system

Abstract

Provide a system. [Solution] a means for acquiring an image; means for transmitting the acquired image to a server via a communication network; means for analyzing the image in the server and generating an outfit evaluation score; A means for generating coordination suggestions based on the user's preferences and past history in the server; means for transmitting the generated coordination proposal to a user terminal; a means for displaying coordination suggestions on a user terminal; A system including:

Description

[Technical Field]

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

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

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

[0004] In today's world, choosing the right fashion for individuals is a major concern. Choosing the right outfit and coordinating it for each day is a difficult task for many people. Coordinating outfits every day takes time and effort, especially for those who lack confidence in their fashion knowledge and sense. Furthermore, with so much fashion information available, it is not easy to find the style that best suits you. Therefore, there is a need for an effective means to solve this problem and support users in making fashion choices. [Means for solving the problem]

[0005] The present invention relates to a system that allows a user to acquire an image of their own clothing, transmit it to a server, generate an evaluation score for the clothing, and generate and provide appropriate outfit suggestions based on the user's preferences and past history. Specifically, the system includes a means for acquiring an image, a means for transmitting the acquired image to a server via a communication network, a means for analyzing the image in the server and generating an evaluation score for the clothing, a means for generating outfit suggestions in the server based on the user's preferences and past history, a means for transmitting the generated outfit suggestions to a user terminal, and a means for displaying the outfit suggestions on the user terminal. This system allows users who are not confident in their own fashion sense to easily find the perfect outfit with the help of AI.

[0006] "Means for acquiring images" refers to the method and device that allows a user to take an image of clothing and store and process it as digital data.

[0007] A "communications network" is a system for sending and receiving data, and refers to the infrastructure that allows multiple devices to communicate with each other, such as the Internet or a home network.

[0008] "Server" refers to a computer system designed to provide a specific function, particularly storing and processing data and providing services to clients.

[0009] The "clothing evaluation score" refers to a numerical value or grade used to evaluate the style, combination, etc. of an outfit image taken by a user.

[0010] "User preferences and past history" refers to data relating to the coordination and preferences selected by the user in the past, including information relating to individual fashion styles.

[0011] "Coordination suggestions" refer to specific combinations of fashion items suggested based on the user's preferences, past history, and clothing evaluation scores.

[0012] A "user terminal" is a device that is directly operated by a user, and refers to hardware such as a smartphone, tablet, or PC.

[0013] "AI Model" refers to a mathematical or statistical model that uses artificial intelligence algorithms to analyze data and perform specific tasks.

[0014] A "database" refers to an information management system that systematically organizes and stores data so that it can be quickly searched and retrieved in response to queries. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and provides coordination suggestions based on the user's preferences and past history. Specific embodiments of this system are described in detail below.

[0037] 1. Get the user's clothing image

[0038] 1.1 The user takes a photo of the outfit and uploads it to the app

[0039] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select and upload the image. At this time, the device is responsible for validating the format and size of the image file.

[0040] 2. Send the acquired image to the server

[0041] 2.1 The device sends the image to the server

[0042] The terminal sends the successfully validated image to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via the communication network.

[0043] 3. The server analyzes the image and generates an outfit evaluation score.

[0044] 3.1 Server receives image

[0045] The server receives the image data sent from the terminal and stores it in temporary storage, after which it performs analysis using the stored image data.

[0046] 3.2 Image processing on the server

[0047] The server pre-processes the image data before inputting it into the AI ​​model, including resizing and normalizing the image so that the AI ​​model can accurately recognize the image.

[0048] 3.3 Server scores outfits

[0049] The server inputs the preprocessed image into the AI ​​model, which extracts image features. The AI ​​model then calculates an outfit evaluation score based on the extracted features. This score is calculated on a scale of 0 to 100, based on factors such as style and combination.

[0050] 4. Generate coordination suggestions based on user preferences and past history

[0051] 4.1 Server retrieves user history and preferences

[0052] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0053] 4.2 The server generates appropriate coordinates

[0054] The server selects appropriate fashion items from the database based on the acquired user preference data and evaluation scores. These fashion items are then combined optimally, taking into account factors such as style, season, and trends.

[0055] 5. Send the generated coordination proposal to the user device.

[0056] 5.1 Server Sends Coordination Proposal

[0057] The server converts the generated coordination proposals into a data format such as JSON and sends them to the user's terminal.

[0058] 6. Displaying outfit suggestions on the user's device

[0059] 6.1 The device displays outfit suggestions

[0060] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0061] Specific examples

[0062] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits, combining items like jeans, white sneakers, and a simple T-shirt. These suggestions are then sent to the device and displayed for the user to easily review.

[0063] In this way, the system according to the present invention assists the user in selecting fashion and makes everyday coordination easier and more enjoyable.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user takes a photo of their outfit using the camera app on their smartphone. After taking the photo, the user presses the "Upload Image" button in the app to select and upload the image.

[0067] Step 2:

[0068] The device validates the uploaded image by checking the image file type and size to ensure it is in the correct format.

[0069] Step 3:

[0070] The terminal sends the successfully validated image to the server using an HTTP POST request, and the image data is transferred to the server via a communication network.

[0071] Step 4:

[0072] The server receives the image data sent from the terminal and stores it in temporary storage.

[0073] Step 5:

[0074] The server preprocesses the stored image data, which includes resizing and normalizing the images so that the AI ​​model can accurately recognize them.

[0075] Step 6:

[0076] The server inputs the preprocessed image into the AI ​​model, which extracts image features and calculates an outfit evaluation score based on them.

[0077] Step 7:

[0078] The server then uses the evaluation score obtained from the AI ​​model to obtain the user's past outfit history and preferred style data, which is obtained from a database.

[0079] Step 8:

[0080] Based on the user's preference data and evaluation scores, the server selects appropriate fashion items from the database. These items are then combined optimally, taking into account factors such as style, season, and trends.

[0081] Step 9:

[0082] The server converts the generated coordination proposal into a data format such as JSON, and then sends this data to the user's device.

[0083] Step 10:

[0084] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0085] For example, if a user uploads an image and the AI ​​model gives it a rating score of 70 / 100, the server will suggest items based on the user's preferences, such as jeans, white sneakers, and a simple T-shirt, and these suggestions will be displayed on the device for review.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] Conventional fashion coordination systems have difficulty automatically suggesting outfits that suit a user's clothing and style, making it difficult for users to easily find outfit combinations that suit them. Furthermore, manual image evaluation and outfit suggestions are time-consuming and inefficient. Furthermore, the lack of customized suggestions that reflect individual users' preferences and past history results in low user satisfaction.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes means for a user to take an image of an outfit and upload it to a terminal, means for transmitting the acquired image to the server via a communication network, means for preprocessing the image in the server and extracting image features to generate an outfit evaluation score, means for generating outfit suggestions in the server based on the user's preferences and past history, means for converting the generated outfit suggestions into a data format and transmitting it to the user terminal, and means for analyzing and displaying the outfit suggestions in the user terminal. This enables the user to quickly and effectively obtain appropriate outfit suggestions based on their preferences and past history.

[0091] "User" refers to an individual who uses the system to upload images of their own clothing and receive coordination suggestions.

[0092] "Terminal" refers to a computer device such as a smartphone or tablet used by a User, through which images are uploaded and outfit suggestions are received and displayed.

[0093] The term "server" refers to a remote computer system that receives data sent from a terminal and performs image analysis and coordinated suggestion generation.

[0094] "Communications network" refers to an infrastructure for transmitting and receiving data, including the Internet.

[0095] "Image preprocessing" refers to the process of resizing and normalizing the image data received by the server in order to input it into the AI ​​model.

[0096] "Image features" refer to the important data points that the AI ​​model extracts from an image to generate an evaluation score.

[0097] "Evaluation score" refers to the score that indicates the quality of the clothing calculated by the AI ​​model based on the extracted features.

[0098] "Coordination suggestions" refer to suggestions for combining multiple fashion items that are generated by the server based on the user's preferences, past history, and evaluation scores.

[0099] "Data format" refers to a format such as JSON or XML that represents the coordination suggestion information sent from the server to the terminal.

[0100] "Preprocessing" refers to a series of operations that change the attributes of an image and make it easier for an AI model to analyze.

[0101] "User interface" refers to the screen and operation method that allows the user to visually check coordination suggestions on the terminal.

[0102] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and provides coordination suggestions based on the user's preferences and past history. A specific embodiment of this system will be described in detail.

[0103] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the image, they press the "Upload Image" button in the app to select and upload the image. At this time, the device validates the image file format (e.g., JPEG or PNG) and size (e.g., maximum 10MB). Images that pass validation will proceed to the next step.

[0104] The device sends the successfully validated image to the server using an HTTP POST request. This transmission is performed over a communication network (e.g., the Internet). Data encryption is performed to ensure communication security.

[0105] The server receives image data sent from the device and stores it in temporary storage. It then uses an image processing library such as OpenCV to perform preprocessing such as resizing (e.g., converting to 224x224 pixels) and normalization (e.g., normalizing to the [0,1] range).

[0106] Once preprocessing is complete, the image is input into a generative AI model such as TENSORFLOW (registered trademark). The server uses this AI model to extract image features and calculates an outfit evaluation score based on them. The evaluation score is determined on a scale of 0 to 100 based on the style and combination of the outfit.

[0107] Next, the server retrieves the user's past outfit history and preferred style data from a database such as Amazon RDS. This information is managed based on the user's profile. The server then selects appropriate fashion items from the database based on the retrieved data and evaluation scores. This is done using an algorithm that takes into account factors such as style, season, and trends.

[0108] The generated outfit suggestions are converted into a data format such as JSON and sent to the user's device as an HTTP response. The device analyzes the received data and visually displays the outfit suggestions to the user through a user interface. The suggested fashion items are displayed using images and text, making it easy for the user to check the details.

[0109] As a concrete example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device then sends the image to the server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits that combine appropriate items such as jeans, white sneakers, and a simple T-shirt. These suggestions are then sent to the device and displayed for the user to easily review.

[0110] An example of a prompt is as follows:

[0111] "Users take a photo of their outfit and upload it to the app. Once uploaded, the server analyzes the image and assigns a score to your outfit. The app then recommends the perfect outfit items based on your preferred style and past history."

[0112] In this way, the system according to the present invention assists the user in easily selecting fashion and makes everyday coordination more enjoyable.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] The user takes a photo of the outfit and uploads it to the app.

[0116] Specifically, the user takes a photo of the outfit using the camera on their smartphone or tablet. This image is saved in JPEG or PNG format. The user then presses the "Upload Image" button in the app, selects the image they took, and uploads it. The input data is the outfit image taken by the user, and the output is an image file that passes validation.

[0117] Step 2:

[0118] The device sends the image to the server.

[0119] Specifically, the device validates the image format and size. If successful, it sends the image to the server using an HTTP POST request. At this time, data encryption is performed over the communication network to ensure secure communication. The input data is the image file that has passed validation, and the output is a notification to the server that transmission has been completed.

[0120] Step 3:

[0121] The server receives the images and performs pre-processing.

[0122] Specifically, the server receives images sent from the device and stores them in temporary storage. It then uses an image processing library such as OpenCV to perform preprocessing such as resizing (e.g., converting to 224x224 pixels) and normalization (e.g., normalizing to the range [0,1]). The input data is the received image file, and the output is the preprocessed image data.

[0123] Step 4:

[0124] The server extracts image features and generates an evaluation score.

[0125] Specifically, the server inputs the preprocessed image into a generative AI model (e.g., TensorFlow) to extract features. The AI ​​model calculates an evaluation score for the outfit based on the features. This score is determined on a scale from 0 to 100. The input data is the preprocessed image data, and the output is the evaluation score.

[0126] Step 5:

[0127] The server retrieves the user's history and preferences.

[0128] Specifically, the server retrieves the user's past outfit history and preferred style data from a database (e.g., Amazon RDS). This information is managed based on the user profile. The input data is the user ID, and the output is the user's past outfit history and preferred style data.

[0129] Step 6:

[0130] The server generates the appropriate coordinates.

[0131] Specifically, the server selects appropriate fashion items from a database based on the acquired user preference data and evaluation scores. It uses an algorithm to generate optimal combinations, taking into account style, season, trends, etc. The input data are the evaluation scores and the user's history and preference data, and the output is coordination suggestions.

[0132] Step 7:

[0133] The server converts the coordination proposal into a data format and transmits it to the user terminal.

[0134] Specifically, the server converts the generated coordination proposal into a data format such as JSON and sends it to the user's device as an HTTP response. The input data is the coordination proposal, and the output is the coordination proposal converted into a data format.

[0135] Step 8:

[0136] The terminal analyzes and displays the coordination suggestions.

[0137] Specifically, the user device parses the JSON data received from the server and visually displays outfit suggestions to the user through a user interface. Suggested fashion items are displayed using images and text. The input data is the outfit suggestions converted into a data format, and the output is a display of the parsed outfit suggestions.

[0138] This series of processes enables the user to quickly and effectively obtain coordination suggestions based on their own preferences and past history.

[0139] (Application example 1)

[0140] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] Conventional clothing coordination suggestion systems simply calculate an evaluation score and provide coordination suggestions. As a result, users have no way to check how the suggested items will actually look on them, which prevents them from appreciating the usefulness of the suggestions or increasing their willingness to actually purchase them. Furthermore, they lack the convenience of directly purchasing the suggested items. This can lead to lower user satisfaction and a decrease in the frequency of system usage.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0143] In this invention, the server includes a means for performing a virtual try-on using an augmented reality function on the user terminal and a means for providing a link for purchasing the suggested items. This allows the user to virtually try on the suggested coordinating items and check how they look. It also makes it easy to directly purchase the items after trying them on, improving user satisfaction and system usage frequency.

[0144] The "means for acquiring an image" is a device including a camera and an interface that allows the user to take a picture of their own outfit and upload the image to the application.

[0145] "Means for transmitting to a server via a communication network" refers to a mechanism for transmitting image data acquired from a user terminal to a remote server using the Internet.

[0146] "Means for analyzing images and generating an evaluation score for clothing" refers to the process of using an AI model on the server side to analyze the clothing in the image and quantify the quality of the style and combination.

[0147] The "means for generating coordination suggestions" is a system in which the server selects and combines other fashion items that go well with the suggested outfit based on the user's past preferences and history.

[0148] The "means for transmitting the generated coordination proposal to the user terminal" is a procedure for transmitting the coordination proposal generated on the server side in data format to the terminal used by the user.

[0149] The "means for displaying coordinated outfit suggestions on a user terminal" refers to an interface that visually displays coordinated outfit suggestions on a terminal so that the user can confirm the suggested coordinated outfits.

[0150] "Means for performing virtual try-on using augmented reality functions" refers to a function that uses a smartphone camera and AR technology to allow users to virtually try on suggested clothing items.

[0151] "Means for providing links to purchase suggested items" refers to a mechanism for providing web links or a shopping cart function for directly purchasing suggested fashion items within the application.

[0152] As one embodiment of the present invention, a virtual style assistant system using a smartphone application will be described in detail.

[0153] The system mainly uses the following hardware and software. The hardware includes a user device such as a smartphone or tablet with a camera function. This device is used by the user to take pictures of the clothing and upload them to the application. The software includes Flask (web framework), TensorFlow (AI model), and PIL (image processing library). The system communicates with a server via a communications network, and the server performs advanced data analysis.

[0154] 1. Image Acquisition

[0155] Users use their smartphones to take photos of their outfits and upload them to the application, which validates the image format and size and prepares it for transmission to the server in the appropriate format.

[0156] 2. Sending images

[0157] The user terminal transmits the validated image to the server via a communication network, for example, using an HTTP POST request.

[0158] 3. Image analysis and evaluation score generation

[0159] The server receives the sent image and stores it in temporary storage. It then preprocesses the image and converts it into a format that can be input to the AI ​​model. The AI ​​model extracts the characteristics of the clothing in the image and calculates an evaluation score. This evaluation score quantifies the quality of the user's clothing style and combination.

[0160] 4. Coordination Proposal Generation

[0161] The server generates optimal outfit suggestions based on the evaluation scores, the user's preferences, and past history data. This suggestion is made by selecting appropriate fashion items from a database and combining them.

[0162] 5. Submit your proposal

[0163] The generated coordination proposals are converted into a data format such as JSON and sent to the user's device.

[0164] 6. Viewing proposals and virtual try-on

[0165] The user device analyzes the outfit suggestions received from the server and displays them through a user interface. The suggested fashion items are visually presented using images and text. The user can also virtually try on the suggested items using the augmented reality function of their smartphone.

[0166] 7. Purchasing Items

[0167] If the user likes a suggested item, they are provided with a link to purchase it directly from within the application, effectively purchasing all suggested items.

[0168] As a concrete example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits, combining items like jeans, white sneakers, and a simple T-shirt. These suggestions are sent to the device and displayed for easy review. Using augmented reality, the user can virtually try on the outfit and, if they like it, purchase it directly from within the app.

[0169] Example prompt for a generative AI model:

[0170] Extract features from user-uploaded clothing images and convert the image's style into a rating score, which should be provided on a scale from 0 to 100.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] A user uses a smartphone to take a photo of their outfit and upload it to the application. The device validates the image format and size and saves it in the appropriate format. The input is the outfit image taken by the user, and the output is the validated image file. The specific operation is for the user to press the "Upload Image" button, select an image from the gallery or camera, and upload it.

[0174] Step 2:

[0175] The terminal sends the validated image to the server via the communication network. An HTTP POST request is used to transfer the uploaded image data to the server. The input is the validated image file stored on the user terminal, and the output is the completion of the transfer of the image data to the server. The specific operation is to send a POST request using the network communication module.

[0176] Step 3:

[0177] The server receives the transmitted image data and saves it in temporary storage. The input is the image data transmitted from the user terminal, and the output is an image file saved in temporary storage. Specifically, the request handler on the server side receives the image data and saves it in a specific directory.

[0178] Step 4:

[0179] The server preprocesses the images stored in temporary storage and converts them into a format that can be input to the generative AI model. The input is the image file stored in temporary storage, and the output is the preprocessed image data. Specific operations include preprocessing the images, such as resizing and normalizing.

[0180] Step 5:

[0181] The server inputs the preprocessed image data into the generative AI model, extracts the clothing features in the image, and calculates an evaluation score. The input is the preprocessed image data, and the output is the calculated evaluation score. The specific operation is to supply the image data to the AI ​​model and obtain a score as the inference result.

[0182] Step 6:

[0183] The server generates optimal outfit suggestions based on the evaluation scores, the user's preferences, and past history data. The input is the evaluation scores and user profile data, and the output is outfit suggestions. The specific operation is to select appropriate fashion items from the database and generate the optimal combination.

[0184] Step 7:

[0185] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's device. The input is the generated coordination proposal, and the output is the completion of sending the data to the user's device. The specific operation is to serialize the data and send it via a POST request.

[0186] Step 8:

[0187] The user terminal analyzes the outfit suggestions received from the server and displays them through a user interface. The input is the outfit suggestion data received from the server, and the output is the display on the user interface. The specific operation is to parse the received data and display it on the screen.

[0188] Step 9:

[0189] The user uses the smartphone's augmented reality (AR) function to virtually try on the suggested items. The input is image data of the suggested outfit items, and the output is a virtual try-on video displayed through the AR function. The specific operation is to perform the virtual try-on using a camera and AR software.

[0190] Step 10:

[0191] If the user likes a suggested item, they are provided with a link to purchase it directly from within the application. The input is a purchase link based on the outfit suggestion, and the output is a purchase screen displayed on the user's device. Specific operations include displaying web links and a shopping cart function, and connecting to an online shopping system.

[0192] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0193] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and uses an emotion engine that recognizes the user's emotions to provide coordination suggestions based on the user's preferences and past history. Specific embodiments of this system are described in detail below.

[0194] 1. Get the user's clothing image

[0195] 1.1 The user takes a photo of the outfit and uploads it to the app

[0196] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select and upload the image. At this time, the device is responsible for validating the format and size of the image file.

[0197] 2. Send the acquired image to the server

[0198] 2.1 The device sends the image to the server

[0199] The terminal sends the successfully validated image to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via a communication network.

[0200] 3. The server analyzes the image and generates an outfit evaluation score.

[0201] 3.1 Server receives image

[0202] The server receives the image data sent from the terminal and stores it in temporary storage, after which it performs analysis using the stored image data.

[0203] 3.2 Image processing on the server

[0204] The server pre-processes the image data before inputting it into the AI ​​model, including resizing and normalizing the image so that the AI ​​model can accurately recognize the image.

[0205] 3.3 Server scores outfits

[0206] The server inputs the preprocessed image into the AI ​​model, which extracts image features. The AI ​​model then calculates an outfit evaluation score based on the extracted features. This score is calculated on a scale of 0 to 100, based on factors such as style and combination.

[0207] 4. Recognizing user emotions and generating outfit suggestions

[0208] 4.1 The server recognizes the user's emotional state

[0209] The server uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to extract the user's emotional state. The emotional state is recorded in a database and used to suggest subsequent outfits.

[0210] 4.2 Server retrieves user history and preferences

[0211] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0212] 4.3 The server generates suitable coordinates

[0213] The server selects appropriate fashion items from the database based on the acquired user preference data, evaluation scores, and emotional state. These items are then combined optimally, taking into account the user's current emotional state.

[0214] 5. Send the generated coordination proposal to the user device.

[0215] 5.1 Server Sends Coordination Proposal

[0216] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's terminal.

[0217] 6. Displaying outfit suggestions on the user's device

[0218] 6.1 The device displays outfit suggestions

[0219] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0220] Specific examples

[0221] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then analyzes the user's facial expressions to recognize their emotional state, such as "happy" or "calm." Based on the user's past preference data, the server then suggests outfits that combine casual and relaxed items like jeans, white sneakers, and a simple T-shirt. These suggestions are displayed on the device for viewing.

[0222] As described above, the system according to the present invention incorporating emotion recognition can assist the user in selecting fashion and easily provide the user with the most appropriate outfit for their emotional state.

[0223] The processing flow will be explained below.

[0224] Step 1:

[0225] The user takes a photo of their outfit using the camera app on their smartphone. After taking the photo, the user presses the "Upload Image" button in the app to select and upload the image.

[0226] Step 2:

[0227] The device validates the uploaded image by checking the image file type and size to ensure it is in the correct format.

[0228] Step 3:

[0229] The terminal sends the successfully validated image to the server using an HTTP POST request, and the image data is transferred to the server via a communication network.

[0230] Step 4:

[0231] The server receives the image data sent from the terminal and stores it in temporary storage.

[0232] Step 5:

[0233] The server preprocesses the stored image data, which includes resizing and normalizing the images to ensure that the AI ​​model can accurately recognize them.

[0234] Step 6:

[0235] The server inputs the preprocessed image into the AI ​​model, extracts image features, and the AI ​​model calculates an outfit evaluation score based on the extracted features.

[0236] Step 7:

[0237] The server analyzes the user's facial expressions and tone of voice and uses an emotion engine to recognize the user's emotional state, which is then recorded in a database.

[0238] Step 8:

[0239] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0240] Step 9:

[0241] The server selects appropriate fashion items from the database based on the user's preference data, evaluation scores, and recognized emotional state. These items are then combined optimally, taking into account the user's current emotional state.

[0242] Step 10:

[0243] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's terminal.

[0244] Step 11:

[0245] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0246] For example, if a user uploads an image and the AI ​​model gives it a score of 70 / 100, the server analyzes the user's facial expressions and recognizes their emotional state, such as "happy" or "calm." Based on the user's past preferences, the server then suggests items such as casual and relaxing jeans, white sneakers, and a simple T-shirt. These suggestions are displayed on the device for viewing.

[0247] Example 2

[0248] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0249] While personal style and fashion options have become increasingly diverse in recent years, there is a lack of systems that evaluate users' clothing and suggest optimal outfits. This makes it difficult for users to objectively evaluate their own style and select appropriate items. Furthermore, since outfit suggestions based on the user's emotions are not available, it is difficult to select a style that suits their mood or situation.

[0250] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0251] In this invention, the server includes means for analyzing image data and generating an evaluation score for clothing, means for recognizing the user's emotions, and means for generating outfit suggestions based on the user's preferences and past history, thereby enabling the user to objectively evaluate their own style and further enabling optimal outfit suggestions based on the user's emotional state at the time.

[0252] "Devices that acquire image data" are devices that allow users to take images of clothing using a smartphone, tablet, etc., and store and manage that image data.

[0253] The "device that transmits acquired image data to a server device via a communication network" is a device that has the function of transferring acquired image data to a server device via a communication network such as the Internet.

[0254] The "device in the server device that analyzes image data and generates an evaluation score for clothing" is a device necessary for analyzing received image data and calculating an evaluation score for clothing using an artificial intelligence model or the like.

[0255] The "device for recognizing the user's emotion in the server device" is a device for analyzing the user's facial expression and voice data to identify and record the emotion.

[0256] The "server device that generates coordination suggestions based on the user's preferences and past history" is a device that is necessary to refer to the user's past history and preference data and suggest optimal combinations of fashion items.

[0257] The "device that transmits the generated coordination proposal to the user terminal device" is a device that has the function of transmitting the coordination proposal generated by the server device to the user terminal device via a communication network.

[0258] The "device for displaying coordination suggestions on a user terminal device" is a device for analyzing received coordination suggestions and visually displaying them through a user interface.

[0259] The system of this invention allows users to upload images of their own clothing, analyzes those images to calculate an evaluation score, and then uses an emotion engine that recognizes the user's emotions to provide coordination suggestions based on the user's preferences and past history.

[0260] 1. Acquisition and upload of image data

[0261] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select the image, and the device validates the format and size of the image file when uploading it.

[0262] 2. Sending image data to the server

[0263] The terminal sends the image that has been successfully validated to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via a communication network.

[0264] 3. Receipt and analysis of image data

[0265] The server receives the image data sent from the device and stores it in temporary storage. It then performs analysis using this stored image data. The server uses the Python PIL (Pillow) library to resize and normalize the image, allowing the AI ​​model to accurately recognize the image.

[0266] 4. Calculating the clothing evaluation score

[0267] The server inputs image data into an AI model trained using TensorFlow and PyTorch, extracts features from the image, and calculates an outfit evaluation score ranging from 0 to 100 based on the extracted features.

[0268] 5. Recognition of User Emotions

[0269] The server uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to extract the user's emotional state. The emotional state is recorded in a database and used to suggest subsequent outfits.

[0270] 6. Coordination Proposal Generation

[0271] The server retrieves the user's past outfit history and preferred style data from the database, integrates this information, selects appropriate fashion items from the database, and generates optimal outfit suggestions that take into account the user's current emotional state.

[0272] 7. Sending and displaying outfit suggestions

[0273] The server converts the generated outfit suggestions into a data format such as JSON and sends them to the user's device. The device then analyzes the received data and displays it to the user through a user interface. The user can then view the recommended fashion item combinations in both images and text.

[0274] Specific examples

[0275] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device then sends the image to a server, which analyzes it and calculates a rating score of 70 / 100. The server then analyzes the user's emotional information and recognizes their emotional state, such as "happy" or "calm." Based on the user's past preference data, the server then suggests outfits that combine items such as casual and relaxing jeans, white sneakers, and a simple T-shirt. These suggestions are displayed as images and text for viewing on the device.

[0276] Example prompt sentence:

[0277] "We'll suggest a casual outfit based on your mood today: How about some relaxed jeans, white sneakers, and a simple T-shirt?"

[0278] In this way, a system incorporating emotion recognition can assist users in choosing fashion and provide them with outfits that are optimal for their emotional state.

[0279] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0280] Step 1:

[0281] The user acquires and uploads clothing images.

[0282] Users take a photo of their outfit using a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app, select the image, and upload it. The input data is the captured image file, and the output data is the validated image file. The device checks the image format and size, and allows uploading if it meets the conditions of JPEG or PNG and a maximum of 5MB.

[0283] Step 2:

[0284] The device sends the image to the server

[0285] The device sends successfully validated images to the server using an HTTP POST request. The input data is the validated image file, and the output data is a message indicating that the image file has been transferred to the server. The device sends the image data to the server via the Internet and monitors the status of the network connection in real time.

[0286] Step 3:

[0287] The server receives the image and stores it in temporary storage.

[0288] The server saves the received image data in temporary storage. The input data is the transferred image file, and the output data is the path to the image file saved in storage. The server records the completion of saving the image file as a log.

[0289] Step 4:

[0290] The server processes the images

[0291] The server uses Python's PIL (Pillow) library to resize the image to an appropriate size, correct brightness and contrast, normalize the image data, and convert it into a format that can be input to the AI ​​model. The input data is the image file stored in storage, and the output data is the preprocessed image data.

[0292] Step 5:

[0293] The server grades the outfit.

[0294] The server inputs the preprocessed images into an AI model trained with TensorFlow or PyTorch. The AI ​​model extracts image features and calculates an outfit evaluation score based on those features. The input data is the preprocessed image data, and the output data is an evaluation score ranging from 0 to 100. The server records the calculated score in a database.

[0295] Step 6:

[0296] The server recognizes the user's emotional state

[0297] The server analyzes inputs from the user, facial expressions, and voice data, and uses an emotion engine to recognize the user's emotional state. The input data is information about the user's emotions, and the output data is the analyzed emotional state. The server records the emotional state in a database.

[0298] Step 7:

[0299] The server retrieves the user's history and preferences

[0300] The server retrieves the user's past outfit history and preference data from the database. The input data is the user's ID, and the output data is the retrieved history and preference data. The server stores this data in its internal memory for use in proposing the next outfit.

[0301] Step 8:

[0302] The server generates the appropriate coordinates

[0303] The server uses the emotion data, evaluation scores, and user history data to select the most suitable fashion items from the database and generate outfit suggestions. The input data is the emotional state, evaluation scores, and user history data, and the output data is the generated outfit suggestions. The server converts the suggestions into JSON format.

[0304] Step 9:

[0305] The server sends the generated coordination proposal to the user terminal.

[0306] The server sends the generated coordination proposal in JSON format to the user's device. The input data is the generated coordination proposal, and the output data is a message that transmission is complete to the user's device. The server records a transmission log of the proposal data.

[0307] Step 10:

[0308] The device displays outfit suggestions

[0309] The device parses the JSON data received from the server and visually displays outfit suggestions to the user through a user interface. The input data is the received JSON data, and the output data is the displayed outfit suggestions. The device also provides user evaluation and feedback functions.

[0310] (Application example 2)

[0311] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0312] While the convenience of online shopping has improved in recent years, there is a problem in that it takes a lot of time and effort for users to find the right outfit. In addition, current systems do not take into account the user's emotional state when proposing outfits, so an improvement in the user experience is required. Furthermore, there is a lack of systems that can be easily used on devices such as smartphones.

[0313] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing an image and generating an evaluation score for clothing, means for recognizing the user's emotional state and optimizing coordination suggestions based on the recognition, and means for transmitting the generated coordination suggestions to the user terminal. This makes it possible to provide optimal coordination suggestions based on the user's emotional state and past history.

[0314] The "means for acquiring an image" refers to a device or method by which the terminal captures an image of the user's clothing and acquires the image data.

[0315] The "means for transmitting the acquired image to the server via a communication network" refers to a device or method for transferring image data from the terminal to the server using the network.

[0316] "Means for analyzing images on a server and generating an evaluation score for clothing" refers to a device or method that uses artificial intelligence to evaluate clothing based on image data received by the server and calculates a score based on that evaluation.

[0317] "Means for generating coordination suggestions based on the user's preferences and past history on the server" refers to a device or method in which the server selects suitable fashion items and suggests coordination based on the user's saved data.

[0318] The "means for transmitting the generated coordination proposal to the user terminal" refers to a device or method by which the server converts the coordination proposal into a data format and transfers it to the user's terminal.

[0319] The "means for displaying a coordination suggestion at a user terminal" refers to a device or method by which the user's terminal visually displays the received coordination suggestion.

[0320] "Means for recognizing the user's emotional state and optimizing coordination suggestions based on that" refers to a device or method that uses emotion recognition technology to analyze the user's emotions and makes optimal fashion suggestions to the user based on the analysis results.

[0321] This invention is a system that analyzes clothing images uploaded by users, generates an evaluation score, and then recognizes the user's emotional state to provide optimal outfit suggestions. This system consists of three main components: the user's device, a server, and an emotion recognition engine.

[0322] User terminal

[0323] The user terminal is a device such as a smartphone or tablet. The user takes a photo of their own outfit with the device's camera and uploads the image to the application. The application validates the format and size of the uploaded image and sends the image to the server in the appropriate format. When the user selects an image through the input form and presses the "Submit" button, the system begins operation.

[0324] server

[0325] The server receives the modified image data and stores it in temporary storage. This stored image data is then used for analysis using the AI ​​model. Before that, preprocessing such as image resizing and normalization is performed to adjust the AI ​​model so that it can function properly. Once the analysis is complete, the server generates an evaluation score for the outfit.

[0326] The server also incorporates an emotion engine that recognizes the user's emotions by capturing and analyzing facial images and voice data. Based on the results of this analysis and past history data, optimal outfit suggestions are generated.

[0327] Emotion Recognition Engine

[0328] Emotion engineering uses the DeepFace library and other emotion recognition software to analyze a user's emotions. The analyzed emotion data is stored in a database, which the server then uses to suggest outfits appropriate for the user's emotional state.

[0329] Sending and viewing outfit suggestions

[0330] The outfit suggestions generated by the server are converted into a data format such as JSON and sent to the user's device. The application on the user's device analyzes the received data and visually displays the suggestions to the user. This allows the user to see the optimal outfit based on their emotional state and past history, and use it for shopping and closet management.

[0331] Specific examples

[0332] For example, a user can take a photo of their outfit with their smartphone and upload it to the application. The server receives the image and generates an evaluation score for the outfit using an AI model. The emotion recognition engine then recognizes emotions such as "happiness" and suggests casual item combinations based on past preference data. These suggestions are visually displayed on the smartphone, allowing the user to decide their next move.

[0333] Example prompts to input to the generative AI model

[0334] We want the system to upload images of the user's outfits and generate optimal outfit suggestions in conjunction with the emotion engine. We will use the following data:

[0335] Image data: {Image data}

[0336] Emotional state: {Emotional state}

[0337] Past history: {Past history}

[0338] Please return the best outfit suggestions in the following format:

[0339] Suggested Item Name

[0340] category

[0341] More Information

[0342] Evaluation score

[0343] The above is a specific embodiment for carrying out the present invention.

[0344] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0345] Step 1:

[0346] A user takes a photo of their own outfit using a device such as a smartphone. The input is the image data taken by the user, and the output is an image file saved on the device. This image file is temporarily saved for later transmission to the server.

[0347] Step 2:

[0348] The device validates the captured image data to ensure it is in the correct format and size. The input is the image file captured by the user, and the output is a validated image file. If the validation process is successful, the user uploads the image to the app.

[0349] Step 3:

[0350] The terminal sends the validated image file to the server via a communication network. The input is the validated image file, and the output is the image data sent to the server. Communication is performed via a protocol such as an HTTP POST request.

[0351] Step 4:

[0352] The server stores the received image data in temporary storage and performs preprocessing on the image. The input is the image data sent to the server, and the output is the preprocessed image data. Preprocessing includes image resizing and normalization.

[0353] Step 5:

[0354] The server inputs the preprocessed image data into the AI ​​model to generate an outfit evaluation score. The input is the preprocessed image data, and the output is the outfit evaluation score. This score is calculated by the AI ​​model by analyzing the image features.

[0355] Step 6:

[0356] The server uses an emotion recognition engine to recognize the user's emotional state. The input is the user's facial image and voice data, and the output is the recognized emotional state. The emotion recognition engine uses libraries such as DeepFace.

[0357] Step 7:

[0358] The server generates optimal outfit suggestions based on the user's emotional state, rating score, and past history data. The inputs are the emotional state, rating score, and past history data, and the output is outfit suggestions. The suggestions are generated by selecting and combining appropriate items from the database.

[0359] Step 8:

[0360] The server converts the generated outfit suggestions into a data format such as JSON and sends it to the user's device. The input is the outfit suggestion data, and the output is the data sent to the user's device. This data is converted into a format appropriate for display on the user's device.

[0361] Step 9:

[0362] The user terminal analyzes the coordination suggestions received from the server and visually displays them to the user. The input is the data sent from the server, and the output is the coordination suggestions displayed on the user interface. The user can check this and decide what to do next.

[0363] The specific operations and data flow in each step have been described above.

[0364] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0365] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0366] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0367] [Second embodiment]

[0368] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0369] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0370] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0371] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0372] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0373] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0374] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0375] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0376] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0377] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0378] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0379] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0380] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and provides coordination suggestions based on the user's preferences and past history. Specific embodiments of this system are described in detail below.

[0381] 1. Get the user's clothing image

[0382] 1.1 The user takes a photo of the outfit and uploads it to the app

[0383] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select and upload the image. At this time, the device is responsible for validating the format and size of the image file.

[0384] 2. Send the acquired image to the server

[0385] 2.1 The device sends the image to the server

[0386] The terminal sends the successfully validated image to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via the communication network.

[0387] 3. The server analyzes the image and generates an outfit evaluation score.

[0388] 3.1 Server receives image

[0389] The server receives the image data sent from the terminal and stores it in temporary storage, after which it performs analysis using the stored image data.

[0390] 3.2 Image processing on the server

[0391] The server pre-processes the image data before inputting it into the AI ​​model, including resizing and normalizing the image so that the AI ​​model can accurately recognize the image.

[0392] 3.3 Server scores outfits

[0393] The server inputs the preprocessed image into the AI ​​model, which extracts image features. The AI ​​model then calculates an outfit evaluation score based on the extracted features. This score is calculated on a scale of 0 to 100, based on factors such as style and combination.

[0394] 4. Generate coordination suggestions based on user preferences and past history

[0395] 4.1 Server retrieves user history and preferences

[0396] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0397] 4.2 The server generates appropriate coordinates

[0398] The server selects appropriate fashion items from the database based on the acquired user preference data and evaluation scores. These fashion items are then combined optimally, taking into account factors such as style, season, and trends.

[0399] 5. Send the generated coordination proposal to the user device.

[0400] 5.1 Server Sends Coordination Proposal

[0401] The server converts the generated coordination proposals into a data format such as JSON and sends them to the user's terminal.

[0402] 6. Displaying outfit suggestions on the user's device

[0403] 6.1 The device displays outfit suggestions

[0404] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0405] Specific examples

[0406] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits, combining items like jeans, white sneakers, and a simple T-shirt. These suggestions are then sent to the device and displayed for the user to easily review.

[0407] In this way, the system according to the present invention assists the user in selecting fashion and makes everyday coordination easier and more enjoyable.

[0408] The processing flow will be explained below.

[0409] Step 1:

[0410] The user takes a photo of their outfit using the camera app on their smartphone. After taking the photo, the user presses the "Upload Image" button in the app to select and upload the image.

[0411] Step 2:

[0412] The device validates the uploaded image by checking the image file type and size to ensure it is in the correct format.

[0413] Step 3:

[0414] The terminal sends the successfully validated image to the server using an HTTP POST request, and the image data is transferred to the server via a communication network.

[0415] Step 4:

[0416] The server receives the image data sent from the terminal and stores it in temporary storage.

[0417] Step 5:

[0418] The server preprocesses the stored image data, which includes resizing and normalizing the images so that the AI ​​model can accurately recognize them.

[0419] Step 6:

[0420] The server inputs the preprocessed image into the AI ​​model, which extracts image features and calculates an outfit evaluation score based on them.

[0421] Step 7:

[0422] The server then uses the evaluation score obtained from the AI ​​model to obtain the user's past outfit history and preferred style data, which is obtained from a database.

[0423] Step 8:

[0424] Based on the user's preference data and evaluation scores, the server selects appropriate fashion items from the database. These items are then combined optimally, taking into account factors such as style, season, and trends.

[0425] Step 9:

[0426] The server converts the generated coordination proposal into a data format such as JSON, and then sends this data to the user's device.

[0427] Step 10:

[0428] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0429] For example, if a user uploads an image and the AI ​​model gives it a rating score of 70 / 100, the server will suggest items based on the user's preferences, such as jeans, white sneakers, and a simple T-shirt, and these suggestions will be displayed on the device for review.

[0430] Example 1

[0431] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0432] Conventional fashion coordination systems have difficulty automatically suggesting outfits that suit a user's clothing and style, making it difficult for users to easily find outfit combinations that suit them. Furthermore, manual image evaluation and outfit suggestions are time-consuming and inefficient. Furthermore, the lack of customized suggestions that reflect individual users' preferences and past history results in low user satisfaction.

[0433] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0434] In this invention, the server includes means for a user to take an image of an outfit and upload it to a terminal, means for transmitting the acquired image to the server via a communication network, means for preprocessing the image in the server and extracting image features to generate an outfit evaluation score, means for generating outfit suggestions in the server based on the user's preferences and past history, means for converting the generated outfit suggestions into a data format and transmitting it to the user terminal, and means for analyzing and displaying the outfit suggestions in the user terminal. This enables the user to quickly and effectively obtain appropriate outfit suggestions based on their preferences and past history.

[0435] "User" refers to an individual who uses the system to upload images of their own clothing and receive coordination suggestions.

[0436] "Terminal" refers to a computer device such as a smartphone or tablet used by a User, through which images are uploaded and outfit suggestions are received and displayed.

[0437] The term "server" refers to a remote computer system that receives data sent from a terminal and performs image analysis and coordinated suggestion generation.

[0438] "Communications network" refers to an infrastructure for transmitting and receiving data, including the Internet.

[0439] "Image preprocessing" refers to the process of resizing and normalizing the image data received by the server in order to input it into the AI ​​model.

[0440] "Image features" refer to the important data points that the AI ​​model extracts from an image to generate an evaluation score.

[0441] "Evaluation score" refers to the score that indicates the quality of the clothing calculated by the AI ​​model based on the extracted features.

[0442] "Coordination suggestions" refer to suggestions for combining multiple fashion items that are generated by the server based on the user's preferences, past history, and evaluation scores.

[0443] "Data format" refers to a format such as JSON or XML that represents the coordination suggestion information sent from the server to the terminal.

[0444] "Preprocessing" refers to a series of operations that change the attributes of an image and make it easier for an AI model to analyze.

[0445] "User interface" refers to the screen and operation method that allows the user to visually check coordination suggestions on the terminal.

[0446] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and provides coordination suggestions based on the user's preferences and past history. A specific embodiment of this system will be described in detail.

[0447] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the image, they press the "Upload Image" button in the app to select and upload the image. At this time, the device validates the image file format (e.g., JPEG or PNG) and size (e.g., maximum 10MB). Images that pass validation will proceed to the next step.

[0448] The device sends the successfully validated image to the server using an HTTP POST request. This transmission is performed over a communication network (e.g., the Internet). Data encryption is performed to ensure communication security.

[0449] The server receives image data sent from the device and stores it in temporary storage. It then uses an image processing library such as OpenCV to perform preprocessing such as resizing (e.g., converting to 224x224 pixels) and normalization (e.g., normalizing to the [0,1] range).

[0450] After preprocessing, the image is input into a generative AI model such as TensorFlow. The server uses this AI model to extract image features and calculates an outfit evaluation score based on the image features. The evaluation score is determined on a scale of 0 to 100 based on the style and combination of the outfit.

[0451] Next, the server retrieves the user's past outfit history and preferred style data from a database such as Amazon RDS. This information is managed based on the user's profile. The server then selects appropriate fashion items from the database based on the retrieved data and evaluation scores. This is done using an algorithm that takes into account factors such as style, season, and trends.

[0452] The generated outfit suggestions are converted into a data format such as JSON and sent to the user's device as an HTTP response. The device analyzes the received data and visually displays the outfit suggestions to the user through a user interface. The suggested fashion items are displayed using images and text, making it easy for the user to check the details.

[0453] As a concrete example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device then sends the image to the server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits that combine appropriate items such as jeans, white sneakers, and a simple T-shirt. These suggestions are then sent to the device and displayed for the user to easily review.

[0454] An example of a prompt is as follows:

[0455] "Users take a photo of their outfit and upload it to the app. Once uploaded, the server analyzes the image and assigns a score to your outfit. The app then recommends the perfect outfit items based on your preferred style and past history."

[0456] In this way, the system according to the present invention assists the user in easily selecting fashion and makes everyday coordination more enjoyable.

[0457] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0458] Step 1:

[0459] The user takes a photo of the outfit and uploads it to the app.

[0460] Specifically, the user takes a photo of the outfit using the camera on their smartphone or tablet. This image is saved in JPEG or PNG format. The user then presses the "Upload Image" button in the app, selects the image they took, and uploads it. The input data is the outfit image taken by the user, and the output is an image file that passes validation.

[0461] Step 2:

[0462] The device sends the image to the server.

[0463] Specifically, the device validates the image format and size. If successful, it sends the image to the server using an HTTP POST request. At this time, data encryption is performed over the communication network to ensure secure communication. The input data is the image file that has passed validation, and the output is a notification to the server that transmission has been completed.

[0464] Step 3:

[0465] The server receives the images and performs pre-processing.

[0466] Specifically, the server receives images sent from the device and stores them in temporary storage. It then uses an image processing library such as OpenCV to perform preprocessing such as resizing (e.g., converting to 224x224 pixels) and normalization (e.g., normalizing to the range [0,1]). The input data is the received image file, and the output is the preprocessed image data.

[0467] Step 4:

[0468] The server extracts image features and generates an evaluation score.

[0469] Specifically, the server inputs the preprocessed image into a generative AI model (e.g., TensorFlow) to extract features. The AI ​​model calculates an evaluation score for the outfit based on the features. This score is determined on a scale from 0 to 100. The input data is the preprocessed image data, and the output is the evaluation score.

[0470] Step 5:

[0471] The server retrieves the user's history and preferences.

[0472] Specifically, the server retrieves the user's past outfit history and preferred style data from a database (e.g., Amazon RDS). This information is managed based on the user profile. The input data is the user ID, and the output is the user's past outfit history and preferred style data.

[0473] Step 6:

[0474] The server generates the appropriate coordinates.

[0475] Specifically, the server selects appropriate fashion items from a database based on the acquired user preference data and evaluation scores. It uses an algorithm to generate optimal combinations, taking into account style, season, trends, etc. The input data are the evaluation scores and the user's history and preference data, and the output is coordination suggestions.

[0476] Step 7:

[0477] The server converts the coordination proposal into a data format and transmits it to the user terminal.

[0478] Specifically, the server converts the generated coordination proposal into a data format such as JSON and sends it to the user's device as an HTTP response. The input data is the coordination proposal, and the output is the coordination proposal converted into a data format.

[0479] Step 8:

[0480] The terminal analyzes and displays the coordination suggestions.

[0481] Specifically, the user device parses the JSON data received from the server and visually displays outfit suggestions to the user through a user interface. Suggested fashion items are displayed using images and text. The input data is the outfit suggestions converted into a data format, and the output is a display of the parsed outfit suggestions.

[0482] This series of processes enables the user to quickly and effectively obtain coordination suggestions based on their own preferences and past history.

[0483] (Application example 1)

[0484] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0485] Conventional clothing coordination suggestion systems simply calculate an evaluation score and provide coordination suggestions. As a result, users have no way to check how the suggested items will actually look on them, which prevents them from appreciating the usefulness of the suggestions or increasing their willingness to actually purchase them. Furthermore, they lack the convenience of directly purchasing the suggested items. This can lead to lower user satisfaction and a decrease in the frequency of system usage.

[0486] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0487] In this invention, the server includes a means for performing a virtual try-on using an augmented reality function on the user terminal and a means for providing a link for purchasing the suggested items. This allows the user to virtually try on the suggested coordinating items and check how they look. It also makes it easy to directly purchase the items after trying them on, improving user satisfaction and system usage frequency.

[0488] The "means for acquiring an image" is a device including a camera and an interface that allows the user to take a picture of their own outfit and upload the image to the application.

[0489] "Means for transmitting to a server via a communication network" refers to a mechanism for transmitting image data acquired from a user terminal to a remote server using the Internet.

[0490] "Means for analyzing images and generating an evaluation score for clothing" refers to the process of using an AI model on the server side to analyze the clothing in the image and quantify the quality of the style and combination.

[0491] The "means for generating coordination suggestions" is a system in which the server selects and combines other fashion items that go well with the suggested outfit based on the user's past preferences and history.

[0492] The "means for transmitting the generated coordination proposal to the user terminal" is a procedure for transmitting the coordination proposal generated on the server side in data format to the terminal used by the user.

[0493] The "means for displaying coordinated outfit suggestions on a user terminal" refers to an interface that visually displays coordinated outfit suggestions on a terminal so that the user can confirm the suggested coordinated outfits.

[0494] "Means for performing virtual try-on using augmented reality functions" refers to a function that uses a smartphone camera and AR technology to allow users to virtually try on suggested clothing items.

[0495] "Means for providing links to purchase suggested items" refers to a mechanism for providing web links or a shopping cart function for directly purchasing suggested fashion items within the application.

[0496] As one embodiment of the present invention, a virtual style assistant system using a smartphone application will be described in detail.

[0497] The system mainly uses the following hardware and software. The hardware includes a user device such as a smartphone or tablet with a camera function. This device is used by the user to take pictures of the clothing and upload them to the application. The software includes Flask (web framework), TensorFlow (AI model), and PIL (image processing library). The system communicates with a server via a communications network, and the server performs advanced data analysis.

[0498] 1. Image Acquisition

[0499] Users use their smartphones to take photos of their outfits and upload them to the application, which validates the image format and size and prepares it for transmission to the server in the appropriate format.

[0500] 2. Sending images

[0501] The user terminal transmits the validated image to the server via a communication network, for example, using an HTTP POST request.

[0502] 3. Image analysis and evaluation score generation

[0503] The server receives the sent image and stores it in temporary storage. It then preprocesses the image and converts it into a format that can be input to the AI ​​model. The AI ​​model extracts the characteristics of the clothing in the image and calculates an evaluation score. This evaluation score quantifies the quality of the user's clothing style and combination.

[0504] 4. Coordination Proposal Generation

[0505] The server generates optimal outfit suggestions based on the evaluation scores, the user's preferences, and past history data. This suggestion is made by selecting appropriate fashion items from a database and combining them.

[0506] 5. Submit your proposal

[0507] The generated coordination proposals are converted into a data format such as JSON and sent to the user's device.

[0508] 6. Viewing proposals and virtual try-on

[0509] The user device analyzes the outfit suggestions received from the server and displays them through a user interface. The suggested fashion items are visually presented using images and text. The user can also virtually try on the suggested items using the augmented reality function of their smartphone.

[0510] 7. Purchasing Items

[0511] If the user likes a suggested item, they are provided with a link to purchase it directly from within the application, effectively purchasing all suggested items.

[0512] As a concrete example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits, combining items like jeans, white sneakers, and a simple T-shirt. These suggestions are sent to the device and displayed for easy review. Using augmented reality, the user can virtually try on the outfit and, if they like it, purchase it directly from within the app.

[0513] Example prompt for a generative AI model:

[0514] Extract features from user-uploaded clothing images and convert the image's style into a rating score, which should be provided on a scale from 0 to 100.

[0515] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0516] Step 1:

[0517] A user uses a smartphone to take a photo of their outfit and upload it to the application. The device validates the image format and size and saves it in the appropriate format. The input is the outfit image taken by the user, and the output is the validated image file. The specific operation is for the user to press the "Upload Image" button, select an image from the gallery or camera, and upload it.

[0518] Step 2:

[0519] The terminal sends the validated image to the server via the communication network. An HTTP POST request is used to transfer the uploaded image data to the server. The input is the validated image file stored on the user terminal, and the output is the completion of the transfer of the image data to the server. The specific operation is to send a POST request using the network communication module.

[0520] Step 3:

[0521] The server receives the transmitted image data and saves it in temporary storage. The input is the image data transmitted from the user terminal, and the output is an image file saved in temporary storage. Specifically, the request handler on the server side receives the image data and saves it in a specific directory.

[0522] Step 4:

[0523] The server preprocesses the images stored in temporary storage and converts them into a format that can be input to the generative AI model. The input is the image file stored in temporary storage, and the output is the preprocessed image data. Specific operations include preprocessing the images, such as resizing and normalizing.

[0524] Step 5:

[0525] The server inputs the preprocessed image data into the generative AI model, extracts the clothing features in the image, and calculates an evaluation score. The input is the preprocessed image data, and the output is the calculated evaluation score. The specific operation is to supply the image data to the AI ​​model and obtain a score as the inference result.

[0526] Step 6:

[0527] The server generates optimal outfit suggestions based on the evaluation scores, the user's preferences, and past history data. The input is the evaluation scores and user profile data, and the output is outfit suggestions. The specific operation is to select appropriate fashion items from the database and generate the optimal combination.

[0528] Step 7:

[0529] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's device. The input is the generated coordination proposal, and the output is the completion of sending the data to the user's device. The specific operation is to serialize the data and send it via a POST request.

[0530] Step 8:

[0531] The user terminal analyzes the outfit suggestions received from the server and displays them through a user interface. The input is the outfit suggestion data received from the server, and the output is the display on the user interface. The specific operation is to parse the received data and display it on the screen.

[0532] Step 9:

[0533] The user uses the smartphone's augmented reality (AR) function to virtually try on the suggested items. The input is image data of the suggested outfit items, and the output is a virtual try-on video displayed through the AR function. The specific operation is to perform the virtual try-on using a camera and AR software.

[0534] Step 10:

[0535] If the user likes a suggested item, they are provided with a link to purchase it directly from within the application. The input is a purchase link based on the outfit suggestion, and the output is a purchase screen displayed on the user's device. Specific operations include displaying web links and a shopping cart function, and connecting to an online shopping system.

[0536] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0537] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and uses an emotion engine that recognizes the user's emotions to provide coordination suggestions based on the user's preferences and past history. Specific embodiments of this system are described in detail below.

[0538] 1. Get the user's clothing image

[0539] 1.1 The user takes a photo of the outfit and uploads it to the app

[0540] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select and upload the image. At this time, the device is responsible for validating the format and size of the image file.

[0541] 2. Send the acquired image to the server

[0542] 2.1 The device sends the image to the server

[0543] The terminal sends the successfully validated image to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via a communication network.

[0544] 3. The server analyzes the image and generates an outfit evaluation score.

[0545] 3.1 Server receives image

[0546] The server receives the image data sent from the terminal and stores it in temporary storage, after which it performs analysis using the stored image data.

[0547] 3.2 Image processing on the server

[0548] The server pre-processes the image data before inputting it into the AI ​​model, including resizing and normalizing the image so that the AI ​​model can accurately recognize the image.

[0549] 3.3 Server scores outfits

[0550] The server inputs the preprocessed image into the AI ​​model, which extracts image features. The AI ​​model then calculates an outfit evaluation score based on the extracted features. This score is calculated on a scale of 0 to 100, based on factors such as style and combination.

[0551] 4. Recognizing user emotions and generating outfit suggestions

[0552] 4.1 The server recognizes the user's emotional state

[0553] The server uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to extract the user's emotional state. The emotional state is recorded in a database and used to suggest subsequent outfits.

[0554] 4.2 Server retrieves user history and preferences

[0555] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0556] 4.3 The server generates suitable coordinates

[0557] The server selects appropriate fashion items from the database based on the acquired user preference data, evaluation scores, and emotional state. These items are then combined optimally, taking into account the user's current emotional state.

[0558] 5. Send the generated coordination proposal to the user device.

[0559] 5.1 Server Sends Coordination Proposal

[0560] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's terminal.

[0561] 6. Displaying outfit suggestions on the user's device

[0562] 6.1 The device displays outfit suggestions

[0563] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0564] Specific examples

[0565] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then analyzes the user's facial expressions to recognize their emotional state, such as "happy" or "calm." Based on the user's past preference data, the server then suggests outfits that combine casual and relaxed items like jeans, white sneakers, and a simple T-shirt. These suggestions are displayed on the device for viewing.

[0566] As described above, the system according to the present invention incorporating emotion recognition can assist the user in selecting fashion and easily provide the user with the most appropriate outfit for their emotional state.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] The user takes a photo of their outfit using the camera app on their smartphone. After taking the photo, the user presses the "Upload Image" button in the app to select and upload the image.

[0570] Step 2:

[0571] The device validates the uploaded image by checking the image file type and size to ensure it is in the correct format.

[0572] Step 3:

[0573] The terminal sends the successfully validated image to the server using an HTTP POST request, and the image data is transferred to the server via a communication network.

[0574] Step 4:

[0575] The server receives the image data sent from the terminal and stores it in temporary storage.

[0576] Step 5:

[0577] The server preprocesses the stored image data, which includes resizing and normalizing the images to ensure that the AI ​​model can accurately recognize them.

[0578] Step 6:

[0579] The server inputs the preprocessed image into the AI ​​model, extracts image features, and the AI ​​model calculates an outfit evaluation score based on the extracted features.

[0580] Step 7:

[0581] The server analyzes the user's facial expressions and tone of voice and uses an emotion engine to recognize the user's emotional state, which is then recorded in a database.

[0582] Step 8:

[0583] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0584] Step 9:

[0585] The server selects appropriate fashion items from the database based on the user's preference data, evaluation scores, and recognized emotional state. These items are then combined optimally, taking into account the user's current emotional state.

[0586] Step 10:

[0587] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's terminal.

[0588] Step 11:

[0589] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0590] For example, if a user uploads an image and the AI ​​model gives it a score of 70 / 100, the server analyzes the user's facial expressions and recognizes their emotional state, such as "happy" or "calm." Based on the user's past preferences, the server then suggests items such as casual and relaxing jeans, white sneakers, and a simple T-shirt. These suggestions are displayed on the device for viewing.

[0591] Example 2

[0592] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0593] While personal style and fashion options have become increasingly diverse in recent years, there is a lack of systems that evaluate users' clothing and suggest optimal outfits. This makes it difficult for users to objectively evaluate their own style and select appropriate items. Furthermore, since outfit suggestions based on the user's emotions are not available, it is difficult to select a style that suits their mood or situation.

[0594] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0595] In this invention, the server includes means for analyzing image data and generating an evaluation score for clothing, means for recognizing the user's emotions, and means for generating outfit suggestions based on the user's preferences and past history, thereby enabling the user to objectively evaluate their own style and further enabling optimal outfit suggestions based on the user's emotional state at the time.

[0596] "Devices that acquire image data" are devices that allow users to take images of clothing using a smartphone, tablet, etc., and store and manage that image data.

[0597] The "device that transmits acquired image data to a server device via a communication network" is a device that has the function of transferring acquired image data to a server device via a communication network such as the Internet.

[0598] The "device in the server device that analyzes image data and generates an evaluation score for clothing" is a device necessary for analyzing received image data and calculating an evaluation score for clothing using an artificial intelligence model or the like.

[0599] The "device for recognizing the user's emotion in the server device" is a device for analyzing the user's facial expression and voice data to identify and record the emotion.

[0600] The "server device that generates coordination suggestions based on the user's preferences and past history" is a device that is necessary to refer to the user's past history and preference data and suggest optimal combinations of fashion items.

[0601] The "device that transmits the generated coordination proposal to the user terminal device" is a device that has the function of transmitting the coordination proposal generated by the server device to the user terminal device via a communication network.

[0602] The "device for displaying coordination suggestions on a user terminal device" is a device for analyzing received coordination suggestions and visually displaying them through a user interface.

[0603] The system of this invention allows users to upload images of their own clothing, analyzes those images to calculate an evaluation score, and then uses an emotion engine that recognizes the user's emotions to provide coordination suggestions based on the user's preferences and past history.

[0604] 1. Acquisition and upload of image data

[0605] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select the image, and the device validates the format and size of the image file when uploading it.

[0606] 2. Sending image data to the server

[0607] The terminal sends the image that has been successfully validated to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via a communication network.

[0608] 3. Receipt and analysis of image data

[0609] The server receives the image data sent from the device and stores it in temporary storage. It then performs analysis using this stored image data. The server uses the Python PIL (Pillow) library to resize and normalize the image, allowing the AI ​​model to accurately recognize the image.

[0610] 4. Calculating the clothing evaluation score

[0611] The server inputs image data into an AI model trained using TensorFlow and PyTorch, extracts features from the image, and calculates an outfit evaluation score ranging from 0 to 100 based on the extracted features.

[0612] 5. Recognition of User Emotions

[0613] The server uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to extract the user's emotional state. The emotional state is recorded in a database and used to suggest subsequent outfits.

[0614] 6. Coordination Proposal Generation

[0615] The server retrieves the user's past outfit history and preferred style data from the database, integrates this information, selects appropriate fashion items from the database, and generates optimal outfit suggestions that take into account the user's current emotional state.

[0616] 7. Sending and displaying outfit suggestions

[0617] The server converts the generated outfit suggestions into a data format such as JSON and sends them to the user's device. The device then analyzes the received data and displays it to the user through a user interface. The user can then view the recommended fashion item combinations in both images and text.

[0618] Specific examples

[0619] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device then sends the image to a server, which analyzes it and calculates a rating score of 70 / 100. The server then analyzes the user's emotional information and recognizes their emotional state, such as "happy" or "calm." Based on the user's past preference data, the server then suggests outfits that combine items such as casual and relaxing jeans, white sneakers, and a simple T-shirt. These suggestions are displayed as images and text for viewing on the device.

[0620] Example prompt sentence:

[0621] "We'll suggest a casual outfit based on your mood today: How about some relaxed jeans, white sneakers, and a simple T-shirt?"

[0622] In this way, a system incorporating emotion recognition can assist users in choosing fashion and provide them with outfits that are optimal for their emotional state.

[0623] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0624] Step 1:

[0625] The user acquires and uploads clothing images.

[0626] Users take a photo of their outfit using a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app, select the image, and upload it. The input data is the captured image file, and the output data is the validated image file. The device checks the image format and size, and allows uploading if it meets the conditions of JPEG or PNG and a maximum of 5MB.

[0627] Step 2:

[0628] The device sends the image to the server

[0629] The device sends successfully validated images to the server using an HTTP POST request. The input data is the validated image file, and the output data is a message indicating that the image file has been transferred to the server. The device sends the image data to the server via the Internet and monitors the status of the network connection in real time.

[0630] Step 3:

[0631] The server receives the image and stores it in temporary storage.

[0632] The server saves the received image data in temporary storage. The input data is the transferred image file, and the output data is the path to the image file saved in storage. The server records the completion of saving the image file as a log.

[0633] Step 4:

[0634] The server processes the images

[0635] The server uses Python's PIL (Pillow) library to resize the image to an appropriate size, correct brightness and contrast, normalize the image data, and convert it into a format that can be input to the AI ​​model. The input data is the image file stored in storage, and the output data is the preprocessed image data.

[0636] Step 5:

[0637] The server grades the outfit.

[0638] The server inputs the preprocessed images into an AI model trained with TensorFlow or PyTorch. The AI ​​model extracts image features and calculates an outfit evaluation score based on those features. The input data is the preprocessed image data, and the output data is an evaluation score ranging from 0 to 100. The server records the calculated score in a database.

[0639] Step 6:

[0640] The server recognizes the user's emotional state

[0641] The server analyzes inputs from the user, facial expressions, and voice data, and uses an emotion engine to recognize the user's emotional state. The input data is information about the user's emotions, and the output data is the analyzed emotional state. The server records the emotional state in a database.

[0642] Step 7:

[0643] The server retrieves the user's history and preferences

[0644] The server retrieves the user's past outfit history and preference data from the database. The input data is the user's ID, and the output data is the retrieved history and preference data. The server stores this data in its internal memory for use in proposing the next outfit.

[0645] Step 8:

[0646] The server generates the appropriate coordinates

[0647] The server uses the emotion data, evaluation scores, and user history data to select the most suitable fashion items from the database and generate outfit suggestions. The input data is the emotional state, evaluation scores, and user history data, and the output data is the generated outfit suggestions. The server converts the suggestions into JSON format.

[0648] Step 9:

[0649] The server sends the generated coordination proposal to the user terminal.

[0650] The server sends the generated coordination proposal in JSON format to the user's device. The input data is the generated coordination proposal, and the output data is a message that transmission is complete to the user's device. The server records a transmission log of the proposal data.

[0651] Step 10:

[0652] The device displays outfit suggestions

[0653] The device parses the JSON data received from the server and visually displays outfit suggestions to the user through a user interface. The input data is the received JSON data, and the output data is the displayed outfit suggestions. The device also provides user evaluation and feedback functions.

[0654] (Application example 2)

[0655] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0656] While the convenience of online shopping has improved in recent years, there is a problem in that it takes a lot of time and effort for users to find the right outfit. In addition, current systems do not take into account the user's emotional state when proposing outfits, so an improvement in the user experience is required. Furthermore, there is a lack of systems that can be easily used on devices such as smartphones.

[0657] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing an image and generating an evaluation score for clothing, means for recognizing the user's emotional state and optimizing coordination suggestions based on the recognition, and means for transmitting the generated coordination suggestions to the user terminal. This makes it possible to provide optimal coordination suggestions based on the user's emotional state and past history.

[0658] The "means for acquiring an image" refers to a device or method by which the terminal captures an image of the user's clothing and acquires the image data.

[0659] The "means for transmitting the acquired image to the server via the communication network" refers to a device or method for transferring image data from the terminal to the server using the network.

[0660] "Means for analyzing images on a server and generating an evaluation score for clothing" refers to a device or method that uses artificial intelligence to evaluate clothing based on image data received by the server and calculates a score based on that evaluation.

[0661] "Means for generating coordination suggestions based on the user's preferences and past history on the server" refers to a device or method in which the server selects suitable fashion items and suggests coordination based on the user's saved data.

[0662] The "means for transmitting the generated coordination proposal to the user terminal" refers to a device or method by which the server converts the coordination proposal into a data format and transfers it to the user's terminal.

[0663] The "means for displaying a coordination suggestion on a user terminal" refers to a device or method by which the user's terminal visually displays the received coordination suggestion.

[0664] "Means for recognizing the user's emotional state and optimizing coordination suggestions based on that" refers to a device or method that uses emotion recognition technology to analyze the user's emotions and makes optimal fashion suggestions to the user based on the analysis results.

[0665] This invention is a system that analyzes clothing images uploaded by users, generates an evaluation score, and then recognizes the user's emotional state to provide optimal outfit suggestions. This system consists of three main components: the user's device, a server, and an emotion recognition engine.

[0666] User terminal

[0667] The user terminal is a device such as a smartphone or tablet. The user takes a photo of their own outfit with the device's camera and uploads the image to the application. The application validates the format and size of the uploaded image and sends the image to the server in the appropriate format. When the user selects an image through the input form and presses the "Submit" button, the system begins operation.

[0668] server

[0669] The server receives the modified image data and stores it in temporary storage. This stored image data is then used for analysis using the AI ​​model. Before that, preprocessing such as image resizing and normalization is performed to adjust the AI ​​model so that it can function properly. Once the analysis is complete, the server generates an evaluation score for the outfit.

[0670] The server also incorporates an emotion engine that recognizes the user's emotions by capturing and analyzing facial images and voice data. Based on the results of this analysis and past history data, optimal outfit suggestions are generated.

[0671] Emotion Recognition Engine

[0672] Emotion engineering uses the DeepFace library and other emotion recognition software to analyze a user's emotions. The analyzed emotion data is stored in a database, which the server then uses to suggest outfits appropriate for the user's emotional state.

[0673] Sending and viewing outfit suggestions

[0674] The outfit suggestions generated by the server are converted into a data format such as JSON and sent to the user's device. The application on the user's device analyzes the received data and visually displays the suggestions to the user. This allows the user to see the optimal outfit based on their emotional state and past history, and use it for shopping and closet management.

[0675] Specific examples

[0676] For example, a user can take a photo of their outfit with their smartphone and upload it to the application. The server receives the image and generates an evaluation score for the outfit using an AI model. The emotion recognition engine then recognizes emotions such as "happiness" and suggests casual item combinations based on past preference data. These suggestions are visually displayed on the smartphone, allowing the user to decide their next move.

[0677] Example prompts to input to the generative AI model

[0678] We want the system to upload images of the user's outfits and generate optimal outfit suggestions in conjunction with the emotion engine. We will use the following data:

[0679] Image data: {Image data}

[0680] Emotional state: {Emotional state}

[0681] Past history: {Past history}

[0682] Please return the best outfit suggestions in the following format:

[0683] Suggested Item Name

[0684] category

[0685] More Information

[0686] Evaluation score

[0687] The above is a specific embodiment for carrying out the present invention.

[0688] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0689] Step 1:

[0690] A user takes a photo of their own outfit using a device such as a smartphone. The input is the image data taken by the user, and the output is an image file saved on the device. This image file is temporarily saved for later transmission to the server.

[0691] Step 2:

[0692] The device validates the captured image data to ensure it is in the correct format and size. The input is the image file captured by the user, and the output is a validated image file. If the validation process is successful, the user uploads the image to the app.

[0693] Step 3:

[0694] The terminal sends the validated image file to the server via a communication network. The input is the validated image file, and the output is the image data sent to the server. Communication is performed via a protocol such as an HTTP POST request.

[0695] Step 4:

[0696] The server stores the received image data in temporary storage and performs preprocessing on the image. The input is the image data sent to the server, and the output is the preprocessed image data. Preprocessing includes image resizing and normalization.

[0697] Step 5:

[0698] The server inputs the preprocessed image data into the AI ​​model to generate an outfit evaluation score. The input is the preprocessed image data, and the output is the outfit evaluation score. This score is calculated by the AI ​​model by analyzing the image features.

[0699] Step 6:

[0700] The server uses an emotion recognition engine to recognize the user's emotional state. The input is the user's facial image and voice data, and the output is the recognized emotional state. The emotion recognition engine uses libraries such as DeepFace.

[0701] Step 7:

[0702] The server generates optimal outfit suggestions based on the user's emotional state, rating score, and past history data. The inputs are the emotional state, rating score, and past history data, and the output is outfit suggestions. The suggestions are generated by selecting and combining appropriate items from the database.

[0703] Step 8:

[0704] The server converts the generated outfit suggestions into a data format such as JSON and sends it to the user's device. The input is the outfit suggestion data, and the output is the data sent to the user's device. This data is converted into a format appropriate for display on the user's device.

[0705] Step 9:

[0706] The user terminal analyzes the coordination suggestions received from the server and visually displays them to the user. The input is the data sent from the server, and the output is the coordination suggestions displayed on the user interface. The user can check this and decide what to do next.

[0707] The specific operations and data flow in each step have been described above.

[0708] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0709] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0710] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0714] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0715] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0716] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0717] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0718] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0719] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0720] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0721] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0722] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0723] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0724] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and provides coordination suggestions based on the user's preferences and past history. Specific embodiments of this system are described in detail below.

[0725] 1. Get the user's clothing image

[0726] 1.1 The user takes a photo of the outfit and uploads it to the app

[0727] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select and upload the image. At this time, the device is responsible for validating the format and size of the image file.

[0728] 2. Send the acquired image to the server

[0729] 2.1 The device sends the image to the server

[0730] The terminal sends the successfully validated image to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via the communication network.

[0731] 3. The server analyzes the image and generates an outfit evaluation score.

[0732] 3.1 Server receives image

[0733] The server receives the image data sent from the terminal and stores it in temporary storage, after which it performs analysis using the stored image data.

[0734] 3.2 Image processing on the server

[0735] The server pre-processes the image data before inputting it into the AI ​​model, including resizing and normalizing the image so that the AI ​​model can accurately recognize the image.

[0736] 3.3 Server scores outfits

[0737] The server inputs the preprocessed image into the AI ​​model, which extracts image features. The AI ​​model then calculates an outfit evaluation score based on the extracted features. This score is calculated on a scale of 0 to 100, based on factors such as style and combination.

[0738] 4. Generate coordination suggestions based on user preferences and past history

[0739] 4.1 Server retrieves user history and preferences

[0740] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0741] 4.2 The server generates appropriate coordinates

[0742] The server selects appropriate fashion items from the database based on the acquired user preference data and evaluation scores. These fashion items are then combined optimally, taking into account factors such as style, season, and trends.

[0743] 5. Send the generated coordination proposal to the user device.

[0744] 5.1 Server Sends Coordination Proposal

[0745] The server converts the generated coordination proposals into a data format such as JSON and sends them to the user's terminal.

[0746] 6. Displaying outfit suggestions on the user's device

[0747] 6.1 The device displays outfit suggestions

[0748] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0749] Specific examples

[0750] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits, combining items like jeans, white sneakers, and a simple T-shirt. These suggestions are then sent to the device and displayed for the user to easily review.

[0751] In this way, the system according to the present invention assists the user in selecting fashion and makes everyday coordination easier and more enjoyable.

[0752] The processing flow will be explained below.

[0753] Step 1:

[0754] The user takes a photo of their outfit using the camera app on their smartphone. After taking the photo, the user presses the "Upload Image" button in the app to select and upload the image.

[0755] Step 2:

[0756] The device validates the uploaded image by checking the image file type and size to ensure it is in the correct format.

[0757] Step 3:

[0758] The terminal sends the successfully validated image to the server using an HTTP POST request, and the image data is transferred to the server via a communication network.

[0759] Step 4:

[0760] The server receives the image data sent from the terminal and stores it in temporary storage.

[0761] Step 5:

[0762] The server preprocesses the stored image data, which includes resizing and normalizing the images so that the AI ​​model can accurately recognize them.

[0763] Step 6:

[0764] The server inputs the preprocessed image into the AI ​​model, which extracts image features and calculates an outfit evaluation score based on them.

[0765] Step 7:

[0766] The server then uses the evaluation score obtained from the AI ​​model to obtain the user's past outfit history and preferred style data, which is obtained from a database.

[0767] Step 8:

[0768] Based on the user's preference data and evaluation scores, the server selects appropriate fashion items from the database. These items are then combined optimally, taking into account factors such as style, season, and trends.

[0769] Step 9:

[0770] The server converts the generated coordination proposal into a data format such as JSON, and then sends this data to the user's device.

[0771] Step 10:

[0772] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0773] For example, if a user uploads an image and the AI ​​model gives it a rating score of 70 / 100, the server will suggest items based on the user's preferences, such as jeans, white sneakers, and a simple T-shirt, and these suggestions will be displayed on the device for review.

[0774] Example 1

[0775] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0776] Conventional fashion coordination systems have difficulty automatically suggesting outfits that suit a user's clothing and style, making it difficult for users to easily find outfit combinations that suit them. Furthermore, manual image evaluation and outfit suggestions are time-consuming and inefficient. Furthermore, the lack of customized suggestions that reflect individual users' preferences and past history results in low user satisfaction.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0778] In this invention, the server includes means for a user to take an image of an outfit and upload it to a terminal, means for transmitting the acquired image to the server via a communication network, means for preprocessing the image in the server and extracting image features to generate an outfit evaluation score, means for generating outfit suggestions in the server based on the user's preferences and past history, means for converting the generated outfit suggestions into a data format and transmitting it to the user terminal, and means for analyzing and displaying the outfit suggestions in the user terminal. This enables the user to quickly and effectively obtain appropriate outfit suggestions based on their preferences and past history.

[0779] "User" refers to an individual who uses the system to upload images of their own clothing and receive coordination suggestions.

[0780] "Terminal" refers to a computer device such as a smartphone or tablet used by a User, through which images are uploaded and outfit suggestions are received and displayed.

[0781] The term "server" refers to a remote computer system that receives data sent from a terminal and performs image analysis and coordinated suggestion generation.

[0782] "Communications network" refers to an infrastructure for transmitting and receiving data, including the Internet.

[0783] "Image preprocessing" refers to the process of resizing and normalizing the image data received by the server in order to input it into the AI ​​model.

[0784] "Image features" refer to the important data points that the AI ​​model extracts from an image to generate an evaluation score.

[0785] "Evaluation score" refers to the score that indicates the quality of the clothing calculated by the AI ​​model based on the extracted features.

[0786] "Coordination suggestions" refer to suggestions for combining multiple fashion items that are generated by the server based on the user's preferences, past history, and evaluation scores.

[0787] "Data format" refers to a format such as JSON or XML that represents the coordination suggestion information sent from the server to the terminal.

[0788] "Preprocessing" refers to a series of operations that change the attributes of an image and make it easier for an AI model to analyze.

[0789] "User interface" refers to the screen and operation method that allows the user to visually check coordination suggestions on the terminal.

[0790] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and provides coordination suggestions based on the user's preferences and past history. A specific embodiment of this system will be described in detail.

[0791] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the image, they press the "Upload Image" button in the app to select and upload the image. At this time, the device validates the image file format (e.g., JPEG or PNG) and size (e.g., maximum 10MB). Images that pass validation will proceed to the next step.

[0792] The device sends the successfully validated image to the server using an HTTP POST request. This transmission is performed over a communication network (e.g., the Internet). Data encryption is performed to ensure communication security.

[0793] The server receives image data sent from the device and stores it in temporary storage. It then uses an image processing library such as OpenCV to perform preprocessing such as resizing (e.g., converting to 224x224 pixels) and normalization (e.g., normalizing to the [0,1] range).

[0794] After preprocessing, the image is input into a generative AI model such as TensorFlow. The server uses this AI model to extract image features and calculates an outfit evaluation score based on the image features. The evaluation score is determined on a scale of 0 to 100 based on the style and combination of the outfit.

[0795] Next, the server retrieves the user's past outfit history and preferred style data from a database such as Amazon RDS. This information is managed based on the user's profile. The server then selects appropriate fashion items from the database based on the retrieved data and evaluation scores. This is done using an algorithm that takes into account factors such as style, season, and trends.

[0796] The generated outfit suggestions are converted into a data format such as JSON and sent to the user's device as an HTTP response. The device analyzes the received data and visually displays the outfit suggestions to the user through a user interface. The suggested fashion items are displayed using images and text, making it easy for the user to check the details.

[0797] As a concrete example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device then sends the image to the server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits that combine appropriate items such as jeans, white sneakers, and a simple T-shirt. These suggestions are then sent to the device and displayed for the user to easily review.

[0798] An example of a prompt is as follows:

[0799] "Users take a photo of their outfit and upload it to the app. Once uploaded, the server analyzes the image and assigns a score to your outfit. The app then recommends the perfect outfit items based on your preferred style and past history."

[0800] In this way, the system according to the present invention assists the user in easily selecting fashion and makes everyday coordination more enjoyable.

[0801] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0802] Step 1:

[0803] The user takes a photo of the outfit and uploads it to the app.

[0804] Specifically, the user takes a photo of the outfit using the camera on their smartphone or tablet. This image is saved in JPEG or PNG format. The user then presses the "Upload Image" button in the app, selects the image they took, and uploads it. The input data is the outfit image taken by the user, and the output is an image file that passes validation.

[0805] Step 2:

[0806] The device sends the image to the server.

[0807] Specifically, the device validates the image format and size. If successful, it sends the image to the server using an HTTP POST request. At this time, data encryption is performed over the communication network to ensure secure communication. The input data is the image file that has passed validation, and the output is a notification to the server that transmission has been completed.

[0808] Step 3:

[0809] The server receives the images and performs pre-processing.

[0810] Specifically, the server receives images sent from the device and stores them in temporary storage. It then uses an image processing library such as OpenCV to perform preprocessing such as resizing (e.g., converting to 224x224 pixels) and normalization (e.g., normalizing to the range [0,1]). The input data is the received image file, and the output is the preprocessed image data.

[0811] Step 4:

[0812] The server extracts image features and generates an evaluation score.

[0813] Specifically, the server inputs the preprocessed image into a generative AI model (e.g., TensorFlow) to extract features. The AI ​​model calculates an evaluation score for the outfit based on the features. This score is determined on a scale from 0 to 100. The input data is the preprocessed image data, and the output is the evaluation score.

[0814] Step 5:

[0815] The server retrieves the user's history and preferences.

[0816] Specifically, the server retrieves the user's past outfit history and preferred style data from a database (e.g., Amazon RDS). This information is managed based on the user profile. The input data is the user ID, and the output is the user's past outfit history and preferred style data.

[0817] Step 6:

[0818] The server generates the appropriate coordinates.

[0819] Specifically, the server selects appropriate fashion items from a database based on the acquired user preference data and evaluation scores. It uses an algorithm to generate optimal combinations, taking into account style, season, trends, etc. The input data are the evaluation scores and the user's history and preference data, and the output is coordination suggestions.

[0820] Step 7:

[0821] The server converts the coordination proposal into a data format and transmits it to the user terminal.

[0822] Specifically, the server converts the generated coordination proposal into a data format such as JSON and sends it to the user's device as an HTTP response. The input data is the coordination proposal, and the output is the coordination proposal converted into a data format.

[0823] Step 8:

[0824] The terminal analyzes and displays the coordination suggestions.

[0825] Specifically, the user device parses the JSON data received from the server and visually displays outfit suggestions to the user through a user interface. Suggested fashion items are displayed using images and text. The input data is the outfit suggestions converted into a data format, and the output is a display of the parsed outfit suggestions.

[0826] This series of processes enables the user to quickly and effectively obtain coordination suggestions based on their own preferences and past history.

[0827] (Application example 1)

[0828] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0829] Conventional clothing coordination suggestion systems simply calculate an evaluation score and provide coordination suggestions. As a result, users have no way to check how the suggested items will actually look on them, which prevents them from appreciating the usefulness of the suggestions or increasing their willingness to actually purchase them. Furthermore, they lack the convenience of directly purchasing the suggested items. This can lead to lower user satisfaction and a decrease in the frequency of system usage.

[0830] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0831] In this invention, the server includes a means for performing a virtual try-on using an augmented reality function on the user terminal and a means for providing a link for purchasing the suggested items. This allows the user to virtually try on the suggested coordinating items and check how they look. It also makes it easy to directly purchase the items after trying them on, improving user satisfaction and system usage frequency.

[0832] The "means for acquiring an image" is a device including a camera and an interface that allows the user to take a picture of their own outfit and upload the image to the application.

[0833] "Means for transmitting to a server via a communication network" refers to a mechanism for transmitting image data acquired from a user terminal to a remote server using the Internet.

[0834] "Means for analyzing images and generating an evaluation score for clothing" refers to the process of using an AI model on the server side to analyze the clothing in the image and quantify the quality of the style and combination.

[0835] The "means for generating coordination suggestions" is a system in which the server selects and combines other fashion items that go well with the suggested outfit based on the user's past preferences and history.

[0836] The "means for transmitting the generated coordination proposal to the user terminal" is a procedure for transmitting the coordination proposal generated on the server side in data format to the terminal used by the user.

[0837] The "means for displaying coordinated outfit suggestions on a user terminal" refers to an interface that visually displays coordinated outfit suggestions on a terminal so that the user can confirm the suggested coordinated outfits.

[0838] "Means for performing virtual try-on using augmented reality functions" refers to a function that uses a smartphone camera and AR technology to allow users to virtually try on suggested clothing items.

[0839] "Means for providing links to purchase suggested items" refers to a mechanism for providing web links or a shopping cart function for directly purchasing suggested fashion items within the application.

[0840] As one embodiment of the present invention, a virtual style assistant system using a smartphone application will be described in detail.

[0841] The system mainly uses the following hardware and software. The hardware includes a user device such as a smartphone or tablet with a camera function. This device is used by the user to take pictures of the clothing and upload them to the application. The software includes Flask (web framework), TensorFlow (AI model), and PIL (image processing library). The system communicates with a server via a communications network, and the server performs advanced data analysis.

[0842] 1. Image Acquisition

[0843] Users use their smartphones to take photos of their outfits and upload them to the application, which validates the image format and size and prepares it for transmission to the server in the appropriate format.

[0844] 2. Sending images

[0845] The user terminal transmits the validated image to the server via a communication network, for example, using an HTTP POST request.

[0846] 3. Image analysis and evaluation score generation

[0847] The server receives the sent image and stores it in temporary storage. It then preprocesses the image and converts it into a format that can be input to the AI ​​model. The AI ​​model extracts the characteristics of the clothing in the image and calculates an evaluation score. This evaluation score quantifies the quality of the user's clothing style and combination.

[0848] 4. Coordination Proposal Generation

[0849] The server generates optimal outfit suggestions based on the evaluation scores, the user's preferences, and past history data. This suggestion is made by selecting appropriate fashion items from a database and combining them.

[0850] 5. Submit your proposal

[0851] The generated coordination proposals are converted into a data format such as JSON and sent to the user's device.

[0852] 6. Viewing proposals and virtual try-on

[0853] The user device analyzes the outfit suggestions received from the server and displays them through a user interface. The suggested fashion items are visually presented using images and text. The user can also virtually try on the suggested items using the augmented reality function of their smartphone.

[0854] 7. Purchasing Items

[0855] If the user likes a suggested item, they are provided with a link to purchase it directly from within the application, effectively purchasing all suggested items.

[0856] As a concrete example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits, combining items like jeans, white sneakers, and a simple T-shirt. These suggestions are sent to the device and displayed for easy review. Using augmented reality, the user can virtually try on the outfit and, if they like it, purchase it directly from within the app.

[0857] Example prompt for a generative AI model:

[0858] Extract features from user-uploaded clothing images and convert the image's style into a rating score, which should be provided on a scale from 0 to 100.

[0859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0860] Step 1:

[0861] A user uses a smartphone to take a photo of their outfit and upload it to the application. The device validates the image format and size and saves it in the appropriate format. The input is the outfit image taken by the user, and the output is the validated image file. The specific operation is for the user to press the "Upload Image" button, select an image from the gallery or camera, and upload it.

[0862] Step 2:

[0863] The terminal sends the validated image to the server via the communication network. An HTTP POST request is used to transfer the uploaded image data to the server. The input is the validated image file stored on the user terminal, and the output is the completion of the transfer of the image data to the server. The specific operation is to send a POST request using the network communication module.

[0864] Step 3:

[0865] The server receives the transmitted image data and saves it in temporary storage. The input is the image data transmitted from the user terminal, and the output is an image file saved in temporary storage. Specifically, the request handler on the server side receives the image data and saves it in a specific directory.

[0866] Step 4:

[0867] The server preprocesses the images stored in temporary storage and converts them into a format that can be input to the generative AI model. The input is the image file stored in temporary storage, and the output is the preprocessed image data. Specific operations include preprocessing the images, such as resizing and normalizing.

[0868] Step 5:

[0869] The server inputs the preprocessed image data into the generative AI model, extracts the clothing features in the image, and calculates an evaluation score. The input is the preprocessed image data, and the output is the calculated evaluation score. The specific operation is to supply the image data to the AI ​​model and obtain a score as the inference result.

[0870] Step 6:

[0871] The server generates optimal outfit suggestions based on the evaluation scores, the user's preferences, and past history data. The input is the evaluation scores and user profile data, and the output is outfit suggestions. The specific operation is to select appropriate fashion items from the database and generate the optimal combination.

[0872] Step 7:

[0873] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's device. The input is the generated coordination proposal, and the output is the completion of sending the data to the user's device. The specific operation is to serialize the data and send it via a POST request.

[0874] Step 8:

[0875] The user terminal analyzes the outfit suggestions received from the server and displays them through a user interface. The input is the outfit suggestion data received from the server, and the output is the display on the user interface. The specific operation is to parse the received data and display it on the screen.

[0876] Step 9:

[0877] The user uses the smartphone's augmented reality (AR) function to virtually try on the suggested items. The input is image data of the suggested outfit items, and the output is a virtual try-on video displayed through the AR function. The specific operation is to perform the virtual try-on using a camera and AR software.

[0878] Step 10:

[0879] If the user likes a suggested item, they are provided with a link to purchase it directly from within the application. The input is a purchase link based on the outfit suggestion, and the output is a purchase screen displayed on the user's device. Specific operations include displaying web links and a shopping cart function, and connecting to an online shopping system.

[0880] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0881] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and uses an emotion engine that recognizes the user's emotions to provide coordination suggestions based on the user's preferences and past history. Specific embodiments of this system are described in detail below.

[0882] 1. Get the user's clothing image

[0883] 1.1 The user takes a photo of the outfit and uploads it to the app

[0884] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select and upload the image. At this time, the device is responsible for validating the format and size of the image file.

[0885] 2. Send the acquired image to the server

[0886] 2.1 The device sends the image to the server

[0887] The terminal sends the successfully validated image to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via a communication network.

[0888] 3. The server analyzes the image and generates an outfit evaluation score.

[0889] 3.1 Server receives image

[0890] The server receives the image data sent from the terminal and stores it in temporary storage, after which it performs analysis using the stored image data.

[0891] 3.2 Image processing on the server

[0892] The server pre-processes the image data before inputting it into the AI ​​model, including resizing and normalizing the image so that the AI ​​model can accurately recognize the image.

[0893] 3.3 Server scores outfits

[0894] The server inputs the preprocessed image into the AI ​​model, which extracts image features. The AI ​​model then calculates an outfit evaluation score based on the extracted features. This score is calculated on a scale of 0 to 100, based on factors such as style and combination.

[0895] 4. Recognizing user emotions and generating outfit suggestions

[0896] 4.1 The server recognizes the user's emotional state

[0897] The server uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to extract the user's emotional state. The emotional state is recorded in a database and used to suggest subsequent outfits.

[0898] 4.2 Server retrieves user history and preferences

[0899] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0900] 4.3 The server generates suitable coordinates

[0901] The server selects appropriate fashion items from the database based on the acquired user preference data, evaluation scores, and emotional state. These items are then combined optimally, taking into account the user's current emotional state.

[0902] 5. Send the generated coordination proposal to the user device.

[0903] 5.1 Server Sends Coordination Proposal

[0904] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's terminal.

[0905] 6. Displaying outfit suggestions on the user's device

[0906] 6.1 The device displays outfit suggestions

[0907] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0908] Specific examples

[0909] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then analyzes the user's facial expressions to recognize their emotional state, such as "happy" or "calm." Based on the user's past preference data, the server then suggests outfits that combine casual and relaxed items like jeans, white sneakers, and a simple T-shirt. These suggestions are displayed on the device for viewing.

[0910] As described above, the system according to the present invention incorporating emotion recognition can assist the user in selecting fashion and easily provide the user with the most appropriate outfit for their emotional state.

[0911] The processing flow will be explained below.

[0912] Step 1:

[0913] The user takes a photo of their outfit using the camera app on their smartphone. After taking the photo, the user presses the "Upload Image" button in the app to select and upload the image.

[0914] Step 2:

[0915] The device validates the uploaded image by checking the image file type and size to ensure it is in the correct format.

[0916] Step 3:

[0917] The terminal sends the successfully validated image to the server using an HTTP POST request, and the image data is transferred to the server via a communication network.

[0918] Step 4:

[0919] The server receives the image data sent from the terminal and stores it in temporary storage.

[0920] Step 5:

[0921] The server preprocesses the stored image data, which includes resizing and normalizing the images to ensure that the AI ​​model can accurately recognize them.

[0922] Step 6:

[0923] The server inputs the preprocessed image into the AI ​​model, extracts image features, and the AI ​​model calculates an outfit evaluation score based on the extracted features.

[0924] Step 7:

[0925] The server analyzes the user's facial expressions and tone of voice and uses an emotion engine to recognize the user's emotional state, which is then recorded in a database.

[0926] Step 8:

[0927] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[0928] Step 9:

[0929] The server selects appropriate fashion items from the database based on the user's preference data, evaluation scores, and recognized emotional state. These items are then combined optimally, taking into account the user's current emotional state.

[0930] Step 10:

[0931] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's terminal.

[0932] Step 11:

[0933] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[0934] For example, if a user uploads an image and the AI ​​model gives it a score of 70 / 100, the server analyzes the user's facial expressions and recognizes their emotional state, such as "happy" or "calm." Based on the user's past preferences, the server then suggests items such as casual and relaxing jeans, white sneakers, and a simple T-shirt. These suggestions are displayed on the device for viewing.

[0935] Example 2

[0936] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0937] While personal style and fashion options have become increasingly diverse in recent years, there is a lack of systems that evaluate users' clothing and suggest optimal outfits. This makes it difficult for users to objectively evaluate their own style and select appropriate items. Furthermore, since outfit suggestions based on the user's emotions are not available, it is difficult to select a style that suits their mood or situation.

[0938] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0939] In this invention, the server includes means for analyzing image data and generating an evaluation score for clothing, means for recognizing the user's emotions, and means for generating outfit suggestions based on the user's preferences and past history, thereby enabling the user to objectively evaluate their own style and further enabling optimal outfit suggestions based on the user's emotional state at the time.

[0940] "Devices that acquire image data" are devices that allow users to take images of clothing using a smartphone, tablet, etc., and store and manage that image data.

[0941] The "device that transmits acquired image data to a server device via a communication network" is a device that has the function of transferring acquired image data to a server device via a communication network such as the Internet.

[0942] The "device in the server device that analyzes image data and generates an evaluation score for clothing" is a device necessary for analyzing received image data and calculating an evaluation score for clothing using an artificial intelligence model or the like.

[0943] The "device for recognizing the user's emotion in the server device" is a device for analyzing the user's facial expression and voice data to identify and record the emotion.

[0944] The "server device that generates coordination suggestions based on the user's preferences and past history" is a device that is necessary to refer to the user's past history and preference data and suggest optimal combinations of fashion items.

[0945] The "device that transmits the generated coordination proposal to the user terminal device" is a device that has the function of transmitting the coordination proposal generated by the server device to the user terminal device via a communication network.

[0946] The "device for displaying coordination suggestions on a user terminal device" is a device for analyzing received coordination suggestions and visually displaying them through a user interface.

[0947] The system of this invention allows users to upload images of their own clothing, analyzes those images to calculate an evaluation score, and then uses an emotion engine that recognizes the user's emotions to provide coordination suggestions based on the user's preferences and past history.

[0948] 1. Acquisition and upload of image data

[0949] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select the image, and the device validates the format and size of the image file when uploading it.

[0950] 2. Sending image data to the server

[0951] The terminal sends the image that has been successfully validated to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via a communication network.

[0952] 3. Receipt and analysis of image data

[0953] The server receives the image data sent from the device and stores it in temporary storage. It then performs analysis using this stored image data. The server uses the Python PIL (Pillow) library to resize and normalize the image, allowing the AI ​​model to accurately recognize the image.

[0954] 4. Calculating the clothing evaluation score

[0955] The server inputs image data into an AI model trained using TensorFlow and PyTorch, extracts features from the image, and calculates an outfit evaluation score ranging from 0 to 100 based on the extracted features.

[0956] 5. Recognition of User Emotions

[0957] The server uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to extract the user's emotional state. The emotional state is recorded in a database and used to suggest subsequent outfits.

[0958] 6. Coordination Proposal Generation

[0959] The server retrieves the user's past outfit history and preferred style data from the database, integrates this information, selects appropriate fashion items from the database, and generates optimal outfit suggestions that take into account the user's current emotional state.

[0960] 7. Sending and displaying outfit suggestions

[0961] The server converts the generated outfit suggestions into a data format such as JSON and sends them to the user's device. The device then analyzes the received data and displays it to the user through a user interface. The user can then view the recommended fashion item combinations in both images and text.

[0962] Specific examples

[0963] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device then sends the image to a server, which analyzes it and calculates a rating score of 70 / 100. The server then analyzes the user's emotional information and recognizes their emotional state, such as "happy" or "calm." Based on the user's past preference data, the server then suggests outfits that combine items such as casual and relaxing jeans, white sneakers, and a simple T-shirt. These suggestions are displayed as images and text for viewing on the device.

[0964] Example prompt sentence:

[0965] "We'll suggest a casual outfit based on your mood today: How about some relaxed jeans, white sneakers, and a simple T-shirt?"

[0966] In this way, a system incorporating emotion recognition can assist users in choosing fashion and provide them with outfits that are optimal for their emotional state.

[0967] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0968] Step 1:

[0969] The user acquires and uploads clothing images.

[0970] Users take a photo of their outfit using a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app, select the image, and upload it. The input data is the captured image file, and the output data is the validated image file. The device checks the image format and size, and allows uploading if it meets the conditions of JPEG or PNG and a maximum of 5MB.

[0971] Step 2:

[0972] The device sends the image to the server

[0973] The device sends successfully validated images to the server using an HTTP POST request. The input data is the validated image file, and the output data is a message indicating that the image file has been transferred to the server. The device sends the image data to the server via the Internet and monitors the status of the network connection in real time.

[0974] Step 3:

[0975] The server receives the image and stores it in temporary storage.

[0976] The server saves the received image data in temporary storage. The input data is the transferred image file, and the output data is the path to the image file saved in storage. The server records the completion of saving the image file as a log.

[0977] Step 4:

[0978] The server processes the images

[0979] The server uses Python's PIL (Pillow) library to resize the image to an appropriate size, correct brightness and contrast, normalize the image data, and convert it into a format that can be input to the AI ​​model. The input data is the image file stored in storage, and the output data is the preprocessed image data.

[0980] Step 5:

[0981] The server grades the outfit.

[0982] The server inputs the preprocessed images into an AI model trained with TensorFlow or PyTorch. The AI ​​model extracts image features and calculates an outfit evaluation score based on those features. The input data is the preprocessed image data, and the output data is an evaluation score ranging from 0 to 100. The server records the calculated score in a database.

[0983] Step 6:

[0984] The server recognizes the user's emotional state

[0985] The server analyzes inputs from the user, facial expressions, and voice data, and uses an emotion engine to recognize the user's emotional state. The input data is information about the user's emotions, and the output data is the analyzed emotional state. The server records the emotional state in a database.

[0986] Step 7:

[0987] The server retrieves the user's history and preferences

[0988] The server retrieves the user's past outfit history and preference data from the database. The input data is the user's ID, and the output data is the retrieved history and preference data. The server stores this data in its internal memory for use in proposing the next outfit.

[0989] Step 8:

[0990] The server generates the appropriate coordinates

[0991] The server uses the emotion data, evaluation scores, and user history data to select the most suitable fashion items from the database and generate outfit suggestions. The input data is the emotional state, evaluation scores, and user history data, and the output data is the generated outfit suggestions. The server converts the suggestions into JSON format.

[0992] Step 9:

[0993] The server sends the generated coordination proposal to the user terminal.

[0994] The server sends the generated coordination proposal in JSON format to the user's device. The input data is the generated coordination proposal, and the output data is a message that transmission is complete to the user's device. The server records a transmission log of the proposal data.

[0995] Step 10:

[0996] The device displays outfit suggestions

[0997] The device parses the JSON data received from the server and visually displays outfit suggestions to the user through a user interface. The input data is the received JSON data, and the output data is the displayed outfit suggestions. The device also provides user evaluation and feedback functions.

[0998] (Application example 2)

[0999] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1000] While the convenience of online shopping has improved in recent years, there is a problem in that it takes a lot of time and effort for users to find the right outfit. In addition, current systems do not take into account the user's emotional state when proposing outfits, so an improvement in the user experience is required. Furthermore, there is a lack of systems that can be easily used on devices such as smartphones.

[1001] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing an image and generating an evaluation score for clothing, means for recognizing the user's emotional state and optimizing coordination suggestions based on the recognition, and means for transmitting the generated coordination suggestions to the user terminal. This makes it possible to provide optimal coordination suggestions based on the user's emotional state and past history.

[1002] The "means for acquiring an image" refers to a device or method by which the terminal captures an image of the user's clothing and acquires the image data.

[1003] The "means for transmitting the acquired image to the server via a communication network" refers to a device or method for transferring image data from the terminal to the server using the network.

[1004] "Means for analyzing images on a server and generating an evaluation score for clothing" refers to a device or method that uses artificial intelligence to evaluate clothing based on image data received by the server and calculates a score based on that evaluation.

[1005] "Means for generating coordination suggestions based on the user's preferences and past history on the server" refers to a device or method in which the server selects suitable fashion items and suggests coordination based on the user's saved data.

[1006] The "means for transmitting the generated coordination proposal to the user terminal" refers to a device or method by which the server converts the coordination proposal into a data format and transfers it to the user's terminal.

[1007] The "means for displaying a coordination suggestion at a user terminal" refers to a device or method by which the user's terminal visually displays the received coordination suggestion.

[1008] "Means for recognizing the user's emotional state and optimizing coordination suggestions based on that" refers to a device or method that uses emotion recognition technology to analyze the user's emotions and makes optimal fashion suggestions to the user based on the analysis results.

[1009] This invention is a system that analyzes clothing images uploaded by users, generates an evaluation score, and then recognizes the user's emotional state to provide optimal outfit suggestions. This system consists of three main components: the user's device, a server, and an emotion recognition engine.

[1010] User terminal

[1011] The user terminal is a device such as a smartphone or tablet. The user takes a photo of their own outfit with the device's camera and uploads the image to the application. The application validates the format and size of the uploaded image and sends the image to the server in the appropriate format. When the user selects an image through the input form and presses the "Submit" button, the system begins operation.

[1012] server

[1013] The server receives the modified image data and stores it in temporary storage. This stored image data is then used for analysis using the AI ​​model. Before that, preprocessing such as image resizing and normalization is performed to adjust the AI ​​model so that it can function properly. Once the analysis is complete, the server generates an evaluation score for the outfit.

[1014] The server also incorporates an emotion engine that recognizes the user's emotions by capturing and analyzing facial images and voice data. Based on the results of this analysis and past history data, optimal outfit suggestions are generated.

[1015] Emotion Recognition Engine

[1016] Emotion engineering uses the DeepFace library and other emotion recognition software to analyze a user's emotions. The analyzed emotion data is stored in a database, which the server then uses to suggest outfits appropriate for the user's emotional state.

[1017] Sending and viewing outfit suggestions

[1018] The outfit suggestions generated by the server are converted into a data format such as JSON and sent to the user's device. The application on the user's device analyzes the received data and visually displays the suggestions to the user. This allows the user to see the optimal outfit based on their emotional state and past history, and use it for shopping and closet management.

[1019] Specific examples

[1020] For example, a user can take a photo of their outfit with their smartphone and upload it to the application. The server receives the image and generates an evaluation score for the outfit using an AI model. The emotion recognition engine then recognizes emotions such as "happiness" and suggests casual item combinations based on past preference data. These suggestions are visually displayed on the smartphone, allowing the user to decide their next move.

[1021] Example prompts to input to the generative AI model

[1022] We want the system to upload images of the user's outfits and generate optimal outfit suggestions in conjunction with the emotion engine. We will use the following data:

[1023] Image data: {Image data}

[1024] Emotional state: {Emotional state}

[1025] Past history: {Past history}

[1026] Please return the best outfit suggestions in the following format:

[1027] Suggested Item Name

[1028] category

[1029] More Information

[1030] Evaluation score

[1031] The above is a specific embodiment for carrying out the present invention.

[1032] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1033] Step 1:

[1034] A user takes a photo of their own outfit using a device such as a smartphone. The input is the image data taken by the user, and the output is an image file saved on the device. This image file is temporarily saved for later transmission to the server.

[1035] Step 2:

[1036] The device validates the captured image data to ensure it is in the correct format and size. The input is the image file captured by the user, and the output is a validated image file. If the validation process is successful, the user uploads the image to the app.

[1037] Step 3:

[1038] The terminal sends the validated image file to the server via a communication network. The input is the validated image file, and the output is the image data sent to the server. Communication is performed via a protocol such as an HTTP POST request.

[1039] Step 4:

[1040] The server stores the received image data in temporary storage and performs preprocessing on the image. The input is the image data sent to the server, and the output is the preprocessed image data. Preprocessing includes image resizing and normalization.

[1041] Step 5:

[1042] The server inputs the preprocessed image data into the AI ​​model to generate an outfit evaluation score. The input is the preprocessed image data, and the output is the outfit evaluation score. This score is calculated by the AI ​​model by analyzing the image features.

[1043] Step 6:

[1044] The server uses an emotion recognition engine to recognize the user's emotional state. The input is the user's facial image and voice data, and the output is the recognized emotional state. The emotion recognition engine uses libraries such as DeepFace.

[1045] Step 7:

[1046] The server generates optimal outfit suggestions based on the user's emotional state, rating score, and past history data. The inputs are the emotional state, rating score, and past history data, and the output is outfit suggestions. The suggestions are generated by selecting and combining appropriate items from the database.

[1047] Step 8:

[1048] The server converts the generated outfit suggestions into a data format such as JSON and sends it to the user's device. The input is the outfit suggestion data, and the output is the data sent to the user's device. This data is converted into a format appropriate for display on the user's device.

[1049] Step 9:

[1050] The user terminal analyzes the coordination suggestions received from the server and visually displays them to the user. The input is the data sent from the server, and the output is the coordination suggestions displayed on the user interface. The user can check this and decide what to do next.

[1051] The specific operations and data flow in each step have been described above.

[1052] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1053] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1054] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1055] [Fourth embodiment]

[1056] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1057] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1058] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1059] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1060] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1061] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1062] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1063] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1064] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1065] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1066] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1067] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1068] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1069] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and provides coordination suggestions based on the user's preferences and past history. Specific embodiments of this system are described in detail below.

[1070] 1. Get the user's clothing image

[1071] 1.1 The user takes a photo of the outfit and uploads it to the app

[1072] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select and upload the image. At this time, the device is responsible for validating the format and size of the image file.

[1073] 2. Send the acquired image to the server

[1074] 2.1 The device sends the image to the server

[1075] The terminal sends the successfully validated image to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via the communication network.

[1076] 3. The server analyzes the image and generates an outfit evaluation score.

[1077] 3.1 Server receives image

[1078] The server receives the image data sent from the terminal and stores it in temporary storage, after which it performs analysis using the stored image data.

[1079] 3.2 Image processing on the server

[1080] The server pre-processes the image data before inputting it into the AI ​​model, including resizing and normalizing the image so that the AI ​​model can accurately recognize the image.

[1081] 3.3 Server scores outfits

[1082] The server inputs the preprocessed image into the AI ​​model, which extracts image features. The AI ​​model then calculates an outfit evaluation score based on the extracted features. This score is calculated on a scale of 0 to 100, based on factors such as style and combination.

[1083] 4. Generate coordination suggestions based on user preferences and past history

[1084] 4.1 Server retrieves user history and preferences

[1085] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[1086] 4.2 The server generates appropriate coordinates

[1087] The server selects appropriate fashion items from the database based on the acquired user preference data and evaluation scores. These fashion items are then combined optimally, taking into account factors such as style, season, and trends.

[1088] 5. Send the generated coordination proposal to the user device.

[1089] 5.1 Server Sends Coordination Proposal

[1090] The server converts the generated coordination proposals into a data format such as JSON and sends them to the user's terminal.

[1091] 6. Displaying outfit suggestions on the user's device

[1092] 6.1 The device displays outfit suggestions

[1093] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[1094] Specific examples

[1095] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits, combining items like jeans, white sneakers, and a simple T-shirt. These suggestions are then sent to the device and displayed for the user to easily review.

[1096] In this way, the system according to the present invention assists the user in selecting fashion and makes everyday coordination easier and more enjoyable.

[1097] The processing flow will be explained below.

[1098] Step 1:

[1099] The user takes a photo of their outfit using the camera app on their smartphone. After taking the photo, the user presses the "Upload Image" button in the app to select and upload the image.

[1100] Step 2:

[1101] The device validates the uploaded image by checking the image file type and size to ensure it is in the correct format.

[1102] Step 3:

[1103] The terminal sends the successfully validated image to the server using an HTTP POST request, and the image data is transferred to the server via a communication network.

[1104] Step 4:

[1105] The server receives the image data sent from the terminal and stores it in temporary storage.

[1106] Step 5:

[1107] The server preprocesses the stored image data, which includes resizing and normalizing the images so that the AI ​​model can accurately recognize them.

[1108] Step 6:

[1109] The server inputs the preprocessed image into the AI ​​model, which extracts image features and calculates an outfit evaluation score based on them.

[1110] Step 7:

[1111] The server then uses the evaluation score obtained from the AI ​​model to obtain the user's past outfit history and preferred style data, which is obtained from a database.

[1112] Step 8:

[1113] Based on the user's preference data and evaluation scores, the server selects appropriate fashion items from the database. These items are then combined optimally, taking into account factors such as style, season, and trends.

[1114] Step 9:

[1115] The server converts the generated coordination proposal into a data format such as JSON, and then sends this data to the user's device.

[1116] Step 10:

[1117] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[1118] For example, if a user uploads an image and the AI ​​model gives it a rating score of 70 / 100, the server will suggest items based on the user's preferences, such as jeans, white sneakers, and a simple T-shirt, and these suggestions will be displayed on the device for review.

[1119] Example 1

[1120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1121] Conventional fashion coordination systems have difficulty automatically suggesting outfits that suit a user's clothing and style, making it difficult for users to easily find outfit combinations that suit them. Furthermore, manual image evaluation and outfit suggestions are time-consuming and inefficient. Furthermore, the lack of customized suggestions that reflect individual users' preferences and past history results in low user satisfaction.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1123] In this invention, the server includes means for a user to take an image of an outfit and upload it to a terminal, means for transmitting the acquired image to the server via a communication network, means for preprocessing the image in the server and extracting image features to generate an outfit evaluation score, means for generating outfit suggestions in the server based on the user's preferences and past history, means for converting the generated outfit suggestions into a data format and transmitting it to the user terminal, and means for analyzing and displaying the outfit suggestions in the user terminal. This enables the user to quickly and effectively obtain appropriate outfit suggestions based on their preferences and past history.

[1124] "User" refers to an individual who uses the system to upload images of their own clothing and receive coordination suggestions.

[1125] "Terminal" refers to a computer device such as a smartphone or tablet used by a User, through which images are uploaded and outfit suggestions are received and displayed.

[1126] The term "server" refers to a remote computer system that receives data sent from a terminal and performs image analysis and coordinated suggestion generation.

[1127] "Communications network" refers to an infrastructure for transmitting and receiving data, including the Internet.

[1128] "Image preprocessing" refers to the process of resizing and normalizing the image data received by the server in order to input it into the AI ​​model.

[1129] "Image features" refer to the important data points that the AI ​​model extracts from an image to generate an evaluation score.

[1130] "Evaluation score" refers to the score that indicates the quality of the clothing calculated by the AI ​​model based on the extracted features.

[1131] "Coordination suggestions" refer to suggestions for combining multiple fashion items that are generated by the server based on the user's preferences, past history, and evaluation scores.

[1132] "Data format" refers to a format such as JSON or XML that represents the coordination suggestion information sent from the server to the terminal.

[1133] "Preprocessing" refers to a series of operations that change the attributes of an image and make it easier for an AI model to analyze.

[1134] "User interface" refers to the screen and operation method that allows the user to visually check coordination suggestions on the terminal.

[1135] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and provides coordination suggestions based on the user's preferences and past history. A specific embodiment of this system will be described in detail.

[1136] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the image, they press the "Upload Image" button in the app to select and upload the image. At this time, the device validates the image file format (e.g., JPEG or PNG) and size (e.g., maximum 10MB). Images that pass validation will proceed to the next step.

[1137] The device sends the successfully validated image to the server using an HTTP POST request. This transmission is performed over a communication network (e.g., the Internet). Data encryption is performed to ensure communication security.

[1138] The server receives image data sent from the device and stores it in temporary storage. It then uses an image processing library such as OpenCV to perform preprocessing such as resizing (e.g., converting to 224x224 pixels) and normalization (e.g., normalizing to the [0,1] range).

[1139] After preprocessing, the image is input into a generative AI model such as TensorFlow. The server uses this AI model to extract image features and calculates an outfit evaluation score based on the image features. The evaluation score is determined on a scale of 0 to 100 based on the style and combination of the outfit.

[1140] Next, the server retrieves the user's past outfit history and preferred style data from a database such as Amazon RDS. This information is managed based on the user's profile. The server then selects appropriate fashion items from the database based on the retrieved data and evaluation scores. This is done using an algorithm that takes into account factors such as style, season, and trends.

[1141] The generated outfit suggestions are converted into a data format such as JSON and sent to the user's device as an HTTP response. The device analyzes the received data and visually displays the outfit suggestions to the user through a user interface. The suggested fashion items are displayed using images and text, making it easy for the user to check the details.

[1142] As a concrete example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device then sends the image to the server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits that combine appropriate items such as jeans, white sneakers, and a simple T-shirt. These suggestions are then sent to the device and displayed for the user to easily review.

[1143] An example of a prompt is as follows:

[1144] "Users take a photo of their outfit and upload it to the app. Once uploaded, the server analyzes the image and assigns a score to your outfit. The app then recommends the perfect outfit items based on your preferred style and past history."

[1145] In this way, the system according to the present invention assists the user in easily selecting fashion and makes everyday coordination more enjoyable.

[1146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1147] Step 1:

[1148] The user takes a photo of the outfit and uploads it to the app.

[1149] Specifically, the user takes a photo of the outfit using the camera on their smartphone or tablet. This image is saved in JPEG or PNG format. The user then presses the "Upload Image" button in the app, selects the image they took, and uploads it. The input data is the outfit image taken by the user, and the output is an image file that passes validation.

[1150] Step 2:

[1151] The device sends the image to the server.

[1152] Specifically, the device validates the image format and size. If successful, it sends the image to the server using an HTTP POST request. At this time, data encryption is performed over the communication network to ensure secure communication. The input data is the image file that has passed validation, and the output is a notification to the server that transmission has been completed.

[1153] Step 3:

[1154] The server receives the images and performs pre-processing.

[1155] Specifically, the server receives images sent from the device and stores them in temporary storage. It then uses an image processing library such as OpenCV to perform preprocessing such as resizing (e.g., converting to 224x224 pixels) and normalization (e.g., normalizing to the range [0,1]). The input data is the received image file, and the output is the preprocessed image data.

[1156] Step 4:

[1157] The server extracts image features and generates an evaluation score.

[1158] Specifically, the server inputs the preprocessed image into a generative AI model (e.g., TensorFlow) to extract features. The AI ​​model calculates an evaluation score for the outfit based on the features. This score is determined on a scale from 0 to 100. The input data is the preprocessed image data, and the output is the evaluation score.

[1159] Step 5:

[1160] The server retrieves the user's history and preferences.

[1161] Specifically, the server retrieves the user's past outfit history and preferred style data from a database (e.g., Amazon RDS). This information is managed based on the user profile. The input data is the user ID, and the output is the user's past outfit history and preferred style data.

[1162] Step 6:

[1163] The server generates the appropriate coordinates.

[1164] Specifically, the server selects appropriate fashion items from a database based on the acquired user preference data and evaluation scores. It uses an algorithm to generate optimal combinations, taking into account style, season, trends, etc. The input data are the evaluation scores and the user's history and preference data, and the output is coordination suggestions.

[1165] Step 7:

[1166] The server converts the coordination proposal into a data format and transmits it to the user terminal.

[1167] Specifically, the server converts the generated coordination proposal into a data format such as JSON and sends it to the user's device as an HTTP response. The input data is the coordination proposal, and the output is the coordination proposal converted into a data format.

[1168] Step 8:

[1169] The terminal analyzes and displays the coordination suggestions.

[1170] Specifically, the user device parses the JSON data received from the server and visually displays outfit suggestions to the user through a user interface. Suggested fashion items are displayed using images and text. The input data is the outfit suggestions converted into a data format, and the output is a display of the parsed outfit suggestions.

[1171] This series of processes enables the user to quickly and effectively obtain coordination suggestions based on their own preferences and past history.

[1172] (Application example 1)

[1173] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1174] Conventional clothing coordination suggestion systems simply calculate an evaluation score and provide coordination suggestions. As a result, users have no way to check how the suggested items will actually look on them, which prevents them from appreciating the usefulness of the suggestions or increasing their willingness to actually purchase them. Furthermore, they lack the convenience of directly purchasing the suggested items. This can lead to lower user satisfaction and a decrease in the frequency of system usage.

[1175] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1176] In this invention, the server includes a means for performing a virtual try-on using an augmented reality function on the user terminal and a means for providing a link for purchasing the suggested items. This allows the user to virtually try on the suggested coordinating items and check how they look. It also makes it easy to directly purchase the items after trying them on, improving user satisfaction and system usage frequency.

[1177] The "means for acquiring an image" is a device including a camera and an interface that allows the user to take a picture of their own outfit and upload the image to the application.

[1178] "Means for transmitting to a server via a communication network" refers to a mechanism for transmitting image data acquired from a user terminal to a remote server using the Internet.

[1179] "Means for analyzing images and generating an evaluation score for clothing" refers to the process of using an AI model on the server side to analyze the clothing in the image and quantify the quality of the style and combination.

[1180] The "means for generating coordination suggestions" is a system in which the server selects and combines other fashion items that go well with the suggested outfit based on the user's past preferences and history.

[1181] The "means for transmitting the generated coordination proposal to the user terminal" is a procedure for transmitting the coordination proposal generated on the server side in data format to the terminal used by the user.

[1182] The "means for displaying coordinated outfit suggestions on a user terminal" refers to an interface that visually displays coordinated outfit suggestions on a terminal so that the user can confirm the suggested coordinated outfits.

[1183] "Means for performing virtual try-on using augmented reality functions" refers to a function that uses a smartphone camera and AR technology to allow users to virtually try on suggested clothing items.

[1184] "Means for providing links to purchase suggested items" refers to a mechanism for providing web links or a shopping cart function for directly purchasing suggested fashion items within the application.

[1185] As one embodiment of the present invention, a virtual style assistant system using a smartphone application will be described in detail.

[1186] The system mainly uses the following hardware and software. The hardware includes a user device such as a smartphone or tablet with a camera function. This device is used by the user to take pictures of the clothing and upload them to the application. The software includes Flask (web framework), TensorFlow (AI model), and PIL (image processing library). The system communicates with a server via a communications network, and the server performs advanced data analysis.

[1187] 1. Image Acquisition

[1188] Users use their smartphones to take photos of their outfits and upload them to the application, which validates the image format and size and prepares it for transmission to the server in the appropriate format.

[1189] 2. Sending images

[1190] The user terminal transmits the validated image to the server via a communication network, for example, using an HTTP POST request.

[1191] 3. Image analysis and evaluation score generation

[1192] The server receives the sent image and stores it in temporary storage. It then preprocesses the image and converts it into a format that can be input to the AI ​​model. The AI ​​model extracts the characteristics of the clothing in the image and calculates an evaluation score. This evaluation score quantifies the quality of the user's clothing style and combination.

[1193] 4. Coordination Proposal Generation

[1194] The server generates optimal outfit suggestions based on the evaluation scores, the user's preferences, and past history data. This suggestion is made by selecting appropriate fashion items from a database and combining them.

[1195] 5. Submit your proposal

[1196] The generated coordination proposals are converted into a data format such as JSON and sent to the user's device.

[1197] 6. Viewing proposals and virtual try-on

[1198] The user device analyzes the outfit suggestions received from the server and displays them through a user interface. The suggested fashion items are visually presented using images and text. The user can also virtually try on the suggested items using the augmented reality function of their smartphone.

[1199] 7. Purchasing Items

[1200] If the user likes a suggested item, they are provided with a link to purchase it directly from within the application, effectively purchasing all suggested items.

[1201] As a concrete example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then uses the user's past preferences to suggest outfits, combining items like jeans, white sneakers, and a simple T-shirt. These suggestions are sent to the device and displayed for easy review. Using augmented reality, the user can virtually try on the outfit and, if they like it, purchase it directly from within the app.

[1202] Example prompt for a generative AI model:

[1203] Extract features from user-uploaded clothing images and convert the image's style into a rating score, which should be provided on a scale from 0 to 100.

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

[1205] Step 1:

[1206] A user uses a smartphone to take a photo of their outfit and upload it to the application. The device validates the image format and size and saves it in the appropriate format. The input is the outfit image taken by the user, and the output is the validated image file. The specific operation is for the user to press the "Upload Image" button, select an image from the gallery or camera, and upload it.

[1207] Step 2:

[1208] The terminal sends the validated image to the server via the communication network. An HTTP POST request is used to transfer the uploaded image data to the server. The input is the validated image file stored on the user terminal, and the output is the completion of the transfer of the image data to the server. The specific operation is to send a POST request using the network communication module.

[1209] Step 3:

[1210] The server receives the transmitted image data and saves it in temporary storage. The input is the image data transmitted from the user terminal, and the output is an image file saved in temporary storage. Specifically, the request handler on the server side receives the image data and saves it in a specific directory.

[1211] Step 4:

[1212] The server preprocesses the images stored in temporary storage and converts them into a format that can be input to the generative AI model. The input is the image file stored in temporary storage, and the output is the preprocessed image data. Specific operations include preprocessing the images, such as resizing and normalizing.

[1213] Step 5:

[1214] The server inputs the preprocessed image data into the generative AI model, extracts the clothing features in the image, and calculates an evaluation score. The input is the preprocessed image data, and the output is the calculated evaluation score. The specific operation is to supply the image data to the AI ​​model and obtain a score as the inference result.

[1215] Step 6:

[1216] The server generates optimal outfit suggestions based on the evaluation scores, the user's preferences, and past history data. The input is the evaluation scores and user profile data, and the output is outfit suggestions. The specific operation is to select appropriate fashion items from the database and generate the optimal combination.

[1217] Step 7:

[1218] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's device. The input is the generated coordination proposal, and the output is the completion of sending the data to the user's device. The specific operation is to serialize the data and send it via a POST request.

[1219] Step 8:

[1220] The user terminal analyzes the outfit suggestions received from the server and displays them through a user interface. The input is the outfit suggestion data received from the server, and the output is the display on the user interface. The specific operation is to parse the received data and display it on the screen.

[1221] Step 9:

[1222] The user uses the smartphone's augmented reality (AR) function to virtually try on the suggested items. The input is image data of the suggested outfit items, and the output is a virtual try-on video displayed through the AR function. The specific operation is to perform the virtual try-on using a camera and AR software.

[1223] Step 10:

[1224] If the user likes a suggested item, they are provided with a link to purchase it directly from within the application. The input is a purchase link based on the outfit suggestion, and the output is a purchase screen displayed on the user's device. Specific operations include displaying web links and a shopping cart function, and connecting to an online shopping system.

[1225] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1226] The system of the present invention allows users to upload images of their own clothing, analyzes the images to calculate an evaluation score, and uses an emotion engine that recognizes the user's emotions to provide coordination suggestions based on the user's preferences and past history. Specific embodiments of this system are described in detail below.

[1227] 1. Get the user's clothing image

[1228] 1.1 The user takes a photo of the outfit and uploads it to the app

[1229] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select and upload the image. At this time, the device is responsible for validating the format and size of the image file.

[1230] 2. Send the acquired image to the server

[1231] 2.1 The device sends the image to the server

[1232] The terminal sends the successfully validated image to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via a communication network.

[1233] 3. The server analyzes the image and generates an outfit evaluation score.

[1234] 3.1 Server receives image

[1235] The server receives the image data sent from the terminal and stores it in temporary storage, after which it performs analysis using the stored image data.

[1236] 3.2 Image processing on the server

[1237] The server pre-processes the image data before inputting it into the AI ​​model, including resizing and normalizing the image so that the AI ​​model can accurately recognize the image.

[1238] 3.3 Server scores outfits

[1239] The server inputs the preprocessed image into the AI ​​model, which extracts image features. The AI ​​model then calculates an outfit evaluation score based on the extracted features. This score is calculated on a scale of 0 to 100, based on factors such as style and combination.

[1240] 4. Recognizing user emotions and generating outfit suggestions

[1241] 4.1 The server recognizes the user's emotional state

[1242] The server uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to extract the user's emotional state. The emotional state is recorded in a database and used to suggest subsequent outfits.

[1243] 4.2 Server retrieves user history and preferences

[1244] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[1245] 4.3 The server generates suitable coordinates

[1246] The server selects appropriate fashion items from the database based on the acquired user preference data, evaluation scores, and emotional state. These items are then combined optimally, taking into account the user's current emotional state.

[1247] 5. Send the generated coordination proposal to the user device.

[1248] 5.1 Server Sends Coordination Proposal

[1249] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's terminal.

[1250] 6. Displaying outfit suggestions on the user's device

[1251] 6.1 The device displays outfit suggestions

[1252] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[1253] Specific examples

[1254] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device sends the image to a server, which analyzes it and calculates a score of 70 / 100. The server then analyzes the user's facial expressions to recognize their emotional state, such as "happy" or "calm." Based on the user's past preference data, the server then suggests outfits that combine casual and relaxed items like jeans, white sneakers, and a simple T-shirt. These suggestions are displayed on the device for viewing.

[1255] As described above, the system according to the present invention incorporating emotion recognition can assist the user in selecting fashion and easily provide the user with the most appropriate outfit for their emotional state.

[1256] The processing flow will be explained below.

[1257] Step 1:

[1258] The user takes a photo of their outfit using the camera app on their smartphone. After taking the photo, the user presses the "Upload Image" button in the app to select and upload the image.

[1259] Step 2:

[1260] The device validates the uploaded image by checking the image file type and size to ensure it is in the correct format.

[1261] Step 3:

[1262] The terminal sends the successfully validated image to the server using an HTTP POST request, and the image data is transferred to the server via a communication network.

[1263] Step 4:

[1264] The server receives the image data sent from the terminal and stores it in temporary storage.

[1265] Step 5:

[1266] The server preprocesses the stored image data, which includes resizing and normalizing the images to ensure that the AI ​​model can accurately recognize them.

[1267] Step 6:

[1268] The server inputs the preprocessed image into the AI ​​model, extracts image features, and the AI ​​model calculates an outfit evaluation score based on the extracted features.

[1269] Step 7:

[1270] The server analyzes the user's facial expressions and tone of voice and uses an emotion engine to recognize the user's emotional state, which is then recorded in a database.

[1271] Step 8:

[1272] The server retrieves the user's past outfit history and preferred style data from the database, which is managed based on the user profile.

[1273] Step 9:

[1274] The server selects appropriate fashion items from the database based on the user's preference data, evaluation scores, and recognized emotional state. These items are then combined optimally, taking into account the user's current emotional state.

[1275] Step 10:

[1276] The server converts the generated coordination proposal into a data format such as JSON and sends it to the user's terminal.

[1277] Step 11:

[1278] The terminal analyzes the outfit suggestions received from the server and displays them to the user through a user interface in an easy-to-understand manner. The proposed fashion item combinations are visually presented using images and text.

[1279] For example, if a user uploads an image and the AI ​​model gives it a score of 70 / 100, the server analyzes the user's facial expressions and recognizes their emotional state, such as "happy" or "calm." Based on the user's past preferences, the server then suggests items such as casual and relaxing jeans, white sneakers, and a simple T-shirt. These suggestions are displayed on the device for viewing.

[1280] Example 2

[1281] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1282] While personal style and fashion options have become increasingly diverse in recent years, there is a lack of systems that evaluate users' clothing and suggest optimal outfits. This makes it difficult for users to objectively evaluate their own style and select appropriate items. Furthermore, since outfit suggestions based on the user's emotions are not available, it is difficult to select a style that suits their mood or situation.

[1283] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1284] In this invention, the server includes means for analyzing image data and generating an evaluation score for clothing, means for recognizing the user's emotions, and means for generating outfit suggestions based on the user's preferences and past history, thereby enabling the user to objectively evaluate their own style and further enabling optimal outfit suggestions based on the user's emotional state at the time.

[1285] "Devices that acquire image data" are devices that allow users to take images of clothing using a smartphone, tablet, etc., and store and manage that image data.

[1286] The "device that transmits acquired image data to a server device via a communication network" is a device that has the function of transferring acquired image data to a server device via a communication network such as the Internet.

[1287] The "device in the server device that analyzes image data and generates an evaluation score for clothing" is a device necessary for analyzing received image data and calculating an evaluation score for clothing using an artificial intelligence model or the like.

[1288] The "device for recognizing the user's emotion in the server device" is a device for analyzing the user's facial expression and voice data to identify and record the emotion.

[1289] The "server device that generates coordination suggestions based on the user's preferences and past history" is a device that is necessary to refer to the user's past history and preference data and suggest optimal combinations of fashion items.

[1290] The "device that transmits the generated coordination proposal to the user terminal device" is a device that has the function of transmitting the coordination proposal generated by the server device to the user terminal device via a communication network.

[1291] The "device for displaying coordination suggestions on a user terminal device" is a device for analyzing received coordination suggestions and visually displaying them through a user interface.

[1292] The system of this invention allows users to upload images of their own clothing, analyzes those images to calculate an evaluation score, and then uses an emotion engine that recognizes the user's emotions to provide coordination suggestions based on the user's preferences and past history.

[1293] 1. Acquisition and upload of image data

[1294] Users take a photo of their outfit using a device such as a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app to select the image, and the device validates the format and size of the image file when uploading it.

[1295] 2. Sending image data to the server

[1296] The terminal sends the image that has been successfully validated to the server using an HTTP POST request, etc. At this time, the image data is transferred to the server via a communication network.

[1297] 3. Receipt and analysis of image data

[1298] The server receives the image data sent from the device and stores it in temporary storage. It then performs analysis using this stored image data. The server uses the Python PIL (Pillow) library to resize and normalize the image, allowing the AI ​​model to accurately recognize the image.

[1299] 4. Calculating the clothing evaluation score

[1300] The server inputs image data into an AI model trained using TensorFlow and PyTorch, extracts features from the image, and calculates an outfit evaluation score ranging from 0 to 100 based on the extracted features.

[1301] 5. Recognition of User Emotions

[1302] The server uses an emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's facial expressions and tone of voice to extract the user's emotional state. The emotional state is recorded in a database and used to suggest subsequent outfits.

[1303] 6. Coordination Proposal Generation

[1304] The server retrieves the user's past outfit history and preferred style data from the database, integrates this information, selects appropriate fashion items from the database, and generates optimal outfit suggestions that take into account the user's current emotional state.

[1305] 7. Sending and displaying outfit suggestions

[1306] The server converts the generated outfit suggestions into a data format such as JSON and sends them to the user's device. The device then analyzes the received data and displays it to the user through a user interface. The user can then view the recommended fashion item combinations in both images and text.

[1307] Specific examples

[1308] For example, a user can take a photo of their outfit with their smartphone and upload it to the app. The device then sends the image to a server, which analyzes it and calculates a rating score of 70 / 100. The server then analyzes the user's emotional information and recognizes their emotional state, such as "happy" or "calm." Based on the user's past preference data, the server then suggests outfits that combine items such as casual and relaxing jeans, white sneakers, and a simple T-shirt. These suggestions are displayed as images and text for viewing on the device.

[1309] Example prompt sentence:

[1310] "We'll suggest a casual outfit based on your mood today: How about some relaxed jeans, white sneakers, and a simple T-shirt?"

[1311] In this way, a system incorporating emotion recognition can assist users in choosing fashion and provide them with outfits that are optimal for their emotional state.

[1312] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1313] Step 1:

[1314] The user acquires and uploads clothing images.

[1315] Users take a photo of their outfit using a smartphone or tablet. After taking the photo, they press the "Upload Image" button in the app, select the image, and upload it. The input data is the captured image file, and the output data is the validated image file. The device checks the image format and size, and allows uploading if it meets the conditions of JPEG or PNG and a maximum of 5MB.

[1316] Step 2:

[1317] The device sends the image to the server

[1318] The device sends successfully validated images to the server using an HTTP POST request. The input data is the validated image file, and the output data is a message indicating that the image file has been transferred to the server. The device sends the image data to the server via the Internet and monitors the status of the network connection in real time.

[1319] Step 3:

[1320] The server receives the image and stores it in temporary storage.

[1321] The server saves the received image data in temporary storage. The input data is the transferred image file, and the output data is the path to the image file saved in storage. The server records the completion of saving the image file as a log.

[1322] Step 4:

[1323] The server processes the images

[1324] The server uses Python's PIL (Pillow) library to resize the image to an appropriate size, correct brightness and contrast, normalize the image data, and convert it into a format that can be input to the AI ​​model. The input data is the image file stored in storage, and the output data is the preprocessed image data.

[1325] Step 5:

[1326] The server grades the outfit.

[1327] The server inputs the preprocessed images into an AI model trained with TensorFlow or PyTorch. The AI ​​model extracts image features and calculates an outfit evaluation score based on those features. The input data is the preprocessed image data, and the output data is an evaluation score ranging from 0 to 100. The server records the calculated score in a database.

[1328] Step 6:

[1329] The server recognizes the user's emotional state

[1330] The server analyzes inputs from the user, facial expressions, and voice data, and uses an emotion engine to recognize the user's emotional state. The input data is information about the user's emotions, and the output data is the analyzed emotional state. The server records the emotional state in a database.

[1331] Step 7:

[1332] The server retrieves the user's history and preferences

[1333] The server retrieves the user's past outfit history and preference data from the database. The input data is the user's ID, and the output data is the retrieved history and preference data. The server stores this data in its internal memory for use in proposing the next outfit.

[1334] Step 8:

[1335] The server generates the appropriate coordinates

[1336] The server uses the emotion data, evaluation scores, and user history data to select the most suitable fashion items from the database and generate outfit suggestions. The input data is the emotional state, evaluation scores, and user history data, and the output data is the generated outfit suggestions. The server converts the suggestions into JSON format.

[1337] Step 9:

[1338] The server sends the generated coordination proposal to the user terminal.

[1339] The server sends the generated coordination proposal in JSON format to the user's device. The input data is the generated coordination proposal, and the output data is a message that transmission is complete to the user's device. The server records a transmission log of the proposal data.

[1340] Step 10:

[1341] The device displays outfit suggestions

[1342] The device parses the JSON data received from the server and visually displays outfit suggestions to the user through a user interface. The input data is the received JSON data, and the output data is the displayed outfit suggestions. The device also provides user evaluation and feedback functions.

[1343] (Application example 2)

[1344] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1345] While the convenience of online shopping has improved in recent years, there is a problem in that it takes a lot of time and effort for users to find the right outfit. In addition, current systems do not take into account the user's emotional state when proposing outfits, so an improvement in the user experience is required. Furthermore, there is a lack of systems that can be easily used on devices such as smartphones.

[1346] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing an image and generating an evaluation score for clothing, means for recognizing the user's emotional state and optimizing coordination suggestions based on the recognition, and means for transmitting the generated coordination suggestions to the user terminal. This makes it possible to provide optimal coordination suggestions based on the user's emotional state and past history.

[1347] The "means for acquiring an image" refers to a device or method by which the terminal captures an image of the user's clothing and acquires the image data.

[1348] The "means for transmitting the acquired image to the server via a communication network" refers to a device or method for transferring image data from the terminal to the server using the network.

[1349] "Means for analyzing images on a server and generating an evaluation score for clothing" refers to a device or method that uses artificial intelligence to evaluate clothing based on image data received by the server and calculates a score based on that evaluation.

[1350] "Means for generating coordination suggestions based on the user's preferences and past history on the server" refers to a device or method in which the server selects suitable fashion items and suggests coordination based on the user's saved data.

[1351] The "means for transmitting the generated coordination proposal to the user terminal" refers to a device or method by which the server converts the coordination proposal into a data format and transfers it to the user's terminal.

[1352] The "means for displaying a coordination suggestion at a user terminal" refers to a device or method by which the user's terminal visually displays the received coordination suggestion.

[1353] "Means for recognizing the user's emotional state and optimizing coordination suggestions based on that" refers to a device or method that uses emotion recognition technology to analyze the user's emotions and makes optimal fashion suggestions to the user based on the analysis results.

[1354] This invention is a system that analyzes clothing images uploaded by users, generates an evaluation score, and then recognizes the user's emotional state to provide optimal outfit suggestions. This system consists of three main components: the user's device, a server, and an emotion recognition engine.

[1355] User terminal

[1356] The user terminal is a device such as a smartphone or tablet. The user takes a photo of their own outfit with the device's camera and uploads the image to the application. The application validates the format and size of the uploaded image and sends the image to the server in the appropriate format. When the user selects an image through the input form and presses the "Submit" button, the system begins operation.

[1357] server

[1358] The server receives the modified image data and stores it in temporary storage. This stored image data is then used for analysis using the AI ​​model. Before that, preprocessing such as image resizing and normalization is performed to adjust the AI ​​model so that it can function properly. Once the analysis is complete, the server generates an evaluation score for the outfit.

[1359] The server also incorporates an emotion engine that recognizes the user's emotions by capturing and analyzing facial images and voice data. Based on the results of this analysis and past history data, optimal outfit suggestions are generated.

[1360] Emotion Recognition Engine

[1361] Emotion engineering uses the DeepFace library and other emotion recognition software to analyze a user's emotions. The analyzed emotion data is stored in a database, which the server then uses to suggest outfits appropriate for the user's emotional state.

[1362] Sending and viewing outfit suggestions

[1363] The outfit suggestions generated by the server are converted into a data format such as JSON and sent to the user's device. The application on the user's device analyzes the received data and visually displays the suggestions to the user. This allows the user to see the optimal outfit based on their emotional state and past history, and use it for shopping and closet management.

[1364] Specific examples

[1365] For example, a user can take a photo of their outfit with their smartphone and upload it to the application. The server receives the image and generates an evaluation score for the outfit using an AI model. The emotion recognition engine then recognizes emotions such as "happiness" and suggests casual item combinations based on past preference data. These suggestions are visually displayed on the smartphone, allowing the user to decide their next move.

[1366] Example prompts to input to the generative AI model

[1367] We want the system to upload images of the user's outfits and generate optimal outfit suggestions in conjunction with the emotion engine. We will use the following data:

[1368] Image data: {Image data}

[1369] Emotional state: {Emotional state}

[1370] Past history: {Past history}

[1371] Please return the best outfit suggestions in the following format:

[1372] Suggested Item Name

[1373] category

[1374] More Information

[1375] Evaluation score

[1376] The above is a specific embodiment for carrying out the present invention.

[1377] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1378] Step 1:

[1379] A user takes a photo of their own outfit using a device such as a smartphone. The input is the image data taken by the user, and the output is an image file saved on the device. This image file is temporarily saved for later transmission to the server.

[1380] Step 2:

[1381] The device validates the captured image data to ensure it is in the correct format and size. The input is the image file captured by the user, and the output is a validated image file. If the validation process is successful, the user uploads the image to the app.

[1382] Step 3:

[1383] The terminal sends the validated image file to the server via a communication network. The input is the validated image file, and the output is the image data sent to the server. Communication is performed via a protocol such as an HTTP POST request.

[1384] Step 4:

[1385] The server stores the received image data in temporary storage and performs preprocessing on the image. The input is the image data sent to the server, and the output is the preprocessed image data. Preprocessing includes image resizing and normalization.

[1386] Step 5:

[1387] The server inputs the preprocessed image data into the AI ​​model to generate an outfit evaluation score. The input is the preprocessed image data, and the output is the outfit evaluation score. This score is calculated by the AI ​​model by analyzing the image features.

[1388] Step 6:

[1389] The server uses an emotion recognition engine to recognize the user's emotional state. The input is the user's facial image and voice data, and the output is the recognized emotional state. The emotion recognition engine uses libraries such as DeepFace.

[1390] Step 7:

[1391] The server generates optimal outfit suggestions based on the user's emotional state, rating score, and past history data. The inputs are the emotional state, rating score, and past history data, and the output is outfit suggestions. The suggestions are generated by selecting and combining appropriate items from the database.

[1392] Step 8:

[1393] The server converts the generated outfit suggestions into a data format such as JSON and sends it to the user's device. The input is the outfit suggestion data, and the output is the data sent to the user's device. This data is converted into a format appropriate for display on the user's device.

[1394] Step 9:

[1395] The user terminal analyzes the coordination suggestions received from the server and visually displays them to the user. The input is the data sent from the server, and the output is the coordination suggestions displayed on the user interface. The user can check this and decide what to do next.

[1396] The specific operations and data flow in each step have been described above.

[1397] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1398] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1399] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1400] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1401] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1402] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1403] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1404] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1405] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1406] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1407] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1408] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1409] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1410] 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.

[1411] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1412] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1413] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1414] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1415] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1416] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1417] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1418] The following is further disclosed regarding the above embodiment.

[1419] (Claim 1)

[1420] a means for acquiring an image;

[1421] means for transmitting the acquired image to a server via a communication network;

[1422] means for analyzing the image in the server and generating an outfit evaluation score;

[1423] A means for generating coordination suggestions based on the user's preferences and past history in the server;

[1424] means for transmitting the generated coordination proposal to a user terminal;

[1425] a means for displaying coordination suggestions on a user terminal;

[1426] A system including:

[1427] (Claim 2)

[1428] 2. The system of claim 1, wherein the means for analyzing the image uses an artificial intelligence model to extract image features and calculate a specific score.

[1429] (Claim 3)

[1430] 2. The system according to claim 1, wherein the means for generating a coordination proposal based on the user's preferences and past history selects appropriate items from a coordination database and combines them.

[1431] "Example 1"

[1432] (Claim 1)

[1433] A means for a user to take an image of the outfit and upload it to a terminal;

[1434] means for transmitting the acquired image to a server via a communication network;

[1435] A server pre-processes the image, extracts image features, and generates an evaluation score for the clothing.

[1436] A means for generating coordination suggestions based on the user's preferences and past history in the server;

[1437] means for converting the generated coordination proposal into a data format and transmitting the data to a user terminal;

[1438] means for analyzing and displaying coordination suggestions in a user terminal;

[1439] A system including:

[1440] (Claim 2)

[1441] The system according to claim 1, characterized in that the image is analyzed using an artificial intelligence model, image features are extracted, and an evaluation score is calculated.

[1442] (Claim 3)

[1443] The system according to claim 1, characterized in that it selects appropriate items from a coordinate database based on the user's preferences and past history, and combines them to generate coordinate suggestions.

[1444] "Application Example 1"

[1445] (Claim 1)

[1446] a means for acquiring an image;

[1447] means for transmitting the acquired image to a server via a communication network;

[1448] means for analyzing the image in the server and generating an outfit evaluation score;

[1449] A means for generating coordination suggestions based on the user's preferences and past history in the server;

[1450] means for transmitting the generated coordination proposal to a user terminal;

[1451] a means for displaying coordination suggestions on a user terminal;

[1452] A means for performing a virtual try-on using an augmented reality function in a user terminal;

[1453] a means for providing a link to purchase the suggested item;

[1454] A system including:

[1455] (Claim 2)

[1456] 2. The system of claim 1, wherein an artificial intelligence model is used to extract image features and calculate a specific score.

[1457] (Claim 3)

[1458] The system according to claim 1, characterized in that it selects appropriate items from a coordinate database and combines them to generate coordinate suggestions.

[1459] "Example 2: Combining Emotion Engines"

[1460] (Claim 1)

[1461] a device for acquiring image data;

[1462] a device for transmitting the acquired image data to a server device via a communication network;

[1463] a device in a server device that analyzes image data and generates an evaluation score for the clothing;

[1464] a device for recognizing user emotions in a server device;

[1465] a server device that generates coordination suggestions based on the user's preferences and past history;

[1466] a device for transmitting the generated coordination proposal to a user terminal device;

[1467] a device for displaying coordination suggestions on a user terminal device;

[1468] A system including:

[1469] (Claim 2)

[1470] 2. The system according to claim 1, further comprising a server device that uses an artificial intelligence model to extract image features and calculate a specific score.

[1471] (Claim 3)

[1472] 10. The system according to claim 1, characterized by a device for selecting appropriate items from a coordinate database and combining them.

[1473] "Application example 2 when combining emotion engines"

[1474] (Claim 1)

[1475] a means for acquiring an image;

[1476] means for transmitting the acquired image to a server via a communication network;

[1477] means for analyzing the image in the server and generating an outfit evaluation score;

[1478] A means for generating coordination suggestions based on the user's preferences and past history in the server;

[1479] means for transmitting the generated coordination proposal to a user terminal;

[1480] a means for displaying coordination suggestions on a user terminal;

[1481] means for recognizing a user's emotional state and optimizing outfit suggestions based thereon;

[1482] A system including:

[1483] (Claim 2)

[1484] 2. The system of claim 1, wherein the means for analyzing the image uses an artificial intelligence model to extract image features and calculate a specific score.

[1485] (Claim 3)

[1486] 2. The system according to claim 1, wherein the means for generating coordination suggestions based on the user's preferences and past history selects appropriate items from a coordination database and combines them. [Explanation of symbols]

[1487] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for acquiring an image; means for transmitting the acquired image to a server via a communication network; means for analyzing the image in the server and generating an outfit evaluation score; A means for generating coordination suggestions based on the user's preferences and past history in the server; means for transmitting the generated coordination proposal to a user terminal; a means for displaying coordination suggestions on a user terminal; A system including:

2. 2. The system of claim 1, wherein the means for analyzing the image uses an artificial intelligence model to extract image features and calculate a specific score.

3. 2. The system according to claim 1, wherein the means for generating a coordination proposal based on the user's preferences and past history selects appropriate items from a coordination database and combines them.

Images