions

[1450] The server uses an emotion engine to recognize the user's current emotional state. Specifically, it analyzes the user's facial expression data captured by the camera and voice input to determine the user's emotions (e.g., stress, relief, etc.). The input is the user's facial expression data and voice data, and the output is the recognized emotional information. This provides data to customize the laundry method based on the user's current emotional state.

[1451] Step 5:

[1452] Search for laundry methods

[1453] The server searches the database for the appropriate washing method based on the results of image analysis and emotion recognition. This process queries the database using material information, color information, and the contents of the care label as keys to extract the optimal washing method. The inputs are image analysis results and emotion recognition results, and the output is information on the appropriate washing method.

[1454] Step 6:

[1455] Customized laundry method shaping

[1456] The server combines the searched laundry method with the user's emotional information to customize the optimal laundry method. For example, if the user is feeling stressed, it will prioritize suggesting a simpler laundry method. The input consists of the searched laundry method information and the user's emotional information, and the output is customized laundry method information.

[1457] Step 7:

[1458] Data formatting and transmission

[1459] The server formats the customized laundry instructions into a user-friendly format. Typically, the formatted data is converted to JSON format. Next, the formatted data is sent to the user's terminal as an HTTP response. The input is customized laundry instructions, and the output is the formatted data sent to the user's terminal.

[1460] Step 8:

[1461] Washing instructions

[1462] The user terminal receives laundry instructions in JSON format from the server and displays them in a user-friendly format within the application. For example, it might display specific instructions such as, "Wash this cotton shirt at 30°C or below, use a laundry net, and avoid using a dryer." The input is the JSON response from the server, and the output is the laundry instructions displayed to the user. This step allows the user to easily understand the optimal laundry method.

[1463] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1464] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1465] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1466] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1467] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1468] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1469] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1470] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1471] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1472] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1473] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1474] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1475] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1476] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1477] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1478] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1479] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1480] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1481] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1482] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1483] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1484] The following is further disclosed regarding the embodiments described above.

[1485] (Claim 1)

[1486] A way to upload images of clothing,

[1487] A means for analyzing uploaded images to recognize the material, color, and care label of clothing,

[1488] A means of searching for an appropriate washing method from a database based on recognized information,

[1489] A means of sending the searched laundry method to the user's terminal,

[1490] A system that includes this.

[1491] (Claim 2)

[1492] The system according to claim 1, characterized in that it includes means for searching a database using material information, color information, and laundry care label information as keys when searching for a washing method based on recognized information.

[1493] (Claim 3)

[1494] The system according to claim 1, characterized in that it includes means for converting the search results for laundry methods into a user-friendly format when sending them to the user's terminal.

[1495] "Example 1"

[1496] (Claim 1)

[1497] A means for users to upload images of clothing,

[1498] A means for analyzing uploaded images to recognize the material, color, and care label of clothing,

[1499] A means of searching for an appropriate washing method from a database based on recognized information,

[1500] A means of sending the searched laundry method to the user's terminal,

[1501] A means of displaying the submitted laundry method in a user-friendly format,

[1502] A system that includes this.

[1503] (Claim 2)

[1504] The system according to claim 1, characterized in that it includes means for searching a database using material information, color information, and laundry care label information as keys when searching for a washing method based on recognized information.

[1505] (Claim 3)

[1506] The system according to claim 1, characterized in that the server includes means for analyzing uploaded images using machine learning models and image recognition tools.

[1507] "Application Example 1"

[1508] (Claim 1)

[1509] A way to upload images of clothing,

[1510] A means for analyzing uploaded images to recognize the material, color, and care label of clothing,

[1511] A means of searching for an appropriate washing method from a database based on recognized information,

[1512] A means for transmitting the searched laundry method to a user terminal or wearable device,

[1513] A communication means for providing laundry instructions in real time while the user is on the move,

[1514] A system that includes this.

[1515] (Claim 2)

[1516] The system according to claim 1, characterized in that it includes means for searching a database using material information, color information, and laundry care label information as keys when searching for a washing method based on recognized information.

[1517] (Claim 3)

[1518] The system according to claim 1, characterized in that it includes means for converting the search results for laundry methods into a user-friendly format when transmitting them to a user terminal or wearable device.

[1519] "Example 2 of combining an emotion engine"

[1520] (Claim 1)

[1521] A way to upload images of clothing,

[1522] A means for analyzing uploaded images to recognize the material, color, and care label of clothing,

[1523] A means of using an emotion engine that recognizes the user's emotions,

[1524] A means for searching a database for an appropriate laundry method based on recognized information and user sentiment,

[1525] A means of converting the searched laundry method into a user-friendly format and sending it to the user's terminal,

[1526] A system that includes this.

[1527] (Claim 2)

[1528] The system according to claim 1, characterized in that it includes means for searching a database using material information, color information, laundry care label information, and emotional information as keys when searching for a laundry method based on recognized information and the user's emotions.

[1529] (Claim 3)

[1530] The system according to claim 1, characterized in that it includes means for converting the search results for laundry methods into a user-friendly format when sending them to the user's terminal.

[1531] "Application example 2 of combining emotional engines"

[1532] (Claim 1)

[1533] A way to upload images of clothing,

[1534] A means for analyzing uploaded images to recognize the material, color, and care label of clothing,

[1535] A means of searching for an appropriate washing method from a database based on recognized information,

[1536] A means of recognizing the user's emotions using an emotion engine and customizing the laundry method according to those emotions,

[1537] A means for sending searched and customized laundry methods to the user terminal,

[1538] A system that includes this.

[1539] (Claim 2)

[1540] The system according to claim 1, characterized in that it includes means for searching a database using material information, color information, and laundry care label information as keys when searching for a washing method based on recognized information.

[1541] (Claim 3)

[1542] The system according to claim 1, characterized in that it includes means for converting the laundry method search results to a user-friendly format when sending them to the user's terminal, and means for providing customized information based on emotions. [Explanation of Symbols]

[1543] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A way to upload images of clothing, A means for analyzing uploaded images to recognize the material, color, and care label of clothing, A means of searching for an appropriate washing method from a database based on recognized information, A means of sending the searched laundry method to the user's terminal, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for searching a database using material information, color information, and laundry care label information as keys when searching for a washing method based on recognized information.

3. The system according to claim 1, characterized in that it includes means for converting the search results for laundry methods into a user-friendly format when sending them to the user's terminal.

Images