system

Abstract

We provide the system. [Solution] A means of obtaining images of clothing owned by the user, A means for analyzing the aforementioned image and extracting characteristic data of the clothing, A means for generating different styles of clothing combinations based on the aforementioned feature data, A means for visually displaying the generated clothing combinations, Furthermore, the means of suggesting items and providing information on items that can be purchased from external e-commerce platforms, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern consumers own a large number of clothes, but there is a problem that it is difficult to effectively combine and utilize them. Therefore, it takes time to select clothes, and as a result, there is a tendency for items to remain unused in the closet. Also, when a consumer wants to try a new style, since they don't know how to combine their existing clothes, they often need to make new purchases. Thus, there is a need for a system that can easily receive proposals for new coordinations while making good use of the clothes one already has.

Means for Solving the Problems

[0005] This invention provides a system that acquires images of clothing owned by a user, analyzes them to extract feature data, and then suggests combinations of different clothing styles. Based on the analyzed feature data, the system generates new outfits and displays them visually. It also suggests items that can be added to the clothing styles and provides information on items that can be purchased from external e-commerce platforms, enabling users to efficiently utilize their wardrobe. Furthermore, by incorporating features such as item recommendations that take into account the user's preferences and trends, and the ability to manage clothing feature data in a database, the system achieves more personalized suggestions.

[0006] "Means" refer to the methods or devices used to achieve a specific objective.

[0007] A "user" refers to an individual or group that uses this system.

[0008] "Clothing images" refer to photographs or image data of clothing owned by the user.

[0009] "Analysis" refers to the process of extracting and analyzing specific information from image data.

[0010] "Feature data" refers to attribute information such as the color, design, and material of clothing.

[0011] "Style" refers to the genre or manner of clothing or appearance.

[0012] "Combination" refers to the process of putting multiple elements together to create a single whole.

[0013] "Visual display" means outputting generated information to the screen as an image or graphic.

[0014] An "item" refers to accessories or related products that are used in addition to clothing.

[0015] An "e-commerce platform" refers to services and websites for selling and buying goods on the Internet.

[0016] A "database" refers to a system that manages a large amount of data and enables efficient search and reference.

Brief Description of Drawings

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

Mode for Carrying Out the Invention

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

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

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

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

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

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

[0025] [First Embodiment]

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

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

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

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

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

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

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

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

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0038] This invention is a system that analyzes the characteristics of clothing images owned by the user and suggests various style coordinates. This system supports the user's wardrobe utilization through a variety of functions.

[0039] 1. Image acquisition and analysis

[0040] The user uses a device to take pictures of their clothing and uploads them to the system. The device sends the images to the server, which receives them. The server applies a feature extraction algorithm to the received images to analyze attribute information such as the color, design, and material of the clothing. This information is stored in a database and managed as the user's wardrobe.

[0041] 2. Coordination suggestions

[0042] The server applies a style generation algorithm based on stored clothing feature data to generate clothing combinations that consider multiple style options. The generated outfits are visualized as images in an easy-to-understand format and sent to the terminal. The user can view multiple outfit suggestions through the terminal. For example, to match the user's red skirt, the server suggests a combination of a cute white blouse and a cool black jacket.

[0043] 3. Proposing and supporting the purchase of additional items.

[0044] Furthermore, the server suggests items that can be added to the outfit. These include items available from external e-commerce platforms. The server searches for these items via an online API and selects recommended items based on the user's preferences and current trends. The device then provides information about these items and purchase links, allowing the user to easily complete the purchase process.

[0045] This makes it possible to maximize the potential of a wardrobe by combining new styles from the user's existing clothing and suggesting a "plus one" item as a new shopping experience.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user takes a picture of their clothing, selects the image using their device, and uploads it. The device receives the image and sends it to the server.

[0049] Step 2:

[0050] The server feeds the received image into an analysis algorithm. The server extracts clothing features such as color, design, and material from the image and records this data as structured information in a database.

[0051] Step 3:

[0052] The server retrieves clothing feature data stored in the user's database. The server then applies a style generation algorithm to generate clothing outfits that take multiple style options into consideration.

[0053] Step 4:

[0054] The server creates images and diagrams that visually represent the generated coordination and sends them to the terminal. The terminal displays this visual information on its user interface so that the user can confirm it.

[0055] Step 5:

[0056] The server selects a suitable additional item for the outfit. Considering the user's preferences and current trends, it recommends additional items and gathers information on items available for purchase from relevant e-commerce platforms.

[0057] Step 6:

[0058] The server retrieves information about the plus-one item and sends a purchase link to the device. The device displays this on the user interface, allowing the user to view details about items of interest and purchase them via the purchase link.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Modern consumers own a large wardrobe, but often struggle to create effective style combinations using their existing clothes. Furthermore, the lack of systems that effectively suggest trendy outfits and new items tailored to individual preferences prevents users from maximizing their potential. Additionally, the scattered nature of information when purchasing new items makes it difficult to have an efficient and comfortable shopping experience.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for the user to acquire images of their own clothing and transmit the image data to the server; means for the server to extract attribute information such as the color, shape, and material of the clothing using image analysis technology; and means for generating different clothing combinations by applying a style generation algorithm based on the extracted attribute information. This allows the user to experience a variety of outfits using their own clothing, and also enables the suggestion of new items according to their preferences and trends, as well as efficient purchasing.

[0064] A "user" is an entity that uses the system to coordinate or purchase clothing.

[0065] "Image data" refers to a digital representation of the visual information of clothing photographed by the user.

[0066] A "server" is a computing system that receives data sent by users and performs image analysis and style generation.

[0067] "Image analysis technology" refers to methods for extracting attribute information such as color, shape, and material from image data.

[0068] "Attribute information" refers to data that indicates the characteristics of the analyzed clothing, such as its color, shape, and material.

[0069] A "style generation algorithm" is a computational procedure that creates different clothing combinations based on attribute information.

[0070] An "information storage medium" is a device such as a database or storage system used to record and manage extracted attribute information.

[0071] An "external information system" is an external e-commerce platform that is linked to search for additional items.

[0072] A "purchase link" is a web link that users use to buy selected items.

[0073] This invention is a system that utilizes information about clothing owned by the user to provide efficient and effective style suggestions. This system handles everything from image acquisition and analysis to style suggestions and purchasing support.

[0074] The user takes a picture of their clothing using a portable information terminal. This terminal is equipped with a high-resolution camera that quickly processes the captured image and sends it to the server. An example of user operation is to launch the camera app on a smartphone, take a picture of a specific piece of clothing, and press the "Send" button.

[0075] The server utilizes image analysis techniques to process the received image data. This analysis uses image processing libraries such as OpenCV to extract attribute information such as the color, shape, and material of the clothing. This creates a detailed database of the user's clothing.

[0076] Next, the server applies a style generation algorithm based on the extracted attribute information. This algorithm utilizes a generative AI model to suggest the optimal outfit based on the user's individual preferences and current trends. For example, to match the user's red skirt, a combination of a white blouse and a black jacket might be suggested.

[0077] Furthermore, the system connects to external information systems and suggests additional items that can be added. The server accesses online shopping platforms via APIs to search for and recommend items that match the user's style. Purchase links are provided on the terminal, allowing users to easily access and consider purchasing.

[0078] As an example of a prompt, inputting a request such as "Suggest outfits that go with a red skirt" into the AI ​​model generates a variety of style options. This invention allows users to enjoy a more fulfilling wardrobe experience in their daily lives.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user takes a picture of their clothing with the device. The device uses its built-in camera to capture a high-resolution image and saves it in a file format (e.g., JPEG or PNG). The input is the user's clothing image, and the output is the saved image file.

[0082] Step 2:

[0083] Users upload captured images to the system using a dedicated app on their device. This is done by pressing the "Upload Image" button within the app. The input is the image file captured by the user, and the output is the image data sent to the server.

[0084] Step 3:

[0085] The server analyzes the received image data. The server applies image analysis algorithms and uses libraries such as OpenCV to extract color, shape, and material information from the image. In this process, attributes are identified through pixel data analysis. The input is the transmitted image data, and the output is the extracted clothing attribute information.

[0086] Step 4:

[0087] The server stores the extracted attribute information in the database. The server accesses the database using SQL queries and adds new clothing information to the user's profile. Here, the data is organized based on a schema pre-configured in the database. The input is the extracted attribute information, and the output is the updated database entry.

[0088] Step 5:

[0089] The server executes a style generation algorithm. Using a generation AI model, the server generates the optimal clothing combinations based on the user's attribute information. User preferences and trend information are also considered. The input is attribute information obtained from a database, and the output is the generated coordination options.

[0090] Step 6:

[0091] The server converts the generated outfit into an image and sends it to the terminal. The server uses an image processing library to convert multiple garments into a single composite image and sends the image data to the terminal. The input is the generated outfit data, and the output is the composite outfit image that is sent to the terminal.

[0092] Step 7:

[0093] The terminal displays the received outfit images to the user, allowing the user to review the suggested style. This display is done through the terminal's user interface, enabling the user to scroll through the screen for viewing. The input is image data received from the server, and the output is an outfit that the user can visually review.

[0094] Step 8:

[0095] The server accesses external information systems to search for additional items. Using APIs, it retrieves relevant items from online shopping platforms and recommends them based on the user's preferences and style. Input is the user's style information, and output is related item information and purchase links.

[0096] Step 9:

[0097] The terminal displays information about an additional item and a purchase link to the user. The user can use the displayed link to proceed with the purchase process on an external website. The input is item information sent from the server, and the output is the purchasable item information displayed to the user.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] Modern consumers face the challenge of choosing the perfect outfit to suit their style, given the vast array of clothing options available. Furthermore, it's difficult to imagine how a potential purchase will fit into their existing wardrobe, making the decision to buy challenging. Real-time fashion advice on the ground is therefore essential.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes an image acquisition means, which is a method for obtaining images of clothing owned by the user; an analysis means for analyzing the images and extracting characteristic data of the clothing; and a generation means for generating different styles of clothing combinations based on the characteristic data. This enables the provision of real-time and visually optimal coordination suggestions, making it easier for consumers to make purchasing decisions.

[0103] "Image acquisition method" refers to a method of acquiring images of clothing owned by a user using a digital device.

[0104] "Analysis method" refers to the process of extracting characteristic data such as the color, material, and design of clothing from acquired images.

[0105] The "generation method" is an algorithm for creating different styles of clothing combinations based on the feature data obtained by the analysis method.

[0106] "Display means" refers to technology for presenting generated clothing combinations to the user in a visually easy-to-understand manner.

[0107] "Means of provision" refers to a function that connects to external online trading platforms, retrieves information on items available for purchase by users, and presents it to them.

[0108] "Presentation method" refers to a method of displaying coordination suggestions in real time via a smart device, thereby providing users with options.

[0109] An "information storage device" is a data storage system for recording and managing the characteristic data of analyzed clothing.

[0110] "User preferences and trends" is a concept that refers to the styles that users are interested in and the fashion trends in the market.

[0111] In implementing this invention, the system is constructed as follows: The server, terminal, and user elements cooperate to suggest clothing coordinates.

[0112] The server first receives images of clothing sent by the user. The image acquisition method used here involves taking pictures of the clothing with a camera on a digital device and uploading the data to the server via the network. A commonly used protocol is used for transferring the images at this time.

[0113] The server uses specific analysis software (such as an image processing library like OpenCV) to extract feature data such as the color, design, and material of the clothing from the received images. This analysis clarifies the characteristics of each garment.

[0114] Next, the server uses a generation mechanism to utilize a generative AI model and generate different clothing combinations based on the extracted feature data. This generative AI model proposes the optimal outfit by considering the user's past preferences and market trends. Finally, the results are converted into a format that can be visually displayed.

[0115] The device retrieves outfit suggestions sent from the server and presents them visually to the user. This information is typically delivered to the user via a smartphone or smart glasses.

[0116] As a concrete example, if a user takes a picture of blue jeans and uploads it to the system, the server will use a generative AI model to suggest tops and accessories that would go well with those jeans. For example, a prompt message like, "Please suggest casual tops and jackets to go with blue jeans. Please also suggest recommended color combinations, taking current trends into consideration," might be used.

[0117] By viewing these suggestions through their devices, users can make clearer purchasing and coordination decisions. Furthermore, if the suggested clothing items are immediately available for purchase through external online trading platforms, this information can also be provided to the user.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user takes a picture of the clothing using their own device. The captured image is saved on the device as a JPEG or PNG file.

[0121] Step 2:

[0122] The device uploads images taken by the user to the server. During this process, image data is transmitted via the HTTP protocol, and the server stores the images in its data storage.

[0123] Step 3:

[0124] The server processes the received image data using an analysis algorithm (for example, the image analysis function of OpenCV). Here, feature data such as the color, design, and material of the clothing are extracted. This process analyzes the image's pixel data to identify hue and shape patterns.

[0125] Step 4:

[0126] The server uses a generative AI model based on feature data to generate prompt sentences and suggest different clothing combinations. For example, it might input the prompt "Suggest a casual top and jacket to go with blue jeans" into the generative AI model and generate outfits.

[0127] Step 5:

[0128] The server converts the generated coordinates into a visually understandable format (e.g., images or text) and sends them to the terminal. This conversion uses a filtering algorithm for visual enhancement.

[0129] Step 6:

[0130] The terminal receives coordination information sent from the server and displays it to the user. Coordination suggestions are presented in real time through the user interface on the smart device, and the user can view them.

[0131] Step 7:

[0132] The user reviews the details of the suggested outfit and clicks a link to purchase related items from an external online trading platform if necessary. At this point, a web browser is launched and the purchase process begins.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention combines a system that uses images of clothing owned by the user, analyzes those images to acquire feature data, and an emotion engine that recognizes the user's emotions to provide more personalized style suggestions. This system adjusts clothing combinations and item recommendations based on emotion data, thereby providing a service that responds to the user's emotional state.

[0135] 1. Image acquisition and analysis

[0136] The user takes a picture of their clothing on their device and uploads the image to the system. The device sends the image to a server, which uses an image analysis module to extract features such as the color and design of the clothing. This feature data is stored in a database and forms the basis for subsequent outfit suggestions.

[0137] 2. Emotion recognition

[0138] The device acquires emotional data using the user's voice, facial expressions, text, etc., and sends it to the server. The server uses an emotion engine to analyze the received data to determine the user's emotional state (e.g., joy, sadness, surprise, etc.). The emotion engine uses a machine learning model to classify emotions into multiple emotional categories.

[0139] 3. Outfit suggestions and additional items

[0140] The server adjusts the style and colors of the outfits based on the user's emotional state. For example, it suggests bright colors when the user is happy and recommends calmer color combinations when the user is feeling down. The server also uses external e-commerce platforms to provide links where users can purchase additional items that match their current mood.

[0141] 4. Optimizing the user experience

[0142] Based on emotion recognition, the interface on the device is also dynamically changed. For example, it may provide an interface that calms the user with a relaxed design, or use uplifting visual effects. Specifically, a user who is recognized as feeling tired may be shown a coordinated outfit of relaxing wear along with a background in soft colors.

[0143] Thus, the present invention provides a more personalized fashion experience by responding to the user's emotional state. Through the above functions, it becomes possible to make the user's daily style selection seamless and realize a shopping experience that is attentive to their emotions.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The user takes a picture of their own clothing, selects the image using their device, and uploads it. The device then sends this image to the server.

[0147] Step 2:

[0148] The server sends the received images to an analysis module, which extracts feature data such as the color, shape, and pattern of the clothing. The server then stores this feature data in a database.

[0149] Step 3:

[0150] The device collects user emotion data. The user inputs their emotional state (e.g., joy, sadness, excitement) into the device through facial recognition, voice input, question answering, etc. The device then sends this emotion data to a server.

[0151] Step 4:

[0152] The server uses an emotion engine to analyze collected emotion data and determine the user's current emotional state. Based on this emotional state, the server generates a style and color scheme that is appropriate for the user.

[0153] Step 5:

[0154] The server visualizes the generated outfit and sends an image of the outfit to the terminal. The terminal displays this in its user interface, allowing the user to review the suggested outfit.

[0155] Step 6:

[0156] The server searches for additional items that match the user's emotional state and retrieves relevant product information and purchase links from external e-commerce platforms.

[0157] Step 7:

[0158] The server retrieves information about additional items and sends it to the terminal. The terminal displays information about items available for purchase to the user, helping them to quickly buy items they are interested in.

[0159] Step 8:

[0160] The user interface dynamically changes colors and designs according to the user's emotional state. For example, by providing a design that promotes relaxation, it creates a comfortable environment for the user.

[0161] (Example 2)

[0162] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0163] Modern consumers seek personalized suggestions in their daily fashion choices, and in particular, they desire fashion coordination that aligns with their emotional state at any given time. However, conventional systems have struggled to provide style suggestions that take into account the user's emotional state or to offer dynamically adjusted interfaces. Therefore, the present invention aims to provide a means to improve user satisfaction by offering personalized fashion suggestions based on the user's emotional state and dynamically adjusting the user interface.

[0164] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0165] In this invention, the server includes means for acquiring images of clothing owned by the user, means for extracting characteristic data of the clothing, and means for analyzing the user's emotional state using an emotion engine. This enables personalized fashion suggestions that respond to the user's emotions, and further, dynamic adjustment of the interface to match that emotional state makes it possible to provide a more satisfying service experience to the user.

[0166] "Means for obtaining images of clothing owned by a user" refers to the process by which a user uses their device to take pictures of their own clothing and sends that image data to a server.

[0167] "Means for extracting clothing characteristic data" refers to a process that uses an image analysis module to identify features such as color, pattern, and design from images of submitted clothing and store them in a database.

[0168] "Methods for analyzing a user's emotional state using an emotion engine" refers to the process of using voice, facial expression, and text data obtained from the user, analyzing it with an emotion engine, and classifying the user's emotions into specific categories (e.g., joy, sadness).

[0169] "Means for generating different styles of clothing combinations" refers to a process that proposes new clothing combinations suitable for the user based on extracted characteristic data and analyzed emotional states.

[0170] "A means of visually displaying and further suggesting additional items that correspond to emotions from an external e-commerce platform" refers to a process that not only visually displays the generated clothing combinations on the user's device but also recommends additional items that match the user's emotional state from an external e-commerce site.

[0171] "Means of dynamically adjusting the user interface" refers to a process of appropriately changing the interface design and color scheme on a device according to the user's emotional state, in order to provide a more comfortable operating environment for the user.

[0172] The embodiments for carrying out the invention are described below.

[0173] This invention is a system that provides style suggestions using clothing owned by the user. The user takes pictures of their clothing using the camera on their device and sends those images to a server via a network. The device used is mainly a mobile device such as a smartphone or tablet.

[0174] The server uses an image analysis module on the received images. This module implements a generative AI model and extracts characteristic data such as the color, shape, and pattern of the clothing. This characteristic data is stored in a database and used in subsequent processing.

[0175] Furthermore, to acquire user emotion data, the device uses voice input, camera-based facial expression capture, or text input. This data is sent to a server, where an emotion engine performs analysis. The emotion engine uses machine learning models to categorize various emotional states. This information, along with characteristic data, is used to suggest the optimal fashion style.

[0176] The server collaborates with external e-commerce platforms to recommend appropriate items based on the user's emotional state and clothing characteristics. Furthermore, the user interface dynamically adjusts according to the user's emotional state. For example, a depressed user will see an interface with soothing colors and be recommended relaxing outfits.

[0177] A concrete example of a prompt is, "Analyze images of the user's clothing and suggest outfits that match the user's emotional state." This prompt functions as an instruction for the generative AI model to begin suggesting fashion styles.

[0178] As described above, the present invention provides a personalized fashion experience by linking the user's emotions with the characteristics of clothing. This enables sophisticated style suggestions that resonate with the user's emotions and serves as an effective means of supporting the user's daily choices.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The user takes a picture of their clothing using the device's camera function. It is recommended that the image be clear and taken in natural light. The input is the image data of the clothing, and the output is saved on the device.

[0182] Step 2:

[0183] The terminal sends the stored image data to the server. The image data is transferred to the server via the network connection. The input is the image data on the user's terminal, and the output is the image data stored on the server.

[0184] Step 3:

[0185] The server analyzes the received images. The image analysis module uses a generated AI model to extract characteristics such as the color, design, and pattern of the clothing. The input is image data on the server, and the output is stored in the database as characteristic data.

[0186] Step 4:

[0187] Users report their emotional state by inputting voice, text, or facial expression information through their device. Input can be an audio file, text information, or facial expression image, and output is structured emotion data on the device.

[0188] Step 5:

[0189] The device sends emotional data to the server. The data is transferred over the network, with the input being the emotional data on the device and the output being the emotional data received on the server.

[0190] Step 6:

[0191] The server analyzes the received emotional data using an emotion engine. It utilizes a generative AI model to classify emotional states into categories. The input is the emotional data on the server, and the output is the classified emotional state.

[0192] Step 7:

[0193] The server generates style suggestions based on clothing characteristic data and the user's emotional state. These suggestions include color images and outfit recommendations. The input is characteristic data and emotional state, and the output is data formatted as style suggestions.

[0194] Step 8:

[0195] The server recommends items related to the style suggestion from an external e-commerce platform. It generates relevant links and provides the user with purchase options. The input is the style suggestion, and the output is links to the recommended products.

[0196] Step 9:

[0197] The terminal displays style suggestions and recommended links received from the server to the user. The user interface dynamically adjusts to the user's mood. The input is data received from the server, and the output is a visually displayed style suggestion and links.

[0198] (Application Example 2)

[0199] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0200] In modern commercial facilities, it is difficult for customers to quickly select products that suit their individual style and emotions. In particular, providing optimal product recommendations based on individual customers' emotions requires accurately analyzing their diverse emotional states and providing product information accordingly. However, current technology makes it difficult to efficiently provide such services, limiting the improvement of the user experience on-site.

[0201] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0202] In this invention, the server includes a device for acquiring images of accessories owned by the user, a device for acquiring and analyzing the user's emotional data, and a device for adjusting the style and color tone of the accessories based on the emotional data. This enables real-time style suggestions that correspond to the customer's emotional state, providing a more personalized product selection experience.

[0203] "Ornaments" are items that users can wear, intended to provide visual and functional appeal.

[0204] "Devices for acquiring images" refer to tools for collecting visual data of accessories owned by the user, and include cameras, scanners, and other similar devices.

[0205] A "feature data extraction device" is a system that identifies attributes such as the shape, color, and texture of decorative items from acquired images and converts them into data.

[0206] A "device for generating combinations of decorative items of different styles" is a system that creates combinations of decorative items based on diverse styles and themes, using extracted feature data.

[0207] "Devices for visual presentation" refer to equipment such as displays and projectors that are intended to show users the combination of decorative items that have been created.

[0208] An "external web service" is an online platform that provides product information and purchasing procedures via the internet.

[0209] A "device for acquiring and analyzing emotional data" refers to a technology that includes machine learning to collect and analyze a user's emotional state from their voice and facial expressions.

[0210] A "device for adjusting style and color tone" is a system for changing the visual elements of a style suggested based on the user's emotions.

[0211] A "device that dynamically changes the method of presenting information" is a system that changes the layout and presentation of information display according to the user's emotional state.

[0212] The system for realizing this invention utilizes images of the user's accessories and emotional data to provide more personalized fashion suggestions. A specific embodiment of this system is described below.

[0213] The server acquires images of the accessories worn by the user through the smart glasses' camera and performs image analysis. Machine learning libraries such as TENSORFLOW® are used for the analysis to extract feature data such as the shape, color, and texture of the accessories. At the same time, to collect emotional data, the server uses computer vision technology similar to OpenCV to analyze the user's voice and facial expressions to determine emotional states such as joy, sadness, and excitement.

[0214] The device sends this data to the server, which then uses the feature data and emotion data to suggest the most suitable style. Specifically, when the emotional state is bright, it suggests outfits centered around brightly colored accessories, while when the emotional state is calm, it recommends combinations of muted colors. These generated suggestions are then displayed on the user's smart glasses.

[0215] Furthermore, the server integrates with external web services to provide information on where suggested products are available in-store and links to online purchases. The presentation method also dynamically changes according to the user's emotional state. For example, when the user is tired, a background with calming colors is displayed.

[0216] For example, if a user is wearing a blue shirt and their emotions are recognized as positive, the system will suggest brightly colored ties and accessories and provide information on the location of related product shelves in the store.

[0217] As an example of a prompt to input into the generating AI model, we will use phrases that request specific accessories or emotionally-based suggestions, such as "Tell me what accessories would be best suited to my current outfit."

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The user uses the smart glasses' camera to capture images of their accessories. The input is a real-time image of the accessories, and the output is stored as digital image data on the device. The device then transmits this data to the server via Bluetooth.

[0221] Step 2:

[0222] The server receives the transmitted image data and performs image analysis using TensorFlow. The input is digital image data, and feature data such as color, shape, and texture are extracted. The output is this feature data, which is stored in the system's database. This data is used to generate styles for decorative items.

[0223] Step 3:

[0224] The system collects the user's facial expression data using the smart glasses' camera and acquires audio data via the microphone. The input consists of facial expressions and audio information. This data is sent from the device to a server. The output is emotion data, which is classified into states such as "joy" and "sadness" through analysis using OpenCV.

[0225] Step 4:

[0226] The server uses feature data and sentiment data to generate optimal style coordination. The input is feature data and sentiment data, and the output is a suggestion of specific accessory combinations. Colors and styles are adjusted according to the sentiment, and the information is prepared for the user to see.

[0227] Step 5:

[0228] The server sends the generated coordination information to the smart glasses. The input is style suggestion data, and the output is a visual display that allows the user to see the combination of accessories. Specifically, the displayed products change color according to the user's emotional state.

[0229] Step 6:

[0230] The server interacts with external web services to search for information on where suggested accessories are available within the store and generates online links. The input is style suggestion data, and the output is product location information and purchase links. This allows users to quickly find where to look in the store or where to purchase items online.

[0231] Step 7:

[0232] Smart glasses dynamically respond to the user's emotional state by changing the display method. The input is emotional data, and the output is a modified visual effect. For example, if the user is tired, the screen background will adopt calming colors to provide a more relaxing atmosphere.

[0233] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0234] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0235] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0239] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0240] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0241] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0243] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0244] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0245] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0246] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0247] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0248] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0249] This invention is a system that analyzes the characteristics of clothing images owned by the user and suggests various style coordinates. This system supports the user's wardrobe utilization through a variety of functions.

[0250] 1. Image acquisition and analysis

[0251] The user uses a device to take pictures of their clothing and uploads them to the system. The device sends the images to the server, which receives them. The server applies a feature extraction algorithm to the received images to analyze attribute information such as the color, design, and material of the clothing. This information is stored in a database and managed as the user's wardrobe.

[0252] 2. Coordination suggestions

[0253] The server applies a style generation algorithm based on stored clothing feature data to generate clothing combinations that consider multiple style options. The generated outfits are visualized as images in an easy-to-understand format and sent to the terminal. The user can view multiple outfit suggestions through the terminal. For example, to match the user's red skirt, the server suggests a combination of a cute white blouse and a cool black jacket.

[0254] 3. Proposing and supporting the purchase of additional items.

[0255] Furthermore, the server suggests items that can be added to the outfit. These include items available from external e-commerce platforms. The server searches for these items via an online API and selects recommended items based on the user's preferences and current trends. The device then provides information about these items and purchase links, allowing the user to easily complete the purchase process.

[0256] This makes it possible to maximize the potential of a wardrobe by combining new styles from the user's existing clothing and suggesting a "plus one" item as a new shopping experience.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The user takes a picture of their clothing, selects the image using their device, and uploads it. The device receives the image and sends it to the server.

[0260] Step 2:

[0261] The server feeds the received image into an analysis algorithm. The server extracts clothing features such as color, design, and material from the image and records this data as structured information in a database.

[0262] Step 3:

[0263] The server retrieves clothing feature data stored in the user's database. The server then applies a style generation algorithm to generate clothing outfits that take multiple style options into consideration.

[0264] Step 4:

[0265] The server creates images and diagrams that visually represent the generated coordination and sends them to the terminal. The terminal displays this visual information on its user interface so that the user can confirm it.

[0266] Step 5:

[0267] The server selects a suitable additional item for the outfit. Considering the user's preferences and current trends, it recommends additional items and gathers information on items available for purchase from relevant e-commerce platforms.

[0268] Step 6:

[0269] The server retrieves information about the plus-one item and sends a purchase link to the device. The device displays this on the user interface, allowing the user to view details about items of interest and purchase them via the purchase link.

[0270] (Example 1)

[0271] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] Modern consumers own a large wardrobe, but often struggle to create effective style combinations using their existing clothes. Furthermore, the lack of systems that effectively suggest trendy outfits and new items tailored to individual preferences prevents users from maximizing their potential. Additionally, the scattered nature of information when purchasing new items makes it difficult to have an efficient and comfortable shopping experience.

[0273] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0274] In this invention, the server includes means for the user to acquire images of their own clothing and transmit the image data to the server; means for the server to extract attribute information such as the color, shape, and material of the clothing using image analysis technology; and means for generating different clothing combinations by applying a style generation algorithm based on the extracted attribute information. This allows the user to experience a variety of outfits using their own clothing, and also enables the suggestion of new items according to their preferences and trends, as well as efficient purchasing.

[0275] A "user" is an entity that uses the system to coordinate or purchase clothing.

[0276] "Image data" refers to a digital representation of the visual information of clothing photographed by the user.

[0277] A "server" is a computing system that receives data sent by users and performs image analysis and style generation.

[0278] "Image analysis technology" refers to methods for extracting attribute information such as color, shape, and material from image data.

[0279] "Attribute information" refers to data indicating characteristics such as the color, shape, and material of the analyzed clothing.

[0280] "Style generation algorithm" is a computational procedure for creating combinations of different clothing based on attribute information.

[0281] "Information storage medium" is a device such as a database or storage for recording and managing the extracted attribute information.

[0282] "External information system" is an external e-commerce platform that is linked to search for additional items.

[0283] "Purchase link" is a web link used when a user purchases the selected item.

[0284] The present invention is a system for effectively and efficiently making style proposals by utilizing information on clothing owned by a user. This system consistently performs operations from image acquisition and analysis to style proposal and purchase support.

[0285] The user uses a portable information terminal to take a picture of their own clothing. This terminal is equipped with a high-resolution camera, which quickly processes the taken image and transmits it to the server. As an example of the user's operation, there is an operation of launching the camera application of a smartphone, taking a picture of a specific piece of clothing, and pressing the "send" button.

[0286] The server uses image analysis technology to process the received image data. For this analysis, an image processing library such as OpenCV is used to extract attribute information such as the color, shape, and material of the clothing. As a result, a detailed database of the clothing owned by the user is constructed.

[0287] Next, the server applies a style generation algorithm based on the extracted attribute information. This algorithm utilizes a generative AI model to suggest the optimal outfit based on the user's individual preferences and current trends. For example, to match the user's red skirt, a combination of a white blouse and a black jacket might be suggested.

[0288] Furthermore, the system connects to external information systems and suggests additional items that can be added. The server accesses online shopping platforms via APIs to search for and recommend items that match the user's style. Purchase links are provided on the terminal, allowing users to easily access and consider purchasing.

[0289] As an example of a prompt, inputting a request such as "Suggest outfits that go with a red skirt" into the AI ​​model generates a variety of style options. This invention allows users to enjoy a more fulfilling wardrobe experience in their daily lives.

[0290] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0291] Step 1:

[0292] The user takes a picture of their clothing with the device. The device uses its built-in camera to capture a high-resolution image and saves it in a file format (e.g., JPEG or PNG). The input is the user's clothing image, and the output is the saved image file.

[0293] Step 2:

[0294] Users upload captured images to the system using a dedicated app on their device. This is done by pressing the "Upload Image" button within the app. The input is the image file captured by the user, and the output is the image data sent to the server.

[0295] Step 3:

[0296] The server analyzes the received image data. The server applies image analysis algorithms and uses libraries such as OpenCV to extract color, shape, and material information from the image. In this process, attributes are identified through pixel data analysis. The input is the transmitted image data, and the output is the extracted clothing attribute information.

[0297] Step 4:

[0298] The server stores the extracted attribute information in the database. The server accesses the database using SQL queries and adds new clothing information to the user's profile. Here, the data is organized based on a schema pre-configured in the database. The input is the extracted attribute information, and the output is the updated database entry.

[0299] Step 5:

[0300] The server executes a style generation algorithm. Using a generation AI model, the server generates the optimal clothing combinations based on the user's attribute information. User preferences and trend information are also considered. The input is attribute information obtained from a database, and the output is the generated coordination options.

[0301] Step 6:

[0302] The server converts the generated outfit into an image and sends it to the terminal. The server uses an image processing library to convert multiple garments into a single composite image and sends the image data to the terminal. The input is the generated outfit data, and the output is the composite outfit image that is sent to the terminal.

[0303] Step 7:

[0304] The terminal displays the received coordinate image to the user, and the user checks the proposed style. This display is performed through the user interface of the terminal, enabling the user to scroll and view the screen. As input, there is image data received from the server, and as output, coordinates that can be visually confirmed by the user are obtained.

[0305] Step 8:

[0306] The server accesses an external information system to search for plus-one items. It uses an API to obtain related items from an online shopping platform and recommends them based on the user's preferences and style. As input, there is the user's style information, and as output, related item information and purchase links are obtained.

[0307] Step 9:

[0308] The terminal displays the information of the plus-one item and the purchase link to the user. The user can use the displayed link to proceed with the purchase procedure on an external site. As input, there is the item information sent from the server, and as output, purchasable item information displayed to the user is obtained.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] Modern consumers are faced with a wealth of clothing options and have the problem that it is difficult to easily determine the optimal coordinates that suit their style. Also, it is difficult to imagine how the clothing under consideration will fit into the existing wardrobe, making the purchase decision difficult. There is a demand for real-time fashion proposals on-site.

[0312] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0313] In this invention, the server includes an image acquisition means, which is a method for obtaining images of clothing owned by the user; an analysis means for analyzing the images and extracting characteristic data of the clothing; and a generation means for generating different styles of clothing combinations based on the characteristic data. This enables the provision of real-time and visually optimal coordination suggestions, making it easier for consumers to make purchasing decisions.

[0314] "Image acquisition method" refers to a method of acquiring images of clothing owned by a user using a digital device.

[0315] "Analysis method" refers to the process of extracting characteristic data such as the color, material, and design of clothing from acquired images.

[0316] The "generation method" is an algorithm for creating different styles of clothing combinations based on the feature data obtained by the analysis method.

[0317] "Display means" refers to technology for presenting generated clothing combinations to the user in a visually easy-to-understand manner.

[0318] "Means of provision" refers to a function that connects to external online trading platforms, retrieves information on items available for purchase by users, and presents it to them.

[0319] "Presentation method" refers to a method of displaying coordination suggestions in real time via a smart device, thereby providing users with options.

[0320] An "information storage device" is a data storage system for recording and managing the characteristic data of analyzed clothing.

[0321] "User preferences and trends" is a concept that refers to the styles that users are interested in and the fashion trends in the market.

[0322] In implementing this invention, the system is constructed as follows: The server, terminal, and user elements cooperate to suggest clothing coordinates.

[0323] The server first receives images of clothing sent by the user. The image acquisition method used here involves taking pictures of the clothing with a camera on a digital device and uploading the data to the server via the network. A commonly used protocol is used for transferring the images at this time.

[0324] The server uses specific analysis software (such as an image processing library like OpenCV) to extract feature data such as the color, design, and material of the clothing from the received images. This analysis clarifies the characteristics of each garment.

[0325] Next, the server uses a generation mechanism to utilize a generative AI model and generate different clothing combinations based on the extracted feature data. This generative AI model proposes the optimal outfit by considering the user's past preferences and market trends. Finally, the results are converted into a format that can be visually displayed.

[0326] The device retrieves outfit suggestions sent from the server and presents them visually to the user. This information is typically delivered to the user via a smartphone or smart glasses.

[0327] As a concrete example, if a user takes a picture of blue jeans and uploads it to the system, the server will use a generative AI model to suggest tops and accessories that would go well with those jeans. For example, a prompt message like, "Please suggest casual tops and jackets to go with blue jeans. Please also suggest recommended color combinations, taking current trends into consideration," might be used.

[0328] By viewing these suggestions through their devices, users can make clearer purchasing and coordination decisions. Furthermore, if the suggested clothing items are immediately available for purchase through external online trading platforms, this information can also be provided to the user.

[0329] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0330] Step 1:

[0331] The user takes a picture of the clothing using their own device. The captured image is saved on the device as a JPEG or PNG file.

[0332] Step 2:

[0333] The device uploads images taken by the user to the server. During this process, image data is transmitted via the HTTP protocol, and the server stores the images in its data storage.

[0334] Step 3:

[0335] The server processes the received image data using an analysis algorithm (for example, the image analysis function of OpenCV). Here, feature data such as the color, design, and material of the clothing are extracted. This process analyzes the image's pixel data to identify hue and shape patterns.

[0336] Step 4:

[0337] The server uses a generative AI model based on feature data to generate prompt sentences and suggest different clothing combinations. For example, it might input the prompt "Suggest a casual top and jacket to go with blue jeans" into the generative AI model and generate outfits.

[0338] Step 5:

[0339] The server converts the generated coordinates into a visually understandable format (e.g., images or text) and sends them to the terminal. This conversion uses a filtering algorithm for visual enhancement.

[0340] Step 6:

[0341] The terminal receives coordination information sent from the server and displays it to the user. Coordination suggestions are presented in real time through the user interface on the smart device, and the user can view them.

[0342] Step 7:

[0343] The user reviews the details of the suggested outfit and clicks a link to purchase related items from an external online trading platform if necessary. At this point, a web browser is launched and the purchase process begins.

[0344] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0345] This invention combines a system that uses images of clothing owned by the user, analyzes those images to acquire feature data, and an emotion engine that recognizes the user's emotions to provide more personalized style suggestions. This system adjusts clothing combinations and item recommendations based on emotion data, thereby providing a service that responds to the user's emotional state.

[0346] 1. Image acquisition and analysis

[0347] The user takes a picture of their clothing on their device and uploads the image to the system. The device sends the image to a server, which uses an image analysis module to extract features such as the color and design of the clothing. This feature data is stored in a database and forms the basis for subsequent outfit suggestions.

[0348] 2. Emotion recognition

[0349] The device acquires emotional data using the user's voice, facial expressions, text, etc., and sends it to the server. The server uses an emotion engine to analyze the received data to determine the user's emotional state (e.g., joy, sadness, surprise, etc.). The emotion engine uses a machine learning model to classify emotions into multiple emotional categories.

[0350] 3. Outfit suggestions and additional items

[0351] The server adjusts the style and colors of the outfits based on the user's emotional state. For example, it suggests bright colors when the user is happy and recommends calmer color combinations when the user is feeling down. The server also uses external e-commerce platforms to provide links where users can purchase additional items that match their current mood.

[0352] 4. Optimizing the user experience

[0353] Based on emotion recognition, the interface on the device is also dynamically changed. For example, it may provide an interface that calms the user with a relaxed design, or use uplifting visual effects. Specifically, a user who is recognized as feeling tired may be shown a coordinated outfit of relaxing wear along with a background in soft colors.

[0354] Thus, the present invention provides a more personalized fashion experience by responding to the user's emotional state. Through the above functions, it becomes possible to make the user's daily style selection seamless and realize a shopping experience that is attentive to their emotions.

[0355] The following describes the processing flow.

[0356] Step 1:

[0357] The user takes a picture of their own clothing, selects the image using their device, and uploads it. The device then sends this image to the server.

[0358] Step 2:

[0359] The server sends the received images to an analysis module, which extracts feature data such as the color, shape, and pattern of the clothing. The server then stores this feature data in a database.

[0360] Step 3:

[0361] The device collects user emotion data. The user inputs their emotional state (e.g., joy, sadness, excitement) into the device through facial recognition, voice input, question answering, etc. The device then sends this emotion data to a server.

[0362] Step 4:

[0363] The server uses an emotion engine to analyze collected emotion data and determine the user's current emotional state. Based on this emotional state, the server generates a style and color scheme that is appropriate for the user.

[0364] Step 5:

[0365] The server visualizes the generated outfit and sends an image of the outfit to the terminal. The terminal displays this in its user interface, allowing the user to review the suggested outfit.

[0366] Step 6:

[0367] The server searches for additional items that match the user's emotional state and retrieves relevant product information and purchase links from external e-commerce platforms.

[0368] Step 7:

[0369] The server retrieves information about additional items and sends it to the terminal. The terminal displays information about items available for purchase to the user, helping them to quickly buy items they are interested in.

[0370] Step 8:

[0371] The user interface dynamically changes colors and designs according to the user's emotional state. For example, by providing a design that promotes relaxation, it creates a comfortable environment for the user.

[0372] (Example 2)

[0373] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0374] Modern consumers seek personalized suggestions in their daily fashion choices, and in particular, they desire fashion coordination that aligns with their emotional state at any given time. However, conventional systems have struggled to provide style suggestions that take into account the user's emotional state or to offer dynamically adjusted interfaces. Therefore, the present invention aims to provide a means to improve user satisfaction by offering personalized fashion suggestions based on the user's emotional state and dynamically adjusting the user interface.

[0375] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0376] In this invention, the server includes means for acquiring images of clothing owned by the user, means for extracting characteristic data of the clothing, and means for analyzing the user's emotional state using an emotion engine. This enables personalized fashion suggestions that respond to the user's emotions, and further, dynamic adjustment of the interface to match that emotional state makes it possible to provide a more satisfying service experience to the user.

[0377] "Means for obtaining images of clothing owned by a user" refers to the process by which a user uses their device to take pictures of their own clothing and sends that image data to a server.

[0378] "Means for extracting clothing characteristic data" refers to a process that uses an image analysis module to identify features such as color, pattern, and design from images of submitted clothing and store them in a database.

[0379] "Methods for analyzing a user's emotional state using an emotion engine" refers to the process of using voice, facial expression, and text data obtained from the user, analyzing it with an emotion engine, and classifying the user's emotions into specific categories (e.g., joy, sadness).

[0380] "Means for generating different styles of clothing combinations" refers to a process that proposes new clothing combinations suitable for the user based on extracted characteristic data and analyzed emotional states.

[0381] "A means of visually displaying and further suggesting additional items that correspond to emotions from an external e-commerce platform" refers to a process that not only visually displays the generated clothing combinations on the user's device but also recommends additional items that match the user's emotional state from an external e-commerce site.

[0382] "Means of dynamically adjusting the user interface" refers to a process of appropriately changing the interface design and color scheme on a device according to the user's emotional state, in order to provide a more comfortable operating environment for the user.

[0383] The embodiments for carrying out the invention are described below.

[0384] This invention is a system that provides style suggestions using clothing owned by the user. The user takes pictures of their clothing using the camera on their device and sends those images to a server via a network. The device used is mainly a mobile device such as a smartphone or tablet.

[0385] The server uses an image analysis module on the received images. This module implements a generative AI model and extracts characteristic data such as the color, shape, and pattern of the clothing. This characteristic data is stored in a database and used in subsequent processing.

[0386] Furthermore, to acquire user emotion data, the device uses voice input, camera-based facial expression capture, or text input. This data is sent to a server, where an emotion engine performs analysis. The emotion engine uses machine learning models to categorize various emotional states. This information, along with characteristic data, is used to suggest the optimal fashion style.

[0387] The server collaborates with external e-commerce platforms to recommend appropriate items based on the user's emotional state and clothing characteristics. Furthermore, the user interface dynamically adjusts according to the user's emotional state. For example, a depressed user will see an interface with soothing colors and be recommended relaxing outfits.

[0388] A concrete example of a prompt is, "Analyze images of the user's clothing and suggest outfits that match the user's emotional state." This prompt functions as an instruction for the generative AI model to begin suggesting fashion styles.

[0389] As described above, the present invention provides a personalized fashion experience by linking the user's emotions with the characteristics of clothing. This enables sophisticated style suggestions that resonate with the user's emotions and serves as an effective means of supporting the user's daily choices.

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] The user takes a picture of their clothing using the device's camera function. It is recommended that the image be clear and taken in natural light. The input is the image data of the clothing, and the output is saved on the device.

[0393] Step 2:

[0394] The terminal sends the stored image data to the server. The image data is transferred to the server via the network connection. The input is the image data on the user's terminal, and the output is the image data stored on the server.

[0395] Step 3:

[0396] The server analyzes the received images. The image analysis module uses a generated AI model to extract characteristics such as the color, design, and pattern of the clothing. The input is image data on the server, and the output is stored in the database as characteristic data.

[0397] Step 4:

[0398] Users report their emotional state by inputting voice, text, or facial expression information through their device. Input can be an audio file, text information, or facial expression image, and output is structured emotion data on the device.

[0399] Step 5:

[0400] The device sends emotional data to the server. The data is transferred over the network, with the input being the emotional data on the device and the output being the emotional data received on the server.

[0401] Step 6:

[0402] The server analyzes the received emotional data using an emotion engine. It utilizes a generative AI model to classify emotional states into categories. The input is the emotional data on the server, and the output is the classified emotional state.

[0403] Step 7:

[0404] The server generates style suggestions based on clothing characteristic data and the user's emotional state. These suggestions include color images and outfit recommendations. The input is characteristic data and emotional state, and the output is data formatted as style suggestions.

[0405] Step 8:

[0406] The server recommends items related to the style suggestion from an external e-commerce platform. It generates relevant links and provides the user with purchase options. The input is the style suggestion, and the output is links to the recommended products.

[0407] Step 9:

[0408] The terminal displays style suggestions and recommended links received from the server to the user. The user interface dynamically adjusts to the user's mood. The input is data received from the server, and the output is a visually displayed style suggestion and links.

[0409] (Application Example 2)

[0410] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0411] In modern commercial facilities, it is difficult for customers to quickly select products that suit their individual style and emotions. In particular, providing optimal product recommendations based on individual customers' emotions requires accurately analyzing their diverse emotional states and providing product information accordingly. However, current technology makes it difficult to efficiently provide such services, limiting the improvement of the user experience on-site.

[0412] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0413] In this invention, the server includes a device for acquiring images of accessories owned by the user, a device for acquiring and analyzing the user's emotional data, and a device for adjusting the style and color tone of the accessories based on the emotional data. This enables real-time style suggestions that correspond to the customer's emotional state, providing a more personalized product selection experience.

[0414] "Ornaments" are items that users can wear, intended to provide visual and functional appeal.

[0415] "Devices for acquiring images" refer to tools for collecting visual data of accessories owned by the user, and include cameras, scanners, and other similar devices.

[0416] A "feature data extraction device" is a system that identifies attributes such as the shape, color, and texture of decorative items from acquired images and converts them into data.

[0417] A "device for generating combinations of decorative items of different styles" is a system that creates combinations of decorative items based on diverse styles and themes, using extracted feature data.

[0418] "Devices for visual presentation" refer to equipment such as displays and projectors that are intended to show users the combination of decorative items that have been created.

[0419] An "external web service" is an online platform that provides product information and purchasing procedures via the internet.

[0420] A "device for acquiring and analyzing emotional data" refers to a technology that includes machine learning to collect and analyze a user's emotional state from their voice and facial expressions.

[0421] A "device for adjusting style and color tone" is a system for changing the visual elements of a style suggested based on the user's emotions.

[0422] A "device that dynamically changes the method of presenting information" is a system that changes the layout and presentation of information display according to the user's emotional state.

[0423] The system for realizing this invention utilizes images of the user's accessories and emotional data to provide more personalized fashion suggestions. A specific embodiment of this system is described below.

[0424] The server acquires images of the accessories worn by the user through the smart glasses' camera and performs image analysis. Machine learning libraries such as TensorFlow are used for the analysis to extract feature data such as the shape, color, and texture of the accessories. At the same time, to collect emotional data, computer vision technology similar to OpenCV is used to analyze the user's voice and facial expressions to determine emotional states such as joy, sadness, and excitement.

[0425] The device sends this data to the server, which then uses the feature data and emotion data to suggest the most suitable style. Specifically, when the emotional state is bright, it suggests outfits centered around brightly colored accessories, while when the emotional state is calm, it recommends combinations of muted colors. These generated suggestions are then displayed on the user's smart glasses.

[0426] Furthermore, the server integrates with external web services to provide information on where suggested products are available in-store and links to online purchases. The presentation method also dynamically changes according to the user's emotional state. For example, when the user is tired, a background with calming colors is displayed.

[0427] For example, if a user is wearing a blue shirt and their emotions are recognized as positive, the system will suggest brightly colored ties and accessories and provide information on the location of related product shelves in the store.

[0428] As an example of a prompt to input into the generating AI model, we will use phrases that request specific accessories or emotionally-based suggestions, such as "Tell me what accessories would be best suited to my current outfit."

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The user uses the smart glasses' camera to capture images of their accessories. The input is a real-time image of the accessories, and the output is stored as digital image data on the device. The device then transmits this data to the server via Bluetooth.

[0432] Step 2:

[0433] The server receives the transmitted image data and performs image analysis using TensorFlow. The input is digital image data, and feature data such as color, shape, and texture are extracted. The output is this feature data, which is stored in the system's database. This data is used to generate styles for decorative items.

[0434] Step 3:

[0435] The system collects the user's facial expression data using the smart glasses' camera and acquires audio data via the microphone. The input consists of facial expressions and audio information. This data is sent from the device to a server. The output is emotion data, which is classified into states such as "joy" and "sadness" through analysis using OpenCV.

[0436] Step 4:

[0437] The server uses feature data and sentiment data to generate optimal style coordination. The input is feature data and sentiment data, and the output is a suggestion of specific accessory combinations. Colors and styles are adjusted according to the sentiment, and the information is prepared for the user to see.

[0438] Step 5:

[0439] The server sends the generated coordination information to the smart glasses. The input is style suggestion data, and the output is a visual display that allows the user to see the combination of accessories. Specifically, the displayed products change color according to the user's emotional state.

[0440] Step 6:

[0441] The server interacts with external web services to search for information on where suggested accessories are available within the store and generates online links. The input is style suggestion data, and the output is product location information and purchase links. This allows users to quickly find where to look in the store or where to purchase items online.

[0442] Step 7:

[0443] Smart glasses dynamically respond to the user's emotional state by changing the display method. The input is emotional data, and the output is a modified visual effect. For example, if the user is tired, the screen background will adopt calming colors to provide a more relaxing atmosphere.

[0444] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0445] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0446] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0450] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0451] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0452] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0453] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0454] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0455] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0456] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0457] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0458] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0459] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0460] This invention is a system that analyzes the characteristics of clothing images owned by the user and suggests various style coordinates. This system supports the user's wardrobe utilization through a variety of functions.

[0461] 1. Image acquisition and analysis

[0462] The user uses a device to take pictures of their clothing and uploads them to the system. The device sends the images to the server, which receives them. The server applies a feature extraction algorithm to the received images to analyze attribute information such as the color, design, and material of the clothing. This information is stored in a database and managed as the user's wardrobe.

[0463] 2. Coordination suggestions

[0464] The server applies a style generation algorithm based on stored clothing feature data to generate clothing combinations that consider multiple style options. The generated outfits are visualized as images in an easy-to-understand format and sent to the terminal. The user can view multiple outfit suggestions through the terminal. For example, to match the user's red skirt, the server suggests a combination of a cute white blouse and a cool black jacket.

[0465] 3. Proposing and supporting the purchase of additional items.

[0466] Furthermore, the server suggests items that can be added to the outfit. These include items available from external e-commerce platforms. The server searches for these items via an online API and selects recommended items based on the user's preferences and current trends. The device then provides information about these items and purchase links, allowing the user to easily complete the purchase process.

[0467] This makes it possible to maximize the potential of a wardrobe by combining new styles from the user's existing clothing and suggesting a "plus one" item as a new shopping experience.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The user takes a picture of their clothing, selects the image using their device, and uploads it. The device receives the image and sends it to the server.

[0471] Step 2:

[0472] The server feeds the received image into an analysis algorithm. The server extracts clothing features such as color, design, and material from the image and records this data as structured information in a database.

[0473] Step 3:

[0474] The server retrieves clothing feature data stored in the user's database. The server then applies a style generation algorithm to generate clothing outfits that take multiple style options into consideration.

[0475] Step 4:

[0476] The server creates images and diagrams that visually represent the generated coordination and sends them to the terminal. The terminal displays this visual information on its user interface so that the user can confirm it.

[0477] Step 5:

[0478] The server selects a suitable additional item for the outfit. Considering the user's preferences and current trends, it recommends additional items and gathers information on items available for purchase from relevant e-commerce platforms.

[0479] Step 6:

[0480] The server retrieves information about the plus-one item and sends a purchase link to the device. The device displays this on the user interface, allowing the user to view details about items of interest and purchase them via the purchase link.

[0481] (Example 1)

[0482] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0483] Modern consumers own a large wardrobe, but often struggle to create effective style combinations using their existing clothes. Furthermore, the lack of systems that effectively suggest trendy outfits and new items tailored to individual preferences prevents users from maximizing their potential. Additionally, the scattered nature of information when purchasing new items makes it difficult to have an efficient and comfortable shopping experience.

[0484] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0485] In this invention, the server includes means for the user to acquire images of their own clothing and transmit the image data to the server; means for the server to extract attribute information such as the color, shape, and material of the clothing using image analysis technology; and means for generating different clothing combinations by applying a style generation algorithm based on the extracted attribute information. This allows the user to experience a variety of outfits using their own clothing, and also enables the suggestion of new items according to their preferences and trends, as well as efficient purchasing.

[0486] A "user" is an entity that uses the system to coordinate or purchase clothing.

[0487] "Image data" refers to a digital representation of the visual information of clothing photographed by the user.

[0488] A "server" is a computing system that receives data sent by users and performs image analysis and style generation.

[0489] "Image analysis technology" refers to methods for extracting attribute information such as color, shape, and material from image data.

[0490] "Attribute information" refers to data that indicates the characteristics of the analyzed clothing, such as its color, shape, and material.

[0491] A "style generation algorithm" is a computational procedure that creates different clothing combinations based on attribute information.

[0492] An "information storage medium" is a device such as a database or storage system used to record and manage extracted attribute information.

[0493] An "external information system" is an external e-commerce platform that is linked to search for additional items.

[0494] A "purchase link" is a web link that users use to buy selected items.

[0495] This invention is a system that utilizes information about clothing owned by the user to provide efficient and effective style suggestions. This system handles everything from image acquisition and analysis to style suggestions and purchasing support.

[0496] The user takes a picture of their clothing using a portable information terminal. This terminal is equipped with a high-resolution camera that quickly processes the captured image and sends it to the server. An example of user operation is to launch the camera app on a smartphone, take a picture of a specific piece of clothing, and press the "Send" button.

[0497] The server utilizes image analysis techniques to process the received image data. This analysis uses image processing libraries such as OpenCV to extract attribute information such as the color, shape, and material of the clothing. This creates a detailed database of the user's clothing.

[0498] Next, the server applies a style generation algorithm based on the extracted attribute information. This algorithm utilizes a generative AI model to suggest the optimal outfit based on the user's individual preferences and current trends. For example, to match the user's red skirt, a combination of a white blouse and a black jacket might be suggested.

[0499] Furthermore, the system connects to external information systems and suggests additional items that can be added. The server accesses online shopping platforms via APIs to search for and recommend items that match the user's style. Purchase links are provided on the terminal, allowing users to easily access and consider purchasing.

[0500] As an example of a prompt, inputting a request such as "Suggest outfits that go with a red skirt" into the AI ​​model generates a variety of style options. This invention allows users to enjoy a more fulfilling wardrobe experience in their daily lives.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The user takes a picture of their clothing with the device. The device uses its built-in camera to capture a high-resolution image and saves it in a file format (e.g., JPEG or PNG). The input is the user's clothing image, and the output is the saved image file.

[0504] Step 2:

[0505] Users upload captured images to the system using a dedicated app on their device. This is done by pressing the "Upload Image" button within the app. The input is the image file captured by the user, and the output is the image data sent to the server.

[0506] Step 3:

[0507] The server analyzes the received image data. The server applies image analysis algorithms and uses libraries such as OpenCV to extract color, shape, and material information from the image. In this process, attributes are identified through pixel data analysis. The input is the transmitted image data, and the output is the extracted clothing attribute information.

[0508] Step 4:

[0509] The server stores the extracted attribute information in the database. The server accesses the database using SQL queries and adds new clothing information to the user's profile. Here, the data is organized based on a schema pre-configured in the database. The input is the extracted attribute information, and the output is the updated database entry.

[0510] Step 5:

[0511] The server executes a style generation algorithm. Using a generation AI model, the server generates the optimal clothing combinations based on the user's attribute information. User preferences and trend information are also considered. The input is attribute information obtained from a database, and the output is the generated coordination options.

[0512] Step 6:

[0513] The server converts the generated outfit into an image and sends it to the terminal. The server uses an image processing library to convert multiple garments into a single composite image and sends the image data to the terminal. The input is the generated outfit data, and the output is the composite outfit image that is sent to the terminal.

[0514] Step 7:

[0515] The terminal displays the received outfit images to the user, allowing the user to review the suggested style. This display is done through the terminal's user interface, enabling the user to scroll through the screen for viewing. The input is image data received from the server, and the output is an outfit that the user can visually review.

[0516] Step 8:

[0517] The server accesses external information systems to search for additional items. Using APIs, it retrieves relevant items from online shopping platforms and recommends them based on the user's preferences and style. Input is the user's style information, and output is related item information and purchase links.

[0518] Step 9:

[0519] The terminal displays information about an additional item and a purchase link to the user. The user can use the displayed link to proceed with the purchase process on an external website. The input is item information sent from the server, and the output is the purchasable item information displayed to the user.

[0520] (Application Example 1)

[0521] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] Modern consumers face the challenge of choosing the perfect outfit to suit their style, given the vast array of clothing options available. Furthermore, it's difficult to imagine how a potential purchase will fit into their existing wardrobe, making the decision to buy challenging. Real-time fashion advice on the ground is therefore essential.

[0523] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0524] In this invention, the server includes an image acquisition means, which is a method for obtaining images of clothing owned by the user; an analysis means for analyzing the images and extracting characteristic data of the clothing; and a generation means for generating different styles of clothing combinations based on the characteristic data. This enables the provision of real-time and visually optimal coordination suggestions, making it easier for consumers to make purchasing decisions.

[0525] "Image acquisition method" refers to a method of acquiring images of clothing owned by a user using a digital device.

[0526] "Analysis method" refers to the process of extracting characteristic data such as the color, material, and design of clothing from acquired images.

[0527] The "generation method" is an algorithm for creating different styles of clothing combinations based on the feature data obtained by the analysis method.

[0528] "Display means" refers to technology for presenting generated clothing combinations to the user in a visually easy-to-understand manner.

[0529] "Means of provision" refers to a function that connects to external online trading platforms, retrieves information on items available for purchase by users, and presents it to them.

[0530] "Presentation method" refers to a method of displaying coordination suggestions in real time via a smart device, thereby providing users with options.

[0531] An "information storage device" is a data storage system for recording and managing the characteristic data of analyzed clothing.

[0532] "User preferences and trends" is a concept that refers to the styles that users are interested in and the fashion trends in the market.

[0533] In implementing this invention, the system is constructed as follows: The server, terminal, and user elements cooperate to suggest clothing coordinates.

[0534] The server first receives images of clothing sent by the user. The image acquisition method used here involves taking pictures of the clothing with a camera on a digital device and uploading the data to the server via the network. A commonly used protocol is used for transferring the images at this time.

[0535] The server uses specific analysis software (such as an image processing library like OpenCV) to extract feature data such as the color, design, and material of the clothing from the received images. This analysis clarifies the characteristics of each garment.

[0536] Next, the server uses a generation mechanism to utilize a generative AI model and generate different clothing combinations based on the extracted feature data. This generative AI model proposes the optimal outfit by considering the user's past preferences and market trends. Finally, the results are converted into a format that can be visually displayed.

[0537] The device retrieves outfit suggestions sent from the server and presents them visually to the user. This information is typically delivered to the user via a smartphone or smart glasses.

[0538] As a concrete example, if a user takes a picture of blue jeans and uploads it to the system, the server will use a generative AI model to suggest tops and accessories that would go well with those jeans. For example, a prompt message like, "Please suggest casual tops and jackets to go with blue jeans. Please also suggest recommended color combinations, taking current trends into consideration," might be used.

[0539] By viewing these suggestions through their devices, users can make clearer purchasing and coordination decisions. Furthermore, if the suggested clothing items are immediately available for purchase through external online trading platforms, this information can also be provided to the user.

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] The user takes a picture of the clothing using their own device. The captured image is saved on the device as a JPEG or PNG file.

[0543] Step 2:

[0544] The device uploads images taken by the user to the server. During this process, image data is transmitted via the HTTP protocol, and the server stores the images in its data storage.

[0545] Step 3:

[0546] The server processes the received image data using an analysis algorithm (for example, the image analysis function of OpenCV). Here, feature data such as the color, design, and material of the clothing are extracted. This process analyzes the image's pixel data to identify hue and shape patterns.

[0547] Step 4:

[0548] The server uses a generative AI model based on feature data to generate prompt sentences and suggest different clothing combinations. For example, it might input the prompt "Suggest a casual top and jacket to go with blue jeans" into the generative AI model and generate outfits.

[0549] Step 5:

[0550] The server converts the generated coordinates into a visually understandable format (e.g., images or text) and sends them to the terminal. This conversion uses a filtering algorithm for visual enhancement.

[0551] Step 6:

[0552] The terminal receives coordination information sent from the server and displays it to the user. Coordination suggestions are presented in real time through the user interface on the smart device, and the user can view them.

[0553] Step 7:

[0554] The user reviews the details of the suggested outfit and clicks a link to purchase related items from an external online trading platform if necessary. At this point, a web browser is launched and the purchase process begins.

[0555] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0556] This invention combines a system that uses images of clothing owned by the user, analyzes those images to acquire feature data, and an emotion engine that recognizes the user's emotions to provide more personalized style suggestions. This system adjusts clothing combinations and item recommendations based on emotion data, thereby providing a service that responds to the user's emotional state.

[0557] 1. Image acquisition and analysis

[0558] The user takes a picture of their clothing on their device and uploads the image to the system. The device sends the image to a server, which uses an image analysis module to extract features such as the color and design of the clothing. This feature data is stored in a database and forms the basis for subsequent outfit suggestions.

[0559] 2. Emotion recognition

[0560] The device acquires emotional data using the user's voice, facial expressions, text, etc., and sends it to the server. The server uses an emotion engine to analyze the received data to determine the user's emotional state (e.g., joy, sadness, surprise, etc.). The emotion engine uses a machine learning model to classify emotions into multiple emotional categories.

[0561] 3. Outfit suggestions and additional items

[0562] The server adjusts the style and colors of the outfits based on the user's emotional state. For example, it suggests bright colors when the user is happy and recommends calmer color combinations when the user is feeling down. The server also uses external e-commerce platforms to provide links where users can purchase additional items that match their current mood.

[0563] 4. Optimizing the user experience

[0564] Based on emotion recognition, the interface on the device is also dynamically changed. For example, it may provide an interface that calms the user with a relaxed design, or use uplifting visual effects. Specifically, a user who is recognized as feeling tired may be shown a coordinated outfit of relaxing wear along with a background in soft colors.

[0565] Thus, the present invention provides a more personalized fashion experience by responding to the user's emotional state. Through the above functions, it becomes possible to make the user's daily style selection seamless and realize a shopping experience that is attentive to their emotions.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The user takes a picture of their own clothing, selects the image using their device, and uploads it. The device then sends this image to the server.

[0569] Step 2:

[0570] The server sends the received images to an analysis module, which extracts feature data such as the color, shape, and pattern of the clothing. The server then stores this feature data in a database.

[0571] Step 3:

[0572] The device collects user emotion data. The user inputs their emotional state (e.g., joy, sadness, excitement) into the device through facial recognition, voice input, question answering, etc. The device then sends this emotion data to a server.

[0573] Step 4:

[0574] The server uses an emotion engine to analyze collected emotion data and determine the user's current emotional state. Based on this emotional state, the server generates a style and color scheme that is appropriate for the user.

[0575] Step 5:

[0576] The server visualizes the generated outfit and sends an image of the outfit to the terminal. The terminal displays this in its user interface, allowing the user to review the suggested outfit.

[0577] Step 6:

[0578] The server searches for additional items that match the user's emotional state and retrieves relevant product information and purchase links from external e-commerce platforms.

[0579] Step 7:

[0580] The server retrieves information about additional items and sends it to the terminal. The terminal displays information about items available for purchase to the user, helping them to quickly buy items they are interested in.

[0581] Step 8:

[0582] The user interface dynamically changes colors and designs according to the user's emotional state. For example, by providing a design that promotes relaxation, it creates a comfortable environment for the user.

[0583] (Example 2)

[0584] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0585] Modern consumers seek personalized suggestions in their daily fashion choices, and in particular, they desire fashion coordination that aligns with their emotional state at any given time. However, conventional systems have struggled to provide style suggestions that take into account the user's emotional state or to offer dynamically adjusted interfaces. Therefore, the present invention aims to provide a means to improve user satisfaction by offering personalized fashion suggestions based on the user's emotional state and dynamically adjusting the user interface.

[0586] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0587] In this invention, the server includes means for acquiring images of clothing owned by the user, means for extracting characteristic data of the clothing, and means for analyzing the user's emotional state using an emotion engine. This enables personalized fashion suggestions that respond to the user's emotions, and further, dynamic adjustment of the interface to match that emotional state makes it possible to provide a more satisfying service experience to the user.

[0588] "Means for obtaining images of clothing owned by a user" refers to the process by which a user uses their device to take pictures of their own clothing and sends that image data to a server.

[0589] "Means for extracting clothing characteristic data" refers to a process that uses an image analysis module to identify features such as color, pattern, and design from images of submitted clothing and store them in a database.

[0590] "Methods for analyzing a user's emotional state using an emotion engine" refers to the process of using voice, facial expression, and text data obtained from the user, analyzing it with an emotion engine, and classifying the user's emotions into specific categories (e.g., joy, sadness).

[0591] "Means for generating different styles of clothing combinations" refers to a process that proposes new clothing combinations suitable for the user based on extracted characteristic data and analyzed emotional states.

[0592] "A means of visually displaying and further suggesting additional items that correspond to emotions from an external e-commerce platform" refers to a process that not only visually displays the generated clothing combinations on the user's device but also recommends additional items that match the user's emotional state from an external e-commerce site.

[0593] "Means of dynamically adjusting the user interface" refers to a process of appropriately changing the interface design and color scheme on a device according to the user's emotional state, in order to provide a more comfortable operating environment for the user.

[0594] The embodiments for carrying out the invention are described below.

[0595] This invention is a system that provides style suggestions using clothing owned by the user. The user takes pictures of their clothing using the camera on their device and sends those images to a server via a network. The device used is mainly a mobile device such as a smartphone or tablet.

[0596] The server uses an image analysis module on the received images. This module implements a generative AI model and extracts characteristic data such as the color, shape, and pattern of the clothing. This characteristic data is stored in a database and used in subsequent processing.

[0597] Furthermore, to acquire user emotion data, the device uses voice input, camera-based facial expression capture, or text input. This data is sent to a server, where an emotion engine performs analysis. The emotion engine uses machine learning models to categorize various emotional states. This information, along with characteristic data, is used to suggest the optimal fashion style.

[0598] The server collaborates with external e-commerce platforms to recommend appropriate items based on the user's emotional state and clothing characteristics. Furthermore, the user interface dynamically adjusts according to the user's emotional state. For example, a depressed user will see an interface with soothing colors and be recommended relaxing outfits.

[0599] A concrete example of a prompt is, "Analyze images of the user's clothing and suggest outfits that match the user's emotional state." This prompt functions as an instruction for the generative AI model to begin suggesting fashion styles.

[0600] As described above, the present invention provides a personalized fashion experience by linking the user's emotions with the characteristics of clothing. This enables sophisticated style suggestions that resonate with the user's emotions and serves as an effective means of supporting the user's daily choices.

[0601] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0602] Step 1:

[0603] The user takes a picture of their clothing using the device's camera function. It is recommended that the image be clear and taken in natural light. The input is the image data of the clothing, and the output is saved on the device.

[0604] Step 2:

[0605] The terminal sends the stored image data to the server. The image data is transferred to the server via the network connection. The input is the image data on the user's terminal, and the output is the image data stored on the server.

[0606] Step 3:

[0607] The server analyzes the received images. The image analysis module uses a generated AI model to extract characteristics such as the color, design, and pattern of the clothing. The input is image data on the server, and the output is stored in the database as characteristic data.

[0608] Step 4:

[0609] Users report their emotional state by inputting voice, text, or facial expression information through their device. Input can be an audio file, text information, or facial expression image, and output is structured emotion data on the device.

[0610] Step 5:

[0611] The device sends emotional data to the server. The data is transferred over the network, with the input being the emotional data on the device and the output being the emotional data received on the server.

[0612] Step 6:

[0613] The server analyzes the received emotional data using an emotion engine. It utilizes a generative AI model to classify emotional states into categories. The input is the emotional data on the server, and the output is the classified emotional state.

[0614] Step 7:

[0615] The server generates style suggestions based on clothing characteristic data and the user's emotional state. These suggestions include color images and outfit recommendations. The input is characteristic data and emotional state, and the output is data formatted as style suggestions.

[0616] Step 8:

[0617] The server recommends items related to the style suggestion from an external e-commerce platform. It generates relevant links and provides the user with purchase options. The input is the style suggestion, and the output is links to the recommended products.

[0618] Step 9:

[0619] The terminal displays style suggestions and recommended links received from the server to the user. The user interface dynamically adjusts to the user's mood. The input is data received from the server, and the output is a visually displayed style suggestion and links.

[0620] (Application Example 2)

[0621] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0622] In modern commercial facilities, it is difficult for customers to quickly select products that suit their individual style and emotions. In particular, providing optimal product recommendations based on individual customers' emotions requires accurately analyzing their diverse emotional states and providing product information accordingly. However, current technology makes it difficult to efficiently provide such services, limiting the improvement of the user experience on-site.

[0623] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0624] In this invention, the server includes a device for acquiring images of accessories owned by the user, a device for acquiring and analyzing the user's emotional data, and a device for adjusting the style and color tone of the accessories based on the emotional data. This enables real-time style suggestions that correspond to the customer's emotional state, providing a more personalized product selection experience.

[0625] "Ornaments" are items that users can wear, intended to provide visual and functional appeal.

[0626] "Devices for acquiring images" refer to tools for collecting visual data of accessories owned by the user, and include cameras, scanners, and other similar devices.

[0627] A "feature data extraction device" is a system that identifies attributes such as the shape, color, and texture of decorative items from acquired images and converts them into data.

[0628] A "device for generating combinations of decorative items of different styles" is a system that creates combinations of decorative items based on diverse styles and themes, using extracted feature data.

[0629] "Devices for visual presentation" refer to equipment such as displays and projectors that are intended to show users the combination of decorative items that have been created.

[0630] An "external web service" is an online platform that provides product information and purchasing procedures via the internet.

[0631] A "device for acquiring and analyzing emotional data" refers to a technology that includes machine learning to collect and analyze a user's emotional state from their voice and facial expressions.

[0632] A "device for adjusting style and color tone" is a system for changing the visual elements of a style suggested based on the user's emotions.

[0633] A "device that dynamically changes the method of presenting information" is a system that changes the layout and presentation of information display according to the user's emotional state.

[0634] The system for realizing this invention utilizes images of the user's accessories and emotional data to provide more personalized fashion suggestions. A specific embodiment of this system is described below.

[0635] The server acquires images of the accessories worn by the user through the smart glasses' camera and performs image analysis. Machine learning libraries such as TensorFlow are used for the analysis to extract feature data such as the shape, color, and texture of the accessories. At the same time, to collect emotional data, computer vision technology similar to OpenCV is used to analyze the user's voice and facial expressions to determine emotional states such as joy, sadness, and excitement.

[0636] The device sends this data to the server, which then uses the feature data and emotion data to suggest the most suitable style. Specifically, when the emotional state is bright, it suggests outfits centered around brightly colored accessories, while when the emotional state is calm, it recommends combinations of muted colors. These generated suggestions are then displayed on the user's smart glasses.

[0637] Furthermore, the server integrates with external web services to provide information on where suggested products are available in-store and links to online purchases. The presentation method also dynamically changes according to the user's emotional state. For example, when the user is tired, a background with calming colors is displayed.

[0638] For example, if a user is wearing a blue shirt and their emotions are recognized as positive, the system will suggest brightly colored ties and accessories and provide information on the location of related product shelves in the store.

[0639] As an example of a prompt to input into the generating AI model, we will use phrases that request specific accessories or emotionally-based suggestions, such as "Tell me what accessories would be best suited to my current outfit."

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The user uses the smart glasses' camera to capture images of their accessories. The input is a real-time image of the accessories, and the output is stored as digital image data on the device. The device then transmits this data to the server via Bluetooth.

[0643] Step 2:

[0644] The server receives the transmitted image data and performs image analysis using TensorFlow. The input is digital image data, and feature data such as color, shape, and texture are extracted. The output is this feature data, which is stored in the system's database. This data is used to generate styles for decorative items.

[0645] Step 3:

[0646] The system collects the user's facial expression data using the smart glasses' camera and acquires audio data via the microphone. The input consists of facial expressions and audio information. This data is sent from the device to a server. The output is emotion data, which is classified into states such as "joy" and "sadness" through analysis using OpenCV.

[0647] Step 4:

[0648] The server uses feature data and sentiment data to generate optimal style coordination. The input is feature data and sentiment data, and the output is a suggestion of specific accessory combinations. Colors and styles are adjusted according to the sentiment, and the information is prepared for the user to see.

[0649] Step 5:

[0650] The server sends the generated coordination information to the smart glasses. The input is style suggestion data, and the output is a visual display that allows the user to see the combination of accessories. Specifically, the displayed products change color according to the user's emotional state.

[0651] Step 6:

[0652] The server interacts with external web services to search for information on where suggested accessories are available within the store and generates online links. The input is style suggestion data, and the output is product location information and purchase links. This allows users to quickly find where to look in the store or where to purchase items online.

[0653] Step 7:

[0654] Smart glasses dynamically respond to the user's emotional state by changing the display method. The input is emotional data, and the output is a modified visual effect. For example, if the user is tired, the screen background will adopt calming colors to provide a more relaxing atmosphere.

[0655] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0656] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0657] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0658] [Fourth Embodiment]

[0659] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0660] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0661] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0662] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0663] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0664] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0665] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0666] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0667] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0668] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0669] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0670] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0671] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0672] This invention is a system that analyzes the characteristics of clothing images owned by the user and suggests various style coordinates. This system supports the user's wardrobe utilization through a variety of functions.

[0673] 1. Image acquisition and analysis

[0674] The user uses a device to take pictures of their clothing and uploads them to the system. The device sends the images to the server, which receives them. The server applies a feature extraction algorithm to the received images to analyze attribute information such as the color, design, and material of the clothing. This information is stored in a database and managed as the user's wardrobe.

[0675] 2. Coordination suggestions

[0676] The server applies a style generation algorithm based on stored clothing feature data to generate clothing combinations that consider multiple style options. The generated outfits are visualized as images in an easy-to-understand format and sent to the terminal. The user can view multiple outfit suggestions through the terminal. For example, to match the user's red skirt, the server suggests a combination of a cute white blouse and a cool black jacket.

[0677] 3. Proposing and supporting the purchase of additional items.

[0678] Furthermore, the server suggests items that can be added to the outfit. These include items available from external e-commerce platforms. The server searches for these items via an online API and selects recommended items based on the user's preferences and current trends. The device then provides information about these items and purchase links, allowing the user to easily complete the purchase process.

[0679] This makes it possible to maximize the potential of a wardrobe by combining new styles from the user's existing clothing and suggesting a "plus one" item as a new shopping experience.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The user takes a picture of their clothing, selects the image using their device, and uploads it. The device receives the image and sends it to the server.

[0683] Step 2:

[0684] The server feeds the received image into an analysis algorithm. The server extracts clothing features such as color, design, and material from the image and records this data as structured information in a database.

[0685] Step 3:

[0686] The server retrieves clothing feature data stored in the user's database. The server then applies a style generation algorithm to generate clothing outfits that take multiple style options into consideration.

[0687] Step 4:

[0688] The server creates images and diagrams that visually represent the generated coordination and sends them to the terminal. The terminal displays this visual information on its user interface so that the user can confirm it.

[0689] Step 5:

[0690] The server selects a suitable additional item for the outfit. Considering the user's preferences and current trends, it recommends additional items and gathers information on items available for purchase from relevant e-commerce platforms.

[0691] Step 6:

[0692] The server retrieves information about the plus-one item and sends a purchase link to the device. The device displays this on the user interface, allowing the user to view details about items of interest and purchase them via the purchase link.

[0693] (Example 1)

[0694] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] Modern consumers own a large wardrobe, but often struggle to create effective style combinations using their existing clothes. Furthermore, the lack of systems that effectively suggest trendy outfits and new items tailored to individual preferences prevents users from maximizing their potential. Additionally, the scattered nature of information when purchasing new items makes it difficult to have an efficient and comfortable shopping experience.

[0696] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0697] In this invention, the server includes means for the user to acquire images of their own clothing and transmit the image data to the server; means for the server to extract attribute information such as the color, shape, and material of the clothing using image analysis technology; and means for generating different clothing combinations by applying a style generation algorithm based on the extracted attribute information. This allows the user to experience a variety of outfits using their own clothing, and also enables the suggestion of new items according to their preferences and trends, as well as efficient purchasing.

[0698] A "user" is an entity that uses the system to coordinate or purchase clothing.

[0699] "Image data" refers to a digital representation of the visual information of clothing photographed by the user.

[0700] A "server" is a computing system that receives data sent by users and performs image analysis and style generation.

[0701] "Image analysis technology" refers to methods for extracting attribute information such as color, shape, and material from image data.

[0702] "Attribute information" refers to data that indicates the characteristics of the analyzed clothing, such as its color, shape, and material.

[0703] A "style generation algorithm" is a computational procedure that creates different clothing combinations based on attribute information.

[0704] An "information storage medium" is a device such as a database or storage system used to record and manage extracted attribute information.

[0705] An "external information system" is an external e-commerce platform that is linked to search for additional items.

[0706] A "purchase link" is a web link that users use to buy selected items.

[0707] This invention is a system that utilizes information about clothing owned by the user to provide efficient and effective style suggestions. This system handles everything from image acquisition and analysis to style suggestions and purchasing support.

[0708] The user takes a picture of their clothing using a portable information terminal. This terminal is equipped with a high-resolution camera that quickly processes the captured image and sends it to the server. An example of user operation is to launch the camera app on a smartphone, take a picture of a specific piece of clothing, and press the "Send" button.

[0709] The server utilizes image analysis techniques to process the received image data. This analysis uses image processing libraries such as OpenCV to extract attribute information such as the color, shape, and material of the clothing. This creates a detailed database of the user's clothing.

[0710] Next, the server applies a style generation algorithm based on the extracted attribute information. This algorithm utilizes a generative AI model to suggest the optimal outfit based on the user's individual preferences and current trends. For example, to match the user's red skirt, a combination of a white blouse and a black jacket might be suggested.

[0711] Furthermore, the system connects to external information systems and suggests additional items that can be added. The server accesses online shopping platforms via APIs to search for and recommend items that match the user's style. Purchase links are provided on the terminal, allowing users to easily access and consider purchasing.

[0712] As an example of a prompt, inputting a request such as "Suggest outfits that go with a red skirt" into the AI ​​model generates a variety of style options. This invention allows users to enjoy a more fulfilling wardrobe experience in their daily lives.

[0713] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0714] Step 1:

[0715] The user takes a picture of their clothing with the device. The device uses its built-in camera to capture a high-resolution image and saves it in a file format (e.g., JPEG or PNG). The input is the user's clothing image, and the output is the saved image file.

[0716] Step 2:

[0717] Users upload captured images to the system using a dedicated app on their device. This is done by pressing the "Upload Image" button within the app. The input is the image file captured by the user, and the output is the image data sent to the server.

[0718] Step 3:

[0719] The server analyzes the received image data. The server applies image analysis algorithms and uses libraries such as OpenCV to extract color, shape, and material information from the image. In this process, attributes are identified through pixel data analysis. The input is the transmitted image data, and the output is the extracted clothing attribute information.

[0720] Step 4:

[0721] The server stores the extracted attribute information in the database. The server accesses the database using SQL queries and adds new clothing information to the user's profile. Here, the data is organized based on a schema pre-configured in the database. The input is the extracted attribute information, and the output is the updated database entry.

[0722] Step 5:

[0723] The server executes a style generation algorithm. Using a generation AI model, the server generates the optimal clothing combinations based on the user's attribute information. User preferences and trend information are also considered. The input is attribute information obtained from a database, and the output is the generated coordination options.

[0724] Step 6:

[0725] The server converts the generated outfit into an image and sends it to the terminal. The server uses an image processing library to convert multiple garments into a single composite image and sends the image data to the terminal. The input is the generated outfit data, and the output is the composite outfit image that is sent to the terminal.

[0726] Step 7:

[0727] The terminal displays the received outfit images to the user, allowing the user to review the suggested style. This display is done through the terminal's user interface, enabling the user to scroll through the screen for viewing. The input is image data received from the server, and the output is an outfit that the user can visually review.

[0728] Step 8:

[0729] The server accesses external information systems to search for additional items. Using APIs, it retrieves relevant items from online shopping platforms and recommends them based on the user's preferences and style. Input is the user's style information, and output is related item information and purchase links.

[0730] Step 9:

[0731] The terminal displays information about an additional item and a purchase link to the user. The user can use the displayed link to proceed with the purchase process on an external website. The input is item information sent from the server, and the output is the purchasable item information displayed to the user.

[0732] (Application Example 1)

[0733] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] Modern consumers face the challenge of choosing the perfect outfit to suit their style, given the vast array of clothing options available. Furthermore, it's difficult to imagine how a potential purchase will fit into their existing wardrobe, making the decision to buy challenging. Real-time fashion advice on the ground is therefore essential.

[0735] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0736] In this invention, the server includes an image acquisition means, which is a method for obtaining images of clothing owned by the user; an analysis means for analyzing the images and extracting characteristic data of the clothing; and a generation means for generating different styles of clothing combinations based on the characteristic data. This enables the provision of real-time and visually optimal coordination suggestions, making it easier for consumers to make purchasing decisions.

[0737] "Image acquisition method" refers to a method of acquiring images of clothing owned by a user using a digital device.

[0738] "Analysis method" refers to the process of extracting characteristic data such as the color, material, and design of clothing from acquired images.

[0739] The "generation method" is an algorithm for creating different styles of clothing combinations based on the feature data obtained by the analysis method.

[0740] "Display means" refers to technology for presenting generated clothing combinations to the user in a visually easy-to-understand manner.

[0741] "Means of provision" refers to a function that connects to external online trading platforms, retrieves information on items available for purchase by users, and presents it to them.

[0742] "Presentation method" refers to a method of displaying coordination suggestions in real time via a smart device, thereby providing users with options.

[0743] An "information storage device" is a data storage system for recording and managing the characteristic data of analyzed clothing.

[0744] "User preferences and trends" is a concept that refers to the styles that users are interested in and the fashion trends in the market.

[0745] In implementing this invention, the system is constructed as follows: The server, terminal, and user elements cooperate to suggest clothing coordinates.

[0746] The server first receives images of clothing sent by the user. The image acquisition method used here involves taking pictures of the clothing with a camera on a digital device and uploading the data to the server via the network. A commonly used protocol is used for transferring the images at this time.

[0747] The server uses specific analysis software (such as an image processing library like OpenCV) to extract feature data such as the color, design, and material of the clothing from the received images. This analysis clarifies the characteristics of each garment.

[0748] Next, the server uses a generation mechanism to utilize a generative AI model and generate different clothing combinations based on the extracted feature data. This generative AI model proposes the optimal outfit by considering the user's past preferences and market trends. Finally, the results are converted into a format that can be visually displayed.

[0749] The device retrieves outfit suggestions sent from the server and presents them visually to the user. This information is typically delivered to the user via a smartphone or smart glasses.

[0750] As a concrete example, if a user takes a picture of blue jeans and uploads it to the system, the server will use a generative AI model to suggest tops and accessories that would go well with those jeans. For example, a prompt message like, "Please suggest casual tops and jackets to go with blue jeans. Please also suggest recommended color combinations, taking current trends into consideration," might be used.

[0751] By viewing these suggestions through their devices, users can make clearer purchasing and coordination decisions. Furthermore, if the suggested clothing items are immediately available for purchase through external online trading platforms, this information can also be provided to the user.

[0752] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0753] Step 1:

[0754] The user takes a picture of the clothing using their own device. The captured image is saved on the device as a JPEG or PNG file.

[0755] Step 2:

[0756] The device uploads images taken by the user to the server. During this process, image data is transmitted via the HTTP protocol, and the server stores the images in its data storage.

[0757] Step 3:

[0758] The server processes the received image data using an analysis algorithm (for example, the image analysis function of OpenCV). Here, feature data such as the color, design, and material of the clothing are extracted. This process analyzes the image's pixel data to identify hue and shape patterns.

[0759] Step 4:

[0760] The server uses a generative AI model based on feature data to generate prompt sentences and suggest different clothing combinations. For example, it might input the prompt "Suggest a casual top and jacket to go with blue jeans" into the generative AI model and generate outfits.

[0761] Step 5:

[0762] The server converts the generated coordinates into a visually understandable format (e.g., images or text) and sends them to the terminal. This conversion uses a filtering algorithm for visual enhancement.

[0763] Step 6:

[0764] The terminal receives coordination information sent from the server and displays it to the user. Coordination suggestions are presented in real time through the user interface on the smart device, and the user can view them.

[0765] Step 7:

[0766] The user reviews the details of the suggested outfit and clicks a link to purchase related items from an external online trading platform if necessary. At this point, a web browser is launched and the purchase process begins.

[0767] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0768] This invention combines a system that uses images of clothing owned by the user, analyzes those images to acquire feature data, and an emotion engine that recognizes the user's emotions to provide more personalized style suggestions. This system adjusts clothing combinations and item recommendations based on emotion data, thereby providing a service that responds to the user's emotional state.

[0769] 1. Image acquisition and analysis

[0770] The user takes a picture of their clothing on their device and uploads the image to the system. The device sends the image to a server, which uses an image analysis module to extract features such as the color and design of the clothing. This feature data is stored in a database and forms the basis for subsequent outfit suggestions.

[0771] 2. Emotion recognition

[0772] The device acquires emotional data using the user's voice, facial expressions, text, etc., and sends it to the server. The server uses an emotion engine to analyze the received data to determine the user's emotional state (e.g., joy, sadness, surprise, etc.). The emotion engine uses a machine learning model to classify emotions into multiple emotional categories.

[0773] 3. Outfit suggestions and additional items

[0774] The server adjusts the style and colors of the outfits based on the user's emotional state. For example, it suggests bright colors when the user is happy and recommends calmer color combinations when the user is feeling down. The server also uses external e-commerce platforms to provide links where users can purchase additional items that match their current mood.

[0775] 4. Optimizing the user experience

[0776] Based on emotion recognition, the interface on the device is also dynamically changed. For example, it may provide an interface that calms the user with a relaxed design, or use uplifting visual effects. Specifically, a user who is recognized as feeling tired may be shown a coordinated outfit of relaxing wear along with a background in soft colors.

[0777] Thus, the present invention provides a more personalized fashion experience by responding to the user's emotional state. Through the above functions, it becomes possible to make the user's daily style selection seamless and realize a shopping experience that is attentive to their emotions.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] The user takes a picture of their own clothing, selects the image using their device, and uploads it. The device then sends this image to the server.

[0781] Step 2:

[0782] The server sends the received images to an analysis module, which extracts feature data such as the color, shape, and pattern of the clothing. The server then stores this feature data in a database.

[0783] Step 3:

[0784] The device collects user emotion data. The user inputs their emotional state (e.g., joy, sadness, excitement) into the device through facial recognition, voice input, question answering, etc. The device then sends this emotion data to a server.

[0785] Step 4:

[0786] The server uses an emotion engine to analyze collected emotion data and determine the user's current emotional state. Based on this emotional state, the server generates a style and color scheme that is appropriate for the user.

[0787] Step 5:

[0788] The server visualizes the generated outfit and sends an image of the outfit to the terminal. The terminal displays this in its user interface, allowing the user to review the suggested outfit.

[0789] Step 6:

[0790] The server searches for additional items that match the user's emotional state and retrieves relevant product information and purchase links from external e-commerce platforms.

[0791] Step 7:

[0792] The server retrieves information about additional items and sends it to the terminal. The terminal displays information about items available for purchase to the user, helping them to quickly buy items they are interested in.

[0793] Step 8:

[0794] The user interface dynamically changes colors and designs according to the user's emotional state. For example, by providing a design that promotes relaxation, it creates a comfortable environment for the user.

[0795] (Example 2)

[0796] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0797] Modern consumers seek personalized suggestions in their daily fashion choices, and in particular, they desire fashion coordination that aligns with their emotional state at any given time. However, conventional systems have struggled to provide style suggestions that take into account the user's emotional state or to offer dynamically adjusted interfaces. Therefore, the present invention aims to provide a means to improve user satisfaction by offering personalized fashion suggestions based on the user's emotional state and dynamically adjusting the user interface.

[0798] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0799] In this invention, the server includes means for acquiring images of clothing owned by the user, means for extracting characteristic data of the clothing, and means for analyzing the user's emotional state using an emotion engine. This enables personalized fashion suggestions that respond to the user's emotions, and further, dynamic adjustment of the interface to match that emotional state makes it possible to provide a more satisfying service experience to the user.

[0800] "Means for obtaining images of clothing owned by a user" refers to the process by which a user uses their device to take pictures of their own clothing and sends that image data to a server.

[0801] "Means for extracting clothing characteristic data" refers to a process that uses an image analysis module to identify features such as color, pattern, and design from images of submitted clothing and store them in a database.

[0802] "Methods for analyzing a user's emotional state using an emotion engine" refers to the process of using voice, facial expression, and text data obtained from the user, analyzing it with an emotion engine, and classifying the user's emotions into specific categories (e.g., joy, sadness).

[0803] "Means for generating different styles of clothing combinations" refers to a process that proposes new clothing combinations suitable for the user based on extracted characteristic data and analyzed emotional states.

[0804] "A means of visually displaying and further suggesting additional items that correspond to emotions from an external e-commerce platform" refers to a process that not only visually displays the generated clothing combinations on the user's device but also recommends additional items that match the user's emotional state from an external e-commerce site.

[0805] "Means of dynamically adjusting the user interface" refers to a process of appropriately changing the interface design and color scheme on a device according to the user's emotional state, in order to provide a more comfortable operating environment for the user.

[0806] The embodiments for carrying out the invention are described below.

[0807] This invention is a system that provides style suggestions using clothing owned by the user. The user takes pictures of their clothing using the camera on their device and sends those images to a server via a network. The device used is mainly a mobile device such as a smartphone or tablet.

[0808] The server uses an image analysis module on the received images. This module implements a generative AI model and extracts characteristic data such as the color, shape, and pattern of the clothing. This characteristic data is stored in a database and used in subsequent processing.

[0809] Furthermore, to acquire user emotion data, the device uses voice input, camera-based facial expression capture, or text input. This data is sent to a server, where an emotion engine performs analysis. The emotion engine uses machine learning models to categorize various emotional states. This information, along with characteristic data, is used to suggest the optimal fashion style.

[0810] The server collaborates with external e-commerce platforms to recommend appropriate items based on the user's emotional state and clothing characteristics. Furthermore, the user interface dynamically adjusts according to the user's emotional state. For example, a depressed user will see an interface with soothing colors and be recommended relaxing outfits.

[0811] A concrete example of a prompt is, "Analyze images of the user's clothing and suggest outfits that match the user's emotional state." This prompt functions as an instruction for the generative AI model to begin suggesting fashion styles.

[0812] As described above, the present invention provides a personalized fashion experience by linking the user's emotions with the characteristics of clothing. This enables sophisticated style suggestions that resonate with the user's emotions and serves as an effective means of supporting the user's daily choices.

[0813] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0814] Step 1:

[0815] The user takes a picture of their clothing using the device's camera function. It is recommended that the image be clear and taken in natural light. The input is the image data of the clothing, and the output is saved on the device.

[0816] Step 2:

[0817] The terminal sends the stored image data to the server. The image data is transferred to the server via the network connection. The input is the image data on the user's terminal, and the output is the image data stored on the server.

[0818] Step 3:

[0819] The server analyzes the received images. The image analysis module uses a generated AI model to extract characteristics such as the color, design, and pattern of the clothing. The input is image data on the server, and the output is stored in the database as characteristic data.

[0820] Step 4:

[0821] Users report their emotional state by inputting voice, text, or facial expression information through their device. Input can be an audio file, text information, or facial expression image, and output is structured emotion data on the device.

[0822] Step 5:

[0823] The device sends emotional data to the server. The data is transferred over the network, with the input being the emotional data on the device and the output being the emotional data received on the server.

[0824] Step 6:

[0825] The server analyzes the received emotional data using an emotion engine. It utilizes a generative AI model to classify emotional states into categories. The input is the emotional data on the server, and the output is the classified emotional state.

[0826] Step 7:

[0827] The server generates style suggestions based on clothing characteristic data and the user's emotional state. These suggestions include color images and outfit recommendations. The input is characteristic data and emotional state, and the output is data formatted as style suggestions.

[0828] Step 8:

[0829] The server recommends items related to the style suggestion from an external e-commerce platform. It generates relevant links and provides the user with purchase options. The input is the style suggestion, and the output is links to the recommended products.

[0830] Step 9:

[0831] The terminal displays style suggestions and recommended links received from the server to the user. The user interface dynamically adjusts to the user's mood. The input is data received from the server, and the output is a visually displayed style suggestion and links.

[0832] (Application Example 2)

[0833] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0834] In modern commercial facilities, it is difficult for customers to quickly select products that suit their individual style and emotions. In particular, providing optimal product recommendations based on individual customers' emotions requires accurately analyzing their diverse emotional states and providing product information accordingly. However, current technology makes it difficult to efficiently provide such services, limiting the improvement of the user experience on-site.

[0835] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0836] In this invention, the server includes a device for acquiring images of accessories owned by the user, a device for acquiring and analyzing the user's emotional data, and a device for adjusting the style and color tone of the accessories based on the emotional data. This enables real-time style suggestions that correspond to the customer's emotional state, providing a more personalized product selection experience.

[0837] "Ornaments" are items that users can wear, intended to provide visual and functional appeal.

[0838] "Devices for acquiring images" refer to tools for collecting visual data of accessories owned by the user, and include cameras, scanners, and other similar devices.

[0839] A "feature data extraction device" is a system that identifies attributes such as the shape, color, and texture of decorative items from acquired images and converts them into data.

[0840] A "device for generating combinations of decorative items of different styles" is a system that creates combinations of decorative items based on diverse styles and themes, using extracted feature data.

[0841] "Devices for visual presentation" refer to equipment such as displays and projectors that are intended to show users the combination of decorative items that have been created.

[0842] An "external web service" is an online platform that provides product information and purchasing procedures via the internet.

[0843] A "device for acquiring and analyzing emotional data" refers to a technology that includes machine learning to collect and analyze a user's emotional state from their voice and facial expressions.

[0844] A "device for adjusting style and color tone" is a system for changing the visual elements of a style suggested based on the user's emotions.

[0845] A "device that dynamically changes the method of presenting information" is a system that changes the layout and presentation of information display according to the user's emotional state.

[0846] The system for realizing this invention utilizes images of the user's accessories and emotional data to provide more personalized fashion suggestions. A specific embodiment of this system is described below.

[0847] The server acquires images of the accessories worn by the user through the smart glasses' camera and performs image analysis. Machine learning libraries such as TensorFlow are used for the analysis to extract feature data such as the shape, color, and texture of the accessories. At the same time, to collect emotional data, computer vision technology similar to OpenCV is used to analyze the user's voice and facial expressions to determine emotional states such as joy, sadness, and excitement.

[0848] The device sends this data to the server, which then uses the feature data and emotion data to suggest the most suitable style. Specifically, when the emotional state is bright, it suggests outfits centered around brightly colored accessories, while when the emotional state is calm, it recommends combinations of muted colors. These generated suggestions are then displayed on the user's smart glasses.

[0849] Furthermore, the server integrates with external web services to provide information on where suggested products are available in-store and links to online purchases. The presentation method also dynamically changes according to the user's emotional state. For example, when the user is tired, a background with calming colors is displayed.

[0850] For example, if a user is wearing a blue shirt and their emotions are recognized as positive, the system will suggest brightly colored ties and accessories and provide information on the location of related product shelves in the store.

[0851] As an example of a prompt to input into the generating AI model, we will use phrases that request specific accessories or emotionally-based suggestions, such as "Tell me what accessories would be best suited to my current outfit."

[0852] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0853] Step 1:

[0854] The user uses the smart glasses' camera to capture images of their accessories. The input is a real-time image of the accessories, and the output is stored as digital image data on the device. The device then transmits this data to the server via Bluetooth.

[0855] Step 2:

[0856] The server receives the transmitted image data and performs image analysis using TensorFlow. The input is digital image data, and feature data such as color, shape, and texture are extracted. The output is this feature data, which is stored in the system's database. This data is used to generate styles for decorative items.

[0857] Step 3:

[0858] The system collects the user's facial expression data using the smart glasses' camera and acquires audio data via the microphone. The input consists of facial expressions and audio information. This data is sent from the device to a server. The output is emotion data, which is classified into states such as "joy" and "sadness" through analysis using OpenCV.

[0859] Step 4:

[0860] The server uses feature data and sentiment data to generate optimal style coordination. The input is feature data and sentiment data, and the output is a suggestion of specific accessory combinations. Colors and styles are adjusted according to the sentiment, and the information is prepared for the user to see.

[0861] Step 5:

[0862] The server sends the generated coordination information to the smart glasses. The input is style suggestion data, and the output is a visual display that allows the user to see the combination of accessories. Specifically, the displayed products change color according to the user's emotional state.

[0863] Step 6:

[0864] The server interacts with external web services to search for information on where suggested accessories are available within the store and generates online links. The input is style suggestion data, and the output is product location information and purchase links. This allows users to quickly find where to look in the store or where to purchase items online.

[0865] Step 7:

[0866] Smart glasses dynamically respond to the user's emotional state by changing the display method. The input is emotional data, and the output is a modified visual effect. For example, if the user is tired, the screen background will adopt calming colors to provide a more relaxing atmosphere.

[0867] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0868] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0869] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0870] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0871] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0872] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0873] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0874] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0875] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0876] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0877] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0878] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0879] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0880] 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.

[0881] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0882] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0883] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0884] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0885] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0886] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0887] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0888] The following is further disclosed regarding the embodiments described above.

[0889] (Claim 1)

[0890] A means of obtaining images of clothing owned by the user,

[0891] A means for analyzing the aforementioned image and extracting characteristic data of the clothing,

[0892] A means for generating different styles of clothing combinations based on the aforementioned feature data,

[0893] A means for visually displaying the generated clothing combinations,

[0894] Furthermore, the means of suggesting items and providing information on items that can be purchased from external e-commerce platforms,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, further comprising means for recommending additional items to generated clothing combinations, taking into account user preferences and trends.

[0898] (Claim 3)

[0899] The system according to claim 1, further comprising means for recording and managing the characteristic data of the clothing in a database.

[0900] "Example 1"

[0901] (Claim 1)

[0902] A means for a user to obtain images of their own clothing and send the image data to a server,

[0903] The server uses image analysis technology to extract attribute information such as the color, shape, and material of the clothing.

[0904] A means of generating different clothing combinations by applying a style generation algorithm based on extracted attribute information,

[0905] A means for visualizing the generated clothing combinations using image processing technology and displaying them on a user terminal,

[0906] A means for searching for items that can be added from an external information system and providing users with purchase links,

[0907] A means to allow users to review suggested clothing combinations and provide a new shopping experience,

[0908] A system that includes this.

[0909] (Claim 2)

[0910] The system according to claim 1, further comprising means for selecting and recommending additional items related to a suggested clothing combination, taking into account the user's individual preferences and trends.

[0911] (Claim 3)

[0912] The system according to claim 1, further comprising means for recording and managing attribute information of extracted clothing in an information storage medium.

[0913] "Application Example 1"

[0914] (Claim 1)

[0915] An image acquisition means, which is a method for obtaining images of clothing owned by a user,

[0916] An analysis means for analyzing the aforementioned image and extracting characteristic data of the clothing,

[0917] A generation means for generating different styles of clothing combinations based on the aforementioned feature data,

[0918] A display means for visually displaying the generated clothing combinations,

[0919] Furthermore, a means of providing additional items and item information obtainable from external online trading platforms,

[0920] A presentation method for displaying outfit suggestions in real time on a smart device,

[0921] A system that includes this.

[0922] (Claim 2)

[0923] The system according to claim 1, further comprising a method for recommending additional items to a generated clothing combination, taking into account the user's preferences and trends.

[0924] (Claim 3)

[0925] The system according to claim 1, further comprising a method for recording and managing the characteristic data of the clothing in an information storage device.

[0926] "Example 2 of combining an emotion engine"

[0927] (Claim 1)

[0928] A means of obtaining images of clothing owned by the user,

[0929] A means for analyzing the aforementioned image and extracting characteristic data of the clothing,

[0930] A means for analyzing a user's emotional state using an emotion engine,

[0931] A means for generating different styles of clothing combinations based on the aforementioned characteristic data and emotional state,

[0932] A means of visually displaying the generated clothing combinations and further suggesting additional items that respond to emotions from an external electronic trading platform,

[0933] Means for dynamically adjusting the user interface,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, further comprising means for changing the visual display interface based on the user's individual emotional state.

[0937] (Claim 3)

[0938] The system according to claim 1, further comprising means for recording and managing the characteristic data and emotional data of the clothing in a database.

[0939] "Application example 2 when combining with an emotional engine"

[0940] (Claim 1)

[0941] A device for acquiring images of decorative items owned by the user,

[0942] A device for analyzing the aforementioned image and extracting characteristic data of the ornament,

[0943] A device for generating combinations of ornaments of different styles based on the aforementioned characteristic data,

[0944] A device for visually presenting the generated combination of ornaments,

[0945] Furthermore, the device proposes products and provides product information that can be purchased from external web services,

[0946] A device that acquires and analyzes user emotion data,

[0947] A device that adjusts the style and color tone of decorative items based on the aforementioned emotional data,

[0948] A device that dynamically changes the way information is presented according to the user's emotions,

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, further comprising a device for recommending additional products to the generated combination of decorative items, taking into account user preferences and market trends.

[0952] (Claim 3)

[0953] The system according to claim 1, further comprising a device for recording and managing characteristic data and emotional data of the aforementioned ornaments in a database. [Explanation of Symbols]

[0954] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining images of clothing owned by the user, A means for analyzing the aforementioned image and extracting characteristic data of the clothing, A means for generating different styles of clothing combinations based on the aforementioned feature data, A means for visually displaying the generated clothing combinations, Furthermore, the means of suggesting items and providing information on items that can be purchased from external e-commerce platforms, A system that includes this.

2. The system according to claim 1, further comprising means for recommending additional items to generated clothing combinations, taking into account user preferences and trends.

3. The system according to claim 1, further comprising means for recording and managing the characteristic data of the clothing in a database.

Images