system

Abstract

We provide the system. [Solution] A means to input your mood for the day, what you want to do, and your relationship with the person you're going out with, A means of sending the input data to the server, A means for analyzing transmitted data and generating appropriate clothing suggestions, A means of generating images based on the generated clothing suggestions, A means of sending the generated clothing suggestions and images to the terminal, A means of displaying clothing suggestions and images on a device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, with the diversification of consumers' lifestyles and the increasing interest in fashion, there has been a demand for choosing clothing suitable for a specific mood or activity content. However, in conventional clothing recommendation systems, it is difficult to provide appropriate fashion recommendations considering the user's mood, activity content, and the relationship with companions, and there is a need for a means to automatically and easily provide more specific and visual recommendations.

Means for Solving the Problems

[0005] The present invention includes means for inputting the user's mood, what they want to do, and their relationship with the person they are going out with; means for transmitting the input data to a server; means for analyzing the transmitted data and generating appropriate clothing suggestions; means for generating images based on the generated clothing suggestions; means for transmitting the generated clothing suggestions and images to a terminal; and means for displaying the clothing suggestions and images on the terminal. This system makes it possible to respond to the diverse needs of users and automatically provide specific and visual fashion suggestions based on their mood, activities, and relationship with their companions.

[0006] "Mood of the day" refers to the emotional state or feelings a user experiences in response to a specific situation or environment on that particular day.

[0007] "Things to do" refers to specific activities or plans that the user wants to accomplish on that day.

[0008] "Relationship with the person you're going out with" refers to the type of relationship the user has with the person they are going out with (for example, friend, lover, colleague, etc.).

[0009] "Means of input" refers to the interface through which users provide data to the system.

[0010] "Means of transmission" refers to the means of communication used by a terminal to transfer input data to a server.

[0011] "Means of analysis" refers to the process by which a server extracts and analyzes information based on the data it receives.

[0012] "Generating means" refers to the process of automatically creating appropriate clothing suggestions and images based on the analysis results.

[0013] "Means of generating images" refers to the process of automatically creating images to visually represent fashion proposals.

[0014] The "means for transmitting to the terminal" refers to the communication means for transferring the proposed content and images generated by the server to the user's terminal.

[0015] The "means for displaying" refers to the interface for visually presenting the information received by the terminal to the user.

[0016] The "JSON format" refers to the JavaScript (registered trademark) Object Notation that expresses data in a lightweight and highly readable format and is suitable for data exchange.

[0017] The "explanation text of fashion proposal" refers to the document that expresses the detailed explanation and proposal about the selected clothing.

Brief Explanation of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of the data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of the data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of the data processing device and the smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of the data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of the data processing device and the headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of the data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of the data processing device and the robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention is a system that provides appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. Below, we will generate the program for this system and explain its specific processing details.

[0040] User data entry

[0041] When a user logs into the system, the terminal displays an input form. This form includes fields for entering "how you feel that day," "what you want to do," and "the relationship you have with the person you're going out with." For example, a user might enter "I want to relax" for their mood, "cafe hopping" for what they want to do, and "friend" for their relationship.

[0042] Sending input data

[0043] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. An example of the generated JSON format is {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}.

[0044] Server-based data analysis and proposal generation

[0045] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles based on mood, activity, and relationships. This analysis might select a casual style, such as "a simple T-shirt, denim pants, and sneakers."

[0046] Utilization of AI for image generation

[0047] Next, the server sends instructions to the image generation AI based on the fashion suggestions, and generates an image that captures the atmosphere of the suggested fashion style. For example, it might send an instruction to generate an image of a model wearing a simple T-shirt, denim pants, and sneakers. This image generation AI then generates a high-quality image based on the instructions.

[0048] Submitting and displaying proposal results

[0049] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0050] Specific examples

[0051] As a concrete example, if a user enters conditions such as "feeling energetic" and "going hiking," and that their companion is "a colleague," the server will suggest items suitable for "energetic activity," such as "sporty outerwear, trekking pants, and athletic shoes," and generate images based on those suggestions. In this way, users can visually confirm specific fashion styles that match their activity and mood.

[0052] Thus, the present invention provides an effective fashion support system that meets the diverse needs of users.

[0053] The following describes the processing flow.

[0054] Step 1:

[0055] The device displays a form for the user to input their mood for the day, what they want to do, and their relationship with the person they are going out with.

[0056] Step 2:

[0057] The user enters "how they feel that day," "what they want to do," and "the nature of their relationship with the person they are going out with" into an input form.

[0058] Step 3:

[0059] The terminal converts the input data into JSON format. For example, it formats the data into {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}.

[0060] Step 4:

[0061] The device sends the formatted JSON data to the server using an HTTP request.

[0062] Step 5:

[0063] The server parses the JSON data received from the terminal and generates appropriate fashion suggestions in conjunction with the database.

[0064] Step 6:

[0065] The server selects a list of fashion items suitable for the mood, activity, and relationship based on the received data, and generates a description of the fashion suggestion. For example, it might generate a description like, "A simple T-shirt, denim pants, and sneakers."

[0066] Step 7:

[0067] The server sends instructions to the image generation AI to generate an image based on the selected fashion items.

[0068] Step 8:

[0069] The image generation AI receives instructions from the server and generates images that capture the feel of fashion items based on those instructions.

[0070] Step 9:

[0071] The image generation AI sends the generated image to the server.

[0072] Step 10:

[0073] The server integrates the generated fashion suggestions and image data, and then formats them back into JSON format. Example: {"suggestion": "Simple T-shirt, denim pants, sneakers", "image_url": "http: / / example.com / generated_image.jpg"}

[0074] Step 11:

[0075] The server sends the integrated JSON data to the terminal.

[0076] Step 12:

[0077] The device interprets the JSON data received from the server and displays a description of the fashion suggestion and the generated image to the user.

[0078] (Example 1)

[0079] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0080] In modern times, choosing fashion is a significant burden for consumers. In particular, selecting appropriate attire based on one's mood, activities, and relationship with companions is difficult. Therefore, there is a need for a system that allows users to easily choose the most suitable outfit for the day.

[0081] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0082] In this invention, the server includes means for analyzing transmitted data and generating appropriate clothing suggestions, means for sending prompt messages to an image generation algorithm based on the generated clothing suggestions, and means for sending the clothing suggestions, including the generated images, to the terminal. This allows the user to receive appropriate clothing suggestions and atmospheric images based on their mood, activities, and relationship with their companions on the day.

[0083] "Mood on the day" refers to the emotions and mental state the user is experiencing on that particular day.

[0084] "Things to do" refers to the activities or events that the user wants to do on that day.

[0085] "Relationship with the person you're going out with" refers to the type of relationship the user has with the person they're going out with on that day (for example, friend, family, colleague).

[0086] "Means of input" refers to the interface or device that allows users to input data into a system.

[0087] "Means of sending to the server" refers to the communication functions and protocols used to send data entered by the user to the server.

[0088] "Means for analyzing transmitted data and generating appropriate clothing suggestions" refers to algorithms and processes that analyze data transmitted by users and generate optimal fashion suggestions based on that data.

[0089] "Means for sending prompt statements to an image generation algorithm" refers to the process of generating and sending instruction statements (prompt statements) to an image generation algorithm based on the generated clothing suggestions.

[0090] "Means for sending clothing suggestions, including generated images, to a terminal" refers to communication functions and protocols for sending images received from an image generation algorithm and generated clothing suggestions to a terminal.

[0091] "Means for displaying clothing suggestions and images on a terminal" refers to interfaces or devices for displaying received clothing suggestions and images on the user's terminal.

[0092] This invention is a system that provides appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. A specific embodiment of this system will be described below.

[0093] User data entry

[0094] When a user logs into the system, the terminal displays an input form. This form includes fields for "how you feel that day," "what you want to do," and "the relationship you have with the person you're going out with." For example, a user might enter "I want to relax" for their mood, "cafe hopping" for what they want to do, and "friend" for their relationship.

[0095] Sending input data

[0096] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. The generated JSON data will, for example, send data like the following:

[0097] {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}

[0098] Data analysis on the server and generation of fashion suggestions.

[0099] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles that match the mood, activity, and relationship based on the sent data. This analysis selects a casual style, such as "a simple T-shirt, denim pants, and sneakers."

[0100] Utilization of AI for image generation

[0101] Next, the server sends prompts to the image generation AI based on the fashion suggestions, generating images that capture the atmosphere of the suggested fashion style. For example, a prompt is sent to generate an image of a model wearing a "simple T-shirt, denim pants, and sneakers." This image generation AI generates high-quality images based on the instructions. The following types of prompts are used:

[0102] "Please generate an image of a model wearing a simple T-shirt, denim pants, and sneakers."

[0103] Submitting and displaying proposal results

[0104] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0105] Specific examples

[0106] As a concrete example, if a user enters conditions such as "feeling energetic" and "going hiking," and that their companion is "a colleague," the server will suggest items suitable for "energetic activity," such as "sporty outerwear, trekking pants, and athletic shoes," and generate images based on those suggestions. The prompt text in this case would be as follows:

[0107] "Please generate an image showing a sporty look with a sporty outer layer, trekking pants, and athletic shoes."

[0108] This allows users to receive specific fashion suggestions that they can visually confirm.

[0109] This system helps users choose the most appropriate outfit based on their mood, activities, and relationship with their companions on the day. This provides an effective fashion support system that meets the diverse needs of users.

[0110] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0111] System program processing flow

[0112] Step 1: User input

[0113] When a user logs into the system, the terminal displays a form for them to enter their "mood for the day," "things they want to do," and "relationship with the person they are going out with." The user then uses this form to enter the information.

[0114] Input: User inputs "mood for the day," "things they want to do," and "relationship with the person they are going out with."

[0115] Output: Input data converted to JSON format

[0116] Specific operation: The user enters information into each field in the text field and clicks the submit button.

[0117] Step 2: Convert the data to JSON format

[0118] The device converts user input data into JSON format. For example, if the input data is "I want to relax," "cafe hopping," and "friends," the following JSON data will be generated:

[0119] {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}

[0120] Input: User input data

[0121] Output: Data in JSON format

[0122] Specific operation: The terminal uses a program such as JavaScript to convert the data into JSON format.

[0123] Step 3: Sending data to the server

[0124] The terminal sends the converted JSON data to the server. The data is sent to the server using an HTTP request.

[0125] Input: Data in JSON format

[0126] Output: Data received by the server

[0127] Specific action: The terminal generates an HTTP POST request and sends data to the server.

[0128] Step 4: Data analysis on the server

[0129] The server parses the received JSON data. The server then accesses the database to determine fashion styles based on mood, activity, and relationships.

[0130] Input: Received JSON data

[0131] Output: Appropriate fashion suggestions

[0132] Specific operation: The server performs JSON parsing and issues SQL queries or API requests to retrieve suggestions from the database.

[0133] Step 5: Generating Fashion Proposals

[0134] Based on the analysis results, the server generates appropriate fashion suggestions. For example, it might select a suggestion like "a simple T-shirt, denim pants, and sneakers."

[0135] Input: Fashion style data from database

[0136] Output: Fashion suggestion data

[0137] Specific operation: The server generates text data for fashion suggestions based on information retrieved from the database.

[0138] Step 6: Send a prompt message to the image generation AI.

[0139] The server sends a prompt to the image generation AI based on the generated fashion suggestions. An example of a prompt might be, "Generate an image of a model wearing a simple T-shirt, denim pants, and sneakers."

[0140] Input: Fashion suggestion data

[0141] Output: Prompt message

[0142] Specific operation: The server generates a prompt message and sends it to the image generation AI's API endpoint using an HTTP request.

[0143] Step 7: Image generation

[0144] The image generation AI generates an atmospheric image based on the prompt text. This image is returned to the server.

[0145] Input: Prompt message

[0146] Output: The generated image

[0147] Specific operation: The AI ​​model parses the prompt text and executes an image generation algorithm to generate an image.

[0148] Step 8: Returning and formatting the results to the server

[0149] After the generated image is sent back to the server, the server combines the fashion suggestion and the image into one file and formats it again into JSON format.

[0150] Input: Generated image, fashion suggestion data

[0151] Output: Proposal results in JSON format

[0152] Specific operation: The server combines image data and fashion suggestion data into a JSON object and converts it into a format that can be sent as an HTTP response.

[0153] Step 9: Sending the results to the terminal

[0154] The server sends the formatted JSON data to the terminal. The terminal interprets the received data and displays it to the user.

[0155] Input: Suggestion results in JSON format

[0156] Output: Display of fashion suggestions and images on the device

[0157] Specific operation: The server sends JSON data to the device as an HTTP response, and the device parses the received data and displays it in the UI.

[0158] Step 10: Presentation to the user

[0159] The device displays fashion suggestions and atmospheric images to the user. This allows the user to visually check the most suitable fashion for their mood and activities on that day.

[0160] Input: Fashion suggestions and images interpreted from JSON data

[0161] Output: Fashion suggestions and images displayed in the user interface

[0162] Specific operation: The terminal uses HTML and CSS to display suggestions and images in a graphical user interface.

[0163] This allows users to receive specific and visually understandable fashion suggestions, making daily outfit selection easier and more enjoyable.

[0164] (Application Example 1)

[0165] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0166] Traditional fashion suggestion systems struggled to provide personalized recommendations based on detailed context, such as the user's mood, activities, and relationship with companions. As a result, users often lacked a concrete image to help them make optimal choices. Furthermore, there was a lack of integrated systems for visually confirming suggested styles and directly purchasing items from e-commerce sites.

[0167] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0168] This invention includes a server that provides input for the user's mood, what they want to do, and their relationship with the person they are going out with; a server that provides input data

[0169] "Mood of the day" refers to the subjective feelings or mood that the user is experiencing on that particular day.

[0170] "Things to do" refers to the specific activities or actions that the user wants to perform on that particular day.

[0171] "Relationship with the person you're going out with" refers to the relationship between the user and the person they will be spending time with or going out with on that particular day (for example, a friend, colleague, or family member).

[0172] "Means of input" refers to the interface (such as forms or buttons) that a user uses to provide information to a system.

[0173] "Means of transmission" refers to the processes and technologies used to send data from a terminal to a server.

[0174] "A means of analyzing and generating appropriate clothing suggestions" refers to the process by which a server analyzes user input data and selects the optimal fashion style based on that data.

[0175] An "image generation artificial intelligence model" is an artificial intelligence model that has the ability to generate high-quality images based on input prompt text.

[0176] A "prompt message" is the text content used to request an artificial intelligence model to generate a specific image.

[0177] "Generating means" refers to the process and techniques for generating images that match the proposed clothing style.

[0178] "Means of display" refers to the interface and technology for displaying the generated clothing suggestions and images on the user's device.

[0179] An "e-commerce site" is a website where users can purchase goods via the internet.

[0180] "Means of providing purchasable links" refers to the process and technology of displaying online shopping links for users to purchase suggested fashion items.

[0181] This invention provides a system that offers appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. Users can access this system using their smartphones.

[0182] First, when a user logs into the system, the terminal displays an input form. The form includes fields for the user to enter their mood for the day, what they want to do, and their relationship with the person they are going out with. Once the user enters this information, the terminal formats the data into JSON format and sends it to the server.

[0183] When the server receives data, it begins analysis. The analysis automatically generates optimal fashion suggestions from the database based on the user's mood, activities, and relationships. This analysis determines, for example, that "if you're feeling energetic and going hiking, a sporty outer layer, trekking pants, and athletic shoes would be suitable."

[0184] Next, the server sends a prompt to the image generation AI model based on the generated fashion suggestions. The prompt might take the form of, for example, "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues." Based on this prompt, the AI ​​model generates an image that captures the atmosphere of the suggested fashion style.

[0185] The generated fashion suggestions and atmosphere images are formatted again in JSON format and sent to the device. The device receives this and displays it to the user. Furthermore, the device also provides online shopping links where the suggested clothing items can be purchased. This allows the user to visually confirm the suggested style and purchase the items directly from the e-commerce site.

[0186] The main hardware of this system is a smartphone, and the software includes a web service framework (e.g., Node.js), a database (e.g., MongoDB), an image generation AI (e.g., OpenAI's DALL-E), and a front-end framework (e.g., React Native).

[0187] For example, if a user enters conditions such as "feeling energetic," "going hiking," and "working with colleagues," the server will suggest "sporty outerwear, trekking pants, and running shoes." Based on this, it generates a prompt message, "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues," and sends it to the image generation AI model. By providing the user with the image generated based on this prompt message, the user can visually confirm specific fashion styles and purchase items directly.

[0188] As described above, the present invention realizes a rapid and effective fashion suggestion system that meets the individual needs of users.

[0189] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0190] Step 1:

[0191] When a user logs into the system, the terminal displays an input form. This form includes fields for the user to enter their mood for the day, what they want to do, and their relationship with the person they are going out with. As the user enters this information, it is formatted into the necessary data format for transmission to the server. The input data format is JSON, for example, {"mood": "energetic mood", "activity": "hiking", "relationship": "colleague"}.

[0192] Step 2:

[0193] The terminal formats the data entered by the user into JSON format and sends it to the server. An HTTP POST request is used to send the entered data to the server. The input consists of the user's mood, what they want to do, and their relationship with the person they are going out with, while the output is the JSON data received by the server.

[0194] Step 3:

[0195] The server analyzes the received data. Specifically, the server analyzes data on mood, activity, and relationships to generate optimal fashion suggestions. This analysis involves database access, referencing fashion styles based on mood, activity, and relationships. The input is data in JSON format, and the output is structured data containing appropriate clothing suggestions.

[0196] Step 4:

[0197] The server sends a prompt to the image generation AI model based on the clothing suggestions it generates. For example, from a suggestion of "sporty outerwear, trekking pants, and running shoes," the prompt "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues." is generated. A request is then sent to the image generation AI based on this prompt. The input is the fashion suggestion, and the output is the prompt to the image generation AI.

[0198] Step 5:

[0199] An image generation artificial intelligence model generates an atmospheric image based on a prompt. The generated image is returned to the server. The input is the prompt, and the output is the generated atmospheric image.

[0200] Step 6:

[0201] The server formats the generated fashion suggestions and atmospheric images into JSON format and sends it to the terminal. The data sent to the terminal includes the text of the fashion suggestions and the URLs of the images. The input is the generated fashion suggestions and atmospheric images, and the output is data in JSON format.

[0202] Step 7:

[0203] The device interprets the received data and displays fashion suggestions (text and images) to the user. Furthermore, it provides links to purchase the suggested items on e-commerce sites. The input is JSON data sent from the server, and the output is the fashion suggestions, atmospheric images, and purchase links displayed to the user.

[0204] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0205] This invention is a fashion suggestion system that incorporates an emotion engine to recognize the user's emotions. This system provides clothing suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. The specific program processing and specific examples of this system are described below.

[0206] Data entry using an emotion engine

[0207] The emotion engine recognizes emotions from the user's facial expressions and voice. For example, if a user is smiling at the camera, the emotion engine recognizes this as a "cheerful mood." This result is automatically reflected in the input data as the "mood for the day."

[0208] User data entry

[0209] When a user logs into the system, the terminal displays an input form. In addition to the mood recognized by the emotion engine, the user enters "what they want to do" and "the nature of their relationship with the person they are going out with." For example, a user might enter "cheerful mood," "cafe hopping," and "friend" as the person they are going out with.

[0210] Sending input data

[0211] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. An example of the generated JSON format is {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}.

[0212] Server-based data analysis and proposal generation

[0213] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles based on mood, activity, and relationships. This analysis might select a cheerful style, such as "a colorful blouse and jeans, and casual shoes."

[0214] Utilization of AI for image generation

[0215] Next, the server sends instructions to the image generation AI based on the fashion suggestions, and generates an image that captures the atmosphere of the suggested fashion style. For example, it might send an instruction to generate an image of a model wearing "a colorful blouse, jeans, and casual shoes." This image generation AI then generates a high-quality image based on the instructions.

[0216] Submitting and displaying proposal results

[0217] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0218] Specific examples

[0219] As a concrete example, if a user is identified as feeling "energetic" through the emotion engine, and further inputs conditions such as "going hiking" and "a colleague" as a companion, the server will suggest "sportswear and hiking boots" that are best suited to "energetic activity," and generate images based on those suggestions. In this way, users can visually confirm specific fashion styles that match their activity and mood.

[0220] By combining an emotion engine, this invention makes it possible to provide fashion suggestions that incorporate the user's real-time emotions, thereby realizing a more personalized user experience.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The device activates its emotion engine and enables the camera and microphone to detect the user's face and voice.

[0224] Step 2:

[0225] The emotion engine analyzes the user's facial expression data and voice tone in real time to recognize their "mood for the day." For example, if the user's facial expression is smiling, it recognizes them as being in a "cheerful mood."

[0226] Step 3:

[0227] The device automatically enters the user's "mood for the day" into the "mood" field of the input form. It also displays a form for the user to input "what they want to do" and "their relationship with the person they are going out with."

[0228] Step 4:

[0229] The user enters "what they want to do" and "their relationship with the person they're going out with" into an input form. For example, they might enter "cafe hopping" or "friends."

[0230] Step 5:

[0231] The device converts the data entered by the user and the "mood of the day" recognized by the emotion engine into JSON format. Example: {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}

[0232] Step 6:

[0233] The device sends the formatted JSON data to the server using an HTTP request.

[0234] Step 7:

[0235] The server receives the JSON data sent from the terminal and parses (analyzes) the data.

[0236] Step 8:

[0237] Based on the data analyzed by the server, it queries the database to generate appropriate fashion suggestions. For example, the analysis results might select a style such as "a colorful blouse, jeans, and casual shoes."

[0238] Step 9:

[0239] Based on the fashion suggestions generated by the server, a descriptive text for the suggestions is created. Example: "Today, to enjoy a lively café hopping experience, a colorful blouse, jeans, and casual shoes are recommended."

[0240] Step 10:

[0241] The server sends instructions to the image generation AI to generate images based on the fashion suggestions.

[0242] Step 11:

[0243] The image generation AI receives instructions from the server and generates an image that captures the atmosphere of the specified fashion style. The image generation AI creates an image of a model wearing "a colorful blouse, jeans, and casual shoes."

[0244] Step 12:

[0245] The image generation AI sends the generated image to the server.

[0246] Step 13:

[0247] The server formats both the generated fashion suggestion (text) and the generated image into JSON format and sends them to the terminal. Example: {"suggestion": "A colorful blouse and jeans, casual shoes", "image_url": "http: / / example.com / generated_image.jpg"}

[0248] Step 14:

[0249] The terminal parses the JSON data received from the server and extracts the fashion suggestion description and the generated image URL.

[0250] Step 15:

[0251] The device displays a description and image of the fashion suggestion to the user. The user reviews the suggested fashion and image to help them choose what to wear that day.

[0252] Through the above processing steps, a fashion suggestion system using an emotion engine is realized. Users can receive personalized and specific fashion suggestions through real-time emotion analysis.

[0253] (Example 2)

[0254] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0255] Traditional fashion suggestion systems relied on subjective input data, making it difficult to reflect users' real-time emotions. Furthermore, the means of visually confirming suggested fashion styles were limited. Therefore, personalized fashion suggestions tailored to the user's emotions and circumstances were challenging.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0257] In this invention, the server includes means for recognizing the user's emotions, means for inputting the user's mood for the day, what they want to do, and their relationship with the person they are going out with, means for transmitting the input data to the server, means for analyzing the transmitted data and generating appropriate clothing suggestions, means for generating an atmospheric image based on the generated clothing suggestions, means for transmitting the generated clothing suggestions and image to a terminal, and means for displaying the clothing suggestions and image on the terminal. This enables personalized fashion suggestions that incorporate the user's emotions in real time.

[0258] 1. "Means of recognizing user emotions" refers to technology that uses cameras and microphones to analyze a user's facial expressions and voice to recognize specific emotional states.

[0259] 2. "A means of inputting one's mood, what one wants to do, and the relationship with the person one is going out with" refers to an interface in which the user logs into the system and inputs their emotional state, plans, and the relationship with the person they are going out with through an input form.

[0260] 3. "Means for sending input data to the server" refers to the technology that converts user-input data into JSON format and sends it to the server via the network.

[0261] 4. "Means for analyzing transmitted data and generating appropriate clothing suggestions" refers to an analysis process within the server that generates optimal fashion suggestions based on mood, activity, and relationships, using the received data.

[0262] 5. "Means for generating atmospheric images based on generated clothing suggestions" refers to a technology that uses a generative AI model to generate atmospheric images based on suggested fashion styles.

[0263] 6. "Means for sending generated clothing suggestions and images to the terminal" refers to a technology that formats the generated fashion suggestions and image data into JSON format and sends them to the user's terminal.

[0264] 7. "Means for displaying clothing suggestions and images on a terminal" refers to an interface that visually displays received fashion suggestion information and atmospheric images on the user's terminal screen.

[0265] 8. "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text-based data format for structuring and exchanging data.

[0266] 9. "Means for generating fashion suggestion descriptions based on analyzed data" refers to technology in which a server automatically generates detailed fashion suggestion descriptions for users based on the analysis results.

[0267] This invention relates to a fashion suggestion system that includes an emotion engine for recognizing user emotions. Based on the user's mood, activities, and relationship with companions, the system provides clothing suggestions and images illustrating the atmosphere of those suggestions.

[0268] The process of recognizing emotions

[0269] When a user smiles at the camera, the emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time. The emotion engine uses facial recognition technology such as OpenCV and voice analysis technology (e.g., a voice recognition service). This allows the system to recognize the user's "mood for the day" as "cheerful."

[0270] Data entry and transmission

[0271] When a user logs into the system, the terminal displays an input form using a frontend library such as React. Here, the user inputs their "mood" as recognized by the emotion engine, their "desires," and their "relationship with the person they are going out with." For example, "cheerful mood," "cafe hopping," and "friends" might be entered.

[0272] Once you have finished entering the data and pressed the submit button, the terminal will format the entered data into JSON format and send it to the server. The data sent will be in a format similar to the following:

[0273] json

[0274] {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}

[0275] This conversion uses the JavaScript fetch API.

[0276] Data analysis and proposal generation

[0277] The server begins analyzing the received data. Using Python and data analysis libraries, the server accesses a database (e.g., MySQL® or MongoDB) to refer to a dataset of fashion styles based on the entered mood, activities, and relationships. As a result of this analysis, a cheerful style such as "colorful blouse and jeans, casual shoes" might be selected.

[0278] Generation of Ambience Image

[0279] Next, the server sends a prompt to the image generation AI model. As the AI model to be used, a generation AI model (e.g., image generation AI) is suitable. Examples of the prompt sentences sent by the server are as follows.

[0280] Please generate an image of a model wearing "colorful blouses and jeans, and casual shoes".

[0281] Upon receiving this prompt, the image generation AI model generates a high-quality ambience image and returns it to the server.

[0282] Transmission and Display of Proposal Results

[0283] The server combines the generated fashion proposal and the ambience image, formats them again in JSON format, and sends them to the terminal. The terminal interprets the received JSON data and displays the text of the fashion proposal and the generated image to the user.

[0284] Specific Example

[0285] For example, when the user is recognized as being in an "energetic mood" through the emotion engine and inputs "going hiking", and the accompanying person inputs a condition such as "colleague", the server proposes "sportswear and hiking boots" that are optimal for "energetic activities" and generates an image based on it. Examples of this prompt sentence are as follows.

[0286] Please generate an image of a model wearing "sportswear and hiking boots".

[0287] Thereby, the user can visually confirm a specific fashion style suitable for the activity content and mood.

[0288] This invention enables personalized fashion suggestions that incorporate the user's real-time emotions, thereby providing a better user experience.

[0289] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0290] Step 1: Emotion Recognition

[0291] Subject: User

[0292] Specific action: The user smiles at the camera.

[0293] Input: User's facial expressions and voice data.

[0294] Data processing / calculation: User facial expressions and voice data acquired in real time from the camera and microphone are analyzed using an emotion engine (e.g., OpenCV or voice analysis technology).

[0295] Output: The emotion engine recognizes the user's emotional state as "cheerful."

[0296] Step 2: Data Entry

[0297] Subject: User

[0298] Specific operation: The user logs into the system and enters information such as "how they feel today," "what they want to do," and "the nature of their relationship with the person they are going out with" through an input form.

[0299] Input: User-entered emotional state, activity details, and relationship data with the other party.

[0300] Data processing / calculation: Receive and verify the input data using a front-end interface (such as React).

[0301] Output: The input data is prepared for the next step.

[0302] Step 3: Data Transmission

[0303] Subject: Terminal

[0304] Specific Action: After the user inputs data, press the send button.

[0305] Input: The mood, activity content, and related data input by the user.

[0306] Data Processing / Calculation: Convert the input data into JSON format and send it to the server using the JavaScript fetch API.

[0307] Output: JSON-formatted data (e.g., {"mood": "Happy mood", "activity": "Café hopping", "relationship": "Friend"}) is generated and sent to the server.

[0308] Step 4: Data Analysis

[0309] Subject: Server

[0310] Specific Action: Analyze the data received by the server.

[0311] Input: JSON-formatted data.

[0312] Data Processing / Calculation: Analyze the data using data analysis libraries such as Python and Pandas, and obtain relevant information from databases (MySQL or MongoDB).

[0313] Output: Fashion suggestions based on the analyzed data (e.g., "Colorful blouse and jeans, casual shoes").

[0314] Step 5: Ambience Image Generation

[0315] Subject: Server

[0316] Specific operation: The server sends prompts to the generated AI model.

[0317] Input: Fashion suggestion data.

[0318] Data processing / calculation: A prompt message (e.g., "Generate an image of a model wearing a colorful blouse, jeans, and casual shoes.") is sent to the generating AI model.

[0319] Output: Atmosphere image generated from the generative AI model.

[0320] Step 6: Submit the proposal results

[0321] Subject: Server

[0322] Specific operation: The server combines fashion suggestions and images into a single file, formats it in JSON format, and sends it to the terminal.

[0323] Input: Fashion suggestions and generated images.

[0324] Data processing / calculation: Format the proposed content and images into JSON format (e.g., {"fashion": "Colorful blouse and jeans, casual shoes", "image": "Image data"}).

[0325] Output: Data in JSON format is sent to the terminal.

[0326] Step 7: Displaying the results

[0327] Subject: terminal

[0328] Specific operation: The device interprets the received data and displays fashion suggestion text and generated images to the user.

[0329] Input: Fashion suggestion data and image data in JSON format.

[0330] Data Processing / Calculation: Analyzes received data and converts it into a format that can be displayed on the user interface.

[0331] Output: The fashion suggestion text and images are visually displayed on the user's device screen.

[0332] This allows users to check in real time the optimal fashion style for their mood and plans for the day.

[0333] (Application Example 2)

[0334] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0335] Conventional fashion suggestion systems make suggestions based solely on schedules and companion information, without considering the user's emotions. This results in a low degree of personalization and difficulty in providing suggestions that match the user's real-time emotions. Furthermore, generating atmospheric images that allow users to visually confirm specific clothing suggestions requires multiple manual steps and is time-consuming, thus lacking immediacy.

[0336] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0337] In this invention, the server includes means for recognizing the user's emotions in real time using an emotion recognition engine, means for adjusting the content of fashion suggestions based on the recognized emotions, and means for generating atmospheric images using a generation AI model based on the analyzed data and the user's emotions. As a result, personalized fashion suggestions that reflect the user's real-time emotions are immediately generated, and the user can use the suggestions while visually confirming them.

[0338] "Mood on the day" refers to the emotions and psychological state that the user is currently experiencing.

[0339] "Things you want to do" refers to activities or plans that the user wants to carry out at a specific date and time.

[0340] "Relationship with the person you're going out with" refers to the relationship between the user and the person they are going out with (e.g., friend, colleague, family).

[0341] "Means of input" refers to the hardware and software that users use to input their mood for the day, what they want to do, and their relationship with the people they are going out with.

[0342] "Means of transmission" refers to the process and equipment used to communicate and send the input data to the server.

[0343] "Means of analysis" refers to the technical process of analyzing data transmitted on a server and generating appropriate fashion suggestions.

[0344] "Means for generating images" refers to technologies and devices for generating visual atmosphere images based on generated clothing suggestions.

[0345] "Means of transmission to the terminal" refers to the communication technologies and protocols used to transmit the generated clothing suggestions and images to the user's terminal.

[0346] "Means of display" refers to display technology used to visually present clothing suggestions and images received on the terminal to the user.

[0347] An "emotion recognition engine" refers to software and hardware that recognizes emotions in real time from a user's facial expressions and voice.

[0348] "Methods for adjusting fashion suggestions based on recognized emotions" refers to technologies that dynamically change and adjust fashion suggestions by taking into account the user's emotions recognized by an emotion recognition engine.

[0349] "Methods for converting to JSON format" refers to software technologies for converting input data into a JSON (JavaScript Object Notation) data structure.

[0350] A "generative AI model" refers to an artificial intelligence model that automatically generates high-quality atmospheric images based on input data.

[0351] This invention is a fashion suggestion system that incorporates an emotion recognition engine to recognize the user's emotions. This system suggests clothing based on the user's mood, activities, and relationship with companions, and provides corresponding atmospheric images.

[0352] Required hardware and software

[0353] Hardware:

[0354] Smartphone (iOS / ANDROID(registered trademark))

[0355] Smart Glasses

[0356] software:

[0357] Emotion recognition engine (API)

[0358] Image recognition and speech analysis software

[0359] Database (MySQL)

[0360] Image generation AI (e.g., OpenAI DALL-E)

[0361] Sending data in JSON format

[0362] Virtual reality (VR) framework (Unity)

[0363] Specific implementations of the system

[0364] 1. User mood recognition:

[0365] The device (smartphone or smart glasses) uses its camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion recognition engine (API), where emotions are recognized in real time. The recognition result is reflected in the system as the user's "mood for the day."

[0366] 2. Data entry means:

[0367] When a user logs into their device, a prompt screen appears. Here, the user enters information such as "what they want to do" and "their relationship with the person they are going out with." For example, they can enter data such as "cafe hopping" and "friend."

[0368] 3. Data transmission means:

[0369] The data entered by the user and the mood data obtained by the emotion recognition engine are converted into JSON format and sent to the server. This efficiently structures the data.

[0370] 4. Data analysis and clothing suggestion generation methods:

[0371] The server analyzes the received data and references a dataset of fashion styles stored in the database, based on mood, activities, and relationships. As an example of this analysis, if the conditions are "cheerful mood," "cafe hopping," and "friends," then a suggestion of "casual blouse and jeans" is generated.

[0372] 5. Image generation means:

[0373] Based on the generated clothing suggestions, the server sends instructions to the image generation AI to create an atmospheric image. The image generation AI automatically generates an image of a model wearing a "colorful blouse and jeans." This allows the user to visually confirm the suggestions.

[0374] 6. Means for submitting and displaying proposal results:

[0375] The fashion suggestions and atmosphere images generated on the server are formatted again into JSON and sent to the terminal. The terminal receives this and displays the fashion suggestion text and images to the user. The user can then use this as a reference to make their actual fashion choices.

[0376] Specific example

[0377] For example, if a user is perceived as feeling "energetic" through the emotion recognition engine and inputs "going hiking" and "a colleague" as their companion, the server will suggest "sportswear and hiking boots" appropriate for "energetic activity." The generative AI model will then generate an image of a "model wearing sportswear and hiking boots." In this way, users can visually confirm specific fashion styles that match their activities and mood for the day in real time.

[0378] Example of a prompt

[0379] "I'm feeling cheerful today and planning to go cafe hopping with a friend. Could you recommend some fashionable outfits?"

[0380] Based on this prompt, the AI ​​model can generate fashion suggestions that perfectly match the user's needs.

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The user logs into their device (smartphone or smart glasses) and uses the camera and microphone to capture their facial expressions and voice. This inputs the user's image and voice data. This input data is sent to an emotion recognition engine API, which analyzes the user's emotions in real time. The emotion recognition engine processes the data and outputs an emotion result, such as "cheerful mood."

[0384] Step 2:

[0385] The user enters "what they want to do (e.g., cafe hopping)" and "the relationship with the person they are going out with (e.g., friend)" into a form displayed on the device. This data is registered as input data on the device. After the user completes the input, the device converts the mood data analyzed by the emotion recognition engine and the above input data into JSON format. This JSON data is output in the format { "mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friend"}.

[0386] Step 3:

[0387] The terminal sends the JSON data generated in step 2 to the server. The server analyzes the received data, accesses the database, and searches for fashion styles based on mood, activities, and relationships. For example, based on the conditions "cheerful mood," "cafe hopping," and "friends," the suggestion "colorful blouse and jeans, casual shoes" might be selected. This analysis result becomes the server's output.

[0388] Step 4:

[0389] Next, the server sends a prompt to the image generation AI based on the analysis results from step 3. An example prompt is, "Generate an image of a model wearing a colorful blouse, jeans, and casual shoes." Based on this prompt, the image generation AI generates a high-quality atmospheric image and outputs it to the server.

[0390] Step 5:

[0391] The server reformats the generated fashion suggestions and images back into JSON format and sends this data to the terminal. For example, it outputs in the format { "fashion_style": "Colorful blouse and jeans, casual shoes", "image_url": "generated_image_url"}.

[0392] Step 6:

[0393] The terminal interprets the JSON data received from the server and displays fashion suggestions, along with generated images, to the user. The user can visually confirm this on the terminal and actually select a suggested fashion style. This display result is the final output.

[0394] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0395] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0397] [Second Embodiment]

[0398] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0399] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0400] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0401] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0402] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0403] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0404] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0405] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0406] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0407] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0408] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0409] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0410] This invention is a system that provides appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. Below, we will generate the program for this system and explain its specific processing details.

[0411] User data entry

[0412] When a user logs into the system, the terminal displays an input form. This form includes fields for entering "how you feel that day," "what you want to do," and "the relationship you have with the person you're going out with." For example, a user might enter "I want to relax" for their mood, "cafe hopping" for what they want to do, and "friend" for their relationship.

[0413] Sending input data

[0414] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. An example of the generated JSON format is {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}.

[0415] Server-based data analysis and proposal generation

[0416] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles based on mood, activity, and relationships. This analysis might select a casual style, such as "a simple T-shirt, denim pants, and sneakers."

[0417] Utilization of AI for image generation

[0418] Next, the server sends instructions to the image generation AI based on the fashion suggestions, and generates an image that captures the atmosphere of the suggested fashion style. For example, it might send an instruction to generate an image of a model wearing a simple T-shirt, denim pants, and sneakers. This image generation AI then generates a high-quality image based on the instructions.

[0419] Submitting and displaying proposal results

[0420] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0421] Specific examples

[0422] As a concrete example, if a user enters conditions such as "feeling energetic" and "going hiking," and that their companion is "a colleague," the server will suggest items suitable for "energetic activity," such as "sporty outerwear, trekking pants, and athletic shoes," and generate images based on those suggestions. In this way, users can visually confirm specific fashion styles that match their activity and mood.

[0423] Thus, the present invention provides an effective fashion support system that meets the diverse needs of users.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The device displays a form for the user to input their mood for the day, what they want to do, and their relationship with the person they are going out with.

[0427] Step 2:

[0428] The user enters "how they feel that day," "what they want to do," and "the nature of their relationship with the person they are going out with" into an input form.

[0429] Step 3:

[0430] The terminal converts the input data into JSON format. For example, it formats the data into {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}.

[0431] Step 4:

[0432] The device sends the formatted JSON data to the server using an HTTP request.

[0433] Step 5:

[0434] The server parses the JSON data received from the terminal and generates appropriate fashion suggestions in conjunction with the database.

[0435] Step 6:

[0436] The server selects a list of fashion items suitable for the mood, activity, and relationship based on the received data, and generates a description of the fashion suggestion. For example, it might generate a description like, "A simple T-shirt, denim pants, and sneakers."

[0437] Step 7:

[0438] The server sends instructions to the image generation AI to generate an image based on the selected fashion items.

[0439] Step 8:

[0440] The image generation AI receives instructions from the server and generates images that capture the feel of fashion items based on those instructions.

[0441] Step 9:

[0442] The image generation AI sends the generated image to the server.

[0443] Step 10:

[0444] The server integrates the generated fashion suggestions and image data, and then formats them back into JSON format. Example: {"suggestion": "Simple T-shirt, denim pants, sneakers", "image_url": "http: / / example.com / generated_image.jpg"}

[0445] Step 11:

[0446] The server sends the integrated JSON data to the terminal.

[0447] Step 12:

[0448] The device interprets the JSON data received from the server and displays a description of the fashion suggestion and the generated image to the user.

[0449] (Example 1)

[0450] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0451] In modern times, choosing fashion is a significant burden for consumers. In particular, selecting appropriate attire based on one's mood, activities, and relationship with companions is difficult. Therefore, there is a need for a system that allows users to easily choose the most suitable outfit for the day.

[0452] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0453] In this invention, the server includes means for analyzing transmitted data and generating appropriate clothing suggestions, means for sending prompt messages to an image generation algorithm based on the generated clothing suggestions, and means for sending the clothing suggestions, including the generated images, to the terminal. This allows the user to receive appropriate clothing suggestions and atmospheric images based on their mood, activities, and relationship with their companions on the day.

[0454] "Mood on the day" refers to the emotions and mental state the user is experiencing on that particular day.

[0455] "Things to do" refers to the activities or events that the user wants to do on that day.

[0456] "Relationship with the person you're going out with" refers to the type of relationship the user has with the person they're going out with on that day (for example, friend, family, colleague).

[0457] "Means of input" refers to the interface or device that allows users to input data into a system.

[0458] "Means of sending to the server" refers to the communication functions and protocols used to send data entered by the user to the server.

[0459] "Means for analyzing transmitted data and generating appropriate clothing suggestions" refers to algorithms and processes that analyze data transmitted by users and generate optimal fashion suggestions based on that data.

[0460] "Means for sending prompt statements to an image generation algorithm" refers to the process of generating and sending instruction statements (prompt statements) to an image generation algorithm based on the generated clothing suggestions.

[0461] "Means for sending clothing suggestions, including generated images, to a terminal" refers to communication functions and protocols for sending images received from an image generation algorithm and generated clothing suggestions to a terminal.

[0462] "Means for displaying clothing suggestions and images on a terminal" refers to interfaces or devices for displaying received clothing suggestions and images on the user's terminal.

[0463] This invention is a system that provides appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. A specific embodiment of this system will be described below.

[0464] User data entry

[0465] When a user logs into the system, the terminal displays an input form. This form includes fields for "how you feel that day," "what you want to do," and "the relationship you have with the person you're going out with." For example, a user might enter "I want to relax" for their mood, "cafe hopping" for what they want to do, and "friend" for their relationship.

[0466] Sending input data

[0467] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. The generated JSON data will, for example, send data like the following:

[0468] {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}

[0469] Data analysis on the server and generation of fashion suggestions.

[0470] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles that match the mood, activity, and relationship based on the sent data. This analysis selects a casual style, such as "a simple T-shirt, denim pants, and sneakers."

[0471] Utilization of AI for image generation

[0472] Next, the server sends prompts to the image generation AI based on the fashion suggestions, generating images that capture the atmosphere of the suggested fashion style. For example, a prompt is sent to generate an image of a model wearing a "simple T-shirt, denim pants, and sneakers." This image generation AI generates high-quality images based on the instructions. The following types of prompts are used:

[0473] "Please generate an image of a model wearing a simple T-shirt, denim pants, and sneakers."

[0474] Submitting and displaying proposal results

[0475] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0476] Specific examples

[0477] As a concrete example, if a user enters conditions such as "feeling energetic" and "going hiking," and that their companion is "a colleague," the server will suggest items suitable for "energetic activity," such as "sporty outerwear, trekking pants, and athletic shoes," and generate images based on those suggestions. The prompt text in this case would be as follows:

[0478] "Please generate an image showing a sporty look with a sporty outer layer, trekking pants, and athletic shoes."

[0479] This allows users to receive specific fashion suggestions that they can visually confirm.

[0480] This system allows users to receive assistance in choosing the most suitable outfit based on their mood, activities, and relationship with their companions on the day. This provides an effective fashion support system that meets the diverse needs of users.

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] System program processing flow

[0483] Step 1: User input

[0484] When a user logs into the system, the terminal displays a form for them to enter their "mood for the day," "things they want to do," and "relationship with the person they are going out with." The user then uses this form to enter the information.

[0485] Input: User inputs "mood for the day," "things they want to do," and "relationship with the person they are going out with."

[0486] Output: Input data converted to JSON format

[0487] Specific operation: The user enters information into each field in the text field and clicks the submit button.

[0488] Step 2: Convert the data to JSON format

[0489] The device converts user input data into JSON format. For example, if the input data is "I want to relax," "cafe hopping," and "friends," the following JSON data will be generated:

[0490] {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}

[0491] Input: User input data

[0492] Output: Data in JSON format

[0493] Specific operation: The terminal uses a program such as JavaScript to convert the data into JSON format.

[0494] Step 3: Sending data to the server

[0495] The terminal sends the converted JSON data to the server. The data is sent to the server using an HTTP request.

[0496] Input: Data in JSON format

[0497] Output: Data received by the server

[0498] Specific action: The terminal generates an HTTP POST request and sends data to the server.

[0499] Step 4: Data analysis on the server

[0500] The server parses the received JSON data. The server then accesses the database to determine fashion styles based on mood, activity, and relationships.

[0501] Input: Received JSON data

[0502] Output: Appropriate fashion suggestions

[0503] Specific operation: The server performs JSON parsing and issues SQL queries or API requests to retrieve suggestions from the database.

[0504] Step 5: Generating Fashion Proposals

[0505] The server generates appropriate fashion suggestions based on the analysis results. For example, it might select a suggestion like "a simple T-shirt, denim pants, and sneakers."

[0506] Input: Fashion style data from database

[0507] Output: Fashion suggestion data

[0508] Specific operation: The server generates text data for fashion suggestions based on information retrieved from the database.

[0509] Step 6: Send a prompt message to the image generation AI.

[0510] The server sends a prompt message to the image generation AI based on the generated fashion suggestions. An example of a prompt message might be, "Generate an image of a model wearing a simple T-shirt, denim pants, and sneakers."

[0511] Input: Fashion suggestion data

[0512] Output: Prompt message

[0513] Specific operation: The server generates a prompt message and sends it to the image generation AI's API endpoint using an HTTP request.

[0514] Step 7: Image generation

[0515] The image generation AI generates an atmospheric image based on the prompt text. This image is returned to the server.

[0516] Input: Prompt message

[0517] Output: The generated image

[0518] Specific operation: The AI ​​model parses the prompt text and executes an image generation algorithm to generate an image.

[0519] Step 8: Returning and formatting the results to the server

[0520] After the generated image is sent back to the server, the server combines the fashion suggestion and the image into one file and formats it again into JSON format.

[0521] Input: Generated image, fashion suggestion data

[0522] Output: Proposal results in JSON format

[0523] Specific operation: The server combines image data and fashion suggestion data into a JSON object and converts it into a format that can be sent as an HTTP response.

[0524] Step 9: Sending the results to the terminal

[0525] The server sends the formatted JSON data to the terminal. The terminal interprets the received data and displays it to the user.

[0526] Input: Suggestion results in JSON format

[0527] Output: Display of fashion suggestions and images on the device

[0528] Specific operation: The server sends JSON data to the device as an HTTP response, and the device parses the received data and displays it in the UI.

[0529] Step 10: Presentation to the user

[0530] The device displays fashion suggestions and atmospheric images to the user. This allows the user to visually check the most suitable fashion for their mood and activities on that day.

[0531] Input: Fashion suggestions and images interpreted from JSON data

[0532] Output: Fashion suggestions and images displayed in the user interface

[0533] Specific operation: The terminal uses HTML and CSS to display suggestions and images in a graphical user interface.

[0534] This allows users to receive specific and visually understandable fashion suggestions, making daily outfit selection easier and more enjoyable.

[0535] (Application Example 1)

[0536] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0537] Traditional fashion suggestion systems struggled to provide personalized recommendations based on detailed context, such as the user's mood, activities, and relationship with companions. As a result, users often lacked a concrete image to help them make optimal choices. Furthermore, there was a lack of integrated systems for visually confirming suggested styles and directly purchasing items from e-commerce sites.

[0538] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0539] This invention includes a server that provides input for the user's mood, what they want to do, and their relationship with the person they are going out with; a server that provides input data

[0540] "Mood on the day" refers to the subjective feelings or mood that the user is experiencing on that particular day.

[0541] "Things to do" refers to the specific activities or actions that the user wants to perform on that particular day.

[0542] "Relationship with the person you're going out with" refers to the relationship between the user and the person they will be spending time with or going out with on that particular day (for example, a friend, colleague, or family member).

[0543] "Means of input" refers to the interface (such as forms or buttons) that a user uses to provide information to a system.

[0544] "Means of transmission" refers to the processes and technologies used to send data from a terminal to a server.

[0545] "A means of analyzing and generating appropriate clothing suggestions" refers to the process by which a server analyzes user input data and selects the optimal fashion style based on that data.

[0546] An "image generation artificial intelligence model" is an artificial intelligence model that has the ability to generate high-quality images based on input prompt text.

[0547] A "prompt message" is the text content used to request an artificial intelligence model to generate a specific image.

[0548] "Generating means" refers to the process and techniques for generating images that match the proposed clothing style.

[0549] "Means of display" refers to the interface and technology for displaying the generated clothing suggestions and images on the user's device.

[0550] An "e-commerce site" is a website where users can purchase goods via the internet.

[0551] "Means of providing purchasable links" refers to the process and technology of displaying online shopping links for users to purchase suggested fashion items.

[0552] This invention provides a system that offers appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. Users can access this system using their smartphones.

[0553] First, when a user logs into the system, the terminal displays an input form. The form includes fields for the user to enter their mood for the day, what they want to do, and their relationship with the person they are going out with. Once the user enters this information, the terminal formats the data into JSON format and sends it to the server.

[0554] When the server receives data, it begins analysis. The analysis automatically generates optimal fashion suggestions from the database based on the user's mood, activities, and relationships. This analysis determines, for example, that "if you're feeling energetic and going hiking, a sporty outer layer, trekking pants, and athletic shoes would be suitable."

[0555] Next, the server sends a prompt to the image generation AI model based on the generated fashion suggestions. The prompt might take the form of, for example, "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues." Based on this prompt, the AI ​​model generates an image that captures the atmosphere of the suggested fashion style.

[0556] The generated fashion suggestions and atmosphere images are formatted again in JSON format and sent to the device. The device receives this and displays it to the user. Furthermore, the device also provides online shopping links where the suggested clothing items can be purchased. This allows the user to visually confirm the suggested style and purchase the items directly from the e-commerce site.

[0557] The main hardware of this system is a smartphone, and the software includes a web service framework (e.g., Node.js), a database (e.g., MongoDB), an image generation AI (e.g., OpenAI's DALL-E), and a front-end framework (e.g., React Native).

[0558] For example, if a user enters conditions such as "feeling energetic," "going hiking," and "working with colleagues," the server will suggest "sporty outerwear, trekking pants, and running shoes." Based on this, it generates a prompt message, "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues," and sends it to the image generation AI model. By providing the user with the image generated based on this prompt message, the user can visually confirm specific fashion styles and purchase items directly.

[0559] As described above, the present invention realizes a rapid and effective fashion suggestion system that meets the individual needs of users.

[0560] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0561] Step 1:

[0562] When a user logs into the system, the terminal displays an input form. This form includes fields for the user to enter their mood for the day, what they want to do, and their relationship with the person they are going out with. As the user enters this information, it is formatted into the necessary data format for transmission to the server. The input data format is JSON, for example, {"mood": "energetic mood", "activity": "hiking", "relationship": "colleague"}.

[0563] Step 2:

[0564] The terminal formats the data entered by the user into JSON format and sends it to the server. An HTTP POST request is used to send the entered data to the server. The input consists of the user's mood, what they want to do, and their relationship with the person they are going out with, while the output is the JSON data received by the server.

[0565] Step 3:

[0566] The server analyzes the received data. Specifically, the server analyzes data on mood, activity, and relationships to generate optimal fashion suggestions. This analysis involves database access, referencing fashion styles based on mood, activity, and relationships. The input is data in JSON format, and the output is structured data containing appropriate clothing suggestions.

[0567] Step 4:

[0568] The server sends a prompt to the image generation AI model based on the clothing suggestions it generates. For example, from a suggestion of "sporty outerwear, trekking pants, and running shoes," the prompt "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues." is generated. A request is then sent to the image generation AI based on this prompt. The input is the fashion suggestion, and the output is the prompt to the image generation AI.

[0569] Step 5:

[0570] An image generation artificial intelligence model generates an atmospheric image based on a prompt. The generated image is returned to the server. The input is the prompt, and the output is the generated atmospheric image.

[0571] Step 6:

[0572] The server formats the generated fashion suggestions and atmospheric images into JSON format and sends it to the terminal. The data sent to the terminal includes the text of the fashion suggestions and the URLs of the images. The input is the generated fashion suggestions and atmospheric images, and the output is data in JSON format.

[0573] Step 7:

[0574] The device interprets the received data and displays fashion suggestions (text and images) to the user. Furthermore, it provides links to purchase the suggested items on e-commerce sites. The input is JSON data sent from the server, and the output is the fashion suggestions, atmospheric images, and purchase links displayed to the user.

[0575] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0576] This invention is a fashion suggestion system that incorporates an emotion engine to recognize the user's emotions. This system provides clothing suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. The specific program processing and specific examples of this system are described below.

[0577] Data entry using an emotion engine

[0578] The emotion engine recognizes emotions from the user's facial expressions and voice. For example, if a user is smiling at the camera, the emotion engine recognizes this as a "cheerful mood." This result is automatically reflected in the input data as the "mood for the day."

[0579] User data entry

[0580] When a user logs into the system, the terminal displays an input form. In addition to the mood recognized by the emotion engine, the user enters "what they want to do" and "the nature of their relationship with the person they are going out with." For example, a user might enter "cheerful mood," "cafe hopping," and "friend" as the person they are going out with.

[0581] Sending input data

[0582] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. An example of the generated JSON format is {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}.

[0583] Server-based data analysis and proposal generation

[0584] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles based on mood, activity, and relationships. This analysis might select a cheerful style, such as "a colorful blouse and jeans, and casual shoes."

[0585] Utilization of AI for image generation

[0586] Next, the server sends instructions to the image generation AI based on the fashion suggestions, and generates an image that captures the atmosphere of the suggested fashion style. For example, it might send an instruction to generate an image of a model wearing "a colorful blouse, jeans, and casual shoes." This image generation AI then generates a high-quality image based on the instructions.

[0587] Submitting and displaying proposal results

[0588] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0589] Specific examples

[0590] As a concrete example, if a user is identified as feeling "energetic" through the emotion engine, and further inputs conditions such as "going hiking" and "a colleague" as a companion, the server will suggest "sportswear and hiking boots" that are best suited to "energetic activity," and generate images based on those suggestions. In this way, users can visually confirm specific fashion styles that match their activity and mood.

[0591] By combining an emotion engine, this invention makes it possible to provide fashion suggestions that incorporate the user's real-time emotions, thereby realizing a more personalized user experience.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The device activates its emotion engine and enables the camera and microphone to detect the user's face and voice.

[0595] Step 2:

[0596] The emotion engine analyzes the user's facial expression data and voice tone in real time to recognize their "mood for the day." For example, if the user's facial expression is smiling, it recognizes them as being in a "cheerful mood."

[0597] Step 3:

[0598] The device automatically enters the user's recognized "mood for the day" into the "mood" field of the input form. Additionally, a form is displayed for the user to input "what they want to do" and "their relationship with the person they are going out with."

[0599] Step 4:

[0600] The user enters "what they want to do" and "their relationship with the person they're going out with" into an input form. For example, they might enter "cafe hopping" or "friends."

[0601] Step 5:

[0602] The device converts the data entered by the user and the "mood of the day" recognized by the emotion engine into JSON format. Example: {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}

[0603] Step 6:

[0604] The device sends the formatted JSON data to the server using an HTTP request.

[0605] Step 7:

[0606] The server receives the JSON data sent from the terminal and parses (analyzes) the data.

[0607] Step 8:

[0608] Based on the data analyzed by the server, it queries the database to generate appropriate fashion suggestions. For example, the analysis results might select a style such as "a colorful blouse, jeans, and casual shoes."

[0609] Step 9:

[0610] Based on the fashion suggestions generated by the server, a descriptive text for the suggestions is created. Example: "Today, to enjoy a lively café hopping experience, a colorful blouse, jeans, and casual shoes are recommended."

[0611] Step 10:

[0612] The server sends instructions to the image generation AI to generate images based on the fashion suggestions.

[0613] Step 11:

[0614] The image generation AI receives instructions from the server and generates an image that captures the atmosphere of the specified fashion style. The image generation AI creates an image of a model wearing "a colorful blouse, jeans, and casual shoes."

[0615] Step 12:

[0616] The image generation AI sends the generated image to the server.

[0617] Step 13:

[0618] The server formats both the generated fashion suggestion (text) and the generated image into JSON format and sends them to the terminal. Example: {"suggestion": "A colorful blouse and jeans, casual shoes", "image_url": "http: / / example.com / generated_image.jpg"}

[0619] Step 14:

[0620] The terminal parses the JSON data received from the server and extracts the fashion suggestion description and the generated image URL.

[0621] Step 15:

[0622] The device displays a description and image of the fashion suggestion to the user. The user reviews the suggested fashion and image to help them choose what to wear that day.

[0623] Through the above processing steps, a fashion suggestion system using an emotion engine is realized. Users can receive personalized and specific fashion suggestions through real-time emotion analysis.

[0624] (Example 2)

[0625] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0626] Traditional fashion suggestion systems relied on subjective input data, making it difficult to reflect users' real-time emotions. Furthermore, there were limited ways to visually confirm the suggested fashion styles. Therefore, personalized fashion suggestions tailored to the user's emotions and circumstances were challenging.

[0627] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0628] In this invention, the server includes means for recognizing the user's emotions, means for inputting the user's mood for the day, what they want to do, and their relationship with the person they are going out with, means for transmitting the input data to the server, means for analyzing the transmitted data and generating appropriate clothing suggestions, means for generating an atmospheric image based on the generated clothing suggestions, means for transmitting the generated clothing suggestions and image to a terminal, and means for displaying the clothing suggestions and image on the terminal. This enables personalized fashion suggestions that incorporate the user's emotions in real time.

[0629] 1. "Means of recognizing user emotions" refers to technology that uses cameras and microphones to analyze a user's facial expressions and voice to recognize specific emotional states.

[0630] 2. "A means of inputting one's mood, what one wants to do, and the relationship with the person one is going out with" refers to an interface in which the user logs into the system and inputs their emotional state, plans, and the relationship with the person they are going out with through an input form.

[0631] 3. "Means for sending input data to the server" refers to the technology that converts user-input data into JSON format and sends it to the server via the network.

[0632] 4. "Means for analyzing transmitted data and generating appropriate clothing suggestions" refers to an analysis process within the server that generates optimal fashion suggestions based on mood, activity, and relationships, using the received data.

[0633] 5. "Means for generating atmospheric images based on generated clothing suggestions" refers to a technology that uses a generative AI model to generate atmospheric images based on suggested fashion styles.

[0634] 6. "Means for sending generated clothing suggestions and images to the terminal" refers to a technology that formats the generated fashion suggestions and image data into JSON format and sends them to the user's terminal.

[0635] 7. "Means for displaying clothing suggestions and images on a terminal" refers to an interface that visually displays received fashion suggestion information and atmospheric images on the user's terminal screen.

[0636] 8. "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text-based data format for structuring and exchanging data.

[0637] 9. "Means for generating fashion suggestion descriptions based on analyzed data" refers to technology in which a server automatically generates detailed fashion suggestion descriptions for users based on the analysis results.

[0638] This invention relates to a fashion suggestion system that includes an emotion engine for recognizing user emotions. Based on the user's mood, activities, and relationship with companions, the system provides clothing suggestions and images illustrating their atmosphere.

[0639] The process of recognizing emotions

[0640] When a user smiles at the camera, the emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time. The emotion engine uses facial recognition technology such as OpenCV and voice analysis technology (e.g., a voice recognition service). This allows the system to recognize the user's "mood for the day" as "cheerful."

[0641] Data entry and transmission

[0642] When a user logs into the system, the terminal displays an input form using a frontend library such as React. Here, the user inputs their "mood" as recognized by the emotion engine, their "desires," and their "relationship with the person they are going out with." For example, "cheerful mood," "cafe hopping," and "friends" might be entered.

[0643] Once you have finished entering the data and pressed the submit button, the terminal will format the entered data into JSON format and send it to the server. The data sent will be in a format similar to the following:

[0644] json

[0645] {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}

[0646] This conversion uses the JavaScript fetch API.

[0647] Data analysis and proposal generation

[0648] The server begins analyzing the received data. Using Python and data analysis libraries, the server accesses a database (e.g., MySQL or MongoDB) to refer to a dataset of fashion styles based on the entered mood, activities, and relationships. As a result of this analysis, a cheerful style such as "colorful blouse and jeans, casual shoes" might be selected.

[0649] Generating atmospheric images

[0650] Next, the server sends a prompt to the image generation AI model. A generative AI model (e.g., an image generation AI) is suitable for use. An example of a prompt message sent by the server is as follows:

[0651] Please generate an image of a model wearing a colorful blouse, jeans, and casual shoes.

[0652] Upon receiving this prompt, the image generation AI model generates a high-quality atmospheric image and sends it back to the server.

[0653] Submitting and displaying proposal results

[0654] The server combines the generated fashion suggestions and atmospheric images, formats them back into JSON format, and sends them to the terminal. The terminal interprets the received JSON data and displays the fashion suggestion text and generated images to the user.

[0655] Specific example

[0656] For example, if the user is identified as "feeling energetic" through the emotion engine, and also inputs "going hiking" and conditions such as "a colleague" as a companion, the server will suggest "sportswear and hiking boots" best suited for "energetic activity" and generate an image based on that. An example of this prompt is as follows:

[0657] Please generate images of models wearing sportswear and hiking boots.

[0658] This allows users to visually see specific fashion styles that match their activities and mood.

[0659] This invention enables personalized fashion suggestions that incorporate the user's real-time emotions, thereby providing a better user experience.

[0660] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0661] Step 1: Emotion Recognition

[0662] Subject: User

[0663] Specific action: The user smiles at the camera.

[0664] Input: User's facial expressions and voice data.

[0665] Data processing / calculation: User facial expressions and voice data acquired in real time from the camera and microphone are analyzed using an emotion engine (e.g., OpenCV or voice analysis technology).

[0666] Output: The emotion engine recognizes the user's emotional state as "cheerful."

[0667] Step 2: Data Entry

[0668] Subject: User

[0669] Specific operation: The user logs into the system and enters information such as "how they feel today," "what they want to do," and "the nature of their relationship with the person they are going out with" through an input form.

[0670] Input: User-entered emotional state, activity details, and relationship data with the other party.

[0671] Data processing / calculation: Receive and verify the input data using a front-end interface (such as React).

[0672] Output: The input data is prepared for the next step.

[0673] Step 3: Data transmission

[0674] Subject: terminal

[0675] Specific action: After the user enters data, they press the submit button.

[0676] Input: User-entered mood, activity details, and related data.

[0677] Data processing / calculation: Convert the input data into JSON format and send it to the server using the JavaScript fetch API.

[0678] Output: Data in JSON format (e.g., {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}) is generated and sent to the server.

[0679] Step 4: Data Analysis

[0680] Subject: Server

[0681] Specific operation: The server analyzes the data it receives.

[0682] Input: Data in JSON format.

[0683] Data processing / calculations: Analyze data using data analysis libraries such as Python and Pandas, and retrieve relevant information from databases (MySQL or MongoDB).

[0684] Output: Fashion suggestions based on analyzed data (e.g., "A colorful blouse and jeans, and casual shoes").

[0685] Step 5: Generate atmosphere image

[0686] Subject: Server

[0687] Specific operation: The server sends prompts to the generated AI model.

[0688] Input: Fashion suggestion data.

[0689] Data processing / calculation: A prompt message (e.g., "Generate an image of a model wearing a colorful blouse, jeans, and casual shoes.") is sent to the generating AI model.

[0690] Output: Atmosphere image generated from the generative AI model.

[0691] Step 6: Submit the proposal results

[0692] Subject: Server

[0693] Specific operation: The server combines fashion suggestions and images into a single file, formats it in JSON format, and sends it to the terminal.

[0694] Input: Fashion suggestions and generated images.

[0695] Data processing / calculation: Format the proposed content and images into JSON format (e.g., {"fashion": "Colorful blouse and jeans, casual shoes", "image": "Image data"}).

[0696] Output: Data in JSON format is sent to the terminal.

[0697] Step 7: Displaying the results

[0698] Subject: terminal

[0699] Specific operation: The device interprets the received data and displays fashion suggestion text and generated images to the user.

[0700] Input: Fashion suggestion data and image data in JSON format.

[0701] Data Processing / Calculation: Analyzes received data and converts it into a format that can be displayed on the user interface.

[0702] Output: The fashion suggestion text and images are visually displayed on the user's device screen.

[0703] This allows users to check in real time the optimal fashion style for their mood and plans for the day.

[0704] (Application Example 2)

[0705] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0706] Conventional fashion suggestion systems make suggestions based solely on schedules and companion information, without considering the user's emotions. This results in a low degree of personalization and difficulty in providing suggestions that match the user's real-time emotions. Furthermore, generating atmospheric images that allow users to visually confirm specific clothing suggestions requires multiple manual steps and is time-consuming, thus lacking immediacy.

[0707] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0708] In this invention, the server includes means for recognizing the user's emotions in real time using an emotion recognition engine, means for adjusting the content of fashion suggestions based on the recognized emotions, and means for generating atmospheric images using a generation AI model based on the analyzed data and the user's emotions. As a result, personalized fashion suggestions that reflect the user's real-time emotions are immediately generated, and the user can use the suggestions while visually confirming them.

[0709] "Mood on the day" refers to the emotions and psychological state that the user is currently experiencing.

[0710] "Things you want to do" refers to activities or plans that the user wants to carry out at a specific date and time.

[0711] "Relationship with the person you're going out with" refers to the relationship between the user and the person they are going out with (e.g., friend, colleague, family).

[0712] "Means of input" refers to the hardware and software that users use to input their mood for the day, what they want to do, and their relationship with the people they are going out with.

[0713] "Means of transmission" refers to the process and equipment used to communicate and send the input data to the server.

[0714] "Means of analysis" refers to the technical process of analyzing data transmitted on a server and generating appropriate fashion suggestions.

[0715] "Means for generating images" refers to technologies and devices for generating visual atmosphere images based on generated clothing suggestions.

[0716] "Means of transmission to the terminal" refers to the communication technologies and protocols used to transmit the generated clothing suggestions and images to the user's terminal.

[0717] "Means of display" refers to display technology used to visually present clothing suggestions and images received on the terminal to the user.

[0718] An "emotion recognition engine" refers to software and hardware that recognizes emotions in real time from a user's facial expressions and voice.

[0719] "Methods for adjusting fashion suggestions based on recognized emotions" refers to technologies that dynamically change and adjust fashion suggestions by taking into account the user's emotions recognized by an emotion recognition engine.

[0720] "Methods for converting to JSON format" refers to software technologies for converting input data into a JSON (JavaScript Object Notation) data structure.

[0721] A "generative AI model" refers to an artificial intelligence model that automatically generates high-quality atmospheric images based on input data.

[0722] This invention relates to a fashion suggestion system that incorporates an emotion recognition engine to recognize the user's emotions. This system suggests clothing based on the user's mood, activities, and relationship with companions, and provides corresponding atmospheric images.

[0723] Required hardware and software

[0724] Hardware:

[0725] Smartphones (iOS / Android)

[0726] Smart Glasses

[0727] software:

[0728] Emotion recognition engine (API)

[0729] Image recognition and speech analysis software

[0730] Database (MySQL)

[0731] Image generation AI (e.g., OpenAI DALL-E)

[0732] Sending data in JSON format

[0733] Virtual reality (VR) framework (Unity)

[0734] Specific implementations of the system

[0735] 1. User mood recognition:

[0736] The device (smartphone or smart glasses) uses its camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion recognition engine (API), where emotions are recognized in real time. The recognition result is reflected in the system as the user's "mood for the day."

[0737] 2. Data input means:

[0738] When a user logs into their device, a prompt screen appears. Here, the user enters information such as "what they want to do" and "the relationship with the person they are going out with." For example, they can enter data such as "cafe hopping" and "friend."

[0739] 3. Data transmission means:

[0740] The data entered by the user and the mood data obtained by the emotion recognition engine are converted into JSON format and sent to the server. This efficiently structures the data.

[0741] 4. Data analysis and clothing suggestion generation methods:

[0742] The server analyzes the received data and references a dataset of fashion styles stored in the database, based on mood, activities, and relationships. As an example of this analysis, if the conditions are "cheerful mood," "cafe hopping," and "friends," then a suggestion of "casual blouse and jeans" is generated.

[0743] 5. Image generation means:

[0744] Based on the generated clothing suggestions, the server sends instructions to the image generation AI to create an atmospheric image. The image generation AI automatically generates an image of a model wearing a "colorful blouse and jeans." This allows the user to visually confirm the suggested outfit.

[0745] 6. Means for submitting and displaying proposal results:

[0746] The fashion suggestions and atmosphere images generated on the server are formatted again into JSON and sent to the terminal. The terminal receives this and displays the fashion suggestion text and images to the user. The user can then use this as a reference to make their actual fashion choices.

[0747] Specific example

[0748] For example, if a user is perceived as feeling "energetic" through the emotion recognition engine and inputs "going hiking" and "a colleague" as their companion, the server will suggest "sportswear and hiking boots" appropriate for "energetic activity." The generative AI model will then generate an image of a "model wearing sportswear and hiking boots." In this way, users can visually confirm specific fashion styles that match their activities and mood for the day in real time.

[0749] Example of a prompt

[0750] "I'm feeling cheerful today and planning to go cafe hopping with a friend. Could you recommend some fashionable outfits?"

[0751] Based on this prompt, the AI ​​model can generate fashion suggestions that perfectly match the user's needs.

[0752] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0753] Step 1:

[0754] The user logs into their device (smartphone or smart glasses) and uses the camera and microphone to capture their facial expressions and voice. This inputs the user's image and voice data. This input data is sent to an emotion recognition engine API, which analyzes the user's emotions in real time. The emotion recognition engine processes the data and outputs an emotion result, such as "cheerful mood."

[0755] Step 2:

[0756] The user enters "what they want to do (e.g., cafe hopping)" and "the relationship with the person they are going out with (e.g., friend)" into a form displayed on the device. This data is registered as input data on the device. After the user completes the input, the device converts the mood data analyzed by the emotion recognition engine and the above input data into JSON format. This JSON data is output in the format { "mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friend"}.

[0757] Step 3:

[0758] The terminal sends the JSON data generated in step 2 to the server. The server analyzes the received data, accesses the database, and searches for fashion styles based on mood, activities, and relationships. For example, based on the conditions "cheerful mood," "cafe hopping," and "friends," the suggestion "colorful blouse and jeans, casual shoes" might be selected. This analysis result becomes the server's output.

[0759] Step 4:

[0760] Next, the server sends a prompt to the image generation AI based on the analysis results from step 3. An example prompt is, "Generate an image of a model wearing a colorful blouse, jeans, and casual shoes." Based on this prompt, the image generation AI generates a high-quality atmospheric image and outputs it to the server.

[0761] Step 5:

[0762] The server reformats the generated fashion suggestions and images back into JSON format and sends this data to the terminal. For example, it outputs in the format { "fashion_style": "Colorful blouse and jeans, casual shoes", "image_url": "generated_image_url"}.

[0763] Step 6:

[0764] The terminal interprets the JSON data received from the server and displays fashion suggestions, along with generated images, to the user. The user can visually confirm this on the terminal and actually select a suggested fashion style. This display result is the final output.

[0765] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0766] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0767] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0768] [Third Embodiment]

[0769] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0770] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0771] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0772] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0773] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0774] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0775] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0776] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0777] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0778] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0779] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0780] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0781] This invention is a system that provides appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. Below, we will generate the program for this system and explain its specific processing details.

[0782] User data entry

[0783] When a user logs into the system, the terminal displays an input form. This form includes fields for entering "how you feel that day," "what you want to do," and "the relationship you have with the person you're going out with." For example, a user might enter "I want to relax" for their mood, "cafe hopping" for what they want to do, and "friend" for their relationship.

[0784] Sending input data

[0785] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. An example of the generated JSON format is {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}.

[0786] Server-based data analysis and proposal generation

[0787] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles based on mood, activity, and relationships. This analysis might select a casual style, such as "a simple T-shirt, denim pants, and sneakers."

[0788] Utilization of AI for image generation

[0789] Next, the server sends instructions to the image generation AI based on the fashion suggestions, and generates an image that captures the atmosphere of the suggested fashion style. For example, it might send an instruction to generate an image of a model wearing a simple T-shirt, denim pants, and sneakers. This image generation AI then generates a high-quality image based on the instructions.

[0790] Submitting and displaying proposal results

[0791] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0792] Specific examples

[0793] As a concrete example, if a user enters conditions such as "feeling energetic" and "going hiking," and that their companion is "a colleague," the server will suggest items suitable for "energetic activity," such as "sporty outerwear, trekking pants, and athletic shoes," and generate images based on those suggestions. In this way, users can visually confirm specific fashion styles that match their activity and mood.

[0794] Thus, the present invention provides an effective fashion support system that meets the diverse needs of users.

[0795] The following describes the processing flow.

[0796] Step 1:

[0797] The device displays a form for the user to input their mood for the day, what they want to do, and their relationship with the person they are going out with.

[0798] Step 2:

[0799] The user enters "how they feel that day," "what they want to do," and "the nature of their relationship with the person they are going out with" into an input form.

[0800] Step 3:

[0801] The terminal converts the input data into JSON format. For example, it formats the data into {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}.

[0802] Step 4:

[0803] The device sends the formatted JSON data to the server using an HTTP request.

[0804] Step 5:

[0805] The server parses the JSON data received from the terminal and generates appropriate fashion suggestions in conjunction with the database.

[0806] Step 6:

[0807] The server selects a list of fashion items suitable for the mood, activity, and relationship based on the received data, and generates a description of the fashion suggestion. For example, it might generate a description like, "A simple T-shirt, denim pants, and sneakers."

[0808] Step 7:

[0809] The server sends instructions to the image generation AI to generate an image based on the selected fashion items.

[0810] Step 8:

[0811] The image generation AI receives instructions from the server and generates images that capture the feel of fashion items based on those instructions.

[0812] Step 9:

[0813] The image generation AI sends the generated image to the server.

[0814] Step 10:

[0815] The server integrates the generated fashion suggestions and image data, and then formats them back into JSON format. Example: {"suggestion": "Simple T-shirt, denim pants, sneakers", "image_url": "http: / / example.com / generated_image.jpg"}

[0816] Step 11:

[0817] The server sends the integrated JSON data to the terminal.

[0818] Step 12:

[0819] The device interprets the JSON data received from the server and displays a description of the fashion suggestion and the generated image to the user.

[0820] (Example 1)

[0821] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0822] In modern times, choosing fashion is a significant burden for consumers. In particular, selecting appropriate attire based on one's mood, activities, and relationship with companions is difficult. Therefore, there is a need for a system that allows users to easily choose the most suitable outfit for the day.

[0823] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0824] In this invention, the server includes means for analyzing transmitted data and generating appropriate clothing suggestions, means for sending prompt messages to an image generation algorithm based on the generated clothing suggestions, and means for sending the clothing suggestions, including the generated images, to the terminal. This allows the user to receive appropriate clothing suggestions and atmospheric images based on their mood, activities, and relationship with their companions on the day.

[0825] "Mood on the day" refers to the emotions and mental state the user is experiencing on that particular day.

[0826] "Things to do" refers to the activities or events that the user wants to do on that day.

[0827] "Relationship with the person you're going out with" refers to the type of relationship the user has with the person they're going out with on that day (for example, friend, family, colleague).

[0828] "Means of input" refers to the interface or device that allows users to input data into a system.

[0829] "Means of sending to the server" refers to the communication functions and protocols used to send data entered by the user to the server.

[0830] "Means for analyzing transmitted data and generating appropriate clothing suggestions" refers to algorithms and processes that analyze data transmitted by users and generate optimal fashion suggestions based on that data.

[0831] "Means for sending prompt statements to an image generation algorithm" refers to the process of generating and sending instruction statements (prompt statements) to an image generation algorithm based on the generated clothing suggestions.

[0832] "Means for sending clothing suggestions, including generated images, to a terminal" refers to communication functions and protocols for sending images received from an image generation algorithm and generated clothing suggestions to a terminal.

[0833] "Means for displaying clothing suggestions and images on a terminal" refers to interfaces or devices for displaying received clothing suggestions and images on the user's terminal.

[0834] This invention is a system that provides appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. A specific embodiment of this system will be described below.

[0835] User data entry

[0836] When a user logs into the system, the terminal displays an input form. This form includes fields for "how you feel that day," "what you want to do," and "the relationship you have with the person you're going out with." For example, a user might enter "I want to relax" for their mood, "cafe hopping" for what they want to do, and "friend" for their relationship.

[0837] Sending input data

[0838] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. The generated JSON data will, for example, send data like the following:

[0839] {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}

[0840] Data analysis on the server and generation of fashion suggestions.

[0841] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles that match the mood, activity, and relationship based on the sent data. This analysis selects a casual style, such as "a simple T-shirt, denim pants, and sneakers."

[0842] Utilization of AI for image generation

[0843] Next, the server sends prompts to the image generation AI based on the fashion suggestions, generating images that capture the atmosphere of the suggested fashion style. For example, a prompt is sent to generate an image of a model wearing a "simple T-shirt, denim pants, and sneakers." This image generation AI generates high-quality images based on the instructions. The following types of prompts are used:

[0844] "Please generate an image of a model wearing a simple T-shirt, denim pants, and sneakers."

[0845] Submitting and displaying proposal results

[0846] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0847] Specific examples

[0848] As a concrete example, if a user enters conditions such as "feeling energetic" and "going hiking," and that their companion is "a colleague," the server will suggest items suitable for "energetic activity," such as "sporty outerwear, trekking pants, and athletic shoes," and generate images based on those suggestions. The prompt text in this case would be as follows:

[0849] "Please generate an image showing a sporty look with a sporty outer layer, trekking pants, and athletic shoes."

[0850] This allows users to receive specific fashion suggestions that they can visually confirm.

[0851] This system allows users to receive assistance in choosing the most suitable outfit based on their mood, activities, and relationship with their companions on the day. This provides an effective fashion support system that meets the diverse needs of users.

[0852] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0853] System program processing flow

[0854] Step 1: User input

[0855] When a user logs into the system, the terminal displays a form for them to enter their "mood for the day," "things they want to do," and "relationship with the person they are going out with." The user then uses this form to enter the information.

[0856] Input: User inputs "mood for the day," "things they want to do," and "relationship with the person they are going out with."

[0857] Output: Input data converted to JSON format

[0858] Specific operation: The user enters information into each field in the text field and clicks the submit button.

[0859] Step 2: Convert the data to JSON format

[0860] The device converts user input data into JSON format. For example, if the input data is "I want to relax," "cafe hopping," and "friends," the following JSON data will be generated:

[0861] {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}

[0862] Input: User input data

[0863] Output: Data in JSON format

[0864] Specific operation: The terminal uses a program such as JavaScript to convert the data into JSON format.

[0865] Step 3: Sending data to the server

[0866] The terminal sends the converted JSON data to the server. The data is sent to the server using an HTTP request.

[0867] Input: Data in JSON format

[0868] Output: Data received by the server

[0869] Specific action: The terminal generates an HTTP POST request and sends data to the server.

[0870] Step 4: Data analysis on the server

[0871] The server parses the received JSON data. The server then accesses the database to determine fashion styles based on mood, activity, and relationships.

[0872] Input: Received JSON data

[0873] Output: Appropriate fashion suggestions

[0874] Specific operation: The server performs JSON parsing and issues SQL queries or API requests to retrieve suggestions from the database.

[0875] Step 5: Generating Fashion Proposals

[0876] The server generates appropriate fashion suggestions based on the analysis results. For example, it might select a suggestion like "a simple T-shirt, denim pants, and sneakers."

[0877] Input: Fashion style data from database

[0878] Output: Fashion suggestion data

[0879] Specific operation: The server generates text data for fashion suggestions based on information retrieved from the database.

[0880] Step 6: Send a prompt message to the image generation AI.

[0881] The server sends a prompt message to the image generation AI based on the generated fashion suggestions. An example of a prompt message might be, "Generate an image of a model wearing a simple T-shirt, denim pants, and sneakers."

[0882] Input: Fashion suggestion data

[0883] Output: Prompt message

[0884] Specific operation: The server generates a prompt message and sends it to the image generation AI's API endpoint using an HTTP request.

[0885] Step 7: Image generation

[0886] The image generation AI generates an atmospheric image based on the prompt text. This image is returned to the server.

[0887] Input: Prompt message

[0888] Output: The generated image

[0889] Specific operation: The AI ​​model parses the prompt text and executes an image generation algorithm to generate an image.

[0890] Step 8: Returning and formatting the results to the server

[0891] After the generated image is sent back to the server, the server combines the fashion suggestion and the image into one file and formats it again into JSON format.

[0892] Input: Generated image, fashion suggestion data

[0893] Output: Proposal results in JSON format

[0894] Specific operation: The server combines image data and fashion suggestion data into a JSON object and converts it into a format that can be sent as an HTTP response.

[0895] Step 9: Sending the results to the terminal

[0896] The server sends the formatted JSON data to the terminal. The terminal interprets the received data and displays it to the user.

[0897] Input: Suggestion results in JSON format

[0898] Output: Display of fashion suggestions and images on the device

[0899] Specific operation: The server sends JSON data to the device as an HTTP response, and the device parses the received data and displays it in the UI.

[0900] Step 10: Presentation to the user

[0901] The device displays fashion suggestions and atmospheric images to the user. This allows the user to visually check the most suitable fashion for their mood and activities on that day.

[0902] Input: Fashion suggestions and images interpreted from JSON data

[0903] Output: Fashion suggestions and images displayed in the user interface

[0904] Specific operation: The terminal uses HTML and CSS to display suggestions and images in a graphical user interface.

[0905] This allows users to receive specific and visually understandable fashion suggestions, making daily outfit selection easier and more enjoyable.

[0906] (Application Example 1)

[0907] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0908] Traditional fashion suggestion systems struggled to provide personalized recommendations based on detailed context, such as the user's mood, activities, and relationship with companions. As a result, users often lacked a concrete image to help them make optimal choices. Furthermore, there was a lack of integrated systems for visually confirming suggested styles and directly purchasing items from e-commerce sites.

[0909] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0910] This invention includes a server that provides input for the user's mood, what they want to do, and their relationship with the person they are going out with; a server that provides input data

[0911] "Mood on the day" refers to the subjective feelings or mood that the user is experiencing on that particular day.

[0912] "Things to do" refers to the specific activities or actions that the user wants to perform on that particular day.

[0913] "Relationship with the person you're going out with" refers to the relationship between the user and the person they will be spending time with or going out with on that particular day (for example, a friend, colleague, or family member).

[0914] "Means of input" refers to the interface (such as forms or buttons) that a user uses to provide information to a system.

[0915] "Means of transmission" refers to the processes and technologies used to send data from a terminal to a server.

[0916] "A means of analyzing and generating appropriate clothing suggestions" refers to the process by which a server analyzes user input data and selects the optimal fashion style based on that data.

[0917] An "image generation artificial intelligence model" is an artificial intelligence model that has the ability to generate high-quality images based on input prompt text.

[0918] A "prompt message" is the text content used to request an artificial intelligence model to generate a specific image.

[0919] "Generating means" refers to the process and techniques for generating images that match the proposed clothing style.

[0920] "Means of display" refers to the interface and technology for displaying the generated clothing suggestions and images on the user's device.

[0921] An "e-commerce site" is a website where users can purchase goods via the internet.

[0922] "Means of providing purchasable links" refers to the process and technology of displaying online shopping links for users to purchase suggested fashion items.

[0923] This invention provides a system that offers appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. Users can access this system using their smartphones.

[0924] First, when a user logs into the system, the terminal displays an input form. The form includes fields for the user to enter their mood for the day, what they want to do, and their relationship with the person they are going out with. Once the user enters this information, the terminal formats the data into JSON format and sends it to the server.

[0925] When the server receives data, it begins analysis. The analysis automatically generates optimal fashion suggestions from the database based on the user's mood, activities, and relationships. This analysis determines, for example, that "if you're feeling energetic and going hiking, a sporty outer layer, trekking pants, and athletic shoes would be suitable."

[0926] Next, the server sends a prompt to the image generation AI model based on the generated fashion suggestions. The prompt might take the form of, for example, "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues." Based on this prompt, the AI ​​model generates an image that captures the atmosphere of the suggested fashion style.

[0927] The generated fashion suggestions and atmosphere images are formatted again in JSON format and sent to the device. The device receives this and displays it to the user. Furthermore, the device also provides online shopping links where the suggested clothing items can be purchased. This allows the user to visually confirm the suggested style and purchase the items directly from the e-commerce site.

[0928] The main hardware of this system is a smartphone, and the software includes a web service framework (e.g., Node.js), a database (e.g., MongoDB), an image generation AI (e.g., OpenAI's DALL-E), and a front-end framework (e.g., React Native).

[0929] For example, if a user enters conditions such as "feeling energetic," "going hiking," and "working with colleagues," the server will suggest "sporty outerwear, trekking pants, and running shoes." Based on this, it generates a prompt message, "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues," and sends it to the image generation AI model. By providing the user with the image generated based on this prompt message, the user can visually confirm specific fashion styles and purchase items directly.

[0930] As described above, the present invention realizes a rapid and effective fashion suggestion system that meets the individual needs of users.

[0931] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0932] Step 1:

[0933] When a user logs into the system, the terminal displays an input form. This form includes fields for the user to enter their mood for the day, what they want to do, and their relationship with the person they are going out with. As the user enters this information, it is formatted into the necessary data format for transmission to the server. The input data format is JSON, for example, {"mood": "energetic mood", "activity": "hiking", "relationship": "colleague"}.

[0934] Step 2:

[0935] The terminal formats the data entered by the user into JSON format and sends it to the server. An HTTP POST request is used to send the entered data to the server. The input consists of the user's mood, what they want to do, and their relationship with the person they are going out with, while the output is the JSON data received by the server.

[0936] Step 3:

[0937] The server analyzes the received data. Specifically, the server analyzes data on mood, activity, and relationships to generate optimal fashion suggestions. This analysis involves database access, referencing fashion styles based on mood, activity, and relationships. The input is data in JSON format, and the output is structured data containing appropriate clothing suggestions.

[0938] Step 4:

[0939] The server sends a prompt to the image generation AI model based on the clothing suggestions it generates. For example, from a suggestion of "sporty outerwear, trekking pants, and running shoes," the prompt "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues." is generated. A request is then sent to the image generation AI based on this prompt. The input is the fashion suggestion, and the output is the prompt to the image generation AI.

[0940] Step 5:

[0941] An image generation artificial intelligence model generates an atmospheric image based on a prompt. The generated image is returned to the server. The input is the prompt, and the output is the generated atmospheric image.

[0942] Step 6:

[0943] The server formats the generated fashion suggestions and atmospheric images into JSON format and sends it to the terminal. The data sent to the terminal includes the text of the fashion suggestions and the URLs of the images. The input is the generated fashion suggestions and atmospheric images, and the output is data in JSON format.

[0944] Step 7:

[0945] The device interprets the received data and displays fashion suggestions (text and images) to the user. Furthermore, it provides links to purchase the suggested items on e-commerce sites. The input is JSON data sent from the server, and the output is the fashion suggestions, atmospheric images, and purchase links displayed to the user.

[0946] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0947] This invention is a fashion suggestion system that incorporates an emotion engine to recognize the user's emotions. This system provides clothing suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. The specific program processing and specific examples of this system are described below.

[0948] Data entry using an emotion engine

[0949] The emotion engine recognizes emotions from the user's facial expressions and voice. For example, if a user is smiling at the camera, the emotion engine recognizes this as a "cheerful mood." This result is automatically reflected in the input data as the "mood for the day."

[0950] User data entry

[0951] When a user logs into the system, the terminal displays an input form. In addition to the mood recognized by the emotion engine, the user enters "what they want to do" and "the nature of their relationship with the person they are going out with." For example, a user might enter "cheerful mood," "cafe hopping," and "friend" as the person they are going out with.

[0952] Sending input data

[0953] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. An example of the generated JSON format is {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}.

[0954] Server-based data analysis and proposal generation

[0955] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles based on mood, activity, and relationships. This analysis might select a cheerful style, such as "a colorful blouse and jeans, and casual shoes."

[0956] Utilization of AI for image generation

[0957] Next, the server sends instructions to the image generation AI based on the fashion suggestions, and generates an image that captures the atmosphere of the suggested fashion style. For example, it might send an instruction to generate an image of a model wearing "a colorful blouse, jeans, and casual shoes." This image generation AI then generates a high-quality image based on the instructions.

[0958] Submitting and displaying proposal results

[0959] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[0960] Specific examples

[0961] As a concrete example, if a user is identified as feeling "energetic" through the emotion engine, and further inputs conditions such as "going hiking" and "a colleague" as a companion, the server will suggest "sportswear and hiking boots" that are best suited to "energetic activity," and generate images based on those suggestions. In this way, users can visually confirm specific fashion styles that match their activity and mood.

[0962] By combining an emotion engine, this invention makes it possible to provide fashion suggestions that incorporate the user's real-time emotions, thereby realizing a more personalized user experience.

[0963] The following describes the processing flow.

[0964] Step 1:

[0965] The device activates its emotion engine and enables the camera and microphone to detect the user's face and voice.

[0966] Step 2:

[0967] The emotion engine analyzes the user's facial expression data and voice tone in real time to recognize their "mood for the day." For example, if the user's facial expression is smiling, it recognizes them as being in a "cheerful mood."

[0968] Step 3:

[0969] The device automatically enters the user's recognized "mood for the day" into the "mood" field of the input form. Additionally, a form is displayed for the user to input "what they want to do" and "their relationship with the person they are going out with."

[0970] Step 4:

[0971] The user enters "what they want to do" and "their relationship with the person they're going out with" into an input form. For example, they might enter "cafe hopping" or "friends."

[0972] Step 5:

[0973] The device converts the data entered by the user and the "mood of the day" recognized by the emotion engine into JSON format. Example: {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}

[0974] Step 6:

[0975] The device sends the formatted JSON data to the server using an HTTP request.

[0976] Step 7:

[0977] The server receives the JSON data sent from the terminal and parses (analyzes) the data.

[0978] Step 8:

[0979] Based on the data analyzed by the server, it queries the database to generate appropriate fashion suggestions. For example, the analysis results might select a style such as "a colorful blouse, jeans, and casual shoes."

[0980] Step 9:

[0981] Based on the fashion suggestions generated by the server, a descriptive text for the suggestions is created. Example: "Today, to enjoy a lively café hopping experience, a colorful blouse, jeans, and casual shoes are recommended."

[0982] Step 10:

[0983] The server sends instructions to the image generation AI to generate images based on the fashion suggestions.

[0984] Step 11:

[0985] The image generation AI receives instructions from the server and generates an image that captures the atmosphere of the specified fashion style. The image generation AI creates an image of a model wearing "a colorful blouse, jeans, and casual shoes."

[0986] Step 12:

[0987] The image generation AI sends the generated image to the server.

[0988] Step 13:

[0989] The server formats both the generated fashion suggestion (text) and the generated image into JSON format and sends them to the terminal. Example: {"suggestion": "A colorful blouse and jeans, casual shoes", "image_url": "http: / / example.com / generated_image.jpg"}

[0990] Step 14:

[0991] The terminal parses the JSON data received from the server and extracts the fashion suggestion description and the generated image URL.

[0992] Step 15:

[0993] The device displays a description and image of the fashion suggestion to the user. The user reviews the suggested fashion and image to help them choose what to wear that day.

[0994] Through the above processing steps, a fashion suggestion system using an emotion engine is realized. Users can receive personalized and specific fashion suggestions through real-time emotion analysis.

[0995] (Example 2)

[0996] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0997] Traditional fashion suggestion systems relied on subjective input data, making it difficult to reflect users' real-time emotions. Furthermore, there were limited ways to visually confirm the suggested fashion styles. Therefore, personalized fashion suggestions tailored to the user's emotions and circumstances were challenging.

[0998] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0999] In this invention, the server includes means for recognizing the user's emotions, means for inputting the user's mood for the day, what they want to do, and their relationship with the person they are going out with, means for transmitting the input data to the server, means for analyzing the transmitted data and generating appropriate clothing suggestions, means for generating an atmospheric image based on the generated clothing suggestions, means for transmitting the generated clothing suggestions and image to a terminal, and means for displaying the clothing suggestions and image on the terminal. This enables personalized fashion suggestions that incorporate the user's emotions in real time.

[1000] 1. "Means of recognizing user emotions" refers to technology that uses cameras and microphones to analyze a user's facial expressions and voice to recognize specific emotional states.

[1001] 2. "A means of inputting one's mood, what one wants to do, and the relationship with the person one is going out with" refers to an interface in which the user logs into the system and inputs their emotional state, plans, and the relationship with the person they are going out with through an input form.

[1002] 3. "Means for sending input data to the server" refers to the technology that converts user-input data into JSON format and sends it to the server via the network.

[1003] 4. "Means for analyzing transmitted data and generating appropriate clothing suggestions" refers to an analysis process within the server that generates optimal fashion suggestions based on mood, activity, and relationships, using the received data.

[1004] 5. "Means for generating atmospheric images based on generated clothing suggestions" refers to a technology that uses a generative AI model to generate atmospheric images based on suggested fashion styles.

[1005] 6. "Means for sending generated clothing suggestions and images to the terminal" refers to a technology that formats the generated fashion suggestions and image data into JSON format and sends them to the user's terminal.

[1006] 7. "Means for displaying clothing suggestions and images on a terminal" refers to an interface that visually displays received fashion suggestion information and atmospheric images on the user's terminal screen.

[1007] 8. "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text-based data format for structuring and exchanging data.

[1008] 9. "Means for generating fashion suggestion descriptions based on analyzed data" refers to technology in which a server automatically generates detailed fashion suggestion descriptions for users based on the analysis results.

[1009] This invention relates to a fashion suggestion system that includes an emotion engine for recognizing user emotions. Based on the user's mood, activities, and relationship with companions, the system provides clothing suggestions and images illustrating their atmosphere.

[1010] The process of recognizing emotions

[1011] When a user smiles at the camera, the emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time. The emotion engine uses facial recognition technology such as OpenCV and voice analysis technology (e.g., a voice recognition service). This allows the system to recognize the user's "mood for the day" as "cheerful."

[1012] Data entry and transmission

[1013] When a user logs into the system, the terminal displays an input form using a frontend library such as React. Here, the user inputs their "mood" as recognized by the emotion engine, their "desires," and their "relationship with the person they are going out with." For example, "cheerful mood," "cafe hopping," and "friends" might be entered.

[1014] Once you have finished entering the data and pressed the submit button, the terminal will format the entered data into JSON format and send it to the server. The data sent will be in a format similar to the following:

[1015] json

[1016] {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}

[1017] This conversion uses the JavaScript fetch API.

[1018] Data analysis and proposal generation

[1019] The server begins analyzing the received data. Using Python and data analysis libraries, the server accesses a database (e.g., MySQL or MongoDB) to refer to a dataset of fashion styles based on the entered mood, activities, and relationships. As a result of this analysis, a cheerful style such as "colorful blouse and jeans, casual shoes" might be selected.

[1020] Generating atmospheric images

[1021] Next, the server sends a prompt to the image generation AI model. A generative AI model (e.g., an image generation AI) is suitable for use. An example of a prompt message sent by the server is as follows:

[1022] Please generate an image of a model wearing a colorful blouse, jeans, and casual shoes.

[1023] Upon receiving this prompt, the image generation AI model generates a high-quality atmospheric image and sends it back to the server.

[1024] Submitting and displaying proposal results

[1025] The server combines the generated fashion suggestions and atmospheric images, formats them back into JSON format, and sends them to the terminal. The terminal interprets the received JSON data and displays the fashion suggestion text and generated images to the user.

[1026] Specific example

[1027] For example, if the user is identified as "feeling energetic" through the emotion engine, and also inputs "going hiking" and conditions such as "a colleague" as a companion, the server will suggest "sportswear and hiking boots" best suited for "energetic activity" and generate an image based on that. An example of this prompt is as follows:

[1028] Please generate images of models wearing sportswear and hiking boots.

[1029] This allows users to visually see specific fashion styles that match their activities and mood.

[1030] This invention enables personalized fashion suggestions that incorporate the user's real-time emotions, thereby providing a better user experience.

[1031] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1032] Step 1: Emotion Recognition

[1033] Subject: User

[1034] Specific action: The user smiles at the camera.

[1035] Input: User's facial expressions and voice data.

[1036] Data processing / calculation: User facial expressions and voice data acquired in real time from the camera and microphone are analyzed using an emotion engine (e.g., OpenCV or voice analysis technology).

[1037] Output: The emotion engine recognizes the user's emotional state as "cheerful."

[1038] Step 2: Data Entry

[1039] Subject: User

[1040] Specific operation: The user logs into the system and enters information such as "how they feel today," "what they want to do," and "the nature of their relationship with the person they are going out with" through an input form.

[1041] Input: User-entered emotional state, activity details, and relationship data with the other party.

[1042] Data processing / calculation: Receive and verify the input data using a front-end interface (such as React).

[1043] Output: The input data is prepared for the next step.

[1044] Step 3: Data transmission

[1045] Subject: terminal

[1046] Specific action: After the user enters data, they press the submit button.

[1047] Input: User-entered mood, activity details, and related data.

[1048] Data processing / calculation: Convert the input data into JSON format and send it to the server using the JavaScript fetch API.

[1049] Output: Data in JSON format (e.g., {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}) is generated and sent to the server.

[1050] Step 4: Data Analysis

[1051] Subject: Server

[1052] Specific operation: The server analyzes the data it receives.

[1053] Input: Data in JSON format.

[1054] Data processing / calculations: Analyze data using data analysis libraries such as Python and Pandas, and retrieve relevant information from databases (MySQL or MongoDB).

[1055] Output: Fashion suggestions based on analyzed data (e.g., "A colorful blouse and jeans, and casual shoes").

[1056] Step 5: Generate atmosphere image

[1057] Subject: Server

[1058] Specific operation: The server sends prompts to the generated AI model.

[1059] Input: Fashion suggestion data.

[1060] Data processing / calculation: A prompt message (e.g., "Generate an image of a model wearing a colorful blouse, jeans, and casual shoes.") is sent to the generating AI model.

[1061] Output: Atmosphere image generated from the generative AI model.

[1062] Step 6: Submit the proposal results

[1063] Subject: Server

[1064] Specific operation: The server combines fashion suggestions and images into a single file, formats it in JSON format, and sends it to the terminal.

[1065] Input: Fashion suggestions and generated images.

[1066] Data processing / calculation: Format the proposed content and images into JSON format (e.g., {"fashion": "Colorful blouse and jeans, casual shoes", "image": "Image data"}).

[1067] Output: Data in JSON format is sent to the terminal.

[1068] Step 7: Displaying the results

[1069] Subject: terminal

[1070] Specific operation: The device interprets the received data and displays fashion suggestion text and generated images to the user.

[1071] Input: Fashion suggestion data and image data in JSON format.

[1072] Data Processing / Calculation: Analyzes received data and converts it into a format that can be displayed on the user interface.

[1073] Output: The fashion suggestion text and images are visually displayed on the user's device screen.

[1074] This allows users to check in real time the optimal fashion style for their mood and plans for the day.

[1075] (Application Example 2)

[1076] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1077] Conventional fashion suggestion systems make suggestions based solely on schedules and companion information, without considering the user's emotions. This results in a low degree of personalization and difficulty in providing suggestions that match the user's real-time emotions. Furthermore, generating atmospheric images that allow users to visually confirm specific clothing suggestions requires multiple manual steps and is time-consuming, thus lacking immediacy.

[1078] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1079] In this invention, the server includes means for recognizing the user's emotions in real time using an emotion recognition engine, means for adjusting the content of fashion suggestions based on the recognized emotions, and means for generating atmospheric images using a generation AI model based on the analyzed data and the user's emotions. As a result, personalized fashion suggestions that reflect the user's real-time emotions are immediately generated, and the user can use the suggestions while visually confirming them.

[1080] "Mood on the day" refers to the emotions and psychological state that the user is currently experiencing.

[1081] "Things you want to do" refers to activities or plans that the user wants to carry out at a specific date and time.

[1082] "Relationship with the person you're going out with" refers to the relationship between the user and the person they are going out with (e.g., friend, colleague, family).

[1083] "Means of input" refers to the hardware and software that users use to input their mood for the day, what they want to do, and their relationship with the people they are going out with.

[1084] "Means of transmission" refers to the process and equipment used to communicate and send the input data to the server.

[1085] "Means of analysis" refers to the technical process of analyzing data transmitted on a server and generating appropriate fashion suggestions.

[1086] "Means for generating images" refers to technologies and devices for generating visual atmosphere images based on generated clothing suggestions.

[1087] "Means of transmission to the terminal" refers to the communication technologies and protocols used to transmit the generated clothing suggestions and images to the user's terminal.

[1088] "Means of display" refers to display technology used to visually present clothing suggestions and images received on the terminal to the user.

[1089] An "emotion recognition engine" refers to software and hardware that recognizes emotions in real time from a user's facial expressions and voice.

[1090] "Methods for adjusting fashion suggestions based on recognized emotions" refers to technologies that dynamically change and adjust fashion suggestions by taking into account the user's emotions recognized by an emotion recognition engine.

[1091] "Methods for converting to JSON format" refers to software technologies for converting input data into a JSON (JavaScript Object Notation) data structure.

[1092] A "generative AI model" refers to an artificial intelligence model that automatically generates high-quality atmospheric images based on input data.

[1093] This invention relates to a fashion suggestion system that incorporates an emotion recognition engine to recognize the user's emotions. This system suggests clothing based on the user's mood, activities, and relationship with companions, and provides corresponding atmospheric images.

[1094] Required hardware and software

[1095] Hardware:

[1096] Smartphones (iOS / Android)

[1097] Smart Glasses

[1098] software:

[1099] Emotion recognition engine (API)

[1100] Image recognition and speech analysis software

[1101] Database (MySQL)

[1102] Image generation AI (e.g., OpenAI DALL-E)

[1103] Sending data in JSON format

[1104] Virtual reality (VR) framework (Unity)

[1105] Specific implementations of the system

[1106] 1. User mood recognition:

[1107] The device (smartphone or smart glasses) uses its camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion recognition engine (API), where emotions are recognized in real time. The recognition result is reflected in the system as the user's "mood for the day."

[1108] 2. Data input means:

[1109] When a user logs into their device, a prompt screen appears. Here, the user enters information such as "what they want to do" and "the relationship with the person they are going out with." For example, they can enter data such as "cafe hopping" and "friend."

[1110] 3. Data transmission means:

[1111] The data entered by the user and the mood data obtained by the emotion recognition engine are converted into JSON format and sent to the server. This efficiently structures the data.

[1112] 4. Data analysis and clothing suggestion generation methods:

[1113] The server analyzes the received data and references a dataset of fashion styles stored in the database, based on mood, activities, and relationships. As an example of this analysis, if the conditions are "cheerful mood," "cafe hopping," and "friends," then a suggestion of "casual blouse and jeans" is generated.

[1114] 5. Image generation means:

[1115] Based on the generated clothing suggestions, the server sends instructions to the image generation AI to create an atmospheric image. The image generation AI automatically generates an image of a model wearing a "colorful blouse and jeans." This allows the user to visually confirm the suggested outfit.

[1116] 6. Means for submitting and displaying proposal results:

[1117] The fashion suggestions and atmosphere images generated on the server are formatted again into JSON and sent to the terminal. The terminal receives this and displays the fashion suggestion text and images to the user. The user can then use this as a reference to make their actual fashion choices.

[1118] Specific example

[1119] For example, if a user is perceived as feeling "energetic" through the emotion recognition engine and inputs "going hiking" and "a colleague" as their companion, the server will suggest "sportswear and hiking boots" appropriate for "energetic activity." The generative AI model will then generate an image of a "model wearing sportswear and hiking boots." In this way, users can visually confirm specific fashion styles that match their activities and mood for the day in real time.

[1120] Example of a prompt

[1121] "I'm feeling cheerful today and planning to go cafe hopping with a friend. Could you recommend some fashionable outfits?"

[1122] Based on this prompt, the AI ​​model can generate fashion suggestions that perfectly match the user's needs.

[1123] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1124] Step 1:

[1125] The user logs into their device (smartphone or smart glasses) and uses the camera and microphone to capture their facial expressions and voice. This inputs the user's image and voice data. This input data is sent to an emotion recognition engine API, which analyzes the user's emotions in real time. The emotion recognition engine processes the data and outputs an emotion result, such as "cheerful mood."

[1126] Step 2:

[1127] The user enters "what they want to do (e.g., cafe hopping)" and "the relationship with the person they are going out with (e.g., friend)" into a form displayed on the device. This data is registered as input data on the device. After the user completes the input, the device converts the mood data analyzed by the emotion recognition engine and the above input data into JSON format. This JSON data is output in the format { "mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friend"}.

[1128] Step 3:

[1129] The terminal sends the JSON data generated in step 2 to the server. The server analyzes the received data, accesses the database, and searches for fashion styles based on mood, activities, and relationships. For example, based on the conditions "cheerful mood," "cafe hopping," and "friends," the suggestion "colorful blouse and jeans, casual shoes" might be selected. This analysis result becomes the server's output.

[1130] Step 4:

[1131] Next, the server sends a prompt to the image generation AI based on the analysis results from step 3. An example prompt is, "Generate an image of a model wearing a colorful blouse, jeans, and casual shoes." Based on this prompt, the image generation AI generates a high-quality atmospheric image and outputs it to the server.

[1132] Step 5:

[1133] The server reformats the generated fashion suggestions and images back into JSON format and sends this data to the terminal. For example, it outputs in the format { "fashion_style": "Colorful blouse and jeans, casual shoes", "image_url": "generated_image_url"}.

[1134] Step 6:

[1135] The terminal interprets the JSON data received from the server and displays fashion suggestions, along with generated images, to the user. The user can visually confirm this on the terminal and actually select a suggested fashion style. This display result is the final output.

[1136] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1137] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1138] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1139] [Fourth Embodiment]

[1140] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1141] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1144] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1147] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1148] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1149] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1150] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1151] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1152] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1153] This invention is a system that provides appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. Below, we will generate the program for this system and explain its specific processing details.

[1154] User data entry

[1155] When a user logs into the system, the terminal displays an input form. This form includes fields for entering "how you feel that day," "what you want to do," and "the relationship you have with the person you're going out with." For example, a user might enter "I want to relax" for their mood, "cafe hopping" for what they want to do, and "friend" for their relationship.

[1156] Sending input data

[1157] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. An example of the generated JSON format is {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}.

[1158] Server-based data analysis and proposal generation

[1159] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles based on mood, activity, and relationships. This analysis might select a casual style, such as "a simple T-shirt, denim pants, and sneakers."

[1160] Utilization of AI for image generation

[1161] Next, the server sends instructions to the image generation AI based on the fashion suggestions, and generates an image that captures the atmosphere of the suggested fashion style. For example, it might send an instruction to generate an image of a model wearing a simple T-shirt, denim pants, and sneakers. This image generation AI then generates a high-quality image based on the instructions.

[1162] Submitting and displaying proposal results

[1163] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[1164] Specific examples

[1165] As a concrete example, if a user enters conditions such as "feeling energetic" and "going hiking," and that their companion is "a colleague," the server will suggest items suitable for "energetic activity," such as "sporty outerwear, trekking pants, and athletic shoes," and generate images based on those suggestions. In this way, users can visually confirm specific fashion styles that match their activity and mood.

[1166] Thus, the present invention provides an effective fashion support system that meets the diverse needs of users.

[1167] The following describes the processing flow.

[1168] Step 1:

[1169] The device displays a form for the user to input their mood for the day, what they want to do, and their relationship with the person they are going out with.

[1170] Step 2:

[1171] The user enters "how they feel that day," "what they want to do," and "the nature of their relationship with the person they are going out with" into an input form.

[1172] Step 3:

[1173] The terminal converts the input data into JSON format. For example, it formats the data into {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}.

[1174] Step 4:

[1175] The device sends the formatted JSON data to the server using an HTTP request.

[1176] Step 5:

[1177] The server parses the JSON data received from the terminal and generates appropriate fashion suggestions in conjunction with the database.

[1178] Step 6:

[1179] The server selects a list of fashion items suitable for the mood, activity, and relationship based on the received data, and generates a description of the fashion suggestion. For example, it might generate a description like, "A simple T-shirt, denim pants, and sneakers."

[1180] Step 7:

[1181] The server sends instructions to the image generation AI to generate an image based on the selected fashion items.

[1182] Step 8:

[1183] The image generation AI receives instructions from the server and generates images that capture the feel of fashion items based on those instructions.

[1184] Step 9:

[1185] The image generation AI sends the generated image to the server.

[1186] Step 10:

[1187] The server integrates the generated fashion suggestions and image data, and then formats them back into JSON format. Example: {"suggestion": "Simple T-shirt, denim pants, sneakers", "image_url": "http: / / example.com / generated_image.jpg"}

[1188] Step 11:

[1189] The server sends the integrated JSON data to the terminal.

[1190] Step 12:

[1191] The device interprets the JSON data received from the server and displays a description of the fashion suggestion and the generated image to the user.

[1192] (Example 1)

[1193] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1194] In modern times, choosing fashion is a significant burden for consumers. In particular, selecting appropriate attire based on one's mood, activities, and relationship with companions is difficult. Therefore, there is a need for a system that allows users to easily choose the most suitable outfit for the day.

[1195] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1196] In this invention, the server includes means for analyzing transmitted data and generating appropriate clothing suggestions, means for sending prompt messages to an image generation algorithm based on the generated clothing suggestions, and means for sending the clothing suggestions, including the generated images, to the terminal. This allows the user to receive appropriate clothing suggestions and atmospheric images based on their mood, activities, and relationship with their companions on the day.

[1197] "Mood on the day" refers to the emotions and mental state the user is experiencing on that particular day.

[1198] "Things to do" refers to the activities or events that the user wants to do on that day.

[1199] "Relationship with the person you're going out with" refers to the type of relationship the user has with the person they're going out with on that day (for example, friend, family, colleague).

[1200] "Means of input" refers to the interface or device that allows users to input data into a system.

[1201] "Means of sending to the server" refers to the communication functions and protocols used to send data entered by the user to the server.

[1202] "Means for analyzing transmitted data and generating appropriate clothing suggestions" refers to algorithms and processes that analyze data transmitted by users and generate optimal fashion suggestions based on that data.

[1203] "Means for sending prompt statements to an image generation algorithm" refers to the process of generating and sending instruction statements (prompt statements) to an image generation algorithm based on the generated clothing suggestions.

[1204] "Means for sending clothing suggestions, including generated images, to a terminal" refers to communication functions and protocols for sending images received from an image generation algorithm and generated clothing suggestions to a terminal.

[1205] "Means for displaying clothing suggestions and images on a terminal" refers to interfaces or devices for displaying received clothing suggestions and images on the user's terminal.

[1206] This invention is a system that provides appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. A specific embodiment of this system will be described below.

[1207] User data entry

[1208] When a user logs into the system, the terminal displays an input form. This form includes fields for "how you feel that day," "what you want to do," and "the relationship you have with the person you're going out with." For example, a user might enter "I want to relax" for their mood, "cafe hopping" for what they want to do, and "friend" for their relationship.

[1209] Sending input data

[1210] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. The generated JSON data will, for example, send data like the following:

[1211] {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}

[1212] Data analysis on the server and generation of fashion suggestions.

[1213] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles that match the mood, activity, and relationship based on the sent data. This analysis selects a casual style, such as "a simple T-shirt, denim pants, and sneakers."

[1214] Utilization of AI for image generation

[1215] Next, the server sends prompts to the image generation AI based on the fashion suggestions, generating images that capture the atmosphere of the suggested fashion style. For example, a prompt is sent to generate an image of a model wearing a "simple T-shirt, denim pants, and sneakers." This image generation AI generates high-quality images based on the instructions. The following types of prompts are used:

[1216] "Please generate an image of a model wearing a simple T-shirt, denim pants, and sneakers."

[1217] Submitting and displaying proposal results

[1218] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[1219] Specific examples

[1220] As a concrete example, if a user enters conditions such as "feeling energetic" and "going hiking," and that their companion is "a colleague," the server will suggest items suitable for "energetic activity," such as "sporty outerwear, trekking pants, and athletic shoes," and generate images based on those suggestions. The prompt text in this case would be as follows:

[1221] "Please generate an image showing a sporty look with a sporty outer layer, trekking pants, and athletic shoes."

[1222] This allows users to receive specific fashion suggestions that they can visually confirm.

[1223] This system allows users to receive assistance in choosing the most suitable outfit based on their mood, activities, and relationship with their companions on the day. This provides an effective fashion support system that meets the diverse needs of users.

[1224] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1225] System program processing flow

[1226] Step 1: User input

[1227] When a user logs into the system, the terminal displays a form for them to enter their "mood for the day," "things they want to do," and "relationship with the person they are going out with." The user then uses this form to enter the information.

[1228] Input: User inputs "mood for the day," "things they want to do," and "relationship with the person they are going out with."

[1229] Output: Input data converted to JSON format

[1230] Specific operation: The user enters information into each field in the text field and clicks the submit button.

[1231] Step 2: Convert the data to JSON format

[1232] The device converts user input data into JSON format. For example, if the input data is "I want to relax," "cafe hopping," and "friends," the following JSON data will be generated:

[1233] {"mood": "I want to relax", "activity": "cafe hopping", "relationship": "friends"}

[1234] Input: User input data

[1235] Output: Data in JSON format

[1236] Specific operation: The terminal uses a program such as JavaScript to convert the data into JSON format.

[1237] Step 3: Sending data to the server

[1238] The terminal sends the converted JSON data to the server. The data is sent to the server using an HTTP request.

[1239] Input: Data in JSON format

[1240] Output: Data received by the server

[1241] Specific action: The terminal generates an HTTP POST request and sends data to the server.

[1242] Step 4: Data analysis on the server

[1243] The server parses the received JSON data. The server then accesses the database to determine fashion styles based on mood, activity, and relationships.

[1244] Input: Received JSON data

[1245] Output: Appropriate fashion suggestions

[1246] Specific operation: The server performs JSON parsing and issues SQL queries or API requests to retrieve suggestions from the database.

[1247] Step 5: Generating Fashion Proposals

[1248] The server generates appropriate fashion suggestions based on the analysis results. For example, it might select a suggestion like "a simple T-shirt, denim pants, and sneakers."

[1249] Input: Fashion style data from database

[1250] Output: Fashion suggestion data

[1251] Specific operation: The server generates text data for fashion suggestions based on information retrieved from the database.

[1252] Step 6: Send a prompt message to the image generation AI.

[1253] The server sends a prompt message to the image generation AI based on the generated fashion suggestions. An example of a prompt message might be, "Generate an image of a model wearing a simple T-shirt, denim pants, and sneakers."

[1254] Input: Fashion suggestion data

[1255] Output: Prompt message

[1256] Specific operation: The server generates a prompt message and sends it to the image generation AI's API endpoint using an HTTP request.

[1257] Step 7: Image generation

[1258] The image generation AI generates an atmospheric image based on the prompt text. This image is returned to the server.

[1259] Input: Prompt message

[1260] Output: The generated image

[1261] Specific operation: The AI ​​model parses the prompt text and executes an image generation algorithm to generate an image.

[1262] Step 8: Returning and formatting the results to the server

[1263] After the generated image is sent back to the server, the server combines the fashion suggestion and the image into one file and formats it again into JSON format.

[1264] Input: Generated image, fashion suggestion data

[1265] Output: Proposal results in JSON format

[1266] Specific operation: The server combines image data and fashion suggestion data into a JSON object and converts it into a format that can be sent as an HTTP response.

[1267] Step 9: Sending the results to the terminal

[1268] The server sends the formatted JSON data to the terminal. The terminal interprets the received data and displays it to the user.

[1269] Input: Suggestion results in JSON format

[1270] Output: Display of fashion suggestions and images on the device

[1271] Specific operation: The server sends JSON data to the device as an HTTP response, and the device parses the received data and displays it in the UI.

[1272] Step 10: Presentation to the user

[1273] The device displays fashion suggestions and atmospheric images to the user. This allows the user to visually check the most suitable fashion for their mood and activities on that day.

[1274] Input: Fashion suggestions and images interpreted from JSON data

[1275] Output: Fashion suggestions and images displayed in the user interface

[1276] Specific operation: The terminal uses HTML and CSS to display suggestions and images in a graphical user interface.

[1277] This allows users to receive specific and visually understandable fashion suggestions, making daily outfit selection easier and more enjoyable.

[1278] (Application Example 1)

[1279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1280] Traditional fashion suggestion systems struggled to provide personalized recommendations based on detailed context, such as the user's mood, activities, and relationship with companions. As a result, users often lacked a concrete image to help them make optimal choices. Furthermore, there was a lack of integrated systems for visually confirming suggested styles and directly purchasing items from e-commerce sites.

[1281] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1282] This invention includes a server that provides input for the user's mood, what they want to do, and their relationship with the person they are going out with; a server that provides input data

[1283] "Mood on the day" refers to the subjective feelings or mood that the user is experiencing on that particular day.

[1284] "Things to do" refers to the specific activities or actions that the user wants to perform on that particular day.

[1285] "Relationship with the person you're going out with" refers to the relationship between the user and the person they will be spending time with or going out with on that particular day (for example, a friend, colleague, or family member).

[1286] "Means of input" refers to the interface (such as forms or buttons) that a user uses to provide information to a system.

[1287] "Means of transmission" refers to the processes and technologies used to send data from a terminal to a server.

[1288] "A means of analyzing and generating appropriate clothing suggestions" refers to the process by which a server analyzes user input data and selects the optimal fashion style based on that data.

[1289] An "image generation artificial intelligence model" is an artificial intelligence model that has the ability to generate high-quality images based on input prompt text.

[1290] A "prompt message" is the text content used to request an artificial intelligence model to generate a specific image.

[1291] "Generating means" refers to the process and techniques for generating images that match the proposed clothing style.

[1292] "Means of display" refers to the interface and technology for displaying the generated clothing suggestions and images on the user's device.

[1293] An "e-commerce site" is a website where users can purchase goods via the internet.

[1294] "Means of providing purchasable links" refers to the process and technology of displaying online shopping links for users to purchase suggested fashion items.

[1295] This invention provides a system that offers appropriate fashion suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. Users can access this system using their smartphones.

[1296] First, when a user logs into the system, the terminal displays an input form. The form includes fields for the user to enter their mood for the day, what they want to do, and their relationship with the person they are going out with. Once the user enters this information, the terminal formats the data into JSON format and sends it to the server.

[1297] When the server receives data, it begins analysis. The analysis automatically generates optimal fashion suggestions from the database based on the user's mood, activities, and relationships. This analysis determines, for example, that "if you're feeling energetic and going hiking, a sporty outer layer, trekking pants, and athletic shoes would be suitable."

[1298] Next, the server sends a prompt to the image generation AI model based on the generated fashion suggestions. The prompt might take the form of, for example, "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues." Based on this prompt, the AI ​​model generates an image that captures the atmosphere of the suggested fashion style.

[1299] The generated fashion suggestions and atmosphere images are formatted again in JSON format and sent to the device. The device receives this and displays it to the user. Furthermore, the device also provides online shopping links where the suggested clothing items can be purchased. This allows the user to visually confirm the suggested style and purchase the items directly from the e-commerce site.

[1300] The main hardware of this system is a smartphone, and the software includes a web service framework (e.g., Node.js), a database (e.g., MongoDB), an image generation AI (e.g., OpenAI's DALL-E), and a front-end framework (e.g., React Native).

[1301] For example, if a user enters conditions such as "feeling energetic," "going hiking," and "working with colleagues," the server will suggest "sporty outerwear, trekking pants, and running shoes." Based on this, it generates a prompt message, "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues," and sends it to the image generation AI model. By providing the user with the image generated based on this prompt message, the user can visually confirm specific fashion styles and purchase items directly.

[1302] As described above, the present invention realizes a rapid and effective fashion suggestion system that meets the individual needs of users.

[1303] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1304] Step 1:

[1305] When a user logs into the system, the terminal displays an input form. This form includes fields for the user to enter their mood for the day, what they want to do, and their relationship with the person they are going out with. As the user enters this information, it is formatted into the necessary data format for transmission to the server. The input data format is JSON, for example, {"mood": "energetic mood", "activity": "hiking", "relationship": "colleague"}.

[1306] Step 2:

[1307] The terminal formats the data entered by the user into JSON format and sends it to the server. An HTTP POST request is used to send the entered data to the server. The input consists of the user's mood, what they want to do, and their relationship with the person they are going out with, while the output is the JSON data received by the server.

[1308] Step 3:

[1309] The server analyzes the received data. Specifically, the server analyzes data on mood, activity, and relationships to generate optimal fashion suggestions. This analysis involves database access, referencing fashion styles based on mood, activity, and relationships. The input is data in JSON format, and the output is structured data containing appropriate clothing suggestions.

[1310] Step 4:

[1311] The server sends a prompt to the image generation AI model based on the clothing suggestions it generates. For example, from a suggestion of "sporty outerwear, trekking pants, and running shoes," the prompt "A model wearing a sporty outerwear, trekking pants, and running shoes for a hiking trip with colleagues." is generated. A request is then sent to the image generation AI based on this prompt. The input is the fashion suggestion, and the output is the prompt to the image generation AI.

[1312] Step 5:

[1313] An image generation artificial intelligence model generates an atmospheric image based on a prompt. The generated image is returned to the server. The input is the prompt, and the output is the generated atmospheric image.

[1314] Step 6:

[1315] The server formats the generated fashion suggestions and atmospheric images into JSON format and sends it to the terminal. The data sent to the terminal includes the text of the fashion suggestions and the URLs of the images. The input is the generated fashion suggestions and atmospheric images, and the output is data in JSON format.

[1316] Step 7:

[1317] The device interprets the received data and displays fashion suggestions (text and images) to the user. Furthermore, it provides links to purchase the suggested items on e-commerce sites. The input is JSON data sent from the server, and the output is the fashion suggestions, atmospheric images, and purchase links displayed to the user.

[1318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1319] This invention is a fashion suggestion system that incorporates an emotion engine to recognize the user's emotions. This system provides clothing suggestions and atmospheric images based on the user's mood, activities, and relationship with companions. The specific program processing and specific examples of this system are described below.

[1320] Data entry using an emotion engine

[1321] The emotion engine recognizes emotions from the user's facial expressions and voice. For example, if a user is smiling at the camera, the emotion engine recognizes this as a "cheerful mood." This result is automatically reflected in the input data as the "mood for the day."

[1322] User data entry

[1323] When a user logs into the system, the terminal displays an input form. In addition to the mood recognized by the emotion engine, the user enters "what they want to do" and "the nature of their relationship with the person they are going out with." For example, a user might enter "cheerful mood," "cafe hopping," and "friend" as the person they are going out with.

[1324] Sending input data

[1325] Once the user completes the input and presses the submit button, the device converts this data into JSON format and sends it to the server. An example of the generated JSON format is {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}.

[1326] Server-based data analysis and proposal generation

[1327] The server receives data sent from the terminal and begins analysis to generate appropriate fashion suggestions. The server accesses a database and refers to a dataset of fashion styles based on mood, activity, and relationships. This analysis might select a cheerful style, such as "a colorful blouse and jeans, and casual shoes."

[1328] Utilization of AI for image generation

[1329] Next, the server sends instructions to the image generation AI based on the fashion suggestions, and generates an image that captures the atmosphere of the suggested fashion style. For example, it might send an instruction to generate an image of a model wearing "a colorful blouse, jeans, and casual shoes." This image generation AI then generates a high-quality image based on the instructions.

[1330] Submitting and displaying proposal results

[1331] The server combines the generated fashion suggestions and atmospheric images into a single file, formats it back into JSON format, and sends it to the device. The device interprets the received JSON data and displays the fashion suggestion text and generated images to the user. The user can use this as a reference to choose the most suitable outfit for the day.

[1332] Specific examples

[1333] As a concrete example, if a user is identified as feeling "energetic" through the emotion engine, and further inputs conditions such as "going hiking" and "a colleague" as a companion, the server will suggest "sportswear and hiking boots" that are best suited to "energetic activity," and generate images based on those suggestions. In this way, users can visually confirm specific fashion styles that match their activity and mood.

[1334] By combining an emotion engine, this invention makes it possible to provide fashion suggestions that incorporate the user's real-time emotions, thereby realizing a more personalized user experience.

[1335] The following describes the processing flow.

[1336] Step 1:

[1337] The device activates its emotion engine and enables the camera and microphone to detect the user's face and voice.

[1338] Step 2:

[1339] The emotion engine analyzes the user's facial expression data and voice tone in real time to recognize their "mood for the day." For example, if the user's facial expression is smiling, it recognizes them as being in a "cheerful mood."

[1340] Step 3:

[1341] The device automatically enters the user's recognized "mood for the day" into the "mood" field of the input form. Additionally, a form is displayed for the user to input "what they want to do" and "their relationship with the person they are going out with."

[1342] Step 4:

[1343] The user enters "what they want to do" and "their relationship with the person they're going out with" into an input form. For example, they might enter "cafe hopping" or "friends."

[1344] Step 5:

[1345] The device converts the data entered by the user and the "mood of the day" recognized by the emotion engine into JSON format. Example: {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}

[1346] Step 6:

[1347] The device sends the formatted JSON data to the server using an HTTP request.

[1348] Step 7:

[1349] The server receives the JSON data sent from the terminal and parses (analyzes) the data.

[1350] Step 8:

[1351] Based on the data analyzed by the server, it queries the database to generate appropriate fashion suggestions. For example, the analysis results might select a style such as "a colorful blouse, jeans, and casual shoes."

[1352] Step 9:

[1353] Based on the fashion suggestions generated by the server, a descriptive text for the suggestions is created. Example: "Today, to enjoy a lively café hopping experience, a colorful blouse, jeans, and casual shoes are recommended."

[1354] Step 10:

[1355] The server sends instructions to the image generation AI to generate images based on the fashion suggestions.

[1356] Step 11:

[1357] The image generation AI receives instructions from the server and generates an image that captures the atmosphere of the specified fashion style. The image generation AI creates an image of a model wearing "a colorful blouse, jeans, and casual shoes."

[1358] Step 12:

[1359] The image generation AI sends the generated image to the server.

[1360] Step 13:

[1361] The server formats both the generated fashion suggestion (text) and the generated image into JSON format and sends them to the terminal. Example: {"suggestion": "A colorful blouse and jeans, casual shoes", "image_url": "http: / / example.com / generated_image.jpg"}

[1362] Step 14:

[1363] The terminal parses the JSON data received from the server and extracts the fashion suggestion description and the generated image URL.

[1364] Step 15:

[1365] The device displays a description and image of the fashion suggestion to the user. The user reviews the suggested fashion and image to help them choose what to wear that day.

[1366] Through the above processing steps, a fashion suggestion system using an emotion engine is realized. Users can receive personalized and specific fashion suggestions through real-time emotion analysis.

[1367] (Example 2)

[1368] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1369] Traditional fashion suggestion systems relied on subjective input data, making it difficult to reflect users' real-time emotions. Furthermore, there were limited ways to visually confirm the suggested fashion styles. Therefore, personalized fashion suggestions tailored to the user's emotions and circumstances were challenging.

[1370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1371] In this invention, the server includes means for recognizing the user's emotions, means for inputting the user's mood for the day, what they want to do, and their relationship with the person they are going out with, means for transmitting the input data to the server, means for analyzing the transmitted data and generating appropriate clothing suggestions, means for generating an atmospheric image based on the generated clothing suggestions, means for transmitting the generated clothing suggestions and image to a terminal, and means for displaying the clothing suggestions and image on the terminal. This enables personalized fashion suggestions that incorporate the user's emotions in real time.

[1372] 1. "Means of recognizing user emotions" refers to technology that uses cameras and microphones to analyze a user's facial expressions and voice to recognize specific emotional states.

[1373] 2. "A means of inputting one's mood, what one wants to do, and the relationship with the person one is going out with" refers to an interface in which the user logs into the system and inputs their emotional state, plans, and the relationship with the person they are going out with through an input form.

[1374] 3. "Means for sending input data to the server" refers to the technology that converts user-input data into JSON format and sends it to the server via the network.

[1375] 4. "Means for analyzing transmitted data and generating appropriate clothing suggestions" refers to an analysis process within the server that generates optimal fashion suggestions based on mood, activity, and relationships, using the received data.

[1376] 5. "Means for generating atmospheric images based on generated clothing suggestions" refers to a technology that uses a generative AI model to generate atmospheric images based on suggested fashion styles.

[1377] 6. "Means for sending generated clothing suggestions and images to the terminal" refers to a technology that formats the generated fashion suggestions and image data into JSON format and sends them to the user's terminal.

[1378] 7. "Means for displaying clothing suggestions and images on a terminal" refers to an interface that visually displays received fashion suggestion information and atmospheric images on the user's terminal screen.

[1379] 8. "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a text-based data format for structuring and exchanging data.

[1380] 9. "Means for generating fashion suggestion descriptions based on analyzed data" refers to technology in which a server automatically generates detailed fashion suggestion descriptions for users based on the analysis results.

[1381] This invention relates to a fashion suggestion system that includes an emotion engine for recognizing user emotions. Based on the user's mood, activities, and relationship with companions, the system provides clothing suggestions and images illustrating their atmosphere.

[1382] The process of recognizing emotions

[1383] When a user smiles at the camera, the emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time. The emotion engine uses facial recognition technology such as OpenCV and voice analysis technology (e.g., a voice recognition service). This allows the system to recognize the user's "mood for the day" as "cheerful."

[1384] Data entry and transmission

[1385] When a user logs into the system, the terminal displays an input form using a frontend library such as React. Here, the user inputs their "mood" as recognized by the emotion engine, their "desires," and their "relationship with the person they are going out with." For example, "cheerful mood," "cafe hopping," and "friends" might be entered.

[1386] Once you have finished entering the data and pressed the submit button, the terminal will format the entered data into JSON format and send it to the server. The data sent will be in a format similar to the following:

[1387] json

[1388] {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}

[1389] This conversion uses the JavaScript fetch API.

[1390] Data analysis and proposal generation

[1391] The server begins analyzing the received data. Using Python and data analysis libraries, the server accesses a database (e.g., MySQL or MongoDB) to refer to a dataset of fashion styles based on the entered mood, activities, and relationships. As a result of this analysis, a cheerful style such as "colorful blouse and jeans, casual shoes" might be selected.

[1392] Generating atmospheric images

[1393] Next, the server sends a prompt to the image generation AI model. A generative AI model (e.g., an image generation AI) is suitable for use. An example of a prompt message sent by the server is as follows:

[1394] Please generate an image of a model wearing a colorful blouse, jeans, and casual shoes.

[1395] Upon receiving this prompt, the image generation AI model generates a high-quality atmospheric image and sends it back to the server.

[1396] Submitting and displaying proposal results

[1397] The server combines the generated fashion suggestions and atmospheric images, formats them back into JSON format, and sends them to the terminal. The terminal interprets the received JSON data and displays the fashion suggestion text and generated images to the user.

[1398] Specific example

[1399] For example, if the user is identified as "feeling energetic" through the emotion engine, and also inputs "going hiking" and conditions such as "a colleague" as a companion, the server will suggest "sportswear and hiking boots" best suited for "energetic activity" and generate an image based on that. An example of this prompt is as follows:

[1400] Please generate images of models wearing sportswear and hiking boots.

[1401] This allows users to visually see specific fashion styles that match their activities and mood.

[1402] This invention enables personalized fashion suggestions that incorporate the user's real-time emotions, thereby providing a better user experience.

[1403] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1404] Step 1: Emotion Recognition

[1405] Subject: User

[1406] Specific action: The user smiles at the camera.

[1407] Input: User's facial expressions and voice data.

[1408] Data processing / calculation: User facial expressions and voice data acquired in real time from the camera and microphone are analyzed using an emotion engine (e.g., OpenCV or voice analysis technology).

[1409] Output: The emotion engine recognizes the user's emotional state as "cheerful."

[1410] Step 2: Data Entry

[1411] Subject: User

[1412] Specific operation: The user logs into the system and enters information such as "how they feel today," "what they want to do," and "the nature of their relationship with the person they are going out with" through an input form.

[1413] Input: User-entered emotional state, activity details, and relationship data with the other party.

[1414] Data processing / calculation: Receive and verify the input data using a front-end interface (such as React).

[1415] Output: The input data is prepared for the next step.

[1416] Step 3: Data transmission

[1417] Subject: terminal

[1418] Specific action: After the user enters data, they press the submit button.

[1419] Input: User-entered mood, activity details, and related data.

[1420] Data processing / calculation: Convert the input data into JSON format and send it to the server using the JavaScript fetch API.

[1421] Output: Data in JSON format (e.g., {"mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friends"}) is generated and sent to the server.

[1422] Step 4: Data Analysis

[1423] Subject: Server

[1424] Specific operation: The server analyzes the data it receives.

[1425] Input: Data in JSON format.

[1426] Data processing / calculations: Analyze data using data analysis libraries such as Python and Pandas, and retrieve relevant information from databases (MySQL or MongoDB).

[1427] Output: Fashion suggestions based on analyzed data (e.g., "A colorful blouse and jeans, and casual shoes").

[1428] Step 5: Generate atmosphere image

[1429] Subject: Server

[1430] Specific operation: The server sends prompts to the generated AI model.

[1431] Input: Fashion suggestion data.

[1432] Data processing / calculation: A prompt message (e.g., "Generate an image of a model wearing a colorful blouse, jeans, and casual shoes.") is sent to the generating AI model.

[1433] Output: Atmosphere image generated from the generative AI model.

[1434] Step 6: Submit the proposal results

[1435] Subject: Server

[1436] Specific operation: The server combines fashion suggestions and images into a single file, formats it in JSON format, and sends it to the terminal.

[1437] Input: Fashion suggestions and generated images.

[1438] Data processing / calculation: Format the proposed content and images into JSON format (e.g., {"fashion": "Colorful blouse and jeans, casual shoes", "image": "Image data"}).

[1439] Output: Data in JSON format is sent to the terminal.

[1440] Step 7: Displaying the results

[1441] Subject: terminal

[1442] Specific operation: The device interprets the received data and displays fashion suggestion text and generated images to the user.

[1443] Input: Fashion suggestion data and image data in JSON format.

[1444] Data Processing / Calculation: Analyzes received data and converts it into a format that can be displayed on the user interface.

[1445] Output: The fashion suggestion text and images are visually displayed on the user's device screen.

[1446] This allows users to check in real time the optimal fashion style for their mood and plans for the day.

[1447] (Application Example 2)

[1448] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1449] Conventional fashion suggestion systems make suggestions based solely on schedules and companion information, without considering the user's emotions. This results in a low degree of personalization and difficulty in providing suggestions that match the user's real-time emotions. Furthermore, generating atmospheric images that allow users to visually confirm specific clothing suggestions requires multiple manual steps and is time-consuming, thus lacking immediacy.

[1450] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1451] In this invention, the server includes means for recognizing the user's emotions in real time using an emotion recognition engine, means for adjusting the content of fashion suggestions based on the recognized emotions, and means for generating atmospheric images using a generation AI model based on the analyzed data and the user's emotions. As a result, personalized fashion suggestions that reflect the user's real-time emotions are immediately generated, and the user can use the suggestions while visually confirming them.

[1452] "Mood on the day" refers to the emotions and psychological state that the user is currently experiencing.

[1453] "Things you want to do" refers to activities or plans that the user wants to carry out at a specific date and time.

[1454] "Relationship with the person you're going out with" refers to the relationship between the user and the person they are going out with (e.g., friend, colleague, family).

[1455] "Means of input" refers to the hardware and software that users use to input their mood for the day, what they want to do, and their relationship with the people they are going out with.

[1456] "Means of transmission" refers to the process and equipment used to communicate and send the input data to the server.

[1457] "Means of analysis" refers to the technical process of analyzing data transmitted on a server and generating appropriate fashion suggestions.

[1458] "Means for generating images" refers to technologies and devices for generating visual atmosphere images based on generated clothing suggestions.

[1459] "Means of transmission to the terminal" refers to the communication technologies and protocols used to transmit the generated clothing suggestions and images to the user's terminal.

[1460] "Means of display" refers to display technology used to visually present clothing suggestions and images received on the terminal to the user.

[1461] An "emotion recognition engine" refers to software and hardware that recognizes emotions in real time from a user's facial expressions and voice.

[1462] "Methods for adjusting fashion suggestions based on recognized emotions" refers to technologies that dynamically change and adjust fashion suggestions by taking into account the user's emotions recognized by an emotion recognition engine.

[1463] "Methods for converting to JSON format" refers to software technologies for converting input data into a JSON (JavaScript Object Notation) data structure.

[1464] A "generative AI model" refers to an artificial intelligence model that automatically generates high-quality atmospheric images based on input data.

[1465] This invention relates to a fashion suggestion system that incorporates an emotion recognition engine to recognize the user's emotions. This system suggests clothing based on the user's mood, activities, and relationship with companions, and provides corresponding atmospheric images.

[1466] Required hardware and software

[1467] Hardware:

[1468] Smartphones (iOS / Android)

[1469] Smart Glasses

[1470] software:

[1471] Emotion recognition engine (API)

[1472] Image recognition and speech analysis software

[1473] Database (MySQL)

[1474] Image generation AI (e.g., OpenAI DALL-E)

[1475] Sending data in JSON format

[1476] Virtual reality (VR) framework (Unity)

[1477] Specific implementations of the system

[1478] 1. User mood recognition:

[1479] The device (smartphone or smart glasses) uses its camera and microphone to capture the user's facial expressions and voice. This data is sent to an emotion recognition engine (API), where emotions are recognized in real time. The recognition result is reflected in the system as the user's "mood for the day."

[1480] 2. Data input means:

[1481] When a user logs into their device, a prompt screen appears. Here, the user enters information such as "what they want to do" and "the relationship with the person they are going out with." For example, they can enter data such as "cafe hopping" and "friend."

[1482] 3. Data transmission means:

[1483] The data entered by the user and the mood data obtained by the emotion recognition engine are converted into JSON format and sent to the server. This efficiently structures the data.

[1484] 4. Data analysis and clothing suggestion generation methods:

[1485] The server analyzes the received data and references a dataset of fashion styles stored in the database, based on mood, activities, and relationships. As an example of this analysis, if the conditions are "cheerful mood," "cafe hopping," and "friends," then a suggestion of "casual blouse and jeans" is generated.

[1486] 5. Image generation means:

[1487] Based on the generated clothing suggestions, the server sends instructions to the image generation AI to create an atmospheric image. The image generation AI automatically generates an image of a model wearing a "colorful blouse and jeans." This allows the user to visually confirm the suggested outfit.

[1488] 6. Means for submitting and displaying proposal results:

[1489] The fashion suggestions and atmosphere images generated on the server are formatted again into JSON and sent to the terminal. The terminal receives this and displays the fashion suggestion text and images to the user. The user can then use this as a reference to make their actual fashion choices.

[1490] Specific example

[1491] For example, if a user is perceived as feeling "energetic" through the emotion recognition engine and inputs "going hiking" and "a colleague" as their companion, the server will suggest "sportswear and hiking boots" appropriate for "energetic activity." The generative AI model will then generate an image of a "model wearing sportswear and hiking boots." In this way, users can visually confirm specific fashion styles that match their activities and mood for the day in real time.

[1492] Example of a prompt

[1493] "I'm feeling cheerful today and planning to go cafe hopping with a friend. Could you recommend some fashionable outfits?"

[1494] Based on this prompt, the AI ​​model can generate fashion suggestions that perfectly match the user's needs.

[1495] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1496] Step 1:

[1497] The user logs into their device (smartphone or smart glasses) and uses the camera and microphone to capture their facial expressions and voice. This inputs the user's image and voice data. This input data is sent to an emotion recognition engine API, which analyzes the user's emotions in real time. The emotion recognition engine processes the data and outputs an emotion result, such as "cheerful mood."

[1498] Step 2:

[1499] The user enters "what they want to do (e.g., cafe hopping)" and "the relationship with the person they are going out with (e.g., friend)" into a form displayed on the device. This data is registered as input data on the device. After the user completes the input, the device converts the mood data analyzed by the emotion recognition engine and the above input data into JSON format. This JSON data is output in the format { "mood": "cheerful mood", "activity": "cafe hopping", "relationship": "friend"}.

[1500] Step 3:

[1501] The terminal sends the JSON data generated in step 2 to the server. The server analyzes the received data, accesses the database, and searches for fashion styles based on mood, activities, and relationships. For example, based on the conditions "cheerful mood," "cafe hopping," and "friends," the suggestion "colorful blouse and jeans, casual shoes" might be selected. This analysis result becomes the server's output.

[1502] Step 4:

[1503] Next, the server sends a prompt to the image generation AI based on the analysis results from step 3. An example prompt is, "Generate an image of a model wearing a colorful blouse, jeans, and casual shoes." Based on this prompt, the image generation AI generates a high-quality atmospheric image and outputs it to the server.

[1504] Step 5:

[1505] The server reformats the generated fashion suggestions and images back into JSON format and sends this data to the terminal. For example, it outputs in the format { "fashion_style": "Colorful blouse and jeans, casual shoes", "image_url": "generated_image_url"}.

[1506] Step 6:

[1507] The terminal interprets the JSON data received from the server and displays fashion suggestions, along with generated images, to the user. The user can visually confirm this on the terminal and actually select a suggested fashion style. This display result is the final output.

[1508] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1509] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1510] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1511] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1512] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1513] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1514] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1515] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1516] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1517] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1518] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1519] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1520] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1521] 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.

[1522] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1523] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1524] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1525] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1526] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1527] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1528] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1529] The following is further disclosed regarding the embodiments described above.

[1530] (Claim 1)

[1531] A way to input your mood for the day, what you want to do, and your relationship with the person you're going out with,

[1532] A means of sending the input data to the server,

[1533] A means for analyzing transmitted data and generating appropriate clothing suggestions,

[1534] A means of generating images based on the generated clothing suggestions,

[1535] A means of sending the generated clothing suggestions and images to the terminal,

[1536] A means of displaying clothing suggestions and images on a device,

[1537] A system that includes this.

[1538] (Claim 2)

[1539] The system according to claim 1, further comprising means for formatting input data into JSON format.

[1540] (Claim 3)

[1541] The system according to claim 1, further comprising means for generating a descriptive text for fashion suggestions based on analyzed data.

[1542] "Example 1"

[1543] (Claim 1)

[1544] A way to input your mood for the day, what you want to do, and your relationship with the person you're going out with,

[1545] A means of sending the input data to the server,

[1546] A means for analyzing transmitted data and generating appropriate clothing suggestions,

[1547] A means for sending a prompt message to an image generation algorithm based on the generated clothing suggestions,

[1548] A means for sending clothing suggestions, including the generated image, to a terminal,

[1549] A means of displaying clothing suggestions and images on a device,

[1550] A system that includes this.

[1551] (Claim 2)

[1552] The system according to claim 1, further comprising means for formatting input data into JSON format.

[1553] (Claim 3)

[1554] The system according to claim 1, further comprising means for generating a descriptive text for fashion suggestions based on analyzed data.

[1555] "Application Example 1"

[1556] (Claim 1)

[1557] A way to input your mood for the day, what you want to do, and your relationship with the person you're going out with,

[1558] A means of sending the input data to the server,

[1559] A means for analyzing transmitted data and generating appropriate clothing suggestions,

[1560] A means of sending a prompt message to an image generation artificial intelligence model based on the generated clothing suggestions, and generating an image,

[1561] A means of sending the generated clothing suggestions and images to the terminal,

[1562] A means of displaying clothing suggestions and images on a device and providing a link to purchase them on an e-commerce site,

[1563] A system that includes this.

[1564] (Claim 2)

[1565] The system according to claim 1, further comprising means for formatting input data into JSON format.

[1566] (Claim 3)

[1567] The system according to claim 1, further comprising means for generating a descriptive text for fashion suggestions based on analyzed data.

[1568] "Example 2 of combining an emotion engine"

[1569] (Claim 1)

[1570] Means of recognizing user emotions,

[1571] A way to input your mood for the day, what you want to do, and your relationship with the person you're going out with,

[1572] A means of sending the input data to the server,

[1573] A means for analyzing transmitted data and generating appropriate clothing suggestions,

[1574] A means of generating atmospheric images based on the generated clothing suggestions,

[1575] A means of sending the generated clothing suggestions and images to the terminal,

[1576] A means of displaying clothing suggestions and images on a device,

[1577] A system that includes this.

[1578] (Claim 2)

[1579] The system according to claim 1, further comprising means for formatting input data into JSON format.

[1580] (Claim 3)

[1581] The system according to claim 1, further comprising means for generating a descriptive text for fashion suggestions based on analyzed data.

[1582] "Application example 2 when combining with an emotional engine"

[1583] (Claim 1)

[1584] A way to input your mood for the day, what you want to do, and your relationship with the person you're going out with,

[1585] A means of sending the input data to the server,

[1586] A means for analyzing transmitted data and generating appropriate clothing suggestions,

[1587] A means of generating images based on the generated clothing suggestions,

[1588] A means of sending the generated clothing suggestions and images to the terminal,

[1589] A means of displaying clothing suggestions and images on a device,

[1590] and means for recognizing the user's emotions in real time using an emotion recognition engine,

[1591] A means of adjusting the content of fashion suggestions based on recognized emotions,

[1592] A system that includes this.

[1593] (Claim 2)

[1594] The system according to claim 1, further comprising means for converting input data into JSON format and means for synthesizing it with recognized emotions.

[1595] (Claim 3)

[1596] The system according to claim 1, further comprising means for generating an atmospheric image using a generation AI model based on analyzed data and user emotions. [Explanation of Symbols]

[1597] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A way to input your mood for the day, what you want to do, and your relationship with the person you're going out with, A means of sending the input data to the server, A means for analyzing transmitted data and generating appropriate clothing suggestions, A means of generating images based on the generated clothing suggestions, A means of sending the generated clothing suggestions and images to the terminal, A means of displaying clothing suggestions and images on a device, A system that includes this.

2. The system according to claim 1, further comprising means for formatting input data into JSON format.

3. The system according to claim 1, further comprising means for generating a descriptive text for fashion suggestions based on analyzed data.

Images