System

Abstract

A system is provided.SOLUTION: A system comprising: means for registering a plurality of clothing items in a closet of a user; means for acquiring weather information based on current location information of the user; means for inputting the weather information and situation information; means for generating an optimal coordinate in a AI based on the weather information, the situation information, and item information in the closet of the user; means for generating and recommending a recommended coordinate based on a similar item from past coordinate posts; and means for posting the suggested coordinate and receiving an incentive for the posting.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] ---

[0006] Existing fashion coordination applications make it difficult for users to efficiently utilize the items registered in their closets, often leaving them unsure of what to wear. Choosing an appropriate outfit for each weather, temperature, and situation can be time-consuming, making everyday clothing choices tedious. Furthermore, they lack functionality that effectively utilizes data obtained from users' past outfits to suggest recommended items and outfits. There is a need for a system that can solve these issues, efficiently utilize the items in users' closets, and support users in choosing appropriate outfits. [Means for solving the problem]

[0007] The present invention provides a means for a user to register multiple clothing items in their closet, a means for a terminal to acquire weather information based on the user's current location information, and a means for the user to input the weather information and situation information. Furthermore, a server provides a means for generating optimal outfits using AI based on the weather information, situation information, and information about items in the user's closet. Additionally, the server provides a means for generating and recommending recommended outfits based on similar items from past outfit posts, allowing users to efficiently select outfits. Users can post suggested outfits and receive incentives for their posts, further enhancing the enjoyment of outfit selection. The system improves user convenience by providing a means for automatically adding items purchased on an online shopping platform to the closet and a means for awarding points for each post that can be used for shopping.

[0008] ---

[0009] The above is the content of the "problem that the invention aims to solve" and "means for solving the problem" in the patent specification.

[0010] ---

[0011] A "user" is an entity that uses the application to manage items in a closet and receive coordination suggestions.

[0012] A "closet" is a database for registering and managing clothing items owned by a user.

[0013] "Items" are various clothing items and fashion accessories that are registered in a user's closet.

[0014] A "terminal" is a device used by a user, such as a smartphone, tablet, or PC, and is a medium for executing applications.

[0015] "Current location information" is the geographical location information of the user that is acquired by the terminal using technology such as GPS.

[0016] "Weather information" is data about current and future weather, such as weather and temperature.

[0017] "Situation information" is information about the purpose or occasion (e.g., casual, business, etc.) of the clothing selected by the user.

[0018] The "server" is a central system that processes the user's closet data, weather information, and situation information, and makes coordination suggestions.

[0019] "AI" stands for artificial intelligence, a program that analyzes data and generates optimal coordination.

[0020] A "coordination" is a combination of multiple items selected by a user according to a specific context or condition.

[0021] "Posting" is the act of a user publishing and sharing an outfit through an application.

[0022] "Incentives" are points or rewards awarded based on users' posts and activities.

[0023] An "online shopping platform" is an e-commerce venue for users to purchase clothing items.

[0024] A "database" is a storage device for storing information such as a user's closet, item information, and outfit information.

[0025] ---

[0026] These are the definitions of important words. [Brief explanation of the drawings]

[0027] [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

[0028] 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.

[0029] First, the terms used in the following description will be explained.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] [First embodiment]

[0036] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0037] 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.

[0038] 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).

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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."

[0048] ---

[0049] This system efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits according to the weather, temperature, and situation. Furthermore, it analyzes past outfit posting data from users and provides recommended items and outfits, simplifying outfit selection and improving the shopping experience.

[0050] The system of the present invention is implemented mainly by the following method:

[0051] 1. Register your closet items

[0052] Users register their clothing items in their closets. They operate their devices to take photos of the items or upload existing photos, and enter information such as the item's category, color, brand, and size. Items purchased through the online shopping platform are automatically added to the user's closet.

[0053] 2. Obtaining weather, temperature, and situation

[0054] The device obtains weather information based on the current location. The device uses its GPS function to obtain the coordinates of the current location and transmits that information to a weather information service to obtain the weather and temperature data for that day. The user inputs situation information into the device according to the day and circumstances. This situation information is selected from options provided on the app interface.

[0055] 3. Coordination suggestions

[0056] The server uses AI to generate optimal outfits based on information about the items in the user's closet, acquired weather information, and situation information. The AI ​​analyzes this information and suggests combinations of items that suit the user. This outfit information is sent to the device and displayed on the device.

[0057] 4. Recommendations from past posts

[0058] The server analyzes the outfit data posted by the user and other users in the past and generates recommended items and outfits that match the current weather information and situation. The generated outfit information includes recommendations for similar items and links to online shopping platforms. This information is also sent to the device and displayed for the user to use.

[0059] 5. Post your outfit

[0060] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user can enter information about the weather, temperature, and situation, and post it along with the outfit. Once posted, the server receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive.

[0061] Specific examples

[0062] For example, consider the case where a user is deciding on an outfit for work. The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects "business attire." The server then suggests a shirt, pants, and jacket from the user's closet. Based on data on business outfits posted by other users in the past, the server also recommends similar items (such as ties) and displays a link to an online shopping platform. If the suggested outfit satisfies the user, the user can wear the outfit and post a photo to the app. The server then awards the user points as an incentive for posting.

[0063] As described above, the system of the present invention not only makes it easier for users to select clothes and provides optimal coordination, but also improves the shopping experience.

[0064] ---

[0065] The above is the "Form for implementing the invention."

[0066] The processing flow will be explained below.

[0067] ---

[0068] Step 1: Register your closet items

[0069] A user logs in to the app and opens the "Closet" section. They take a photo of an item or upload an existing photo, and enter details such as the item's category, color, brand, and size. They press the "Save" button to send the information to the app.

[0070] ---

[0071] Step 2: Save your closet data

[0072] The server stores the item information sent by the user in a database and also adds the item information to the database when a request for adding an item purchased on the online shopping platform to the closet is received, since the item may be automatically added to the closet.

[0073] ---

[0074] Step 3: Get the weather and temperature

[0075] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[0076] ---

[0077] Step 4: Enter situation information

[0078] The user selects the situation information for the day, for example, choosing a scene such as "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[0079] ---

[0080] Step 5: Generate coordinates

[0081] The server uses AI to generate optimal outfits based on information about the items in the user's closet, acquired weather information, and situation information, and then sends the generated outfit information to the user's device.

[0082] ---

[0083] Step 6: Displaying your outfits

[0084] The device displays the coordinated outfit information received from the server on the app screen, allowing the user to check the proposed outfit.

[0085] ---

[0086] Step 7: Analyze past posts

[0087] The server searches the user's past outfit posting data and analyzes outfits that contain items similar to the weather and situation information. Based on the analysis results, it generates recommended items and outfits, adds online shopping links, and sends them to the user's device.

[0088] ---

[0089] Step 8: Display recommended outfits

[0090] The device displays the recommended outfit information and purchase links received from the server on the screen, allowing the user to purchase the items based on this information.

[0091] ---

[0092] Step 9: Post your outfit

[0093] The user actually wears the suggested outfit and takes a photo. They post the photo to the app, inputting weather, temperature, and situation information. Then, they press the "Post new outfit" button.

[0094] ---

[0095] Step 10: Save submitted data and assign points

[0096] The server saves the new coordinate posting data received from the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[0097] ---

[0098] The above is the content of the program processing of the system of the present invention, divided into specific steps.

[0099] Example 1

[0100] 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."

[0101] Until now, it has been difficult for users to efficiently manage the clothing items in their closets and choose the best outfit for the day's weather, temperature, and situation. Furthermore, since there was no system that provided recommended items and outfits based on past outfit posts, users had to spend time and effort choosing their outfits. Furthermore, there was also a lack of a convenient way for users to purchase the items they wanted.

[0102] 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.

[0103] In this invention, the server includes: means for a user to register multiple clothing items in their closet; means for a terminal to acquire weather information based on the user's current location information; means for the user to input the weather information and situation information; means for generating an optimal outfit based on the weather information, situation information, and item information in the user's closet using a generative AI model; means for generating and recommending recommended outfits based on similar items from past outfit posts; and means for the user to post the suggested outfits and receive incentives for the posts. This not only makes the user's clothing selection more efficient and suggests optimal outfits, but also improves the user's shopping experience.

[0104] "User" refers to an individual who uses the system to register their own clothing items and receive coordination suggestions.

[0105] A "terminal" is an electronic device operated by a user, and includes a smartphone, tablet, personal computer, etc.

[0106] "Weather information" refers to data that indicates weather conditions such as the weather and temperature at the user's current location.

[0107] "Situation information" is information input by the user regarding the plans and activities for the day, and includes, for example, "business scene" and "casual scene".

[0108] "Server" refers to the central processing unit that aggregates, analyzes, stores, and coordinates data on the system.

[0109] "Item information in the closet" is information about clothing items that the user has registered in the closet, and includes attributes such as category, color, brand, and size.

[0110] A "generative AI model" refers to an algorithm or software that uses artificial intelligence technology to generate optimal outfits for users.

[0111] "Past coordination posts" are coordination data that the user or other users have previously posted to the system.

[0112] "Recommended coordination" refers to a coordination generated by the server and recommended to the user based on past coordination submission data and current weather and situation information.

[0113] "Incentives" are rewards or benefits that users receive for using the system, and include points, for example.

[0114] An "e-commerce platform" is a website or application where goods are bought and sold over the internet, and where users can purchase clothing items online.

[0115] The present invention is a system that efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits according to the weather, temperature, and situation. Furthermore, by analyzing the user's past outfit posting data, the system offers recommended items and outfits, simplifying outfit selection and improving the shopping experience.

[0116] The system of the present invention is implemented mainly by the following method.

[0117] Registering closet items

[0118] A user uses an application on their device to register clothing items in their closet. The user launches the application and registers new items on the closet management screen. They take a photo of the item or upload an existing photo, and enter information such as the item's category, color, brand, and size. Items purchased through the online shopping platform are automatically added to the user's closet.

[0119] As a specific example, a user uploads a photo of a jacket taken with a smartphone app and enters the category as "outerwear," the color as "black," the brand as "Uniqlo," and the size as "M."

[0120] Obtaining weather, temperature, and situation information

[0121] The device uses its GPS function to obtain the coordinates of its current location. Based on the obtained coordinates, it queries a weather information service (e.g., OpenWeatherMap) to obtain weather and temperature data. This information is displayed on the device screen. The user can then enter information about the situation for that day and select a specific situation (e.g., "Business Scene").

[0122] For example, the device acquires the user's current location using GPS, queries OpenWeatherMap to obtain information such as "sunny, temperature 25 degrees," and displays it in the app. The user selects "Business scene."

[0123] Coordination suggestions

[0124] The server obtains information about the items in the closet. Based on the obtained weather and situation information, it uses a generative AI model (e.g., TensorFlow) to generate the optimal outfit. This suggestion is sent to the device and displayed.

[0125] For example, a combination of "white shirt," "gray pants," and "black jacket" is suggested.

[0126] Recommendations from past posts

[0127] The server analyzes the outfit data posted by the user and other users in the past. Based on the analysis results, it generates recommended items and outfits that match the current weather information and situation. This includes recommendations for similar items and links to online shopping platforms. The generated recommended information is sent to the device and displayed.

[0128] As a specific example, it analyzes outfits posted in the past with the tag "business scene" and recommends items such as "ties" and "business shoes."

[0129] Coordination Post

[0130] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user then enters information about the weather, temperature, and situation, and posts the photo. Once posted, the server receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive.

[0131] For example, when a user wears a suggested outfit and takes a photo, they upload it to the app, entering the information "sunny, 25 degrees, business setting." The server saves the data and awards points to the user.

[0132] Specific actions

[0133] For example, consider the case where a user is deciding on an outfit for work. The user launches the app, and the device retrieves weather and temperature information for the user's current location. If the weather is sunny and the temperature is 25 degrees, the user selects "business attire." The server then suggests a shirt, pants, and jacket from the user's closet. Based on data on business outfits posted by other users in the past, the server also recommends similar items (such as ties) and displays a link to an online shopping platform. If a suggested outfit satisfies the user, the user can wear the outfit and post a photo within the app. The server then awards the user points as an incentive for posting.

[0134] As described above, the system of the present invention not only makes it easier for users to select clothes and provides optimal coordination, but also improves the shopping experience.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Program processing flow

[0137] Step 1: Register your closet items

[0138] Step 1-1:

[0139] The user launches the app on their device and opens the closet management screen.

[0140] Input: The app launch operation.

[0141] Output: The closet management screen will be displayed on the device.

[0142] Step 1-2:

[0143] The user selects the Register New Item button and takes a photo or uploads an existing photo.

[0144] Input: Image of new item (taken or uploaded).

[0145] Output: Image data of the new item.

[0146] Step 1-3:

[0147] The user enters the item information (category, color, brand, size, etc.) and presses the save button.

[0148] Input: Item information (category, color, brand, size).

[0149] Output: Item registration data.

[0150] Steps 1-4:

[0151] The server receives the registered information and stores it in a database.

[0152] Input: Registration information data.

[0153] Output: Save to closet database.

[0154] Specific behavior:

[0155] Users take a photo of the jacket with their smartphone camera and upload it to the app.

[0156] The user enters "Category: Outerwear", "Color: Black", "Brand: Uniqlo", and "Size: M".

[0157] The server stores the received information in the closet database.

[0158] Step 2: Obtaining weather, temperature, and situation

[0159] Step 2-1:

[0160] The device uses GPS to obtain the coordinates of the current location.

[0161] Input: GPS function activation operation.

[0162] Output: Current location information (coordinate data).

[0163] Step 2-2:

[0164] The device sends its current location coordinates to a weather information service and obtains weather and temperature data.

[0165] Input: coordinate data.

[0166] Output: Weather information, temperature information.

[0167] Step 2-3:

[0168] The device displays the weather and temperature data it has obtained.

[0169] Input: Weather information, temperature information.

[0170] Output: Display weather and temperature information on the terminal screen.

[0171] Step 2-4:

[0172] The user inputs the situation information for that day.

[0173] Input: Situation information (e.g., business situation).

[0174] Output: Situation information data.

[0175] Specific behavior:

[0176] The device obtains the user's current location using GPS and queries the weather information service.

[0177] The device obtains the information "Sunny, 25 degrees" and displays it in the app.

[0178] The user selects "Business Scene" from the app's situation selection screen.

[0179] Step 3: Coordination proposal

[0180] Step 3-1:

[0181] The server retrieves information about items in the closet.

[0182] Input: Information from the closet database.

[0183] Output: The retrieved item information.

[0184] Step 3-2:

[0185] The server uses a generative AI model based on the acquired weather and situation information to generate the optimal outfit.

[0186] Input: Weather information, temperature information, situation information, item information.

[0187] Output: Optimal coordination suggestions.

[0188] Step 3-3:

[0189] The server sends the generated coordinate information to the terminal and displays it.

[0190] Input: Optimal coordination suggestions.

[0191] Output: Display of suggested coordinates on terminal screen.

[0192] Specific behavior:

[0193] The server retrieves information about "white shirt," "gray pants," and "black jacket" from the closet.

[0194] The server uses an AI model to generate an outfit suitable for a sunny day with a temperature of 25 degrees and a business setting.

[0195] The server sends the suggested outfits to the smartphone and displays them on the app.

[0196] Step 4: Recommendations from past posts

[0197] Step 4-1:

[0198] The server acquires coordinate data posted in the past by the user and other users.

[0199] Input: Coordinated submission database.

[0200] Output: Past posting data.

[0201] Step 4-2:

[0202] The server performs analysis and generates recommended items and outfits that match the current weather information and situation.

[0203] Input: Past posting data, weather information, situation information.

[0204] Output: Recommended item information, recommended coordination information.

[0205] Step 4-3:

[0206] The server sends the generated recommendation information to the terminal and displays it.

[0207] Input: Recommended coordination information.

[0208] Output: Recommended outfits displayed on the device screen.

[0209] Specific behavior:

[0210] The server retrieves and analyzes past coordination posting data tagged with "business scene."

[0211] The server generates recommendations such as "ties" and "business shoes" based on past data.

[0212] The server sends the generated recommendation information to the smartphone and displays it in the app.

[0213] Step 5: Post your outfit

[0214] Step 5-1:

[0215] The user wears the suggested outfit and takes a photo.

[0216] Input: A photo of the outfit you're wearing.

[0217] Output: The captured photo data.

[0218] Step 5-2:

[0219] Users upload photos to the app and enter weather, temperature, and situation information.

[0220] Input: Photo data, weather information, temperature information, situation information.

[0221] output: The post data.

[0222] Step 5-3:

[0223] The server receives the posted information and stores it in a database.

[0224] Input: Post data.

[0225] Output: Save to database.

[0226] Step 5-4:

[0227] The server will credit the points to the user's account.

[0228] Input: Post data, user information.

[0229] Output: Points awarded.

[0230] Specific behavior:

[0231] The user puts on the suggested outfit of a white shirt, gray pants, and black jacket and takes a photo with their smartphone.

[0232] The user enters the weather as "sunny," the temperature as "25 degrees," and the situation as "business scene," and posts a photo.

[0233] The server receives the posted data, stores it in the database, and awards the user 10 points.

[0234] (Application example 1)

[0235] 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."

[0236] There are already systems that efficiently utilize multiple clothing items registered in a user's closet to suggest optimal outfits based on the weather, temperature, and situation. However, these systems lack the ability to provide a sense of actual fitting and lack a means to reflect feedback on the outfits provided by the user. This can result in low user satisfaction with the outfit suggestions. Furthermore, there is a need for a method to enhance users' visual recognition of the suggested outfits by providing a try-on experience in a virtual space.

[0237] 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.

[0238] In this invention, the server includes a means for allowing a user to create an avatar for virtual try-on and for having the avatar try on coordinated clothing, a means for using a generative AI model to create prompt sentences based on items in the user's closet and weather information, and for the AI ​​to use the prompt sentences to generate an optimal outfit, and a means for acquiring weather information based on the user's current location information, thereby allowing the user to try on coordinated clothing in a virtual space and visually check it.

[0239] A "user" is an individual who uses this system to manage their own clothing items and receive coordination suggestions.

[0240] "Clothing items" are decorative items such as clothes and accessories that a user registers in his or her closet.

[0241] A "closet" is a digital repository of clothing items that a user registers through their device.

[0242] A "terminal" is an electronic device such as a smartphone, tablet, or PC that allows a user to access and operate the system.

[0243] "Current location information" is information about the user's physical location obtained using the terminal's GPS function or the like.

[0244] "Weather information" is environmental data such as weather and temperature acquired based on the user's current location information.

[0245] "Situation information" is information about an event or situation that is input by the user when receiving a coordination suggestion.

[0246] The "server" is a computer system that processes the user's closet information, weather information, and the like, and generates coordination suggestions.

[0247] "AI" stands for artificial intelligence, a technology that analyzes information about items in a user's closet, weather information, situational information, etc. to generate optimal outfits.

[0248] "Coordination" refers to a combination of clothing items worn by a user.

[0249] "Past coordination posts" are data on past coordination posts posted to the system by the user and other users.

[0250] "Virtual try-on" means that a user tries on clothing items in a digital space using an avatar.

[0251] An "avatar" is a digital character that resembles the user and is used during virtual fitting.

[0252] A "generative AI model" is an artificial intelligence model that generates optimal outfits based on the items in the user's closet and weather information.

[0253] A "prompt statement" is an instruction given to a generative AI model to make it output appropriate coordinates.

[0254] An "online shopping platform" is an e-commerce system over the Internet through which users purchase clothing items.

[0255] "Incentives" are rewards or benefits that users receive by posting their suggested outfits.

[0256] The present invention is a system that effectively utilizes multiple clothing items registered in a user's closet to suggest optimal outfits according to the weather, temperature, and situation, and allows the user to check the outfits in a virtual try-on environment. The system of the present invention is mainly implemented in the following manner.

[0257] 1. Register your closet items

[0258] Users can register clothing items in their closets using their own devices by taking photos of the items or uploading existing photos and entering information such as the item's category, color, brand, and size. In addition, users can set items purchased from online shopping platforms to be automatically added to their closets.

[0259] 2. Obtaining weather information

[0260] The device uses the GPS function to obtain the user's current location information, and then uses that information to obtain the day's weather and temperature data from a weather information service, thereby providing real-time weather information.

[0261] 3. Enter situation information

[0262] The user inputs situation information (e.g., business, casual date, outdoors, etc.) into the device based on the day's schedule and circumstances. This situation information is selected from options provided on the app interface.

[0263] 4. Coordination suggestions and generative AI models

[0264] The server uses AI to generate the optimal outfit based on the weather information, situation information, and information about items in the user's closet. This involves creating a prompt using a generative AI model, and then the AI ​​generates the optimal outfit based on that prompt. For example, the AI ​​might be given the following prompt:

[0265] "What would be an appropriate outfit for a business setting on a sunny day when the temperature is 25 degrees? In my closet I have a blue shirt, a white shirt, black pants, jeans, a suit jacket, and a casual jacket."

[0266] 5. Providing a virtual try-on environment

[0267] The server provides a means for users to create an avatar for virtual try-on and virtually try on suggested outfits for that avatar. This function allows users to visually check the suggested clothing items in a virtual environment. If the user is satisfied with the outfit, they can select it and actually wear it.

[0268] 6. Posts and Incentives

[0269] Users can try on the suggested outfits, take photos, and post them to the app. The server then receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive, which can be used on online shopping platforms.

[0270] The system configured as described above allows users to efficiently receive coordination suggestions and visually confirm them while making comfortable clothing selections, thereby improving the user's shopping experience and reducing the stress of selecting clothing.

[0271] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0272] Step 1:

[0273] Registering closet items

[0274] Users use their devices to register clothing items in their closets. They take photos of the items or upload existing photos, and enter information such as the item's category, color, brand, and size. The images and text data are then sent to the server and stored in a database. Items purchased on the online shopping platform are automatically added to the user's closet based on the purchase information.

[0275] Input: Item photo, category, color, brand, size, and other information

[0276] Data processing: Classification and registration of input image and text data

[0277] Output: Registered item information is saved in the database

[0278] Step 2:

[0279] Obtaining weather information

[0280] The device uses the GPS function to obtain the user's current location information, and then uses that information to obtain the day's weather and temperature data from a weather information service. The obtained weather information is used in the next step.

[0281] Input: User's current location information

[0282] Data processing: Send an API request based on current location information to obtain weather data

[0283] Output: Weather information and temperature data

[0284] Step 3:

[0285] Entering situation information

[0286] The user inputs situation information (e.g., business, casual date, outdoors, etc.) into the terminal to receive coordination suggestions. The situation information is sent to the server along with the previous weather information.

[0287] Input: Situation information (choose from options)

[0288] Data processing: collecting and integrating situational data

[0289] Output: Situation information combined with weather information

[0290] Step 4:

[0291] Use of outfit suggestions and generative AI models

[0292] The server uses AI to generate the optimal outfit based on the weather information, situation information, and information about the items in the user's closet. In this process, a generative AI model is used to create a prompt, and the AI ​​generates the optimal outfit based on that prompt.

[0293] Input: Weather information, situation information, item information in the user's closet

[0294] Data processing: Prompt sentence generation and coordination suggestions using AI models

[0295] Output: Generated coordinate information

[0296] Step 5:

[0297] Providing a virtual try-on environment

[0298] The server creates an avatar for the user to virtually try on the suggested outfits, allowing the user to visually check the suggested clothing items in a virtual environment.

[0299] Input: Generated coordinate information, user avatar

[0300] Data processing: 3D rendering to apply coordinate information to an avatar

[0301] Output: Visualized virtual try-on image

[0302] Step 6:

[0303] Posts and Incentives

[0304] Users try on the suggested outfits, take photos, and post them to the app. The posted information is sent to a server and stored in a database. At the same time, users are awarded points as an incentive, which can be used on the online shopping platform.

[0305] Input: User's outfit photo, related information

[0306] Data processing: Saving submitted data and calculating incentive points

[0307] Output: Save to database and give points

[0308] Through the above steps, the system of the present invention enables the user to efficiently receive coordination suggestions and comfortably select clothes.

[0309] 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.

[0310] ---

[0311] The present invention is a system that efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits based on the weather, temperature, situation, and the user's emotions. Furthermore, by analyzing the user's past outfit posting data and providing recommended items and outfits, the system simplifies outfit selection and improves the shopping experience.

[0312] The system of the present invention is implemented mainly by the following method:

[0313] 1. Register your closet items

[0314] The user registers their clothing items in the closet. Using their device, the user takes a photo of the item or uploads an existing photo, and enters details such as the item's category, color, brand, and size. Then, the user presses the "Save" button to send the information to the app.

[0315] The server stores the item information sent by the user in a database and also adds the item information to the database when a request for adding an item purchased on the online shopping platform to the user's closet is received, since the item may be automatically added to the user's closet.

[0316] 2. Obtaining weather, temperature, and situation information

[0317] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[0318] The user selects the situation information for the day, for example, choosing a scene such as "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[0319] 3. Emotional Recognition

[0320] The device receives the user's facial expressions and voice as input and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and recognizes their emotions. For example, it uses a camera to take a picture of the user's face and identifies their emotions through image analysis.

[0321] 4. Coordination suggestions

[0322] The server uses AI to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotional data obtained from the emotion engine. The AI ​​analyzes this information and suggests combinations of items that suit the user. This outfit information is sent to the device and displayed on the device.

[0323] 5. Recommendations from past posts

[0324] The server analyzes the outfit data posted by the user and other users in the past, generates recommended items and outfits that match the current weather information, situation information, and emotion data, and adds online shopping links. This information is also sent to the device and displayed for the user to use.

[0325] 6. Post your outfit

[0326] The user actually wears the suggested outfit and takes a photo. They post the photo to the app, inputting weather, temperature, and situation information. Then, they press the "Post new outfit" button.

[0327] The server saves the new coordinate posting data received from the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[0328] Specific examples

[0329] For example, consider the case where a user is choosing an outfit for going out with friends.

[0330] The user launches the app and retrieves the weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects the "Casual Scene" setting. The device takes a photo of the user's face with the camera, and the emotion engine recognizes the user's facial expression as "happy."

[0331] The server suggests suitable shirts, pants, and sneakers from the user's closet. It also recommends similar items (such as caps) based on outfit data posted by other users in the past, and provides a link to an online shopping platform.

[0332] The user wears the proposed outfit, takes a photo, and posts it to the app. The server then awards points to the user as an incentive.

[0333] As described above, the system of the present invention not only streamlines the user's clothing selection process and provides optimal coordination, but also makes suggestions that take the user's emotions into consideration, thereby further improving the shopping experience.

[0334] ---

[0335] The above is the "Form for implementing the invention."

[0336] The processing flow will be explained below.

[0337] ---

[0338] Step 1: Register your closet items

[0339] A user logs in to the app and opens the "Closet" section. They take a photo of an item or upload an existing photo, and enter details such as the item's category, color, brand, and size. They press the "Save" button to send the information to the app.

[0340] ---

[0341] Step 2: Save your closet data

[0342] The server receives the item information sent by the user and stores it in a database. In addition, since an item purchased on the online shopping platform may be automatically added to the user's closet, the server adds the item information to the database when a request for this is received.

[0343] ---

[0344] Step 3: Get the weather and temperature

[0345] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[0346] ---

[0347] Step 4: Enter situation information

[0348] The user selects the situation information for the day, for example, "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[0349] ---

[0350] Step 5: Recognize emotions

[0351] The device captures the user's facial expressions and voice using a camera and microphone and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and identifies their emotions. For example, it may recognize "they look happy" through image analysis.

[0352] ---

[0353] Step 6: Coordination proposal

[0354] The server uses AI to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotion data obtained from the emotion engine, and then sends the generated outfit information to the device.

[0355] ---

[0356] Step 7: Displaying Coordination

[0357] The device displays the coordinated information received from the server on the app screen, and the user can confirm the proposed coordinated outfit.

[0358] ---

[0359] Step 8: Analyze past posts

[0360] The server analyzes the outfit data posted by the user and other users in the past, and generates recommended items and outfits that match the current weather information, situation information, and emotional data. The generated outfit information includes recommendations for similar items and online shopping links. This information is also sent to the device.

[0361] ---

[0362] Step 9: Display recommended outfits

[0363] The device displays the recommended outfit information and purchase links received from the server on the screen, allowing the user to purchase the items based on this information.

[0364] ---

[0365] Step 10: Post your outfit

[0366] The user actually wears the suggested outfit and takes a photo. The photo is then posted to the app, along with weather, temperature, and situation information. The user then presses the "Post new outfit" button.

[0367] ---

[0368] Step 11: Save submitted data and assign points

[0369] The server saves the new coordinate posting data sent by the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[0370] ---

[0371] The above is the content of the program processing of the system of the present invention, divided into specific steps.

[0372] Example 2

[0373] 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."

[0374] Previous systems lacked a means for users to efficiently register and use a large number of clothing items, and were inadequate in suggesting outfits based on weather information, situations, or emotions. Furthermore, the system was incomplete in utilizing data from users' past outfit submissions to make new suggestions, and there were also issues with providing incentives for suggested outfits. For these reasons, a new system is needed to further improve users' clothing selection and shopping experience.

[0375] 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.

[0376] In this invention, the server includes a means for registering clothing items in the user's closet, a means for acquiring weather information, a means for recognizing situation information and emotions, a means for proposing optimal outfits using a generative AI model, and a means for generating and recommending recommended outfits based on past outfit posts. This allows users to efficiently manage their clothing items and receive optimal outfit suggestions based on the weather, situation, and emotions. Furthermore, by providing new suggestions and incentives using past posting data, the system further improves clothing selection and the shopping experience.

[0377] A "user" is an individual who uses the system to manage their own clothing items and receive suggestions for optimal coordination.

[0378] A "closet" is a virtual storage space that manages multiple clothing items registered by a user on a database.

[0379] A "terminal" is an electronic device that allows a user to access the system and perform input operations or receive data. Examples include smartphones, tablets, and personal computers.

[0380] "Weather information" is data relating to the weather, temperature, and other climate information based on the user's current location.

[0381] "Situation information" is data relating to scenes and circumstances that a user sets according to the schedule and activities of the day. For example, it includes categories such as "casual" and "business."

[0382] An "emotion engine" is software or an algorithm that analyzes a user's facial expressions and voice to recognize their emotions.

[0383] A "generative AI model" is a machine learning model that suggests optimal outfits based on information about items in the user's closet, weather information, situational information, and emotional data.

[0384] "Past coordinated outfit posts" is historical data about coordinated outfits posted by the user or other users on the system.

[0385] "Recommended outfits" are new clothing combinations suggested by a generative AI model based on past outfit posting data.

[0386] "Incentives" are rewards, points, or other benefits given to users when they post new outfits.

[0387] The system of this invention efficiently utilizes multiple clothing items registered in a user's closet and proposes optimal outfits based on the weather, temperature, situation, and the user's emotions. Furthermore, by analyzing the user's past outfit posting data and providing recommended items and outfits, it simplifies clothing selection and improves the shopping experience.

[0388] Registering closet items

[0389] Users register their clothing items through their devices. They take a photo of the clothing using the device's camera or upload an existing photo. They then enter detailed information such as the item's category (e.g., shirt, pants), color, brand, and size, and press the "Save" button to send it to the server.

[0390] The server stores the received clothing item information in a database, and also automatically adds items purchased through the online shopping platform to the closet.

[0391] Obtaining weather, temperature, and situation information

[0392] The device acquires the user's current location information using the GPS function, then transmits the location information to the weather information service to acquire weather and temperature data.

[0393] The user selects the situation of the day on the app interface, choosing a scene such as "casual" or "business," and then presses the "Set Situation" button to send it to the server.

[0394] Emotion recognition

[0395] The device sends the user's facial expressions and voice as input to the emotion engine. The device's camera captures a picture of the user's face, or the microphone captures voice data. The emotion engine analyzes this data and identifies the user's emotions. Specifically, it analyzes emotions using OpenFace or the Google Cloud Speech-to-Text API.

[0396] Coordination suggestions

[0397] The server uses a generative AI model to suggest optimal outfits based on information about items in the user's closet, weather information, situation information, and emotional data. The server then analyzes the data using an AI model (e.g., TensorFlow) to generate optimal outfits. This generated outfit information is then sent to the device.

[0398] Recommendations from past posts

[0399] The server analyzes the outfit data posted by the user and other users in the past, and generates recommended items and outfits based on current weather information, situation information, and emotional data. It also generates appropriate online shopping links and sends them to the device.

[0400] Coordination Post

[0401] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user then enters the weather, temperature, and situation information along with the photo, and presses the "Post new outfit" button.

[0402] The server stores the received posting data of the new coordinates in a database, awards points as an incentive for the user's posting, and reflects the information in the user's account.

[0403] Specific examples

[0404] For example, consider the case where a user is choosing an outfit for going out with friends.

[0405] The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects the "Casual Scene" setting. The device then takes a photo of the user's face with the camera, and the emotion engine recognizes the user's facial expression as "happy."

[0406] The server suggests suitable shirts, pants, and sneakers from the user's closet, and also recommends additional items such as caps based on data posted by other users in the past, along with links to online shopping platforms.

[0407] The user wears the suggested outfit, takes a photo, and posts it to the app. After posting, the server gives the user points as an incentive and reflects the information in the user's account.

[0408] Prompt Sentence Examples

[0409] "Please suggest the best outfit for a casual occasion when the weather is sunny, the temperature is 25 degrees, and you are feeling happy."

[0410] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0411] Program processing flow

[0412] The program processing of this system is divided into the following steps:

[0413] Step 1: Register your closet items

[0414] Specific behavior:

[0415] The user takes a photo of the clothing item with the device's camera or uploads an existing photo.

[0416] The user enters details such as clothing item category, color, brand, size, etc. and presses the "Save" button.

[0417] Input: Clothing item photo and details

[0418] The terminal sends this information to the server.

[0419] The server stores the received information in a database.

[0420] Output: New clothing item information stored in the database

[0421] Step 2: Obtaining weather, temperature, and situation information

[0422] Specific behavior:

[0423] The device uses GPS to obtain the current location.

[0424] Input: Latitude and longitude information of current location

[0425] The device transmits its current location information to a weather information service and obtains weather and temperature data.

[0426] The user selects and enters the situation information for the day on the app interface.

[0427] Input: Situation information (casual, business, etc.)

[0428] Output: Obtained weather and temperature information and situation information

[0429] Step 3: Recognize emotions

[0430] Specific behavior:

[0431] The device captures the user's facial expressions with a camera or records their voice with a microphone.

[0432] Input: User's facial expression and voice data

[0433] The device sends this data to the emotion engine.

[0434] The emotion engine analyzes the data and identifies the user's emotion (e.g., "happy").

[0435] Output: User emotion data

[0436] Step 4: Coordination proposal

[0437] Specific behavior:

[0438] The server uses a generative AI model to generate the optimal outfit based on information about the items in the user's closet, acquired weather and temperature information, situation information, and emotional data.

[0439] Input: Closet item information, weather and temperature information, situation information, emotional data

[0440] An AI model analyzes this data and suggests optimal outfits.

[0441] The server sends the generated coordinate information to the terminal.

[0442] Output: Proposed coordinate information

[0443] Step 5: Recommendations from past posts

[0444] Specific behavior:

[0445] The server analyzes the coordination data posted by the user and other users in the past.

[0446] Input: Past coordination posting data

[0447] The server compares current weather information, situation information, and emotional data to generate recommended items and outfits.

[0448] The server adds a link to the online shopping platform and sends it to the terminal.

[0449] Output: Recommended items, outfit information, and related shopping links

[0450] Step 6: Post your outfit

[0451] Specific behavior:

[0452] The user wears the suggested outfit, takes a photo, and posts it to the app.

[0453] Input: outfit photo, weather and temperature information, situation information

[0454] The server stores the received posting data in a database and adds points to the user's account as an incentive.

[0455] Output: Post data and awarded points stored in the database

[0456] (Application example 2)

[0457] 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."

[0458] Conventional clothing selection support systems only suggest outfits based on the weather and situation, and have the problem of not being able to make suggestions that reflect the user's emotional state. Furthermore, they do not fully utilize the user's past outfit data, so there is a need to improve the shopping experience and the accuracy of suggestions.

[0459] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to register multiple clothing items in their closet; a means for a terminal to acquire weather information based on the user's current location information; a means for a user to input the weather information and situation information; a means for the server to generate an optimal outfit using AI based on the weather information, situation information, and item information in the user's closet; a means for the terminal to acquire the user's facial expressions and voice and analyze them with an emotion engine; a means for the server to generate an optimal outfit based on the weather information, situation information, emotion data, and item information in the user's closet; a means for the server to generate and recommend a recommended outfit based on similar items from past outfit posts; and a means for a user to post the suggested outfit and receive an incentive for the post. This enables personalized clothing selection based on the user's emotional state, improving the accuracy of suggestions and the shopping experience.

[0460] "User" refers to an entity that registers clothing items and receives coordination suggestions.

[0461] "Clothing items" refer to various elements of clothing that a user registers in their closet and uses to coordinate outfits.

[0462] A "closet" refers to a virtual storage space that stores information about clothing items owned by a user.

[0463] "Terminal" refers to a device that a user operates to acquire and display information. Examples include smartphones, tablets, and personal computers.

[0464] "Location information" refers to the physical location of a user, typically obtained using the Global Positioning System (GPS).

[0465] "Weather information" refers to environmental data such as the weather and temperature at the user's current location.

[0466] "Situation information" refers to information about a particular everyday or special situation specified by the user, such as "casual" or "business."

[0467] "Server" refers to a computer system that processes information sent by users, makes various suggestions, and manages data.

[0468] "Item information" refers to detailed information about a clothing item, such as category, color, brand, size, etc.

[0469] "AI (artificial intelligence)" refers to the machine learning or data analysis algorithms used by the server to generate the coordinates.

[0470] An "emotion engine" refers to a system that recognizes a user's emotional state by analyzing their facial expressions and voice.

[0471] "Emotion data" refers to information that indicates the user's emotional state as analyzed by the emotion engine.

[0472] "Coordination" refers to a combination of multiple clothing items suggested to the user.

[0473] "Past coordination posts" refer to coordination suggestions and results previously made by the user and other users.

[0474] "Recommendation" refers to the act of the server presenting new outfits or items to the user.

[0475] "Posting" refers to the act of a user wearing a suggested outfit and uploading the result to the system.

[0476] "Incentives" refer to rewards, points, and other benefits that users receive when they post content.

[0477] This invention is a system that efficiently utilizes multiple clothing items registered in a user's virtual closet and suggests optimal coordination based on the weather, temperature, situation, and the user's emotions.

[0478] 1. Register your closet items

[0479] A user registers a clothing item in their closet using their device. They take a photo of the clothing item using the device's camera and enter details such as the item's category, color, brand, and size. The device is equipped with an image recognition library (e.g., TensorFlow) that analyzes the image to obtain item information. This information is sent to the server and stored in a database.

[0480] 2. Obtaining weather, temperature, and situation information

[0481] The device uses its GPS function to obtain the user's current location information. It then sends this information to the weather information service's API (e.g., OpenWeatherMap) to obtain weather and temperature data. The user then uses the application's interface to select the situation for the day. For example, the user can enter a situation such as "casual" or "business."

[0482] 3. Emotional Recognition

[0483] The device acquires the user's facial expressions and voice and sends them to an emotion recognition engine (e.g., Microsoft Azure Cognitive Services). The emotion engine analyzes the user's facial expressions and voice and recognizes their emotions. For example, it can take a picture of the user's face with a camera and perform image analysis to determine emotions such as "happy" or "sad."

[0484] 4. Coordination suggestions

[0485] The server uses AI (e.g., TensorFlow) to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotion data obtained from an emotion engine. This outfit information is sent to the device and displayed to the user.

[0486] 5. Recommendations from past posts

[0487] The server analyzes the coordination data posted by the user and other users in the past, and generates recommended items and coordinations that match the current weather information, situation information, and emotion data. This recommendation information is also sent to the terminal and displayed for the user to use.

[0488] 6. Post your outfit

[0489] The user actually wears the suggested outfit, takes a photo, and posts it to the application. The user then re-enters weather, temperature, and situation information for the photo and posts it. The server saves the received new outfit posting data in a database, awards points as an incentive based on the user's post, and reflects the information in the user's account.

[0490] Specific examples

[0491] For example, consider a situation where a user is choosing an outfit for going out with friends. The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects "casual scene." The device takes a photo of the user's face with a camera, and the emotion engine recognizes the user's facial expression as "happy." The server then suggests suitable shirts, pants, and sneakers from the user's closet. Based on outfit data posted by other users in the past, the server also recommends similar items (e.g., caps) and displays a link to an online shopping platform.

[0492] Prompt Sentence Examples

[0493] "The user wants to go to lunch with a friend in the spring sunshine, so please suggest the perfect casual outfit from his closet. The temperature is 22 degrees and the weather is sunny. The user looks excited."

[0494] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0495] Step 1:

[0496] The user takes a photo of a clothing item using the device's camera. The photo is analyzed by an image recognition library (e.g., TensorFlow) to obtain detailed information about the item, such as its category, color, brand, and size. This information is sent to the server via the device and stored in a database. The input is a photo of the clothing item, and the output is detailed information about the item.

[0497] Step 2:

[0498] The device uses its GPS function to obtain the user's current location information. The obtained location information is sent to the API of a weather information service (e.g., OpenWeatherMap) to obtain weather and temperature data. Based on this location information, the API returns weather and temperature data. The input is the current location information, and the output is weather and temperature data.

[0499] Step 3:

[0500] The user uses the application interface to input situation information for the day, for example, by selecting a category such as "casual" or "business." The input is the situation information, and the output is the selected category.

[0501] Step 4:

[0502] The device captures the user's facial expressions and voice. Facial expression data is captured by the smartphone's camera, and voice data is captured by the microphone. This data is sent to an emotion recognition engine (e.g., Microsoft Azure Cognitive Services), which analyzes it to identify emotions. The input is facial expression and voice data, and the output is recognized emotion data.

[0503] Step 5:

[0504] The server uses AI (e.g., TensorFlow) to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotional data. The input is information about items in the closet, weather information, situation information, and emotional data, and the output is the optimal outfit.

[0505] Step 6:

[0506] The server generates recommended outfits based on similar items from past outfit posts and prepares the data to recommend to users. The input is past outfit data and current conditions (weather information, situation information, emotion data), and the output is the recommended outfit.

[0507] Step 7:

[0508] The user actually wears the proposed outfit, takes a photo, and posts it to the application. The photo also includes weather, temperature, and situation information. This new outfit posting data is sent to the server and stored in a database. The input is a photo of the proposed outfit and related information, and the output is the outfit posting data stored in the database.

[0509] Step 8:

[0510] The server awards points as an incentive based on the user's posts and reflects the information in the user's account. The input is the posted coordinate data, and the output is the awarded point information.

[0511] In this way, personalized clothing selection based on the user's emotional state is possible, improving the accuracy of recommendations and the shopping experience.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] [Second embodiment]

[0516] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0517] 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.

[0518] 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).

[0519] 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.

[0520] 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.

[0521] 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).

[0522] 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. 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.

[0523] 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.

[0524] 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.

[0525] 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.

[0526] In the smart glasses 214, 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.

[0527] 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."

[0528] ---

[0529] This system efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits according to the weather, temperature, and situation. Furthermore, it analyzes past outfit posting data from users and provides recommended items and outfits, simplifying outfit selection and improving the shopping experience.

[0530] The system of the present invention is implemented mainly by the following method:

[0531] 1. Register your closet items

[0532] Users register their clothing items in their closets. They operate their devices to take photos of the items or upload existing photos, and enter information such as the item's category, color, brand, and size. Items purchased through the online shopping platform are automatically added to the user's closet.

[0533] 2. Obtaining weather, temperature, and situation

[0534] The device obtains weather information based on the current location. The device uses its GPS function to obtain the coordinates of the current location and transmits that information to a weather information service to obtain the weather and temperature data for that day. The user inputs situation information into the device according to the day and circumstances. This situation information is selected from options provided on the app interface.

[0535] 3. Coordination suggestions

[0536] The server uses AI to generate optimal outfits based on information about the items in the user's closet, acquired weather information, and situation information. The AI ​​analyzes this information and suggests combinations of items that suit the user. This outfit information is sent to the device and displayed on the device.

[0537] 4. Recommendations from past posts

[0538] The server analyzes the outfit data posted by the user and other users in the past and generates recommended items and outfits that match the current weather information and situation. The generated outfit information includes recommendations for similar items and links to online shopping platforms. This information is also sent to the device and displayed for the user to use.

[0539] 5. Post your outfit

[0540] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user can enter information about the weather, temperature, and situation, and post it along with the outfit. Once posted, the server receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive.

[0541] Specific examples

[0542] For example, consider the case where a user is deciding on an outfit for work. The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects "business attire." The server then suggests a shirt, pants, and jacket from the user's closet. Based on data on business outfits posted by other users in the past, the server also recommends similar items (such as ties) and displays a link to an online shopping platform. If the suggested outfit satisfies the user, the user can wear the outfit and post a photo to the app. The server then awards the user points as an incentive for posting.

[0543] As described above, the system of the present invention not only makes it easier for users to select clothes and provides optimal coordination, but also improves the shopping experience.

[0544] ---

[0545] The above is the "Form for implementing the invention."

[0546] The processing flow will be explained below.

[0547] ---

[0548] Step 1: Register your closet items

[0549] A user logs in to the app and opens the "Closet" section. They take a photo of an item or upload an existing photo, and enter details such as the item's category, color, brand, and size. They press the "Save" button to send the information to the app.

[0550] ---

[0551] Step 2: Save your closet data

[0552] The server stores the item information sent by the user in a database and also adds the item information to the database when a request for adding an item purchased on the online shopping platform to the closet is received, since the item may be automatically added to the closet.

[0553] ---

[0554] Step 3: Get the weather and temperature

[0555] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[0556] ---

[0557] Step 4: Enter situation information

[0558] The user selects the situation information for the day, for example, choosing a scene such as "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[0559] ---

[0560] Step 5: Generate coordinates

[0561] The server uses AI to generate optimal outfits based on information about the items in the user's closet, acquired weather information, and situation information, and then sends the generated outfit information to the user's device.

[0562] ---

[0563] Step 6: Displaying your outfits

[0564] The device displays the coordinated outfit information received from the server on the app screen, allowing the user to check the proposed outfit.

[0565] ---

[0566] Step 7: Analyze past posts

[0567] The server searches the user's past outfit posting data and analyzes outfits that contain items similar to the weather and situation information. Based on the analysis results, it generates recommended items and outfits, adds online shopping links, and sends them to the user's device.

[0568] ---

[0569] Step 8: Display recommended outfits

[0570] The device displays the recommended outfit information and purchase links received from the server on the screen, allowing the user to purchase the items based on this information.

[0571] ---

[0572] Step 9: Post your outfit

[0573] The user actually wears the suggested outfit and takes a photo. They post the photo to the app, inputting weather, temperature, and situation information. Then, they press the "Post new outfit" button.

[0574] ---

[0575] Step 10: Save submitted data and assign points

[0576] The server saves the new coordinate posting data received from the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[0577] ---

[0578] The above is the content of the program processing of the system of the present invention, divided into specific steps.

[0579] Example 1

[0580] 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."

[0581] Until now, it has been difficult for users to efficiently manage the clothing items in their closets and choose the best outfit for the day's weather, temperature, and situation. Furthermore, since there was no system that provided recommended items and outfits based on past outfit posts, users had to spend time and effort choosing their outfits. Furthermore, there was also a lack of a convenient way for users to purchase the items they wanted.

[0582] 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.

[0583] In this invention, the server includes: means for a user to register multiple clothing items in their closet; means for a terminal to acquire weather information based on the user's current location information; means for the user to input the weather information and situation information; means for generating an optimal outfit based on the weather information, situation information, and item information in the user's closet using a generative AI model; means for generating and recommending recommended outfits based on similar items from past outfit posts; and means for the user to post the suggested outfits and receive incentives for the posts. This not only makes the user's clothing selection more efficient and suggests optimal outfits, but also improves the user's shopping experience.

[0584] "User" refers to an individual who uses the system to register their own clothing items and receive coordination suggestions.

[0585] A "terminal" is an electronic device operated by a user, and includes a smartphone, tablet, personal computer, etc.

[0586] "Weather information" refers to data that indicates weather conditions such as the weather and temperature at the user's current location.

[0587] "Situation information" is information input by the user regarding the plans and activities for the day, and includes, for example, "business scene" and "casual scene".

[0588] "Server" refers to the central processing unit that aggregates, analyzes, stores, and coordinates data on the system.

[0589] "Item information in the closet" is information about clothing items that the user has registered in the closet, and includes attributes such as category, color, brand, and size.

[0590] A "generative AI model" refers to an algorithm or software that uses artificial intelligence technology to generate optimal outfits for users.

[0591] "Past coordination posts" are coordination data that the user or other users have previously posted to the system.

[0592] "Recommended coordination" refers to a coordination generated by the server and recommended to the user based on past coordination submission data and current weather and situation information.

[0593] "Incentives" are rewards or benefits that users receive for using the system, and include points, for example.

[0594] An "e-commerce platform" is a website or application where goods are bought and sold over the internet, and where users can purchase clothing items online.

[0595] The present invention is a system that efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits according to the weather, temperature, and situation. Furthermore, by analyzing the user's past outfit posting data, the system offers recommended items and outfits, simplifying outfit selection and improving the shopping experience.

[0596] The system of the present invention is implemented mainly by the following method.

[0597] Registering closet items

[0598] A user uses an application on their device to register clothing items in their closet. The user launches the application and registers new items on the closet management screen. They take a photo of the item or upload an existing photo, and enter information such as the item's category, color, brand, and size. Items purchased through the online shopping platform are automatically added to the user's closet.

[0599] As a specific example, a user uploads a photo of a jacket taken with a smartphone app and enters the category as "outerwear," the color as "black," the brand as "Uniqlo," and the size as "M."

[0600] Obtaining weather, temperature, and situation information

[0601] The device uses its GPS function to obtain the coordinates of its current location. Based on the obtained coordinates, it queries a weather information service (e.g., OpenWeatherMap) to obtain weather and temperature data. This information is displayed on the device screen. The user can then enter information about the situation for that day and select a specific situation (e.g., "Business Scene").

[0602] For example, the device acquires the user's current location using GPS, queries OpenWeatherMap to obtain information such as "sunny, temperature 25 degrees," and displays it in the app. The user selects "Business scene."

[0603] Coordination suggestions

[0604] The server obtains information about the items in the closet. Based on the obtained weather and situation information, it uses a generative AI model (e.g., TensorFlow) to generate the optimal outfit. This suggestion is sent to the device and displayed.

[0605] For example, a combination of "white shirt," "gray pants," and "black jacket" is suggested.

[0606] Recommendations from past posts

[0607] The server analyzes the outfit data posted by the user and other users in the past. Based on the analysis results, it generates recommended items and outfits that match the current weather information and situation. This includes recommendations for similar items and links to online shopping platforms. The generated recommended information is sent to the device and displayed.

[0608] As a specific example, it analyzes outfits posted in the past with the tag "business scene" and recommends items such as "ties" and "business shoes."

[0609] Coordination Post

[0610] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user then enters information about the weather, temperature, and situation, and posts the photo. Once posted, the server receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive.

[0611] For example, when a user wears a suggested outfit and takes a photo, they upload it to the app, entering the information "sunny, 25 degrees, business setting." The server saves the data and awards points to the user.

[0612] Specific actions

[0613] For example, consider the case where a user is deciding on an outfit for work. The user launches the app, and the device retrieves weather and temperature information for the user's current location. If the weather is sunny and the temperature is 25 degrees, the user selects "business attire." The server then suggests a shirt, pants, and jacket from the user's closet. Based on data on business outfits posted by other users in the past, the server also recommends similar items (such as ties) and displays a link to an online shopping platform. If a suggested outfit satisfies the user, the user can wear the outfit and post a photo within the app. The server then awards the user points as an incentive for posting.

[0614] As described above, the system of the present invention not only makes it easier for users to select clothes and provides optimal coordination, but also improves the shopping experience.

[0615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0616] Program processing flow

[0617] Step 1: Register your closet items

[0618] Step 1-1:

[0619] The user launches the app on their device and opens the closet management screen.

[0620] Input: The app launch operation.

[0621] Output: The closet management screen will be displayed on the device.

[0622] Step 1-2:

[0623] The user selects the Register New Item button and takes a photo or uploads an existing photo.

[0624] Input: Image of new item (taken or uploaded).

[0625] Output: Image data of the new item.

[0626] Step 1-3:

[0627] The user enters the item information (category, color, brand, size, etc.) and presses the save button.

[0628] Input: Item information (category, color, brand, size).

[0629] Output: Item registration data.

[0630] Steps 1-4:

[0631] The server receives the registered information and stores it in a database.

[0632] Input: Registration information data.

[0633] Output: Save to closet database.

[0634] Specific behavior:

[0635] Users take a photo of the jacket with their smartphone camera and upload it to the app.

[0636] The user enters "Category: Outerwear", "Color: Black", "Brand: Uniqlo", and "Size: M".

[0637] The server stores the received information in the closet database.

[0638] Step 2: Obtaining weather, temperature, and situation

[0639] Step 2-1:

[0640] The device uses GPS to obtain the coordinates of the current location.

[0641] Input: GPS function activation operation.

[0642] Output: Current location information (coordinate data).

[0643] Step 2-2:

[0644] The device sends its current location coordinates to a weather information service and obtains weather and temperature data.

[0645] Input: coordinate data.

[0646] Output: Weather information, temperature information.

[0647] Step 2-3:

[0648] The device displays the weather and temperature data it has obtained.

[0649] Input: Weather information, temperature information.

[0650] Output: Display weather and temperature information on the terminal screen.

[0651] Step 2-4:

[0652] The user inputs the situation information for that day.

[0653] Input: Situation information (e.g., business situation).

[0654] Output: Situation information data.

[0655] Specific behavior:

[0656] The device obtains the user's current location using GPS and queries the weather information service.

[0657] The device obtains the information "Sunny, 25 degrees" and displays it in the app.

[0658] The user selects "Business Scene" from the app's situation selection screen.

[0659] Step 3: Coordination proposal

[0660] Step 3-1:

[0661] The server retrieves information about items in the closet.

[0662] Input: Information from the closet database.

[0663] Output: The retrieved item information.

[0664] Step 3-2:

[0665] The server uses a generative AI model based on the acquired weather and situation information to generate the optimal outfit.

[0666] Input: Weather information, temperature information, situation information, item information.

[0667] Output: Optimal coordination suggestions.

[0668] Step 3-3:

[0669] The server sends the generated coordinate information to the terminal and displays it.

[0670] Input: Optimal coordination suggestions.

[0671] Output: Display of suggested coordinates on terminal screen.

[0672] Specific behavior:

[0673] The server retrieves information about "white shirt," "gray pants," and "black jacket" from the closet.

[0674] The server uses an AI model to generate an outfit suitable for a sunny day with a temperature of 25 degrees and a business setting.

[0675] The server sends the suggested outfits to the smartphone and displays them on the app.

[0676] Step 4: Recommendations from past posts

[0677] Step 4-1:

[0678] The server acquires coordinate data posted in the past by the user and other users.

[0679] Input: Coordinated submission database.

[0680] Output: Past posting data.

[0681] Step 4-2:

[0682] The server performs analysis and generates recommended items and outfits that match the current weather information and situation.

[0683] Input: Past posting data, weather information, situation information.

[0684] Output: Recommended item information, recommended coordination information.

[0685] Step 4-3:

[0686] The server sends the generated recommendation information to the terminal and displays it.

[0687] Input: Recommended coordination information.

[0688] Output: Recommended outfits displayed on the device screen.

[0689] Specific behavior:

[0690] The server retrieves and analyzes past coordination posting data tagged with "business scene."

[0691] The server generates recommendations such as "ties" and "business shoes" based on past data.

[0692] The server sends the generated recommendation information to the smartphone and displays it in the app.

[0693] Step 5: Post your outfit

[0694] Step 5-1:

[0695] The user wears the suggested outfit and takes a photo.

[0696] Input: A photo of the outfit you're wearing.

[0697] Output: The captured photo data.

[0698] Step 5-2:

[0699] Users upload photos to the app and enter weather, temperature, and situation information.

[0700] Input: Photo data, weather information, temperature information, situation information.

[0701] output: The post data.

[0702] Step 5-3:

[0703] The server receives the posted information and stores it in a database.

[0704] Input: Post data.

[0705] Output: Save to database.

[0706] Step 5-4:

[0707] The server will credit the points to the user's account.

[0708] Input: Post data, user information.

[0709] Output: Points awarded.

[0710] Specific behavior:

[0711] The user puts on the suggested outfit of a white shirt, gray pants, and black jacket and takes a photo with their smartphone.

[0712] The user enters the weather as "sunny," the temperature as "25 degrees," and the situation as "business scene," and posts a photo.

[0713] The server receives the posted data, stores it in the database, and awards the user 10 points.

[0714] (Application example 1)

[0715] 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."

[0716] There are already systems that efficiently utilize multiple clothing items registered in a user's closet to suggest optimal outfits based on the weather, temperature, and situation. However, these systems lack the ability to provide a sense of actual fitting and lack a means to reflect feedback on the outfits provided by the user. This can result in low user satisfaction with the outfit suggestions. Furthermore, there is a need for a method to enhance users' visual recognition of the suggested outfits by providing a try-on experience in a virtual space.

[0717] 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.

[0718] In this invention, the server includes a means for allowing a user to create an avatar for virtual try-on and for having the avatar try on coordinated clothing, a means for using a generative AI model to create prompt sentences based on items in the user's closet and weather information, and for the AI ​​to use the prompt sentences to generate an optimal outfit, and a means for acquiring weather information based on the user's current location information, thereby allowing the user to try on coordinated clothing in a virtual space and visually check it.

[0719] A "user" is an individual who uses this system to manage their own clothing items and receive coordination suggestions.

[0720] "Clothing items" are decorative items such as clothes and accessories that a user registers in his or her closet.

[0721] A "closet" is a digital repository of clothing items that a user registers through their device.

[0722] A "terminal" is an electronic device such as a smartphone, tablet, or PC that allows a user to access and operate the system.

[0723] "Current location information" is information about the user's physical location obtained using the terminal's GPS function or the like.

[0724] "Weather information" is environmental data such as weather and temperature acquired based on the user's current location information.

[0725] "Situation information" is information about an event or situation that is input by the user when receiving a coordination suggestion.

[0726] The "server" is a computer system that processes the user's closet information, weather information, and the like, and generates coordination suggestions.

[0727] "AI" stands for artificial intelligence, a technology that analyzes information about items in a user's closet, weather information, situational information, etc. to generate optimal outfits.

[0728] "Coordination" refers to a combination of clothing items worn by a user.

[0729] "Past coordination posts" are data on past coordination posts posted to the system by the user and other users.

[0730] "Virtual try-on" means that a user tries on clothing items in a digital space using an avatar.

[0731] An "avatar" is a digital character that resembles the user and is used during virtual fitting.

[0732] A "generative AI model" is an artificial intelligence model that generates optimal outfits based on the items in the user's closet and weather information.

[0733] A "prompt statement" is an instruction given to a generative AI model to make it output appropriate coordinates.

[0734] An "online shopping platform" is an e-commerce system over the Internet through which users purchase clothing items.

[0735] "Incentives" are rewards or benefits that users receive by posting their suggested outfits.

[0736] The present invention is a system that effectively utilizes multiple clothing items registered in a user's closet to suggest optimal outfits according to the weather, temperature, and situation, and allows the user to check the outfits in a virtual try-on environment. The system of the present invention is mainly implemented in the following manner.

[0737] 1. Register your closet items

[0738] Users can register clothing items in their closets using their own devices by taking photos of the items or uploading existing photos and entering information such as the item's category, color, brand, and size. In addition, users can set items purchased from online shopping platforms to be automatically added to their closets.

[0739] 2. Obtaining weather information

[0740] The device uses the GPS function to obtain the user's current location information, and then uses that information to obtain the day's weather and temperature data from a weather information service, thereby providing real-time weather information.

[0741] 3. Enter situation information

[0742] The user inputs situation information (e.g., business, casual date, outdoors, etc.) into the device based on the day's schedule and circumstances. This situation information is selected from options provided on the app interface.

[0743] 4. Coordination suggestions and generative AI models

[0744] The server uses AI to generate the optimal outfit based on the weather information, situation information, and information about items in the user's closet. This involves creating a prompt using a generative AI model, and then the AI ​​generates the optimal outfit based on that prompt. For example, the AI ​​might be given the following prompt:

[0745] "What would be an appropriate outfit for a business setting on a sunny day when the temperature is 25 degrees? In my closet I have a blue shirt, a white shirt, black pants, jeans, a suit jacket, and a casual jacket."

[0746] 5. Providing a virtual try-on environment

[0747] The server provides a means for users to create an avatar for virtual try-on and virtually try on suggested outfits for that avatar. This function allows users to visually check the suggested clothing items in a virtual environment. If the user is satisfied with the outfit, they can select it and actually wear it.

[0748] 6. Posts and Incentives

[0749] Users can try on the suggested outfits, take photos, and post them to the app. The server then receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive, which can be used on online shopping platforms.

[0750] The system configured as described above allows users to efficiently receive coordination suggestions and visually confirm them while making comfortable clothing selections, thereby improving the user's shopping experience and reducing the stress of selecting clothing.

[0751] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0752] Step 1:

[0753] Registering closet items

[0754] Users use their devices to register clothing items in their closets. They take photos of the items or upload existing photos, and enter information such as the item's category, color, brand, and size. The images and text data are then sent to the server and stored in a database. Items purchased on the online shopping platform are automatically added to the user's closet based on the purchase information.

[0755] Input: Item photo, category, color, brand, size, and other information

[0756] Data processing: Classification and registration of input image and text data

[0757] Output: Registered item information is saved in the database

[0758] Step 2:

[0759] Obtaining weather information

[0760] The device uses the GPS function to obtain the user's current location information, and then uses that information to obtain the day's weather and temperature data from a weather information service. The obtained weather information is used in the next step.

[0761] Input: User's current location information

[0762] Data processing: Send an API request based on current location information to obtain weather data

[0763] Output: Weather information and temperature data

[0764] Step 3:

[0765] Entering situation information

[0766] The user inputs situation information (e.g., business, casual date, outdoors, etc.) into the terminal to receive coordination suggestions. The situation information is sent to the server along with the previous weather information.

[0767] Input: Situation information (choose from options)

[0768] Data processing: collecting and integrating situational data

[0769] Output: Situation information combined with weather information

[0770] Step 4:

[0771] Use of outfit suggestions and generative AI models

[0772] The server uses AI to generate the optimal outfit based on the weather information, situation information, and information about the items in the user's closet. In this process, a generative AI model is used to create a prompt, and the AI ​​generates the optimal outfit based on that prompt.

[0773] Input: Weather information, situation information, item information in the user's closet

[0774] Data processing: Prompt sentence generation and coordination suggestions using AI models

[0775] Output: Generated coordinate information

[0776] Step 5:

[0777] Providing a virtual try-on environment

[0778] The server creates an avatar for the user to virtually try on the suggested outfits, allowing the user to visually check the suggested clothing items in a virtual environment.

[0779] Input: Generated coordinate information, user avatar

[0780] Data processing: 3D rendering to apply coordinate information to an avatar

[0781] Output: Visualized virtual try-on image

[0782] Step 6:

[0783] Posts and Incentives

[0784] Users try on the suggested outfits, take photos, and post them to the app. The posted information is sent to a server and stored in a database. At the same time, users are awarded points as an incentive, which can be used on the online shopping platform.

[0785] Input: User's outfit photo, related information

[0786] Data processing: Saving submitted data and calculating incentive points

[0787] Output: Save to database and give points

[0788] Through the above steps, the system of the present invention enables the user to efficiently receive coordination suggestions and comfortably select clothes.

[0789] 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.

[0790] ---

[0791] The present invention is a system that efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits based on the weather, temperature, situation, and the user's emotions. Furthermore, by analyzing the user's past outfit posting data and providing recommended items and outfits, the system simplifies outfit selection and improves the shopping experience.

[0792] The system of the present invention is implemented mainly by the following method:

[0793] 1. Register your closet items

[0794] The user registers their clothing items in the closet. Using their device, the user takes a photo of the item or uploads an existing photo, and enters details such as the item's category, color, brand, and size. Then, the user presses the "Save" button to send the information to the app.

[0795] The server stores the item information sent by the user in a database and also adds the item information to the database when a request for adding an item purchased on the online shopping platform to the user's closet is received, since the item may be automatically added to the user's closet.

[0796] 2. Obtaining weather, temperature, and situation information

[0797] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[0798] The user selects the situation information for the day, for example, choosing a scene such as "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[0799] 3. Emotional Recognition

[0800] The device receives the user's facial expressions and voice as input and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and recognizes their emotions. For example, it uses a camera to take a picture of the user's face and identifies their emotions through image analysis.

[0801] 4. Coordination suggestions

[0802] The server uses AI to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotional data obtained from the emotion engine. The AI ​​analyzes this information and suggests combinations of items that suit the user. This outfit information is sent to the device and displayed on the device.

[0803] 5. Recommendations from past posts

[0804] The server analyzes the outfit data posted by the user and other users in the past, generates recommended items and outfits that match the current weather information, situation information, and emotion data, and adds online shopping links. This information is also sent to the device and displayed for the user to use.

[0805] 6. Post your outfit

[0806] The user actually wears the suggested outfit and takes a photo. They post the photo to the app, inputting weather, temperature, and situation information. Then, they press the "Post new outfit" button.

[0807] The server saves the new coordinate posting data received from the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[0808] Specific examples

[0809] For example, consider the case where a user is choosing an outfit for going out with friends.

[0810] The user launches the app and retrieves the weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects the "Casual Scene" setting. The device takes a photo of the user's face with the camera, and the emotion engine recognizes the user's facial expression as "happy."

[0811] The server suggests suitable shirts, pants, and sneakers from the user's closet. It also recommends similar items (such as caps) based on outfit data posted by other users in the past, and provides a link to an online shopping platform.

[0812] The user wears the proposed outfit, takes a photo, and posts it to the app. The server then awards points to the user as an incentive.

[0813] As described above, the system of the present invention not only streamlines the user's clothing selection process and provides optimal coordination, but also makes suggestions that take the user's emotions into consideration, thereby further improving the shopping experience.

[0814] ---

[0815] The above is the "Form for implementing the invention."

[0816] The processing flow will be explained below.

[0817] ---

[0818] Step 1: Register your closet items

[0819] A user logs in to the app and opens the "Closet" section. They take a photo of an item or upload an existing photo, and enter details such as the item's category, color, brand, and size. They press the "Save" button to send the information to the app.

[0820] ---

[0821] Step 2: Save your closet data

[0822] The server receives the item information sent by the user and stores it in a database. In addition, since an item purchased on the online shopping platform may be automatically added to the user's closet, the server adds the item information to the database when a request for this is received.

[0823] ---

[0824] Step 3: Get the weather and temperature

[0825] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[0826] ---

[0827] Step 4: Enter situation information

[0828] The user selects the situation information for the day, for example, "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[0829] ---

[0830] Step 5: Recognize emotions

[0831] The device captures the user's facial expressions and voice using a camera and microphone and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and identifies their emotions. For example, it may recognize "they look happy" through image analysis.

[0832] ---

[0833] Step 6: Coordination proposal

[0834] The server uses AI to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotion data obtained from the emotion engine, and then sends the generated outfit information to the device.

[0835] ---

[0836] Step 7: Displaying Coordination

[0837] The device displays the coordinated information received from the server on the app screen, and the user can confirm the proposed coordinated outfit.

[0838] ---

[0839] Step 8: Analyze past posts

[0840] The server analyzes the outfit data posted by the user and other users in the past, and generates recommended items and outfits that match the current weather information, situation information, and emotional data. The generated outfit information includes recommendations for similar items and online shopping links. This information is also sent to the device.

[0841] ---

[0842] Step 9: Display recommended outfits

[0843] The device displays the recommended outfit information and purchase links received from the server on the screen, allowing the user to purchase the items based on this information.

[0844] ---

[0845] Step 10: Post your outfit

[0846] The user actually wears the suggested outfit and takes a photo. The photo is then posted to the app, along with weather, temperature, and situation information. The user then presses the "Post new outfit" button.

[0847] ---

[0848] Step 11: Save submitted data and assign points

[0849] The server saves the new coordinate posting data sent by the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[0850] ---

[0851] The above is the content of the program processing of the system of the present invention, divided into specific steps.

[0852] Example 2

[0853] 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."

[0854] Previous systems lacked a means for users to efficiently register and use a large number of clothing items, and were inadequate in suggesting outfits based on weather information, situations, or emotions. Furthermore, the system was incomplete in utilizing data from users' past outfit submissions to make new suggestions, and there were also issues with providing incentives for suggested outfits. For these reasons, a new system is needed to further improve users' clothing selection and shopping experience.

[0855] 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.

[0856] In this invention, the server includes a means for registering clothing items in the user's closet, a means for acquiring weather information, a means for recognizing situation information and emotions, a means for proposing optimal outfits using a generative AI model, and a means for generating and recommending recommended outfits based on past outfit posts. This allows users to efficiently manage their clothing items and receive optimal outfit suggestions based on the weather, situation, and emotions. Furthermore, by providing new suggestions and incentives using past posting data, the system further improves clothing selection and the shopping experience.

[0857] A "user" is an individual who uses the system to manage their own clothing items and receive suggestions for optimal coordination.

[0858] A "closet" is a virtual storage space that manages multiple clothing items registered by a user on a database.

[0859] A "terminal" is an electronic device that allows a user to access the system and perform input operations or receive data. Examples include smartphones, tablets, and personal computers.

[0860] "Weather information" is data relating to the weather, temperature, and other climate information based on the user's current location.

[0861] "Situation information" is data relating to scenes and circumstances that a user sets according to the schedule and activities of the day. For example, it includes categories such as "casual" and "business."

[0862] An "emotion engine" is software or an algorithm that analyzes a user's facial expressions and voice to recognize their emotions.

[0863] A "generative AI model" is a machine learning model that suggests optimal outfits based on information about items in the user's closet, weather information, situational information, and emotional data.

[0864] "Past coordinated outfit posts" is historical data about coordinated outfits posted by the user or other users on the system.

[0865] "Recommended outfits" are new clothing combinations suggested by a generative AI model based on past outfit posting data.

[0866] "Incentives" are rewards, points, or other benefits given to users when they post new outfits.

[0867] The system of this invention efficiently utilizes multiple clothing items registered in a user's closet and proposes optimal outfits based on the weather, temperature, situation, and the user's emotions. Furthermore, by analyzing the user's past outfit posting data and providing recommended items and outfits, it simplifies clothing selection and improves the shopping experience.

[0868] Registering closet items

[0869] Users register their clothing items through their devices. They take a photo of the clothing using the device's camera or upload an existing photo. They then enter detailed information such as the item's category (e.g., shirt, pants), color, brand, and size, and press the "Save" button to send it to the server.

[0870] The server stores the received clothing item information in a database, and also automatically adds items purchased through the online shopping platform to the closet.

[0871] Obtaining weather, temperature, and situation information

[0872] The device acquires the user's current location information using the GPS function, then transmits the location information to the weather information service to acquire weather and temperature data.

[0873] The user selects the situation of the day on the app interface, choosing a scene such as "casual" or "business," and then presses the "Set Situation" button to send it to the server.

[0874] Emotion recognition

[0875] The device sends the user's facial expressions and voice as input to the emotion engine. The device's camera captures a picture of the user's face, or the microphone captures voice data. The emotion engine analyzes this data and identifies the user's emotions. Specifically, it analyzes emotions using OpenFace or the Google Cloud Speech-to-Text API.

[0876] Coordination suggestions

[0877] The server uses a generative AI model to suggest optimal outfits based on information about items in the user's closet, weather information, situation information, and emotional data. The server then analyzes the data using an AI model (e.g., TensorFlow) to generate optimal outfits. This generated outfit information is then sent to the device.

[0878] Recommendations from past posts

[0879] The server analyzes the outfit data posted by the user and other users in the past, and generates recommended items and outfits based on current weather information, situation information, and emotional data. It also generates appropriate online shopping links and sends them to the device.

[0880] Coordination Post

[0881] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user then enters the weather, temperature, and situation information along with the photo, and presses the "Post new outfit" button.

[0882] The server stores the received posting data of the new coordinates in a database, awards points as an incentive for the user's posting, and reflects the information in the user's account.

[0883] Specific examples

[0884] For example, consider the case where a user is choosing an outfit for going out with friends.

[0885] The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects the "Casual Scene" setting. The device then takes a photo of the user's face with the camera, and the emotion engine recognizes the user's facial expression as "happy."

[0886] The server suggests suitable shirts, pants, and sneakers from the user's closet, and also recommends additional items such as caps based on data posted by other users in the past, along with links to online shopping platforms.

[0887] The user wears the suggested outfit, takes a photo, and posts it to the app. After posting, the server gives the user points as an incentive and reflects the information in the user's account.

[0888] Prompt Sentence Examples

[0889] "Please suggest the best outfit for a casual occasion when the weather is sunny, the temperature is 25 degrees, and you are feeling happy."

[0890] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0891] Program processing flow

[0892] The program processing of this system is divided into the following steps:

[0893] Step 1: Register your closet items

[0894] Specific behavior:

[0895] The user takes a photo of the clothing item with the device's camera or uploads an existing photo.

[0896] The user enters details such as clothing item category, color, brand, size, etc. and presses the "Save" button.

[0897] Input: Clothing item photo and details

[0898] The terminal sends this information to the server.

[0899] The server stores the received information in a database.

[0900] Output: New clothing item information stored in the database

[0901] Step 2: Obtaining weather, temperature, and situation information

[0902] Specific behavior:

[0903] The device uses GPS to obtain the current location.

[0904] Input: Latitude and longitude information of current location

[0905] The device transmits its current location information to a weather information service and obtains weather and temperature data.

[0906] The user selects and enters the situation information for the day on the app interface.

[0907] Input: Situation information (casual, business, etc.)

[0908] Output: Obtained weather and temperature information and situation information

[0909] Step 3: Recognize emotions

[0910] Specific behavior:

[0911] The device captures the user's facial expressions with a camera or records their voice with a microphone.

[0912] Input: User's facial expression and voice data

[0913] The device sends this data to the emotion engine.

[0914] The emotion engine analyzes the data and identifies the user's emotion (e.g., "happy").

[0915] Output: User emotion data

[0916] Step 4: Coordination proposal

[0917] Specific behavior:

[0918] The server uses a generative AI model to generate the optimal outfit based on information about the items in the user's closet, acquired weather and temperature information, situation information, and emotional data.

[0919] Input: Closet item information, weather and temperature information, situation information, emotional data

[0920] An AI model analyzes this data and suggests optimal outfits.

[0921] The server sends the generated coordinate information to the terminal.

[0922] Output: Proposed coordinate information

[0923] Step 5: Recommendations from past posts

[0924] Specific behavior:

[0925] The server analyzes the coordination data posted by the user and other users in the past.

[0926] Input: Past coordination posting data

[0927] The server compares current weather information, situation information, and emotional data to generate recommended items and outfits.

[0928] The server adds a link to the online shopping platform and sends it to the terminal.

[0929] Output: Recommended items, outfit information, and related shopping links

[0930] Step 6: Post your outfit

[0931] Specific behavior:

[0932] The user wears the suggested outfit, takes a photo, and posts it to the app.

[0933] Input: outfit photo, weather and temperature information, situation information

[0934] The server stores the received posting data in a database and adds points to the user's account as an incentive.

[0935] Output: Post data and awarded points stored in the database

[0936] (Application example 2)

[0937] 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."

[0938] Conventional clothing selection support systems only suggest outfits based on the weather and situation, and have the problem of not being able to make suggestions that reflect the user's emotional state. Furthermore, they do not fully utilize the user's past outfit data, so there is a need to improve the shopping experience and the accuracy of suggestions.

[0939] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to register multiple clothing items in their closet; a means for a terminal to acquire weather information based on the user's current location information; a means for a user to input the weather information and situation information; a means for the server to generate an optimal outfit using AI based on the weather information, situation information, and item information in the user's closet; a means for the terminal to acquire the user's facial expressions and voice and analyze them with an emotion engine; a means for the server to generate an optimal outfit based on the weather information, situation information, emotion data, and item information in the user's closet; a means for the server to generate and recommend a recommended outfit based on similar items from past outfit posts; and a means for a user to post the suggested outfit and receive an incentive for the post. This enables personalized clothing selection based on the user's emotional state, improving the accuracy of suggestions and the shopping experience.

[0940] "User" refers to an entity that registers clothing items and receives coordination suggestions.

[0941] "Clothing items" refer to various elements of clothing that a user registers in their closet and uses to coordinate outfits.

[0942] A "closet" refers to a virtual storage space that stores information about clothing items owned by a user.

[0943] "Terminal" refers to a device that a user operates to acquire and display information. Examples include smartphones, tablets, and personal computers.

[0944] "Location information" refers to the physical location of a user, typically obtained using the Global Positioning System (GPS).

[0945] "Weather information" refers to environmental data such as the weather and temperature at the user's current location.

[0946] "Situation information" refers to information about a particular everyday or special situation specified by the user, such as "casual" or "business."

[0947] "Server" refers to a computer system that processes information sent by users, makes various suggestions, and manages data.

[0948] "Item information" refers to detailed information about a clothing item, such as category, color, brand, size, etc.

[0949] "AI (artificial intelligence)" refers to the machine learning or data analysis algorithms used by the server to generate the coordinates.

[0950] An "emotion engine" refers to a system that recognizes a user's emotional state by analyzing their facial expressions and voice.

[0951] "Emotion data" refers to information that indicates the user's emotional state as analyzed by the emotion engine.

[0952] "Coordination" refers to a combination of multiple clothing items suggested to the user.

[0953] "Past coordination posts" refer to coordination suggestions and results previously made by the user and other users.

[0954] "Recommendation" refers to the act of the server presenting new outfits or items to the user.

[0955] "Posting" refers to the act of a user wearing a suggested outfit and uploading the result to the system.

[0956] "Incentives" refer to rewards, points, and other benefits that users receive when they post content.

[0957] This invention is a system that efficiently utilizes multiple clothing items registered in a user's virtual closet and suggests optimal coordination based on the weather, temperature, situation, and the user's emotions.

[0958] 1. Register your closet items

[0959] A user registers a clothing item in their closet using their device. They take a photo of the clothing item using the device's camera and enter details such as the item's category, color, brand, and size. The device is equipped with an image recognition library (e.g., TensorFlow) that analyzes the image to obtain item information. This information is sent to the server and stored in a database.

[0960] 2. Obtaining weather, temperature, and situation information

[0961] The device uses its GPS function to obtain the user's current location information. It then sends this information to the weather information service's API (e.g., OpenWeatherMap) to obtain weather and temperature data. The user then uses the application's interface to select the situation for the day. For example, the user can enter a situation such as "casual" or "business."

[0962] 3. Emotional Recognition

[0963] The device acquires the user's facial expressions and voice and sends them to an emotion recognition engine (e.g., Microsoft Azure Cognitive Services). The emotion engine analyzes the user's facial expressions and voice and recognizes their emotions. For example, it can take a picture of the user's face with a camera and perform image analysis to determine emotions such as "happy" or "sad."

[0964] 4. Coordination suggestions

[0965] The server uses AI (e.g., TensorFlow) to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotion data obtained from an emotion engine. This outfit information is sent to the device and displayed to the user.

[0966] 5. Recommendations from past posts

[0967] The server analyzes the coordination data posted by the user and other users in the past, and generates recommended items and coordinations that match the current weather information, situation information, and emotion data. This recommendation information is also sent to the terminal and displayed for the user to use.

[0968] 6. Post your outfit

[0969] The user actually wears the suggested outfit, takes a photo, and posts it to the application. The user then re-enters weather, temperature, and situation information for the photo and posts it. The server saves the received new outfit posting data in a database, awards points as an incentive based on the user's post, and reflects the information in the user's account.

[0970] Specific examples

[0971] For example, consider a situation where a user is choosing an outfit for going out with friends. The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects "casual scene." The device takes a photo of the user's face with a camera, and the emotion engine recognizes the user's facial expression as "happy." The server then suggests suitable shirts, pants, and sneakers from the user's closet. Based on outfit data posted by other users in the past, the server also recommends similar items (e.g., caps) and displays a link to an online shopping platform.

[0972] Prompt Sentence Examples

[0973] "The user wants to go to lunch with a friend in the spring sunshine, so please suggest the perfect casual outfit from his closet. The temperature is 22 degrees and the weather is sunny. The user looks excited."

[0974] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0975] Step 1:

[0976] The user takes a photo of a clothing item using the device's camera. The photo is analyzed by an image recognition library (e.g., TensorFlow) to obtain detailed information about the item, such as its category, color, brand, and size. This information is sent to the server via the device and stored in a database. The input is a photo of the clothing item, and the output is detailed information about the item.

[0977] Step 2:

[0978] The device uses its GPS function to obtain the user's current location information. The obtained location information is sent to the API of a weather information service (e.g., OpenWeatherMap) to obtain weather and temperature data. Based on this location information, the API returns weather and temperature data. The input is the current location information, and the output is weather and temperature data.

[0979] Step 3:

[0980] The user uses the application interface to input situation information for the day, for example, by selecting a category such as "casual" or "business." The input is the situation information, and the output is the selected category.

[0981] Step 4:

[0982] The device captures the user's facial expressions and voice. Facial expression data is captured by the smartphone's camera, and voice data is captured by the microphone. This data is sent to an emotion recognition engine (e.g., Microsoft Azure Cognitive Services), which analyzes it to identify emotions. The input is facial expression and voice data, and the output is recognized emotion data.

[0983] Step 5:

[0984] The server uses AI (e.g., TensorFlow) to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotional data. The input is information about items in the closet, weather information, situation information, and emotional data, and the output is the optimal outfit.

[0985] Step 6:

[0986] The server generates recommended outfits based on similar items from past outfit posts and prepares the data to recommend to users. The input is past outfit data and current conditions (weather information, situation information, emotion data), and the output is the recommended outfit.

[0987] Step 7:

[0988] The user actually wears the proposed outfit, takes a photo, and posts it to the application. The photo also includes weather, temperature, and situation information. This new outfit posting data is sent to the server and stored in a database. The input is a photo of the proposed outfit and related information, and the output is the outfit posting data stored in the database.

[0989] Step 8:

[0990] The server awards points as an incentive based on the user's posts and reflects the information in the user's account. The input is the posted coordinate data, and the output is the awarded point information.

[0991] In this way, personalized clothing selection based on the user's emotional state can be achieved, improving the accuracy of recommendations and the shopping experience.

[0992] 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.

[0993] 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.

[0994] 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.

[0995] [Third embodiment]

[0996] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0997] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0998] 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).

[0999] 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.

[1000] 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.

[1001] 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).

[1002] 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. 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.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] 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.

[1007] 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."

[1008] ---

[1009] This system efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits according to the weather, temperature, and situation. Furthermore, it analyzes past outfit posting data from users and provides recommended items and outfits, simplifying outfit selection and improving the shopping experience.

[1010] The system of the present invention is implemented mainly by the following method:

[1011] 1. Register your closet items

[1012] Users register their clothing items in their closets. They operate their devices to take photos of the items or upload existing photos, and enter information such as the item's category, color, brand, and size. Items purchased through the online shopping platform are automatically added to the user's closet.

[1013] 2. Obtaining weather, temperature, and situation

[1014] The device obtains weather information based on the current location. The device uses its GPS function to obtain the coordinates of the current location and transmits that information to a weather information service to obtain the weather and temperature data for that day. The user inputs situation information into the device according to the day and circumstances. This situation information is selected from options provided on the app interface.

[1015] 3. Coordination suggestions

[1016] The server uses AI to generate optimal outfits based on information about the items in the user's closet, acquired weather information, and situation information. The AI ​​analyzes this information and suggests combinations of items that suit the user. This outfit information is sent to the device and displayed on the device.

[1017] 4. Recommendations from past posts

[1018] The server analyzes the outfit data posted by the user and other users in the past and generates recommended items and outfits that match the current weather information and situation. The generated outfit information includes recommendations for similar items and links to online shopping platforms. This information is also sent to the device and displayed for the user to use.

[1019] 5. Post your outfit

[1020] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user can enter information about the weather, temperature, and situation, and post it along with the outfit. Once posted, the server receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive.

[1021] Specific examples

[1022] For example, consider the case where a user is deciding on an outfit for work. The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects "business attire." The server then suggests a shirt, pants, and jacket from the user's closet. Based on data on business outfits posted by other users in the past, the server also recommends similar items (such as ties) and displays a link to an online shopping platform. If the suggested outfit satisfies the user, the user can wear the outfit and post a photo to the app. The server then awards the user points as an incentive for posting.

[1023] As described above, the system of the present invention not only makes it easier for users to select clothes and provides optimal coordination, but also improves the shopping experience.

[1024] ---

[1025] The above is the "Form for implementing the invention."

[1026] The processing flow will be explained below.

[1027] ---

[1028] Step 1: Register your closet items

[1029] A user logs in to the app and opens the "Closet" section. They take a photo of an item or upload an existing photo, and enter details such as the item's category, color, brand, and size. They press the "Save" button to send the information to the app.

[1030] ---

[1031] Step 2: Save your closet data

[1032] The server stores the item information sent by the user in a database and also adds the item information to the database when a request for adding an item purchased on the online shopping platform to the closet is received, since the item may be automatically added to the closet.

[1033] ---

[1034] Step 3: Get the weather and temperature

[1035] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[1036] ---

[1037] Step 4: Enter situation information

[1038] The user selects the situation information for the day, for example, choosing a scene such as "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[1039] ---

[1040] Step 5: Generate coordinates

[1041] The server uses AI to generate optimal outfits based on information about the items in the user's closet, acquired weather information, and situation information, and then sends the generated outfit information to the user's device.

[1042] ---

[1043] Step 6: Displaying your outfits

[1044] The device displays the coordinated outfit information received from the server on the app screen, allowing the user to check the proposed outfit.

[1045] ---

[1046] Step 7: Analyze past posts

[1047] The server searches the user's past outfit posting data and analyzes outfits that contain items similar to the weather and situation information. Based on the analysis results, it generates recommended items and outfits, adds online shopping links, and sends them to the user's device.

[1048] ---

[1049] Step 8: Display recommended outfits

[1050] The device displays the recommended outfit information and purchase links received from the server on the screen, allowing the user to purchase the items based on this information.

[1051] ---

[1052] Step 9: Post your outfit

[1053] The user actually wears the suggested outfit and takes a photo. They post the photo to the app, inputting weather, temperature, and situation information. Then, they press the "Post new outfit" button.

[1054] ---

[1055] Step 10: Save submitted data and assign points

[1056] The server saves the new coordinate posting data received from the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[1057] ---

[1058] The above is the content of the program processing of the system of the present invention, divided into specific steps.

[1059] Example 1

[1060] 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."

[1061] Until now, it has been difficult for users to efficiently manage the clothing items in their closets and choose the best outfit for the day's weather, temperature, and situation. Furthermore, since there was no system that provided recommended items and outfits based on past outfit posts, users had to spend time and effort choosing their outfits. Furthermore, there was also a lack of a convenient way for users to purchase the items they wanted.

[1062] 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.

[1063] In this invention, the server includes: means for a user to register multiple clothing items in their closet; means for a terminal to acquire weather information based on the user's current location information; means for the user to input the weather information and situation information; means for generating an optimal outfit based on the weather information, situation information, and item information in the user's closet using a generative AI model; means for generating and recommending recommended outfits based on similar items from past outfit posts; and means for the user to post the suggested outfits and receive incentives for the posts. This not only makes the user's clothing selection more efficient and suggests optimal outfits, but also improves the user's shopping experience.

[1064] "User" refers to an individual who uses the system to register their own clothing items and receive coordination suggestions.

[1065] A "terminal" is an electronic device operated by a user, and includes a smartphone, tablet, personal computer, etc.

[1066] "Weather information" refers to data that indicates weather conditions such as the weather and temperature at the user's current location.

[1067] "Situation information" is information input by the user regarding the plans and activities for the day, and includes, for example, "business scene" and "casual scene".

[1068] "Server" refers to the central processing unit that aggregates, analyzes, stores, and coordinates data on the system.

[1069] "Item information in the closet" is information about clothing items that the user has registered in the closet, and includes attributes such as category, color, brand, and size.

[1070] A "generative AI model" refers to an algorithm or software that uses artificial intelligence technology to generate optimal outfits for users.

[1071] "Past coordination posts" are coordination data that the user or other users have previously posted to the system.

[1072] "Recommended coordination" refers to a coordination generated by the server and recommended to the user based on past coordination submission data and current weather and situation information.

[1073] "Incentives" are rewards or benefits that users receive for using the system, and include points, for example.

[1074] An "e-commerce platform" is a website or application where goods are bought and sold over the internet, and where users can purchase clothing items online.

[1075] The present invention is a system that efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits according to the weather, temperature, and situation. Furthermore, by analyzing the user's past outfit posting data, the system offers recommended items and outfits, simplifying outfit selection and improving the shopping experience.

[1076] The system of the present invention is implemented mainly by the following method.

[1077] Registering closet items

[1078] A user uses an application on their device to register clothing items in their closet. The user launches the application and registers new items on the closet management screen. They take a photo of the item or upload an existing photo, and enter information such as the item's category, color, brand, and size. Items purchased through the online shopping platform are automatically added to the user's closet.

[1079] As a specific example, a user uploads a photo of a jacket taken with a smartphone app and enters the category as "outerwear," the color as "black," the brand as "Uniqlo," and the size as "M."

[1080] Obtaining weather, temperature, and situation information

[1081] The device uses its GPS function to obtain the coordinates of its current location. Based on the obtained coordinates, it queries a weather information service (e.g., OpenWeatherMap) to obtain weather and temperature data. This information is displayed on the device screen. The user can then enter information about the situation for that day and select a specific situation (e.g., "Business Scene").

[1082] For example, the device acquires the user's current location using GPS, queries OpenWeatherMap to obtain information such as "sunny, temperature 25 degrees," and displays it in the app. The user selects "Business scene."

[1083] Coordination suggestions

[1084] The server obtains information about the items in the closet. Based on the obtained weather and situation information, it uses a generative AI model (e.g., TensorFlow) to generate the optimal outfit. This suggestion is sent to the device and displayed.

[1085] For example, a combination of "white shirt," "gray pants," and "black jacket" is suggested.

[1086] Recommendations from past posts

[1087] The server analyzes the outfit data posted by the user and other users in the past. Based on the analysis results, it generates recommended items and outfits that match the current weather information and situation. This includes recommendations for similar items and links to online shopping platforms. The generated recommended information is sent to the device and displayed.

[1088] As a specific example, it analyzes outfits posted in the past with the tag "business scene" and recommends items such as "ties" and "business shoes."

[1089] Coordination Post

[1090] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user then enters information about the weather, temperature, and situation, and posts the photo. Once posted, the server receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive.

[1091] For example, when a user wears a suggested outfit and takes a photo, they upload it to the app, entering the information "sunny, 25 degrees, business setting." The server saves the data and awards points to the user.

[1092] Specific actions

[1093] For example, consider the case where a user is deciding on an outfit for work. The user launches the app, and the device retrieves weather and temperature information for the user's current location. If the weather is sunny and the temperature is 25 degrees, the user selects "business attire." The server then suggests a shirt, pants, and jacket from the user's closet. Based on data on business outfits posted by other users in the past, the server also recommends similar items (such as ties) and displays a link to an online shopping platform. If a suggested outfit satisfies the user, the user can wear the outfit and post a photo within the app. The server then awards the user points as an incentive for posting.

[1094] As described above, the system of the present invention not only makes it easier for users to select clothes and provides optimal coordination, but also improves the shopping experience.

[1095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1096] Program processing flow

[1097] Step 1: Register your closet items

[1098] Step 1-1:

[1099] The user launches the app on their device and opens the closet management screen.

[1100] Input: The app launch operation.

[1101] Output: The closet management screen will be displayed on the device.

[1102] Step 1-2:

[1103] The user selects the Register New Item button and takes a photo or uploads an existing photo.

[1104] Input: Image of new item (taken or uploaded).

[1105] Output: Image data of the new item.

[1106] Step 1-3:

[1107] The user enters the item information (category, color, brand, size, etc.) and presses the save button.

[1108] Input: Item information (category, color, brand, size).

[1109] Output: Item registration data.

[1110] Steps 1-4:

[1111] The server receives the registered information and stores it in a database.

[1112] Input: Registration information data.

[1113] Output: Save to closet database.

[1114] Specific behavior:

[1115] Users take a photo of the jacket with their smartphone camera and upload it to the app.

[1116] The user enters "Category: Outerwear", "Color: Black", "Brand: Uniqlo", and "Size: M".

[1117] The server stores the received information in the closet database.

[1118] Step 2: Obtaining weather, temperature, and situation

[1119] Step 2-1:

[1120] The device uses GPS to obtain the coordinates of the current location.

[1121] Input: GPS function activation operation.

[1122] Output: Current location information (coordinate data).

[1123] Step 2-2:

[1124] The device sends its current location coordinates to a weather information service and obtains weather and temperature data.

[1125] Input: coordinate data.

[1126] Output: Weather information, temperature information.

[1127] Step 2-3:

[1128] The device displays the weather and temperature data it has obtained.

[1129] Input: Weather information, temperature information.

[1130] Output: Display weather and temperature information on the terminal screen.

[1131] Step 2-4:

[1132] The user inputs the situation information for that day.

[1133] Input: Situation information (e.g., business situation).

[1134] Output: Situation information data.

[1135] Specific behavior:

[1136] The device obtains the user's current location using GPS and queries the weather information service.

[1137] The device obtains the information "Sunny, 25 degrees" and displays it in the app.

[1138] The user selects "Business Scene" from the app's situation selection screen.

[1139] Step 3: Coordination proposal

[1140] Step 3-1:

[1141] The server retrieves information about items in the closet.

[1142] Input: Information from the closet database.

[1143] Output: The retrieved item information.

[1144] Step 3-2:

[1145] The server uses a generative AI model based on the acquired weather and situation information to generate the optimal outfit.

[1146] Input: Weather information, temperature information, situation information, item information.

[1147] Output: Optimal coordination suggestions.

[1148] Step 3-3:

[1149] The server sends the generated coordinate information to the terminal and displays it.

[1150] Input: Optimal coordination suggestions.

[1151] Output: Display of suggested coordinates on terminal screen.

[1152] Specific behavior:

[1153] The server retrieves information about "white shirt," "gray pants," and "black jacket" from the closet.

[1154] The server uses an AI model to generate an outfit suitable for a sunny day with a temperature of 25 degrees and a business setting.

[1155] The server sends the suggested outfits to the smartphone and displays them on the app.

[1156] Step 4: Recommendations from past posts

[1157] Step 4-1:

[1158] The server acquires coordinate data posted in the past by the user and other users.

[1159] Input: Coordinated submission database.

[1160] Output: Past posting data.

[1161] Step 4-2:

[1162] The server performs analysis and generates recommended items and outfits that match the current weather information and situation.

[1163] Input: Past posting data, weather information, situation information.

[1164] Output: Recommended item information, recommended coordination information.

[1165] Step 4-3:

[1166] The server sends the generated recommendation information to the terminal and displays it.

[1167] Input: Recommended coordination information.

[1168] Output: Recommended outfits displayed on the device screen.

[1169] Specific behavior:

[1170] The server retrieves and analyzes past coordination posting data tagged with "business scene."

[1171] The server generates recommendations such as "ties" and "business shoes" based on past data.

[1172] The server sends the generated recommendation information to the smartphone and displays it in the app.

[1173] Step 5: Post your outfit

[1174] Step 5-1:

[1175] The user wears the suggested outfit and takes a photo.

[1176] Input: A photo of the outfit you're wearing.

[1177] Output: The captured photo data.

[1178] Step 5-2:

[1179] Users upload photos to the app and enter weather, temperature, and situation information.

[1180] Input: Photo data, weather information, temperature information, situation information.

[1181] output: The post data.

[1182] Step 5-3:

[1183] The server receives the posted information and stores it in a database.

[1184] Input: Post data.

[1185] Output: Save to database.

[1186] Step 5-4:

[1187] The server will credit the points to the user's account.

[1188] Input: Post data, user information.

[1189] Output: Points awarded.

[1190] Specific behavior:

[1191] The user puts on the suggested outfit of a white shirt, gray pants, and black jacket and takes a photo with their smartphone.

[1192] The user enters the weather as "sunny," the temperature as "25 degrees," and the situation as "business scene," and posts a photo.

[1193] The server receives the posted data, stores it in the database, and awards the user 10 points.

[1194] (Application example 1)

[1195] 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."

[1196] There are already systems that efficiently utilize multiple clothing items registered in a user's closet to suggest optimal outfits based on the weather, temperature, and situation. However, these systems lack the ability to provide a sense of actual fitting and lack a means to reflect feedback on the outfits provided by the user. This can result in low user satisfaction with the outfit suggestions. Furthermore, there is a need for a method to enhance users' visual recognition of the suggested outfits by providing a try-on experience in a virtual space.

[1197] 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.

[1198] In this invention, the server includes a means for allowing a user to create an avatar for virtual try-on and for having the avatar try on coordinated clothing, a means for using a generative AI model to create prompt sentences based on items in the user's closet and weather information, and for the AI ​​to use the prompt sentences to generate an optimal outfit, and a means for acquiring weather information based on the user's current location information, thereby allowing the user to try on coordinated clothing in a virtual space and visually check it.

[1199] A "user" is an individual who uses this system to manage their own clothing items and receive coordination suggestions.

[1200] "Clothing items" are decorative items such as clothes and accessories that a user registers in his or her closet.

[1201] A "closet" is a digital repository of clothing items that a user registers through their device.

[1202] A "terminal" is an electronic device such as a smartphone, tablet, or PC that allows a user to access and operate the system.

[1203] "Current location information" is information about the user's physical location obtained using the terminal's GPS function or the like.

[1204] "Weather information" is environmental data such as weather and temperature acquired based on the user's current location information.

[1205] "Situation information" is information about an event or situation that is input by the user when receiving a coordination suggestion.

[1206] The "server" is a computer system that processes the user's closet information, weather information, and the like, and generates coordination suggestions.

[1207] "AI" stands for artificial intelligence, a technology that analyzes information about items in a user's closet, weather information, situational information, etc. to generate optimal outfits.

[1208] "Coordination" refers to a combination of clothing items worn by a user.

[1209] "Past coordination posts" are data on past coordination posts posted to the system by the user and other users.

[1210] "Virtual try-on" means that a user tries on clothing items in a digital space using an avatar.

[1211] An "avatar" is a digital character that resembles the user and is used during virtual fitting.

[1212] A "generative AI model" is an artificial intelligence model that generates optimal outfits based on the items in the user's closet and weather information.

[1213] A "prompt statement" is an instruction given to a generative AI model to make it output appropriate coordinates.

[1214] An "online shopping platform" is an e-commerce system over the Internet through which users purchase clothing items.

[1215] "Incentives" are rewards or benefits that users receive by posting their suggested outfits.

[1216] The present invention is a system that effectively utilizes multiple clothing items registered in a user's closet to suggest optimal outfits according to the weather, temperature, and situation, and allows the user to check the outfits in a virtual try-on environment. The system of the present invention is mainly implemented in the following manner.

[1217] 1. Register your closet items

[1218] Users can register clothing items in their closets using their own devices by taking photos of the items or uploading existing photos and entering information such as the item's category, color, brand, and size. In addition, users can set items purchased from online shopping platforms to be automatically added to their closets.

[1219] 2. Obtaining weather information

[1220] The device uses the GPS function to obtain the user's current location information, and then uses that information to obtain the day's weather and temperature data from a weather information service, thereby providing real-time weather information.

[1221] 3. Enter situation information

[1222] The user inputs situation information (e.g., business, casual date, outdoors, etc.) into the device based on the day's schedule and circumstances. This situation information is selected from options provided on the app interface.

[1223] 4. Coordination suggestions and generative AI models

[1224] The server uses AI to generate the optimal outfit based on the weather information, situation information, and information about items in the user's closet. This involves creating a prompt using a generative AI model, and then the AI ​​generates the optimal outfit based on that prompt. For example, the AI ​​might be given the following prompt:

[1225] "What would be an appropriate outfit for a business setting on a sunny day when the temperature is 25 degrees? In my closet I have a blue shirt, a white shirt, black pants, jeans, a suit jacket, and a casual jacket."

[1226] 5. Providing a virtual try-on environment

[1227] The server provides a means for users to create an avatar for virtual try-on and virtually try on suggested outfits for that avatar. This function allows users to visually check the suggested clothing items in a virtual environment. If the user is satisfied with the outfit, they can select it and actually wear it.

[1228] 6. Posts and Incentives

[1229] Users can try on the suggested outfits, take photos, and post them to the app. The server then receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive, which can be used on online shopping platforms.

[1230] The system configured as described above allows users to efficiently receive coordination suggestions and visually confirm them while making comfortable clothing selections, thereby improving the user's shopping experience and reducing the stress of selecting clothing.

[1231] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1232] Step 1:

[1233] Registering closet items

[1234] Users use their devices to register clothing items in their closets. They take photos of the items or upload existing photos, and enter information such as the item's category, color, brand, and size. The images and text data are then sent to the server and stored in a database. Items purchased on the online shopping platform are automatically added to the user's closet based on the purchase information.

[1235] Input: Item photo, category, color, brand, size, and other information

[1236] Data processing: Classification and registration of input image and text data

[1237] Output: Registered item information is saved in the database

[1238] Step 2:

[1239] Obtaining weather information

[1240] The device uses the GPS function to obtain the user's current location information, and then uses that information to obtain the day's weather and temperature data from a weather information service. The obtained weather information is used in the next step.

[1241] Input: User's current location information

[1242] Data processing: Send an API request based on current location information to obtain weather data

[1243] Output: Weather information and temperature data

[1244] Step 3:

[1245] Entering situation information

[1246] The user inputs situation information (e.g., business, casual date, outdoors, etc.) into the terminal to receive coordination suggestions. The situation information is sent to the server along with the previous weather information.

[1247] Input: Situation information (choose from options)

[1248] Data processing: collecting and integrating situational data

[1249] Output: Situation information combined with weather information

[1250] Step 4:

[1251] Use of outfit suggestions and generative AI models

[1252] The server uses AI to generate the optimal outfit based on the weather information, situation information, and information about the items in the user's closet. In this process, a generative AI model is used to create a prompt, and the AI ​​generates the optimal outfit based on that prompt.

[1253] Input: Weather information, situation information, item information in the user's closet

[1254] Data processing: Prompt sentence generation and coordination suggestions using AI models

[1255] Output: Generated coordinate information

[1256] Step 5:

[1257] Providing a virtual try-on environment

[1258] The server creates an avatar for the user to virtually try on the suggested outfits, allowing the user to visually check the suggested clothing items in a virtual environment.

[1259] Input: Generated coordinate information, user avatar

[1260] Data processing: 3D rendering to apply coordinate information to an avatar

[1261] Output: Visualized virtual try-on image

[1262] Step 6:

[1263] Posts and Incentives

[1264] Users try on the suggested outfits, take photos, and post them to the app. The posted information is sent to a server and stored in a database. At the same time, users are awarded points as an incentive, which can be used on the online shopping platform.

[1265] Input: User's outfit photo, related information

[1266] Data processing: Saving submitted data and calculating incentive points

[1267] Output: Save to database and give points

[1268] Through the above steps, the system of the present invention enables the user to efficiently receive coordination suggestions and comfortably select clothes.

[1269] 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.

[1270] ---

[1271] The present invention is a system that efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits based on the weather, temperature, situation, and the user's emotions. Furthermore, by analyzing the user's past outfit posting data and providing recommended items and outfits, the system simplifies outfit selection and improves the shopping experience.

[1272] The system of the present invention is implemented mainly by the following method:

[1273] 1. Register your closet items

[1274] The user registers their clothing items in the closet. Using their device, the user takes a photo of the item or uploads an existing photo, and enters details such as the item's category, color, brand, and size. Then, the user presses the "Save" button to send the information to the app.

[1275] The server stores the item information sent by the user in a database and also adds the item information to the database when a request for adding an item purchased on the online shopping platform to the user's closet is received, since the item may be automatically added to the user's closet.

[1276] 2. Obtaining weather, temperature, and situation information

[1277] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[1278] The user selects the situation information for the day, for example, choosing a scene such as "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[1279] 3. Emotional Recognition

[1280] The device receives the user's facial expressions and voice as input and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and recognizes their emotions. For example, it uses a camera to take a picture of the user's face and identifies their emotions through image analysis.

[1281] 4. Coordination suggestions

[1282] The server uses AI to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotional data obtained from the emotion engine. The AI ​​analyzes this information and suggests combinations of items that suit the user. This outfit information is sent to the device and displayed on the device.

[1283] 5. Recommendations from past posts

[1284] The server analyzes the outfit data posted by the user and other users in the past, generates recommended items and outfits that match the current weather information, situation information, and emotion data, and adds online shopping links. This information is also sent to the device and displayed for the user to use.

[1285] 6. Post your outfit

[1286] The user actually wears the suggested outfit and takes a photo. They post the photo to the app, inputting weather, temperature, and situation information. Then, they press the "Post new outfit" button.

[1287] The server saves the new coordinate posting data received from the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[1288] Specific examples

[1289] For example, consider the case where a user is choosing an outfit for going out with friends.

[1290] The user launches the app and retrieves the weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects the "Casual Scene" setting. The device takes a photo of the user's face with the camera, and the emotion engine recognizes the user's facial expression as "happy."

[1291] The server suggests suitable shirts, pants, and sneakers from the user's closet. It also recommends similar items (such as caps) based on outfit data posted by other users in the past, and provides a link to an online shopping platform.

[1292] The user wears the proposed outfit, takes a photo, and posts it to the app. The server then awards points to the user as an incentive.

[1293] As described above, the system of the present invention not only streamlines the user's clothing selection process and provides optimal coordination, but also makes suggestions that take the user's emotions into consideration, thereby further improving the shopping experience.

[1294] ---

[1295] The above is the "Form for implementing the invention."

[1296] The processing flow will be explained below.

[1297] ---

[1298] Step 1: Register your closet items

[1299] A user logs in to the app and opens the "Closet" section. They take a photo of an item or upload an existing photo, and enter details such as the item's category, color, brand, and size. They press the "Save" button to send the information to the app.

[1300] ---

[1301] Step 2: Save your closet data

[1302] The server receives the item information sent by the user and stores it in a database. In addition, since an item purchased on the online shopping platform may be automatically added to the user's closet, the server adds the item information to the database when a request for this is received.

[1303] ---

[1304] Step 3: Get the weather and temperature

[1305] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[1306] ---

[1307] Step 4: Enter situation information

[1308] The user selects the situation information for the day, for example, "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[1309] ---

[1310] Step 5: Recognize emotions

[1311] The device captures the user's facial expressions and voice using a camera and microphone and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and identifies their emotions. For example, it may recognize "they look happy" through image analysis.

[1312] ---

[1313] Step 6: Coordination proposal

[1314] The server uses AI to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotion data obtained from the emotion engine, and then sends the generated outfit information to the device.

[1315] ---

[1316] Step 7: Displaying Coordination

[1317] The device displays the coordinated information received from the server on the app screen, and the user can confirm the proposed coordinated outfit.

[1318] ---

[1319] Step 8: Analyze past posts

[1320] The server analyzes the outfit data posted by the user and other users in the past, and generates recommended items and outfits that match the current weather information, situation information, and emotional data. The generated outfit information includes recommendations for similar items and online shopping links. This information is also sent to the device.

[1321] ---

[1322] Step 9: Display recommended outfits

[1323] The device displays the recommended outfit information and purchase links received from the server on the screen, allowing the user to purchase the items based on this information.

[1324] ---

[1325] Step 10: Post your outfit

[1326] The user actually wears the suggested outfit and takes a photo. The photo is then posted to the app, along with weather, temperature, and situation information. The user then presses the "Post new outfit" button.

[1327] ---

[1328] Step 11: Save submitted data and assign points

[1329] The server saves the new coordinate posting data sent by the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[1330] ---

[1331] The above is the content of the program processing of the system of the present invention, divided into specific steps.

[1332] Example 2

[1333] 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."

[1334] Previous systems lacked a means for users to efficiently register and use a large number of clothing items, and were inadequate in suggesting outfits based on weather information, situations, or emotions. Furthermore, the system was incomplete in utilizing data from users' past outfit submissions to make new suggestions, and there were also issues with providing incentives for suggested outfits. For these reasons, a new system is needed to further improve users' clothing selection and shopping experience.

[1335] 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.

[1336] In this invention, the server includes a means for registering clothing items in the user's closet, a means for acquiring weather information, a means for recognizing situation information and emotions, a means for proposing optimal outfits using a generative AI model, and a means for generating and recommending recommended outfits based on past outfit posts. This allows users to efficiently manage their clothing items and receive optimal outfit suggestions based on the weather, situation, and emotions. Furthermore, by providing new suggestions and incentives using past posting data, the system further improves clothing selection and the shopping experience.

[1337] A "user" is an individual who uses the system to manage their own clothing items and receive suggestions for optimal coordination.

[1338] A "closet" is a virtual storage space that manages multiple clothing items registered by a user on a database.

[1339] A "terminal" is an electronic device that allows a user to access the system and perform input operations or receive data. Examples include smartphones, tablets, and personal computers.

[1340] "Weather information" is data relating to the weather, temperature, and other climate information based on the user's current location.

[1341] "Situation information" is data relating to scenes and circumstances that a user sets according to the schedule and activities of the day. For example, it includes categories such as "casual" and "business."

[1342] An "emotion engine" is software or an algorithm that analyzes a user's facial expressions and voice to recognize their emotions.

[1343] A "generative AI model" is a machine learning model that suggests optimal outfits based on information about items in the user's closet, weather information, situational information, and emotional data.

[1344] "Past coordinated outfit posts" is historical data about coordinated outfits posted by the user or other users on the system.

[1345] "Recommended outfits" are new clothing combinations suggested by a generative AI model based on past outfit posting data.

[1346] "Incentives" are rewards, points, or other benefits given to users when they post new outfits.

[1347] The system of this invention efficiently utilizes multiple clothing items registered in a user's closet and proposes optimal outfits based on the weather, temperature, situation, and the user's emotions. Furthermore, by analyzing the user's past outfit posting data and providing recommended items and outfits, it simplifies clothing selection and improves the shopping experience.

[1348] Registering closet items

[1349] Users register their clothing items through their devices. They take a photo of the clothing using the device's camera or upload an existing photo. They then enter detailed information such as the item's category (e.g., shirt, pants), color, brand, and size, and press the "Save" button to send it to the server.

[1350] The server stores the received clothing item information in a database, and also automatically adds items purchased through the online shopping platform to the closet.

[1351] Obtaining weather, temperature, and situation information

[1352] The device acquires the user's current location information using the GPS function, then transmits the location information to the weather information service to acquire weather and temperature data.

[1353] The user selects the situation of the day on the app interface, choosing a scene such as "casual" or "business," and then presses the "Set Situation" button to send it to the server.

[1354] Emotion recognition

[1355] The device sends the user's facial expressions and voice as input to the emotion engine. The device's camera captures a picture of the user's face, or the microphone captures voice data. The emotion engine analyzes this data and identifies the user's emotions. Specifically, it analyzes emotions using OpenFace or the Google Cloud Speech-to-Text API.

[1356] Coordination suggestions

[1357] The server uses a generative AI model to suggest optimal outfits based on information about items in the user's closet, weather information, situation information, and emotional data. The server then analyzes the data using an AI model (e.g., TensorFlow) to generate optimal outfits. This generated outfit information is then sent to the device.

[1358] Recommendations from past posts

[1359] The server analyzes the outfit data posted by the user and other users in the past, and generates recommended items and outfits based on current weather information, situation information, and emotional data. It also generates appropriate online shopping links and sends them to the device.

[1360] Coordination Post

[1361] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user then enters the weather, temperature, and situation information along with the photo, and presses the "Post new outfit" button.

[1362] The server stores the received posting data of the new coordinates in a database, awards points as an incentive for the user's posting, and reflects the information in the user's account.

[1363] Specific examples

[1364] For example, consider the case where a user is choosing an outfit for going out with friends.

[1365] The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects the "Casual Scene" setting. The device then takes a photo of the user's face with the camera, and the emotion engine recognizes the user's facial expression as "happy."

[1366] The server suggests suitable shirts, pants, and sneakers from the user's closet, and also recommends additional items such as caps based on data posted by other users in the past, along with links to online shopping platforms.

[1367] The user wears the suggested outfit, takes a photo, and posts it to the app. After posting, the server gives the user points as an incentive and reflects the information in the user's account.

[1368] Prompt Sentence Examples

[1369] "Please suggest the best outfit for a casual occasion when the weather is sunny, the temperature is 25 degrees, and you are feeling happy."

[1370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1371] Program processing flow

[1372] The program processing of this system is divided into the following steps:

[1373] Step 1: Register your closet items

[1374] Specific behavior:

[1375] The user takes a photo of the clothing item with the device's camera or uploads an existing photo.

[1376] The user enters details such as clothing item category, color, brand, size, etc. and presses the "Save" button.

[1377] Input: Clothing item photo and details

[1378] The terminal sends this information to the server.

[1379] The server stores the received information in a database.

[1380] Output: New clothing item information stored in the database

[1381] Step 2: Obtaining weather, temperature, and situation information

[1382] Specific behavior:

[1383] The device uses GPS to obtain the current location.

[1384] Input: Latitude and longitude information of current location

[1385] The device transmits its current location information to a weather information service and obtains weather and temperature data.

[1386] The user selects and enters the situation information for the day on the app interface.

[1387] Input: Situation information (casual, business, etc.)

[1388] Output: Obtained weather and temperature information and situation information

[1389] Step 3: Recognize emotions

[1390] Specific behavior:

[1391] The device captures the user's facial expressions with a camera or records their voice with a microphone.

[1392] Input: User's facial expression and voice data

[1393] The device sends this data to the emotion engine.

[1394] The emotion engine analyzes the data and identifies the user's emotion (e.g., "happy").

[1395] Output: User emotion data

[1396] Step 4: Coordination proposal

[1397] Specific behavior:

[1398] The server uses a generative AI model to generate the optimal outfit based on information about the items in the user's closet, acquired weather and temperature information, situation information, and emotional data.

[1399] Input: Closet item information, weather and temperature information, situation information, emotional data

[1400] An AI model analyzes this data and suggests optimal outfits.

[1401] The server sends the generated coordinate information to the terminal.

[1402] Output: Proposed coordinate information

[1403] Step 5: Recommendations from past posts

[1404] Specific behavior:

[1405] The server analyzes the coordination data posted by the user and other users in the past.

[1406] Input: Past coordination posting data

[1407] The server compares current weather information, situation information, and emotional data to generate recommended items and outfits.

[1408] The server adds a link to the online shopping platform and sends it to the terminal.

[1409] Output: Recommended items, outfit information, and related shopping links

[1410] Step 6: Post your outfit

[1411] Specific behavior:

[1412] The user wears the suggested outfit, takes a photo, and posts it to the app.

[1413] Input: outfit photo, weather and temperature information, situation information

[1414] The server stores the received posting data in a database and adds points to the user's account as an incentive.

[1415] Output: Post data and awarded points stored in the database

[1416] (Application example 2)

[1417] 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."

[1418] Conventional clothing selection support systems only suggest outfits based on the weather and situation, and have the problem of not being able to make suggestions that reflect the user's emotional state. Furthermore, they do not fully utilize the user's past outfit data, so there is a need to improve the shopping experience and the accuracy of suggestions.

[1419] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to register multiple clothing items in their closet; a means for a terminal to acquire weather information based on the user's current location information; a means for a user to input the weather information and situation information; a means for the server to generate an optimal outfit using AI based on the weather information, situation information, and item information in the user's closet; a means for the terminal to acquire the user's facial expressions and voice and analyze them with an emotion engine; a means for the server to generate an optimal outfit based on the weather information, situation information, emotion data, and item information in the user's closet; a means for the server to generate and recommend a recommended outfit based on similar items from past outfit posts; and a means for a user to post the suggested outfit and receive an incentive for the post. This enables personalized clothing selection based on the user's emotional state, improving the accuracy of suggestions and the shopping experience.

[1420] "User" refers to an entity that registers clothing items and receives coordination suggestions.

[1421] "Clothing items" refer to various elements of clothing that a user registers in their closet and uses to coordinate outfits.

[1422] A "closet" refers to a virtual storage space that stores information about clothing items owned by a user.

[1423] "Terminal" refers to a device that a user operates to acquire and display information. Examples include smartphones, tablets, and personal computers.

[1424] "Location information" refers to the physical location of a user, typically obtained using the Global Positioning System (GPS).

[1425] "Weather information" refers to environmental data such as the weather and temperature at the user's current location.

[1426] "Situation information" refers to information about a particular everyday or special situation specified by the user, such as "casual" or "business."

[1427] "Server" refers to a computer system that processes information sent by users, makes various suggestions, and manages data.

[1428] "Item information" refers to detailed information about a clothing item, such as category, color, brand, size, etc.

[1429] "AI (artificial intelligence)" refers to the machine learning or data analysis algorithms used by the server to generate the coordinates.

[1430] An "emotion engine" refers to a system that recognizes a user's emotional state by analyzing their facial expressions and voice.

[1431] "Emotion data" refers to information that indicates the user's emotional state as analyzed by the emotion engine.

[1432] "Coordination" refers to a combination of multiple clothing items suggested to the user.

[1433] "Past coordination posts" refer to coordination suggestions and results previously made by the user and other users.

[1434] "Recommendation" refers to the act of the server presenting new outfits or items to the user.

[1435] "Posting" refers to the act of a user wearing a suggested outfit and uploading the result to the system.

[1436] "Incentives" refer to rewards, points, and other benefits that users receive when they post content.

[1437] This invention is a system that efficiently utilizes multiple clothing items registered in a user's virtual closet and suggests optimal coordination based on the weather, temperature, situation, and the user's emotions.

[1438] 1. Register your closet items

[1439] A user registers a clothing item in their closet using their device. They take a photo of the clothing item using the device's camera and enter details such as the item's category, color, brand, and size. The device is equipped with an image recognition library (e.g., TensorFlow) that analyzes the image to obtain item information. This information is sent to the server and stored in a database.

[1440] 2. Obtaining weather, temperature, and situation information

[1441] The device uses its GPS function to obtain the user's current location information. It then sends this information to the weather information service's API (e.g., OpenWeatherMap) to obtain weather and temperature data. The user then uses the application's interface to select the situation for the day. For example, the user can enter a situation such as "casual" or "business."

[1442] 3. Emotional Recognition

[1443] The device acquires the user's facial expressions and voice and sends them to an emotion recognition engine (e.g., Microsoft Azure Cognitive Services). The emotion engine analyzes the user's facial expressions and voice and recognizes their emotions. For example, it can take a picture of the user's face with a camera and perform image analysis to determine emotions such as "happy" or "sad."

[1444] 4. Coordination suggestions

[1445] The server uses AI (e.g., TensorFlow) to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotion data obtained from an emotion engine. This outfit information is sent to the device and displayed to the user.

[1446] 5. Recommendations from past posts

[1447] The server analyzes the coordination data posted by the user and other users in the past, and generates recommended items and coordinations that match the current weather information, situation information, and emotion data. This recommendation information is also sent to the terminal and displayed for the user to use.

[1448] 6. Post your outfit

[1449] The user actually wears the suggested outfit, takes a photo, and posts it to the application. The user then re-enters weather, temperature, and situation information for the photo and posts it. The server saves the received new outfit posting data in a database, awards points as an incentive based on the user's post, and reflects the information in the user's account.

[1450] Specific examples

[1451] For example, consider a situation where a user is choosing an outfit for going out with friends. The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects "casual scene." The device takes a photo of the user's face with a camera, and the emotion engine recognizes the user's facial expression as "happy." The server then suggests suitable shirts, pants, and sneakers from the user's closet. Based on outfit data posted by other users in the past, the server also recommends similar items (e.g., caps) and displays a link to an online shopping platform.

[1452] Prompt Sentence Examples

[1453] "The user wants to go to lunch with a friend in the spring sunshine, so please suggest the perfect casual outfit from his closet. The temperature is 22 degrees and the weather is sunny. The user looks excited."

[1454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1455] Step 1:

[1456] The user takes a photo of a clothing item using the device's camera. The photo is analyzed by an image recognition library (e.g., TensorFlow) to obtain detailed information about the item, such as its category, color, brand, and size. This information is sent to the server via the device and stored in a database. The input is a photo of the clothing item, and the output is detailed information about the item.

[1457] Step 2:

[1458] The device uses its GPS function to obtain the user's current location information. The obtained location information is sent to the API of a weather information service (e.g., OpenWeatherMap) to obtain weather and temperature data. Based on this location information, the API returns weather and temperature data. The input is the current location information, and the output is weather and temperature data.

[1459] Step 3:

[1460] The user uses the application interface to input situation information for the day, for example, by selecting a category such as "casual" or "business." The input is the situation information, and the output is the selected category.

[1461] Step 4:

[1462] The device captures the user's facial expressions and voice. Facial expression data is captured by the smartphone's camera, and voice data is captured by the microphone. This data is sent to an emotion recognition engine (e.g., Microsoft Azure Cognitive Services), which analyzes it to identify emotions. The input is facial expression and voice data, and the output is recognized emotion data.

[1463] Step 5:

[1464] The server uses AI (e.g., TensorFlow) to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotional data. The input is information about items in the closet, weather information, situation information, and emotional data, and the output is the optimal outfit.

[1465] Step 6:

[1466] The server generates recommended outfits based on similar items from past outfit posts and prepares the data to recommend to users. The input is past outfit data and current conditions (weather information, situation information, emotion data), and the output is the recommended outfit.

[1467] Step 7:

[1468] The user actually wears the proposed outfit, takes a photo, and posts it to the application. The photo also includes weather, temperature, and situation information. This new outfit posting data is sent to the server and stored in a database. The input is a photo of the proposed outfit and related information, and the output is the outfit posting data stored in the database.

[1469] Step 8:

[1470] The server awards points as an incentive based on the user's posts and reflects the information in the user's account. The input is the posted coordinate data, and the output is the awarded point information.

[1471] In this way, personalized clothing selection based on the user's emotional state is possible, improving the accuracy of recommendations and the shopping experience.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] [Fourth embodiment]

[1476] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1477] 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.

[1478] 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).

[1479] 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.

[1480] 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.

[1481] 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).

[1482] 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. 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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."

[1489] ---

[1490] This system efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits according to the weather, temperature, and situation. Furthermore, it analyzes past outfit posting data from users and provides recommended items and outfits, simplifying outfit selection and improving the shopping experience.

[1491] The system of the present invention is implemented mainly by the following method:

[1492] 1. Register your closet items

[1493] Users register their clothing items in their closets. They operate their devices to take photos of the items or upload existing photos, and enter information such as the item's category, color, brand, and size. Items purchased through the online shopping platform are automatically added to the user's closet.

[1494] 2. Obtaining weather, temperature, and situation

[1495] The device obtains weather information based on the current location. The device uses its GPS function to obtain the coordinates of the current location and transmits that information to a weather information service to obtain the weather and temperature data for that day. The user inputs situation information into the device according to the day and circumstances. This situation information is selected from options provided on the app interface.

[1496] 3. Coordination suggestions

[1497] The server uses AI to generate optimal outfits based on information about the items in the user's closet, acquired weather information, and situation information. The AI ​​analyzes this information and suggests combinations of items that suit the user. This outfit information is sent to the device and displayed on the device.

[1498] 4. Recommendations from past posts

[1499] The server analyzes the outfit data posted by the user and other users in the past and generates recommended items and outfits that match the current weather information and situation. The generated outfit information includes recommendations for similar items and links to online shopping platforms. This information is also sent to the device and displayed for the user to use.

[1500] 5. Post your outfit

[1501] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user can enter information about the weather, temperature, and situation, and post it along with the outfit. Once posted, the server receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive.

[1502] Specific examples

[1503] For example, consider the case where a user is deciding on an outfit for work. The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects "business attire." The server then suggests a shirt, pants, and jacket from the user's closet. Based on data on business outfits posted by other users in the past, the server also recommends similar items (such as ties) and displays a link to an online shopping platform. If the suggested outfit satisfies the user, the user can wear the outfit and post a photo to the app. The server then awards the user points as an incentive for posting.

[1504] As described above, the system of the present invention not only makes it easier for users to select clothes and provides optimal coordination, but also improves the shopping experience.

[1505] ---

[1506] The above is the "Form for implementing the invention."

[1507] The processing flow will be explained below.

[1508] ---

[1509] Step 1: Register your closet items

[1510] A user logs in to the app and opens the "Closet" section. They take a photo of an item or upload an existing photo, and enter details such as the item's category, color, brand, and size. They press the "Save" button to send the information to the app.

[1511] ---

[1512] Step 2: Save your closet data

[1513] The server stores the item information sent by the user in a database and also adds the item information to the database when a request for adding an item purchased on the online shopping platform to the closet is received, since the item may be automatically added to the closet.

[1514] ---

[1515] Step 3: Get the weather and temperature

[1516] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[1517] ---

[1518] Step 4: Enter situation information

[1519] The user selects the situation information for the day, for example, choosing a scene such as "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[1520] ---

[1521] Step 5: Generate coordinates

[1522] The server uses AI to generate optimal outfits based on information about the items in the user's closet, acquired weather information, and situation information, and then sends the generated outfit information to the user's device.

[1523] ---

[1524] Step 6: Displaying your outfits

[1525] The device displays the coordinated outfit information received from the server on the app screen, allowing the user to check the proposed outfit.

[1526] ---

[1527] Step 7: Analyze past posts

[1528] The server searches the user's past outfit posting data and analyzes outfits that contain items similar to the weather and situation information. Based on the analysis results, it generates recommended items and outfits, adds online shopping links, and sends them to the user's device.

[1529] ---

[1530] Step 8: Display recommended outfits

[1531] The device displays the recommended outfit information and purchase links received from the server on the screen, allowing the user to purchase the items based on this information.

[1532] ---

[1533] Step 9: Post your outfit

[1534] The user actually wears the suggested outfit and takes a photo. They post the photo to the app, inputting weather, temperature, and situation information. Then, they press the "Post new outfit" button.

[1535] ---

[1536] Step 10: Save submitted data and assign points

[1537] The server saves the new coordinate posting data received from the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[1538] ---

[1539] The above is the content of the program processing of the system of the present invention, divided into specific steps.

[1540] Example 1

[1541] 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."

[1542] Until now, it has been difficult for users to efficiently manage the clothing items in their closets and choose the best outfit for the day's weather, temperature, and situation. Furthermore, since there was no system that provided recommended items and outfits based on past outfit posts, users had to spend time and effort choosing their outfits. Furthermore, there was also a lack of a convenient way for users to purchase the items they wanted.

[1543] 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.

[1544] In this invention, the server includes: means for a user to register multiple clothing items in their closet; means for a terminal to acquire weather information based on the user's current location information; means for the user to input the weather information and situation information; means for generating an optimal outfit based on the weather information, situation information, and item information in the user's closet using a generative AI model; means for generating and recommending recommended outfits based on similar items from past outfit posts; and means for the user to post the suggested outfits and receive incentives for the posts. This not only makes the user's clothing selection more efficient and suggests optimal outfits, but also improves the user's shopping experience.

[1545] "User" refers to an individual who uses the system to register their own clothing items and receive coordination suggestions.

[1546] A "terminal" is an electronic device operated by a user, and includes a smartphone, tablet, personal computer, etc.

[1547] "Weather information" refers to data that indicates weather conditions such as the weather and temperature at the user's current location.

[1548] "Situation information" is information input by the user regarding the plans and activities for the day, and includes, for example, "business scene" and "casual scene".

[1549] "Server" refers to the central processing unit that aggregates, analyzes, stores, and coordinates data on the system.

[1550] "Item information in the closet" is information about clothing items that the user has registered in the closet, and includes attributes such as category, color, brand, and size.

[1551] A "generative AI model" refers to an algorithm or software that uses artificial intelligence technology to generate optimal outfits for users.

[1552] "Past coordination posts" are coordination data that the user or other users have previously posted to the system.

[1553] "Recommended coordination" refers to a coordination generated by the server and recommended to the user based on past coordination submission data and current weather and situation information.

[1554] "Incentives" are rewards or benefits that users receive for using the system, and include points, for example.

[1555] An "e-commerce platform" is a website or application where goods are bought and sold over the internet, and where users can purchase clothing items online.

[1556] The present invention is a system that efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits according to the weather, temperature, and situation. Furthermore, by analyzing the user's past outfit posting data, the system offers recommended items and outfits, simplifying outfit selection and improving the shopping experience.

[1557] The system of the present invention is implemented mainly by the following method.

[1558] Registering closet items

[1559] A user uses an application on their device to register clothing items in their closet. The user launches the application and registers new items on the closet management screen. They take a photo of the item or upload an existing photo, and enter information such as the item's category, color, brand, and size. Items purchased through the online shopping platform are automatically added to the user's closet.

[1560] As a specific example, a user uploads a photo of a jacket taken with a smartphone app and enters the category as "outerwear," the color as "black," the brand as "Uniqlo," and the size as "M."

[1561] Obtaining weather, temperature, and situation information

[1562] The device uses its GPS function to obtain the coordinates of its current location. Based on the obtained coordinates, it queries a weather information service (e.g., OpenWeatherMap) to obtain weather and temperature data. This information is displayed on the device screen. The user can then enter information about the situation for that day and select a specific situation (e.g., "Business Scene").

[1563] For example, the device acquires the user's current location using GPS, queries OpenWeatherMap to obtain information such as "sunny, temperature 25 degrees," and displays it in the app. The user selects "Business scene."

[1564] Coordination suggestions

[1565] The server obtains information about the items in the closet. Based on the obtained weather and situation information, it uses a generative AI model (e.g., TensorFlow) to generate the optimal outfit. This suggestion is sent to the device and displayed.

[1566] For example, a combination of "white shirt," "gray pants," and "black jacket" is suggested.

[1567] Recommendations from past posts

[1568] The server analyzes the outfit data posted by the user and other users in the past. Based on the analysis results, it generates recommended items and outfits that match the current weather information and situation. This includes recommendations for similar items and links to online shopping platforms. The generated recommended information is sent to the device and displayed.

[1569] As a specific example, it analyzes outfits posted in the past with the tag "business scene" and recommends items such as "ties" and "business shoes."

[1570] Coordination Post

[1571] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user then enters information about the weather, temperature, and situation, and posts the photo. Once posted, the server receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive.

[1572] For example, when a user wears a suggested outfit and takes a photo, they upload it to the app, entering the information "sunny, 25 degrees, business setting." The server saves the data and awards points to the user.

[1573] Specific actions

[1574] For example, consider the case where a user is deciding on an outfit for work. The user launches the app, and the device retrieves weather and temperature information for the user's current location. If the weather is sunny and the temperature is 25 degrees, the user selects "business attire." The server then suggests a shirt, pants, and jacket from the user's closet. Based on data on business outfits posted by other users in the past, the server also recommends similar items (such as ties) and displays a link to an online shopping platform. If a suggested outfit satisfies the user, the user can wear the outfit and post a photo within the app. The server then awards the user points as an incentive for posting.

[1575] As described above, the system of the present invention not only makes it easier for users to select clothes and provides optimal coordination, but also improves the shopping experience.

[1576] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1577] Program processing flow

[1578] Step 1: Register your closet items

[1579] Step 1-1:

[1580] The user launches the app on their device and opens the closet management screen.

[1581] Input: The app launch operation.

[1582] Output: The closet management screen will be displayed on the device.

[1583] Step 1-2:

[1584] The user selects the Register New Item button and takes a photo or uploads an existing photo.

[1585] Input: Image of new item (taken or uploaded).

[1586] Output: Image data of the new item.

[1587] Step 1-3:

[1588] The user enters the item information (category, color, brand, size, etc.) and presses the save button.

[1589] Input: Item information (category, color, brand, size).

[1590] Output: Item registration data.

[1591] Steps 1-4:

[1592] The server receives the registered information and stores it in a database.

[1593] Input: Registration information data.

[1594] Output: Save to closet database.

[1595] Specific behavior:

[1596] Users take a photo of the jacket with their smartphone camera and upload it to the app.

[1597] The user enters "Category: Outerwear", "Color: Black", "Brand: Uniqlo", and "Size: M".

[1598] The server stores the received information in the closet database.

[1599] Step 2: Obtaining weather, temperature, and situation

[1600] Step 2-1:

[1601] The device uses GPS to obtain the coordinates of the current location.

[1602] Input: GPS function activation operation.

[1603] Output: Current location information (coordinate data).

[1604] Step 2-2:

[1605] The device sends its current location coordinates to a weather information service and obtains weather and temperature data.

[1606] Input: coordinate data.

[1607] Output: Weather information, temperature information.

[1608] Step 2-3:

[1609] The device displays the weather and temperature data it has obtained.

[1610] Input: Weather information, temperature information.

[1611] Output: Display weather and temperature information on the terminal screen.

[1612] Step 2-4:

[1613] The user inputs the situation information for that day.

[1614] Input: Situation information (e.g., business situation).

[1615] Output: Situation information data.

[1616] Specific behavior:

[1617] The device obtains the user's current location using GPS and queries the weather information service.

[1618] The device obtains the information "Sunny, 25 degrees" and displays it in the app.

[1619] The user selects "Business Scene" from the app's situation selection screen.

[1620] Step 3: Coordination proposal

[1621] Step 3-1:

[1622] The server retrieves information about items in the closet.

[1623] Input: Information from the closet database.

[1624] Output: The retrieved item information.

[1625] Step 3-2:

[1626] The server uses a generative AI model based on the acquired weather and situation information to generate the optimal outfit.

[1627] Input: Weather information, temperature information, situation information, item information.

[1628] Output: Optimal coordination suggestions.

[1629] Step 3-3:

[1630] The server sends the generated coordinate information to the terminal and displays it.

[1631] Input: Optimal coordination suggestions.

[1632] Output: Display of suggested coordinates on terminal screen.

[1633] Specific behavior:

[1634] The server retrieves information about "white shirt," "gray pants," and "black jacket" from the closet.

[1635] The server uses an AI model to generate an outfit suitable for a sunny day with a temperature of 25 degrees and a business setting.

[1636] The server sends the suggested outfits to the smartphone and displays them on the app.

[1637] Step 4: Recommendations from past posts

[1638] Step 4-1:

[1639] The server acquires coordinate data posted in the past by the user and other users.

[1640] Input: Coordinated submission database.

[1641] Output: Past posting data.

[1642] Step 4-2:

[1643] The server performs analysis and generates recommended items and outfits that match the current weather information and situation.

[1644] Input: Past posting data, weather information, situation information.

[1645] Output: Recommended item information, recommended coordination information.

[1646] Step 4-3:

[1647] The server sends the generated recommendation information to the terminal and displays it.

[1648] Input: Recommended coordination information.

[1649] Output: Recommended outfits displayed on the device screen.

[1650] Specific behavior:

[1651] The server retrieves and analyzes past coordination posting data tagged with "business scene."

[1652] The server generates recommendations such as "ties" and "business shoes" based on past data.

[1653] The server sends the generated recommendation information to the smartphone and displays it in the app.

[1654] Step 5: Post your outfit

[1655] Step 5-1:

[1656] The user wears the suggested outfit and takes a photo.

[1657] Input: A photo of the outfit you're wearing.

[1658] Output: The captured photo data.

[1659] Step 5-2:

[1660] Users upload photos to the app and enter weather, temperature, and situation information.

[1661] Input: Photo data, weather information, temperature information, situation information.

[1662] output: The post data.

[1663] Step 5-3:

[1664] The server receives the posted information and stores it in a database.

[1665] Input: Post data.

[1666] Output: Save to database.

[1667] Step 5-4:

[1668] The server will credit the points to the user's account.

[1669] Input: Post data, user information.

[1670] Output: Points awarded.

[1671] Specific behavior:

[1672] The user puts on the suggested outfit of a white shirt, gray pants, and black jacket and takes a photo with their smartphone.

[1673] The user enters the weather as "sunny," the temperature as "25 degrees," and the situation as "business scene," and posts a photo.

[1674] The server receives the posted data, stores it in the database, and awards the user 10 points.

[1675] (Application example 1)

[1676] 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."

[1677] There are already systems that efficiently utilize multiple clothing items registered in a user's closet to suggest optimal outfits based on the weather, temperature, and situation. However, these systems lack the ability to provide a sense of actual fitting and lack a means to reflect feedback on the outfits provided by the user. This can result in low user satisfaction with the outfit suggestions. Furthermore, there is a need for a method to enhance users' visual recognition of the suggested outfits by providing a try-on experience in a virtual space.

[1678] 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.

[1679] In this invention, the server includes a means for allowing a user to create an avatar for virtual try-on and for having the avatar try on coordinated clothing, a means for using a generative AI model to create prompt sentences based on items in the user's closet and weather information, and for the AI ​​to use the prompt sentences to generate an optimal outfit, and a means for acquiring weather information based on the user's current location information, thereby allowing the user to try on coordinated clothing in a virtual space and visually check it.

[1680] A "user" is an individual who uses this system to manage their own clothing items and receive coordination suggestions.

[1681] "Clothing items" are decorative items such as clothes and accessories that a user registers in his or her closet.

[1682] A "closet" is a digital repository of clothing items that a user registers through their device.

[1683] A "terminal" is an electronic device such as a smartphone, tablet, or PC that allows a user to access and operate the system.

[1684] "Current location information" is information about the user's physical location obtained using the terminal's GPS function or the like.

[1685] "Weather information" is environmental data such as weather and temperature acquired based on the user's current location information.

[1686] "Situation information" is information about an event or situation that is input by the user when receiving a coordination suggestion.

[1687] The "server" is a computer system that processes the user's closet information, weather information, and the like, and generates coordination suggestions.

[1688] "AI" stands for artificial intelligence, a technology that analyzes information about items in a user's closet, weather information, situational information, etc. to generate optimal outfits.

[1689] "Coordination" refers to a combination of clothing items worn by a user.

[1690] "Past coordination posts" are data on past coordination posts posted to the system by the user and other users.

[1691] "Virtual try-on" means that a user tries on clothing items in a digital space using an avatar.

[1692] An "avatar" is a digital character that resembles the user and is used during virtual fitting.

[1693] A "generative AI model" is an artificial intelligence model that generates optimal outfits based on the items in the user's closet and weather information.

[1694] A "prompt statement" is an instruction given to a generative AI model to make it output appropriate coordinates.

[1695] An "online shopping platform" is an e-commerce system over the Internet through which users purchase clothing items.

[1696] "Incentives" are rewards or benefits that users receive by posting their suggested outfits.

[1697] The present invention is a system that effectively utilizes multiple clothing items registered in a user's closet to suggest optimal outfits according to the weather, temperature, and situation, and allows the user to check the outfits in a virtual try-on environment. The system of the present invention is mainly implemented in the following manner.

[1698] 1. Register your closet items

[1699] Users can register clothing items in their closets using their own devices by taking photos of the items or uploading existing photos and entering information such as the item's category, color, brand, and size. In addition, users can set items purchased from online shopping platforms to be automatically added to their closets.

[1700] 2. Obtaining weather information

[1701] The device uses the GPS function to obtain the user's current location information, and then uses that information to obtain the day's weather and temperature data from a weather information service, thereby providing real-time weather information.

[1702] 3. Enter situation information

[1703] The user inputs situation information (e.g., business, casual date, outdoors, etc.) into the device based on the day's schedule and circumstances. This situation information is selected from options provided on the app interface.

[1704] 4. Coordination suggestions and generative AI models

[1705] The server uses AI to generate the optimal outfit based on the weather information, situation information, and information about items in the user's closet. This involves creating a prompt using a generative AI model, and then the AI ​​generates the optimal outfit based on that prompt. For example, the AI ​​might be given the following prompt:

[1706] "What would be an appropriate outfit for a business setting on a sunny day when the temperature is 25 degrees? In my closet I have a blue shirt, a white shirt, black pants, jeans, a suit jacket, and a casual jacket."

[1707] 5. Providing a virtual try-on environment

[1708] The server provides a means for users to create an avatar for virtual try-on and virtually try on suggested outfits for that avatar. This function allows users to visually check the suggested clothing items in a virtual environment. If the user is satisfied with the outfit, they can select it and actually wear it.

[1709] 6. Posts and Incentives

[1710] Users can try on the suggested outfits, take photos, and post them to the app. The server then receives the information and stores it in a database. At the same time, points are awarded to the user's account as an incentive, which can be used on online shopping platforms.

[1711] The system configured as described above allows users to efficiently receive coordination suggestions and visually confirm them while making comfortable clothing selections, thereby improving the user's shopping experience and reducing the stress of selecting clothing.

[1712] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1713] Step 1:

[1714] Registering closet items

[1715] Users use their devices to register clothing items in their closets. They take photos of the items or upload existing photos, and enter information such as the item's category, color, brand, and size. The images and text data are then sent to the server and stored in a database. Items purchased on the online shopping platform are automatically added to the user's closet based on the purchase information.

[1716] Input: Item photo, category, color, brand, size, and other information

[1717] Data processing: Classification and registration of input image and text data

[1718] Output: Registered item information is saved in the database

[1719] Step 2:

[1720] Obtaining weather information

[1721] The device uses the GPS function to obtain the user's current location information, and then uses that information to obtain the day's weather and temperature data from a weather information service. The obtained weather information is used in the next step.

[1722] Input: User's current location information

[1723] Data processing: Send an API request based on current location information to obtain weather data

[1724] Output: Weather information and temperature data

[1725] Step 3:

[1726] Entering situation information

[1727] The user inputs situation information (e.g., business, casual date, outdoors, etc.) into the terminal to receive coordination suggestions. The situation information is sent to the server along with the previous weather information.

[1728] Input: Situation information (choose from options)

[1729] Data processing: collecting and integrating situational data

[1730] Output: Situation information combined with weather information

[1731] Step 4:

[1732] Use of outfit suggestions and generative AI models

[1733] The server uses AI to generate the optimal outfit based on the weather information, situation information, and information about the items in the user's closet. In this process, a generative AI model is used to create a prompt, and the AI ​​generates the optimal outfit based on that prompt.

[1734] Input: Weather information, situation information, item information in the user's closet

[1735] Data processing: Prompt sentence generation and coordination suggestions using AI models

[1736] Output: Generated coordinate information

[1737] Step 5:

[1738] Providing a virtual try-on environment

[1739] The server creates an avatar for the user to virtually try on the suggested outfits, allowing the user to visually check the suggested clothing items in a virtual environment.

[1740] Input: Generated coordinate information, user avatar

[1741] Data processing: 3D rendering to apply coordinate information to an avatar

[1742] Output: Visualized virtual try-on image

[1743] Step 6:

[1744] Posts and Incentives

[1745] Users try on the suggested outfits, take photos, and post them to the app. The posted information is sent to a server and stored in a database. At the same time, users are awarded points as an incentive, which can be used on the online shopping platform.

[1746] Input: User's outfit photo, related information

[1747] Data processing: Saving submitted data and calculating incentive points

[1748] Output: Save to database and give points

[1749] Through the above steps, the system of the present invention enables the user to efficiently receive coordination suggestions and comfortably select clothes.

[1750] 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.

[1751] ---

[1752] The present invention is a system that efficiently utilizes multiple clothing items registered in a user's closet and suggests optimal outfits based on the weather, temperature, situation, and the user's emotions. Furthermore, by analyzing the user's past outfit posting data and providing recommended items and outfits, the system simplifies outfit selection and improves the shopping experience.

[1753] The system of the present invention is implemented mainly by the following method:

[1754] 1. Register your closet items

[1755] The user registers their clothing items in the closet. Using their device, the user takes a photo of the item or uploads an existing photo, and enters details such as the item's category, color, brand, and size. Then, the user presses the "Save" button to send the information to the app.

[1756] The server stores the item information sent by the user in a database and also adds the item information to the database when a request for adding an item purchased on the online shopping platform to the user's closet is received, since the item may be automatically added to the user's closet.

[1757] 2. Obtaining weather, temperature, and situation information

[1758] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[1759] The user selects the situation information for the day, for example, "casual" or "business" on the app's interface screen, and then presses the "Set Situation" button.

[1760] 3. Emotional Recognition

[1761] The device receives the user's facial expressions and voice as input and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and recognizes their emotions. For example, it uses a camera to take a picture of the user's face and identifies their emotions through image analysis.

[1762] 4. Coordination suggestions

[1763] The server uses AI to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotional data obtained from the emotion engine. The AI ​​analyzes this information and suggests combinations of items that suit the user. This outfit information is sent to the device and displayed on the device.

[1764] 5. Recommendations from past posts

[1765] The server analyzes the outfit data posted by the user and other users in the past, generates recommended items and outfits that match the current weather information, situation information, and emotion data, and adds online shopping links. This information is also sent to the device and displayed for the user to use.

[1766] 6. Post your outfit

[1767] The user actually wears the suggested outfit and takes a photo. They post the photo to the app, inputting weather, temperature, and situation information. Then they press the "Post new outfit" button.

[1768] The server saves the new coordinate posting data received from the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[1769] Specific examples

[1770] For example, consider the case where a user is choosing an outfit for going out with friends.

[1771] The user launches the app and retrieves the weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects the "Casual Scene" setting. The device takes a photo of the user's face with the camera, and the emotion engine recognizes the user's facial expression as "happy."

[1772] The server suggests suitable shirts, pants, and sneakers from the user's closet. It also recommends similar items (such as caps) based on outfit data posted by other users in the past, and provides a link to an online shopping platform.

[1773] The user wears the proposed outfit, takes a photo, and posts it to the app. The server then awards points to the user as an incentive.

[1774] As described above, the system of the present invention not only streamlines the user's clothing selection process and provides optimal coordination, but also makes suggestions that take the user's emotions into consideration, thereby further improving the shopping experience.

[1775] ---

[1776] The above is the "Form for implementing the invention."

[1777] The processing flow will be explained below.

[1778] ---

[1779] Step 1: Register your closet items

[1780] A user logs in to the app and opens the "Closet" section. They take a photo of an item or upload an existing photo, and enter details such as the item's category, color, brand, and size. They press the "Save" button to send the information to the app.

[1781] ---

[1782] Step 2: Save your closet data

[1783] The server receives the item information sent by the user and stores it in a database. In addition, since an item purchased on the online shopping platform may be automatically added to the user's closet, the server adds the item information to the database when a request for this is received.

[1784] ---

[1785] Step 3: Get the weather and temperature

[1786] The device acquires its current location information using its GPS function, then transmits the information to a weather information service to acquire weather and temperature data.

[1787] ---

[1788] Step 4: Enter situation information

[1789] The user selects the situation information for the day, for example, "casual" or "business" on the app's interface screen, and then presses the "Set situation" button.

[1790] ---

[1791] Step 5: Recognize emotions

[1792] The device captures the user's facial expressions and voice using a camera and microphone and sends them to the emotion engine. The emotion engine analyzes the user's facial expressions and voice and identifies their emotions. For example, it may recognize "they look happy" through image analysis.

[1793] ---

[1794] Step 6: Coordination proposal

[1795] The server uses AI to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotion data obtained from the emotion engine, and then sends the generated outfit information to the device.

[1796] ---

[1797] Step 7: Displaying Coordination

[1798] The device displays the coordinated information received from the server on the app screen, and the user can confirm the proposed coordinated outfit.

[1799] ---

[1800] Step 8: Analyze past posts

[1801] The server analyzes the outfit data posted by the user and other users in the past, and generates recommended items and outfits that match the current weather information, situation information, and emotional data. The generated outfit information includes recommendations for similar items and online shopping links. This information is also sent to the device.

[1802] ---

[1803] Step 9: Display recommended outfits

[1804] The device displays the recommended outfit information and purchase links received from the server on the screen, allowing the user to purchase the items based on this information.

[1805] ---

[1806] Step 10: Post your outfit

[1807] The user actually wears the suggested outfit and takes a photo. The photo is then posted to the app, along with weather, temperature, and situation information. The user then presses the "Post new outfit" button.

[1808] ---

[1809] Step 11: Save submitted data and assign points

[1810] The server saves the new coordinate posting data sent by the user in the database. At the same time, points are awarded according to the user's posting and the information is reflected in the user's account.

[1811] ---

[1812] The above is the content of the program processing of the system of the present invention, divided into specific steps.

[1813] Example 2

[1814] 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."

[1815] Previous systems lacked a means for users to efficiently register and use a large number of clothing items, and were inadequate in suggesting outfits based on weather information, situations, or emotions. Furthermore, the system was incomplete in utilizing data from users' past outfit submissions to make new suggestions, and there were also issues with providing incentives for suggested outfits. For these reasons, a new system is needed to further improve users' clothing selection and shopping experience.

[1816] 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.

[1817] In this invention, the server includes a means for registering clothing items in the user's closet, a means for acquiring weather information, a means for recognizing situation information and emotions, a means for proposing optimal outfits using a generative AI model, and a means for generating and recommending recommended outfits based on past outfit posts. This allows users to efficiently manage their clothing items and receive optimal outfit suggestions based on the weather, situation, and emotions. Furthermore, by providing new suggestions and incentives using past posting data, the system further improves clothing selection and the shopping experience.

[1818] A "user" is an individual who uses the system to manage their own clothing items and receive suggestions for optimal coordination.

[1819] A "closet" is a virtual storage space that manages multiple clothing items registered by a user on a database.

[1820] A "terminal" is an electronic device that allows a user to access the system and perform input operations or receive data. Examples include smartphones, tablets, and personal computers.

[1821] "Weather information" is data relating to the weather, temperature, and other climate information based on the user's current location.

[1822] "Situation information" is data relating to scenes and circumstances that a user sets according to the schedule and activities of the day. For example, it includes categories such as "casual" and "business."

[1823] An "emotion engine" is software or an algorithm that analyzes a user's facial expressions and voice to recognize their emotions.

[1824] A "generative AI model" is a machine learning model that suggests optimal outfits based on information about items in the user's closet, weather information, situational information, and emotional data.

[1825] "Past coordinated outfit posts" is historical data about coordinated outfits posted by the user or other users on the system.

[1826] "Recommended outfits" are new clothing combinations suggested by a generative AI model based on past outfit posting data.

[1827] "Incentives" are rewards, points, or other benefits given to users when they post new outfits.

[1828] The system of this invention efficiently utilizes multiple clothing items registered in a user's closet and proposes optimal outfits based on the weather, temperature, situation, and the user's emotions. Furthermore, by analyzing the user's past outfit posting data and providing recommended items and outfits, it simplifies clothing selection and improves the shopping experience.

[1829] Registering closet items

[1830] Users register their clothing items through their devices. They take a photo of the clothing using the device's camera or upload an existing photo. They then enter detailed information such as the item's category (e.g., shirt, pants), color, brand, and size, and press the "Save" button to send it to the server.

[1831] The server stores the received clothing item information in a database, and also automatically adds items purchased through the online shopping platform to the closet.

[1832] Obtaining weather, temperature, and situation information

[1833] The device acquires the user's current location information using the GPS function, then transmits the location information to the weather information service to acquire weather and temperature data.

[1834] The user selects the situation of the day on the app interface, choosing a scene such as "casual" or "business," and then presses the "Set Situation" button to send it to the server.

[1835] Emotion recognition

[1836] The device sends the user's facial expressions and voice as input to the emotion engine. The device's camera captures a picture of the user's face, or the microphone captures voice data. The emotion engine analyzes this data and identifies the user's emotions. Specifically, it analyzes emotions using OpenFace or the Google Cloud Speech-to-Text API.

[1837] Coordination suggestions

[1838] The server uses a generative AI model to suggest optimal outfits based on information about items in the user's closet, weather information, situation information, and emotional data. The server then analyzes the data using an AI model (e.g., TensorFlow) to generate optimal outfits. This generated outfit information is then sent to the device.

[1839] Recommendations from past posts

[1840] The server analyzes the outfit data posted by the user and other users in the past, and generates recommended items and outfits based on current weather information, situation information, and emotional data. It also generates appropriate online shopping links and sends them to the device.

[1841] Coordination Post

[1842] The user actually wears the suggested outfit, takes a photo, and posts it to the app. The user then enters the weather, temperature, and situation information along with the photo, and presses the "Post new outfit" button.

[1843] The server stores the received posting data of the new coordinates in a database, awards points as an incentive for the user's posting, and reflects the information in the user's account.

[1844] Specific examples

[1845] For example, consider the case where a user is choosing an outfit for going out with friends.

[1846] The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects the "Casual Scene" setting. The device then takes a photo of the user's face with the camera, and the emotion engine recognizes the user's facial expression as "happy."

[1847] The server suggests suitable shirts, pants, and sneakers from the user's closet, and also recommends additional items such as caps based on data posted by other users in the past, along with links to online shopping platforms.

[1848] The user wears the suggested outfit, takes a photo, and posts it to the app. After posting, the server gives the user points as an incentive and reflects the information in the user's account.

[1849] Prompt Sentence Examples

[1850] "Please suggest the best outfit for a casual occasion when the weather is sunny, the temperature is 25 degrees, and you are feeling happy."

[1851] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1852] Program processing flow

[1853] The program processing of this system is divided into the following steps:

[1854] Step 1: Register your closet items

[1855] Specific behavior:

[1856] The user takes a photo of the clothing item with the device's camera or uploads an existing photo.

[1857] The user enters details such as clothing item category, color, brand, size, etc. and presses the "Save" button.

[1858] Input: Clothing item photo and details

[1859] The terminal sends this information to the server.

[1860] The server stores the received information in a database.

[1861] Output: New clothing item information stored in the database

[1862] Step 2: Obtaining weather, temperature, and situation information

[1863] Specific behavior:

[1864] The device uses GPS to obtain the current location.

[1865] Input: Latitude and longitude information of current location

[1866] The device transmits its current location information to a weather information service and obtains weather and temperature data.

[1867] The user selects and enters the situation information for the day on the app interface.

[1868] Input: Situation information (casual, business, etc.)

[1869] Output: Obtained weather and temperature information and situation information

[1870] Step 3: Recognize emotions

[1871] Specific behavior:

[1872] The device captures the user's facial expressions with a camera or records their voice with a microphone.

[1873] Input: User's facial expression and voice data

[1874] The device sends this data to the emotion engine.

[1875] The emotion engine analyzes the data and identifies the user's emotion (e.g., "happy").

[1876] Output: User emotion data

[1877] Step 4: Coordination proposal

[1878] Specific behavior:

[1879] The server uses a generative AI model to generate the optimal outfit based on information about the items in the user's closet, acquired weather and temperature information, situation information, and emotional data.

[1880] Input: Closet item information, weather and temperature information, situation information, emotional data

[1881] An AI model analyzes this data and suggests optimal outfits.

[1882] The server sends the generated coordinate information to the terminal.

[1883] Output: Proposed coordinate information

[1884] Step 5: Recommendations from past posts

[1885] Specific behavior:

[1886] The server analyzes the coordination data posted by the user and other users in the past.

[1887] Input: Past coordination posting data

[1888] The server compares current weather information, situation information, and emotional data to generate recommended items and outfits.

[1889] The server adds a link to the online shopping platform and sends it to the terminal.

[1890] Output: Recommended items, outfit information, and related shopping links

[1891] Step 6: Post your outfit

[1892] Specific behavior:

[1893] The user wears the suggested outfit, takes a photo, and posts it to the app.

[1894] Input: outfit photo, weather and temperature information, situation information

[1895] The server stores the received posting data in a database and adds points to the user's account as an incentive.

[1896] Output: Post data and awarded points stored in the database

[1897] (Application example 2)

[1898] 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."

[1899] Conventional clothing selection support systems only suggest outfits based on the weather and situation, and have the problem of not being able to make suggestions that reflect the user's emotional state. Furthermore, they do not fully utilize the user's past outfit data, so there is a need to improve the shopping experience and the accuracy of suggestions.

[1900] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to register multiple clothing items in their closet; a means for a terminal to acquire weather information based on the user's current location information; a means for a user to input the weather information and situation information; a means for the server to generate an optimal outfit using AI based on the weather information, situation information, and item information in the user's closet; a means for the terminal to acquire the user's facial expressions and voice and analyze them with an emotion engine; a means for the server to generate an optimal outfit based on the weather information, situation information, emotion data, and item information in the user's closet; a means for the server to generate and recommend a recommended outfit based on similar items from past outfit posts; and a means for a user to post the suggested outfit and receive an incentive for the post. This enables personalized clothing selection based on the user's emotional state, improving the accuracy of suggestions and the shopping experience.

[1901] "User" refers to an entity that registers clothing items and receives coordination suggestions.

[1902] "Clothing items" refer to various elements of clothing that a user registers in their closet and uses to coordinate outfits.

[1903] A "closet" refers to a virtual storage space that stores information about clothing items owned by a user.

[1904] "Terminal" refers to a device that a user operates to acquire and display information. Examples include smartphones, tablets, and personal computers.

[1905] "Location information" refers to the physical location of a user, typically obtained using the Global Positioning System (GPS).

[1906] "Weather information" refers to environmental data such as the weather and temperature at the user's current location.

[1907] "Situation information" refers to information about a particular everyday or special situation specified by the user, such as "casual" or "business."

[1908] "Server" refers to a computer system that processes information sent by users, makes various suggestions, and manages data.

[1909] "Item information" refers to detailed information about a clothing item, such as category, color, brand, size, etc.

[1910] "AI (artificial intelligence)" refers to the machine learning or data analysis algorithms used by the server to generate the coordinates.

[1911] An "emotion engine" refers to a system that recognizes a user's emotional state by analyzing their facial expressions and voice.

[1912] "Emotion data" refers to information that indicates the user's emotional state as analyzed by the emotion engine.

[1913] "Coordination" refers to a combination of multiple clothing items suggested to the user.

[1914] "Past coordination posts" refer to coordination suggestions and results previously made by the user and other users.

[1915] "Recommendation" refers to the act of the server presenting new outfits or items to the user.

[1916] "Posting" refers to the act of a user wearing a suggested outfit and uploading the result to the system.

[1917] "Incentives" refer to rewards, points, and other benefits that users receive when they post content.

[1918] This invention is a system that efficiently utilizes multiple clothing items registered in a user's virtual closet and suggests optimal coordination based on the weather, temperature, situation, and the user's emotions.

[1919] 1. Register your closet items

[1920] A user registers a clothing item in their closet using their device. They take a photo of the clothing item using the device's camera and enter details such as the item's category, color, brand, and size. The device is equipped with an image recognition library (e.g., TensorFlow) that analyzes the image to obtain item information. This information is sent to the server and stored in a database.

[1921] 2. Obtaining weather, temperature, and situation information

[1922] The device uses its GPS function to obtain the user's current location information. It then sends this information to the weather information service's API (e.g., OpenWeatherMap) to obtain weather and temperature data. The user then uses the application's interface to select the situation for the day. For example, the user can enter a situation such as "casual" or "business."

[1923] 3. Emotional Recognition

[1924] The device acquires the user's facial expressions and voice and sends them to an emotion recognition engine (e.g., Microsoft Azure Cognitive Services). The emotion engine analyzes the user's facial expressions and voice and recognizes their emotions. For example, it can take a picture of the user's face with a camera and perform image analysis to determine emotions such as "happy" or "sad."

[1925] 4. Coordination suggestions

[1926] The server uses AI (e.g., TensorFlow) to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotion data obtained from an emotion engine. This outfit information is sent to the device and displayed to the user.

[1927] 5. Recommendations from past posts

[1928] The server analyzes the coordination data posted by the user and other users in the past, and generates recommended items and coordinations that match the current weather information, situation information, and emotion data. This recommendation information is also sent to the terminal and displayed for the user to use.

[1929] 6. Post your outfit

[1930] The user actually wears the suggested outfit, takes a photo, and posts it to the application. The user then re-enters weather, temperature, and situation information for the photo and posts it. The server saves the received new outfit posting data in a database, awards points as an incentive based on the user's post, and reflects the information in the user's account.

[1931] Specific examples

[1932] For example, consider a situation where a user is choosing an outfit for going out with friends. The user launches the app and obtains weather and temperature information for their current location. If the weather is sunny and the temperature is 25 degrees, the user selects "casual scene." The device takes a photo of the user's face with a camera, and the emotion engine recognizes the user's facial expression as "happy." The server then suggests suitable shirts, pants, and sneakers from the user's closet. Based on outfit data posted by other users in the past, the server also recommends similar items (e.g., caps) and displays a link to an online shopping platform.

[1933] Prompt Sentence Examples

[1934] "The user wants to go to lunch with a friend in the spring sunshine, so please suggest the perfect casual outfit from his closet. The temperature is 22 degrees and the weather is sunny. The user looks excited."

[1935] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1936] Step 1:

[1937] The user takes a photo of a clothing item using the device's camera. The photo is analyzed by an image recognition library (e.g., TensorFlow) to obtain detailed information about the item, such as its category, color, brand, and size. This information is sent to the server via the device and stored in a database. The input is a photo of the clothing item, and the output is detailed information about the item.

[1938] Step 2:

[1939] The device uses its GPS function to obtain the user's current location information. The obtained location information is sent to the API of a weather information service (e.g., OpenWeatherMap) to obtain weather and temperature data. Based on this location information, the API returns weather and temperature data. The input is the current location information, and the output is weather and temperature data.

[1940] Step 3:

[1941] The user uses the application interface to input situation information for the day, for example, by selecting a category such as "casual" or "business." The input is the situation information, and the output is the selected category.

[1942] Step 4:

[1943] The device captures the user's facial expressions and voice. Facial expression data is captured by the smartphone's camera, and voice data is captured by the microphone. This data is sent to an emotion recognition engine (e.g., Microsoft Azure Cognitive Services), which analyzes it to identify emotions. The input is facial expression and voice data, and the output is recognized emotion data.

[1944] Step 5:

[1945] The server uses AI (e.g., TensorFlow) to generate optimal outfits based on information about items in the user's closet, acquired weather information, situation information, and emotional data. The input is information about items in the closet, weather information, situation information, and emotional data, and the output is the optimal outfit.

[1946] Step 6:

[1947] The server generates recommended outfits based on similar items from past outfit posts and prepares the data to recommend to users. The input is past outfit data and current conditions (weather information, situation information, emotion data), and the output is the recommended outfit.

[1948] Step 7:

[1949] The user actually wears the proposed outfit, takes a photo, and posts it to the application. The photo also includes weather, temperature, and situation information. This new outfit posting data is sent to the server and stored in a database. The input is a photo of the proposed outfit and related information, and the output is the outfit posting data stored in the database.

[1950] Step 8:

[1951] The server awards points as an incentive based on the user's posts and reflects the information in the user's account. The input is the posted coordinate data, and the output is the awarded point information.

[1952] In this way, personalized clothing selection based on the user's emotional state can be achieved, improving the accuracy of recommendations and the shopping experience.

[1953] 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.

[1954] 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.

[1955] 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.

[1956] 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.

[1957] 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.

[1958] 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.

[1959] 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).

[1960] 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.

[1961] 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."

[1962] 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.

[1963] 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).

[1964] 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.

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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.

[1969] 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.

[1970] 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.

[1971] 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.

[1972] 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.

[1973] 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.

[1974] The following is further disclosed regarding the above embodiment.

[1975] ---

[1976] (Claim 1)

[1977] A means for a user to register a plurality of clothing items in his / her closet;

[1978] A means for the terminal to acquire weather information based on current location information of the user;

[1979] A means for a user to input the weather information and situation information;

[1980] A means for the server to generate an optimal coordination using AI based on the weather information, situation information, and item information in the user's closet;

[1981] A means for the server to generate and recommend recommended outfits based on similar items from past outfit posts;

[1982] A means for users to post suggested outfits and receive incentives for their posts;

[1983] A system including:

[1984] (Claim 2)

[1985] The system of claim 1 , further comprising means for the user to automatically add items purchased from an online shopping platform to a closet.

[1986] (Claim 3)

[1987] The system of claim 1 , wherein the server further comprises means for awarding points for each coordinated post of a user and making the points available for use on an online shopping platform.

[1988] ---

[1989] The above is the draft of the patent claims.

[1990] "Example 1"

[1991] (Claim 1)

[1992] A means for a user to register a plurality of clothing items in his / her closet;

[1993] A means for the terminal to acquire weather information based on current location information of the user;

[1994] A means for a user to input the weather information and situation information;

[1995] A means for the server to generate an optimal coordination using a generative AI model based on the weather information, situation information, and item information in the user's closet;

[1996] A server generates and recommends recommended outfits based on similar items from past outfit posts;

[1997] A means for users to post suggested outfits and receive incentives for their posts;

[1998] A system including:

[1999] (Claim 2)

[2000] The system of claim 1 , further comprising means for the user to automatically add items purchased on the e-commerce platform to a closet.

[2001] (Claim 3)

[2002] 10. The system of claim 1, wherein the server further comprises means for awarding points for each coordinated post of a user and making the points available for use on an e-commerce platform.

[2003] "Application Example 1"

[2004] (Claim 1)

[2005] A means for a user to register a plurality of clothing items in his / her closet;

[2006] A means for the terminal to acquire weather information based on current location information of the user;

[2007] A means for a user to input the weather information and situation information;

[2008] A means for the server to generate an optimal coordination using AI based on the weather information, situation information, and item information in the user's closet;

[2009] A means for the server to generate and recommend recommended outfits based on similar items from past outfit posts;

[2010] A means for users to post suggested outfits and receive incentives for their posts;

[2011] A means for a user to create an avatar for virtual try-on and have the avatar try on coordinated clothing;

[2012] A means for the server to use a generative AI model to create a prompt sentence based on the items in the user's closet and weather information, and for the AI ​​to use the prompt sentence to generate an optimal outfit;

[2013] A system including:

[2014] (Claim 2)

[2015] The system of claim 1 , further comprising means for the user to automatically add items purchased from an online shopping platform to a closet.

[2016] (Claim 3)

[2017] The system of claim 1 , wherein the server further comprises means for awarding points for each coordinated post of a user and making the points available for use on an online shopping platform.

[2018] "Example 2: Combining Emotion Engines"

[2019] (Claim 1)

[2020] A means for a user to register a plurality of clothing items in his / her clo...

Claims

1. A means for a user to register a plurality of clothing items in his / her closet; A means for the terminal to acquire weather information based on current location information of the user; A means for a user to input the weather information and situation information; A means for the server to generate an optimal coordination using AI based on the weather information, situation information, and item information in the user's closet; A means for the server to generate and recommend recommended outfits based on similar items from past outfit posts; A means for users to post suggested outfits and receive incentives for their posts; A system including:

2. The system of claim 1 , further comprising means for the user to automatically add items purchased from an online shopping platform to a closet.

3. The system of claim 1 , wherein the server further comprises means for awarding points for each coordinated post of a user and making the points available for use on an online shopping platform.

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