System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving style information provided by a user; means for receiving preference information; means for selecting an optimal product based on the style information and the preference information; means for generating an overall coordinate image using the selected product; means for presenting the coordinate image to the user; means for confirming purchase based on the presented coordinate image; and means for executing a purchase procedure.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] Currently, many users spend a lot of time and effort trying to find the perfect fashion item for them. In particular, gathering style information, understanding preferences, and considering how to coordinate outfits are all cumbersome, and even once they find the perfect item, the purchasing process is complicated. [Means for solving the problem]

[0005] The present invention solves this problem by providing a system including: means for receiving style information provided by a user; means for receiving preference information; means for selecting optimal products based on the style information and the preference information; means for generating an overall coordination image using the selected products; means for presenting the coordination image to the user; means for confirming a purchase based on the presented coordination image; and means for executing a purchase procedure. This system further includes means for receiving a full-body photo or measurement suit data of the user as the style information, thereby precisely acquiring the user's body type information, and is characterized in that the preference information is fashion style classification information based on the user's selection, making it possible to make suggestions that accurately reflect the user's fashion preferences.

[0006] "User" refers to an individual who uses the system to input their own style information and fashion preferences and purchase suggested outfits.

[0007] "Terminal" means an electronic device used by a User to operate the System, including a smartphone, tablet, or PC.

[0008] A "server" is a computer system that receives and processes input information from a user.

[0009] "Style information" is data about the user's body type and appearance, including full-body photos and measurement suit data.

[0010] "Preference information" refers to classification information of the fashion style that the user prefers, and includes styles such as casual, formal, and street style.

[0011] "Products" refers to fashion items, including clothing and accessories selected by the server based on the user's style and preference information.

[0012] A "coordination image" is a visual representation of the overall look of a combination of multiple selected fashion items.

[0013] "Purchase" refers to the act of a user selecting a suggested fashion item and completing the payment and delivery procedures.

[0014] "Purchase procedure" refers to the series of processes required for a user to actually purchase the item selected by the user, including entering payment information and specifying a delivery address. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that automatically suggests optimal fashion coordination and allows users to purchase the coordination as it is by providing their own style information and preference information. Specific embodiments of the system will be described below.

[0037] Overall system flow

[0038] The system mainly consists of the following main parts:

[0039] 1. Enter style information

[0040] 2. Enter your preferences

[0041] 3. Coordination suggestions

[0042] 4. Coordination suggestions

[0043] 5. Confirmation of purchase

[0044] 6. Purchase Processing

[0045] Entering style information

[0046] Users use their devices to provide their own style information. The devices are equipped with a camera and a dedicated application, allowing users to take full-body photos and upload the image data. Users can also wear measuring suits such as the ZOZO Suit to obtain detailed body data. This style information is used as basic data for the AI ​​algorithm to select appropriate fashion items.

[0047] Enter your preferences

[0048] Users input their fashion preferences using a device. Preference information is a classification of fashion styles, such as casual, formal, and street style, and users can select from a list. This information is essential for the AI ​​to suggest outfits that suit the user.

[0049] Coordination suggestions

[0050] The server receives style and preference information provided by the user and analyzes the data. Based on the analysis results, it uses an AI algorithm to select the most suitable products. It extracts appropriate items from the product catalog and generates an overall coordination image for the user. This image includes multiple selected fashion items.

[0051] Coordination suggestions

[0052] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually check it. The user can check the combination of selected fashion items based on this overall coordinated image.

[0053] Confirm your purchase

[0054] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm their purchase. The device recognizes this operation and sends purchase request data to the server. This request data includes information on the selected item, quantity, delivery address, etc.

[0055] Purchase Processing

[0056] The server receives the purchase request and initiates the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[0057] Specific examples

[0058] For users who prefer a casual style

[0059] 1. The user takes a full-body photo using their device and uploads it.

[0060] 2. The server receives the photo and analyzes it.

[0061] 3. The user selects "casual style" and enters it into the device.

[0062] 4. The server selects casual fashion items based on the style and preference information.

[0063] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0064] 6. The device displays the coordinated image to the user.

[0065] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[0066] 8. The server processes the purchase and sends a confirmation message to the device.

[0067] 9. The device displays a confirmation message to the user and the purchase is complete.

[0068] In this way, users can easily select and purchase fashion items that suit their tastes through simple operations.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] Users log in to their device and enter their style information. Specifically, they can either take a full-body photo using the device's camera or wear the ZOZO Suit to obtain measurement data, which they then upload to their device.

[0072] Step 2:

[0073] The device sends the captured photos or measurement data to the server. At this time, the data format is standardized and error checks are performed to ensure reliable transmission to the server.

[0074] Step 3:

[0075] The server analyzes the received style information using image processing technology and machine learning models to extract and store the user's body shape data.

[0076] Step 4:

[0077] The user inputs their fashion preferences into the device, and then selects a style such as "casual," "formal," or "street" from a list displayed on the device.

[0078] Step 5:

[0079] The device sends the user's preferences to the server, which formats and transmits the data to accurately reflect the user's choices.

[0080] Step 6:

[0081] The server analyzes the preference information received, combines it with style information, and uses an AI algorithm to select the most suitable fashion items from a product catalog.

[0082] Step 7:

[0083] The server generates an overall coordinated image based on the selected items. Specifically, it runs a program that combines the selected items and generates CG and mockups.

[0084] Step 8:

[0085] The server sends the generated coordinated image and item list to the device. The data package includes high-resolution images and detailed item information.

[0086] Step 9:

[0087] The device displays an outfit image and a list of items to the user, who can then check the details of each item and evaluate the overall outfit.

[0088] Step 10:

[0089] The user selects a favorite outfit and presses the purchase button. The device recognizes this operation and generates purchase request data.

[0090] Step 11:

[0091] The terminal sends purchase request data to the server, which includes information about the selected item, the quantity, the delivery address, etc.

[0092] Step 12:

[0093] The server receives the purchase request and initiates the user's purchase process, including verifying payment information, securing inventory, and arranging shipping.

[0094] Step 13:

[0095] The server generates a confirmation message that the purchase has been confirmed and sends it to the user's device, including details about the purchase and an estimated delivery date.

[0096] Step 14:

[0097] The device displays a purchase confirmation message to the user, so that the user knows the purchase was successful.

[0098] Example 1

[0099] 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."

[0100] Conventional fashion coordination systems require users to take the time and effort to select and combine products themselves, and have difficulty in proposing optimal outfits based on the user's body type and preferences. Furthermore, a separate purchasing procedure is required, making the overall user experience cumbersome. To solve these issues, there is a need for a system that can suggest optimal fashion coordinations with simple and intuitive operation for users, and can guide users through the entire process to purchase.

[0101] 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.

[0102] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means including a generative AI model for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for presenting the coordination image to the user, means for confirming a purchase based on the presented coordination image, means for carrying out a purchase procedure, and means for sending a confirmation message to the user's terminal that the purchase has been confirmed. This eliminates the need for the user to select products themselves, and allows the user to receive suggestions for optimal fashion coordination through simple and intuitive operations, and then to carry out the purchase procedure in an integrated manner.

[0103] "User" refers to an individual who uses the system and provides their own style and preference information.

[0104] "Style information" is information that indicates the user's body type and fashion characteristics, and is data obtained from a full-body photograph and measurement suit data.

[0105] "Preference information" is information relating to the type and attributes of a fashion style that a user prefers, and is data that is input based on the user's selection.

[0106] The "generative AI model" is an artificial intelligence model that selects the most suitable products and generates coordination images based on style and preference information provided by the user.

[0107] A "coordinate image" is a visual image of an overall coordinated look created by combining a number of selected fashion items.

[0108] A "terminal" is an electronic device used by a user to input information or check outfit ideas, and includes smartphones and tablets.

[0109] The "server" is a computer system that receives and analyzes information provided by users, suggests fashion coordination, and carries out purchasing procedures.

[0110] "Purchase request data" refers to data that includes detailed information about the purchase, such as information about the items selected by the user, the quantity, and the delivery address.

[0111] A "confirmation message" is a message that notifies the user that the purchase procedure has been completed, and is a notification that is displayed on the terminal.

[0112] MODE FOR CARRYING OUT THE INVENTION

[0113] The present invention is a system that automatically suggests optimal fashion coordinations and allows users to purchase them as they are by providing their own style information and preference information. Specific embodiments of this system are described below.

[0114] Hardware and software used

[0115] First, smartphones and tablets are suitable as devices for users. These devices are equipped with a camera function and dedicated applications. A cloud-based computer system (e.g., AWS) is used as the server, which receives and analyzes the user's style and preference information. The server uses machine learning libraries such as TensorFlow and PyTorch to run AI algorithms. Computer vision is also used for image analysis technology.

[0116] Entering style information

[0117] Users take a full-body photo using their own device and upload the image data to a server using a dedicated application. Furthermore, by wearing a measuring suit such as the ZOZO Suit, users can obtain detailed body shape data. This style information is analyzed by an AI algorithm and used as basic data to select appropriate fashion items.

[0118] Enter your preferences

[0119] Users use a dedicated application on their device to input their preferred fashion style. This information includes fashion style classifications such as casual, formal, and street style, as well as color and design preferences. This information can be selected from a list and input, and the AI ​​will use it to suggest outfits that suit the user.

[0120] Coordination suggestions

[0121] The server receives style and preference information provided by the user and analyzes it using an AI algorithm. Based on the analysis results, it selects the most suitable products from the product catalog and generates an overall coordination image that includes multiple selected fashion items.

[0122] Coordination suggestions

[0123] The server sends the generated coordinated image to the user's device, which then displays the received image and item list to the user, allowing them to visually confirm the combination of their selected fashion items based on this overall coordinated image.

[0124] Confirm your purchase

[0125] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device then sends purchase request data to the server. This request data includes information about the selected item, the quantity, the delivery address, etc.

[0126] Purchase Processing

[0127] The server receives the purchase request and initiates the purchase process, including verifying payment information, securing inventory, and arranging shipping. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The device then displays this confirmation message to the user, completing the purchase process.

[0128] Specific examples

[0129] For users who prefer a casual style

[0130] 1. The user takes a full-body photo using their device and uploads it.

[0131] 2. The server receives the photo and analyzes it.

[0132] 3. The user selects "casual style" and enters it into the device.

[0133] 4. The server selects casual fashion items based on the style and preference information.

[0134] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0135] 6. The device displays the coordinated image to the user.

[0136] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[0137] 8. The server processes the purchase and sends a confirmation message to the device.

[0138] 9. The device displays a confirmation message to the user and the purchase is complete.

[0139] Example prompts to be input to the generative AI model

[0140] "Please suggest a casual fashion coordination. Style information: full-body photo, preference information: casual style."

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1: Entering style information

[0143] Users use their device to provide their own style information. First, they open a dedicated application, take a full-body photo, and upload the image data. Furthermore, if the user is wearing a measuring suit such as the ZOZO Suit, detailed body shape data is also obtained through the dedicated application and sent to the server. The device then sends this data to the server. The server saves the received image data and body shape data for analysis and registers it as style information.

[0144] Input: User's full-body photo, measurement suit data

[0145] Output: Style information (user's body shape and posture data)

[0146] Step 2: Enter your preferences

[0147] Users input their fashion preferences through a dedicated application, selecting, for example, their fashion style classification (e.g., casual, formal, street style) as well as their color and design preferences. This preference information is sent from the device to the server, which then stores it as data necessary for analysis.

[0148] Input: User's fashion style preferences

[0149] Output: Data saved as preference information

[0150] Step 3: Coordination suggestions

[0151] The server receives style and preference information provided by the user and analyzes this data. Specifically, it uses AI algorithms (e.g., TensorFlow or PyTorch) to select the best fashion items from a product catalog that suit the user's body type and preferences. The AI ​​algorithm uses image analysis technology to understand the user's body type and posture and select appropriate items. The server selects the best products and combines them to generate an outfit image. This outfit image also includes detailed information about each fashion item.

[0152] Input: Style information, preference information

[0153] Output: Selection of optimal fashion items and generated coordination images

[0154] Step 4: Show your outfit

[0155] The server sends the generated coordinated image to the user's device. The device displays the received image to the user through a dedicated application. The displayed content includes the coordinated image and detailed information about each item (price, brand, size, etc.). The user can visually check this and confirm the combination of selected fashion items.

[0156] Input: Generated coordinate image

[0157] Output: Coordinate image and detailed item information displayed on the user's device

[0158] Step 5: Confirm your purchase

[0159] The user checks the displayed coordinated image and presses the "Purchase" button in the dedicated application if they wish to confirm the purchase. The device recognizes this operation and sends purchase request data to the server. This data includes information on the selected item, quantity, delivery address, etc. The server receives this and starts the purchase process.

[0160] Input: User's purchase intent (pressing the purchase button)

[0161] Output: Purchase request data sent to server

[0162] Step 6: Purchase Process

[0163] The server receives the purchase request and initiates the purchase process. Specifically, it connects to a third-party payment gateway (e.g., Stripe or PayPal) to verify payment information. It then secures inventory and verifies that the product is available. It then connects to a logistics system to process the delivery. If the purchase is confirmed, the server generates a confirmation message indicating that the purchase is complete and sends it to the user's device. The device displays this confirmation message to the user, completing the purchase process.

[0164] Input: Purchase request data

[0165] Output: Payment confirmation, inventory reservation, shipping process, generation and sending of confirmation message

[0166] (Application example 1)

[0167] 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."

[0168] Conventional fashion suggestion systems have had problems in that it is difficult for users to find the best products that suit their style, and it takes time to select the appropriate outfit. Also, try-on and fitting must be done in a physical store, and there is a problem that size and style mismatches are likely to occur when shopping online. The present invention aims to solve these problems and provide a system that allows users to efficiently find the best fashion outfit and confirm the appropriate fit through virtual try-on.

[0169] 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.

[0170] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for displaying the coordination image as a virtual try-on on the user's device, means for confirming a purchase based on the presented coordination image, and means for executing a purchase procedure. This allows the user to receive suggested fashion items optimal to the user based on their style information and preference information, check the items in a virtual try-on, and easily purchase them.

[0171] "User" refers to an individual who provides information about their own fashion style and preferences and receives outfit suggestions from the system.

[0172] "Style information" refers to information about the user's body shape and fashion style, such as a full-body photo of the user and measurement suit data.

[0173] "Preference information" refers to information regarding the classification of fashion style selected by the user, such as casual, formal, street, etc.

[0174] "Optimal products" refer to the fashion items that best suit a user, selected by AI based on the user's style and preference information.

[0175] "Coordination image" refers to the visual image of the overall fashion style presented to the user by combining multiple selected fashion items.

[0176] "Virtual try-on" refers to a method of using digital technology to display selected fashion items on a user's device, providing a visual confirmation that they are actually trying on.

[0177] "Device" refers to an information terminal used by a user, such as a smartphone, smart glasses, or head-mounted display.

[0178] "Confirming purchase" refers to the act of a user officially deciding to purchase a fashion item selected based on the presented coordination image.

[0179] "Purchase procedure" refers to a series of processes that are carried out after a purchase is confirmed, such as confirming payment information, securing inventory, and shipping procedures.

[0180] The present invention is a system that proposes optimal fashion coordination based on a user's style information and preference information, and then allows the user to virtually try on the clothes and complete the purchase procedure. Specific embodiments of this system are described below.

[0181] Entering style information

[0182] Users take a full-body photo using a device (e.g., a smartphone or smart glasses) and upload the image data to the server. They can also obtain detailed body shape data using a measurement suit (e.g., a measurement suit), and this data is also sent to the server as style information.

[0183] Enter your preferences

[0184] The user inputs their own fashion preference information using the terminal. The preference information is a classification of fashion styles such as casual, formal, street, etc., and the selection made from the selection list is sent to the server.

[0185] Coordination suggestions

[0186] The server receives style and preference information provided by the user and analyzes the data. An AI algorithm (e.g., FashionAI) is used for the analysis. The AI ​​algorithm selects the most suitable products and extracts appropriate items from the product catalog. Based on these extracted items, an overall outfit image is generated.

[0187] Coordination presentation and virtual try-on

[0188] The server sends the generated coordinated image to the user's device. The device (e.g., a smartphone or smart glasses) displays the received image and item list, allowing the user to visually confirm it. Additionally, using virtual try-on technology, the selected fashion items are displayed on the user's device, providing a visual confirmation as if they were actually being tried on.

[0189] Confirmation and checkout

[0190] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device recognizes this operation and sends the purchase request data to the server. The server receives the purchase request, confirms payment information, secures inventory, and processes delivery. It also generates a confirmation message indicating that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[0191] Specific examples

[0192] For users who prefer a casual style

[0193] 1. The user takes a full-body photo using their device and uploads it.

[0194] 2. The server receives the photo and analyzes it.

[0195] 3. The user selects "casual style" and enters it into the device.

[0196] 4. The server selects casual fashion items based on the style and preference information.

[0197] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0198] 6. The device displays outfit images to the user, allowing them to virtually try them on.

[0199] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[0200] 8. The server processes the purchase and sends a confirmation message to the device.

[0201] 9. The device displays a confirmation message to the user and the purchase is complete.

[0202] Prompt Sentence Examples

[0203] "The user takes a full-body photo and selects a casual style. The AI ​​then suggests the best fashion coordination. Then, it generates a code that allows the user to purchase the suggested items."

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] Users take a full-body photo using a device (e.g., a smartphone) and upload the image data to the server. This input data includes information about the user's body shape and style. The uploaded image data is used as the basis for subsequent fashion item selection.

[0207] Step 2:

[0208] The user inputs their fashion preference information using the terminal. This input data includes classification information of the user's selected fashion style (e.g., casual, formal). The server receives the preference information and stores it in a database.

[0209] Step 3:

[0210] The server analyzes the style and preference information. An AI algorithm (e.g., FashionAI) is used for this analysis. Based on the style and preference information in the input data, the server selects the most suitable fashion items. The list of selected items is used to generate outfits in the next step.

[0211] Step 4:

[0212] The server combines the selected fashion items to generate an overall coordinated image. This image generation is performed by applying the selected items to a virtual model. The generated coordinated image is used as output data to present to the user.

[0213] Step 5:

[0214] The server sends the generated coordinated image to the user's device, which then displays the coordinated image. Using virtual try-on technology, the user visually checks the selected fashion items as if they were actually being tried on.

[0215] Step 6:

[0216] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device recognizes this operation and sends the purchase request data to the server. The input data includes information on the selected item, quantity, delivery address, etc.

[0217] Step 7:

[0218] The server receives the purchase request, verifies payment information, secures inventory, and processes shipping. This completes the user's purchase. Finally, it generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The device displays this confirmation message to the user, completing the purchase process.

[0219] 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.

[0220] The present invention allows users to provide their own style and preference information, and the system then suggests optimal fashion coordinations that can then be purchased. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to achieve more personalized suggestions.

[0221] Overall system flow

[0222] The system consists of the following main parts:

[0223] 1. Enter style information

[0224] 2. Enter your preferences

[0225] 3. Introducing the Emotion Engine

[0226] 4. Coordination suggestions

[0227] 5. Coordination suggestions

[0228] 6. Confirmation of purchase

[0229] 7. Purchase Processing

[0230] Entering style information

[0231] Users use their devices to provide their own style information. The devices are equipped with a camera and a dedicated application, allowing users to take full-body photos and upload the image data. Users can also wear measuring suits such as the ZOZO Suit to obtain detailed body data. This style information is used as basic data for the AI ​​algorithm to select appropriate fashion items.

[0232] Enter your preferences

[0233] Users input their fashion preferences using a device. Preference information is a classification of fashion styles, such as casual, formal, and street style, and users can select from a list. This information is essential for the AI ​​to suggest outfits that suit the user.

[0234] Introducing the Emotion Engine

[0235] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expression data and voice data to estimate the user's emotions. For example, if the user is smiling while taking a photo, the emotion engine will infer that the user is in a positive state. This information influences product selection and the generation of outfit images.

[0236] Coordination suggestions

[0237] The server receives and analyzes style information, preference information, and emotion data obtained by the emotion engine provided by the user. Based on the analysis results, it uses an AI algorithm to select optimal fashion items from a product catalog. It then extracts appropriate items from the product catalog and generates an overall outfit image for the user. This image includes multiple selected fashion items.

[0238] Coordination suggestions

[0239] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually check it. The user can check the combination of selected fashion items based on this overall coordinated image.

[0240] Confirm your purchase

[0241] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm their purchase. The device recognizes this operation and generates purchase request data. This request data includes information on the selected item, quantity, delivery address, etc.

[0242] Purchase Processing

[0243] The server receives the purchase request and initiates the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[0244] Specific examples

[0245] For users who prefer a casual style

[0246] 1. The user takes a full-body photo using their device and uploads it.

[0247] 2. The server receives the photo and analyzes it.

[0248] 3. The user selects "casual style" and enters it into the device.

[0249] 4. The server selects casual fashion items based on the style and preference information.

[0250] 5. The server uses an emotion engine to analyze the user's facial expression data and determine that the user is in a positive state.

[0251] 6. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0252] 7. The device displays the coordinated image to the user.

[0253] 8. The user presses the "Purchase" button, sending a purchase request to the server.

[0254] 9. The server processes the purchase and sends a confirmation message to the device.

[0255] 10. The device displays a confirmation message to the user and the purchase is complete.

[0256] In this way, more personalized suggestions are possible depending on the user's emotional state, improving the user experience.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] Users log in to the device and enter their style information. Specifically, they can take a full-body photo using the device's camera or wear a measuring suit to obtain measurement data, which they then upload to the device.

[0260] Step 2:

[0261] The device sends the captured photos or measurement data to the server. At this time, the data format is standardized and error checks are performed to ensure reliable transmission to the server.

[0262] Step 3:

[0263] The server analyzes the received style information using image processing technology and machine learning models to extract and store the user's body shape data.

[0264] Step 4:

[0265] The user inputs their fashion preferences into the device, and then selects a style such as "casual," "formal," or "street" from a list displayed on the device.

[0266] Step 5:

[0267] The device sends the user's preferences to the server, which formats and transmits the data to accurately reflect the user's choices.

[0268] Step 6:

[0269] The server analyzes the received preference and style information and selects the most suitable items from the product catalog based on that information. It uses AI algorithms to narrow down the options and identify the most suitable fashion items for the user.

[0270] Step 7:

[0271] The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's facial expression data and voice data and infers their emotions based on that information. For example, if the user is smiling while taking a photo, it is determined to be in a positive state.

[0272] Step 8:

[0273] The server generates the final outfit image based on the analysis results, including emotional data. The selected items are combined to create an outfit that takes into account the user's emotional state.

[0274] Step 9:

[0275] The server sends the generated coordinated image and item list to the device. The data package includes high-resolution images and detailed item information.

[0276] Step 10:

[0277] The device displays an outfit image and a list of items to the user, who can then check the details of each item and evaluate the overall outfit.

[0278] Step 11:

[0279] The user selects a favorite outfit and presses the purchase button. The device recognizes this operation and generates purchase request data.

[0280] Step 12:

[0281] The terminal sends purchase request data to the server, which includes information about the selected item, the quantity, the delivery address, etc.

[0282] Step 13:

[0283] The server receives the purchase request and initiates the user's purchase process, including verifying payment information, securing inventory, and arranging shipping.

[0284] Step 14:

[0285] The server generates a confirmation message that the purchase has been confirmed and sends it to the user's device, including details about the purchase and an estimated delivery date.

[0286] Step 15:

[0287] The device displays a purchase confirmation message to the user, so that the user knows the purchase was successful.

[0288] Example 2

[0289] 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."

[0290] The problem to be solved by the present invention is to realize more accurate suggestions that take into account the user's emotional state when suggesting personalized fashion coordination based on the user's style information and preference information, and to improve user convenience by providing a system that allows the suggested fashion items to be purchased as is.

[0291] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for acquiring and analyzing user emotion data, means for reflecting the analyzed emotion data in product selection, means for presenting the coordination image to the user, means for confirming purchase based on the presented coordination image, and means for executing purchase procedures. This enables personalized coordination suggestions that take into account the user's emotional state in addition to the style information and preference information provided by the user, thereby significantly improving user convenience.

[0292] A "user" is an individual or group that utilizes the system to provide their style and preference information.

[0293] "Style information" refers to data relating to the user's body type and appearance, such as a full-body photo of the user and measurement suit data.

[0294] "Preference information" is classification information of a fashion style selected by a user, and includes information such as casual, formal, and street style.

[0295] "Emotion data" is data that represents the user's current emotional state, obtained by analyzing the user's facial expression data and voice data.

[0296] The "server" is a computer system that receives style information, preference information, and emotional data sent by the user, analyzes this, and makes coordination suggestions.

[0297] A "coordination image" is a visual image of a specific combination of fashion items that is generated based on information analyzed by the server.

[0298] "Purchase request data" is data generated when a user confirms a purchase based on a coordination image, and includes information on the selected items, the quantity, the delivery address, and so on.

[0299] MODE FOR CARRYING OUT THE INVENTION

[0300] This invention is a system that suggests optimal fashion coordination based on style information, preference information, and the user's emotional state, and allows the user to purchase the outfit immediately. The system is mainly composed of a terminal and a server, and operates in cooperation with an emotion recognition engine, AI algorithms, and a product catalog.

[0301] Hardware and software used

[0302] Device:

[0303] Smartphones, tablets, PCs, etc.

[0304] Camera features

[0305] Dedicated application (fashion coordination app)

[0306] server:

[0307] Web server, database server

[0308] Emotion recognition engine (facial expression analysis, voice analysis)

[0309] Software for running AI algorithms (e.g., TensorFlow or PyTorch)

[0310] Product catalog database

[0311] data:

[0312] Style information (full-body photo, suit data for measurements)

[0313] Preference information (user-selected fashion style classification)

[0314] Emotion data (user's facial expression data, voice data)

[0315] Detailed explanation of the system's operation

[0316] 1. Providing style information:

[0317] Users use a dedicated application to take a full-body photo of themselves and upload it to the server from their device. Users also wear a measurement suit to obtain detailed body shape data, which is then sent to the server.

[0318] 2. Enter your preferences:

[0319] The user selects the classification information of their fashion style and sends it to the server from their terminal. This preference information is categorized into categories such as casual, formal, and street style.

[0320] 3. Acquiring emotion data:

[0321] The user captures their facial expression using the device's camera and sends the data to the server, where the server's emotion recognition engine analyzes the facial expression data and estimates the user's emotional state (positive, neutral, negative, etc.).

[0322] 4. Coordination proposal generation:

[0323] The server integrates the received style, preference, and emotion data and uses an AI algorithm to select the most suitable fashion items. Based on this, it selects appropriate items from a product catalog and creates an overall coordination image.

[0324] 5. Coordination suggestions:

[0325] The generated coordinated image is sent from the server to the user's terminal, which displays the image to the user, allowing the user to visually confirm the proposed coordinated look.

[0326] 6. Purchasing process:

[0327] When the user likes the suggested outfit and presses the purchase button, the device sends the purchase request data to the server. The server verifies payment information, secures inventory, and processes delivery procedures, and finally sends a confirmation message to the user's device. The device displays this confirmation message to the user, completing the purchase.

[0328] Specific examples

[0329] For example, if a user desires a casual style, the following steps are performed.

[0330] 1. The user takes a full-body photo using their device and uploads it to the server.

[0331] 2. The user selects "casual style" and enters it into the terminal.

[0332] 3. The server analyzes the user's facial expression data and determines their positive emotional state.

[0333] 4. The server generates an image of a coordinated outfit consisting of a denim jacket, a casual T-shirt, and chino pants, and sends it to the device.

[0334] 5. The device displays the coordinated image, and the user presses the "Purchase" button to proceed with the purchase.

[0335] Prompt Sentence Examples

[0336] "I'm a woman who likes casual style. I'd like you to suggest denim jackets, casual T-shirts, and chino pants. The sentiment is positive."

[0337] This invention allows for personalized coordination suggestions that take into account the user's emotional state and seamless purchase of the items, improving the user experience.

[0338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0339] Step 1: Entering style information

[0340] Specific behavior:

[0341] 1. The user launches a dedicated application using the device.

[0342] 2. The user follows the application's instructions to take a full-body photo.

[0343] 3. The device uploads the captured image data to the server.

[0344] 4. The device also collects data from measurement suits such as the ZOZO Suit and sends it to the server.

[0345] Input: A full-body photo taken by the user and measurement suit data.

[0346] Output: Style information (full-body photo, suit data for measurements) sent to the server.

[0347] Step 2: Enter your preferences

[0348] Specific behavior:

[0349] 1. The user displays the fashion style selection screen on their device.

[0350] 2. The user selects their preferred fashion style, such as "casual" or "formal."

[0351] 3. The device sends the selected preference information to the server.

[0352] Input: The user's chosen fashion style (e.g. casual, formal, etc.).

[0353] Output: Preference information sent to the server.

[0354] Step 3: Obtaining emotion data

[0355] Specific behavior:

[0356] 1. The user uses the camera function to capture their facial expression.

[0357] 2. The device collects facial expression data and sends it to the server.

[0358] 3. The server uses an emotion engine to analyze the received facial expression data and estimate the current emotional state.

[0359] Input: User's facial expression data.

[0360] Output: The emotional state inferred by the emotion engine (e.g., positive, neutral, negative, etc.).

[0361] Step 4: Analyze the information and generate coordination proposals

[0362] Specific behavior:

[0363] 1. The server integrates and analyzes the style information, preference information, and emotion data received from the user.

[0364] 2. The server uses an AI algorithm to select the most suitable fashion items.

[0365] 3. The server generates an overall coordinated image based on the items selected.

[0366] Input: Style information, preference information, emotion data.

[0367] Output: Coordination image (combination of selected fashion items).

[0368] Step 5: Show your outfit

[0369] Specific behavior:

[0370] 1. The server sends the generated coordinate image to the terminal.

[0371] 2. The terminal displays the coordinated image received to the user.

[0372] 3. Allow the user to visually check the suggested outfits.

[0373] Input: Server-generated coordinate image.

[0374] Output: Coordinate image displayed on the device.

[0375] Step 6: Confirm your purchase

[0376] Specific behavior:

[0377] 1. The user checks the coordinated image displayed.

[0378] 2. The user presses the "Purchase" button.

[0379] 3. The terminal generates a purchase request and sends it to the server. This request data includes information about the selected item, the quantity, the delivery address, etc.

[0380] Input: User confirms outfit and confirms purchase.

[0381] Output: Purchase request data sent to the server.

[0382] Step 7: Purchase Processing

[0383] Specific behavior:

[0384] 1. The server receives the purchase request and verifies the payment information.

[0385] 2. The server checks and secures product inventory.

[0386] 3. The server initiates the delivery procedure.

[0387] 4. The server generates a confirmation message confirming the purchase and sends it to the user's device.

[0388] 5. The device displays a confirmation message to the user, informing them that the purchase is complete.

[0389] Input: Purchase request data sent to the server.

[0390] Output: Purchase confirmation message and purchase completion notification.

[0391] (Application example 2)

[0392] 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."

[0393] Conventional fashion coordination suggestion systems only suggest products based on style and preference information, which means they lack personalized suggestions that take into account the user's emotional state. As a result, it is difficult to improve user satisfaction, and the quality of the shopping experience can decline. This can also affect sales by reducing purchasing motivation.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0395] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for acquiring emotion data using an emotion engine that recognizes the user's emotions, means for selecting optimal products based on the style information, preference information, and emotion data, means for generating an overall coordination image using the selected products, means for presenting the coordination image to the user, means for confirming a purchase based on the presented coordination image, and means for executing a purchase procedure, thereby making it possible to provide a more personalized fashion suggestion and purchasing experience according to the user's emotional state.

[0396] "Style information provided by the user" refers to information provided by the user, such as a full-body photo of the user and suit measurement data, and is basic data used by the system to understand the user's body type and fashion trends.

[0397] "Preference information" is classification information of a fashion style selected by a user, and is information based on the user's preferences, such as casual, formal, street, etc.

[0398] The "emotion engine that recognizes user emotions" is software and algorithms that analyze the user's facial expression data and voice data to estimate the user's emotional state.

[0399] The "means for selecting the most suitable product based on the style information, preference information, and emotional data" refers to an algorithm that analyzes the style information, preference information, and emotional data obtained from the user and selects the most suitable fashion item from a product catalog based on the information.

[0400] The "means for generating an overall coordination image using the selected products" refers to software or algorithms for combining multiple selected fashion items to create an overall coordination image to be proposed to the user.

[0401] The "means for presenting the coordinated image to the user" refers to a technique for transmitting the generated coordinated image to the user's terminal and displaying it in a visually confirmable manner.

[0402] The "means for confirming a purchase based on the presented coordinated image" refers to an interface and process for a user to confirm a displayed coordinated image and confirm a purchase.

[0403] The "means of completing the purchase process" is the process of verifying payment information, securing inventory, arranging shipping, and sending a confirmation message to the user after the purchase is confirmed.

[0404] The system that realizes this invention proposes optimal fashion coordination based on the user's style information and preference information, and further provides more personalized proposals by recognizing the user's emotional state using an emotion engine. The hardware and software required to implement this system are described below.

[0405] Overall system configuration

[0406] 1. Enter style information

[0407] Users take a full-body photo of themselves using a device such as a smartphone and upload the image data through a dedicated application. They can also wear a measurement suit to obtain detailed body data. The hardware used is the smartphone's camera function, and the software is a dedicated application for processing the image data.

[0408] 2. Enter your preferences

[0409] The user inputs his or her fashion preferences using the terminal. This preference information is a classification of fashion styles such as casual, formal, street, etc., and the user selects from a list.

[0410] 3. Introducing the Emotion Engine

[0411] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expression data and voice data to estimate the user's emotional state. The software used is Keras and OpenCV, which use machine learning models to recognize emotions.

[0412] 4. Coordination suggestions

[0413] The server receives and analyzes style and preference information provided by the user, as well as emotional data obtained by the emotion engine. Based on the analysis results, an AI algorithm is used to select the most suitable fashion items from the product catalog. The software used for this process is Python-based conditional branching and filtering logic.

[0414] 5. Coordination suggestions

[0415] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually confirm the image. The software used is the UI component of a dedicated application.

[0416] 6. Confirmation of purchase

[0417] The user checks the displayed coordinated image and presses the purchase button if they wish to finalize the purchase. The terminal recognizes this operation and generates purchase request data.

[0418] 7. Purchase Processing

[0419] The server receives the purchase request and starts the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes shipping. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The software used is the requests library for processing HTTP requests.

[0420] Specific examples

[0421] For users who prefer a casual style

[0422] 1. The user takes a full-body photo using their smartphone and uploads it.

[0423] 2. The server receives the photo and analyzes it.

[0424] 3. The user selects "casual style" and enters it into the device.

[0425] 4. The server selects casual fashion items based on the style and preference information.

[0426] 5. The server uses an emotion engine to analyze the user's facial expression data and determine that the user is in a positive state.

[0427] 6. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0428] 7. The device displays the coordinated image to the user.

[0429] 8. The user presses the "Purchase" button, sending a purchase request to the server.

[0430] 9. The server processes the purchase and sends a confirmation message to the device.

[0431] 10. The device displays a confirmation message to the user and the purchase is complete.

[0432] Prompt Sentence Examples

[0433] Generate a Python program that suggests casual and comfortable fashion items when a user takes a full-body photo using a smartphone app and the emotion recognition engine determines that the user is "happy."

[0434] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0435] Step 1:

[0436] The user takes a full-body photo using their smartphone and launches a dedicated application.

[0437] Input: A full-body photo of the user.

[0438] Output: Full-body photo data.

[0439] How it works: The user takes a full-body photo of themselves using the camera on their smartphone, and the image data is uploaded to a dedicated application.

[0440] Step 2:

[0441] The terminal transmits the captured full-body photograph to the server.

[0442] Input: Full-body photo data.

[0443] Output: The photo data sent to the server.

[0444] Specific operation: A dedicated application on the device generates and sends an HTTP request to send the captured full-body photo data to the server.

[0445] Step 3:

[0446] The server analyzes the received full-body photo data and extracts style information.

[0447] Input: Full-body photo data.

[0448] Output: Style information data.

[0449] Specific operation: The server uses an image processing algorithm to extract information about the user's body shape and the clothes they are wearing from the full-body photo data.

[0450] Step 4:

[0451] The user inputs his / her preference information (casual, formal, street style, etc.) using the terminal.

[0452] Input: User preference information.

[0453] Output: Preference information data.

[0454] Specific operation: The user selects his / her fashion preferences using the dedicated application interface. The selected preference information is sent from the terminal to the server.

[0455] Step 5:

[0456] The server receives the user-entered preference information.

[0457] Input: Preference information data.

[0458] Output: Preference information data stored in the server.

[0459] Specific operation: The server processes the HTTP request, receives and stores preference information data.

[0460] Step 6:

[0461] The server uses an emotion engine to recognize emotions from the user's facial expression data.

[0462] Input: Facial expression data.

[0463] Output: Emotion data.

[0464] Specific operation: The server runs an emotion engine (using Keras and OpenCV) that analyzes facial expression data and recognizes emotional states such as positive and negative.

[0465] Step 7:

[0466] The server selects the most suitable product based on the style information, preference information, and emotion data.

[0467] Input: Style information data, preference information data, emotion data.

[0468] Output: A list of the best products.

[0469] Specific operation: The server uses an AI algorithm to select the most suitable fashion items from the product catalog that match the user's style information, preference information, and emotional data.

[0470] Step 8:

[0471] The server generates an overall coordinated image using the selected products.

[0472] Input: A list of best products.

[0473] Output: Coordinated image.

[0474] Specific operation: The server performs image processing to combine the selected fashion items and generate coordinated images.

[0475] Step 9:

[0476] The server transmits the generated coordinated image to the user's terminal.

[0477] Input: Coordinated image.

[0478] Output: Coordinated image sent to user device.

[0479] Specific operation: The server generates and sends an HTTP response to send the coordinated image to the user's device.

[0480] Step 10:

[0481] The terminal displays the received coordinated image to the user.

[0482] Input: Coordinated image.

[0483] Output: Coordinate image displayed on the device.

[0484] Specific operation: The user's device displays the coordinated image using the UI component of the dedicated application.

[0485] Step 11:

[0486] The user checks the displayed coordinated image and presses a button to confirm the purchase.

[0487] Input: User taps.

[0488] Output: Purchase request data.

[0489] Specific operation: When a user taps the purchase button of an application, this action is recognized on the device and purchase request data is generated.

[0490] Step 12:

[0491] The terminal transmits the generated purchase request data to the server.

[0492] Input: Purchase request data.

[0493] Output: Purchase request data sent to the server.

[0494] Specific operation: The terminal generates and sends an HTTP request to send the purchase request data to the server.

[0495] Step 13:

[0496] The server receives the purchase request and processes the purchase.

[0497] Input: Purchase request data.

[0498] Output: Purchase completion message.

[0499] Specific operation: The server processes the purchase request data, verifies payment information, secures inventory, and arranges for delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device.

[0500] Step 14:

[0501] The terminal will display a message to the user indicating that the purchase has been completed.

[0502] Input: Purchase completion message.

[0503] Output: The completion message displayed to the user.

[0504] Specific behavior: The user's device displays a notification in the UI component that the purchase process has been completed, informing the user that the purchase was successful.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] [Second embodiment]

[0509] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0510] 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.

[0511] 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).

[0512] 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.

[0513] 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.

[0514] 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).

[0515] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] 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."

[0521] The present invention is a system that automatically suggests optimal fashion coordination and allows users to purchase the coordination as it is by providing their own style information and preference information. Specific embodiments of the system will be described below.

[0522] Overall system flow

[0523] The system mainly consists of the following main parts:

[0524] 1. Enter style information

[0525] 2. Enter your preferences

[0526] 3. Coordination suggestions

[0527] 4. Coordination suggestions

[0528] 5. Confirmation of purchase

[0529] 6. Purchase Processing

[0530] Entering style information

[0531] Users use their devices to provide their own style information. The devices are equipped with a camera and a dedicated application, allowing users to take full-body photos and upload the image data. Users can also wear measuring suits such as the ZOZO Suit to obtain detailed body data. This style information is used as basic data for the AI ​​algorithm to select appropriate fashion items.

[0532] Enter your preferences

[0533] Users input their fashion preferences using a device. Preference information is a classification of fashion styles, such as casual, formal, and street style, and users can select from a list. This information is essential for the AI ​​to suggest outfits that suit the user.

[0534] Coordination suggestions

[0535] The server receives style and preference information provided by the user and analyzes the data. Based on the analysis results, it uses an AI algorithm to select the most suitable products. It extracts appropriate items from the product catalog and generates an overall coordination image for the user. This image includes multiple selected fashion items.

[0536] Coordination suggestions

[0537] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually check it. The user can check the combination of selected fashion items based on this overall coordinated image.

[0538] Confirm your purchase

[0539] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm their purchase. The device recognizes this operation and sends purchase request data to the server. This request data includes information on the selected item, quantity, delivery address, etc.

[0540] Purchase Processing

[0541] The server receives the purchase request and initiates the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[0542] Specific examples

[0543] For users who prefer a casual style

[0544] 1. The user takes a full-body photo using their device and uploads it.

[0545] 2. The server receives the photo and analyzes it.

[0546] 3. The user selects "casual style" and enters it into the device.

[0547] 4. The server selects casual fashion items based on the style and preference information.

[0548] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0549] 6. The device displays the coordinated image to the user.

[0550] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[0551] 8. The server processes the purchase and sends a confirmation message to the device.

[0552] 9. The device displays a confirmation message to the user and the purchase is complete.

[0553] In this way, users can easily select and purchase fashion items that suit their tastes through simple operations.

[0554] The processing flow will be explained below.

[0555] Step 1:

[0556] Users log in to their device and enter their style information. Specifically, they can either take a full-body photo using the device's camera or wear the ZOZO Suit to obtain measurement data, which they then upload to their device.

[0557] Step 2:

[0558] The device sends the captured photos or measurement data to the server. At this time, the data format is standardized and error checks are performed to ensure reliable transmission to the server.

[0559] Step 3:

[0560] The server analyzes the received style information using image processing technology and machine learning models to extract and store the user's body shape data.

[0561] Step 4:

[0562] The user inputs their fashion preferences into the device, and then selects a style such as "casual," "formal," or "street" from a list displayed on the device.

[0563] Step 5:

[0564] The device sends the user's preferences to the server, which formats and transmits the data to accurately reflect the user's choices.

[0565] Step 6:

[0566] The server analyzes the preference information received, combines it with style information, and uses an AI algorithm to select the most suitable fashion items from a product catalog.

[0567] Step 7:

[0568] The server generates an overall coordinated image based on the selected items. Specifically, it runs a program that combines the selected items and generates CG and mockups.

[0569] Step 8:

[0570] The server sends the generated coordinated image and item list to the device. The data package includes high-resolution images and detailed item information.

[0571] Step 9:

[0572] The device displays an outfit image and a list of items to the user, who can then check the details of each item and evaluate the overall outfit.

[0573] Step 10:

[0574] The user selects a favorite outfit and presses the purchase button. The device recognizes this operation and generates purchase request data.

[0575] Step 11:

[0576] The terminal sends purchase request data to the server, which includes information about the selected item, the quantity, the delivery address, etc.

[0577] Step 12:

[0578] The server receives the purchase request and initiates the user's purchase process, including verifying payment information, securing inventory, and arranging shipping.

[0579] Step 13:

[0580] The server generates a confirmation message that the purchase has been confirmed and sends it to the user's device, including details about the purchase and an estimated delivery date.

[0581] Step 14:

[0582] The device displays a purchase confirmation message to the user, so that the user knows the purchase was successful.

[0583] Example 1

[0584] 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."

[0585] Conventional fashion coordination systems require users to take the time and effort to select and combine products themselves, and have difficulty in proposing optimal outfits based on the user's body type and preferences. Furthermore, a separate purchasing procedure is required, making the overall user experience cumbersome. To solve these issues, there is a need for a system that can suggest optimal fashion coordinations with simple and intuitive operation for users, and can guide users through the entire process to purchase.

[0586] 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.

[0587] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means including a generative AI model for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for presenting the coordination image to the user, means for confirming a purchase based on the presented coordination image, means for carrying out a purchase procedure, and means for sending a confirmation message to the user's terminal that the purchase has been confirmed. This eliminates the need for the user to select products themselves, and allows the user to receive suggestions for optimal fashion coordination through simple and intuitive operations, and then to carry out the purchase procedure in an integrated manner.

[0588] "User" refers to an individual who uses the system and provides their own style and preference information.

[0589] "Style information" is information that indicates the user's body type and fashion characteristics, and is data obtained from a full-body photograph and measurement suit data.

[0590] "Preference information" is information relating to the type and attributes of a fashion style that a user prefers, and is data that is input based on the user's selection.

[0591] The "generative AI model" is an artificial intelligence model that selects the most suitable products and generates coordination images based on style and preference information provided by the user.

[0592] A "coordinate image" is a visual image of an overall coordinated look created by combining a number of selected fashion items.

[0593] A "terminal" is an electronic device used by a user to input information or check outfit ideas, and includes smartphones and tablets.

[0594] The "server" is a computer system that receives and analyzes information provided by users, suggests fashion coordination, and carries out purchasing procedures.

[0595] "Purchase request data" refers to data that includes detailed information about the purchase, such as information about the items selected by the user, the quantity, and the delivery address.

[0596] A "confirmation message" is a message that notifies the user that the purchase procedure has been completed, and is a notification that is displayed on the terminal.

[0597] MODE FOR CARRYING OUT THE INVENTION

[0598] The present invention is a system that automatically suggests optimal fashion coordinations and allows users to purchase them as they are by providing their own style information and preference information. Specific embodiments of this system are described below.

[0599] Hardware and software used

[0600] First, smartphones and tablets are suitable as devices for users. These devices are equipped with a camera function and dedicated applications. A cloud-based computer system (e.g., AWS) is used as the server, which receives and analyzes the user's style and preference information. The server uses machine learning libraries such as TensorFlow and PyTorch to run AI algorithms. Computer vision is also used for image analysis technology.

[0601] Entering style information

[0602] Users take a full-body photo using their own device and upload the image data to a server using a dedicated application. Furthermore, by wearing a measuring suit such as the ZOZO Suit, users can obtain detailed body shape data. This style information is analyzed by an AI algorithm and used as basic data to select appropriate fashion items.

[0603] Enter your preferences

[0604] Users use a dedicated application on their device to input their preferred fashion style. This information includes fashion style classifications such as casual, formal, and street style, as well as color and design preferences. This information can be selected from a list and input, and the AI ​​will use it to suggest outfits that suit the user.

[0605] Coordination suggestions

[0606] The server receives style and preference information provided by the user and analyzes it using an AI algorithm. Based on the analysis results, it selects the most suitable products from the product catalog and generates an overall coordination image that includes multiple selected fashion items.

[0607] Coordination suggestions

[0608] The server sends the generated coordinated image to the user's device, which then displays the received image and item list to the user, allowing them to visually confirm the combination of their selected fashion items based on this overall coordinated image.

[0609] Confirm your purchase

[0610] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device then sends purchase request data to the server. This request data includes information about the selected item, the quantity, the delivery address, etc.

[0611] Purchase Processing

[0612] The server receives the purchase request and initiates the purchase process, including verifying payment information, securing inventory, and arranging shipping. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The device then displays this confirmation message to the user, completing the purchase process.

[0613] Specific examples

[0614] For users who prefer a casual style

[0615] 1. The user takes a full-body photo using their device and uploads it.

[0616] 2. The server receives the photo and analyzes it.

[0617] 3. The user selects "casual style" and enters it into the device.

[0618] 4. The server selects casual fashion items based on the style and preference information.

[0619] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0620] 6. The device displays the coordinated image to the user.

[0621] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[0622] 8. The server processes the purchase and sends a confirmation message to the device.

[0623] 9. The device displays a confirmation message to the user and the purchase is complete.

[0624] Example prompts to be input to the generative AI model

[0625] "Please suggest a casual fashion coordination. Style information: full-body photo, preference information: casual style."

[0626] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0627] Step 1: Entering style information

[0628] Users use their device to provide their own style information. First, they open a dedicated application, take a full-body photo, and upload the image data. Furthermore, if the user is wearing a measuring suit such as the ZOZO Suit, detailed body shape data is also obtained through the dedicated application and sent to the server. The device then sends this data to the server. The server saves the received image data and body shape data for analysis and registers it as style information.

[0629] Input: User's full-body photo, measurement suit data

[0630] Output: Style information (user's body shape and posture data)

[0631] Step 2: Enter your preferences

[0632] Users input their fashion preferences through a dedicated application, selecting, for example, their fashion style classification (e.g., casual, formal, street style) as well as their color and design preferences. This preference information is sent from the device to the server, which then stores it as data necessary for analysis.

[0633] Input: User's fashion style preferences

[0634] Output: Data saved as preference information

[0635] Step 3: Coordination suggestions

[0636] The server receives style and preference information provided by the user and analyzes this data. Specifically, it uses AI algorithms (e.g., TensorFlow or PyTorch) to select the best fashion items from a product catalog that suit the user's body type and preferences. The AI ​​algorithm uses image analysis technology to understand the user's body type and posture and select appropriate items. The server selects the best products and combines them to generate an outfit image. This outfit image also includes detailed information about each fashion item.

[0637] Input: Style information, preference information

[0638] Output: Selection of optimal fashion items and generated coordination images

[0639] Step 4: Show your outfit

[0640] The server sends the generated coordinated image to the user's device. The device displays the received image to the user through a dedicated application. The displayed content includes the coordinated image and detailed information about each item (price, brand, size, etc.). The user can visually check this and confirm the combination of selected fashion items.

[0641] Input: Generated coordinate image

[0642] Output: Coordinate image and detailed item information displayed on the user's device

[0643] Step 5: Confirm your purchase

[0644] The user checks the displayed coordinated image and presses the "Purchase" button in the dedicated application if they wish to confirm the purchase. The device recognizes this operation and sends purchase request data to the server. This data includes information on the selected item, quantity, delivery address, etc. The server receives this and starts the purchase process.

[0645] Input: User's purchase intent (pressing the purchase button)

[0646] Output: Purchase request data sent to server

[0647] Step 6: Purchase Process

[0648] The server receives the purchase request and initiates the purchase process. Specifically, it connects to a third-party payment gateway (e.g., Stripe or PayPal) to verify payment information. It then secures inventory and verifies that the product is available. It then connects to a logistics system to process the delivery. If the purchase is confirmed, the server generates a confirmation message indicating that the purchase is complete and sends it to the user's device. The device displays this confirmation message to the user, completing the purchase process.

[0649] Input: Purchase request data

[0650] Output: Payment confirmation, inventory reservation, shipping process, generation and sending of confirmation message

[0651] (Application example 1)

[0652] 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."

[0653] Conventional fashion suggestion systems have had problems in that it is difficult for users to find the best products that suit their style, and it takes time to select the appropriate outfit. Also, try-on and fitting must be done in a physical store, and there is a problem that size and style mismatches are likely to occur when shopping online. The present invention aims to solve these problems and provide a system that allows users to efficiently find the best fashion outfit and confirm the appropriate fit through virtual try-on.

[0654] 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.

[0655] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for displaying the coordination image as a virtual try-on on the user's device, means for confirming a purchase based on the presented coordination image, and means for executing a purchase procedure. This allows the user to receive suggested fashion items optimal to the user based on their style information and preference information, check the items in a virtual try-on, and easily purchase them.

[0656] "User" refers to an individual who provides information about their own fashion style and preferences and receives outfit suggestions from the system.

[0657] "Style information" refers to information about the user's body shape and fashion style, such as a full-body photo of the user and measurement suit data.

[0658] "Preference information" refers to information regarding the classification of fashion style selected by the user, such as casual, formal, street, etc.

[0659] "Optimal products" refer to the fashion items that best suit a user, selected by AI based on the user's style and preference information.

[0660] "Coordination image" refers to the visual image of the overall fashion style presented to the user by combining multiple selected fashion items.

[0661] "Virtual try-on" refers to a method of using digital technology to display selected fashion items on a user's device, providing a visual confirmation that they are actually trying on.

[0662] "Device" refers to an information terminal used by a user, such as a smartphone, smart glasses, or head-mounted display.

[0663] "Confirming purchase" refers to the act of a user officially deciding to purchase a fashion item selected based on the presented coordination image.

[0664] "Purchase procedure" refers to a series of processes that are carried out after a purchase is confirmed, such as confirming payment information, securing inventory, and shipping procedures.

[0665] The present invention is a system that proposes optimal fashion coordination based on a user's style information and preference information, and then allows the user to virtually try on the clothes and complete the purchase procedure. Specific embodiments of this system are described below.

[0666] Entering style information

[0667] Users take a full-body photo using a device (e.g., a smartphone or smart glasses) and upload the image data to the server. They can also obtain detailed body shape data using a measurement suit (e.g., a measurement suit), and this data is also sent to the server as style information.

[0668] Enter your preferences

[0669] The user inputs their own fashion preference information using the terminal. The preference information is a classification of fashion styles such as casual, formal, street, etc., and the selection made from the selection list is sent to the server.

[0670] Coordination suggestions

[0671] The server receives style and preference information provided by the user and analyzes the data. An AI algorithm (e.g., FashionAI) is used for the analysis. The AI ​​algorithm selects the most suitable products and extracts appropriate items from the product catalog. Based on these extracted items, an overall outfit image is generated.

[0672] Coordination presentation and virtual try-on

[0673] The server sends the generated coordinated image to the user's device. The device (e.g., a smartphone or smart glasses) displays the received image and item list, allowing the user to visually confirm it. Additionally, using virtual try-on technology, the selected fashion items are displayed on the user's device, providing a visual confirmation as if they were actually being tried on.

[0674] Confirmation and checkout

[0675] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device recognizes this operation and sends the purchase request data to the server. The server receives the purchase request, confirms payment information, secures inventory, and processes delivery. It also generates a confirmation message indicating that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[0676] Specific examples

[0677] For users who prefer a casual style

[0678] 1. The user takes a full-body photo using their device and uploads it.

[0679] 2. The server receives the photo and analyzes it.

[0680] 3. The user selects "casual style" and enters it into the device.

[0681] 4. The server selects casual fashion items based on the style and preference information.

[0682] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0683] 6. The device displays outfit images to the user, allowing them to virtually try them on.

[0684] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[0685] 8. The server processes the purchase and sends a confirmation message to the device.

[0686] 9. The device displays a confirmation message to the user and the purchase is complete.

[0687] Prompt Sentence Examples

[0688] "The user takes a full-body photo and selects a casual style. The AI ​​then suggests the best fashion coordination. Then, it generates a code that allows the user to purchase the suggested items."

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

[0690] Step 1:

[0691] Users take a full-body photo using a device (e.g., a smartphone) and upload the image data to the server. This input data includes information about the user's body shape and style. The uploaded image data is used as the basis for subsequent fashion item selection.

[0692] Step 2:

[0693] The user inputs their fashion preference information using the terminal. This input data includes classification information of the user's selected fashion style (e.g., casual, formal). The server receives the preference information and stores it in a database.

[0694] Step 3:

[0695] The server analyzes the style and preference information. An AI algorithm (e.g., FashionAI) is used for this analysis. Based on the style and preference information in the input data, the server selects the most suitable fashion items. The list of selected items is used to generate outfits in the next step.

[0696] Step 4:

[0697] The server combines the selected fashion items to generate an overall coordinated image. This image generation is performed by applying the selected items to a virtual model. The generated coordinated image is used as output data to present to the user.

[0698] Step 5:

[0699] The server sends the generated coordinated image to the user's device, which then displays the coordinated image. Using virtual try-on technology, the user visually checks the selected fashion items as if they were actually being tried on.

[0700] Step 6:

[0701] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device recognizes this operation and sends the purchase request data to the server. The input data includes information on the selected item, quantity, delivery address, etc.

[0702] Step 7:

[0703] The server receives the purchase request, verifies payment information, secures inventory, and processes shipping. This completes the user's purchase. Finally, it generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The device displays this confirmation message to the user, completing the purchase process.

[0704] 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.

[0705] The present invention allows users to provide their own style and preference information, and the system then suggests optimal fashion coordinations that can then be purchased. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to achieve more personalized suggestions.

[0706] Overall system flow

[0707] The system consists of the following main parts:

[0708] 1. Enter style information

[0709] 2. Enter your preferences

[0710] 3. Introducing the Emotion Engine

[0711] 4. Coordination suggestions

[0712] 5. Coordination suggestions

[0713] 6. Confirmation of purchase

[0714] 7. Purchase Processing

[0715] Entering style information

[0716] Users use their devices to provide their own style information. The devices are equipped with a camera and a dedicated application, allowing users to take full-body photos and upload the image data. Users can also wear measuring suits such as the ZOZO Suit to obtain detailed body data. This style information is used as basic data for the AI ​​algorithm to select appropriate fashion items.

[0717] Enter your preferences

[0718] Users input their fashion preferences using a device. Preference information is a classification of fashion styles, such as casual, formal, and street style, and users can select from a list. This information is essential for the AI ​​to suggest outfits that suit the user.

[0719] Introducing the Emotion Engine

[0720] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expression data and voice data to estimate the user's emotions. For example, if the user is smiling while taking a photo, the emotion engine will infer that the user is in a positive state. This information influences product selection and the generation of outfit images.

[0721] Coordination suggestions

[0722] The server receives and analyzes style information, preference information, and emotion data obtained by the emotion engine provided by the user. Based on the analysis results, it uses an AI algorithm to select optimal fashion items from a product catalog. It then extracts appropriate items from the product catalog and generates an overall outfit image for the user. This image includes multiple selected fashion items.

[0723] Coordination suggestions

[0724] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually check it. The user can check the combination of selected fashion items based on this overall coordinated image.

[0725] Confirm your purchase

[0726] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm their purchase. The device recognizes this operation and generates purchase request data. This request data includes information on the selected item, quantity, delivery address, etc.

[0727] Purchase Processing

[0728] The server receives the purchase request and initiates the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[0729] Specific examples

[0730] For users who prefer a casual style

[0731] 1. The user takes a full-body photo using their device and uploads it.

[0732] 2. The server receives the photo and analyzes it.

[0733] 3. The user selects "casual style" and enters it into the device.

[0734] 4. The server selects casual fashion items based on the style and preference information.

[0735] 5. The server uses an emotion engine to analyze the user's facial expression data and determine that the user is in a positive state.

[0736] 6. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0737] 7. The device displays the coordinated image to the user.

[0738] 8. The user presses the "Purchase" button, sending a purchase request to the server.

[0739] 9. The server processes the purchase and sends a confirmation message to the device.

[0740] 10. The device displays a confirmation message to the user and the purchase is complete.

[0741] In this way, more personalized suggestions are possible depending on the user's emotional state, improving the user experience.

[0742] The processing flow will be explained below.

[0743] Step 1:

[0744] Users log in to the device and enter their style information. Specifically, they can take a full-body photo using the device's camera or wear a measuring suit to obtain measurement data, which they then upload to the device.

[0745] Step 2:

[0746] The device sends the captured photos or measurement data to the server. At this time, the data format is standardized and error checks are performed to ensure reliable transmission to the server.

[0747] Step 3:

[0748] The server analyzes the received style information using image processing technology and machine learning models to extract and store the user's body shape data.

[0749] Step 4:

[0750] The user inputs their fashion preferences into the device, and then selects a style such as "casual," "formal," or "street" from a list displayed on the device.

[0751] Step 5:

[0752] The device sends the user's preferences to the server, which formats and transmits the data to accurately reflect the user's choices.

[0753] Step 6:

[0754] The server analyzes the received preference and style information and selects the most suitable items from the product catalog based on that information. It uses AI algorithms to narrow down the options and identify the most suitable fashion items for the user.

[0755] Step 7:

[0756] The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's facial expression data and voice data and infers their emotions based on that information. For example, if the user is smiling while taking a photo, it is determined to be in a positive state.

[0757] Step 8:

[0758] The server generates the final outfit image based on the analysis results, including emotional data. The selected items are combined to create an outfit that takes into account the user's emotional state.

[0759] Step 9:

[0760] The server sends the generated coordinated image and item list to the device. The data package includes high-resolution images and detailed item information.

[0761] Step 10:

[0762] The device displays an outfit image and a list of items to the user, who can then check the details of each item and evaluate the overall outfit.

[0763] Step 11:

[0764] The user selects a favorite outfit and presses the purchase button. The device recognizes this operation and generates purchase request data.

[0765] Step 12:

[0766] The terminal sends purchase request data to the server, which includes information about the selected item, the quantity, the delivery address, etc.

[0767] Step 13:

[0768] The server receives the purchase request and initiates the user's purchase process, including verifying payment information, securing inventory, and arranging shipping.

[0769] Step 14:

[0770] The server generates a confirmation message that the purchase has been confirmed and sends it to the user's device, including details about the purchase and an estimated delivery date.

[0771] Step 15:

[0772] The device displays a purchase confirmation message to the user, so that the user knows the purchase was successful.

[0773] Example 2

[0774] 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."

[0775] The problem to be solved by the present invention is to realize more accurate suggestions that take into account the user's emotional state when suggesting personalized fashion coordination based on the user's style information and preference information, and to improve user convenience by providing a system that allows the suggested fashion items to be purchased as is.

[0776] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for acquiring and analyzing user emotion data, means for reflecting the analyzed emotion data in product selection, means for presenting the coordination image to the user, means for confirming purchase based on the presented coordination image, and means for executing purchase procedures. This enables personalized coordination suggestions that take into account the user's emotional state in addition to the style information and preference information provided by the user, thereby significantly improving user convenience.

[0777] A "user" is an individual or group that utilizes the system to provide their style and preference information.

[0778] "Style information" refers to data relating to the user's body type and appearance, such as a full-body photo of the user and measurement suit data.

[0779] "Preference information" is classification information of a fashion style selected by a user, and includes information such as casual, formal, and street style.

[0780] "Emotion data" is data that represents the user's current emotional state, obtained by analyzing the user's facial expression data and voice data.

[0781] The "server" is a computer system that receives style information, preference information, and emotional data sent by the user, analyzes this, and makes coordination suggestions.

[0782] A "coordination image" is a visual image of a specific combination of fashion items that is generated based on information analyzed by the server.

[0783] "Purchase request data" is data generated when a user confirms a purchase based on a coordination image, and includes information on the selected items, the quantity, the delivery address, and so on.

[0784] MODE FOR CARRYING OUT THE INVENTION

[0785] This invention is a system that suggests optimal fashion coordination based on style information, preference information, and the user's emotional state, and allows the user to purchase the outfit immediately. The system is mainly composed of a terminal and a server, and operates in cooperation with an emotion recognition engine, AI algorithms, and a product catalog.

[0786] Hardware and software used

[0787] Device:

[0788] Smartphones, tablets, PCs, etc.

[0789] Camera features

[0790] Dedicated application (fashion coordination app)

[0791] server:

[0792] Web server, database server

[0793] Emotion recognition engine (facial expression analysis, voice analysis)

[0794] Software for running AI algorithms (e.g., TensorFlow or PyTorch)

[0795] Product catalog database

[0796] data:

[0797] Style information (full-body photo, suit data for measurements)

[0798] Preference information (user-selected fashion style classification)

[0799] Emotion data (user's facial expression data, voice data)

[0800] Detailed explanation of the system's operation

[0801] 1. Providing style information:

[0802] Users use a dedicated application to take a full-body photo of themselves and upload it to the server from their device. Users also wear a measurement suit to obtain detailed body shape data, which is then sent to the server.

[0803] 2. Enter your preferences:

[0804] The user selects the classification information of their fashion style and sends it to the server from their terminal. This preference information is categorized into categories such as casual, formal, and street style.

[0805] 3. Acquiring emotion data:

[0806] The user captures their facial expression using the device's camera and sends the data to the server, where the server's emotion recognition engine analyzes the facial expression data and estimates the user's emotional state (positive, neutral, negative, etc.).

[0807] 4. Coordination proposal generation:

[0808] The server integrates the received style, preference, and emotion data and uses an AI algorithm to select the most suitable fashion items. Based on this, it selects appropriate items from a product catalog and creates an overall coordination image.

[0809] 5. Coordination suggestions:

[0810] The generated coordinated image is sent from the server to the user's terminal, which displays the image to the user, allowing the user to visually confirm the proposed coordinated look.

[0811] 6. Purchasing process:

[0812] When the user likes the suggested outfit and presses the purchase button, the device sends the purchase request data to the server. The server verifies payment information, secures inventory, and processes delivery procedures, and finally sends a confirmation message to the user's device. The device displays this confirmation message to the user, completing the purchase.

[0813] Specific examples

[0814] For example, if a user desires a casual style, the following steps are performed.

[0815] 1. The user takes a full-body photo using their device and uploads it to the server.

[0816] 2. The user selects "casual style" and enters it into the terminal.

[0817] 3. The server analyzes the user's facial expression data and determines their positive emotional state.

[0818] 4. The server generates an image of a coordinated outfit consisting of a denim jacket, a casual T-shirt, and chino pants, and sends it to the device.

[0819] 5. The device displays the coordinated image, and the user presses the "Purchase" button to proceed with the purchase.

[0820] Prompt Sentence Examples

[0821] "I'm a woman who likes casual style. I'd like you to suggest denim jackets, casual T-shirts, and chino pants. The sentiment is positive."

[0822] This invention allows for personalized coordination suggestions that take into account the user's emotional state and seamless purchase of the items, improving the user experience.

[0823] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0824] Step 1: Entering style information

[0825] Specific behavior:

[0826] 1. The user launches a dedicated application using the device.

[0827] 2. The user follows the application's instructions to take a full-body photo.

[0828] 3. The device uploads the captured image data to the server.

[0829] 4. The device also collects data from measurement suits such as the ZOZO Suit and sends it to the server.

[0830] Input: A full-body photo taken by the user and measurement suit data.

[0831] Output: Style information (full-body photo, suit data for measurements) sent to the server.

[0832] Step 2: Enter your preferences

[0833] Specific behavior:

[0834] 1. The user displays the fashion style selection screen on their device.

[0835] 2. The user selects their preferred fashion style, such as "casual" or "formal."

[0836] 3. The device sends the selected preference information to the server.

[0837] Input: The user's chosen fashion style (e.g. casual, formal, etc.).

[0838] Output: Preference information sent to the server.

[0839] Step 3: Obtaining emotion data

[0840] Specific behavior:

[0841] 1. The user uses the camera function to capture their facial expression.

[0842] 2. The device collects facial expression data and sends it to the server.

[0843] 3. The server uses an emotion engine to analyze the received facial expression data and estimate the current emotional state.

[0844] Input: User's facial expression data.

[0845] Output: The emotional state inferred by the emotion engine (e.g., positive, neutral, negative, etc.).

[0846] Step 4: Analyze the information and generate coordination proposals

[0847] Specific behavior:

[0848] 1. The server integrates and analyzes the style information, preference information, and emotion data received from the user.

[0849] 2. The server uses an AI algorithm to select the most suitable fashion items.

[0850] 3. The server generates an overall coordinated image based on the items selected.

[0851] Input: Style information, preference information, emotion data.

[0852] Output: Coordination image (combination of selected fashion items).

[0853] Step 5: Show your outfit

[0854] Specific behavior:

[0855] 1. The server sends the generated coordinate image to the terminal.

[0856] 2. The terminal displays the coordinated image received to the user.

[0857] 3. Allow the user to visually check the suggested outfits.

[0858] Input: Server-generated coordinate image.

[0859] Output: Coordinate image displayed on the device.

[0860] Step 6: Confirm your purchase

[0861] Specific behavior:

[0862] 1. The user checks the coordinated image displayed.

[0863] 2. The user presses the "Purchase" button.

[0864] 3. The terminal generates a purchase request and sends it to the server. This request data includes information about the selected item, the quantity, the delivery address, etc.

[0865] Input: User confirms outfit and confirms purchase.

[0866] Output: Purchase request data sent to the server.

[0867] Step 7: Purchase Processing

[0868] Specific behavior:

[0869] 1. The server receives the purchase request and verifies the payment information.

[0870] 2. The server checks and secures product inventory.

[0871] 3. The server initiates the delivery procedure.

[0872] 4. The server generates a confirmation message confirming the purchase and sends it to the user's device.

[0873] 5. The device displays a confirmation message to the user, informing them that the purchase is complete.

[0874] Input: Purchase request data sent to the server.

[0875] Output: Purchase confirmation message and purchase completion notification.

[0876] (Application example 2)

[0877] 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."

[0878] Conventional fashion coordination suggestion systems only suggest products based on style and preference information, which means they lack personalized suggestions that take into account the user's emotional state. As a result, it is difficult to improve user satisfaction, and the quality of the shopping experience can decline. This can also affect sales by reducing purchasing motivation.

[0879] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0880] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for acquiring emotion data using an emotion engine that recognizes the user's emotions, means for selecting optimal products based on the style information, preference information, and emotion data, means for generating an overall coordination image using the selected products, means for presenting the coordination image to the user, means for confirming a purchase based on the presented coordination image, and means for executing a purchase procedure, thereby making it possible to provide a more personalized fashion suggestion and purchasing experience according to the user's emotional state.

[0881] "Style information provided by the user" refers to information provided by the user, such as a full-body photo of the user and suit measurement data, and is basic data used by the system to understand the user's body type and fashion trends.

[0882] "Preference information" is classification information of a fashion style selected by a user, and is information based on the user's preferences, such as casual, formal, street, etc.

[0883] The "emotion engine that recognizes user emotions" is software and algorithms that analyze the user's facial expression data and voice data to estimate the user's emotional state.

[0884] The "means for selecting the most suitable product based on the style information, preference information, and emotional data" refers to an algorithm that analyzes the style information, preference information, and emotional data obtained from the user and selects the most suitable fashion item from a product catalog based on the information.

[0885] The "means for generating an overall coordination image using the selected products" refers to software or algorithms for combining multiple selected fashion items to create an overall coordination image to be proposed to the user.

[0886] The "means for presenting the coordinated image to the user" refers to a technique for transmitting the generated coordinated image to the user's terminal and displaying it in a visually confirmable manner.

[0887] The "means for confirming a purchase based on the presented coordinated image" refers to an interface and process for a user to confirm a displayed coordinated image and confirm a purchase.

[0888] The "means of completing the purchase process" is the process of verifying payment information, securing inventory, arranging shipping, and sending a confirmation message to the user after the purchase is confirmed.

[0889] The system that realizes this invention proposes optimal fashion coordination based on the user's style information and preference information, and further provides more personalized proposals by recognizing the user's emotional state using an emotion engine. The hardware and software required to implement this system are described below.

[0890] Overall system configuration

[0891] 1. Enter style information

[0892] Users take a full-body photo of themselves using a device such as a smartphone and upload the image data through a dedicated application. They can also wear a measurement suit to obtain detailed body data. The hardware used is the smartphone's camera function, and the software is a dedicated application for processing the image data.

[0893] 2. Enter your preferences

[0894] The user inputs his or her fashion preferences using the terminal. This preference information is a classification of fashion styles such as casual, formal, street, etc., and the user selects from a list.

[0895] 3. Introducing the Emotion Engine

[0896] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expression data and voice data to estimate the user's emotional state. The software used is Keras and OpenCV, which use machine learning models to recognize emotions.

[0897] 4. Coordination suggestions

[0898] The server receives and analyzes style and preference information provided by the user, as well as emotional data obtained by the emotion engine. Based on the analysis results, an AI algorithm is used to select the most suitable fashion items from the product catalog. The software used for this process is Python-based conditional branching and filtering logic.

[0899] 5. Coordination suggestions

[0900] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually confirm the image. The software used is the UI component of a dedicated application.

[0901] 6. Confirmation of purchase

[0902] The user checks the displayed coordinated image and presses the purchase button if they wish to finalize the purchase. The terminal recognizes this operation and generates purchase request data.

[0903] 7. Purchase Processing

[0904] The server receives the purchase request and starts the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes shipping. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The software used is the requests library for processing HTTP requests.

[0905] Specific examples

[0906] For users who prefer a casual style

[0907] 1. The user takes a full-body photo using their smartphone and uploads it.

[0908] 2. The server receives the photo and analyzes it.

[0909] 3. The user selects "casual style" and enters it into the device.

[0910] 4. The server selects casual fashion items based on the style and preference information.

[0911] 5. The server uses an emotion engine to analyze the user's facial expression data and determine that the user is in a positive state.

[0912] 6. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[0913] 7. The device displays the coordinated image to the user.

[0914] 8. The user presses the "Purchase" button, sending a purchase request to the server.

[0915] 9. The server processes the purchase and sends a confirmation message to the device.

[0916] 10. The device displays a confirmation message to the user and the purchase is complete.

[0917] Prompt Sentence Examples

[0918] Generate a Python program that suggests casual and comfortable fashion items when a user takes a full-body photo using a smartphone app and the emotion recognition engine determines that the user is "happy."

[0919] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0920] Step 1:

[0921] The user takes a full-body photo using their smartphone and launches a dedicated application.

[0922] Input: A full-body photo of the user.

[0923] Output: Full-body photo data.

[0924] How it works: The user takes a full-body photo of themselves using the camera on their smartphone, and the image data is uploaded to a dedicated application.

[0925] Step 2:

[0926] The terminal transmits the captured full-body photograph to the server.

[0927] Input: Full-body photo data.

[0928] Output: The photo data sent to the server.

[0929] Specific operation: A dedicated application on the device generates and sends an HTTP request to send the captured full-body photo data to the server.

[0930] Step 3:

[0931] The server analyzes the received full-body photo data and extracts style information.

[0932] Input: Full-body photo data.

[0933] Output: Style information data.

[0934] Specific operation: The server uses an image processing algorithm to extract information about the user's body shape and the clothes they are wearing from the full-body photo data.

[0935] Step 4:

[0936] The user inputs his / her preference information (casual, formal, street style, etc.) using the terminal.

[0937] Input: User preference information.

[0938] Output: Preference information data.

[0939] Specific operation: The user selects his / her fashion preferences using the dedicated application interface. The selected preference information is sent from the terminal to the server.

[0940] Step 5:

[0941] The server receives the user-entered preference information.

[0942] Input: Preference information data.

[0943] Output: Preference information data stored in the server.

[0944] Specific operation: The server processes the HTTP request, receives and stores preference information data.

[0945] Step 6:

[0946] The server uses an emotion engine to recognize emotions from the user's facial expression data.

[0947] Input: Facial expression data.

[0948] Output: Emotion data.

[0949] Specific operation: The server runs an emotion engine (using Keras and OpenCV) that analyzes facial expression data and recognizes emotional states such as positive and negative.

[0950] Step 7:

[0951] The server selects the most suitable product based on the style information, preference information, and emotion data.

[0952] Input: Style information data, preference information data, emotion data.

[0953] Output: A list of the best products.

[0954] Specific operation: The server uses an AI algorithm to select the most suitable fashion items from the product catalog that match the user's style information, preference information, and emotional data.

[0955] Step 8:

[0956] The server generates an overall coordinated image using the selected products.

[0957] Input: A list of best products.

[0958] Output: Coordinated image.

[0959] Specific operation: The server performs image processing to combine the selected fashion items and generate coordinated images.

[0960] Step 9:

[0961] The server transmits the generated coordinated image to the user's terminal.

[0962] Input: Coordinated image.

[0963] Output: Coordinated image sent to user device.

[0964] Specific operation: The server generates and sends an HTTP response to send the coordinated image to the user's device.

[0965] Step 10:

[0966] The terminal displays the received coordinated image to the user.

[0967] Input: Coordinated image.

[0968] Output: Coordinate image displayed on the device.

[0969] Specific operation: The user's device displays the coordinated image using the UI component of the dedicated application.

[0970] Step 11:

[0971] The user checks the displayed coordinated image and presses a button to confirm the purchase.

[0972] Input: User taps.

[0973] Output: Purchase request data.

[0974] Specific operation: When a user taps the purchase button of an application, this action is recognized on the device and purchase request data is generated.

[0975] Step 12:

[0976] The terminal transmits the generated purchase request data to the server.

[0977] Input: Purchase request data.

[0978] Output: Purchase request data sent to the server.

[0979] Specific operation: The terminal generates and sends an HTTP request to send the purchase request data to the server.

[0980] Step 13:

[0981] The server receives the purchase request and processes the purchase.

[0982] Input: Purchase request data.

[0983] Output: Purchase completion message.

[0984] Specific operation: The server processes the purchase request data, verifies payment information, secures inventory, and arranges for delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device.

[0985] Step 14:

[0986] The terminal will display a message to the user indicating that the purchase has been completed.

[0987] Input: Purchase completion message.

[0988] Output: The completion message displayed to the user.

[0989] Specific behavior: The user's device displays a notification in the UI component that the purchase process has been completed, informing the user that the purchase was successful.

[0990] 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.

[0991] 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.

[0992] 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.

[0993] [Third embodiment]

[0994] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0995] 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.

[0996] 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).

[0997] 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.

[0998] 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.

[0999] 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).

[1000] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1001] 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.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] 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."

[1006] The present invention is a system that automatically suggests optimal fashion coordination and allows users to purchase the coordination as it is by providing their own style information and preference information. Specific embodiments of the system will be described below.

[1007] Overall system flow

[1008] The system mainly consists of the following main parts:

[1009] 1. Enter style information

[1010] 2. Enter your preferences

[1011] 3. Coordination suggestions

[1012] 4. Coordination suggestions

[1013] 5. Confirmation of purchase

[1014] 6. Purchase Processing

[1015] Entering style information

[1016] Users use their devices to provide their own style information. The devices are equipped with a camera and a dedicated application, allowing users to take full-body photos and upload the image data. Users can also wear measuring suits such as the ZOZO Suit to obtain detailed body data. This style information is used as basic data for the AI ​​algorithm to select appropriate fashion items.

[1017] Enter your preferences

[1018] Users input their fashion preferences using a device. Preference information is a classification of fashion styles, such as casual, formal, and street style, and users can select from a list. This information is essential for the AI ​​to suggest outfits that suit the user.

[1019] Coordination suggestions

[1020] The server receives style and preference information provided by the user and analyzes the data. Based on the analysis results, it uses an AI algorithm to select the most suitable products. It extracts appropriate items from the product catalog and generates an overall coordination image for the user. This image includes multiple selected fashion items.

[1021] Coordination suggestions

[1022] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually check it. The user can check the combination of selected fashion items based on this overall coordinated image.

[1023] Confirm your purchase

[1024] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm their purchase. The device recognizes this operation and sends purchase request data to the server. This request data includes information on the selected item, quantity, delivery address, etc.

[1025] Purchase Processing

[1026] The server receives the purchase request and initiates the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[1027] Specific examples

[1028] For users who prefer a casual style

[1029] 1. The user takes a full-body photo using their device and uploads it.

[1030] 2. The server receives the photo and analyzes it.

[1031] 3. The user selects "casual style" and enters it into the device.

[1032] 4. The server selects casual fashion items based on the style and preference information.

[1033] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1034] 6. The device displays the coordinated image to the user.

[1035] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[1036] 8. The server processes the purchase and sends a confirmation message to the device.

[1037] 9. The device displays a confirmation message to the user and the purchase is complete.

[1038] In this way, users can easily select and purchase fashion items that suit their tastes through simple operations.

[1039] The processing flow will be explained below.

[1040] Step 1:

[1041] Users log in to their device and enter their style information. Specifically, they can either take a full-body photo using the device's camera or wear the ZOZO Suit to obtain measurement data, which they then upload to their device.

[1042] Step 2:

[1043] The device sends the captured photos or measurement data to the server. At this time, the data format is standardized and error checks are performed to ensure reliable transmission to the server.

[1044] Step 3:

[1045] The server analyzes the received style information using image processing technology and machine learning models to extract and store the user's body shape data.

[1046] Step 4:

[1047] The user inputs their fashion preferences into the device, and then selects a style such as "casual," "formal," or "street" from a list displayed on the device.

[1048] Step 5:

[1049] The device sends the user's preferences to the server, which formats and transmits the data to accurately reflect the user's choices.

[1050] Step 6:

[1051] The server analyzes the preference information received, combines it with style information, and uses an AI algorithm to select the most suitable fashion items from a product catalog.

[1052] Step 7:

[1053] The server generates an overall coordinated image based on the selected items. Specifically, it runs a program that combines the selected items and generates CG and mockups.

[1054] Step 8:

[1055] The server sends the generated coordinated image and item list to the device. The data package includes high-resolution images and detailed item information.

[1056] Step 9:

[1057] The device displays an outfit image and a list of items to the user, who can then check the details of each item and evaluate the overall outfit.

[1058] Step 10:

[1059] The user selects a favorite outfit and presses the purchase button. The device recognizes this operation and generates purchase request data.

[1060] Step 11:

[1061] The terminal sends purchase request data to the server, which includes information about the selected item, the quantity, the delivery address, etc.

[1062] Step 12:

[1063] The server receives the purchase request and initiates the user's purchase process, including verifying payment information, securing inventory, and arranging shipping.

[1064] Step 13:

[1065] The server generates a confirmation message that the purchase has been confirmed and sends it to the user's device, including details about the purchase and an estimated delivery date.

[1066] Step 14:

[1067] The device displays a purchase confirmation message to the user, so that the user knows the purchase was successful.

[1068] Example 1

[1069] 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."

[1070] Conventional fashion coordination systems require users to take the time and effort to select and combine products themselves, and have difficulty in proposing optimal outfits based on the user's body type and preferences. Furthermore, a separate purchasing procedure is required, making the overall user experience cumbersome. To solve these issues, there is a need for a system that can suggest optimal fashion coordinations with simple and intuitive operation for users, and can guide users through the entire process to purchase.

[1071] 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.

[1072] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means including a generative AI model for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for presenting the coordination image to the user, means for confirming a purchase based on the presented coordination image, means for carrying out a purchase procedure, and means for sending a confirmation message to the user's terminal that the purchase has been confirmed. This eliminates the need for the user to select products themselves, and allows the user to receive suggestions for optimal fashion coordination through simple and intuitive operations, and then to carry out the purchase procedure in an integrated manner.

[1073] "User" refers to an individual who uses the system and provides their own style and preference information.

[1074] "Style information" is information that indicates the user's body type and fashion characteristics, and is data obtained from a full-body photograph and measurement suit data.

[1075] "Preference information" is information relating to the type and attributes of a fashion style that a user prefers, and is data that is input based on the user's selection.

[1076] The "generative AI model" is an artificial intelligence model that selects the most suitable products and generates coordination images based on style and preference information provided by the user.

[1077] A "coordinate image" is a visual image of an overall coordinated look created by combining a number of selected fashion items.

[1078] A "terminal" is an electronic device used by a user to input information or check outfit ideas, and includes smartphones and tablets.

[1079] The "server" is a computer system that receives and analyzes information provided by users, suggests fashion coordination, and carries out purchasing procedures.

[1080] "Purchase request data" refers to data that includes detailed information about the purchase, such as information about the items selected by the user, the quantity, and the delivery address.

[1081] A "confirmation message" is a message that notifies the user that the purchase procedure has been completed, and is a notification that is displayed on the terminal.

[1082] MODE FOR CARRYING OUT THE INVENTION

[1083] The present invention is a system that automatically suggests optimal fashion coordinations and allows users to purchase them as they are by providing their own style information and preference information. Specific embodiments of this system are described below.

[1084] Hardware and software used

[1085] First, smartphones and tablets are suitable as devices for users. These devices are equipped with a camera function and dedicated applications. A cloud-based computer system (e.g., AWS) is used as the server, which receives and analyzes the user's style and preference information. The server uses machine learning libraries such as TensorFlow and PyTorch to run AI algorithms. Computer vision is also used for image analysis technology.

[1086] Entering style information

[1087] Users take a full-body photo using their own device and upload the image data to a server using a dedicated application. Furthermore, by wearing a measuring suit such as the ZOZO Suit, users can obtain detailed body shape data. This style information is analyzed by an AI algorithm and used as basic data to select appropriate fashion items.

[1088] Enter your preferences

[1089] Users use a dedicated application on their device to input their preferred fashion style. This information includes fashion style classifications such as casual, formal, and street style, as well as color and design preferences. This information can be selected from a list and input, and the AI ​​will use it to suggest outfits that suit the user.

[1090] Coordination suggestions

[1091] The server receives style and preference information provided by the user and analyzes it using an AI algorithm. Based on the analysis results, it selects the most suitable products from the product catalog and generates an overall coordination image that includes multiple selected fashion items.

[1092] Coordination suggestions

[1093] The server sends the generated coordinated image to the user's device, which then displays the received image and item list to the user, allowing them to visually confirm the combination of their selected fashion items based on this overall coordinated image.

[1094] Confirm your purchase

[1095] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device then sends purchase request data to the server. This request data includes information about the selected item, the quantity, the delivery address, etc.

[1096] Purchase Processing

[1097] The server receives the purchase request and initiates the purchase process, including verifying payment information, securing inventory, and arranging shipping. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The device then displays this confirmation message to the user, completing the purchase process.

[1098] Specific examples

[1099] For users who prefer a casual style

[1100] 1. The user takes a full-body photo using their device and uploads it.

[1101] 2. The server receives the photo and analyzes it.

[1102] 3. The user selects "casual style" and enters it into the device.

[1103] 4. The server selects casual fashion items based on the style and preference information.

[1104] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1105] 6. The device displays the coordinated image to the user.

[1106] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[1107] 8. The server processes the purchase and sends a confirmation message to the device.

[1108] 9. The device displays a confirmation message to the user and the purchase is complete.

[1109] Example prompts to be input to the generative AI model

[1110] "Please suggest a casual fashion coordination. Style information: full-body photo, preference information: casual style."

[1111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1112] Step 1: Entering style information

[1113] Users use their device to provide their own style information. First, they open a dedicated application, take a full-body photo, and upload the image data. Furthermore, if the user is wearing a measuring suit such as the ZOZO Suit, detailed body shape data is also obtained through the dedicated application and sent to the server. The device then sends this data to the server. The server saves the received image data and body shape data for analysis and registers it as style information.

[1114] Input: User's full-body photo, measurement suit data

[1115] Output: Style information (user's body shape and posture data)

[1116] Step 2: Enter your preferences

[1117] Users input their fashion preferences through a dedicated application, selecting, for example, their fashion style classification (e.g., casual, formal, street style) as well as their color and design preferences. This preference information is sent from the device to the server, which then stores it as data necessary for analysis.

[1118] Input: User's fashion style preferences

[1119] Output: Data saved as preference information

[1120] Step 3: Coordination suggestions

[1121] The server receives style and preference information provided by the user and analyzes this data. Specifically, it uses AI algorithms (e.g., TensorFlow or PyTorch) to select the best fashion items from a product catalog that suit the user's body type and preferences. The AI ​​algorithm uses image analysis technology to understand the user's body type and posture and select appropriate items. The server selects the best products and combines them to generate an outfit image. This outfit image also includes detailed information about each fashion item.

[1122] Input: Style information, preference information

[1123] Output: Selection of optimal fashion items and generated coordination images

[1124] Step 4: Show your outfit

[1125] The server sends the generated coordinated image to the user's device. The device displays the received image to the user through a dedicated application. The displayed content includes the coordinated image and detailed information about each item (price, brand, size, etc.). The user can visually check this and confirm the combination of selected fashion items.

[1126] Input: Generated coordinate image

[1127] Output: Coordinate image and detailed item information displayed on the user's device

[1128] Step 5: Confirm your purchase

[1129] The user checks the displayed coordinated image and presses the "Purchase" button in the dedicated application if they wish to confirm the purchase. The device recognizes this operation and sends purchase request data to the server. This data includes information on the selected item, quantity, delivery address, etc. The server receives this and starts the purchase process.

[1130] Input: User's purchase intent (pressing the purchase button)

[1131] Output: Purchase request data sent to server

[1132] Step 6: Purchase Process

[1133] The server receives the purchase request and initiates the purchase process. Specifically, it connects to a third-party payment gateway (e.g., Stripe or PayPal) to verify payment information. It then secures inventory and verifies that the product is available. It then connects to a logistics system to process the delivery. If the purchase is confirmed, the server generates a confirmation message indicating that the purchase is complete and sends it to the user's device. The device displays this confirmation message to the user, completing the purchase process.

[1134] Input: Purchase request data

[1135] Output: Payment confirmation, inventory reservation, shipping process, generation and sending of confirmation message

[1136] (Application example 1)

[1137] 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."

[1138] Conventional fashion suggestion systems have had problems in that it is difficult for users to find the best products that suit their style, and it takes time to select the appropriate outfit. Also, try-on and fitting must be done in a physical store, and there is a problem that size and style mismatches are likely to occur when shopping online. The present invention aims to solve these problems and provide a system that allows users to efficiently find the best fashion outfit and confirm the appropriate fit through virtual try-on.

[1139] 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.

[1140] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for displaying the coordination image as a virtual try-on on the user's device, means for confirming a purchase based on the presented coordination image, and means for executing a purchase procedure. This allows the user to receive suggested fashion items optimal to the user based on their style information and preference information, check the items in a virtual try-on, and easily purchase them.

[1141] "User" refers to an individual who provides information about their own fashion style and preferences and receives outfit suggestions from the system.

[1142] "Style information" refers to information about the user's body shape and fashion style, such as a full-body photo of the user and measurement suit data.

[1143] "Preference information" refers to information regarding the classification of fashion style selected by the user, such as casual, formal, street, etc.

[1144] "Optimal products" refer to the fashion items that best suit a user, selected by AI based on the user's style and preference information.

[1145] "Coordination image" refers to the visual image of the overall fashion style presented to the user by combining multiple selected fashion items.

[1146] "Virtual try-on" refers to a method of using digital technology to display selected fashion items on a user's device, providing a visual confirmation that they are actually trying on.

[1147] "Device" refers to an information terminal used by a user, such as a smartphone, smart glasses, or head-mounted display.

[1148] "Confirming purchase" refers to the act of a user officially deciding to purchase a fashion item selected based on the presented coordination image.

[1149] "Purchase procedure" refers to a series of processes that are carried out after a purchase is confirmed, such as confirming payment information, securing inventory, and shipping procedures.

[1150] The present invention is a system that proposes optimal fashion coordination based on a user's style information and preference information, and then allows the user to virtually try on the clothes and complete the purchase procedure. Specific embodiments of this system are described below.

[1151] Entering style information

[1152] Users take a full-body photo using a device (e.g., a smartphone or smart glasses) and upload the image data to the server. They can also obtain detailed body shape data using a measurement suit (e.g., a measurement suit), and this data is also sent to the server as style information.

[1153] Enter your preferences

[1154] The user inputs their own fashion preference information using the terminal. The preference information is a classification of fashion styles such as casual, formal, street, etc., and the selection made from the selection list is sent to the server.

[1155] Coordination suggestions

[1156] The server receives style and preference information provided by the user and analyzes the data. An AI algorithm (e.g., FashionAI) is used for the analysis. The AI ​​algorithm selects the most suitable products and extracts appropriate items from the product catalog. Based on these extracted items, an overall outfit image is generated.

[1157] Coordination presentation and virtual try-on

[1158] The server sends the generated coordinated image to the user's device. The device (e.g., a smartphone or smart glasses) displays the received image and item list, allowing the user to visually confirm it. Additionally, using virtual try-on technology, the selected fashion items are displayed on the user's device, providing a visual confirmation as if they were actually being tried on.

[1159] Confirmation and checkout

[1160] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device recognizes this operation and sends the purchase request data to the server. The server receives the purchase request, confirms payment information, secures inventory, and processes delivery. It also generates a confirmation message indicating that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[1161] Specific examples

[1162] For users who prefer a casual style

[1163] 1. The user takes a full-body photo using their device and uploads it.

[1164] 2. The server receives the photo and analyzes it.

[1165] 3. The user selects "casual style" and enters it into the device.

[1166] 4. The server selects casual fashion items based on the style and preference information.

[1167] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1168] 6. The device displays outfit images to the user, allowing them to virtually try them on.

[1169] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[1170] 8. The server processes the purchase and sends a confirmation message to the device.

[1171] 9. The device displays a confirmation message to the user and the purchase is complete.

[1172] Prompt Sentence Examples

[1173] "The user takes a full-body photo and selects a casual style. The AI ​​then suggests the best fashion coordination. Then, it generates a code that allows the user to purchase the suggested items."

[1174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1175] Step 1:

[1176] Users take a full-body photo using a device (e.g., a smartphone) and upload the image data to the server. This input data includes information about the user's body shape and style. The uploaded image data is used as the basis for subsequent fashion item selection.

[1177] Step 2:

[1178] The user inputs their fashion preference information using the terminal. This input data includes classification information of the user's selected fashion style (e.g., casual, formal). The server receives the preference information and stores it in a database.

[1179] Step 3:

[1180] The server analyzes the style and preference information. An AI algorithm (e.g., FashionAI) is used for this analysis. Based on the style and preference information in the input data, the server selects the most suitable fashion items. The list of selected items is used to generate outfits in the next step.

[1181] Step 4:

[1182] The server combines the selected fashion items to generate an overall coordinated image. This image generation is performed by applying the selected items to a virtual model. The generated coordinated image is used as output data to present to the user.

[1183] Step 5:

[1184] The server sends the generated coordinated image to the user's device, which then displays the coordinated image. Using virtual try-on technology, the user visually checks the selected fashion items as if they were actually being tried on.

[1185] Step 6:

[1186] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device recognizes this operation and sends the purchase request data to the server. The input data includes information on the selected item, quantity, delivery address, etc.

[1187] Step 7:

[1188] The server receives the purchase request, verifies payment information, secures inventory, and processes shipping. This completes the user's purchase. Finally, it generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The device displays this confirmation message to the user, completing the purchase process.

[1189] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1190] The present invention allows users to provide their own style and preference information, and the system then suggests optimal fashion coordinations that can then be purchased. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to achieve more personalized suggestions.

[1191] Overall system flow

[1192] The system consists of the following main parts:

[1193] 1. Enter style information

[1194] 2. Enter your preferences

[1195] 3. Introducing the Emotion Engine

[1196] 4. Coordination suggestions

[1197] 5. Coordination suggestions

[1198] 6. Confirmation of purchase

[1199] 7. Purchase Processing

[1200] Entering style information

[1201] Users use their devices to provide their own style information. The devices are equipped with a camera and a dedicated application, allowing users to take full-body photos and upload the image data. Users can also wear measuring suits such as the ZOZO Suit to obtain detailed body data. This style information is used as basic data for the AI ​​algorithm to select appropriate fashion items.

[1202] Enter your preferences

[1203] Users input their fashion preferences using a device. Preference information is a classification of fashion styles, such as casual, formal, and street style, and users can select from a list. This information is essential for the AI ​​to suggest outfits that suit the user.

[1204] Introducing the Emotion Engine

[1205] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expression data and voice data to estimate the user's emotions. For example, if the user is smiling while taking a photo, the emotion engine will infer that the user is in a positive state. This information influences product selection and the generation of outfit images.

[1206] Coordination suggestions

[1207] The server receives and analyzes style information, preference information, and emotion data obtained by the emotion engine provided by the user. Based on the analysis results, it uses an AI algorithm to select optimal fashion items from a product catalog. It then extracts appropriate items from the product catalog and generates an overall outfit image for the user. This image includes multiple selected fashion items.

[1208] Coordination suggestions

[1209] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually check it. The user can check the combination of selected fashion items based on this overall coordinated image.

[1210] Confirm your purchase

[1211] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm their purchase. The device recognizes this operation and generates purchase request data. This request data includes information on the selected item, quantity, delivery address, etc.

[1212] Purchase Processing

[1213] The server receives the purchase request and initiates the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[1214] Specific examples

[1215] For users who prefer a casual style

[1216] 1. The user takes a full-body photo using their device and uploads it.

[1217] 2. The server receives the photo and analyzes it.

[1218] 3. The user selects "casual style" and enters it into the device.

[1219] 4. The server selects casual fashion items based on the style and preference information.

[1220] 5. The server uses an emotion engine to analyze the user's facial expression data and determine that the user is in a positive state.

[1221] 6. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1222] 7. The device displays the coordinated image to the user.

[1223] 8. The user presses the "Purchase" button, sending a purchase request to the server.

[1224] 9. The server processes the purchase and sends a confirmation message to the device.

[1225] 10. The device displays a confirmation message to the user and the purchase is complete.

[1226] In this way, more personalized suggestions are possible depending on the user's emotional state, improving the user experience.

[1227] The processing flow will be explained below.

[1228] Step 1:

[1229] Users log in to the device and enter their style information. Specifically, they can take a full-body photo using the device's camera or wear a measuring suit to obtain measurement data, which they then upload to the device.

[1230] Step 2:

[1231] The device sends the captured photos or measurement data to the server. At this time, the data format is standardized and error checks are performed to ensure reliable transmission to the server.

[1232] Step 3:

[1233] The server analyzes the received style information using image processing technology and machine learning models to extract and store the user's body shape data.

[1234] Step 4:

[1235] The user inputs their fashion preferences into the device, and then selects a style such as "casual," "formal," or "street" from a list displayed on the device.

[1236] Step 5:

[1237] The device sends the user's preferences to the server, which formats and transmits the data to accurately reflect the user's choices.

[1238] Step 6:

[1239] The server analyzes the received preference and style information and selects the most suitable items from the product catalog based on that information. It uses AI algorithms to narrow down the options and identify the most suitable fashion items for the user.

[1240] Step 7:

[1241] The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's facial expression data and voice data and infers their emotions based on that information. For example, if the user is smiling while taking a photo, it is determined to be in a positive state.

[1242] Step 8:

[1243] The server generates the final outfit image based on the analysis results, including emotional data. The selected items are combined to create an outfit that takes into account the user's emotional state.

[1244] Step 9:

[1245] The server sends the generated coordinated image and item list to the device. The data package includes high-resolution images and detailed item information.

[1246] Step 10:

[1247] The device displays an outfit image and a list of items to the user, who can then check the details of each item and evaluate the overall outfit.

[1248] Step 11:

[1249] The user selects a favorite outfit and presses the purchase button. The device recognizes this operation and generates purchase request data.

[1250] Step 12:

[1251] The terminal sends purchase request data to the server, which includes information about the selected item, the quantity, the delivery address, etc.

[1252] Step 13:

[1253] The server receives the purchase request and initiates the user's purchase process, including verifying payment information, securing inventory, and arranging shipping.

[1254] Step 14:

[1255] The server generates a confirmation message that the purchase has been confirmed and sends it to the user's device, including details about the purchase and an estimated delivery date.

[1256] Step 15:

[1257] The device displays a purchase confirmation message to the user, so that the user knows the purchase was successful.

[1258] Example 2

[1259] 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."

[1260] The problem to be solved by the present invention is to realize more accurate suggestions that take into account the user's emotional state when suggesting personalized fashion coordination based on the user's style information and preference information, and to improve user convenience by providing a system that allows the suggested fashion items to be purchased as is.

[1261] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for acquiring and analyzing user emotion data, means for reflecting the analyzed emotion data in product selection, means for presenting the coordination image to the user, means for confirming purchase based on the presented coordination image, and means for executing purchase procedures. This enables personalized coordination suggestions that take into account the user's emotional state in addition to the style information and preference information provided by the user, thereby significantly improving user convenience.

[1262] A "user" is an individual or group that utilizes the system to provide their style and preference information.

[1263] "Style information" refers to data relating to the user's body type and appearance, such as a full-body photo of the user and measurement suit data.

[1264] "Preference information" is classification information of a fashion style selected by a user, and includes information such as casual, formal, and street style.

[1265] "Emotion data" is data that represents the user's current emotional state, obtained by analyzing the user's facial expression data and voice data.

[1266] The "server" is a computer system that receives style information, preference information, and emotional data sent by the user, analyzes this, and makes coordination suggestions.

[1267] A "coordination image" is a visual image of a specific combination of fashion items that is generated based on information analyzed by the server.

[1268] "Purchase request data" is data generated when a user confirms a purchase based on a coordination image, and includes information on the selected items, the quantity, the delivery address, and so on.

[1269] MODE FOR CARRYING OUT THE INVENTION

[1270] This invention is a system that suggests optimal fashion coordination based on style information, preference information, and the user's emotional state, and allows the user to purchase the outfit immediately. The system is mainly composed of a terminal and a server, and operates in cooperation with an emotion recognition engine, AI algorithms, and a product catalog.

[1271] Hardware and software used

[1272] Device:

[1273] Smartphones, tablets, PCs, etc.

[1274] Camera features

[1275] Dedicated application (fashion coordination app)

[1276] server:

[1277] Web server, database server

[1278] Emotion recognition engine (facial expression analysis, voice analysis)

[1279] Software for running AI algorithms (e.g., TensorFlow or PyTorch)

[1280] Product catalog database

[1281] data:

[1282] Style information (full-body photo, suit data for measurements)

[1283] Preference information (user-selected fashion style classification)

[1284] Emotion data (user's facial expression data, voice data)

[1285] Detailed explanation of the system's operation

[1286] 1. Providing style information:

[1287] Users use a dedicated application to take a full-body photo of themselves and upload it to the server from their device. Users also wear a measurement suit to obtain detailed body shape data, which is then sent to the server.

[1288] 2. Enter your preferences:

[1289] The user selects the classification information of their fashion style and sends it to the server from their terminal. This preference information is categorized into categories such as casual, formal, and street style.

[1290] 3. Acquiring emotion data:

[1291] The user captures their facial expression using the device's camera and sends the data to the server, where the server's emotion recognition engine analyzes the facial expression data and estimates the user's emotional state (positive, neutral, negative, etc.).

[1292] 4. Coordination proposal generation:

[1293] The server integrates the received style, preference, and emotion data and uses an AI algorithm to select the most suitable fashion items. Based on this, it selects appropriate items from a product catalog and creates an overall coordination image.

[1294] 5. Coordination suggestions:

[1295] The generated coordinated image is sent from the server to the user's terminal, which displays the image to the user, allowing the user to visually confirm the proposed coordinated look.

[1296] 6. Purchasing process:

[1297] When the user likes the suggested outfit and presses the purchase button, the device sends the purchase request data to the server. The server verifies payment information, secures inventory, and processes delivery procedures, and finally sends a confirmation message to the user's device. The device displays this confirmation message to the user, completing the purchase.

[1298] Specific examples

[1299] For example, if a user desires a casual style, the following steps are performed.

[1300] 1. The user takes a full-body photo using their device and uploads it to the server.

[1301] 2. The user selects "casual style" and enters it into the terminal.

[1302] 3. The server analyzes the user's facial expression data and determines their positive emotional state.

[1303] 4. The server generates an image of a coordinated outfit consisting of a denim jacket, a casual T-shirt, and chino pants, and sends it to the device.

[1304] 5. The device displays the coordinated image, and the user presses the "Purchase" button to proceed with the purchase.

[1305] Prompt Sentence Examples

[1306] "I'm a woman who likes casual style. I'd like you to suggest denim jackets, casual T-shirts, and chino pants. The sentiment is positive."

[1307] This invention allows for personalized coordination suggestions that take into account the user's emotional state and seamless purchase of the items, improving the user experience.

[1308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1309] Step 1: Entering style information

[1310] Specific behavior:

[1311] 1. The user launches a dedicated application using the device.

[1312] 2. The user follows the application's instructions to take a full-body photo.

[1313] 3. The device uploads the captured image data to the server.

[1314] 4. The device also collects data from measurement suits such as the ZOZO Suit and sends it to the server.

[1315] Input: A full-body photo taken by the user and measurement suit data.

[1316] Output: Style information (full-body photo, suit data for measurements) sent to the server.

[1317] Step 2: Enter your preferences

[1318] Specific behavior:

[1319] 1. The user displays the fashion style selection screen on their device.

[1320] 2. The user selects their preferred fashion style, such as "casual" or "formal."

[1321] 3. The device sends the selected preference information to the server.

[1322] Input: The user's chosen fashion style (e.g. casual, formal, etc.).

[1323] Output: Preference information sent to the server.

[1324] Step 3: Obtaining emotion data

[1325] Specific behavior:

[1326] 1. The user uses the camera function to capture their facial expression.

[1327] 2. The device collects facial expression data and sends it to the server.

[1328] 3. The server uses an emotion engine to analyze the received facial expression data and estimate the current emotional state.

[1329] Input: User's facial expression data.

[1330] Output: The emotional state inferred by the emotion engine (e.g., positive, neutral, negative, etc.).

[1331] Step 4: Analyze the information and generate coordination proposals

[1332] Specific behavior:

[1333] 1. The server integrates and analyzes the style information, preference information, and emotion data received from the user.

[1334] 2. The server uses an AI algorithm to select the most suitable fashion items.

[1335] 3. The server generates an overall coordinated image based on the items selected.

[1336] Input: Style information, preference information, emotion data.

[1337] Output: Coordination image (combination of selected fashion items).

[1338] Step 5: Show your outfit

[1339] Specific behavior:

[1340] 1. The server sends the generated coordinate image to the terminal.

[1341] 2. The terminal displays the coordinated image received to the user.

[1342] 3. Allow the user to visually check the suggested outfits.

[1343] Input: Server-generated coordinate image.

[1344] Output: Coordinate image displayed on the device.

[1345] Step 6: Confirm your purchase

[1346] Specific behavior:

[1347] 1. The user checks the coordinated image displayed.

[1348] 2. The user presses the "Purchase" button.

[1349] 3. The terminal generates a purchase request and sends it to the server. This request data includes information about the selected item, the quantity, the delivery address, etc.

[1350] Input: User confirms outfit and confirms purchase.

[1351] Output: Purchase request data sent to the server.

[1352] Step 7: Purchase Processing

[1353] Specific behavior:

[1354] 1. The server receives the purchase request and verifies the payment information.

[1355] 2. The server checks and secures product inventory.

[1356] 3. The server initiates the delivery procedure.

[1357] 4. The server generates a confirmation message confirming the purchase and sends it to the user's device.

[1358] 5. The device displays a confirmation message to the user, informing them that the purchase is complete.

[1359] Input: Purchase request data sent to the server.

[1360] Output: Purchase confirmation message and purchase completion notification.

[1361] (Application example 2)

[1362] 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."

[1363] Conventional fashion coordination suggestion systems only suggest products based on style and preference information, which means they lack personalized suggestions that take into account the user's emotional state. As a result, it is difficult to improve user satisfaction, and the quality of the shopping experience can decline. This can also affect sales by reducing purchasing motivation.

[1364] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1365] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for acquiring emotion data using an emotion engine that recognizes the user's emotions, means for selecting optimal products based on the style information, preference information, and emotion data, means for generating an overall coordination image using the selected products, means for presenting the coordination image to the user, means for confirming a purchase based on the presented coordination image, and means for executing a purchase procedure, thereby making it possible to provide a more personalized fashion suggestion and purchasing experience according to the user's emotional state.

[1366] "Style information provided by the user" refers to information provided by the user, such as a full-body photo of the user and suit measurement data, and is basic data used by the system to understand the user's body type and fashion trends.

[1367] "Preference information" is classification information of a fashion style selected by a user, and is information based on the user's preferences, such as casual, formal, street, etc.

[1368] The "emotion engine that recognizes user emotions" is software and algorithms that analyze the user's facial expression data and voice data to estimate the user's emotional state.

[1369] The "means for selecting the most suitable product based on the style information, preference information, and emotional data" refers to an algorithm that analyzes the style information, preference information, and emotional data obtained from the user and selects the most suitable fashion item from a product catalog based on the information.

[1370] The "means for generating an overall coordination image using the selected products" refers to software or algorithms for combining multiple selected fashion items to create an overall coordination image to be proposed to the user.

[1371] The "means for presenting the coordinated image to the user" refers to a technique for transmitting the generated coordinated image to the user's terminal and displaying it in a visually confirmable manner.

[1372] The "means for confirming a purchase based on the presented coordinated image" refers to an interface and process for a user to confirm a displayed coordinated image and confirm a purchase.

[1373] The "means of completing the purchase process" is the process of verifying payment information, securing inventory, arranging shipping, and sending a confirmation message to the user after the purchase is confirmed.

[1374] The system that realizes this invention proposes optimal fashion coordination based on the user's style information and preference information, and further provides more personalized proposals by recognizing the user's emotional state using an emotion engine. The hardware and software required to implement this system are described below.

[1375] Overall system configuration

[1376] 1. Enter style information

[1377] Users take a full-body photo of themselves using a device such as a smartphone and upload the image data through a dedicated application. They can also wear a measurement suit to obtain detailed body data. The hardware used is the smartphone's camera function, and the software is a dedicated application for processing the image data.

[1378] 2. Enter your preferences

[1379] The user inputs his or her fashion preferences using the terminal. This preference information is a classification of fashion styles such as casual, formal, street, etc., and the user selects from a list.

[1380] 3. Introducing the Emotion Engine

[1381] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expression data and voice data to estimate the user's emotional state. The software used is Keras and OpenCV, which use machine learning models to recognize emotions.

[1382] 4. Coordination suggestions

[1383] The server receives and analyzes style and preference information provided by the user, as well as emotional data obtained by the emotion engine. Based on the analysis results, an AI algorithm is used to select the most suitable fashion items from the product catalog. The software used for this process is Python-based conditional branching and filtering logic.

[1384] 5. Coordination suggestions

[1385] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually confirm the image. The software used is the UI component of a dedicated application.

[1386] 6. Confirmation of purchase

[1387] The user checks the displayed coordinated image and presses the purchase button if they wish to finalize the purchase. The terminal recognizes this operation and generates purchase request data.

[1388] 7. Purchase Processing

[1389] The server receives the purchase request and starts the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes shipping. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The software used is the requests library for processing HTTP requests.

[1390] Specific examples

[1391] For users who prefer a casual style

[1392] 1. The user takes a full-body photo using their smartphone and uploads it.

[1393] 2. The server receives the photo and analyzes it.

[1394] 3. The user selects "casual style" and enters it into the device.

[1395] 4. The server selects casual fashion items based on the style and preference information.

[1396] 5. The server uses an emotion engine to analyze the user's facial expression data and determine that the user is in a positive state.

[1397] 6. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1398] 7. The device displays the coordinated image to the user.

[1399] 8. The user presses the "Purchase" button, sending a purchase request to the server.

[1400] 9. The server processes the purchase and sends a confirmation message to the device.

[1401] 10. The device displays a confirmation message to the user and the purchase is complete.

[1402] Prompt Sentence Examples

[1403] Generate a Python program that suggests casual and comfortable fashion items when a user takes a full-body photo using a smartphone app and the emotion recognition engine determines that the user is "happy."

[1404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1405] Step 1:

[1406] The user takes a full-body photo using their smartphone and launches a dedicated application.

[1407] Input: A full-body photo of the user.

[1408] Output: Full-body photo data.

[1409] How it works: The user takes a full-body photo of themselves using the camera on their smartphone, and the image data is uploaded to a dedicated application.

[1410] Step 2:

[1411] The terminal transmits the captured full-body photograph to the server.

[1412] Input: Full-body photo data.

[1413] Output: The photo data sent to the server.

[1414] Specific operation: A dedicated application on the device generates and sends an HTTP request to send the captured full-body photo data to the server.

[1415] Step 3:

[1416] The server analyzes the received full-body photo data and extracts style information.

[1417] Input: Full-body photo data.

[1418] Output: Style information data.

[1419] Specific operation: The server uses an image processing algorithm to extract information about the user's body shape and the clothes they are wearing from the full-body photo data.

[1420] Step 4:

[1421] The user inputs his / her preference information (casual, formal, street style, etc.) using the terminal.

[1422] Input: User preference information.

[1423] Output: Preference information data.

[1424] Specific operation: The user selects his / her fashion preferences using the dedicated application interface. The selected preference information is sent from the terminal to the server.

[1425] Step 5:

[1426] The server receives the user-entered preference information.

[1427] Input: Preference information data.

[1428] Output: Preference information data stored in the server.

[1429] Specific operation: The server processes the HTTP request, receives and stores preference information data.

[1430] Step 6:

[1431] The server uses an emotion engine to recognize emotions from the user's facial expression data.

[1432] Input: Facial expression data.

[1433] Output: Emotion data.

[1434] Specific operation: The server runs an emotion engine (using Keras and OpenCV) that analyzes facial expression data and recognizes emotional states such as positive and negative.

[1435] Step 7:

[1436] The server selects the most suitable product based on the style information, preference information, and emotion data.

[1437] Input: Style information data, preference information data, emotion data.

[1438] Output: A list of the best products.

[1439] Specific operation: The server uses an AI algorithm to select the most suitable fashion items from the product catalog that match the user's style information, preference information, and emotional data.

[1440] Step 8:

[1441] The server generates an overall coordinated image using the selected products.

[1442] Input: A list of best products.

[1443] Output: Coordinated image.

[1444] Specific operation: The server performs image processing to combine the selected fashion items and generate coordinated images.

[1445] Step 9:

[1446] The server transmits the generated coordinated image to the user's terminal.

[1447] Input: Coordinated image.

[1448] Output: Coordinated image sent to user device.

[1449] Specific operation: The server generates and sends an HTTP response to send the coordinated image to the user's device.

[1450] Step 10:

[1451] The terminal displays the received coordinated image to the user.

[1452] Input: Coordinated image.

[1453] Output: Coordinate image displayed on the device.

[1454] Specific operation: The user's device displays the coordinated image using the UI component of the dedicated application.

[1455] Step 11:

[1456] The user checks the displayed coordinated image and presses a button to confirm the purchase.

[1457] Input: User taps.

[1458] Output: Purchase request data.

[1459] Specific operation: When a user taps the purchase button of an application, this action is recognized on the device and purchase request data is generated.

[1460] Step 12:

[1461] The terminal transmits the generated purchase request data to the server.

[1462] Input: Purchase request data.

[1463] Output: Purchase request data sent to the server.

[1464] Specific operation: The terminal generates and sends an HTTP request to send the purchase request data to the server.

[1465] Step 13:

[1466] The server receives the purchase request and processes the purchase.

[1467] Input: Purchase request data.

[1468] Output: Purchase completion message.

[1469] Specific operation: The server processes the purchase request data, verifies payment information, secures inventory, and arranges for delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device.

[1470] Step 14:

[1471] The terminal will display a message to the user indicating that the purchase has been completed.

[1472] Input: Purchase completion message.

[1473] Output: The completion message displayed to the user.

[1474] Specific behavior: The user's device displays a notification in the UI component that the purchase process has been completed, informing the user that the purchase was successful.

[1475] 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.

[1476] 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.

[1477] 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.

[1478] [Fourth embodiment]

[1479] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1480] 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.

[1481] 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).

[1482] 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.

[1483] 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.

[1484] 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).

[1485] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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."

[1492] The present invention is a system that automatically suggests optimal fashion coordination and allows users to purchase the coordination as it is by providing their own style information and preference information. Specific embodiments of the system will be described below.

[1493] Overall system flow

[1494] The system mainly consists of the following main parts:

[1495] 1. Enter style information

[1496] 2. Enter your preferences

[1497] 3. Coordination suggestions

[1498] 4. Coordination suggestions

[1499] 5. Confirmation of purchase

[1500] 6. Purchase Processing

[1501] Entering style information

[1502] Users use their devices to provide their own style information. The devices are equipped with a camera and a dedicated application, allowing users to take full-body photos and upload the image data. Users can also wear measuring suits such as the ZOZO Suit to obtain detailed body data. This style information is used as basic data for the AI ​​algorithm to select appropriate fashion items.

[1503] Enter your preferences

[1504] Users input their fashion preferences using a device. Preference information is a classification of fashion styles, such as casual, formal, and street style, and users can select from a list. This information is essential for the AI ​​to suggest outfits that suit the user.

[1505] Coordination suggestions

[1506] The server receives style and preference information provided by the user and analyzes the data. Based on the analysis results, it uses an AI algorithm to select the most suitable products. It extracts appropriate items from the product catalog and generates an overall coordination image for the user. This image includes multiple selected fashion items.

[1507] Coordination suggestions

[1508] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually check it. The user can check the combination of selected fashion items based on this overall coordinated image.

[1509] Confirm your purchase

[1510] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm their purchase. The device recognizes this operation and sends purchase request data to the server. This request data includes information on the selected item, quantity, delivery address, etc.

[1511] Purchase Processing

[1512] The server receives the purchase request and initiates the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[1513] Specific examples

[1514] For users who prefer a casual style

[1515] 1. The user takes a full-body photo using their device and uploads it.

[1516] 2. The server receives the photo and analyzes it.

[1517] 3. The user selects "casual style" and enters it into the device.

[1518] 4. The server selects casual fashion items based on the style and preference information.

[1519] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1520] 6. The device displays the coordinated image to the user.

[1521] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[1522] 8. The server processes the purchase and sends a confirmation message to the device.

[1523] 9. The device displays a confirmation message to the user and the purchase is complete.

[1524] In this way, users can easily select and purchase fashion items that suit their tastes through simple operations.

[1525] The processing flow will be explained below.

[1526] Step 1:

[1527] Users log in to their device and enter their style information. Specifically, they can either take a full-body photo using the device's camera or wear the ZOZO Suit to obtain measurement data, which they then upload to their device.

[1528] Step 2:

[1529] The device sends the captured photos or measurement data to the server. At this time, the data format is standardized and error checks are performed to ensure reliable transmission to the server.

[1530] Step 3:

[1531] The server analyzes the received style information using image processing technology and machine learning models to extract and store the user's body shape data.

[1532] Step 4:

[1533] The user inputs their fashion preferences into the device, and then selects a style such as "casual," "formal," or "street" from a list displayed on the device.

[1534] Step 5:

[1535] The device sends the user's preferences to the server, which formats and transmits the data to accurately reflect the user's choices.

[1536] Step 6:

[1537] The server analyzes the preference information received, combines it with style information, and uses an AI algorithm to select the most suitable fashion items from a product catalog.

[1538] Step 7:

[1539] The server generates an overall coordinated image based on the selected items. Specifically, it runs a program that combines the selected items and generates CG and mockups.

[1540] Step 8:

[1541] The server sends the generated coordinated image and item list to the device. The data package includes high-resolution images and detailed item information.

[1542] Step 9:

[1543] The device displays an outfit image and a list of items to the user, who can then check the details of each item and evaluate the overall outfit.

[1544] Step 10:

[1545] The user selects a favorite outfit and presses the purchase button. The device recognizes this operation and generates purchase request data.

[1546] Step 11:

[1547] The terminal sends purchase request data to the server, which includes information about the selected item, the quantity, the delivery address, etc.

[1548] Step 12:

[1549] The server receives the purchase request and initiates the user's purchase process, including verifying payment information, securing inventory, and arranging shipping.

[1550] Step 13:

[1551] The server generates a confirmation message that the purchase has been confirmed and sends it to the user's device, including details about the purchase and an estimated delivery date.

[1552] Step 14:

[1553] The device displays a purchase confirmation message to the user, so that the user knows the purchase was successful.

[1554] Example 1

[1555] 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."

[1556] Conventional fashion coordination systems require users to take the time and effort to select and combine products themselves, and have difficulty in proposing optimal outfits based on the user's body type and preferences. Furthermore, a separate purchasing procedure is required, making the overall user experience cumbersome. To solve these issues, there is a need for a system that can suggest optimal fashion coordinations with simple and intuitive operation for users, and can guide users through the entire process to purchase.

[1557] 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.

[1558] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means including a generative AI model for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for presenting the coordination image to the user, means for confirming a purchase based on the presented coordination image, means for carrying out a purchase procedure, and means for sending a confirmation message to the user's terminal that the purchase has been confirmed. This eliminates the need for the user to select products themselves, and allows the user to receive suggestions for optimal fashion coordination through simple and intuitive operations, and then to carry out the purchase procedure in an integrated manner.

[1559] "User" refers to an individual who uses the system and provides their own style and preference information.

[1560] "Style information" is information that indicates the user's body type and fashion characteristics, and is data obtained from a full-body photograph and measurement suit data.

[1561] "Preference information" is information relating to the type and attributes of a fashion style that a user prefers, and is data that is input based on the user's selection.

[1562] The "generative AI model" is an artificial intelligence model that selects the most suitable products and generates coordination images based on style and preference information provided by the user.

[1563] A "coordinate image" is a visual image of an overall coordinated look created by combining a number of selected fashion items.

[1564] A "terminal" is an electronic device used by a user to input information or check outfit ideas, and includes smartphones and tablets.

[1565] The "server" is a computer system that receives and analyzes information provided by users, suggests fashion coordination, and carries out purchasing procedures.

[1566] "Purchase request data" refers to data that includes detailed information about the purchase, such as information about the items selected by the user, the quantity, and the delivery address.

[1567] A "confirmation message" is a message that notifies the user that the purchase procedure has been completed, and is a notification that is displayed on the terminal.

[1568] MODE FOR CARRYING OUT THE INVENTION

[1569] The present invention is a system that automatically suggests optimal fashion coordinations and allows users to purchase them as they are by providing their own style information and preference information. Specific embodiments of this system are described below.

[1570] Hardware and software used

[1571] First, smartphones and tablets are suitable as devices for users. These devices are equipped with a camera function and dedicated applications. A cloud-based computer system (e.g., AWS) is used as the server, which receives and analyzes the user's style and preference information. The server uses machine learning libraries such as TensorFlow and PyTorch to run AI algorithms. Computer vision is also used for image analysis technology.

[1572] Entering style information

[1573] Users take a full-body photo using their own device and upload the image data to a server using a dedicated application. Furthermore, by wearing a measuring suit such as the ZOZO Suit, users can obtain detailed body shape data. This style information is analyzed by an AI algorithm and used as basic data to select appropriate fashion items.

[1574] Enter your preferences

[1575] Users use a dedicated application on their device to input their preferred fashion style. This information includes fashion style classifications such as casual, formal, and street style, as well as color and design preferences. This information can be selected from a list and input, and the AI ​​will use it to suggest outfits that suit the user.

[1576] Coordination suggestions

[1577] The server receives style and preference information provided by the user and analyzes it using an AI algorithm. Based on the analysis results, it selects the most suitable products from the product catalog and generates an overall coordination image that includes multiple selected fashion items.

[1578] Coordination suggestions

[1579] The server sends the generated coordinated image to the user's device, which then displays the received image and item list to the user, allowing them to visually confirm the combination of their selected fashion items based on this overall coordinated image.

[1580] Confirm your purchase

[1581] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device then sends purchase request data to the server. This request data includes information about the selected item, the quantity, the delivery address, etc.

[1582] Purchase Processing

[1583] The server receives the purchase request and initiates the purchase process, including verifying payment information, securing inventory, and arranging shipping. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The device then displays this confirmation message to the user, completing the purchase process.

[1584] Specific examples

[1585] For users who prefer a casual style

[1586] 1. The user takes a full-body photo using their device and uploads it.

[1587] 2. The server receives the photo and analyzes it.

[1588] 3. The user selects "casual style" and enters it into the device.

[1589] 4. The server selects casual fashion items based on the style and preference information.

[1590] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1591] 6. The device displays the coordinated image to the user.

[1592] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[1593] 8. The server processes the purchase and sends a confirmation message to the device.

[1594] 9. The device displays a confirmation message to the user and the purchase is complete.

[1595] Example prompts to be input to the generative AI model

[1596] "Please suggest a casual fashion coordination. Style information: full-body photo, preference information: casual style."

[1597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1598] Step 1: Entering style information

[1599] Users use their device to provide their own style information. First, they open a dedicated application, take a full-body photo, and upload the image data. Furthermore, if the user is wearing a measuring suit such as the ZOZO Suit, detailed body shape data is also obtained through the dedicated application and sent to the server. The device then sends this data to the server. The server saves the received image data and body shape data for analysis and registers it as style information.

[1600] Input: User's full-body photo, measurement suit data

[1601] Output: Style information (user's body shape and posture data)

[1602] Step 2: Enter your preferences

[1603] Users input their fashion preferences through a dedicated application, selecting, for example, their fashion style classification (e.g., casual, formal, street style) as well as their color and design preferences. This preference information is sent from the device to the server, which then stores it as data necessary for analysis.

[1604] Input: User's fashion style preferences

[1605] Output: Data saved as preference information

[1606] Step 3: Coordination suggestions

[1607] The server receives style and preference information provided by the user and analyzes this data. Specifically, it uses AI algorithms (e.g., TensorFlow or PyTorch) to select the best fashion items from a product catalog that suit the user's body type and preferences. The AI ​​algorithm uses image analysis technology to understand the user's body type and posture and select appropriate items. The server selects the best products and combines them to generate an outfit image. This outfit image also includes detailed information about each fashion item.

[1608] Input: Style information, preference information

[1609] Output: Selection of optimal fashion items and generated coordination images

[1610] Step 4: Show your outfit

[1611] The server sends the generated coordinated image to the user's device. The device displays the received image to the user through a dedicated application. The displayed content includes the coordinated image and detailed information about each item (price, brand, size, etc.). The user can visually check this and confirm the combination of selected fashion items.

[1612] Input: Generated coordinate image

[1613] Output: Coordinate image and detailed item information displayed on the user's device

[1614] Step 5: Confirm your purchase

[1615] The user checks the displayed coordinated image and presses the "Purchase" button in the dedicated application if they wish to confirm the purchase. The device recognizes this operation and sends purchase request data to the server. This data includes information on the selected item, quantity, delivery address, etc. The server receives this and starts the purchase process.

[1616] Input: User's purchase intent (pressing the purchase button)

[1617] Output: Purchase request data sent to server

[1618] Step 6: Purchase Process

[1619] The server receives the purchase request and initiates the purchase process. Specifically, it connects to a third-party payment gateway (e.g., Stripe or PayPal) to verify payment information. It then secures inventory and verifies that the product is available. It then connects to a logistics system to process the delivery. If the purchase is confirmed, the server generates a confirmation message indicating that the purchase is complete and sends it to the user's device. The device displays this confirmation message to the user, completing the purchase process.

[1620] Input: Purchase request data

[1621] Output: Payment confirmation, inventory reservation, shipping process, generation and sending of confirmation message

[1622] (Application example 1)

[1623] 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."

[1624] Conventional fashion suggestion systems have had problems in that it is difficult for users to find the best products that suit their style, and it takes time to select the appropriate outfit. Also, try-on and fitting must be done in a physical store, and there is a problem that size and style mismatches are likely to occur when shopping online. The present invention aims to solve these problems and provide a system that allows users to efficiently find the best fashion outfit and confirm the appropriate fit through virtual try-on.

[1625] 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.

[1626] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for displaying the coordination image as a virtual try-on on the user's device, means for confirming a purchase based on the presented coordination image, and means for executing a purchase procedure. This allows the user to receive suggested fashion items optimal to the user based on their style information and preference information, check the items in a virtual try-on, and easily purchase them.

[1627] "User" refers to an individual who provides information about their own fashion style and preferences and receives outfit suggestions from the system.

[1628] "Style information" refers to information about the user's body shape and fashion style, such as a full-body photo of the user and measurement suit data.

[1629] "Preference information" refers to information regarding the classification of fashion style selected by the user, such as casual, formal, street, etc.

[1630] "Optimal products" refer to the fashion items that best suit a user, selected by AI based on the user's style and preference information.

[1631] "Coordination image" refers to the visual image of the overall fashion style presented to the user by combining multiple selected fashion items.

[1632] "Virtual try-on" refers to a method of using digital technology to display selected fashion items on a user's device, providing a visual confirmation that they are actually trying on.

[1633] "Device" refers to an information terminal used by a user, such as a smartphone, smart glasses, or head-mounted display.

[1634] "Confirming purchase" refers to the act of a user officially deciding to purchase a fashion item selected based on the presented coordination image.

[1635] "Purchase procedure" refers to a series of processes that are carried out after a purchase is confirmed, such as confirming payment information, securing inventory, and shipping procedures.

[1636] The present invention is a system that proposes optimal fashion coordination based on a user's style information and preference information, and then allows the user to virtually try on the clothes and complete the purchase procedure. Specific embodiments of this system are described below.

[1637] Entering style information

[1638] Users take a full-body photo using a device (e.g., a smartphone or smart glasses) and upload the image data to the server. They can also obtain detailed body shape data using a measurement suit (e.g., a measurement suit), and this data is also sent to the server as style information.

[1639] Enter your preferences

[1640] The user inputs their own fashion preference information using the terminal. The preference information is a classification of fashion styles such as casual, formal, street, etc., and the selection made from the selection list is sent to the server.

[1641] Coordination suggestions

[1642] The server receives style and preference information provided by the user and analyzes the data. An AI algorithm (e.g., FashionAI) is used for the analysis. The AI ​​algorithm selects the most suitable products and extracts appropriate items from the product catalog. Based on these extracted items, an overall outfit image is generated.

[1643] Coordination presentation and virtual try-on

[1644] The server sends the generated coordinated image to the user's device. The device (e.g., a smartphone or smart glasses) displays the received image and item list, allowing the user to visually confirm it. Additionally, using virtual try-on technology, the selected fashion items are displayed on the user's device, providing a visual confirmation as if they were actually being tried on.

[1645] Confirmation and checkout

[1646] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device recognizes this operation and sends the purchase request data to the server. The server receives the purchase request, confirms payment information, secures inventory, and processes delivery. It also generates a confirmation message indicating that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[1647] Specific examples

[1648] For users who prefer a casual style

[1649] 1. The user takes a full-body photo using their device and uploads it.

[1650] 2. The server receives the photo and analyzes it.

[1651] 3. The user selects "casual style" and enters it into the device.

[1652] 4. The server selects casual fashion items based on the style and preference information.

[1653] 5. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1654] 6. The device displays outfit images to the user, allowing them to virtually try them on.

[1655] 7. The user presses the "Purchase" button, sending a purchase request to the server.

[1656] 8. The server processes the purchase and sends a confirmation message to the device.

[1657] 9. The device displays a confirmation message to the user and the purchase is complete.

[1658] Prompt Sentence Examples

[1659] "The user takes a full-body photo and selects a casual style. The AI ​​then suggests the best fashion coordination. Then, it generates a code that allows the user to purchase the suggested items."

[1660] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1661] Step 1:

[1662] Users take a full-body photo using a device (e.g., a smartphone) and upload the image data to the server. This input data includes information about the user's body shape and style. The uploaded image data is used as the basis for subsequent fashion item selection.

[1663] Step 2:

[1664] The user inputs their fashion preference information using the terminal. This input data includes classification information of the user's selected fashion style (e.g., casual, formal). The server receives the preference information and stores it in a database.

[1665] Step 3:

[1666] The server analyzes the style and preference information. An AI algorithm (e.g., FashionAI) is used for this analysis. Based on the style and preference information in the input data, the server selects the most suitable fashion items. The list of selected items is used to generate outfits in the next step.

[1667] Step 4:

[1668] The server combines the selected fashion items to generate an overall coordinated image. This image generation is performed by applying the selected items to a virtual model. The generated coordinated image is used as output data to present to the user.

[1669] Step 5:

[1670] The server sends the generated coordinated image to the user's device, which then displays the coordinated image. Using virtual try-on technology, the user visually checks the selected fashion items as if they were actually being tried on.

[1671] Step 6:

[1672] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm the purchase. The device recognizes this operation and sends the purchase request data to the server. The input data includes information on the selected item, quantity, delivery address, etc.

[1673] Step 7:

[1674] The server receives the purchase request, verifies payment information, secures inventory, and processes shipping. This completes the user's purchase. Finally, it generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The device displays this confirmation message to the user, completing the purchase process.

[1675] 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.

[1676] The present invention allows users to provide their own style and preference information, and the system then suggests optimal fashion coordinations that can then be purchased. Furthermore, the present invention combines an emotion engine that recognizes the user's emotions to achieve more personalized suggestions.

[1677] Overall system flow

[1678] The system consists of the following main parts:

[1679] 1. Enter style information

[1680] 2. Enter your preferences

[1681] 3. Introducing the Emotion Engine

[1682] 4. Coordination suggestions

[1683] 5. Coordination suggestions

[1684] 6. Confirmation of purchase

[1685] 7. Purchase Processing

[1686] Entering style information

[1687] Users use their devices to provide their own style information. The devices are equipped with a camera and a dedicated application, allowing users to take full-body photos and upload the image data. Users can also wear measuring suits such as the ZOZO Suit to obtain detailed body data. This style information is used as basic data for the AI ​​algorithm to select appropriate fashion items.

[1688] Enter your preferences

[1689] Users input their fashion preferences using a device. Preference information is a classification of fashion styles, such as casual, formal, and street style, and users can select from a list. This information is essential for the AI ​​to suggest outfits that suit the user.

[1690] Introducing the Emotion Engine

[1691] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expression data and voice data to estimate the user's emotions. For example, if the user is smiling while taking a photo, the emotion engine will infer that the user is in a positive state. This information influences product selection and the generation of outfit images.

[1692] Coordination suggestions

[1693] The server receives and analyzes style information, preference information, and emotion data obtained by the emotion engine provided by the user. Based on the analysis results, it uses an AI algorithm to select optimal fashion items from a product catalog. It then extracts appropriate items from the product catalog and generates an overall outfit image for the user. This image includes multiple selected fashion items.

[1694] Coordination suggestions

[1695] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually check it. The user can check the combination of selected fashion items based on this overall coordinated image.

[1696] Confirm your purchase

[1697] The user checks the displayed coordinated image and presses the purchase button if they wish to confirm their purchase. The device recognizes this operation and generates purchase request data. This request data includes information on the selected item, quantity, delivery address, etc.

[1698] Purchase Processing

[1699] The server receives the purchase request and initiates the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. Finally, the device displays this confirmation message to the user, completing the purchase process.

[1700] Specific examples

[1701] For users who prefer a casual style

[1702] 1. The user takes a full-body photo using their device and uploads it.

[1703] 2. The server receives the photo and analyzes it.

[1704] 3. The user selects "casual style" and enters it into the device.

[1705] 4. The server selects casual fashion items based on the style and preference information.

[1706] 5. The server uses an emotion engine to analyze the user's facial expression data and determine that the user is in a positive state.

[1707] 6. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1708] 7. The device displays the coordinated image to the user.

[1709] 8. The user presses the "Purchase" button, sending a purchase request to the server.

[1710] 9. The server processes the purchase and sends a confirmation message to the device.

[1711] 10. The device displays a confirmation message to the user and the purchase is complete.

[1712] In this way, more personalized suggestions are possible depending on the user's emotional state, improving the user experience.

[1713] The processing flow will be explained below.

[1714] Step 1:

[1715] Users log in to the device and enter their style information. Specifically, they can take a full-body photo using the device's camera or wear a measuring suit to obtain measurement data, which they then upload to the device.

[1716] Step 2:

[1717] The device sends the captured photos or measurement data to the server. At this time, the data format is standardized and error checks are performed to ensure reliable transmission to the server.

[1718] Step 3:

[1719] The server analyzes the received style information using image processing technology and machine learning models to extract and store the user's body shape data.

[1720] Step 4:

[1721] The user inputs their fashion preferences into the device, and then selects a style such as "casual," "formal," or "street" from a list displayed on the device.

[1722] Step 5:

[1723] The device sends the user's preferences to the server, which formats and transmits the data to accurately reflect the user's choices.

[1724] Step 6:

[1725] The server analyzes the received preference and style information and selects the most suitable items from the product catalog based on that information. It uses AI algorithms to narrow down the options and identify the most suitable fashion items for the user.

[1726] Step 7:

[1727] The server uses an emotion engine to recognize the user's emotions. Specifically, it analyzes the user's facial expression data and voice data and infers their emotions based on that information. For example, if the user is smiling while taking a photo, it is determined to be in a positive state.

[1728] Step 8:

[1729] The server generates the final outfit image based on the analysis results, including emotional data. The selected items are combined to create an outfit that takes into account the user's emotional state.

[1730] Step 9:

[1731] The server sends the generated coordinated image and item list to the device. The data package includes high-resolution images and detailed item information.

[1732] Step 10:

[1733] The device displays an outfit image and a list of items to the user, who can then check the details of each item and evaluate the overall outfit.

[1734] Step 11:

[1735] The user selects a favorite outfit and presses the purchase button. The device recognizes this operation and generates purchase request data.

[1736] Step 12:

[1737] The terminal sends purchase request data to the server, which includes information about the selected item, the quantity, the delivery address, etc.

[1738] Step 13:

[1739] The server receives the purchase request and initiates the user's purchase process, including verifying payment information, securing inventory, and arranging shipping.

[1740] Step 14:

[1741] The server generates a confirmation message that the purchase has been confirmed and sends it to the user's device, including details about the purchase and an estimated delivery date.

[1742] Step 15:

[1743] The device displays a purchase confirmation message to the user, so that the user knows the purchase was successful.

[1744] Example 2

[1745] 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."

[1746] The problem to be solved by the present invention is to realize more accurate suggestions that take into account the user's emotional state when suggesting personalized fashion coordination based on the user's style information and preference information, and to improve user convenience by providing a system that allows the suggested fashion items to be purchased as is.

[1747] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for selecting optimal products based on the style information and the preference information, means for generating an overall coordination image using the selected products, means for acquiring and analyzing user emotion data, means for reflecting the analyzed emotion data in product selection, means for presenting the coordination image to the user, means for confirming purchase based on the presented coordination image, and means for executing purchase procedures. This enables personalized coordination suggestions that take into account the user's emotional state in addition to the style information and preference information provided by the user, thereby significantly improving user convenience.

[1748] A "user" is an individual or group that utilizes the system to provide their style and preference information.

[1749] "Style information" refers to data relating to the user's body type and appearance, such as a full-body photo of the user and measurement suit data.

[1750] "Preference information" is classification information of a fashion style selected by a user, and includes information such as casual, formal, and street style.

[1751] "Emotion data" is data that represents the user's current emotional state, obtained by analyzing the user's facial expression data and voice data.

[1752] The "server" is a computer system that receives style information, preference information, and emotional data sent by the user, analyzes this, and makes coordination suggestions.

[1753] A "coordination image" is a visual image of a specific combination of fashion items that is generated based on information analyzed by the server.

[1754] "Purchase request data" is data generated when a user confirms a purchase based on a coordination image, and includes information on the selected items, the quantity, the delivery address, and so on.

[1755] MODE FOR CARRYING OUT THE INVENTION

[1756] This invention is a system that suggests optimal fashion coordination based on style information, preference information, and the user's emotional state, and allows the user to purchase the outfit immediately. The system is mainly composed of a terminal and a server, and operates in cooperation with an emotion recognition engine, AI algorithms, and a product catalog.

[1757] Hardware and software used

[1758] Device:

[1759] Smartphones, tablets, PCs, etc.

[1760] Camera features

[1761] Dedicated application (fashion coordination app)

[1762] server:

[1763] Web server, database server

[1764] Emotion recognition engine (facial expression analysis, voice analysis)

[1765] Software for running AI algorithms (e.g., TensorFlow or PyTorch)

[1766] Product catalog database

[1767] data:

[1768] Style information (full-body photo, suit data for measurements)

[1769] Preference information (user-selected fashion style classification)

[1770] Emotion data (user's facial expression data, voice data)

[1771] Detailed explanation of the system's operation

[1772] 1. Providing style information:

[1773] Users use a dedicated application to take a full-body photo of themselves and upload it to the server from their device. Users also wear a measurement suit to obtain detailed body shape data, which is then sent to the server.

[1774] 2. Enter your preferences:

[1775] The user selects the classification information of their fashion style and sends it to the server from their terminal. This preference information is categorized into categories such as casual, formal, and street style.

[1776] 3. Acquiring emotion data:

[1777] The user captures their facial expression using the device's camera and sends the data to the server, where the server's emotion recognition engine analyzes the facial expression data and estimates the user's emotional state (positive, neutral, negative, etc.).

[1778] 4. Coordination proposal generation:

[1779] The server integrates the received style, preference, and emotion data and uses an AI algorithm to select the most suitable fashion items. Based on this, it selects appropriate items from a product catalog and creates an overall coordination image.

[1780] 5. Coordination suggestions:

[1781] The generated coordinated image is sent from the server to the user's terminal, which displays the image to the user, allowing the user to visually confirm the proposed coordinated look.

[1782] 6. Purchasing process:

[1783] When the user likes the suggested outfit and presses the purchase button, the device sends the purchase request data to the server. The server verifies payment information, secures inventory, and processes delivery procedures, and finally sends a confirmation message to the user's device. The device displays this confirmation message to the user, completing the purchase.

[1784] Specific examples

[1785] For example, if a user desires a casual style, the following steps are performed.

[1786] 1. The user takes a full-body photo using their device and uploads it to the server.

[1787] 2. The user selects "casual style" and enters it into the terminal.

[1788] 3. The server analyzes the user's facial expression data and determines their positive emotional state.

[1789] 4. The server generates an image of a coordinated outfit consisting of a denim jacket, a casual T-shirt, and chino pants, and sends it to the device.

[1790] 5. The device displays the coordinated image, and the user presses the "Purchase" button to proceed with the purchase.

[1791] Prompt Sentence Examples

[1792] "I'm a woman who likes casual style. I'd like you to suggest denim jackets, casual T-shirts, and chino pants. The sentiment is positive."

[1793] This invention allows for personalized coordination suggestions that take into account the user's emotional state and seamless purchase of the items, improving the user experience.

[1794] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1795] Step 1: Entering style information

[1796] Specific behavior:

[1797] 1. The user launches a dedicated application using the device.

[1798] 2. The user follows the application's instructions to take a full-body photo.

[1799] 3. The device uploads the captured image data to the server.

[1800] 4. The device also collects data from measurement suits such as the ZOZO Suit and sends it to the server.

[1801] Input: A full-body photo taken by the user and measurement suit data.

[1802] Output: Style information (full-body photo, suit data for measurements) sent to the server.

[1803] Step 2: Enter your preferences

[1804] Specific behavior:

[1805] 1. The user displays the fashion style selection screen on their device.

[1806] 2. The user selects their preferred fashion style, such as "casual" or "formal."

[1807] 3. The device sends the selected preference information to the server.

[1808] Input: The user's chosen fashion style (e.g. casual, formal, etc.).

[1809] Output: Preference information sent to the server.

[1810] Step 3: Obtaining emotion data

[1811] Specific behavior:

[1812] 1. The user uses the camera function to capture their facial expression.

[1813] 2. The device collects facial expression data and sends it to the server.

[1814] 3. The server uses an emotion engine to analyze the received facial expression data and estimate the current emotional state.

[1815] Input: User's facial expression data.

[1816] Output: The emotional state inferred by the emotion engine (e.g., positive, neutral, negative, etc.).

[1817] Step 4: Analyze the information and generate coordination proposals

[1818] Specific behavior:

[1819] 1. The server integrates and analyzes the style information, preference information, and emotion data received from the user.

[1820] 2. The server uses an AI algorithm to select the most suitable fashion items.

[1821] 3. The server generates an overall coordinated image based on the items selected.

[1822] Input: Style information, preference information, emotion data.

[1823] Output: Coordination image (combination of selected fashion items).

[1824] Step 5: Show your outfit

[1825] Specific behavior:

[1826] 1. The server sends the generated coordinate image to the terminal.

[1827] 2. The terminal displays the coordinated image received to the user.

[1828] 3. Allow the user to visually check the suggested outfits.

[1829] Input: Server-generated coordinate image.

[1830] Output: Coordinate image displayed on the device.

[1831] Step 6: Confirm your purchase

[1832] Specific behavior:

[1833] 1. The user checks the coordinated image displayed.

[1834] 2. The user presses the "Purchase" button.

[1835] 3. The terminal generates a purchase request and sends it to the server. This request data includes information about the selected item, the quantity, the delivery address, etc.

[1836] Input: User confirms outfit and confirms purchase.

[1837] Output: Purchase request data sent to the server.

[1838] Step 7: Purchase Processing

[1839] Specific behavior:

[1840] 1. The server receives the purchase request and verifies the payment information.

[1841] 2. The server checks and secures product inventory.

[1842] 3. The server initiates the delivery procedure.

[1843] 4. The server generates a confirmation message confirming the purchase and sends it to the user's device.

[1844] 5. The device displays a confirmation message to the user, informing them that the purchase is complete.

[1845] Input: Purchase request data sent to the server.

[1846] Output: Purchase confirmation message and purchase completion notification.

[1847] (Application example 2)

[1848] 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."

[1849] Conventional fashion coordination suggestion systems only suggest products based on style and preference information, which means they lack personalized suggestions that take into account the user's emotional state. As a result, it is difficult to improve user satisfaction, and the quality of the shopping experience can decline. This can also affect sales by reducing purchasing motivation.

[1850] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1851] In this invention, the server includes means for receiving style information provided by a user, means for receiving preference information, means for acquiring emotion data using an emotion engine that recognizes the user's emotions, means for selecting optimal products based on the style information, preference information, and emotion data, means for generating an overall coordination image using the selected products, means for presenting the coordination image to the user, means for confirming a purchase based on the presented coordination image, and means for executing a purchase procedure, thereby making it possible to provide a more personalized fashion suggestion and purchasing experience according to the user's emotional state.

[1852] "Style information provided by the user" refers to information provided by the user, such as a full-body photo of the user and suit measurement data, and is basic data used by the system to understand the user's body type and fashion trends.

[1853] "Preference information" is classification information of a fashion style selected by a user, and is information based on the user's preferences, such as casual, formal, street, etc.

[1854] The "emotion engine that recognizes user emotions" is software and algorithms that analyze the user's facial expression data and voice data to estimate the user's emotional state.

[1855] The "means for selecting the most suitable product based on the style information, preference information, and emotional data" refers to an algorithm that analyzes the style information, preference information, and emotional data obtained from the user and selects the most suitable fashion item from a product catalog based on the information.

[1856] The "means for generating an overall coordination image using the selected products" refers to software or algorithms for combining multiple selected fashion items to create an overall coordination image to be proposed to the user.

[1857] The "means for presenting the coordinated image to the user" refers to a technique for transmitting the generated coordinated image to the user's terminal and displaying it in a visually confirmable manner.

[1858] The "means for confirming a purchase based on the presented coordinated image" refers to an interface and process for a user to confirm a displayed coordinated image and confirm a purchase.

[1859] The "means of completing the purchase process" is the process of verifying payment information, securing inventory, arranging shipping, and sending a confirmation message to the user after the purchase is confirmed.

[1860] The system that realizes this invention proposes optimal fashion coordination based on the user's style information and preference information, and further provides more personalized proposals by recognizing the user's emotional state using an emotion engine. The hardware and software required to implement this system are described below.

[1861] Overall system configuration

[1862] 1. Enter style information

[1863] Users take a full-body photo of themselves using a device such as a smartphone and upload the image data through a dedicated application. They can also wear a measurement suit to obtain detailed body data. The hardware used is the smartphone's camera function, and the software is a dedicated application for processing the image data.

[1864] 2. Enter your preferences

[1865] The user inputs his or her fashion preferences using the terminal. This preference information is a classification of fashion styles such as casual, formal, street, etc., and the user selects from a list.

[1866] 3. Introducing the Emotion Engine

[1867] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expression data and voice data to estimate the user's emotional state. The software used is Keras and OpenCV, which use machine learning models to recognize emotions.

[1868] 4. Coordination suggestions

[1869] The server receives and analyzes style and preference information provided by the user, as well as emotional data obtained by the emotion engine. Based on the analysis results, an AI algorithm is used to select the most suitable fashion items from the product catalog. The software used for this process is Python-based conditional branching and filtering logic.

[1870] 5. Coordination suggestions

[1871] The server sends the generated coordinated image to the user's device. The device displays the received image and item list to the user, allowing them to visually confirm the image. The software used is the UI component of a dedicated application.

[1872] 6. Confirmation of purchase

[1873] The user checks the displayed coordinated image and presses the purchase button if they wish to finalize the purchase. The terminal recognizes this operation and generates purchase request data.

[1874] 7. Purchase Processing

[1875] The server receives the purchase request and starts the user's purchase process. Specifically, it verifies payment information, secures inventory, and processes shipping. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device. The software used is the requests library for processing HTTP requests.

[1876] Specific examples

[1877] For users who prefer a casual style

[1878] 1. The user takes a full-body photo using their smartphone and uploads it.

[1879] 2. The server receives the photo and analyzes it.

[1880] 3. The user selects "casual style" and enters it into the device.

[1881] 4. The server selects casual fashion items based on the style and preference information.

[1882] 5. The server uses an emotion engine to analyze the user's facial expression data and determine that the user is in a positive state.

[1883] 6. The server generates an outfit image of a denim jacket, a casual T-shirt, and chino pants and sends it to the device.

[1884] 7. The device displays the coordinated image to the user.

[1885] 8. The user presses the "Purchase" button, sending a purchase request to the server.

[1886] 9. The server processes the purchase and sends a confirmation message to the device.

[1887] 10. The device displays a confirmation message to the user and the purchase is complete.

[1888] Prompt Sentence Examples

[1889] Generate a Python program that suggests casual and comfortable fashion items when a user takes a full-body photo using a smartphone app and the emotion recognition engine determines that the user is "happy."

[1890] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1891] Step 1:

[1892] The user takes a full-body photo using their smartphone and launches a dedicated application.

[1893] Input: A full-body photo of the user.

[1894] Output: Full-body photo data.

[1895] How it works: The user takes a full-body photo of themselves using the camera on their smartphone, and the image data is uploaded to a dedicated application.

[1896] Step 2:

[1897] The terminal transmits the captured full-body photograph to the server.

[1898] Input: Full-body photo data.

[1899] Output: The photo data sent to the server.

[1900] Specific operation: A dedicated application on the device generates and sends an HTTP request to send the captured full-body photo data to the server.

[1901] Step 3:

[1902] The server analyzes the received full-body photo data and extracts style information.

[1903] Input: Full-body photo data.

[1904] Output: Style information data.

[1905] Specific operation: The server uses an image processing algorithm to extract information about the user's body shape and the clothes they are wearing from the full-body photo data.

[1906] Step 4:

[1907] The user inputs his / her preference information (casual, formal, street style, etc.) using the terminal.

[1908] Input: User preference information.

[1909] Output: Preference information data.

[1910] Specific operation: The user selects his / her fashion preferences using the dedicated application interface. The selected preference information is sent from the terminal to the server.

[1911] Step 5:

[1912] The server receives the user-entered preference information.

[1913] Input: Preference information data.

[1914] Output: Preference information data stored in the server.

[1915] Specific operation: The server processes the HTTP request, receives and stores preference information data.

[1916] Step 6:

[1917] The server uses an emotion engine to recognize emotions from the user's facial expression data.

[1918] Input: Facial expression data.

[1919] Output: Emotion data.

[1920] Specific operation: The server runs an emotion engine (using Keras and OpenCV) that analyzes facial expression data and recognizes emotional states such as positive and negative.

[1921] Step 7:

[1922] The server selects the most suitable product based on the style information, preference information, and emotion data.

[1923] Input: Style information data, preference information data, emotion data.

[1924] Output: A list of the best products.

[1925] Specific operation: The server uses an AI algorithm to select the most suitable fashion items from the product catalog that match the user's style information, preference information, and emotional data.

[1926] Step 8:

[1927] The server generates an overall coordinated image using the selected products.

[1928] Input: A list of best products.

[1929] Output: Coordinated image.

[1930] Specific operation: The server performs image processing to combine the selected fashion items and generate coordinated images.

[1931] Step 9:

[1932] The server transmits the generated coordinated image to the user's terminal.

[1933] Input: Coordinated image.

[1934] Output: Coordinated image sent to user device.

[1935] Specific operation: The server generates and sends an HTTP response to send the coordinated image to the user's device.

[1936] Step 10:

[1937] The terminal displays the received coordinated image to the user.

[1938] Input: Coordinated image.

[1939] Output: Coordinate image displayed on the device.

[1940] Specific operation: The user's device displays the coordinated image using the UI component of the dedicated application.

[1941] Step 11:

[1942] The user checks the displayed coordinated image and presses a button to confirm the purchase.

[1943] Input: User taps.

[1944] Output: Purchase request data.

[1945] Specific operation: When a user taps the purchase button of an application, this action is recognized on the device and purchase request data is generated.

[1946] Step 12:

[1947] The terminal transmits the generated purchase request data to the server.

[1948] Input: Purchase request data.

[1949] Output: Purchase request data sent to the server.

[1950] Specific operation: The terminal generates and sends an HTTP request to send the purchase request data to the server.

[1951] Step 13:

[1952] The server receives the purchase request and processes the purchase.

[1953] Input: Purchase request data.

[1954] Output: Purchase completion message.

[1955] Specific operation: The server processes the purchase request data, verifies payment information, secures inventory, and arranges for delivery. It also generates a confirmation message that the purchase has been confirmed and sends it to the user's device.

[1956] Step 14:

[1957] The terminal will display a message to the user indicating that the purchase has been completed.

[1958] Input: Purchase completion message.

[1959] Output: The completion message displayed to the user.

[1960] Specific behavior: The user's device displays a notification in the UI component that the purchase process has been completed, informing the user that the purchase was successful.

[1961] 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.

[1962] 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.

[1963] 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.

[1964] 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.

[1965] 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.

[1966] 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.

[1967] 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).

[1968] 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.

[1969] 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."

[1970] 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.

[1971] 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).

[1972] 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.

[1973] 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.

[1974] 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.

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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.

[1982] The following is further disclosed regarding the above embodiment.

[1983] (Claim 1)

[1984] means for receiving style information provided by a user;

[1985] means for receiving preference information;

[1986] a means for selecting an optimal product based on the style information and the preference information;

[1987] means for generating an overall coordinated image using the selected products;

[1988] means for presenting the coordinated image to a user;

[1989] A means for confirming a purchase based on the presented coordinated image;

[1990] A means of completing the purchase process;

[1991] A system including:

[1992] (Claim 2)

[1993] 2. The system according to claim 1, further comprising means for receiving a full-body photograph or measurement suit data of the user as the style information.

[1994] (Claim 3)

[1995] 2. The system according to claim 1, wherein the preference information is classification information of fashion styles based on user selection.

[1996] "Example 1"

[1997] (Claim 1)

[1998] means for receiving style information provided by a user;

[1999] means for receiving preference information;

[2000] means including a generative AI model that selects optimal products based on the style information and the preference information;

[2001] means for generating an overall coordinated image using the selected products;

[2002] means for presenting the coordinated image to a user;

[2003] A means for confirming a purchase based on the presented coordinated image;

[2004] means for carrying out the purchase procedure;

[2005] means for sending a confirmation message to the user's terminal that the purchase has been confirmed;

[2006] A system including:

[2007] (Claim 2)

[2008] 2. The system according to claim 1, further comprising means for receiving a full-body photograph or measurement suit data of the user as the style information.

[2009] (Claim 3)

[2010] 2. The system according to claim 1, wherein the preference information is classification information of fashion styles based on user selection.

[2011] "Application Example 1"

[2012] (Claim 1)

[2013] means for receiving style information provided by a user;

[2014] means for receiving preference information;

[2015] a means for selecting an optimal product based on the style information and the preference information;

[2016] means for generating an overall coordinated image using the selected products;

[2017] means for presenting the coordinated image to a user;

[2018] A means for displaying the presented coordinated image on a user's device as a virtual try-on;

[2019] A means for confirming a purchase based on the presented coordinated image;

[2020] A means of completing the purchase process;

[2021] A system including:

[2022] (Claim 2)

[2023] 2. The system according to claim 1, further comprising means for receiving a full-body photograph or measurement suit data of the user as the style information.

[2024] (Claim 3)

[2025] 2. The system according to claim 1, wherein the preference information is classification information of fashion styles based on user selection.

[2026] "Example 2: Combining Emotion Engines"

[2027] (Claim 1)

[2028] means for receiving style information provided by a user;

[2029] means for receiving preference information;

[2030] a means for selecting an optimal product based on the style information and the preference information;

[2031] means for generating an overall coordinated image using the selected products;

[2032] means for acquiring and analyzing user emotion data;

[2033] a means for reflecting the analyzed emotion data in product selection;

[2034] means for presenting the coordinated image to a user;

[2035] A means for confirming a purchase based on the presented coordinated image;

[2036] A means of completing the purchase process;

[2037] A system including:

[2038] (Claim 2)

[2039] 2. The system according to claim 1, further comprising means for receiving a full-body photograph or measurement suit data of the user as the style information.

[2040] (Claim 3)

[2041] 2. The system according to claim 1, wherein the preference information is classification information of fashion styles based on user selection.

[2042] "Application example 2 when combining emotion engines"

[2043] (Claim 1)

[2044] means for receiving style information provided by a user;

[2045] means for receiving preference information;

[2046] A means for acquiring emotion data using an emotion engine that recognizes the emotion of a user;

[2047] a means for selecting an optimal product based on the style information, the preference information, and the emotion data;

[2048] means for generating an overall coordinated image using the selected products;

[2049] means for presenting the coordinated image to a user;

[2050] A means for confirming a purchase based on the presented coordinated image;

[2051] A means of completing the purchase process;

[2052] A system including:

[2053] (Claim 2)

[2054] 2. The system according to claim 1, further comprising means for receiving a full-body photograph or measurement suit data of the user as the style information.

[2055] (Claim 3)

[2056] 2. The system according to claim 1, wherein the preference information is classification information of fashion styles based on user selection. [Explanation of symbols]

[2057] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving style information provided by a user; means for receiving preference information; a means for selecting an optimal product based on the style information and the preference information; means for generating an overall coordinated image using the selected products; means for presenting the coordinated image to a user; A means for confirming a purchase based on the presented coordinated image; A means of completing the purchase process; A system including:

2. The system according to claim 1 , further comprising means for receiving a full-body photograph or suit measurement data of the user as the style information.

3. 2. The system according to claim 1, wherein the preference information is classification information of fashion styles based on user selection.

Images