system

Abstract

Provide a system. The system includes a means for users to register photos of their own clothing, A means for transmitting information entered by the user, such as mood, date, weather, and temperature, to a server; A means for the server to search a database of clothing owned by the user and an external clothing database to generate an optimal outfit; A means for transmitting the generated coordinates from the server to the user's terminal; A means for displaying the coordinates received by the user terminal; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional fashion coordination suggestion systems have difficulty combining the user's clothing with the latest products in an external database, making it difficult to suggest outfits that make the most of the user's clothing. Furthermore, there is a need for more accurate suggestions by taking into account external environmental information such as weather, temperature, and the user's mood. The objective of this invention is to solve these problems and suggest the optimal outfits for the user. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: a system that includes a means for allowing a user to register photos of their own clothing, a means for transmitting information entered by the user, such as mood, date, weather, and temperature, to a server, a means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, a means for transmitting the generated outfit from the server to the user's device, and a means for displaying the outfit received by the user's device. This makes it possible to combine the user's own clothing with the latest products in the external database and to suggest optimal outfits that take into account information such as weather, temperature, and the user's mood.

[0006] "User" refers to an individual or corporation that uses this system to register the clothing they own and receive suggestions for fashion coordination.

[0007] "Clothing" refers to all clothing and accessories owned by a User that are the subject of coordination suggestions.

[0008] "Photo" refers to image data of clothing registered by the user, an image file from which attribute information is extracted through analysis.

[0009] "Attribute information" refers to characteristic information about clothing, such as category, color, brand, etc.

[0010] "Mood" refers to a user's current emotional state and specific fashion preferences.

[0011] "Date" refers to information indicating the date on which the proposed coordination will be applied.

[0012] "Weather" refers to the weather conditions on the day that should be taken into consideration when proposing outfits.

[0013] "Temperature" refers to the temperature information on the day of the coordinated outfit proposal, entered by the user or obtained by the system.

[0014] "Server" refers to the central server that receives, processes, stores and analyzes data sent by users.

[0015] "Database" refers to a collection of data within the system for storing user registered clothing data and external clothing data in a searchable format.

[0016] "External Clothing Database" refers to an external database containing up-to-date clothing product data that is not owned by the User.

[0017] "Coordination" refers to a set of fashion suggestions that combine clothing from the user and an external database.

[0018] "Means for generating" refers to a series of processes that create the optimal coordination based on input information from the user.

[0019] "Transmitting means" refers to a function for sending the generated coordinates to a user terminal.

[0020] "Display means" refers to a function for displaying the received coordinates on the screen of the user terminal. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] The present invention relates to a system that allows users to register their own clothing and combine it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, a server, and a database.

[0043] Clothing Registration

[0044] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0045] External clothing database integration

[0046] The server connects to an external clothing database (e.g., an online fashion store's database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[0047] Coordination suggestions

[0048] The user taps the "Suggest outfit" button in the app and enters information such as their current mood, date, weather, and temperature. This information is sent from the user's device to the server. The server uses the received information to search the user's registered clothing database and an external clothing database to generate the optimal outfit. For example, if the user selects "feeling casual" and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize casual items. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[0049] Coordination notification

[0050] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[0051] Specific examples

[0052] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," the following specific process will occur:

[0053] 1. User clothing registration

[0054] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0055] Device: Sends the photo and input information to the server.

[0056] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[0057] 2. External clothing database integration

[0058] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[0059] 3. Coordination suggestions

[0060] User: Enters "casual," "sunny," and "20 degrees" into the app.

[0061] Terminal: Sends input information to the server.

[0062] Server: Based on the input information, a black T-shirt, blue jeans, and white sneakers are selected and an outfit is generated.

[0063] 4. Coordination notification

[0064] Server: Sends the generated coordinates to the user's device.

[0065] Device: The suggested outfits are displayed on the screen and notified to the user.

[0066] This allows users to make the most of the clothes they already own and easily create the perfect outfit for the weather, temperature, and mood.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user opens the smartphone app or website and taps the "Register Clothing" button to register the clothing item.

[0070] Step 2:

[0071] Users take a photo of their clothing and enter information such as category, color, brand, etc. For example, they can take a photo of a black Uniqlo T-shirt and enter "tops, black, Uniqlo."

[0072] Step 3:

[0073] The device sends the information entered by the user and the photograph taken to the server.

[0074] Step 4:

[0075] The server analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information (color, shape, etc.) from the images.

[0076] Step 5:

[0077] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0078] Step 6:

[0079] The server periodically sends requests to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing product data.

[0080] Step 7:

[0081] The external clothing data acquired by the server is stored in an internal database, and categories and attribute information are added to make it searchable.

[0082] Step 8:

[0083] The user taps the "Suggest an outfit" button on a smartphone app or website.

[0084] Step 9:

[0085] The user inputs information such as the mood of the day (casual, formal, etc.), date, weather, and temperature. For example, the user inputs information such as casual, sunny, and 20 degrees.

[0086] Step 10:

[0087] The terminal transmits the input information to the server.

[0088] Step 11:

[0089] The server searches the user's registered clothing database and an external clothing database, and generates the optimal outfit based on the input information (mood, weather, temperature, etc.).

[0090] Step 12:

[0091] The server sends the generated coordinates to the terminal in a data format such as JSON.

[0092] Step 13:

[0093] The device then displays the received outfit information on the screen and notifies the user, suggesting a combination of, for example, a black T-shirt, blue jeans, and white sneakers.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] One of the problems many people face today is the time and effort it takes to think about their daily outfits. They also struggle with how to efficiently combine and use the clothes they own. Furthermore, there are currently only a limited number of systems that automatically suggest optimal outfits that take into account external factors such as weather and temperature, and achieving this requires a lot of effort.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes means for allowing a user to register images of their own clothing, means for transmitting information such as mood, date, weather, and temperature input by the user to the server, means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, means for transmitting the generated outfit from the server to the user's terminal, means for the user's terminal to display the outfit received, means for analyzing the image with an image analysis algorithm using the information input by the user and extracting attribute information, and means for linking with an external clothing database to periodically obtain the latest clothing data and save it in a searchable state. This allows users to effectively utilize their own clothing and effortlessly obtain optimal outfits that take external factors such as weather and temperature into consideration.

[0099] "User" refers to an individual who uses this system to register the clothing they own and receive coordination suggestions.

[0100] "Server" refers to the central computer system that receives information from users, links to a database, analyzes it, generates coordinates, and transmits the results to user terminals.

[0101] "Terminal" refers to a device operated by a user (e.g., smartphone, tablet, PC) that exchanges information with a server.

[0102] A "clothing database" refers to a database system that stores the user's clothing and the latest clothing information obtained from external sources, and manages it in a searchable format.

[0103] "Image analysis algorithm" refers to an algorithm that analyzes photos of clothing received by the server and extracts attribute information such as color, shape, and material.

[0104] "External clothing database" refers to a database that stores clothing information provided by online fashion stores or other external providers.

[0105] "Coordination" refers to the optimal fashion combination generated by combining the clothing data registered by the user with external clothing data.

[0106] "Attribute information" refers to detailed characteristic information about clothing, such as color, shape, material, and brand.

[0107] "Searchable" refers to a state in which the data in a database is organized so that it can be quickly searched and referenced based on specific criteria or queries.

[0108] "JSON format" is a data exchange format and refers to a lightweight data format used when exchanging coordination information between a server and a user device.

[0109] The present invention relates to a system that allows users to register their own clothing and combine it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, a server, and a database.

[0110] Clothing Registration

[0111] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm (e.g., TENSORFLOW (registered trademark), OpenCV) and automatically extracts clothing attribute information from the photos. The manually entered information and the automatically extracted attribute information are then combined, and the final clothing data is stored in a database.

[0112] Examples:

[0113] The user registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0114] The user's device sends the photo and input information to the server.

[0115] The server performs image analysis, extracts attribute information, integrates it with manually entered information, and stores it in a database.

[0116] External clothing database integration

[0117] The server connects to an external clothing database (e.g., an online fashion store database) via API and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database (e.g., MySQL (registered trademark), MongoDB), and is organized into a searchable format with categories and attribute information added.

[0118] Examples:

[0119] The server retrieves the latest clothing product data from the external database and stores it in the internal database.

[0120] Coordination suggestions

[0121] Users tap the "Suggest outfits" button in the app and enter information such as their current mood, date, weather, and temperature. This information is sent from the user's device to the server. Based on the received information, the server searches the user's registered clothing database and external clothing databases to generate the optimal outfit.

[0122] Examples:

[0123] A user enters "casual," "sunny," and "20 degrees" into the app.

[0124] The user terminal sends the input information to the server.

[0125] Based on the input information, the server generates an outfit consisting of a black T-shirt, blue jeans, and white sneakers.

[0126] Coordination notification

[0127] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[0128] Examples:

[0129] The server sends the generated coordinates to the user terminal.

[0130] The user's device will display the suggested outfit on the screen and notify the user.

[0131] Example prompt sentence:

[0132] "I have a black T-shirt and blue jeans. Can you suggest an outfit for a casual, sunny, 20-degree day?"

[0133] This invention allows users to maximize the use of their existing clothing and easily find the perfect outfit for the weather, temperature, and mood. To achieve this, the user's device and server work together to perform image analysis and data processing based on the user's input. Furthermore, by linking with an external database, suggestions are made that take into account the latest fashion information.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] Users register the clothing they own.

[0137] Specific operation:

[0138] Users open the smartphone app or website, access the "Clothing Registration" page, and take a photo of a black Uniqlo T-shirt and blue Levi's jeans.

[0139] Users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand along with a photo.

[0140] Input: Photo, Category information, Color, Brand

[0141] Output: Clothing registration information

[0142] Step 2:

[0143] The terminal transmits the user input information to the server.

[0144] Specific operation:

[0145] The device acquires the clothing information and photos entered by the user and sends them to the server.

[0146] Input: User-entered information, photo

[0147] Output: Data sent to the server

[0148] Step 3:

[0149] The server analyzes the received photos using an image analysis algorithm and extracts attribute information.

[0150] Specific operation:

[0151] The server analyzes the received photos using image analysis algorithms such as TensorFlow and OpenCV to extract clothing attribute information (e.g., color, shape, material).

[0152] Input: Received photo

[0153] Output: Extracted attribute information

[0154] Step 4:

[0155] The server integrates the manually input information and the extracted attribute information and stores the information in a database.

[0156] Specific operation:

[0157] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0158] Input: Manually entered information, automatically extracted attribute information

[0159] Output: Clothing data stored in a database

[0160] Step 5:

[0161] The server connects to an external clothing database to obtain the latest clothing data.

[0162] Specific operation:

[0163] The server periodically calls the API endpoint of an external online fashion store to retrieve the latest clothing data.

[0164] Input: API endpoint of external clothing database

[0165] Output: Latest clothing data obtained

[0166] Step 6:

[0167] The server stores the acquired external data in an internal database and organizes it in a searchable format.

[0168] Specific operation:

[0169] The server assigns category and attribute information to the acquired data and stores it in an internal database.

[0170] Input: Retrieved external data

[0171] Output: A cleaned database

[0172] Step 7:

[0173] Users send coordination requests through the app.

[0174] Specific operation:

[0175] Users tap the "Suggest an outfit" button in the app and enter information such as their mood, date, weather, and temperature.

[0176] Input: Mood, Date, Weather, Temperature

[0177] Output: Request data

[0178] Step 8:

[0179] The terminal transmits the request data to the server.

[0180] Specific operation:

[0181] The terminal transmits the input request data to the server.

[0182] Input: Request data

[0183] Output: Request data sent to the server

[0184] Step 9:

[0185] The server searches the user's registered clothing data and external clothing data to generate the optimal coordination.

[0186] Specific operation:

[0187] The server searches the user's registered clothing database and an external clothing database based on the user's mood, weather, and temperature to generate the optimal outfit. For example, if the weather is sunny and the temperature is 20 degrees, it will suggest a casual outfit.

[0188] Input: User's registered clothing data, external clothing data, request data

[0189] Output: Generated coordinates

[0190] Step 10:

[0191] The server sends the generated coordinates to the user's device in JSON format.

[0192] Specific operation:

[0193] The server generates coordinate information and sends it to the user's device in JSON format.

[0194] Input: Generated coordinates

[0195] Output: JSON format coordinate data

[0196] Step 11:

[0197] The terminal displays the received coordinate information on the screen and notifies the user.

[0198] Specific operation:

[0199] The terminal displays the received coordinate information on the screen and notifies the user.

[0200] Input: JSON format coordinate data

[0201] Output: Displayed coordinates, user notification

[0202] Example prompt sentence:

[0203] "I have a black T-shirt and blue jeans. Can you suggest an outfit for a casual, sunny, 20-degree day?"

[0204] (Application example 1)

[0205] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0206] With conventional fashion coordination systems, users could only receive coordination suggestions based on the clothes they already own, making it difficult to get an idea of ​​how to coordinate a new item. Furthermore, the system was unable to fully consider in-store inventory, resulting in a suboptimal shopping experience. This prevented users from efficiently considering their outfits and delayed their purchasing decisions.

[0207] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0208] In this invention, the server includes means for allowing a user to register photos of their own clothing, means for transmitting information such as mood, date, weather, and temperature input by the user to the server, means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, means for transmitting the generated outfit from the server to the user's terminal, means for displaying the outfit received by the user's terminal, means for scanning devices installed in the store to obtain store inventory information, and means for combining the obtained inventory information with information on the user's clothing to propose an optimal outfit. This makes it possible to propose optimal outfits in real time that combine store inventory with the user's own clothing.

[0209] "A means for users to register photos of clothing they own" refers to a function that allows users to take pictures of clothing they own using a smartphone or digital camera, and register those pictures in a database.

[0210] "Means for sending information entered by the user, such as mood, date, weather, and temperature, to the server" refers to a function that allows a user to use a device such as a smartphone or computer to enter information such as their current mood, a specified date, weather, and temperature, and then send that information to the server.

[0211] "Means for the server to search the database of clothing owned by the user and an external clothing database to generate the optimal coordination" refers to a function in which the server searches for clothing information registered in the user's database and clothing information obtained from an external database, and combines these to automatically generate the optimal fashion coordination.

[0212] "Means for transmitting the coordinated outfits generated from the server to the user's device" refers to a function for transmitting the fashion coordinated outfit information generated by the server to the user's device such as a smartphone or PC via the Internet or other communication means.

[0213] The "means for displaying the coordinated outfits received by the user terminal" is a function for visually displaying the fashion coordinated outfit information received by the user terminal from the server.

[0214] "Means for scanning devices installed in the store to obtain inventory information within the store" refers to a function that allows users to obtain inventory information within a store by scanning QR codes (registered trademark) or NFC tags installed within the store using a smartphone or other device.

[0215] "A means for proposing optimal coordination by combining acquired inventory information with information on clothing owned by the user" is a function that proposes fashion coordination that optimally combines inventory information from physical stores and information on clothing owned by the user registered.

[0216] The present invention relates to a system that allows users to register their own clothing and link with an external database to provide optimal fashion coordination. This system utilizes the user's smartphone or in-store devices to suggest optimal coordination in real time.

[0217] Hardware and software used

[0218] To realize this system, the following hardware and software are used.

[0219] Smartphone: Used by users to take photos of clothing and enter information.

[0220] Server: A central processing unit that communicates with the database, analyzes images, and proposes coordinated results. The API server is built using Node.js and Express.

[0221] Database: Used to store user clothing information and external database information. MongoDB is used.

[0222] Image analysis software: Uses OpenCV and TensorFlow to analyze clothing information registered by users.

[0223] IoT devices: Use QR codes and NFC tags to obtain in-store inventory information.

[0224] System operation procedure

[0225] 1. Clothing Registration:

[0226] Users use their smartphones to take photos of their clothing, and then input information such as the clothing category, color, and brand into the smartphone app.

[0227] The smartphone sends the photo and the entered information to the server.

[0228] The server analyzes the received photos using OpenCV and TensorFlow, automatically extracts clothing attribute information from the images, then combines it with manually entered information and stores the final clothing data in a MongoDB database.

[0229] 2. Get in-store inventory information:

[0230] Users can scan QR codes or NFC tags installed in stores with their smartphones to obtain store inventory information.

[0231] Inventory information is sent to the server via smartphone and stored in a database on the server side.

[0232] 3. Coordination suggestions:

[0233] Users tap the "Suggest outfits" button on the smartphone app and enter information such as their current mood, date, weather, and temperature, which is then sent to the server.

[0234] Based on the received information, the server searches the user's registered clothing database and the store's inventory database to generate the optimal outfit.

[0235] The coordinated outfits are sent from the server to the user's smartphone, where the user can check the suggested outfits on their smartphone.

[0236] Specific examples

[0237] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," the following specific process will occur:

[0238] User clothing registration: The user registers a black T-shirt and blue jeans. They take a photo and enter the information into the smartphone app.

[0239] In-store scanning: Users scan a QR code in the store with their smartphone to get availability information for white sneakers and a red jacket.

[0240] Outfit suggestion: The server selects a black T-shirt, blue jeans, and white sneakers and generates an outfit.

[0241] Coordination notification: Suggested coordination will be displayed on your smartphone.

[0242] Example prompt sentence:

[0243] Please suggest the best fashion coordination based on the user's clothing data. The user owns a "black T-shirt" and "blue jeans." The store's inventory data is "white sneakers" and "red jacket." The user wants a "casual" style.

[0244] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0245] Step 1:

[0246] Users register photos of their clothing. They take photos of the clothing using their smartphone camera and enter information such as clothing category, color, and brand into the smartphone app. The entered information and photo are sent from the smartphone to the server. The server uses OpenCV and TensorFlow to perform image analysis and extract clothing attribute information. The extracted attribute information is integrated with manually entered information, and the final clothing data is stored in a database.

[0247] Input: Manually enter information such as clothing photos, category, color, brand, etc.

[0248] Data processing: Image analysis (OpenCV, TensorFlow)

[0249] Output: Clothing data with integrated attribute information

[0250] Step 2:

[0251] Users scan QR codes or NFC tags installed in stores with their smartphones to obtain inventory information for the store. The information is then sent from the smartphone to a server, which stores it in a database.

[0252] Input: QR code or NFC tag scan data

[0253] Data processing: Analysis of scan data

[0254] Output: In-store inventory information

[0255] Step 3:

[0256] Users tap the "Suggest outfits" button on their smartphone app and enter information such as their current mood, date, weather, and temperature, which is then sent to the server.

[0257] Input: Mood, date, weather, temperature, and other information

[0258] Data processing: Format conversion of input information

[0259] Output: The formatted input information

[0260] Step 4:

[0261] Based on the received information, the server searches the user's registered clothing database and the acquired store's inventory database to generate the optimal outfit. It uses a generative AI model to find the best combination that meets the user's requirements.

[0262] Input: User mood, date, weather, temperature, user registered clothing database, store inventory database

[0263] Data processing: Search algorithms, combinatorial calculations using generative AI models

[0264] Output: Optimal Coordination

[0265] Step 5:

[0266] The server sends the generated coordinates to the user's smartphone in a data format such as JSON.

[0267] Input: Best outfit

[0268] Data processing: Data format conversion (JSON)

[0269] Output: JSON format coordinate data

[0270] Step 6:

[0271] The smartphone displays the received coordination information on its screen and notifies the user, who can then check the suggested coordination.

[0272] Input: JSON format coordinate data

[0273] Data processing: JSON parsing, visual display

[0274] Output: Visual representation of the coordinates

[0275] Through the above processing steps, the user can obtain the optimal fashion coordination in real time by combining in-store inventory with clothing that the user already owns.

[0276] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0277] The present invention combines an emotion engine with a system that allows users to register their own clothing and combines it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, an emotion engine, a server, and a database.

[0278] Clothing Registration

[0279] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0280] External clothing database integration

[0281] The server connects to an external clothing database (e.g., an online fashion store's database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[0282] Emotion recognition by emotion engine

[0283] The user device is equipped with an emotion engine that uses sensors such as cameras and microphones to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. This emotional information is used as input when suggesting outfits to the user.

[0284] Coordination suggestions

[0285] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotions and sends them to the server along with their current mood, date, weather, temperature, etc. The server then searches the user's registered clothing database and an external clothing database based on the received information to generate the optimal outfit. For example, if a user selects "feeling casual," and the emotion engine recognizes that the user is feeling "joyful," and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize selecting items with a casual and positive vibe. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[0286] Coordination notification

[0287] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[0288] Specific examples

[0289] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects "joy," the following specific process will take place:

[0290] 1. User clothing registration

[0291] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0292] Device: Sends the photo and input information to the server.

[0293] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[0294] 2. External clothing database integration

[0295] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[0296] 3. Emotion recognition

[0297] On the device: Recognizes user emotions using a camera and microphone.

[0298] Emotion engine: Determines whether the user is feeling "joy."

[0299] 4. Coordination suggestions

[0300] User: Enters "casual," "sunny," and "20 degrees" into the app.

[0301] Device: Sends input information and emotion information to the server.

[0302] Server: Based on the input information and emotional information, it selects a black T-shirt, blue jeans, and white sneakers and generates a coordination.

[0303] 5. Coordination notification

[0304] Server: Sends the generated coordinates to the user's device.

[0305] Device: The suggested outfits are displayed on the screen and notified to the user.

[0306] This allows users to make the most of the clothes they own and easily create the perfect outfit that suits the weather, temperature, mood, and even emotion.

[0307] The processing flow will be explained below.

[0308] Step 1:

[0309] The user opens the smartphone app or website and taps the "Register Clothing" button to register the clothing item.

[0310] Step 2:

[0311] Users take a photo of their clothing and enter information such as category, color, brand, etc. For example, they can take a photo of a black Uniqlo T-shirt and enter "tops, black, Uniqlo."

[0312] Step 3:

[0313] The device sends the information entered by the user and the photograph taken to the server.

[0314] Step 4:

[0315] The server analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information (color, shape, etc.) from the images.

[0316] Step 5:

[0317] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0318] Step 6:

[0319] The server periodically sends requests to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing product data.

[0320] Step 7:

[0321] The external clothing data acquired by the server is stored in an internal database, and categories and attribute information are added to make it searchable.

[0322] Step 8:

[0323] The user taps the "Suggest an outfit" button on a smartphone app or website.

[0324] Step 9:

[0325] The emotion engine uses the camera and microphone to recognize the user's emotions. For example, if the user is smiling in front of the smartphone, the emotion engine will recognize "joy."

[0326] Step 10:

[0327] The user inputs information such as the mood of the day (casual, formal, etc.), date, weather, and temperature. For example, the user inputs information such as casual, sunny, and 20 degrees.

[0328] Step 11:

[0329] The terminal transmits the input information and the emotion information recognized by the emotion engine to the server.

[0330] Step 12:

[0331] The server searches the user's registered clothing database and an external clothing database, and generates the optimal outfit based on the input information (mood, weather, temperature, emotion, etc.) For example, if the user is in a "casual mood" and feeling "joyful," and inputs that the weather is "sunny" and the temperature is "20 degrees," the server will prioritize selecting items that have a casual and positive vibe.

[0332] Step 13:

[0333] The server sends the generated coordinates to the terminal in a data format such as JSON.

[0334] Step 14:

[0335] The device then displays the received outfit information on the screen and notifies the user, suggesting a combination of, for example, a black T-shirt, blue jeans, and white sneakers.

[0336] Example 2

[0337] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0338] Conventional fashion coordination suggestion systems do not take into account the user's emotions when making suggestions, making it difficult for users to find the appropriate outfit that matches their mood and emotions at the time. Furthermore, current systems that efficiently integrate clothing items in users' possession with clothing from online stores to make optimal suggestions are not sufficient. Therefore, the challenge is to automatically suggest the optimal fashion coordination that matches the user's emotions and mood.

[0339] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0340] In this invention, the server includes means for allowing users to register photos of their own clothing, means for transmitting information entered by the user such as mood, date, weather, and temperature to the server, means for acquiring emotion information using a user terminal equipped with an emotion engine that recognizes the user's emotions, and means for transmitting the emotion information to the server and using it to generate outfits. This makes it possible to propose optimal fashion coordination based on the user's emotions and mood.

[0341] "User" refers to an individual who uses the system, registering the clothing they own through a smartphone app or website and receiving coordination suggestions.

[0342] "Terminal" refers to devices used by users, such as computers, smartphones, and tablets, and has functions such as registering clothing, requesting coordination suggestions, and obtaining emotional data.

[0343] A "server" refers to a computer system that manages data for the entire system and processes coordinate generation, generating optimal coordinates based on data input from users and external data acquired.

[0344] "Database" refers to a data structure that stores clothing information registered by users and clothing data obtained from external sources and is organized so that it can be searched.

[0345] An "emotion engine" refers to software or hardware technology for recognizing a user's emotions, using sensors such as a camera or microphone to identify the user's emotional state.

[0346] "Coordination suggestions" refers to the function in which the server generates optimal fashion combinations based on the user's clothing data and external data, taking into account the user's emotions, mood, weather, temperature, etc., and notifies the user of these combinations.

[0347] "Clothing registration" refers to the process in which a user enters a photo of the clothing they own into the system and provides attribute information such as category, color, and brand to the system.

[0348] "Image analysis" refers to a technology in which a server analyzes photos of clothing sent by users and automatically extracts attribute information such as color, material, and design.

[0349] "External clothing database integration" refers to the process in which the server integrates with an external clothing database, periodically obtains the latest clothing data, and stores it in the internal database.

[0350] The present invention combines an emotion engine with a system that allows users to register their own clothing and combines it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, an emotion engine, a server, and a database.

[0351] Clothing Registration

[0352] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm (e.g., a general image analysis API) and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in an internal database.

[0353] External clothing database integration

[0354] The server connects to an external clothing database (e.g., an online fashion store database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[0355] Emotion recognition by emotion engine

[0356] The user's device is equipped with an emotion engine (e.g., a general emotion recognition API) that uses sensors such as a camera and microphone to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. This emotional information is used as input when suggesting outfits to the user.

[0357] Coordination suggestions

[0358] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotions and sends them to the server along with their current mood, date, weather, temperature, etc. The server then searches the user's registered clothing database and an external clothing database based on the received information to generate the optimal outfit. For example, if a user selects "feeling casual," and the emotion engine recognizes that the user is feeling "joyful," and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize selecting items with a casual and positive vibe. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[0359] Coordination notification

[0360] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[0361] Specific examples

[0362] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects "joy," the following specific process will occur:

[0363] 1. User clothing registration

[0364] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0365] Device: Sends the photo and input information to the server.

[0366] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[0367] 2. External clothing database integration

[0368] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[0369] 3. Emotion recognition

[0370] On-device: Uses a camera and microphone to recognize user emotions.

[0371] Emotion engine: Determines whether the user is feeling "joy."

[0372] 4. Coordination suggestions

[0373] User: Enters "casual," "sunny," and "20 degrees" into the app.

[0374] Terminal: Sends input information and emotion information to the server.

[0375] Server: Based on the input information and emotional information, it selects a black T-shirt, blue jeans, and white sneakers and generates an outfit.

[0376] 5. Coordination notification

[0377] Server: Sends the generated coordinates to the user device.

[0378] Device: The suggested outfits are displayed on the screen and notified to the user.

[0379] Examples of prompt statements

[0380] "Today I'm feeling casual, it's sunny, and the temperature is 20 degrees. What outfit would you suggest if you're feeling joy?"

[0381] This allows users to make the most of the clothes they own and easily create the perfect outfit that suits the weather, temperature, mood, and even emotion.

[0382] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0383] Step 1: Enter clothing information

[0384] Users can register their own clothing items by tapping the "Register Clothing" button and taking a photo of the item using a smartphone app or website. They can also manually enter information such as the clothing's category, color, and brand.

[0385] Input: Clothing photo, category, color, brand information

[0386] Output: Temporary data stored on the user's device

[0387] Step 2: Submit your clothing information

[0388] The device receives the photos and manually entered information registered by the user and sends them to the server, often using HTTP as the communication protocol.

[0389] Input: Photos of clothing registered by the user and manual input information

[0390] Output: Data sent to the server

[0391] Step 3: Image analysis and data storage

[0392] The server analyzes the received photos using an image analysis algorithm (e.g., a general image analysis API) and automatically extracts clothing attribute information (e.g., color, material, design, etc.). It then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in an internal database.

[0393] Input: Clothing photos sent to the server and manual input information

[0394] Output: Final clothing data stored in a database

[0395] Step 4: Link with external clothing database

[0396] The server periodically connects with an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing data. The obtained data is stored in an internal database and organized in a searchable format with categories and attribute information.

[0397] Input: Data obtained from an external clothing database

[0398] Output: Organized data stored in an internal database

[0399] Step 5: Obtaining emotion data

[0400] The device uses sensors such as a camera and microphone to capture user emotion data. For example, the front camera captures the user's facial expressions while using an app.

[0401] Input: User emotion data obtained by sensors

[0402] Output: Emotion data stored on the user's device

[0403] Step 6: Analyze the sentiment data

[0404] The emotion engine analyzes sensor data obtained from the camera and microphone, identifies the user's emotions (e.g., joy, sadness, anger, etc.), and stores the results as data.

[0405] Input: Emotion data obtained by the sensor

[0406] Output: Parsed emotion information

[0407] Step 7: Enter your coordination request

[0408] Users tap the "Suggest an outfit" button in the app and enter information such as their mood, date, weather, and temperature.

[0409] Input: Mood, date, weather, temperature, and other information

[0410] Output: Request information saved on the user's device

[0411] Step 8: Sending a coordination request and emotion data

[0412] The terminal transmits the input request information and the emotion data identified by the emotion engine to the server.

[0413] Input: Request information, analyzed emotion information

[0414] Output: Data sent to the server

[0415] Step 9: Coordinate Generation

[0416] The server searches the user's registered clothing database and an external clothing database based on the received request information and emotion data to generate the optimal outfit. For example, it selects casual clothing based on the conditions of "casual mood" and "joy," the weather being "sunny," and the temperature being "20 degrees."

[0417] Input: Request information, analyzed emotion information, clothing data from the database

[0418] Output: Generated coordinate data

[0419] Step 10: Coordination Notification

[0420] The server sends the generated coordinated outfits to the user's device in a data format such as JSON. The device then displays the received coordinated outfit information on the screen and notifies the user.

[0421] Input: Generated coordinate data

[0422] Output: Coordinate information displayed on the user's device

[0423] (Application example 2)

[0424] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0425] In recent years, there has been a demand for systems that can coordinate with the user's clothing in fashion coordination. However, existing systems do not take the user's emotions into consideration and lack coordination suggestions that combine the user's own clothing with store products. Therefore, a means is needed to provide optimal coordination that reflects the user's mood and emotions.

[0426] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0427] In this invention, the server includes a means for recognizing the user's emotions and having an emotion engine used when generating outfits, a means for linking with the product database of the physical store to suggest outfits, and a means for periodically acquiring the latest clothing data from an external clothing database and saving it in a searchable state. This makes it possible to respond to the user's mood and emotions and provide optimal outfits that combine products from the physical store with the user's own clothing.

[0428] "A means equipped with an emotion engine that recognizes the user's emotions and is used when generating outfits" refers to technology that analyzes the user's emotions from biometric information such as facial expressions and voice, and reflects the results of that analysis in outfit suggestions.

[0429] "A means of making coordination suggestions in collaboration with the product database of a physical store" is a technology that links with product data held by a physical store and, based on that data, makes coordination suggestions that combine clothing owned by the user with products in the store.

[0430] "Means for periodically obtaining the latest clothing data from external clothing databases and saving it in a searchable state" refers to technology that obtains the latest clothing information from external databases such as online fashion stores, saves it in an internal database, and makes it available for searches and suggestions.

[0431] "A means for users to register photos of clothing they own" is a function that allows users to take photos of clothing they own and register them in the system.

[0432] "Means for sending information entered by the user, such as mood, date, weather, and temperature, to the server" refers to technology that sends information entered by the user to the server and uses that information to generate outfits.

[0433] "Means for the server to search the user's clothing database and an external clothing database to generate the optimal outfit" refers to a technology that simultaneously searches the user's clothing database and an external clothing database, and generates the optimal outfit for the user based on the results.

[0434] "Means for transmitting coordinate information generated from the server to the user's terminal" refers to a technique for transmitting coordinate information generated by the server to the user's terminal.

[0435] "Means for displaying coordinated items received by the user's terminal" refers to a function for displaying coordinated items received by the user's terminal on a display or the like to inform the user.

[0436] This invention is a system that allows users to manage their own clothing and suggests optimal outfits based on their mood, emotions, and environmental information. The system is composed of a user terminal, an emotion engine, a server, and a database.

[0437] User clothing registration

[0438] Users use smart glasses or a smartphone app to register photos of their clothing. During the registration process, they manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The manually entered information and the automatically extracted attribute information are then combined and the final clothing data is stored in a database.

[0439] External database integration

[0440] The server periodically connects to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing data. The obtained data is stored in an internal database, where it is organized into a searchable format with categories and attribute information.

[0441] Emotion recognition and external environment data acquisition

[0442] The user's device is equipped with an emotion engine that uses sensors such as a camera and microphone to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. It also uses a weather API to obtain current weather and temperature data. This information is used as input when suggesting outfits to the user.

[0443] Coordination suggestions

[0444] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotion and sends it to the server along with information such as their current mood, date, weather, and temperature. The server then uses the received information to search the user's registered clothing database and external clothing databases to generate the optimal outfit. This allows users to make the most of their existing clothing and easily find the perfect outfit based on the weather, temperature, mood, and even their emotion.

[0445] Specific examples

[0446] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects that the user is "happy," the following specific process will occur:

[0447] 1. The user registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0448] 2. The user device sends the photo and input information to the server.

[0449] 3. The server extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[0450] 4. The user device uses a camera and microphone to recognize the user's emotions, and the emotion engine detects "joy."

[0451] 5. The user enters "casual," "sunny," and "20 degrees" into the app, and the input information and emotion information are sent to the server.

[0452] 6. The server generates an outfit by selecting a black T-shirt, blue jeans, and white sneakers based on the input information and emotional information.

[0453] 7. The server sends the generated coordinates to the user terminal, which displays the proposed coordinates on its screen and notifies the user.

[0454] Example prompt: "User emotion: joy, weather: sunny, temperature: 20 degrees, user clothing entry: black UNIQLO T-shirt."

[0455] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0456] Step 1:

[0457] Users use smart glasses or a smartphone app to register photos of their own clothing.

[0458] Input: A photo of the clothing taken by the user, along with information such as clothing category, color, and brand.

[0459] How it works: The device receives the photo and input information and sends it to the server.

[0460] Output: The server receives the clothing photos and information.

[0461] Step 2:

[0462] The server analyzes the received photos using an image analysis algorithm to extract attribute information about the clothing.

[0463] Input: User-submitted clothing photos.

[0464] How it works: Image analysis algorithms automatically extract attribute information such as clothing category, color, design, and brand.

[0465] Output: Extracted clothing attribute information.

[0466] Step 3:

[0467] The extracted attribute information is integrated with manually entered information, and the final clothing data is stored in a database.

[0468] Input: Manually entered information, attribute information extracted through image analysis.

[0469] How it works: The server consolidates this information and stores it in a database.

[0470] Output: Integrated clothing data.

[0471] Step 4:

[0472] The server connects to an external clothing database to obtain the latest clothing data.

[0473] Input: Access information for external clothing database.

[0474] Operation: The server accesses an external database to obtain the latest clothing data.

[0475] Output: Latest clothing data.

[0476] Step 5:

[0477] The acquired data is stored in an internal database, and categories and attribute information are added to it, making it searchable.

[0478] Input: The latest clothing data obtained.

[0479] How it works: The server organizes the data, assigns categories and attribute information, and stores it in an internal database.

[0480] Output: Organized, searchable clothing data.

[0481] Step 6:

[0482] The user device uses a camera and microphone to recognize the user's emotions, and the emotion engine analyzes that information.

[0483] Input: User's facial expression images, voice audio data.

[0484] How it works: The emotion engine analyzes this data to determine the user's emotional state.

[0485] Output: User sentiment information.

[0486] Step 7:

[0487] The user taps the "Suggest outfit" button in the app, enters emotional information, weather, mood, temperature, etc., and sends it to the server.

[0488] Input: User emotional information, weather, mood, temperature, etc.

[0489] Operation: The device sends this information to the server.

[0490] Output: Proposal request information received by the server.

[0491] Step 8:

[0492] Based on the information received by the server, the server searches the user's registered clothing database and an external clothing database to generate the optimal outfit.

[0493] Input: Proposal request information, clothing information from the database.

[0494] How it works: The server searches for clothing information and generates outfits based on emotional and environmental information.

[0495] Output: The generated coordinate information.

[0496] Step 9:

[0497] The server sends the generated coordinates to the user terminal.

[0498] Input: Generated coordinate information.

[0499] Operation: The server sends coordinate information to the terminal.

[0500] Output: Coordination information received by the user device.

[0501] Step 10:

[0502] The user terminal displays the received coordinate information on the screen and notifies the user.

[0503] Input: Received coordinate information.

[0504] Action: The device displays the information on the display and notifies you.

[0505] Output: Outfit suggestions displayed to the user.

[0506] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0507] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0508] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0509] [Second embodiment]

[0510] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0511] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0512] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0513] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0514] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0515] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0516] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0517] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0518] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0519] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0520] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0521] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0522] The present invention relates to a system that allows users to register their own clothing and combine it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, a server, and a database.

[0523] Clothing Registration

[0524] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0525] External clothing database integration

[0526] The server connects to an external clothing database (e.g., an online fashion store's database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[0527] Coordination suggestions

[0528] The user taps the "Suggest outfit" button in the app and enters information such as their current mood, date, weather, and temperature. This information is sent from the user's device to the server. The server uses the received information to search the user's registered clothing database and an external clothing database to generate the optimal outfit. For example, if the user selects "feeling casual" and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize casual items. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[0529] Coordination notification

[0530] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[0531] Specific examples

[0532] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," the following specific process will occur:

[0533] 1. User clothing registration

[0534] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0535] Device: Sends the photo and input information to the server.

[0536] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[0537] 2. External clothing database integration

[0538] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[0539] 3. Coordination suggestions

[0540] User: Enters "casual," "sunny," and "20 degrees" into the app.

[0541] Terminal: Sends input information to the server.

[0542] Server: Based on the input information, a black T-shirt, blue jeans, and white sneakers are selected and an outfit is generated.

[0543] 4. Coordination notification

[0544] Server: Sends the generated coordinates to the user's device.

[0545] Device: The suggested outfits are displayed on the screen and notified to the user.

[0546] This allows users to make the most of the clothes they already own and easily create the perfect outfit for the weather, temperature, and mood.

[0547] The processing flow will be explained below.

[0548] Step 1:

[0549] The user opens the smartphone app or website and taps the "Register Clothing" button to register the clothing item.

[0550] Step 2:

[0551] Users take a photo of their clothing and enter information such as category, color, brand, etc. For example, they can take a photo of a black Uniqlo T-shirt and enter "tops, black, Uniqlo."

[0552] Step 3:

[0553] The device sends the information entered by the user and the photograph taken to the server.

[0554] Step 4:

[0555] The server analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information (color, shape, etc.) from the images.

[0556] Step 5:

[0557] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0558] Step 6:

[0559] The server periodically sends requests to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing product data.

[0560] Step 7:

[0561] The external clothing data acquired by the server is stored in an internal database, and categories and attribute information are added to make it searchable.

[0562] Step 8:

[0563] The user taps the "Suggest an outfit" button on a smartphone app or website.

[0564] Step 9:

[0565] The user inputs information such as the mood of the day (casual, formal, etc.), date, weather, and temperature. For example, the user inputs information such as casual, sunny, and 20 degrees.

[0566] Step 10:

[0567] The terminal transmits the input information to the server.

[0568] Step 11:

[0569] The server searches the user's registered clothing database and an external clothing database, and generates the optimal outfit based on the input information (mood, weather, temperature, etc.).

[0570] Step 12:

[0571] The server sends the generated coordinates to the terminal in a data format such as JSON.

[0572] Step 13:

[0573] The device then displays the received outfit information on the screen and notifies the user, suggesting a combination of, for example, a black T-shirt, blue jeans, and white sneakers.

[0574] Example 1

[0575] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0576] One of the problems many people face today is the time and effort it takes to think about their daily outfits. They also struggle with how to efficiently combine and use the clothes they own. Furthermore, there are currently only a limited number of systems that automatically suggest optimal outfits that take into account external factors such as weather and temperature, and achieving this requires a lot of effort.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0578] In this invention, the server includes means for allowing a user to register images of their own clothing, means for transmitting information such as mood, date, weather, and temperature input by the user to the server, means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, means for transmitting the generated outfit from the server to the user's terminal, means for the user's terminal to display the outfit received, means for analyzing the image with an image analysis algorithm using the information input by the user and extracting attribute information, and means for linking with an external clothing database to periodically obtain the latest clothing data and save it in a searchable state. This allows users to effectively utilize their own clothing and effortlessly obtain optimal outfits that take external factors such as weather and temperature into consideration.

[0579] "User" refers to an individual who uses this system to register the clothing they own and receive coordination suggestions.

[0580] "Server" refers to the central computer system that receives information from users, links to a database, analyzes it, generates coordinates, and transmits the results to user terminals.

[0581] "Terminal" refers to a device operated by a user (e.g., smartphone, tablet, PC) that exchanges information with a server.

[0582] A "clothing database" refers to a database system that stores the user's clothing and the latest clothing information obtained from external sources, and manages it in a searchable format.

[0583] "Image analysis algorithm" refers to an algorithm that analyzes photos of clothing received by the server and extracts attribute information such as color, shape, and material.

[0584] "External clothing database" refers to a database that stores clothing information provided by online fashion stores or other external providers.

[0585] "Coordination" refers to the optimal fashion combination generated by combining the clothing data registered by the user with external clothing data.

[0586] "Attribute information" refers to detailed characteristic information about clothing, such as color, shape, material, and brand.

[0587] "Searchable" refers to a state in which the data in a database is organized so that it can be quickly searched and referenced based on specific criteria or queries.

[0588] "JSON format" is a data exchange format and refers to a lightweight data format used when exchanging coordination information between a server and a user device.

[0589] The present invention relates to a system that allows users to register their own clothing and combine it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, a server, and a database.

[0590] Clothing Registration

[0591] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm (e.g., TensorFlow, OpenCV) and automatically extracts clothing attribute information from the photos. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0592] Examples:

[0593] The user registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0594] The user's device sends the photo and input information to the server.

[0595] The server performs image analysis, extracts attribute information, integrates it with manually entered information, and stores it in a database.

[0596] External clothing database integration

[0597] The server connects to an external clothing database (e.g., an online fashion store's database) via API to periodically retrieve the latest clothing data. The retrieved data is stored in an internal database (e.g., MySQL, MongoDB), where it is organized into a searchable format with categories and attribute information added.

[0598] Examples:

[0599] The server retrieves the latest clothing product data from the external database and stores it in the internal database.

[0600] Coordination suggestions

[0601] Users tap the "Suggest outfits" button in the app and enter information such as their current mood, date, weather, and temperature. This information is sent from the user's device to the server. Based on the received information, the server searches the user's registered clothing database and external clothing databases to generate the optimal outfit.

[0602] Examples:

[0603] A user enters "casual," "sunny," and "20 degrees" into the app.

[0604] The user terminal sends the input information to the server.

[0605] Based on the input information, the server generates an outfit consisting of a black T-shirt, blue jeans, and white sneakers.

[0606] Coordination notification

[0607] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[0608] Examples:

[0609] The server sends the generated coordinates to the user terminal.

[0610] The user's device will display the suggested outfit on the screen and notify the user.

[0611] Example prompt sentence:

[0612] "I have a black T-shirt and blue jeans. Can you suggest an outfit for a casual, sunny, 20-degree day?"

[0613] This invention allows users to maximize the use of their existing clothing and easily find the perfect outfit for the weather, temperature, and mood. To achieve this, the user's device and server work together to perform image analysis and data processing based on the user's input. Furthermore, by linking with an external database, suggestions are made that take into account the latest fashion information.

[0614] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0615] Step 1:

[0616] Users register the clothing they own.

[0617] Specific operation:

[0618] Users open the smartphone app or website, access the "Clothing Registration" page, and take a photo of a black Uniqlo T-shirt and blue Levi's jeans.

[0619] Users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand along with a photo.

[0620] Input: Photo, Category information, Color, Brand

[0621] Output: Clothing registration information

[0622] Step 2:

[0623] The terminal transmits the user input information to the server.

[0624] Specific operation:

[0625] The device acquires the clothing information and photos entered by the user and sends them to the server.

[0626] Input: User-entered information, photo

[0627] Output: Data sent to the server

[0628] Step 3:

[0629] The server analyzes the received photos using an image analysis algorithm and extracts attribute information.

[0630] Specific operation:

[0631] The server analyzes the received photos using image analysis algorithms such as TensorFlow and OpenCV to extract clothing attribute information (e.g., color, shape, material).

[0632] Input: Received photo

[0633] Output: Extracted attribute information

[0634] Step 4:

[0635] The server integrates the manually input information and the extracted attribute information and stores the information in a database.

[0636] Specific operation:

[0637] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0638] Input: Manually entered information, automatically extracted attribute information

[0639] Output: Clothing data stored in a database

[0640] Step 5:

[0641] The server connects to an external clothing database to obtain the latest clothing data.

[0642] Specific operation:

[0643] The server periodically calls the API endpoint of an external online fashion store to retrieve the latest clothing data.

[0644] Input: API endpoint of external clothing database

[0645] Output: Latest clothing data obtained

[0646] Step 6:

[0647] The server stores the acquired external data in an internal database and organizes it in a searchable format.

[0648] Specific operation:

[0649] The server assigns category and attribute information to the acquired data and stores it in an internal database.

[0650] Input: Retrieved external data

[0651] Output: A cleaned database

[0652] Step 7:

[0653] Users send coordination requests through the app.

[0654] Specific operation:

[0655] Users tap the "Suggest an outfit" button in the app and enter information such as their mood, date, weather, and temperature.

[0656] Input: Mood, Date, Weather, Temperature

[0657] Output: Request data

[0658] Step 8:

[0659] The terminal transmits the request data to the server.

[0660] Specific operation:

[0661] The terminal transmits the input request data to the server.

[0662] Input: Request data

[0663] Output: Request data sent to the server

[0664] Step 9:

[0665] The server searches the user's registered clothing data and external clothing data to generate the optimal coordination.

[0666] Specific operation:

[0667] The server searches the user's registered clothing database and an external clothing database based on the user's mood, weather, and temperature to generate the optimal outfit. For example, if the weather is sunny and the temperature is 20 degrees, it will suggest a casual outfit.

[0668] Input: User's registered clothing data, external clothing data, request data

[0669] Output: Generated coordinates

[0670] Step 10:

[0671] The server sends the generated coordinates to the user's device in JSON format.

[0672] Specific operation:

[0673] The server generates coordinate information and sends it to the user's device in JSON format.

[0674] Input: Generated coordinates

[0675] Output: JSON format coordinate data

[0676] Step 11:

[0677] The terminal displays the received coordinate information on the screen and notifies the user.

[0678] Specific operation:

[0679] The terminal displays the received coordinate information on the screen and notifies the user.

[0680] Input: JSON format coordinate data

[0681] Output: Displayed coordinates, user notification

[0682] Example prompt sentence:

[0683] "I have a black T-shirt and blue jeans. Can you suggest an outfit for a casual, sunny, 20-degree day?"

[0684] (Application example 1)

[0685] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0686] With conventional fashion coordination systems, users could only receive coordination suggestions based on the clothes they already own, making it difficult to get an idea of ​​how to coordinate a new item. Furthermore, the system was unable to fully consider in-store inventory, resulting in a suboptimal shopping experience. This prevented users from efficiently considering their outfits and delayed their purchasing decisions.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0688] In this invention, the server includes means for allowing a user to register photos of their own clothing, means for transmitting information such as mood, date, weather, and temperature input by the user to the server, means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, means for transmitting the generated outfit from the server to the user's terminal, means for displaying the outfit received by the user's terminal, means for scanning devices installed in the store to obtain store inventory information, and means for combining the obtained inventory information with information on the user's clothing to propose an optimal outfit. This makes it possible to propose optimal outfits in real time that combine store inventory with the user's own clothing.

[0689] "A means for users to register photos of clothing they own" refers to a function that allows users to take pictures of clothing they own using a smartphone or digital camera, and register those pictures in a database.

[0690] "Means for sending information entered by the user, such as mood, date, weather, and temperature, to the server" refers to a function that allows a user to use a device such as a smartphone or computer to enter information such as their current mood, a specified date, weather, and temperature, and then send that information to the server.

[0691] "Means for the server to search the database of clothing owned by the user and an external clothing database to generate the optimal coordination" refers to a function in which the server searches for clothing information registered in the user's database and clothing information obtained from an external database, and combines these to automatically generate the optimal fashion coordination.

[0692] "Means for transmitting the coordinated outfits generated from the server to the user's device" refers to a function for transmitting the fashion coordinated outfit information generated by the server to the user's device such as a smartphone or PC via the Internet or other communication means.

[0693] The "means for displaying the coordinated outfits received by the user terminal" is a function for visually displaying the fashion coordinated outfit information received by the user terminal from the server.

[0694] "Means of scanning devices installed in stores to obtain in-store inventory information" refers to a function that allows users to obtain store inventory information by scanning QR codes or NFC tags installed in physical stores with a smartphone or other device.

[0695] "A means for proposing optimal coordination by combining acquired inventory information with information on clothing owned by the user" is a function that proposes fashion coordination that optimally combines inventory information from physical stores and information on clothing owned by the user registered.

[0696] The present invention relates to a system that allows users to register their own clothing and link with an external database to provide optimal fashion coordination. This system utilizes the user's smartphone or in-store devices to suggest optimal coordination in real time.

[0697] Hardware and software used

[0698] To realize this system, the following hardware and software are used.

[0699] Smartphone: Used by users to take photos of clothing and enter information.

[0700] Server: A central processing unit that communicates with the database, analyzes images, and proposes coordinated results. The API server is built using Node.js and Express.

[0701] Database: Used to store user clothing information and external database information. MongoDB is used.

[0702] Image analysis software: Uses OpenCV and TensorFlow to analyze clothing information registered by users.

[0703] IoT devices: Use QR codes and NFC tags to obtain in-store inventory information.

[0704] System operation procedure

[0705] 1. Clothing Registration:

[0706] Users use their smartphones to take photos of their clothing, and then input information such as the clothing category, color, and brand into the smartphone app.

[0707] The smartphone sends the photo and the entered information to the server.

[0708] The server analyzes the received photos using OpenCV and TensorFlow, automatically extracts clothing attribute information from the images, then combines it with manually entered information and stores the final clothing data in a MongoDB database.

[0709] 2. Get in-store inventory information:

[0710] Users can scan QR codes or NFC tags installed in stores with their smartphones to obtain store inventory information.

[0711] Inventory information is sent to the server via smartphone and stored in a database on the server side.

[0712] 3. Coordination suggestions:

[0713] Users tap the "Suggest outfits" button on the smartphone app and enter information such as their current mood, date, weather, and temperature, which is then sent to the server.

[0714] Based on the received information, the server searches the user's registered clothing database and the store's inventory database to generate the optimal outfit.

[0715] The coordinated outfits are sent from the server to the user's smartphone, where the user can check the suggested outfits on their smartphone.

[0716] Specific examples

[0717] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," the following specific process will occur:

[0718] User clothing registration: The user registers a black T-shirt and blue jeans. They take a photo and enter the information into the smartphone app.

[0719] In-store scanning: Users scan a QR code in the store with their smartphone to get availability information for white sneakers and a red jacket.

[0720] Outfit suggestion: The server selects a black T-shirt, blue jeans, and white sneakers and generates an outfit.

[0721] Coordination notification: Suggested coordination will be displayed on your smartphone.

[0722] Example prompt sentence:

[0723] Please suggest the best fashion coordination based on the user's clothing data. The user owns a "black T-shirt" and "blue jeans." The store's inventory data is "white sneakers" and "red jacket." The user wants a "casual" style.

[0724] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0725] Step 1:

[0726] Users register photos of their clothing. They take photos of the clothing using their smartphone camera and enter information such as clothing category, color, and brand into the smartphone app. The entered information and photo are sent from the smartphone to the server. The server uses OpenCV and TensorFlow to perform image analysis and extract clothing attribute information. The extracted attribute information is integrated with manually entered information, and the final clothing data is stored in a database.

[0727] Input: Manually enter information such as clothing photos, category, color, brand, etc.

[0728] Data processing: Image analysis (OpenCV, TensorFlow)

[0729] Output: Clothing data with integrated attribute information

[0730] Step 2:

[0731] Users scan QR codes or NFC tags installed in stores with their smartphones to obtain inventory information for the store. The information is then sent from the smartphone to a server, which stores it in a database.

[0732] Input: QR code or NFC tag scan data

[0733] Data processing: Analysis of scan data

[0734] Output: In-store inventory information

[0735] Step 3:

[0736] Users tap the "Suggest outfits" button on their smartphone app and enter information such as their current mood, date, weather, and temperature, which is then sent to the server.

[0737] Input: Mood, date, weather, temperature, and other information

[0738] Data processing: Format conversion of input information

[0739] Output: The formatted input information

[0740] Step 4:

[0741] Based on the received information, the server searches the user's registered clothing database and the acquired store's inventory database to generate the optimal outfit. It uses a generative AI model to find the best combination that meets the user's requirements.

[0742] Input: User mood, date, weather, temperature, user registered clothing database, store inventory database

[0743] Data processing: Search algorithms, combinatorial calculations using generative AI models

[0744] Output: Optimal Coordination

[0745] Step 5:

[0746] The server sends the generated coordinates to the user's smartphone in a data format such as JSON.

[0747] Input: Best outfit

[0748] Data processing: Data format conversion (JSON)

[0749] Output: JSON format coordinate data

[0750] Step 6:

[0751] The smartphone displays the received coordination information on its screen and notifies the user, who can then check the suggested coordination.

[0752] Input: JSON format coordinate data

[0753] Data processing: JSON parsing, visual display

[0754] Output: Visual representation of the coordinates

[0755] Through the above processing steps, the user can obtain the optimal fashion coordination in real time by combining in-store inventory with clothing that the user already owns.

[0756] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0757] The present invention combines an emotion engine with a system that allows users to register their own clothing and combines it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, an emotion engine, a server, and a database.

[0758] Clothing Registration

[0759] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0760] External clothing database integration

[0761] The server connects to an external clothing database (e.g., an online fashion store's database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[0762] Emotion recognition by emotion engine

[0763] The user device is equipped with an emotion engine that uses sensors such as cameras and microphones to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. This emotional information is used as input when suggesting outfits to the user.

[0764] Coordination suggestions

[0765] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotions and sends them to the server along with their current mood, date, weather, temperature, etc. The server then searches the user's registered clothing database and an external clothing database based on the received information to generate the optimal outfit. For example, if a user selects "feeling casual," and the emotion engine recognizes that the user is feeling "joyful," and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize selecting items with a casual and positive vibe. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[0766] Coordination notification

[0767] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[0768] Specific examples

[0769] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects "joy," the following specific process will take place:

[0770] 1. User clothing registration

[0771] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0772] Device: Sends the photo and input information to the server.

[0773] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[0774] 2. External clothing database integration

[0775] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[0776] 3. Emotion recognition

[0777] On the device: Recognizes user emotions using a camera and microphone.

[0778] Emotion engine: Determines whether the user is feeling "joy."

[0779] 4. Coordination suggestions

[0780] User: Enters "casual," "sunny," and "20 degrees" into the app.

[0781] Device: Sends input information and emotion information to the server.

[0782] Server: Based on the input information and emotional information, it selects a black T-shirt, blue jeans, and white sneakers and generates a coordination.

[0783] 5. Coordination notification

[0784] Server: Sends the generated coordinates to the user's device.

[0785] Device: The suggested outfits are displayed on the screen and notified to the user.

[0786] This allows users to make the most of the clothes they own and easily create the perfect outfit that suits the weather, temperature, mood, and even emotion.

[0787] The processing flow will be explained below.

[0788] Step 1:

[0789] The user opens the smartphone app or website and taps the "Register Clothing" button to register the clothing item.

[0790] Step 2:

[0791] Users take a photo of their clothing and enter information such as category, color, brand, etc. For example, they can take a photo of a black Uniqlo T-shirt and enter "tops, black, Uniqlo."

[0792] Step 3:

[0793] The device sends the information entered by the user and the photograph taken to the server.

[0794] Step 4:

[0795] The server analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information (color, shape, etc.) from the images.

[0796] Step 5:

[0797] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[0798] Step 6:

[0799] The server periodically sends requests to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing product data.

[0800] Step 7:

[0801] The external clothing data acquired by the server is stored in an internal database, and categories and attribute information are added to make it searchable.

[0802] Step 8:

[0803] The user taps the "Suggest an outfit" button on a smartphone app or website.

[0804] Step 9:

[0805] The emotion engine uses the camera and microphone to recognize the user's emotions. For example, if the user is smiling in front of the smartphone, the emotion engine will recognize "joy."

[0806] Step 10:

[0807] The user inputs information such as the mood of the day (casual, formal, etc.), date, weather, and temperature. For example, the user inputs information such as casual, sunny, and 20 degrees.

[0808] Step 11:

[0809] The terminal transmits the input information and the emotion information recognized by the emotion engine to the server.

[0810] Step 12:

[0811] The server searches the user's registered clothing database and an external clothing database, and generates the optimal outfit based on the input information (mood, weather, temperature, emotion, etc.) For example, if the user is in a "casual mood" and feeling "joyful," and inputs that the weather is "sunny" and the temperature is "20 degrees," the server will prioritize selecting items that have a casual and positive vibe.

[0812] Step 13:

[0813] The server sends the generated coordinates to the terminal in a data format such as JSON.

[0814] Step 14:

[0815] The device then displays the received outfit information on the screen and notifies the user, suggesting a combination of, for example, a black T-shirt, blue jeans, and white sneakers.

[0816] Example 2

[0817] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0818] Conventional fashion coordination suggestion systems do not take into account the user's emotions when making suggestions, making it difficult for users to find the appropriate outfit that matches their mood and emotions at the time. Furthermore, current systems that efficiently integrate clothing items in users' possession with clothing from online stores to make optimal suggestions are not sufficient. Therefore, the challenge is to automatically suggest the optimal fashion coordination that matches the user's emotions and mood.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0820] In this invention, the server includes means for allowing users to register photos of their own clothing, means for transmitting information entered by the user such as mood, date, weather, and temperature to the server, means for acquiring emotion information using a user terminal equipped with an emotion engine that recognizes the user's emotions, and means for transmitting the emotion information to the server and using it to generate outfits. This makes it possible to propose optimal fashion coordination based on the user's emotions and mood.

[0821] "User" refers to an individual who uses the system, registering the clothing they own through a smartphone app or website and receiving coordination suggestions.

[0822] "Terminal" refers to devices used by users, such as computers, smartphones, and tablets, and has functions such as registering clothing, requesting coordination suggestions, and obtaining emotional data.

[0823] A "server" refers to a computer system that manages data for the entire system and processes coordinate generation, generating optimal coordinates based on data input from users and external data acquired.

[0824] "Database" refers to a data structure that stores clothing information registered by users and clothing data obtained from external sources and is organized so that it can be searched.

[0825] An "emotion engine" refers to software or hardware technology for recognizing a user's emotions, using sensors such as a camera or microphone to identify the user's emotional state.

[0826] "Coordination suggestions" refers to the function in which the server generates optimal fashion combinations based on the user's clothing data and external data, taking into account the user's emotions, mood, weather, temperature, etc., and notifies the user of these combinations.

[0827] "Clothing registration" refers to the process in which a user enters a photo of the clothing they own into the system and provides attribute information such as category, color, and brand to the system.

[0828] "Image analysis" refers to a technology in which a server analyzes photos of clothing sent by users and automatically extracts attribute information such as color, material, and design.

[0829] "External clothing database integration" refers to the process in which the server integrates with an external clothing database, periodically obtains the latest clothing data, and stores it in the internal database.

[0830] The present invention combines an emotion engine with a system that allows users to register their own clothing and combines it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, an emotion engine, a server, and a database.

[0831] Clothing Registration

[0832] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm (e.g., a general image analysis API) and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in an internal database.

[0833] External clothing database integration

[0834] The server connects to an external clothing database (e.g., an online fashion store database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[0835] Emotion recognition by emotion engine

[0836] The user's device is equipped with an emotion engine (e.g., a general emotion recognition API) that uses sensors such as a camera and microphone to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. This emotional information is used as input when suggesting outfits to the user.

[0837] Coordination suggestions

[0838] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotions and sends them to the server along with their current mood, date, weather, temperature, etc. The server then searches the user's registered clothing database and an external clothing database based on the received information to generate the optimal outfit. For example, if a user selects "feeling casual," and the emotion engine recognizes that the user is feeling "joyful," and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize selecting items with a casual and positive vibe. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[0839] Coordination notification

[0840] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[0841] Specific examples

[0842] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects "joy," the following specific process will occur:

[0843] 1. User clothing registration

[0844] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0845] Device: Sends the photo and input information to the server.

[0846] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[0847] 2. External clothing database integration

[0848] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[0849] 3. Emotion recognition

[0850] On-device: Uses a camera and microphone to recognize user emotions.

[0851] Emotion engine: Determines whether the user is feeling "joy."

[0852] 4. Coordination suggestions

[0853] User: Enters "casual," "sunny," and "20 degrees" into the app.

[0854] Terminal: Sends input information and emotion information to the server.

[0855] Server: Based on the input information and emotional information, it selects a black T-shirt, blue jeans, and white sneakers and generates an outfit.

[0856] 5. Coordination notification

[0857] Server: Sends the generated coordinates to the user device.

[0858] Device: The suggested outfits are displayed on the screen and notified to the user.

[0859] Examples of prompt statements

[0860] "Today I'm feeling casual, it's sunny, and the temperature is 20 degrees. What outfit would you suggest if you're feeling joy?"

[0861] This allows users to make the most of the clothes they own and easily create the perfect outfit that suits the weather, temperature, mood, and even emotion.

[0862] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0863] Step 1: Enter clothing information

[0864] Users can register their own clothing items by tapping the "Register Clothing" button and taking a photo of the item using a smartphone app or website. They can also manually enter information such as the clothing's category, color, and brand.

[0865] Input: Clothing photo, category, color, brand information

[0866] Output: Temporary data stored on the user's device

[0867] Step 2: Submit your clothing information

[0868] The device receives the photos and manually entered information registered by the user and sends them to the server, often using HTTP as the communication protocol.

[0869] Input: Photos of clothing registered by the user and manual input information

[0870] Output: Data sent to the server

[0871] Step 3: Image analysis and data storage

[0872] The server analyzes the received photos using an image analysis algorithm (e.g., a general image analysis API) and automatically extracts clothing attribute information (e.g., color, material, design, etc.). It then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in an internal database.

[0873] Input: Clothing photos sent to the server and manual input information

[0874] Output: Final clothing data stored in a database

[0875] Step 4: Link with external clothing database

[0876] The server periodically connects with an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing data. The obtained data is stored in an internal database and organized in a searchable format with categories and attribute information.

[0877] Input: Data obtained from an external clothing database

[0878] Output: Organized data stored in an internal database

[0879] Step 5: Obtaining emotion data

[0880] The device uses sensors such as a camera and microphone to capture user emotion data. For example, the front camera captures the user's facial expressions while using an app.

[0881] Input: User emotion data obtained by sensors

[0882] Output: Emotion data stored on the user's device

[0883] Step 6: Analyze the sentiment data

[0884] The emotion engine analyzes sensor data obtained from the camera and microphone, identifies the user's emotions (e.g., joy, sadness, anger, etc.), and stores the results as data.

[0885] Input: Emotion data obtained by the sensor

[0886] Output: Parsed emotion information

[0887] Step 7: Enter your coordination request

[0888] Users tap the "Suggest an outfit" button in the app and enter information such as their mood, date, weather, and temperature.

[0889] Input: Mood, date, weather, temperature, and other information

[0890] Output: Request information saved on the user's device

[0891] Step 8: Sending a coordination request and emotion data

[0892] The terminal transmits the input request information and the emotion data identified by the emotion engine to the server.

[0893] Input: Request information, analyzed emotion information

[0894] Output: Data sent to the server

[0895] Step 9: Coordinate Generation

[0896] The server searches the user's registered clothing database and an external clothing database based on the received request information and emotion data to generate the optimal outfit. For example, it selects casual clothing based on the conditions of "casual mood" and "joy," the weather being "sunny," and the temperature being "20 degrees."

[0897] Input: Request information, analyzed emotion information, clothing data from the database

[0898] Output: Generated coordinate data

[0899] Step 10: Coordination Notification

[0900] The server sends the generated coordinated outfits to the user's device in a data format such as JSON. The device then displays the received coordinated outfit information on the screen and notifies the user.

[0901] Input: Generated coordinate data

[0902] Output: Coordinate information displayed on the user's device

[0903] (Application example 2)

[0904] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0905] In recent years, there has been a demand for systems that can coordinate with the user's clothing in fashion coordination. However, existing systems do not take the user's emotions into consideration and lack coordination suggestions that combine the user's own clothing with store products. Therefore, a means is needed to provide optimal coordination that reflects the user's mood and emotions.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0907] In this invention, the server includes a means for recognizing the user's emotions and having an emotion engine used when generating outfits, a means for linking with the product database of the physical store to suggest outfits, and a means for periodically acquiring the latest clothing data from an external clothing database and saving it in a searchable state. This makes it possible to respond to the user's mood and emotions and provide optimal outfits that combine products from the physical store with the user's own clothing.

[0908] "A means equipped with an emotion engine that recognizes the user's emotions and is used when generating outfits" refers to technology that analyzes the user's emotions from biometric information such as facial expressions and voice, and reflects the results of that analysis in outfit suggestions.

[0909] "A means of making coordination suggestions in collaboration with the product database of a physical store" is a technology that links with product data held by a physical store and, based on that data, makes coordination suggestions that combine clothing owned by the user with products in the store.

[0910] "Means for periodically obtaining the latest clothing data from external clothing databases and saving it in a searchable state" refers to technology that obtains the latest clothing information from external databases such as online fashion stores, saves it in an internal database, and makes it available for searches and suggestions.

[0911] "A means for users to register photos of clothing they own" is a function that allows users to take photos of clothing they own and register them in the system.

[0912] "Means for sending information entered by the user, such as mood, date, weather, and temperature, to the server" refers to technology that sends information entered by the user to the server and uses that information to generate outfits.

[0913] "Means for the server to search the user's clothing database and an external clothing database to generate the optimal outfit" refers to a technology that simultaneously searches the user's clothing database and an external clothing database, and generates the optimal outfit for the user based on the results.

[0914] "Means for transmitting coordinate information generated from the server to the user's terminal" refers to a technique for transmitting coordinate information generated by the server to the user's terminal.

[0915] "Means for displaying coordinated items received by the user's terminal" refers to a function for displaying coordinated items received by the user's terminal on a display or the like to inform the user.

[0916] This invention is a system that allows users to manage their own clothing and suggests optimal outfits based on their mood, emotions, and environmental information. The system is composed of a user terminal, an emotion engine, a server, and a database.

[0917] User clothing registration

[0918] Users use smart glasses or a smartphone app to register photos of their clothing. During the registration process, they manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The manually entered information and the automatically extracted attribute information are then combined and the final clothing data is stored in a database.

[0919] External database integration

[0920] The server periodically connects to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing data. The obtained data is stored in an internal database, where it is organized into a searchable format with categories and attribute information.

[0921] Emotion recognition and external environment data acquisition

[0922] The user's device is equipped with an emotion engine that uses sensors such as a camera and microphone to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. It also uses a weather API to obtain current weather and temperature data. This information is used as input when suggesting outfits to the user.

[0923] Coordination suggestions

[0924] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotion and sends it to the server along with information such as their current mood, date, weather, and temperature. The server then uses the received information to search the user's registered clothing database and external clothing databases to generate the optimal outfit. This allows users to make the most of their existing clothing and easily find the perfect outfit based on the weather, temperature, mood, and even their emotion.

[0925] Specific examples

[0926] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects that the user is "happy," the following specific process will occur:

[0927] 1. The user registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[0928] 2. The user device sends the photo and input information to the server.

[0929] 3. The server extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[0930] 4. The user device uses a camera and microphone to recognize the user's emotions, and the emotion engine detects "joy."

[0931] 5. The user enters "casual," "sunny," and "20 degrees" into the app, and the input information and emotion information are sent to the server.

[0932] 6. The server generates an outfit by selecting a black T-shirt, blue jeans, and white sneakers based on the input information and emotional information.

[0933] 7. The server sends the generated coordinates to the user terminal, which displays the proposed coordinates on its screen and notifies the user.

[0934] Example prompt: "User emotion: joy, weather: sunny, temperature: 20 degrees, user clothing entry: black UNIQLO T-shirt."

[0935] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0936] Step 1:

[0937] Users use smart glasses or a smartphone app to register photos of their own clothing.

[0938] Input: A photo of the clothing taken by the user, along with information such as clothing category, color, and brand.

[0939] How it works: The device receives the photo and input information and sends it to the server.

[0940] Output: The server receives the clothing photos and information.

[0941] Step 2:

[0942] The server analyzes the received photos using an image analysis algorithm to extract attribute information about the clothing.

[0943] Input: User-submitted clothing photos.

[0944] How it works: Image analysis algorithms automatically extract attribute information such as clothing category, color, design, and brand.

[0945] Output: Extracted clothing attribute information.

[0946] Step 3:

[0947] The extracted attribute information is integrated with manually entered information, and the final clothing data is stored in a database.

[0948] Input: Manually entered information, attribute information extracted through image analysis.

[0949] How it works: The server consolidates this information and stores it in a database.

[0950] Output: Integrated clothing data.

[0951] Step 4:

[0952] The server connects to an external clothing database to obtain the latest clothing data.

[0953] Input: Access information for external clothing database.

[0954] Operation: The server accesses an external database to obtain the latest clothing data.

[0955] Output: Latest clothing data.

[0956] Step 5:

[0957] The acquired data is stored in an internal database, and categories and attribute information are added to it, making it searchable.

[0958] Input: The latest clothing data obtained.

[0959] How it works: The server organizes the data, assigns categories and attribute information, and stores it in an internal database.

[0960] Output: Organized, searchable clothing data.

[0961] Step 6:

[0962] The user device uses a camera and microphone to recognize the user's emotions, and the emotion engine analyzes that information.

[0963] Input: User's facial expression images, voice audio data.

[0964] How it works: The emotion engine analyzes this data to determine the user's emotional state.

[0965] Output: User sentiment information.

[0966] Step 7:

[0967] The user taps the "Suggest outfit" button in the app, enters emotional information, weather, mood, temperature, etc., and sends it to the server.

[0968] Input: User emotional information, weather, mood, temperature, etc.

[0969] Operation: The device sends this information to the server.

[0970] Output: Proposal request information received by the server.

[0971] Step 8:

[0972] Based on the information received by the server, the server searches the user's registered clothing database and an external clothing database to generate the optimal outfit.

[0973] Input: Proposal request information, clothing information from the database.

[0974] How it works: The server searches for clothing information and generates outfits based on emotional and environmental information.

[0975] Output: The generated coordinate information.

[0976] Step 9:

[0977] The server sends the generated coordinates to the user terminal.

[0978] Input: Generated coordinate information.

[0979] Operation: The server sends coordinate information to the terminal.

[0980] Output: Coordination information received by the user device.

[0981] Step 10:

[0982] The user terminal displays the received coordinate information on the screen and notifies the user.

[0983] Input: Received coordinate information.

[0984] Action: The device displays the information on the display and notifies you.

[0985] Output: Outfit suggestions displayed to the user.

[0986] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0987] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0988] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0989] [Third embodiment]

[0990] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0991] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0992] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0993] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0994] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0995] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0996] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0997] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0998] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0999] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1000] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1001] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1002] The present invention relates to a system that allows users to register their own clothing and combine it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, a server, and a database.

[1003] Clothing Registration

[1004] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1005] External clothing database integration

[1006] The server connects to an external clothing database (e.g., an online fashion store's database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[1007] Coordination suggestions

[1008] The user taps the "Suggest outfit" button in the app and enters information such as their current mood, date, weather, and temperature. This information is sent from the user's device to the server. The server uses the received information to search the user's registered clothing database and an external clothing database to generate the optimal outfit. For example, if the user selects "feeling casual" and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize casual items. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[1009] Coordination notification

[1010] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[1011] Specific examples

[1012] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," the following specific process will occur:

[1013] 1. User clothing registration

[1014] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1015] Device: Sends the photo and input information to the server.

[1016] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[1017] 2. External clothing database integration

[1018] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[1019] 3. Coordination suggestions

[1020] User: Enters "casual," "sunny," and "20 degrees" into the app.

[1021] Terminal: Sends input information to the server.

[1022] Server: Based on the input information, a black T-shirt, blue jeans, and white sneakers are selected and an outfit is generated.

[1023] 4. Coordination notification

[1024] Server: Sends the generated coordinates to the user's device.

[1025] Device: The suggested outfits are displayed on the screen and notified to the user.

[1026] This allows users to make the most of the clothes they already own and easily create the perfect outfit for the weather, temperature, and mood.

[1027] The processing flow will be explained below.

[1028] Step 1:

[1029] The user opens the smartphone app or website and taps the "Register Clothing" button to register the clothing item.

[1030] Step 2:

[1031] Users take a photo of their clothing and enter information such as category, color, brand, etc. For example, they can take a photo of a black Uniqlo T-shirt and enter "tops, black, Uniqlo."

[1032] Step 3:

[1033] The device sends the information entered by the user and the photograph taken to the server.

[1034] Step 4:

[1035] The server analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information (color, shape, etc.) from the images.

[1036] Step 5:

[1037] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1038] Step 6:

[1039] The server periodically sends requests to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing product data.

[1040] Step 7:

[1041] The external clothing data acquired by the server is stored in an internal database, and categories and attribute information are added to make it searchable.

[1042] Step 8:

[1043] The user taps the "Suggest an outfit" button on a smartphone app or website.

[1044] Step 9:

[1045] The user inputs information such as the mood of the day (casual, formal, etc.), date, weather, and temperature. For example, the user inputs information such as casual, sunny, and 20 degrees.

[1046] Step 10:

[1047] The terminal transmits the input information to the server.

[1048] Step 11:

[1049] The server searches the user's registered clothing database and an external clothing database, and generates the optimal outfit based on the input information (mood, weather, temperature, etc.).

[1050] Step 12:

[1051] The server sends the generated coordinates to the terminal in a data format such as JSON.

[1052] Step 13:

[1053] The device then displays the received outfit information on the screen and notifies the user, suggesting a combination of, for example, a black T-shirt, blue jeans, and white sneakers.

[1054] Example 1

[1055] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1056] One of the problems many people face today is the time and effort it takes to think about their daily outfits. They also struggle with how to efficiently combine and use the clothes they own. Furthermore, there are currently only a limited number of systems that automatically suggest optimal outfits that take into account external factors such as weather and temperature, and achieving this requires a lot of effort.

[1057] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1058] In this invention, the server includes means for allowing a user to register images of their own clothing, means for transmitting information such as mood, date, weather, and temperature input by the user to the server, means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, means for transmitting the generated outfit from the server to the user's terminal, means for the user's terminal to display the outfit received, means for analyzing the image with an image analysis algorithm using the information input by the user and extracting attribute information, and means for linking with an external clothing database to periodically obtain the latest clothing data and save it in a searchable state. This allows users to effectively utilize their own clothing and effortlessly obtain optimal outfits that take external factors such as weather and temperature into consideration.

[1059] "User" refers to an individual who uses this system to register the clothing they own and receive coordination suggestions.

[1060] "Server" refers to the central computer system that receives information from users, links to a database, analyzes it, generates coordinates, and transmits the results to user terminals.

[1061] "Terminal" refers to a device operated by a user (e.g., smartphone, tablet, PC) that exchanges information with a server.

[1062] A "clothing database" refers to a database system that stores the user's clothing and the latest clothing information obtained from external sources, and manages it in a searchable format.

[1063] "Image analysis algorithm" refers to an algorithm that analyzes photos of clothing received by the server and extracts attribute information such as color, shape, and material.

[1064] "External clothing database" refers to a database that stores clothing information provided by online fashion stores or other external providers.

[1065] "Coordination" refers to the optimal fashion combination generated by combining the clothing data registered by the user with external clothing data.

[1066] "Attribute information" refers to detailed characteristic information about clothing, such as color, shape, material, and brand.

[1067] "Searchable" refers to a state in which the data in a database is organized so that it can be quickly searched and referenced based on specific criteria or queries.

[1068] "JSON format" is a data exchange format and refers to a lightweight data format used when exchanging coordination information between a server and a user device.

[1069] The present invention relates to a system that allows users to register their own clothing and combine it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, a server, and a database.

[1070] Clothing Registration

[1071] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm (e.g., TensorFlow, OpenCV) and automatically extracts clothing attribute information from the photos. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1072] Examples:

[1073] The user registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1074] The user's device sends the photo and input information to the server.

[1075] The server performs image analysis, extracts attribute information, integrates it with manually entered information, and stores it in a database.

[1076] External clothing database integration

[1077] The server connects to an external clothing database (e.g., an online fashion store's database) via API to periodically retrieve the latest clothing data. The retrieved data is stored in an internal database (e.g., MySQL, MongoDB), where it is organized into a searchable format with categories and attribute information added.

[1078] Examples:

[1079] The server retrieves the latest clothing product data from the external database and stores it in the internal database.

[1080] Coordination suggestions

[1081] Users tap the "Suggest outfits" button in the app and enter information such as their current mood, date, weather, and temperature. This information is sent from the user's device to the server. Based on the received information, the server searches the user's registered clothing database and external clothing databases to generate the optimal outfit.

[1082] Examples:

[1083] A user enters "casual," "sunny," and "20 degrees" into the app.

[1084] The user terminal sends the input information to the server.

[1085] Based on the input information, the server generates an outfit consisting of a black T-shirt, blue jeans, and white sneakers.

[1086] Coordination notification

[1087] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[1088] Examples:

[1089] The server sends the generated coordinates to the user terminal.

[1090] The user's device will display the suggested outfit on the screen and notify the user.

[1091] Example prompt sentence:

[1092] "I have a black T-shirt and blue jeans. Can you suggest an outfit for a casual, sunny, 20-degree day?"

[1093] This invention allows users to maximize the use of their existing clothing and easily find the perfect outfit for the weather, temperature, and mood. To achieve this, the user's device and server work together to perform image analysis and data processing based on the user's input. Furthermore, by linking with an external database, suggestions are made that take into account the latest fashion information.

[1094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1095] Step 1:

[1096] Users register the clothing they own.

[1097] Specific operation:

[1098] Users open the smartphone app or website, access the "Clothing Registration" page, and take a photo of a black Uniqlo T-shirt and blue Levi's jeans.

[1099] Users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand along with a photo.

[1100] Input: Photo, Category information, Color, Brand

[1101] Output: Clothing registration information

[1102] Step 2:

[1103] The terminal transmits the user input information to the server.

[1104] Specific operation:

[1105] The device acquires the clothing information and photos entered by the user and sends them to the server.

[1106] Input: User-entered information, photo

[1107] Output: Data sent to the server

[1108] Step 3:

[1109] The server analyzes the received photos using an image analysis algorithm and extracts attribute information.

[1110] Specific operation:

[1111] The server analyzes the received photos using image analysis algorithms such as TensorFlow and OpenCV to extract clothing attribute information (e.g., color, shape, material).

[1112] Input: Received photo

[1113] Output: Extracted attribute information

[1114] Step 4:

[1115] The server integrates the manually input information and the extracted attribute information and stores the information in a database.

[1116] Specific operation:

[1117] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1118] Input: Manually entered information, automatically extracted attribute information

[1119] Output: Clothing data stored in a database

[1120] Step 5:

[1121] The server connects to an external clothing database to obtain the latest clothing data.

[1122] Specific operation:

[1123] The server periodically calls the API endpoint of an external online fashion store to retrieve the latest clothing data.

[1124] Input: API endpoint of external clothing database

[1125] Output: Latest clothing data obtained

[1126] Step 6:

[1127] The server stores the acquired external data in an internal database and organizes it in a searchable format.

[1128] Specific operation:

[1129] The server assigns category and attribute information to the acquired data and stores it in an internal database.

[1130] Input: Retrieved external data

[1131] Output: A cleaned database

[1132] Step 7:

[1133] Users send coordination requests through the app.

[1134] Specific operation:

[1135] Users tap the "Suggest an outfit" button in the app and enter information such as their mood, date, weather, and temperature.

[1136] Input: Mood, Date, Weather, Temperature

[1137] Output: Request data

[1138] Step 8:

[1139] The terminal transmits the request data to the server.

[1140] Specific operation:

[1141] The terminal transmits the input request data to the server.

[1142] Input: Request data

[1143] Output: Request data sent to the server

[1144] Step 9:

[1145] The server searches the user's registered clothing data and external clothing data to generate the optimal coordination.

[1146] Specific operation:

[1147] The server searches the user's registered clothing database and an external clothing database based on the user's mood, weather, and temperature to generate the optimal outfit. For example, if the weather is sunny and the temperature is 20 degrees, it will suggest a casual outfit.

[1148] Input: User's registered clothing data, external clothing data, request data

[1149] Output: Generated coordinates

[1150] Step 10:

[1151] The server sends the generated coordinates to the user's device in JSON format.

[1152] Specific operation:

[1153] The server generates coordinate information and sends it to the user's device in JSON format.

[1154] Input: Generated coordinates

[1155] Output: JSON format coordinate data

[1156] Step 11:

[1157] The terminal displays the received coordinate information on the screen and notifies the user.

[1158] Specific operation:

[1159] The terminal displays the received coordinate information on the screen and notifies the user.

[1160] Input: JSON format coordinate data

[1161] Output: Displayed coordinates, user notification

[1162] Example prompt sentence:

[1163] "I have a black T-shirt and blue jeans. Can you suggest an outfit for a casual, sunny, 20-degree day?"

[1164] (Application example 1)

[1165] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1166] With conventional fashion coordination systems, users could only receive coordination suggestions based on the clothes they already own, making it difficult to get an idea of ​​how to coordinate a new item. Furthermore, the system was unable to fully consider in-store inventory, resulting in a suboptimal shopping experience. This prevented users from efficiently considering their outfits and delayed their purchasing decisions.

[1167] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1168] In this invention, the server includes means for allowing a user to register photos of their own clothing, means for transmitting information such as mood, date, weather, and temperature input by the user to the server, means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, means for transmitting the generated outfit from the server to the user's terminal, means for displaying the outfit received by the user's terminal, means for scanning devices installed in the store to obtain store inventory information, and means for combining the obtained inventory information with information on the user's clothing to propose an optimal outfit. This makes it possible to propose optimal outfits in real time that combine store inventory with the user's own clothing.

[1169] "A means for users to register photos of clothing they own" refers to a function that allows users to take pictures of clothing they own using a smartphone or digital camera, and register those pictures in a database.

[1170] "Means for sending information entered by the user, such as mood, date, weather, and temperature, to the server" refers to a function that allows a user to use a device such as a smartphone or computer to enter information such as their current mood, a specified date, weather, and temperature, and then send that information to the server.

[1171] "Means for the server to search the database of clothing owned by the user and an external clothing database to generate the optimal coordination" refers to a function in which the server searches for clothing information registered in the user's database and clothing information obtained from an external database, and combines these to automatically generate the optimal fashion coordination.

[1172] "Means for transmitting the coordinated outfits generated from the server to the user's device" refers to a function for transmitting the fashion coordinated outfit information generated by the server to the user's device such as a smartphone or PC via the Internet or other communication means.

[1173] The "means for displaying the coordinated outfits received by the user terminal" is a function for visually displaying the fashion coordinated outfit information received by the user terminal from the server.

[1174] "Means of scanning devices installed in stores to obtain in-store inventory information" refers to a function that allows users to obtain store inventory information by scanning QR codes or NFC tags installed in physical stores with a smartphone or other device.

[1175] "A means for proposing optimal coordination by combining acquired inventory information with information on clothing owned by the user" is a function that proposes fashion coordination that optimally combines inventory information from physical stores and information on clothing owned by the user registered.

[1176] The present invention relates to a system that allows users to register their own clothing and link with an external database to provide optimal fashion coordination. This system utilizes the user's smartphone or in-store devices to suggest optimal coordination in real time.

[1177] Hardware and software used

[1178] To realize this system, the following hardware and software are used.

[1179] Smartphone: Used by users to take photos of clothing and enter information.

[1180] Server: A central processing unit that communicates with the database, analyzes images, and proposes coordinated results. The API server is built using Node.js and Express.

[1181] Database: Used to store user clothing information and external database information. MongoDB is used.

[1182] Image analysis software: Uses OpenCV and TensorFlow to analyze clothing information registered by users.

[1183] IoT devices: Use QR codes and NFC tags to obtain in-store inventory information.

[1184] System operation procedure

[1185] 1. Clothing Registration:

[1186] Users use their smartphones to take photos of their clothing, and then input information such as the clothing category, color, and brand into the smartphone app.

[1187] The smartphone sends the photo and the entered information to the server.

[1188] The server analyzes the received photos using OpenCV and TensorFlow, automatically extracts clothing attribute information from the images, then combines it with manually entered information and stores the final clothing data in a MongoDB database.

[1189] 2. Get in-store inventory information:

[1190] Users can scan QR codes or NFC tags installed in stores with their smartphones to obtain store inventory information.

[1191] Inventory information is sent to the server via smartphone and stored in a database on the server side.

[1192] 3. Coordination suggestions:

[1193] Users tap the "Suggest outfits" button on the smartphone app and enter information such as their current mood, date, weather, and temperature, which is then sent to the server.

[1194] Based on the received information, the server searches the user's registered clothing database and the store's inventory database to generate the optimal outfit.

[1195] The coordinated outfits are sent from the server to the user's smartphone, where the user can check the suggested outfits on their smartphone.

[1196] Specific examples

[1197] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," the following specific process will occur:

[1198] User clothing registration: The user registers a black T-shirt and blue jeans. They take a photo and enter the information into the smartphone app.

[1199] In-store scanning: Users scan a QR code in the store with their smartphone to get availability information for white sneakers and a red jacket.

[1200] Outfit suggestion: The server selects a black T-shirt, blue jeans, and white sneakers and generates an outfit.

[1201] Coordination notification: Suggested coordination will be displayed on your smartphone.

[1202] Example prompt sentence:

[1203] Please suggest the best fashion coordination based on the user's clothing data. The user owns a "black T-shirt" and "blue jeans." The store's inventory data is "white sneakers" and "red jacket." The user wants a "casual" style.

[1204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1205] Step 1:

[1206] Users register photos of their clothing. They take photos of the clothing using their smartphone camera and enter information such as clothing category, color, and brand into the smartphone app. The entered information and photo are sent from the smartphone to the server. The server uses OpenCV and TensorFlow to perform image analysis and extract clothing attribute information. The extracted attribute information is integrated with manually entered information, and the final clothing data is stored in a database.

[1207] Input: Manually enter information such as clothing photos, category, color, brand, etc.

[1208] Data processing: Image analysis (OpenCV, TensorFlow)

[1209] Output: Clothing data with integrated attribute information

[1210] Step 2:

[1211] Users scan QR codes or NFC tags installed in stores with their smartphones to obtain inventory information for the store. The information is then sent from the smartphone to a server, which stores it in a database.

[1212] Input: QR code or NFC tag scan data

[1213] Data processing: Analysis of scan data

[1214] Output: In-store inventory information

[1215] Step 3:

[1216] Users tap the "Suggest outfits" button on their smartphone app and enter information such as their current mood, date, weather, and temperature, which is then sent to the server.

[1217] Input: Mood, date, weather, temperature, and other information

[1218] Data processing: Format conversion of input information

[1219] Output: The formatted input information

[1220] Step 4:

[1221] Based on the received information, the server searches the user's registered clothing database and the acquired store's inventory database to generate the optimal outfit. It uses a generative AI model to find the best combination that meets the user's requirements.

[1222] Input: User mood, date, weather, temperature, user registered clothing database, store inventory database

[1223] Data processing: Search algorithms, combinatorial calculations using generative AI models

[1224] Output: Optimal Coordination

[1225] Step 5:

[1226] The server sends the generated coordinates to the user's smartphone in a data format such as JSON.

[1227] Input: Best outfit

[1228] Data processing: Data format conversion (JSON)

[1229] Output: JSON format coordinate data

[1230] Step 6:

[1231] The smartphone displays the received coordination information on its screen and notifies the user, who can then check the suggested coordination.

[1232] Input: JSON format coordinate data

[1233] Data processing: JSON parsing, visual display

[1234] Output: Visual representation of the coordinates

[1235] Through the above processing steps, the user can obtain the optimal fashion coordination in real time by combining in-store inventory with clothing that the user already owns.

[1236] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1237] The present invention combines an emotion engine with a system that allows users to register their own clothing and combines it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, an emotion engine, a server, and a database.

[1238] Clothing Registration

[1239] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1240] External clothing database integration

[1241] The server connects to an external clothing database (e.g., an online fashion store's database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[1242] Emotion recognition by emotion engine

[1243] The user device is equipped with an emotion engine that uses sensors such as cameras and microphones to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. This emotional information is used as input when suggesting outfits to the user.

[1244] Coordination suggestions

[1245] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotions and sends them to the server along with their current mood, date, weather, temperature, etc. The server then searches the user's registered clothing database and an external clothing database based on the received information to generate the optimal outfit. For example, if a user selects "feeling casual," and the emotion engine recognizes that the user is feeling "joyful," and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize selecting items with a casual and positive vibe. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[1246] Coordination notification

[1247] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[1248] Specific examples

[1249] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects "joy," the following specific process will take place:

[1250] 1. User clothing registration

[1251] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1252] Device: Sends the photo and input information to the server.

[1253] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[1254] 2. External clothing database integration

[1255] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[1256] 3. Emotion recognition

[1257] On the device: Recognizes user emotions using a camera and microphone.

[1258] Emotion engine: Determines whether the user is feeling "joy."

[1259] 4. Coordination suggestions

[1260] User: Enters "casual," "sunny," and "20 degrees" into the app.

[1261] Device: Sends input information and emotion information to the server.

[1262] Server: Based on the input information and emotional information, it selects a black T-shirt, blue jeans, and white sneakers and generates a coordination.

[1263] 5. Coordination notification

[1264] Server: Sends the generated coordinates to the user's device.

[1265] Device: The suggested outfits are displayed on the screen and notified to the user.

[1266] This allows users to make the most of the clothes they own and easily create the perfect outfit that suits the weather, temperature, mood, and even emotion.

[1267] The processing flow will be explained below.

[1268] Step 1:

[1269] The user opens the smartphone app or website and taps the "Register Clothing" button to register the clothing item.

[1270] Step 2:

[1271] Users take a photo of their clothing and enter information such as category, color, brand, etc. For example, they can take a photo of a black Uniqlo T-shirt and enter "tops, black, Uniqlo."

[1272] Step 3:

[1273] The device sends the information entered by the user and the photograph taken to the server.

[1274] Step 4:

[1275] The server analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information (color, shape, etc.) from the images.

[1276] Step 5:

[1277] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1278] Step 6:

[1279] The server periodically sends requests to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing product data.

[1280] Step 7:

[1281] The external clothing data acquired by the server is stored in an internal database, and categories and attribute information are added to make it searchable.

[1282] Step 8:

[1283] The user taps the "Suggest an outfit" button on a smartphone app or website.

[1284] Step 9:

[1285] The emotion engine uses the camera and microphone to recognize the user's emotions. For example, if the user is smiling in front of the smartphone, the emotion engine will recognize "joy."

[1286] Step 10:

[1287] The user inputs information such as the mood of the day (casual, formal, etc.), date, weather, and temperature. For example, the user inputs information such as casual, sunny, and 20 degrees.

[1288] Step 11:

[1289] The terminal transmits the input information and the emotion information recognized by the emotion engine to the server.

[1290] Step 12:

[1291] The server searches the user's registered clothing database and an external clothing database, and generates the optimal outfit based on the input information (mood, weather, temperature, emotion, etc.) For example, if the user is in a "casual mood" and feeling "joyful," and inputs that the weather is "sunny" and the temperature is "20 degrees," the server will prioritize selecting items that have a casual and positive vibe.

[1292] Step 13:

[1293] The server sends the generated coordinates to the terminal in a data format such as JSON.

[1294] Step 14:

[1295] The device then displays the received outfit information on the screen and notifies the user, suggesting a combination of, for example, a black T-shirt, blue jeans, and white sneakers.

[1296] Example 2

[1297] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1298] Conventional fashion coordination suggestion systems do not take into account the user's emotions when making suggestions, making it difficult for users to find the appropriate outfit that matches their mood and emotions at the time. Furthermore, current systems that efficiently integrate clothing items in users' possession with clothing from online stores to make optimal suggestions are not sufficient. Therefore, the challenge is to automatically suggest the optimal fashion coordination that matches the user's emotions and mood.

[1299] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1300] In this invention, the server includes means for allowing users to register photos of their own clothing, means for transmitting information entered by the user such as mood, date, weather, and temperature to the server, means for acquiring emotion information using a user terminal equipped with an emotion engine that recognizes the user's emotions, and means for transmitting the emotion information to the server and using it to generate outfits. This makes it possible to propose optimal fashion coordination based on the user's emotions and mood.

[1301] "User" refers to an individual who uses the system, registering the clothing they own through a smartphone app or website and receiving coordination suggestions.

[1302] "Terminal" refers to devices used by users, such as computers, smartphones, and tablets, and has functions such as registering clothing, requesting coordination suggestions, and obtaining emotional data.

[1303] A "server" refers to a computer system that manages data for the entire system and processes coordinate generation, generating optimal coordinates based on data input from users and external data acquired.

[1304] "Database" refers to a data structure that stores clothing information registered by users and clothing data obtained from external sources and is organized so that it can be searched.

[1305] An "emotion engine" refers to software or hardware technology for recognizing a user's emotions, using sensors such as a camera or microphone to identify the user's emotional state.

[1306] "Coordination suggestions" refers to the function in which the server generates optimal fashion combinations based on the user's clothing data and external data, taking into account the user's emotions, mood, weather, temperature, etc., and notifies the user of these combinations.

[1307] "Clothing registration" refers to the process in which a user enters a photo of the clothing they own into the system and provides attribute information such as category, color, and brand to the system.

[1308] "Image analysis" refers to a technology in which a server analyzes photos of clothing sent by users and automatically extracts attribute information such as color, material, and design.

[1309] "External clothing database integration" refers to the process in which the server integrates with an external clothing database, periodically obtains the latest clothing data, and stores it in the internal database.

[1310] The present invention combines an emotion engine with a system that allows users to register their own clothing and combines it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, an emotion engine, a server, and a database.

[1311] Clothing Registration

[1312] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm (e.g., a general image analysis API) and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in an internal database.

[1313] External clothing database integration

[1314] The server connects to an external clothing database (e.g., an online fashion store database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[1315] Emotion recognition by emotion engine

[1316] The user's device is equipped with an emotion engine (e.g., a general emotion recognition API) that uses sensors such as a camera and microphone to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. This emotional information is used as input when suggesting outfits to the user.

[1317] Coordination suggestions

[1318] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotions and sends them to the server along with their current mood, date, weather, temperature, etc. The server then searches the user's registered clothing database and an external clothing database based on the received information to generate the optimal outfit. For example, if a user selects "feeling casual," and the emotion engine recognizes that the user is feeling "joyful," and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize selecting items with a casual and positive vibe. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[1319] Coordination notification

[1320] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[1321] Specific examples

[1322] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects "joy," the following specific process will occur:

[1323] 1. User clothing registration

[1324] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1325] Device: Sends the photo and input information to the server.

[1326] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[1327] 2. External clothing database integration

[1328] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[1329] 3. Emotion recognition

[1330] On-device: Uses a camera and microphone to recognize user emotions.

[1331] Emotion engine: Determines whether the user is feeling "joy."

[1332] 4. Coordination suggestions

[1333] User: Enters "casual," "sunny," and "20 degrees" into the app.

[1334] Terminal: Sends input information and emotion information to the server.

[1335] Server: Based on the input information and emotional information, it selects a black T-shirt, blue jeans, and white sneakers and generates an outfit.

[1336] 5. Coordination notification

[1337] Server: Sends the generated coordinates to the user device.

[1338] Device: The suggested outfits are displayed on the screen and notified to the user.

[1339] Examples of prompt statements

[1340] "Today I'm feeling casual, it's sunny, and the temperature is 20 degrees. What outfit would you suggest if you're feeling joy?"

[1341] This allows users to make the most of the clothes they own and easily create the perfect outfit that suits the weather, temperature, mood, and even emotion.

[1342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1343] Step 1: Enter clothing information

[1344] Users can register their own clothing items by tapping the "Register Clothing" button and taking a photo of the item using a smartphone app or website. They can also manually enter information such as the clothing's category, color, and brand.

[1345] Input: Clothing photo, category, color, brand information

[1346] Output: Temporary data stored on the user's device

[1347] Step 2: Submit your clothing information

[1348] The device receives the photos and manually entered information registered by the user and sends them to the server, often using HTTP as the communication protocol.

[1349] Input: Photos of clothing registered by the user and manual input information

[1350] Output: Data sent to the server

[1351] Step 3: Image analysis and data storage

[1352] The server analyzes the received photos using an image analysis algorithm (e.g., a general image analysis API) and automatically extracts clothing attribute information (e.g., color, material, design, etc.). It then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in an internal database.

[1353] Input: Clothing photos sent to the server and manual input information

[1354] Output: Final clothing data stored in a database

[1355] Step 4: Link with external clothing database

[1356] The server periodically connects with an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing data. The obtained data is stored in an internal database and organized in a searchable format with categories and attribute information.

[1357] Input: Data obtained from an external clothing database

[1358] Output: Organized data stored in an internal database

[1359] Step 5: Obtaining emotion data

[1360] The device uses sensors such as a camera and microphone to capture user emotion data. For example, the front camera captures the user's facial expressions while using an app.

[1361] Input: User emotion data obtained by sensors

[1362] Output: Emotion data stored on the user's device

[1363] Step 6: Analyze the sentiment data

[1364] The emotion engine analyzes sensor data obtained from the camera and microphone, identifies the user's emotions (e.g., joy, sadness, anger, etc.), and stores the results as data.

[1365] Input: Emotion data obtained by the sensor

[1366] Output: Parsed emotion information

[1367] Step 7: Enter your coordination request

[1368] Users tap the "Suggest an outfit" button in the app and enter information such as their mood, date, weather, and temperature.

[1369] Input: Mood, date, weather, temperature, and other information

[1370] Output: Request information saved on the user's device

[1371] Step 8: Sending a coordination request and emotion data

[1372] The terminal transmits the input request information and the emotion data identified by the emotion engine to the server.

[1373] Input: Request information, analyzed emotion information

[1374] Output: Data sent to the server

[1375] Step 9: Coordinate Generation

[1376] The server searches the user's registered clothing database and an external clothing database based on the received request information and emotion data to generate the optimal outfit. For example, it selects casual clothing based on the conditions of "casual mood" and "joy," the weather being "sunny," and the temperature being "20 degrees."

[1377] Input: Request information, analyzed emotion information, clothing data from the database

[1378] Output: Generated coordinate data

[1379] Step 10: Coordination Notification

[1380] The server sends the generated coordinated outfits to the user's device in a data format such as JSON. The device then displays the received coordinated outfit information on the screen and notifies the user.

[1381] Input: Generated coordinate data

[1382] Output: Coordinate information displayed on the user's device

[1383] (Application example 2)

[1384] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1385] In recent years, there has been a demand for systems that can coordinate with the user's clothing in fashion coordination. However, existing systems do not take the user's emotions into consideration and lack coordination suggestions that combine the user's own clothing with store products. Therefore, a means is needed to provide optimal coordination that reflects the user's mood and emotions.

[1386] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1387] In this invention, the server includes a means for recognizing the user's emotions and having an emotion engine used when generating outfits, a means for linking with the product database of the physical store to suggest outfits, and a means for periodically acquiring the latest clothing data from an external clothing database and saving it in a searchable state. This makes it possible to respond to the user's mood and emotions and provide optimal outfits that combine products from the physical store with the user's own clothing.

[1388] "A means equipped with an emotion engine that recognizes the user's emotions and is used when generating outfits" refers to technology that analyzes the user's emotions from biometric information such as facial expressions and voice, and reflects the results of that analysis in outfit suggestions.

[1389] "A means of making coordination suggestions in collaboration with the product database of a physical store" is a technology that links with product data held by a physical store and, based on that data, makes coordination suggestions that combine clothing owned by the user with products in the store.

[1390] "Means for periodically obtaining the latest clothing data from external clothing databases and saving it in a searchable state" refers to technology that obtains the latest clothing information from external databases such as online fashion stores, saves it in an internal database, and makes it available for searches and suggestions.

[1391] "A means for users to register photos of clothing they own" is a function that allows users to take photos of clothing they own and register them in the system.

[1392] "Means for sending information entered by the user, such as mood, date, weather, and temperature, to the server" refers to technology that sends information entered by the user to the server and uses that information to generate outfits.

[1393] "Means for the server to search the user's clothing database and an external clothing database to generate the optimal outfit" refers to a technology that simultaneously searches the user's clothing database and an external clothing database, and generates the optimal outfit for the user based on the results.

[1394] "Means for transmitting coordinate information generated from the server to the user's terminal" refers to a technique for transmitting coordinate information generated by the server to the user's terminal.

[1395] "Means for displaying coordinated items received by the user's terminal" refers to a function for displaying coordinated items received by the user's terminal on a display or the like to inform the user.

[1396] This invention is a system that allows users to manage their own clothing and suggests optimal outfits based on their mood, emotions, and environmental information. The system is composed of a user terminal, an emotion engine, a server, and a database.

[1397] User clothing registration

[1398] Users use smart glasses or a smartphone app to register photos of their clothing. During the registration process, they manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The manually entered information and the automatically extracted attribute information are then combined and the final clothing data is stored in a database.

[1399] External database integration

[1400] The server periodically connects to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing data. The obtained data is stored in an internal database, where it is organized into a searchable format with categories and attribute information.

[1401] Emotion recognition and external environment data acquisition

[1402] The user's device is equipped with an emotion engine that uses sensors such as a camera and microphone to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. It also uses a weather API to obtain current weather and temperature data. This information is used as input when suggesting outfits to the user.

[1403] Coordination suggestions

[1404] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotion and sends it to the server along with information such as their current mood, date, weather, and temperature. The server then uses the received information to search the user's registered clothing database and external clothing databases to generate the optimal outfit. This allows users to make the most of their existing clothing and easily find the perfect outfit based on the weather, temperature, mood, and even their emotion.

[1405] Specific examples

[1406] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects that the user is "happy," the following specific process will occur:

[1407] 1. The user registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1408] 2. The user device sends the photo and input information to the server.

[1409] 3. The server extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[1410] 4. The user device uses a camera and microphone to recognize the user's emotions, and the emotion engine detects "joy."

[1411] 5. The user enters "casual," "sunny," and "20 degrees" into the app, and the input information and emotion information are sent to the server.

[1412] 6. The server generates an outfit by selecting a black T-shirt, blue jeans, and white sneakers based on the input information and emotional information.

[1413] 7. The server sends the generated coordinates to the user terminal, which displays the proposed coordinates on its screen and notifies the user.

[1414] Example prompt: "User emotion: joy, weather: sunny, temperature: 20 degrees, user clothing entry: black UNIQLO T-shirt."

[1415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1416] Step 1:

[1417] Users use smart glasses or a smartphone app to register photos of their own clothing.

[1418] Input: A photo of the clothing taken by the user, along with information such as clothing category, color, and brand.

[1419] How it works: The device receives the photo and input information and sends it to the server.

[1420] Output: The server receives the clothing photos and information.

[1421] Step 2:

[1422] The server analyzes the received photos using an image analysis algorithm to extract attribute information about the clothing.

[1423] Input: User-submitted clothing photos.

[1424] How it works: Image analysis algorithms automatically extract attribute information such as clothing category, color, design, and brand.

[1425] Output: Extracted clothing attribute information.

[1426] Step 3:

[1427] The extracted attribute information is integrated with manually entered information, and the final clothing data is stored in a database.

[1428] Input: Manually entered information, attribute information extracted through image analysis.

[1429] How it works: The server consolidates this information and stores it in a database.

[1430] Output: Integrated clothing data.

[1431] Step 4:

[1432] The server connects to an external clothing database to obtain the latest clothing data.

[1433] Input: Access information for external clothing database.

[1434] Operation: The server accesses an external database to obtain the latest clothing data.

[1435] Output: Latest clothing data.

[1436] Step 5:

[1437] The acquired data is stored in an internal database, and categories and attribute information are added to it, making it searchable.

[1438] Input: The latest clothing data obtained.

[1439] How it works: The server organizes the data, assigns categories and attribute information, and stores it in an internal database.

[1440] Output: Organized, searchable clothing data.

[1441] Step 6:

[1442] The user device uses a camera and microphone to recognize the user's emotions, and the emotion engine analyzes that information.

[1443] Input: User's facial expression images, voice audio data.

[1444] How it works: The emotion engine analyzes this data to determine the user's emotional state.

[1445] Output: User sentiment information.

[1446] Step 7:

[1447] The user taps the "Suggest outfit" button in the app, enters emotional information, weather, mood, temperature, etc., and sends it to the server.

[1448] Input: User emotional information, weather, mood, temperature, etc.

[1449] Operation: The device sends this information to the server.

[1450] Output: Proposal request information received by the server.

[1451] Step 8:

[1452] Based on the information received by the server, the server searches the user's registered clothing database and an external clothing database to generate the optimal outfit.

[1453] Input: Proposal request information, clothing information from the database.

[1454] How it works: The server searches for clothing information and generates outfits based on emotional and environmental information.

[1455] Output: The generated coordinate information.

[1456] Step 9:

[1457] The server sends the generated coordinates to the user terminal.

[1458] Input: Generated coordinate information.

[1459] Operation: The server sends coordinate information to the terminal.

[1460] Output: Coordination information received by the user device.

[1461] Step 10:

[1462] The user terminal displays the received coordinate information on the screen and notifies the user.

[1463] Input: Received coordinate information.

[1464] Action: The device displays the information on the display and notifies you.

[1465] Output: Outfit suggestions displayed to the user.

[1466] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1467] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1468] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1469] [Fourth embodiment]

[1470] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1471] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1472] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1473] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1474] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1475] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1476] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1477] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1478] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1479] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1480] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1481] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1482] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1483] The present invention relates to a system that allows users to register their own clothing and combine it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, a server, and a database.

[1484] Clothing Registration

[1485] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1486] External clothing database integration

[1487] The server connects to an external clothing database (e.g., an online fashion store's database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[1488] Coordination suggestions

[1489] The user taps the "Suggest outfit" button in the app and enters information such as their current mood, date, weather, and temperature. This information is sent from the user's device to the server. The server uses the received information to search the user's registered clothing database and an external clothing database to generate the optimal outfit. For example, if the user selects "feeling casual" and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize casual items. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[1490] Coordination notification

[1491] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[1492] Specific examples

[1493] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," the following specific process will occur:

[1494] 1. User clothing registration

[1495] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1496] Device: Sends the photo and input information to the server.

[1497] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[1498] 2. External clothing database integration

[1499] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[1500] 3. Coordination suggestions

[1501] User: Enters "casual," "sunny," and "20 degrees" into the app.

[1502] Terminal: Sends input information to the server.

[1503] Server: Based on the input information, a black T-shirt, blue jeans, and white sneakers are selected and an outfit is generated.

[1504] 4. Coordination notification

[1505] Server: Sends the generated coordinates to the user's device.

[1506] Device: The suggested outfits are displayed on the screen and notified to the user.

[1507] This allows users to make the most of the clothes they already own and easily create the perfect outfit for the weather, temperature, and mood.

[1508] The processing flow will be explained below.

[1509] Step 1:

[1510] The user opens the smartphone app or website and taps the "Register Clothing" button to register the clothing item.

[1511] Step 2:

[1512] Users take a photo of their clothing and enter information such as category, color, brand, etc. For example, they can take a photo of a black Uniqlo T-shirt and enter "tops, black, Uniqlo."

[1513] Step 3:

[1514] The device sends the information entered by the user and the photograph taken to the server.

[1515] Step 4:

[1516] The server analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information (color, shape, etc.) from the images.

[1517] Step 5:

[1518] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1519] Step 6:

[1520] The server periodically sends requests to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing product data.

[1521] Step 7:

[1522] The external clothing data acquired by the server is stored in an internal database, and categories and attribute information are added to make it searchable.

[1523] Step 8:

[1524] The user taps the "Suggest an outfit" button on a smartphone app or website.

[1525] Step 9:

[1526] The user inputs information such as the mood of the day (casual, formal, etc.), date, weather, and temperature. For example, the user inputs information such as casual, sunny, and 20 degrees.

[1527] Step 10:

[1528] The terminal transmits the input information to the server.

[1529] Step 11:

[1530] The server searches the user's registered clothing database and an external clothing database, and generates the optimal outfit based on the input information (mood, weather, temperature, etc.).

[1531] Step 12:

[1532] The server sends the generated coordinates to the terminal in a data format such as JSON.

[1533] Step 13:

[1534] The device then displays the received outfit information on the screen and notifies the user, suggesting a combination of, for example, a black T-shirt, blue jeans, and white sneakers.

[1535] Example 1

[1536] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1537] One of the problems many people face today is the time and effort it takes to think about their daily outfits. They also struggle with how to efficiently combine and use the clothes they own. Furthermore, there are currently only a limited number of systems that automatically suggest optimal outfits that take into account external factors such as weather and temperature, and achieving this requires a lot of effort.

[1538] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1539] In this invention, the server includes means for allowing a user to register images of their own clothing, means for transmitting information such as mood, date, weather, and temperature input by the user to the server, means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, means for transmitting the generated outfit from the server to the user's terminal, means for the user's terminal to display the outfit received, means for analyzing the image with an image analysis algorithm using the information input by the user and extracting attribute information, and means for linking with an external clothing database to periodically obtain the latest clothing data and save it in a searchable state. This allows users to effectively utilize their own clothing and effortlessly obtain optimal outfits that take external factors such as weather and temperature into consideration.

[1540] "User" refers to an individual who uses this system to register the clothing they own and receive coordination suggestions.

[1541] "Server" refers to the central computer system that receives information from users, links to a database, analyzes it, generates coordinates, and transmits the results to user terminals.

[1542] "Terminal" refers to a device operated by a user (e.g., smartphone, tablet, PC) that exchanges information with a server.

[1543] A "clothing database" refers to a database system that stores the user's clothing and the latest clothing information obtained from external sources, and manages it in a searchable format.

[1544] "Image analysis algorithm" refers to an algorithm that analyzes photos of clothing received by the server and extracts attribute information such as color, shape, and material.

[1545] "External clothing database" refers to a database that stores clothing information provided by online fashion stores or other external providers.

[1546] "Coordination" refers to the optimal fashion combination generated by combining the clothing data registered by the user with external clothing data.

[1547] "Attribute information" refers to detailed characteristic information about clothing, such as color, shape, material, and brand.

[1548] "Searchable" refers to a state in which the data in a database is organized so that it can be quickly searched and referenced based on specific criteria or queries.

[1549] "JSON format" is a data exchange format and refers to a lightweight data format used when exchanging coordination information between a server and a user device.

[1550] The present invention relates to a system that allows users to register their own clothing and combine it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, a server, and a database.

[1551] Clothing Registration

[1552] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm (e.g., TensorFlow, OpenCV) and automatically extracts clothing attribute information from the photos. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1553] Examples:

[1554] The user registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1555] The user's device sends the photo and input information to the server.

[1556] The server performs image analysis, extracts attribute information, integrates it with manually entered information, and stores it in a database.

[1557] External clothing database integration

[1558] The server connects to an external clothing database (e.g., an online fashion store's database) via API to periodically retrieve the latest clothing data. The retrieved data is stored in an internal database (e.g., MySQL, MongoDB), where it is organized into a searchable format with categories and attribute information added.

[1559] Examples:

[1560] The server retrieves the latest clothing product data from the external database and stores it in the internal database.

[1561] Coordination suggestions

[1562] Users tap the "Suggest outfits" button in the app and enter information such as their current mood, date, weather, and temperature. This information is sent from the user's device to the server. Based on the received information, the server searches the user's registered clothing database and external clothing databases to generate the optimal outfit.

[1563] Examples:

[1564] A user enters "casual," "sunny," and "20 degrees" into the app.

[1565] The user terminal sends the input information to the server.

[1566] Based on the input information, the server generates an outfit consisting of a black T-shirt, blue jeans, and white sneakers.

[1567] Coordination notification

[1568] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[1569] Examples:

[1570] The server sends the generated coordinates to the user terminal.

[1571] The user's device will display the suggested outfit on the screen and notify the user.

[1572] Example prompt sentence:

[1573] "I have a black T-shirt and blue jeans. Can you suggest an outfit for a casual, sunny, 20-degree day?"

[1574] This invention allows users to maximize the use of their existing clothing and easily find the perfect outfit for the weather, temperature, and mood. To achieve this, the user's device and server work together to perform image analysis and data processing based on the user's input. Furthermore, by linking with an external database, suggestions are made that take into account the latest fashion information.

[1575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1576] Step 1:

[1577] Users register the clothing they own.

[1578] Specific operation:

[1579] Users open the smartphone app or website, access the "Clothing Registration" page, and take a photo of a black Uniqlo T-shirt and blue Levi's jeans.

[1580] Users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand along with a photo.

[1581] Input: Photo, Category information, Color, Brand

[1582] Output: Clothing registration information

[1583] Step 2:

[1584] The terminal transmits the user input information to the server.

[1585] Specific operation:

[1586] The device acquires the clothing information and photos entered by the user and sends them to the server.

[1587] Input: User-entered information, photo

[1588] Output: Data sent to the server

[1589] Step 3:

[1590] The server analyzes the received photos using an image analysis algorithm and extracts attribute information.

[1591] Specific operation:

[1592] The server analyzes the received photos using image analysis algorithms such as TensorFlow and OpenCV to extract clothing attribute information (e.g., color, shape, material).

[1593] Input: Received photo

[1594] Output: Extracted attribute information

[1595] Step 4:

[1596] The server integrates the manually input information and the extracted attribute information and stores the information in a database.

[1597] Specific operation:

[1598] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1599] Input: Manually entered information, automatically extracted attribute information

[1600] Output: Clothing data stored in a database

[1601] Step 5:

[1602] The server connects to an external clothing database to obtain the latest clothing data.

[1603] Specific operation:

[1604] The server periodically calls the API endpoint of an external online fashion store to retrieve the latest clothing data.

[1605] Input: API endpoint of external clothing database

[1606] Output: Latest clothing data obtained

[1607] Step 6:

[1608] The server stores the acquired external data in an internal database and organizes it in a searchable format.

[1609] Specific operation:

[1610] The server assigns category and attribute information to the acquired data and stores it in an internal database.

[1611] Input: Retrieved external data

[1612] Output: A cleaned database

[1613] Step 7:

[1614] Users send coordination requests through the app.

[1615] Specific operation:

[1616] Users tap the "Suggest an outfit" button in the app and enter information such as their mood, date, weather, and temperature.

[1617] Input: Mood, Date, Weather, Temperature

[1618] Output: Request data

[1619] Step 8:

[1620] The terminal transmits the request data to the server.

[1621] Specific operation:

[1622] The terminal transmits the input request data to the server.

[1623] Input: Request data

[1624] Output: Request data sent to the server

[1625] Step 9:

[1626] The server searches the user's registered clothing data and external clothing data to generate the optimal coordination.

[1627] Specific operation:

[1628] The server searches the user's registered clothing database and an external clothing database based on the user's mood, weather, and temperature to generate the optimal outfit. For example, if the weather is sunny and the temperature is 20 degrees, it will suggest a casual outfit.

[1629] Input: User's registered clothing data, external clothing data, request data

[1630] Output: Generated coordinates

[1631] Step 10:

[1632] The server sends the generated coordinates to the user's device in JSON format.

[1633] Specific operation:

[1634] The server generates coordinate information and sends it to the user's device in JSON format.

[1635] Input: Generated coordinates

[1636] Output: JSON format coordinate data

[1637] Step 11:

[1638] The terminal displays the received coordinate information on the screen and notifies the user.

[1639] Specific operation:

[1640] The terminal displays the received coordinate information on the screen and notifies the user.

[1641] Input: JSON format coordinate data

[1642] Output: Displayed coordinates, user notification

[1643] Example prompt sentence:

[1644] "I have a black T-shirt and blue jeans. Can you suggest an outfit for a casual, sunny, 20-degree day?"

[1645] (Application example 1)

[1646] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1647] With conventional fashion coordination systems, users could only receive coordination suggestions based on the clothes they already own, making it difficult to get an idea of ​​how to coordinate a new item. Furthermore, the system was unable to fully consider in-store inventory, resulting in a suboptimal shopping experience. This prevented users from efficiently considering their outfits and delayed their purchasing decisions.

[1648] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1649] In this invention, the server includes means for allowing a user to register photos of their own clothing, means for transmitting information such as mood, date, weather, and temperature input by the user to the server, means for the server to search a database of the user's clothing and an external clothing database to generate an optimal outfit, means for transmitting the generated outfit from the server to the user's terminal, means for displaying the outfit received by the user's terminal, means for scanning devices installed in the store to obtain store inventory information, and means for combining the obtained inventory information with information on the user's clothing to propose an optimal outfit. This makes it possible to propose optimal outfits in real time that combine store inventory with the user's own clothing.

[1650] "A means for users to register photos of clothing they own" refers to a function that allows users to take pictures of clothing they own using a smartphone or digital camera, and register those pictures in a database.

[1651] "Means for sending information entered by the user, such as mood, date, weather, and temperature, to the server" refers to a function that allows a user to use a device such as a smartphone or computer to enter information such as their current mood, a specified date, weather, and temperature, and then send that information to the server.

[1652] "Means for the server to search the database of clothing owned by the user and an external clothing database to generate the optimal coordination" refers to a function in which the server searches for clothing information registered in the user's database and clothing information obtained from an external database, and combines these to automatically generate the optimal fashion coordination.

[1653] "Means for transmitting the coordinated outfits generated from the server to the user's device" refers to a function for transmitting the fashion coordinated outfit information generated by the server to the user's device such as a smartphone or PC via the Internet or other communication means.

[1654] The "means for displaying the coordinated outfits received by the user terminal" is a function for visually displaying the fashion coordinated outfit information received by the user terminal from the server.

[1655] "Means of scanning devices installed in stores to obtain in-store inventory information" refers to a function that allows users to obtain store inventory information by scanning QR codes or NFC tags installed in physical stores with a smartphone or other device.

[1656] "A means for proposing optimal coordination by combining acquired inventory information with information on clothing owned by the user" is a function that proposes fashion coordination that optimally combines inventory information from physical stores and information on clothing owned by the user registered.

[1657] The present invention relates to a system that allows users to register their own clothing and link with an external database to provide optimal fashion coordination. This system utilizes the user's smartphone or in-store devices to suggest optimal coordination in real time.

[1658] Hardware and software used

[1659] To realize this system, the following hardware and software are used.

[1660] Smartphone: Used by users to take photos of clothing and enter information.

[1661] Server: A central processing unit that communicates with the database, analyzes images, and proposes coordinated results. The API server is built using Node.js and Express.

[1662] Database: Used to store user clothing information and external database information. MongoDB is used.

[1663] Image analysis software: Uses OpenCV and TensorFlow to analyze clothing information registered by users.

[1664] IoT devices: Use QR codes and NFC tags to obtain in-store inventory information.

[1665] System operation procedure

[1666] 1. Clothing Registration:

[1667] Users use their smartphones to take photos of their clothing, and then input information such as the clothing category, color, and brand into the smartphone app.

[1668] The smartphone sends the photo and the entered information to the server.

[1669] The server analyzes the received photos using OpenCV and TensorFlow, automatically extracts clothing attribute information from the images, then combines it with manually entered information and stores the final clothing data in a MongoDB database.

[1670] 2. Get in-store inventory information:

[1671] Users can scan QR codes or NFC tags installed in stores with their smartphones to obtain store inventory information.

[1672] Inventory information is sent to the server via smartphone and stored in a database on the server side.

[1673] 3. Coordination suggestions:

[1674] Users tap the "Suggest outfits" button on the smartphone app and enter information such as their current mood, date, weather, and temperature, which is then sent to the server.

[1675] Based on the received information, the server searches the user's registered clothing database and the store's inventory database to generate the optimal outfit.

[1676] The coordinated outfits are sent from the server to the user's smartphone, where the user can check the suggested outfits on their smartphone.

[1677] Specific examples

[1678] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," the following specific process will occur:

[1679] User clothing registration: The user registers a black T-shirt and blue jeans. They take a photo and enter the information into the smartphone app.

[1680] In-store scanning: Users scan a QR code in the store with their smartphone to get availability information for white sneakers and a red jacket.

[1681] Outfit suggestion: The server selects a black T-shirt, blue jeans, and white sneakers and generates an outfit.

[1682] Coordination notification: Suggested coordination will be displayed on your smartphone.

[1683] Example prompt sentence:

[1684] Please suggest the best fashion coordination based on the user's clothing data. The user owns a "black T-shirt" and "blue jeans." The store's inventory data is "white sneakers" and "red jacket." The user wants a "casual" style.

[1685] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1686] Step 1:

[1687] Users register photos of their clothing. They take photos of the clothing using their smartphone camera and enter information such as clothing category, color, and brand into the smartphone app. The entered information and photo are sent from the smartphone to the server. The server uses OpenCV and TensorFlow to perform image analysis and extract clothing attribute information. The extracted attribute information is integrated with manually entered information, and the final clothing data is stored in a database.

[1688] Input: Manually enter information such as clothing photos, category, color, brand, etc.

[1689] Data processing: Image analysis (OpenCV, TensorFlow)

[1690] Output: Clothing data with integrated attribute information

[1691] Step 2:

[1692] Users scan QR codes or NFC tags installed in stores with their smartphones to obtain inventory information for the store. The information is then sent from the smartphone to a server, which stores it in a database.

[1693] Input: QR code or NFC tag scan data

[1694] Data processing: Analysis of scan data

[1695] Output: In-store inventory information

[1696] Step 3:

[1697] Users tap the "Suggest outfits" button on their smartphone app and enter information such as their current mood, date, weather, and temperature, which is then sent to the server.

[1698] Input: Mood, date, weather, temperature, and other information

[1699] Data processing: Format conversion of input information

[1700] Output: The formatted input information

[1701] Step 4:

[1702] Based on the received information, the server searches the user's registered clothing database and the acquired store's inventory database to generate the optimal outfit. It uses a generative AI model to find the best combination that meets the user's requirements.

[1703] Input: User mood, date, weather, temperature, user registered clothing database, store inventory database

[1704] Data processing: Search algorithms, combinatorial calculations using generative AI models

[1705] Output: Optimal Coordination

[1706] Step 5:

[1707] The server sends the generated coordinates to the user's smartphone in a data format such as JSON.

[1708] Input: Best outfit

[1709] Data processing: Data format conversion (JSON)

[1710] Output: JSON format coordinate data

[1711] Step 6:

[1712] The smartphone displays the received coordination information on its screen and notifies the user, who can then check the suggested coordination.

[1713] Input: JSON format coordinate data

[1714] Data processing: JSON parsing, visual display

[1715] Output: Visual representation of the coordinates

[1716] Through the above processing steps, the user can obtain the optimal fashion coordination in real time by combining in-store inventory with clothing that the user already owns.

[1717] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1718] The present invention combines an emotion engine with a system that allows users to register their own clothing and combines it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, an emotion engine, a server, and a database.

[1719] Clothing Registration

[1720] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user's device then sends this information to a server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1721] External clothing database integration

[1722] The server connects to an external clothing database (e.g., an online fashion store's database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[1723] Emotion recognition by emotion engine

[1724] The user device is equipped with an emotion engine that uses sensors such as cameras and microphones to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. This emotional information is used as input when suggesting outfits to the user.

[1725] Coordination suggestions

[1726] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotions and sends them to the server along with their current mood, date, weather, temperature, etc. The server then searches the user's registered clothing database and an external clothing database based on the received information to generate the optimal outfit. For example, if a user selects "feeling casual," and the emotion engine recognizes that the user is feeling "joyful," and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize selecting items with a casual and positive vibe. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[1727] Coordination notification

[1728] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[1729] Specific examples

[1730] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects "joy," the following specific process will take place:

[1731] 1. User clothing registration

[1732] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1733] Device: Sends the photo and input information to the server.

[1734] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[1735] 2. External clothing database integration

[1736] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[1737] 3. Emotion recognition

[1738] On the device: Recognizes user emotions using a camera and microphone.

[1739] Emotion engine: Determines whether the user is feeling "joy."

[1740] 4. Coordination suggestions

[1741] User: Enters "casual," "sunny," and "20 degrees" into the app.

[1742] Device: Sends input information and emotion information to the server.

[1743] Server: Based on the input information and emotional information, it selects a black T-shirt, blue jeans, and white sneakers and generates a coordination.

[1744] 5. Coordination notification

[1745] Server: Sends the generated coordinates to the user's device.

[1746] Device: The suggested outfits are displayed on the screen and notified to the user.

[1747] This allows users to make the most of the clothes they own and easily create the perfect outfit that suits the weather, temperature, mood, and even emotion.

[1748] The processing flow will be explained below.

[1749] Step 1:

[1750] The user opens the smartphone app or website and taps the "Register Clothing" button to register the clothing item.

[1751] Step 2:

[1752] Users take a photo of their clothing and enter information such as category, color, brand, etc. For example, they can take a photo of a black Uniqlo T-shirt and enter "tops, black, Uniqlo."

[1753] Step 3:

[1754] The device sends the information entered by the user and the photograph taken to the server.

[1755] Step 4:

[1756] The server analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information (color, shape, etc.) from the images.

[1757] Step 5:

[1758] The server integrates the manually entered information with the automatically extracted attribute information and stores the final clothing data in a database.

[1759] Step 6:

[1760] The server periodically sends requests to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing product data.

[1761] Step 7:

[1762] The external clothing data acquired by the server is stored in an internal database, and categories and attribute information are added to make it searchable.

[1763] Step 8:

[1764] The user taps the "Suggest an outfit" button on a smartphone app or website.

[1765] Step 9:

[1766] The emotion engine uses the camera and microphone to recognize the user's emotions. For example, if the user is smiling in front of the smartphone, the emotion engine will recognize "joy."

[1767] Step 10:

[1768] The user inputs information such as the mood of the day (casual, formal, etc.), date, weather, and temperature. For example, the user inputs information such as casual, sunny, and 20 degrees.

[1769] Step 11:

[1770] The terminal transmits the input information and the emotion information recognized by the emotion engine to the server.

[1771] Step 12:

[1772] The server searches the user's registered clothing database and an external clothing database, and generates the optimal outfit based on the input information (mood, weather, temperature, emotion, etc.) For example, if the user is in a "casual mood" and feeling "joyful," and inputs that the weather is "sunny" and the temperature is "20 degrees," the server will prioritize selecting items that have a casual and positive vibe.

[1773] Step 13:

[1774] The server sends the generated coordinates to the terminal in a data format such as JSON.

[1775] Step 14:

[1776] The device then displays the received outfit information on the screen and notifies the user, suggesting a combination of, for example, a black T-shirt, blue jeans, and white sneakers.

[1777] Example 2

[1778] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1779] Conventional fashion coordination suggestion systems do not take into account the user's emotions when making suggestions, making it difficult for users to find the appropriate outfit that matches their mood and emotions at the time. Furthermore, current systems that efficiently integrate clothing items in users' possession with clothing from online stores to make optimal suggestions are not sufficient. Therefore, the challenge is to automatically suggest the optimal fashion coordination that matches the user's emotions and mood.

[1780] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1781] In this invention, the server includes means for allowing users to register photos of their own clothing, means for transmitting information entered by the user such as mood, date, weather, and temperature to the server, means for acquiring emotion information using a user terminal equipped with an emotion engine that recognizes the user's emotions, and means for transmitting the emotion information to the server and using it to generate outfits. This makes it possible to propose optimal fashion coordination based on the user's emotions and mood.

[1782] "User" refers to an individual who uses the system, registering the clothing they own through a smartphone app or website and receiving coordination suggestions.

[1783] "Terminal" refers to devices used by users, such as computers, smartphones, and tablets, and has functions such as registering clothing, requesting coordination suggestions, and obtaining emotional data.

[1784] A "server" refers to a computer system that manages data for the entire system and processes coordinate generation, generating optimal coordinates based on data input from users and external data acquired.

[1785] "Database" refers to a data structure that stores clothing information registered by users and clothing data obtained from external sources and is organized so that it can be searched.

[1786] An "emotion engine" refers to software or hardware technology for recognizing a user's emotions, using sensors such as a camera or microphone to identify the user's emotional state.

[1787] "Coordination suggestions" refers to the function in which the server generates optimal fashion combinations based on the user's clothing data and external data, taking into account the user's emotions, mood, weather, temperature, etc., and notifies the user of these combinations.

[1788] "Clothing registration" refers to the process in which a user enters a photo of the clothing they own into the system and provides attribute information such as category, color, and brand to the system.

[1789] "Image analysis" refers to a technology in which a server analyzes photos of clothing sent by users and automatically extracts attribute information such as color, material, and design.

[1790] "External clothing database integration" refers to the process in which the server integrates with an external clothing database, periodically obtains the latest clothing data, and stores it in the internal database.

[1791] The present invention combines an emotion engine with a system that allows users to register their own clothing and combines it with an external database to provide optimal fashion coordination. The system of the present invention is composed of a user terminal, an emotion engine, a server, and a database.

[1792] Clothing Registration

[1793] Users register photos of their clothing using a smartphone app or website. During the registration process, users manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm (e.g., a general image analysis API) and automatically extracts clothing attribute information from the images. The server then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in an internal database.

[1794] External clothing database integration

[1795] The server connects to an external clothing database (e.g., an online fashion store database) and periodically retrieves the latest clothing data. The retrieved data is stored in an internal database, where it is assigned categories and attribute information and organized in a searchable format.

[1796] Emotion recognition by emotion engine

[1797] The user's device is equipped with an emotion engine (e.g., a general emotion recognition API) that uses sensors such as a camera and microphone to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. This emotional information is used as input when suggesting outfits to the user.

[1798] Coordination suggestions

[1799] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotions and sends them to the server along with their current mood, date, weather, temperature, etc. The server then searches the user's registered clothing database and an external clothing database based on the received information to generate the optimal outfit. For example, if a user selects "feeling casual," and the emotion engine recognizes that the user is feeling "joyful," and enters the weather as "sunny" and the temperature as "20 degrees," the server will prioritize selecting items with a casual and positive vibe. The server can select a black T-shirt and blue jeans from the user's existing clothing and suggest white sneakers from an external database.

[1800] Coordination notification

[1801] The coordinated outfits generated by the server are sent to the user's device in a data format such as JSON. The user's device then displays the received coordinated outfit information on its screen and notifies the user.

[1802] Specific examples

[1803] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects "joy," the following specific process will occur:

[1804] 1. User clothing registration

[1805] User: Registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1806] Device: Sends the photo and input information to the server.

[1807] Server: Extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[1808] 2. External clothing database integration

[1809] Server: Retrieves the latest clothing product data from an external database and stores it in an internal database.

[1810] 3. Emotion recognition

[1811] On-device: Uses a camera and microphone to recognize user emotions.

[1812] Emotion engine: Determines whether the user is feeling "joy."

[1813] 4. Coordination suggestions

[1814] User: Enters "casual," "sunny," and "20 degrees" into the app.

[1815] Terminal: Sends input information and emotion information to the server.

[1816] Server: Based on the input information and emotional information, it selects a black T-shirt, blue jeans, and white sneakers and generates an outfit.

[1817] 5. Coordination notification

[1818] Server: Sends the generated coordinates to the user device.

[1819] Device: The suggested outfits are displayed on the screen and notified to the user.

[1820] Examples of prompt statements

[1821] "Today I'm feeling casual, it's sunny, and the temperature is 20 degrees. What outfit would you suggest if you're feeling joy?"

[1822] This allows users to make the most of the clothes they own and easily create the perfect outfit that suits the weather, temperature, mood, and even emotion.

[1823] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1824] Step 1: Enter clothing information

[1825] Users can register their own clothing items by tapping the "Register Clothing" button and taking a photo of the item using a smartphone app or website. They can also manually enter information such as the clothing's category, color, and brand.

[1826] Input: Clothing photo, category, color, brand information

[1827] Output: Temporary data stored on the user's device

[1828] Step 2: Submit your clothing information

[1829] The device receives the photos and manually entered information registered by the user and sends them to the server, often using HTTP as the communication protocol.

[1830] Input: Photos of clothing registered by the user and manual input information

[1831] Output: Data sent to the server

[1832] Step 3: Image analysis and data storage

[1833] The server analyzes the received photos using an image analysis algorithm (e.g., a general image analysis API) and automatically extracts clothing attribute information (e.g., color, material, design, etc.). It then combines the manually entered information with the automatically extracted attribute information and stores the final clothing data in an internal database.

[1834] Input: Clothing photos sent to the server and manual input information

[1835] Output: Final clothing data stored in a database

[1836] Step 4: Link with external clothing database

[1837] The server periodically connects with an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing data. The obtained data is stored in an internal database and organized in a searchable format with categories and attribute information.

[1838] Input: Data obtained from an external clothing database

[1839] Output: Organized data stored in an internal database

[1840] Step 5: Obtaining emotion data

[1841] The device uses sensors such as a camera and microphone to capture user emotion data. For example, the front camera captures the user's facial expressions while using an app.

[1842] Input: User emotion data obtained by sensors

[1843] Output: Emotion data stored on the user's device

[1844] Step 6: Analyze the sentiment data

[1845] The emotion engine analyzes sensor data obtained from the camera and microphone, identifies the user's emotions (e.g., joy, sadness, anger, etc.), and stores the results as data.

[1846] Input: Emotion data obtained by the sensor

[1847] Output: Parsed emotion information

[1848] Step 7: Enter your coordination request

[1849] Users tap the "Suggest an outfit" button in the app and enter information such as their mood, date, weather, and temperature.

[1850] Input: Mood, date, weather, temperature, and other information

[1851] Output: Request information saved on the user's device

[1852] Step 8: Sending a coordination request and emotion data

[1853] The terminal transmits the input request information and the emotion data identified by the emotion engine to the server.

[1854] Input: Request information, analyzed emotion information

[1855] Output: Data sent to the server

[1856] Step 9: Coordinate Generation

[1857] The server searches the user's registered clothing database and an external clothing database based on the received request information and emotion data to generate the optimal outfit. For example, it selects casual clothing based on the conditions of "casual mood" and "joy," the weather being "sunny," and the temperature being "20 degrees."

[1858] Input: Request information, analyzed emotion information, clothing data from the database

[1859] Output: Generated coordinate data

[1860] Step 10: Coordination Notification

[1861] The server sends the generated coordinated outfits to the user's device in a data format such as JSON. The device then displays the received coordinated outfit information on the screen and notifies the user.

[1862] Input: Generated coordinate data

[1863] Output: Coordinate information displayed on the user's device

[1864] (Application example 2)

[1865] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1866] In recent years, there has been a demand for systems that can coordinate with the user's clothing in fashion coordination. However, existing systems do not take the user's emotions into consideration and lack coordination suggestions that combine the user's own clothing with store products. Therefore, a means is needed to provide optimal coordination that reflects the user's mood and emotions.

[1867] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1868] In this invention, the server includes a means for recognizing the user's emotions and having an emotion engine used when generating outfits, a means for linking with the product database of the physical store to suggest outfits, and a means for periodically acquiring the latest clothing data from an external clothing database and saving it in a searchable state. This makes it possible to respond to the user's mood and emotions and provide optimal outfits that combine products from the physical store with the user's own clothing.

[1869] "A means equipped with an emotion engine that recognizes the user's emotions and is used when generating outfits" refers to technology that analyzes the user's emotions from biometric information such as facial expressions and voice, and reflects the results of that analysis in outfit suggestions.

[1870] "A means of making coordination suggestions in collaboration with the product database of a physical store" is a technology that links with product data held by a physical store and, based on that data, makes coordination suggestions that combine clothing owned by the user with products in the store.

[1871] "Means for periodically obtaining the latest clothing data from external clothing databases and saving it in a searchable state" refers to technology that obtains the latest clothing information from external databases such as online fashion stores, saves it in an internal database, and makes it available for searches and suggestions.

[1872] "A means for users to register photos of clothing they own" is a function that allows users to take photos of clothing they own and register them in the system.

[1873] "Means for sending information entered by the user, such as mood, date, weather, and temperature, to the server" refers to technology that sends information entered by the user to the server and uses that information to generate outfits.

[1874] "Means for the server to search the user's clothing database and an external clothing database to generate the optimal outfit" refers to a technology that simultaneously searches the user's clothing database and an external clothing database, and generates the optimal outfit for the user based on the results.

[1875] "Means for transmitting coordinate information generated from the server to the user's terminal" refers to a technique for transmitting coordinate information generated by the server to the user's terminal.

[1876] "Means for displaying coordinated items received by the user's terminal" refers to a function for displaying coordinated items received by the user's terminal on a display or the like to inform the user.

[1877] This invention is a system that allows users to manage their own clothing and suggests optimal outfits based on their mood, emotions, and environmental information. The system is composed of a user terminal, an emotion engine, a server, and a database.

[1878] User clothing registration

[1879] Users use smart glasses or a smartphone app to register photos of their clothing. During the registration process, they manually enter information such as clothing category (e.g., tops, bottoms), color, and brand. The user device then sends this information to the server. The server then analyzes the received photos using an image analysis algorithm and automatically extracts clothing attribute information from the images. The manually entered information and the automatically extracted attribute information are then combined and the final clothing data is stored in a database.

[1880] External database integration

[1881] The server periodically connects to an external clothing database (e.g., an online fashion store's database) to obtain the latest clothing data. The obtained data is stored in an internal database, where it is organized into a searchable format with categories and attribute information.

[1882] Emotion recognition and external environment data acquisition

[1883] The user's device is equipped with an emotion engine that uses sensors such as a camera and microphone to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and tone of voice to identify emotions such as joy, sadness, and anger. It also uses a weather API to obtain current weather and temperature data. This information is used as input when suggesting outfits to the user.

[1884] Coordination suggestions

[1885] When a user taps the "Suggest Outfit" button in the app, the emotion engine recognizes the user's emotion and sends it to the server along with information such as their current mood, date, weather, and temperature. The server then uses the received information to search the user's registered clothing database and external clothing databases to generate the optimal outfit. This allows users to make the most of their existing clothing and easily find the perfect outfit based on the weather, temperature, mood, and even their emotion.

[1886] Specific examples

[1887] For example, if a user inputs "I'm feeling casual today, it's sunny, and the temperature is 20 degrees," and the emotion engine detects that the user is "happy," the following specific process will occur:

[1888] 1. The user registers a black Uniqlo T-shirt and blue Levi's jeans and takes a photo.

[1889] 2. The user device sends the photo and input information to the server.

[1890] 3. The server extracts clothing attribute information using image analysis, integrates it with manually entered information, and stores it in a database.

[1891] 4. The user device uses a camera and microphone to recognize the user's emotions, and the emotion engine detects "joy."

[1892] 5. The user enters "casual," "sunny," and "20 degrees" into the app, and the input information and emotion information are sent to the server.

[1893] 6. The server generates an outfit by selecting a black T-shirt, blue jeans, and white sneakers based on the input information and emotional information.

[1894] 7. The server sends the generated coordinates to the user terminal, which displays the proposed coordinates on its screen and notifies the user.

[1895] Example prompt: "User emotion: joy, weather: sunny, temperature: 20 degrees, user clothing entry: black UNIQLO T-shirt."

[1896] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1897] Step 1:

[1898] Users use smart glasses or a smartphone app to register photos of their own clothing.

[1899] Input: A photo of the clothing taken by the user, along with information such as clothing category, color, and brand.

[1900] How it works: The device receives the photo and input information and sends it to the server.

[1901] Output: The server receives the clothing photos and information.

[1902] Step 2:

[1903] The server analyzes the received photos using an image analysis algorithm to extract attribute information about the clothing.

[1904] Input: User-submitted clothing photos.

[1905] How it works: Image analysis algorithms automatically extract attribute information such as clothing category, color, design, and brand.

[1906] Output: Extracted clothing attribute information.

[1907] Step 3:

[1908] The extracted attribute information is integrated with manually entered information, and the final clothing data is stored in a database.

[1909] Input: Manually entered information, attribute information extracted through image analysis.

[1910] How it works: The server consolidates this information and stores it in a database.

[1911] Output: Integrated clothing data.

[1912] Step 4:

[1913] The server connects to an external clothing database to obtain the latest clothing data.

[1914] Input: Access information for external clothing database.

[1915] Operation: The server accesses an external database to obtain the latest clothing data.

[1916] Output: Latest clothing data.

[1917] Step 5:

[1918] The acquired data is stored in an internal database, and categories and attribute information are added to it, making it searchable.

[1919] Input: The latest clothing data obtained.

[1920] How it works: The server organizes the data, assigns categories and attribute information, and stores it in an internal database.

[1921] Output: Organized, searchable clothing data.

[1922] Step 6:

[1923] The user device uses a camera and microphone to recognize the user's emotions, and the emotion engine analyzes that information.

[1924] Input: User's facial expression images, voice audio data.

[1925] How it works: The emotion engine analyzes this data to determine the user's emotional state.

[1926] Output: User sentiment information.

[1927] Step 7:

[1928] The user taps the "Suggest outfit" button in the app, enters emotional information, weather, mood, temperature, etc., and sends it to the server.

[1929] Input: User emotional information, weather, mood, temperature, etc.

[1930] Operation: The device sends this information to the server.

[1931] Output: Proposal request information received by the server.

[1932] Step 8:

[1933] Based on the information received by the server, the server searches the user's registered clothing database and an external clothing database to generate the optimal outfit.

[1934] Input: Proposal request information, clothing information from the database.

[1935] How it works: The server searches for clothing information and generates outfits based on emotional and environmental information.

[1936] Output: The generated coordinate information.

[1937] Step 9:

[1938] The server sends the generated coordinates to the user terminal.

[1939] Input: Generated coordinate information.

[1940] Operation: The server sends coordinate information to the terminal.

[1941] Output: Coordination information received by the user device.

[1942] Step 10:

[1943] The user terminal displays the received coordinate information on the screen and notifies the user.

[1944] Input: Received coordinate information.

[1945] Action: The device displays the information on the display and notifies you.

[1946] Output: Outfit suggestions displayed to the user.

[1947] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1948] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1949] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1950] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1951] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1952] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1953] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1954] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1955] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1956] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1957] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1958] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1959] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1960] 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.

[1961] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1962] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1963] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1964] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1965] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1966] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1967] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1968] The following is further disclosed regarding the above embodiment.

[1969] (Claim 1)

[1970] A means for users to register photos of their own clothing,

[1971] A means for transmitting information entered by the user, such as mood, date, weather, and temperature, to a server;

[1972] A means for the server to search a database of clothing owned by the user and an external clothing database to generate an optimal outfit;

[1973] A means for transmitting the generated coordinates from the server to the user's terminal;

[1974] A means for displaying the coordinates received by the user terminal;

[1975] A system including:

[1976] (Claim 2)

[1977] 2. The system according to claim 1, further comprising means for analyzing a photograph of clothing registered by a user and extracting attribute information.

[1978] (Claim 3)

[1979] 2. The system according to claim 1, further comprising means for periodically obtaining the latest clothing data from an external clothing database and storing the same in a searchable state.

[1980] "Example 1"

[1981] (Claim 1)

[1982] A means for users to register images of their own clothing,

[1983] A means for transmitting information entered by the user, such as mood, date, weather, temperature, etc., to a server;

[1984] A means for the server to search a database of clothing owned by the user and an external clothing database to generate an optimal outfit;

[1985] A means for transmitting the generated coordinates from the server to the user's terminal;

[1986] A means for displaying the coordinates received by the user terminal;

[1987] A means for analyzing the image using an image analysis algorithm based on information input by a user to extract attribute information;

[1988] A means to link with an external clothing database to periodically retrieve the latest clothing data and store it in a searchable format;

[1989] A system including:

[1990] (Claim 2)

[1991] 10. The system of claim 1, wherein the user terminal further comprises means for automatically extracting attribute information from the image using an image analysis algorithm and integrating the extracted attribute information with manually input information.

[1992] (Claim 3)

[1993] 2. The system according to claim 1, further comprising means for the server to periodically obtain the latest clothing data from an external database, assign category and attribute information to the data, and store the data in the internal database.

[1994] "Application Example 1"

[1995] (Claim 1)

[1996] A means for users to register photos of their own clothing,

[1997] A means for transmitting information entered by the user, such as mood, date, weather, and temperature, to a server;

[1998] A means for the server to search a database of clothing owned by the user and an external clothing database to generate an optimal outfit;

[1999] A means for transmitting the generated coordinates from the server to the user's terminal;

[2000] A means for displaying the coordinates received by the user terminal;

[2001] means for scanning devices located within the store to obtain store inventory information;

[2002] A method for suggesting optimal outfits by combining acquired inventory information with information on clothing owned by the user, and

[2003] A system including:

[2004] (Claim 2)

[2005] 2. The system according to claim 1, further comprising means for analyzing a photograph of clothing registered by a user and extracting attribute information.

[2006] (Claim 3)

[2007] 2. The system according to claim 1, further comprising means for periodically obtaining the latest clothing data from an external clothing database and storing the same in a searchable state.

[2008] "Example 2: Combining Emotion Engines"

[2009] (Claim 1)

[2010] A means for users to register photos of their own clothing,

[2011] A means for transmitting information entered by the user, such as mood, date, weather, and temperature, to a server;

[2012] A means for the server to search a database of clothing owned by the user and an external clothing database to generate an optimal outfit;

[2013] A means for transmitting the generated coordinates from the server to the user's terminal;

[2014] A means for displaying the coordinates received by the user terminal;

[2015] A means for acquiring emotion information using a user terminal equipped with an emotion engine that recognizes the user's emotions;

[2016] A means for transmitting emotion information to a server and utilizing the emotion information for generating coordinates;

[2017] A system including:

[2018] (Claim 2)

[2019] 2. The system according to claim 1, further comprising means for analyzing a photograph of clothing registered by a user and extracting attribute information.

[2020] (Claim 3)

[2021] 2. The system according to claim 1, further comprising means for periodically obtaining the latest clothing data from an external clothing database and storing the same in a searchable state.

[2022] "Application example 2 when combining emotion engines"

[2023] (Claim 1)

[2024] A means for users to register photos of their own clothing,

[2025] A means for transmitting information entered by the user, such as mood, date, weather, and temperature, to a server;

[2026] A means for the server to search a database of clothing owned by the user and an external clothing database to generate an optimal outfit;

[2027] A means for transmitting the generated coordinates from the server to the user's terminal;

[2028] A means for displaying the coordinates received by the user terminal;

[2029] a means for recognizing a user's emotions and having an emotion engine for use in generating a coordinate;

[2030] A means of linking with the product database of physical stores to make coordination suggestions;

[2031] A system including:

[2032] (Claim 2)

[2033] 2. The system according to claim 1, further comprising means for analyzing a photograph of clothing registered by a user and extracting attribute information.

[2034] (Claim 3)

[2035] 2. The system according to claim 1, further comprising means for periodically obtaining the latest clothing data from an external clothing database and storing the same in a searchable state. [Explanation of symbols]

[2036] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to register photos of their own clothing, A means for transmitting information entered by the user, such as mood, date, weather, and temperature, to a server; A means for the server to search a database of clothing owned by the user and an external clothing database to generate an optimal outfit; A means for transmitting the generated coordinates from the server to the user's terminal; A means for displaying the coordinates received by the user terminal; A system including:

2. The system according to claim 1 , further comprising means for analyzing a photograph of clothing registered by a user and extracting attribute information.

3. 2. The system according to claim 1, further comprising means for periodically obtaining the latest clothing data from an external clothing database and storing the data in a searchable state.

Images