system

Abstract

We provide the system. [Solution] Means for obtaining user biometric data, Means for analyzing the aforementioned biometric data, A means of recommending fashion styles based on analysis results, A means of presenting the aforementioned recommendation to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

[0006] "Biometric data" refers to data that describes a user's physical characteristics, including, for example, height, weight, waist circumference, skin color, and facial features.

[0007] "Means of acquisition" refers to a method or apparatus for collecting biometric data from a user.

[0008] "Means of analysis" refers to a method or apparatus for evaluating user characteristics based on collected biometric data and determining an appropriate fashion style.

[0009] "Recommended means" refers to a method or device for presenting the most suitable fashion items and colors to the user based on the analysis results.

[0010] "Means of presentation" refers to methods or devices for displaying recommendations to a user, including, for example, mobile applications or software interfaces. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered 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.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] Patent Specification

[0033] The system of this invention acquires and analyzes the user's biometric data and proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0034] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends it to the server.

[0035] Next, the server analyzes the received data. The server has an algorithm to recommend suitable colors based on the user's skin tone. For example, if the user's skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. For tall users, it recommends long coats and bootcut pants, while for users below average height, it recommends high-waisted pants and A-line skirts.

[0036] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0037] As a concrete example, suppose a user enters the following data:

[0038] Height: 165 cm

[0039] Weight: 60 kg

[0040] Waist circumference: 75 cm

[0041] Skin tone: Light

[0042] Face shape: Oval

[0043] Based on this data, the server makes the following recommendations:

[0044] Recommended colors: Light blue, pink, white

[0045] Style suggestions: High-waisted pants, A-line skirts, etc.

[0046] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the most suitable fashion style.

[0047] This system allows users to easily find individually optimized fashion styles based on their biometric data, thereby boosting their confidence.

[0048] The following describes the processing flow.

[0049] Program processing steps

[0050] Step 1:

[0051] The device prompts the user to enter biometric data. The user enters information such as height, weight, waist circumference, skin color, and face shape.

[0052] Step 2:

[0053] The terminal sends the entered biometric data to the server. The server formats the received data into the appropriate format and stores it.

[0054] Step 3:

[0055] The server analyzes the biometric data it receives. Specifically, it determines recommended colors based on the user's skin tone. For example, if the skin tone is "light," light blue, pink, and white might be recommended.

[0056] Step 4:

[0057] The server suggests styles based on the user's height and body type. For example, it recommends long coats and bootcut pants for taller users, and high-waisted pants and A-line skirts for users of average or shorter height.

[0058] Step 5:

[0059] The server generates and sends recommended fashion styles to the device. The device receives this data and presents it visually to the user.

[0060] Step 6:

[0061] Users can review the suggested fashion styles presented and use them to help them make their own fashion choices. Specific item images and style combinations are shown, making it easy for users to understand.

[0062] Through the above processing steps, users can find their optimal fashion style based on their biometric data and enjoy fashion with confidence.

[0063] (Example 1)

[0064] 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."

[0065] Conventional fashion style suggestion systems could only offer suggestions based on general styles and trends, making it difficult to provide suggestions optimized for the individual characteristics of each user. Furthermore, because users needed specialized knowledge and experience to choose fashion items based on their biometric data, finding the optimal style was not easy. This resulted in users being unable to enjoy fashion with confidence.

[0066] 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.

[0067] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recommending a fashion style based on the analysis results, and means for presenting the recommendation to the user. This makes it possible for the user to easily find a fashion style that is individually optimized for them based on their own biometric data.

[0068] "Biometric data" refers to data that describes the physical characteristics of individual users, including height, weight, waist circumference, skin color, and facial features.

[0069] "Analysis means" refers to a system component that processes acquired biometric data to determine the optimal fashion style for the user.

[0070] A "means for recommending fashion styles" is a system component that suggests optimal colors and clothing styles to users based on data derived from analytical methods.

[0071] "Means of presenting recommendations to users" refer to system components that visually display fashion style recommendations to users, and include images of specific fashion items, color lists, and examples of style combinations.

[0072] The system of this invention acquires and analyzes the user's biometric data and proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0073] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends it to the server.

[0074] Next, the server analyzes the received data. The server has an algorithm to recommend suitable colors based on the user's skin tone. For example, if the user's skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. For tall users, it recommends long coats and bootcut pants, while for users below average height, it recommends high-waisted pants and A-line skirts.

[0075] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0076] The following hardware and software are required to implement this system.

[0077] Hardware:

[0078] User devices such as smartphones and tablets

[0079] Server for data analysis and recommendation generation

[0080] software:

[0081] Mobile application for user data collection and display

[0082] Algorithms for data analysis and fashion style suggestions

[0083] As a concrete example, suppose a user enters the following data:

[0084] Height: 165 cm

[0085] Weight: 60 kg

[0086] Waist circumference: 75 cm

[0087] Skin tone: Light

[0088] Face shape: Oval

[0089] Based on this data, the server makes the following recommendations:

[0090] Recommended colors: Light blue, pink, white

[0091] Style suggestions: High-waisted pants, A-line skirts, etc.

[0092] Examples of prompt statements:

[0093] "User data: Height 165 cm, weight 60 kg, waist circumference 75 cm, skin tone light, face shape oval. Please tell me your recommended fashion colors and styles."

[0094] The above recommendations are sent to the device, which visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style. This system allows users to easily find individually optimized fashion styles based on their biometric data, thereby boosting their confidence.

[0095] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0096] Step 1:

[0097] The user launches the application.

[0098] Specific actions: The device displays the application's initial screen. An interface prompting the user to enter biometric data appears.

[0099] Input: None

[0100] Output: Displays the data entry screen.

[0101] Step 2:

[0102] The user enters biometric data such as height, weight, waist circumference, skin color, and facial features.

[0103] Specific operation: The terminal provides text fields and dropdown menus, and the user enters their data into these fields.

[0104] Input: User's biometric data (height, weight, waist circumference, skin color, face shape)

[0105] Output: The user's biometric data is stored on the device.

[0106] Step 3:

[0107] The terminal sends the entered biometric data to the server.

[0108] Specific operation: The terminal converts the input data into JSON format and securely sends it to the server using the HTTPS protocol.

[0109] Input: Biometric data entered by the user

[0110] Output: The data, converted to JSON format, is sent to the server.

[0111] Step 4:

[0112] The server analyzes the received data.

[0113] Specific operation: The server uses Python scripts and TENSORFLOW® models to execute algorithms that analyze biometric data. For example, it can recommend suitable colors based on skin tone or suggest styles based on height and weight.

[0114] Input: Biometric data in JSON format

[0115] Output: Analysis results (suggestions for recommended colors and styles)

[0116] Step 5:

[0117] The server generates fashion styles based on the analysis results.

[0118] Specific operation: The server generates fashion style data, including recommended colors and style suggestions, and converts it into JSON format.

[0119] Input: Analysis results (suggestions for recommended colors and styles)

[0120] Output: Fashion style data in JSON format

[0121] Step 6:

[0122] The server sends the generated recommended fashion style data to the device.

[0123] Specific operation: The server uses the HTTPS protocol to send data in JSON format to the terminal.

[0124] Input: Fashion style data in JSON format

[0125] Output: Fashion style data is sent to the terminal.

[0126] Step 7:

[0127] The device visually displays recommended fashion styles received from the server to the user.

[0128] Specific operation: The device uses a UI framework (e.g., React Native or Flutter®) to display the received data to the user in the form of a list of recommended colors, images of specific fashion items, and examples of style combinations.

[0129] Input: Fashion style data in JSON format

[0130] Output: Visual display of recommended fashion styles

[0131] Step 8:

[0132] Users review the suggested fashion styles and select the one that suits them best.

[0133] Specific operation: The user selects a style based on the recommendations displayed on the device.

[0134] Input: Visual display of recommended fashion styles

[0135] Output: User-selected fashion style

[0136] (Application Example 1)

[0137] 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."

[0138] Traditional fashion style suggestion systems recommended appropriate fashion styles based on the user's biometric data, but they lacked a way for users to actually try on and verify those styles. Therefore, it was difficult for users to visualize how the items they selected based on the suggestions would actually look. To solve this inconvenience, a system is needed that allows users to visually check the suggested fashion styles using a virtual fitting room.

[0139] 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.

[0140] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recommending a fashion style based on the analysis results, and means for the user to check the suggested fashion style in a virtual fitting room. This allows the user to visually confirm the fashion style that is best suited to them and actually try it on.

[0141] "Biometric data" refers to information about a user's physical characteristics, specifically including height, weight, waist circumference, skin color, and facial features.

[0142] "Analysis means" refers to algorithms and software that process acquired biometric data to determine fashion styles and colors suitable for the user.

[0143] "Fashion style recommendation methods" refer to functions and processes that suggest the most suitable fashion items and color combinations to users based on analyzed data.

[0144] "Means of presenting to the user" refers to user interfaces and display devices that visually show the user the analysis results and recommendations.

[0145] A "virtual fitting room" refers to a virtual space or technology that allows users to visually check suggested fashion items using 3D avatars or similar displays.

[0146] This invention provides a system that suggests the optimal fashion style for a user based on biometric data and allows them to visually confirm the style using a virtual fitting room.

[0147] First, when a user launches the application, the device prompts the user to input biometric data or capture it using the camera. Biometric data refers to information about the user's physical characteristics, specifically height, weight, waist circumference, skin color, and facial features. This data can be manually entered by the user or automatically acquired using the device's camera function.

[0148] Next, the acquired biometric data is transmitted from the terminal to the server. The server uses analytical means to analyze the received data. Analytical means refer to algorithms and software that process the acquired biometric data and determine fashion styles and colors suitable for the user. Specifically, this includes means for determining recommended colors based on the user's skin tone.

[0149] The server has a fashion style recommendation system that suggests fashion styles based on the analysis results. This system proposes the most suitable fashion items and color combinations to the user. The suggestions are then sent back to the terminal, which presents them in a visually easy-to-understand format. The presentation system refers to the user interface and display device, which show details of the suggested fashion items and combinations.

[0150] Furthermore, the suggested fashion styles can be viewed in a virtual fitting room. A virtual fitting room refers to a virtual space or technology that allows users to visually check suggested fashion items using 3D avatars or other visual aids. This allows users to see exactly how the fashion items fit them.

[0151] The hardware and software used to implement this system include iOS or Android® smartphones, Python (server-side), Flask (API), Unity (3D rendering), and SQLite (database). The program processes as follows:

[0152] 1. Input data acquisition: The user enters biometric data into the app or takes a picture with the camera.

[0153] 2. Server Sending: An HTTP POST request is used to send this data to the server.

[0154] 3. Data Analysis: Data analysis is performed on the server using algorithms written in Python.

[0155] 4. Result reception: Receive the analysis results and display them on the terminal.

[0156] 5. 3D Rendering: The fashion style selected by the user is applied to a 3D avatar in Unity and displayed in a virtual fitting room.

[0157] For example, if a user enters data such as height 165cm, weight 60kg, waist circumference 75cm, light skin tone, and oval face shape, the system will recommend colors such as light blue, pink, and white, and suggest styles such as high-waisted pants and A-line skirts. These suggestions can be visually confirmed in a virtual fitting room.

[0158] Example of a prompt:

[0159] "Please suggest the best fashion items for a user who is 165cm tall, weighs 60kg, has a waist circumference of 75cm, has a light skin tone, and an oval face shape."

[0160] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0161] Step 1:

[0162] When a user launches the application, the device prompts the user to input biometric data. The user can manually enter biometric data such as height, weight, waist circumference, skin color, and face shape, or the device can automatically acquire this data using its camera function. Once the device receives the data, it stores this biometric data in a variable.

[0163] Input: User's biometric data (height, weight, waist circumference, skin color, face shape)

[0164] Output: Biometric data stored on the device

[0165] Step 2:

[0166] The device sends the acquired biometric data to the server. Specifically, it sends the data to the server in JSON format using an HTTP POST request.

[0167] Input: Biometric data stored in the device

[0168] Output: Biometric data sent to the server

[0169] Step 3:

[0170] The server analyzes the biometric data it receives. An algorithm written in Python is executed on the server to recommend the optimal fashion style and colors based on the user's characteristics. For example, if the user's skin tone is light, colors such as light blue, pink, and white will be recommended.

[0171] Input: Biometric data sent to the server

[0172] Output: List of recommended colors and fashion styles

[0173] Step 4:

[0174] The server then sends the list of recommended colors and fashion styles, which are the results of the analysis, back to the terminal.

[0175] Input: List of recommended colors and fashion styles

[0176] Output: A list of recommended colors and fashion styles sent to the terminal.

[0177] Step 5:

[0178] The device displays the analysis results it receives to the user. The interface is designed to allow users to visually check recommended colors and fashion styles, making it easy for them to view the suggestions.

[0179] Input: A list of recommended colors and fashion styles sent to the device.

[0180] Output: Display of fashion style suggestions that the user can visually confirm.

[0181] Step 6:

[0182] Users can view suggested fashion styles in a virtual fitting room. The fashion items selected by the user on their device are applied to a 3D avatar using Unity's 3D rendering technology. This allows users to visually see how the suggested styles fit them.

[0183] Input: Fashion style suggestions that users can visually confirm.

[0184] Output: Visual confirmation of the fashion style applied to the 3D avatar.

[0185] Through the above processing steps, users are offered optimal fashion styles based on their biometric data, which they can then visually confirm in a virtual fitting room. This makes it easier for users to choose the fashion items that best suit them.

[0186] 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.

[0187] Patent Specification

[0188] The present invention provides a system that acquires and analyzes a user's biometric data and emotional state, and then suggests the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0189] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends that data to the server.

[0190] Next, the server uses the device's camera to scan the user's face in order to analyze their facial expressions. This process detects the user's current emotional state. The emotion engine recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[0191] Furthermore, the server analyzes the received biometric and emotional data. It also has an algorithm that determines recommended colors based on the user's skin tone. For example, if the skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. Long coats and bootcut pants are recommended for tall users, while high-waisted pants and A-line skirts are recommended for users of average height or shorter.

[0192] Based on emotional data, recommended fashion styles are further refined. For example, if a user indicates "joy," bright colors and casual styles are recommended. Conversely, if a user indicates "sadness," calm colors and relaxing styles are recommended.

[0193] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is done in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0194] As a concrete example, suppose a user enters the following data:

[0195] Height: 165 cm

[0196] Weight: 60 kg

[0197] Waist circumference: 75 cm

[0198] Skin tone: Light

[0199] Face shape: Oval

[0200] Then, if the emotion engine recognizes the user's current emotional state as joy, the server will make the following recommendation based on this data:

[0201] Recommended colors: Light blue, pink, white

[0202] Style suggestion: High-waisted pants or A-line skirt

[0203] Emotion-based adjustment: Casual style in bright colors

[0204] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style and enjoy fashion with confidence.

[0205] This system allows users to easily find the optimal fashion style based on their biometric data and emotional state, thereby improving their quality of life.

[0206] The following describes the processing flow.

[0207] Program processing steps

[0208] Step 1:

[0209] The device prompts the user to enter biometric data (height, weight, waist circumference, skin color, face shape). The user then enters the required information.

[0210] Step 2:

[0211] The device sends the entered biometric data to the server. The server stores the received data in an appropriate format.

[0212] Step 3:

[0213] The system uses the device's camera to scan the user's face and collect facial expression data. The system automatically acquires data when the user faces the camera.

[0214] Step 4:

[0215] The server analyzes facial expression data and recognizes the user's emotional state (e.g., joy, sadness, anger, surprise, etc.). The emotion engine is responsible for this.

[0216] Step 5:

[0217] The server analyzes the biometric data it receives. Specifically, it uses an algorithm that determines recommended colors based on the user's skin tone. For example, light blue, pink, and white are recommended for a "light" skin tone.

[0218] Step 6:

[0219] The server suggests styles based on the user's height and body type. For example, it recommends long coats and bootcut pants for taller users, and high-waisted pants and A-line skirts for users of average height or shorter.

[0220] Step 7:

[0221] The server further adjusts recommended fashion styles based on emotional data. For example, if a user indicates an emotion of "joy," it recommends bright colors and casual styles. Conversely, if a user indicates an emotion of "sadness," it recommends calm colors and relaxing styles.

[0222] Step 8:

[0223] The server sends the generated recommended fashion style to the device. The device receives this data and presents it visually to the user.

[0224] Step 9:

[0225] The device displays recommended fashion styles to the user. These recommendations include a list of suggested colors and styles, images of specific fashion items, and explanations on how to combine them.

[0226] Step 10:

[0227] Users review the suggested fashion styles presented and use them as a basis for their own fashion choices. They can refer to the system's recommendations when selecting specific items.

[0228] Through the processing steps described above, users can easily find the optimal fashion style based on their biometric data and emotional state. This allows users to enjoy fashion with confidence.

[0229] (Example 2)

[0230] 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".

[0231] Conventional fashion style suggestion systems could make suggestions based on the user's biometric data, but they could not make suggestions that took the user's emotional state into account. Therefore, it was difficult to provide fashion styles that matched the user's current mood and emotions. This invention aims to solve this problem and provide a system that suggests the optimal fashion style by simultaneously considering the user's biometric data and emotional state.

[0232] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recognizing the user's emotional state, means for recommending a fashion style based on the analysis results and the recognized emotional state, and means for presenting the recommendation to the user. This makes it possible to comprehensively analyze the user's biometric data and emotional state and provide an optimal fashion style based on them.

[0233] "User biometric data" refers to information that represents an individual user's physical characteristics, such as height, weight, waist circumference, skin color, and facial features.

[0234] "Analysis methods" refer to means of analyzing each data item based on biometric data obtained from users, and deriving characteristics and trends.

[0235] "Emotional state" refers to feelings such as joy, sadness, anger, and surprise, which can be recognized from the user's facial expressions and other indicators.

[0236] "Recognition means" refers to methods for evaluating a user's facial expressions and actions and identifying their emotional state.

[0237] "Fashion style recommendation methods" refer to methods for suggesting fashion items and styles suitable for the user based on analysis results and recognized emotional states.

[0238] "Presentation methods" refer to means of visually showing a recommended fashion style to the user, and are primarily carried out through displays or application interfaces.

[0239] Modes for carrying out the invention

[0240] The present invention provides a system that acquires and analyzes a user's biometric data and emotional state, and then proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0241] First, when the user launches the mobile application, the device prompts the user to enter biometric data. The user enters the following information within the application:

[0242] height

[0243] body weight

[0244] waist area

[0245] skin color

[0246] Face shape

[0247] Once the user provides this information, the device sends that data to the server. This allows the server to obtain the user's biometric data.

[0248] Next, the server uses the device's camera to scan the user's face and acquires the image data. Through this process, the server analyzes the user's emotional state. The emotion engine recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[0249] Furthermore, the server analyzes the received biometric and emotional data. The analysis includes the following steps:

[0250] Recommended colors are determined based on skin tone. (For example, if your skin tone is light, then light blue, pink, white, etc.)

[0251] We will suggest suitable styles based on your height and body type. (For example, for taller individuals, we might suggest a long coat and bootcut pants.)

[0252] The recommended fashion style is adjusted based on emotional data. (For example, a bright, casual style for joyful occasions, and a calm, relaxed style for sad occasions.)

[0253] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is done in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0254] As a concrete example, suppose a user enters the following data:

[0255] Height: 165 cm

[0256] Weight: 60 kg

[0257] Waist circumference: 75 cm

[0258] Skin tone: Light

[0259] Face shape: Oval

[0260] Then, if the emotion engine recognizes the user's current emotional state as "joy," the server will make the following recommendation based on this data:

[0261] Recommended colors: Light blue, pink, white

[0262] Style suggestion: High-waisted pants or A-line skirt

[0263] Emotion-based adjustment: Casual style in bright colors

[0264] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style and enjoy fashion with confidence.

[0265] An example of a prompt statement is as follows:

[0266] "Please suggest the best fashion styles for a user who is 165 cm tall, weighs 60 kg, has a waist circumference of 75 cm, and has a light skin tone, so that they can feel happy."

[0267] This system allows users to easily find the optimal fashion style based on their biometric data and emotional state, thereby improving their quality of life.

[0268] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0269] System program processing steps

[0270] Step 1: User data entry

[0271] The user launches the mobile application.

[0272] The user enters biometric data (height, weight, waist circumference, skin color, face shape) into the application.

[0273] Input data: height, weight, waist circumference, skin color, face shape

[0274] Output data: Biometric data entered by the user

[0275] Specific operation: The user enters data into each input field and presses the complete button, at which point the entered data is saved to the device.

[0276] Step 2: Send to the server

[0277] The device sends the user's entered biometric data to the server.

[0278] Input data: Biometric data input by the user

[0279] Output data: Biometric data sent to the server

[0280] Specific operation: The terminal sends biometric data to the server through the Internet. After the transmission, a success message is displayed on the terminal.

[0281] Step 3: User face scan and emotion recognition

[0282] The camera of the terminal scans the user's face and sends the image data to the server.

[0283] Input data: Image data of the user's face

[0284] Output data: Image data of the face sent to the server

[0285] Specific operation: The user faces the camera for scanning, and a message "Scanning" is displayed. After the scanning is completed, "Analyzing emotional state" is displayed.

[0286] Step 4: Analysis of biometric data and emotion data by the server

[0287] The server analyzes the emotional state using the biometric data and face image data sent by the user.

[0288] Input data: Biometric data, face image data

[0289] Output data: Analyzed emotional state of the user (e.g., happiness, sadness, anger, surprise)

[0290] Specific operation: The emotion recognition engine on the server side analyzes the image data to identify the user's emotion. After that, it is integrated with the biometric data for further analysis.

[0291] [[ID=5I]] Step 5: Generation of recommended fashion styles

[0292] The server uses the biometric and emotional data it receives to generate recommended fashion styles.

[0293] Input data: biometric data, emotional data

[0294] Output data: Recommended fashion style (recommended colors, style suggestions, emotion-based adjustments)

[0295] Specific operation: The server's algorithm calculates the optimal fashion style based on the user's skin color, height, body type, and emotional state, and generates it as text and image data.

[0296] Step 6: Sending from server to terminal in the recommended style

[0297] The server sends the recommended fashion style it generates to the device.

[0298] Input data: Recommended fashion style

[0299] Output data: Recommended fashion styles sent to the user's device.

[0300] Specific operation: The server converts the recommended content into an appropriate data format such as JSON and sends it to the terminal. Once the terminal receives the data, a confirmation message is displayed.

[0301] Step 7: Recommended style displayed by the device

[0302] The device visually presents the user with recommended fashion styles it has received.

[0303] Input data: Recommended fashion style

[0304] Output data: Visual presentation to the user (recommended colors, style suggestions, images of specific fashion items)

[0305] Specific operations: A list of recommended fashion items and styles is displayed within the application. The user can tap on each item to view detailed information.

[0306] Through the above steps, this system realizes the proposal of an optimal fashion style based on the user's biometric data and emotional state.

[0307] (Application Example 2)

[0308] Next, Application Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0309] In the conventional fashion recommendation system, since fashion styles are recommended only based on the user's static biometric data, there is a problem that appropriate recommendations considering the user's current emotional state cannot be made. As a result, the user is not provided with advice on fashion choices that suit their emotions, and the satisfaction may decrease.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0311] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for acquiring the user's emotional state in real time, means for analyzing the emotional data based on the emotional state, means for recommending a fashion style based on the analysis results of the biometric data and the emotional data, and means for presenting the recommendation to the user. This enables the proposal of an individually optimized fashion considering not only the user's biometric data but also the emotional state. <00​​​

[0313] "Means for analyzing biometric data" refers to methods for analyzing acquired biometric data to determine a fashion style suitable for the user.

[0314] "Means for acquiring a user's emotional state in real time" refers to methods of recognizing a user's facial expressions and actions using cameras and sensors in order to acquire the user's emotions in real time.

[0315] "Means for analyzing emotional data based on emotional state" refers to methods for analyzing the emotional state of acquired users and using the analysis results to suggest fashion styles.

[0316] "A method for recommending fashion styles based on the analysis results of biometric and emotional data" refers to a method for recommending the optimal fashion style based on the analysis results.

[0317] "Means of presenting recommendations to users" refers to methods of visually presenting recommended fashion styles so that users can confirm them.

[0318] Modes for carrying out the invention

[0319] This invention is a system that acquires and analyzes a user's biometric data and emotional state in real time, and recommends the optimal fashion style based on the results. This system is mainly realized by exchanging information between a server, a terminal, and the user, and by comprehensively analyzing various types of data.

[0320] Hardware and software usage

[0321] Hardware: Utilizes the camera, microphone, and internet connection built into the smartphone or tablet.

[0322] software:

[0323] I will build a program in Python and use it for data analysis.

[0324] Perform real-time face recognition using OpenCV.

[0325] We will use Keras to build an emotion recognition model and analyze the user's emotional state.

[0326] Data flow and analysis

[0327] 1. Collection of user biometric data:

[0328] Users input biometric data such as height, weight, waist circumference, skin color, and face shape into the application. This data is then sent from the device to the server.

[0329] 2. Real-time acquisition of emotional state:

[0330] The device's built-in camera is used to capture the user's facial expressions. This facial expression data is sent to a server and analyzed in real time using an emotion recognition model.

[0331] 3. Data Analysis:

[0332] The server analyzes the acquired biometric and emotional data. This analysis determines recommended colors based on the user's skin tone and fashion styles adjusted according to the user's current emotional state.

[0333] 4. Recommended fashion styles:

[0334] Based on the analysis results, the system suggests the optimal fashion style to the user. The suggestions include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0335] Specific example

[0336] Consider a user who is 165 cm tall, weighs 60 kg, has a waist circumference of 75 cm, has light skin tone, and an oval face shape. The emotion recognition model evaluates the user's current emotional state as "joyful." Based on this data, the server makes the following recommendations.

[0337] Recommended colors: Light blue, pink, white

[0338] Style suggestion: High-waisted pants or A-line skirt

[0339] Emotion-based adjustment: Casual style in bright colors

[0340] These recommendations are sent to the device and presented to the user visually. Based on these recommendations, the user can enjoy self-optimized fashion choices.

[0341] Example of a prompt

[0342] "Please tell me your fashion style, given that you are 165 cm tall, weigh 60 kg, have light skin tone, an oval face shape, and your emotional state is joyful."

[0343] This allows the server to suggest the most suitable fashion style to the user. Through this system, users can intuitively receive optimal fashion advice based on biometric data and emotional state, thereby improving their quality of life.

[0344] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0345] Step 1:

[0346] The user enters biometric data (height, weight, waist circumference, skin color, face shape) into the application on their device.

[0347] Input: Height, weight, waist circumference, skin color, face shape

[0348] Output: This data is sent to the server.

[0349] Specific operation: The user launches the application and enters various biometric data according to the instructions. Once the input is complete, they press the submit button to send the data to the server.

[0350] Step 2:

[0351] The device uses its built-in camera to capture the user's facial expressions.

[0352] Input: User's face image

[0353] Output: The facial image is sent to the server.

[0354] Specific operation: The device's camera activates and detects the user's face. When the user makes an appropriate facial expression towards the camera, the image is captured and sent to the server.

[0355] Step 3:

[0356] The server uses the received facial images to analyze the user's emotional state in real time.

[0357] Input: User's face image

[0358] Output: Emotional state (e.g., joy, sadness, anger, surprise, etc.)

[0359] Specific operation: The server inputs a facial image into an emotion recognition model, which analyzes facial features to identify the emotional state. The emotion recognition model is built using a pre-trained Keras model. The analysis result provides the emotional state, which is then used for subsequent processing.

[0360] Step 4:

[0361] The server analyzes both biometric data and emotional state, and based on that, recommends the optimal fashion style.

[0362] Input: Biometric data, emotional state

[0363] Output: Fashion style recommendations (recommended colors, styles)

[0364] Specific operation: The server analyzes biometric data (e.g., skin color, height, weight) to determine suitable colors and styles. Simultaneously, it adjusts recommendations based on emotional state (e.g., bright colors and casual styles for happy moods). This analysis result is compiled into fashion style recommendations.

[0365] Step 5:

[0366] The server sends the recommended fashion style to the device.

[0367] Input: Fashion style recommendations

[0368] Output: Information on recommended fashion styles

[0369] Specific operation: The server compiles data on recommended fashion styles (recommended colors, styles, specific item images, etc.) and sends it to the terminal.

[0370] Step 6:

[0371] The device visually presents the user with recommended fashion styles received from the server.

[0372] Input: Information on recommended fashion styles

[0373] Output: Images and descriptions of fashion styles presented to the user.

[0374] Specific operation: The application on the device displays the received data, and the user can see the recommended fashion style on the screen. Specifically, a list of recommended colors and styles, images of specific fashion items, and explanations of how to combine them are displayed.

[0375] This process allows users to receive personalized fashion style suggestions in real time.

[0376] 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.

[0377] 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 the following. 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 indicated 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.

[0378] 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.

[0379] [Second Embodiment]

[0380] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0381] 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.

[0382] 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).

[0383] 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.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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".

[0392] Patent Specification

[0393] The system of this invention acquires and analyzes the user's biometric data and proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0394] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends it to the server.

[0395] Next, the server analyzes the received data. The server has an algorithm to recommend suitable colors based on the user's skin tone. For example, if the user's skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. For tall users, it recommends long coats and bootcut pants, while for users below average height, it recommends high-waisted pants and A-line skirts.

[0396] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0397] As a concrete example, suppose a user enters the following data:

[0398] Height: 165 cm

[0399] Weight: 60 kg

[0400] Waist circumference: 75 cm

[0401] Skin tone: Light

[0402] Face shape: Oval

[0403] Based on this data, the server makes the following recommendations:

[0404] Recommended colors: Light blue, pink, white

[0405] Style suggestions: High-waisted pants, A-line skirts, etc.

[0406] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the most suitable fashion style.

[0407] This system allows users to easily find individually optimized fashion styles based on their biometric data, thereby boosting their confidence.

[0408] The following describes the processing flow.

[0409] Program processing steps

[0410] Step 1:

[0411] The device prompts the user to enter biometric data. The user enters information such as height, weight, waist circumference, skin color, and face shape.

[0412] Step 2:

[0413] The terminal sends the entered biometric data to the server. The server formats the received data into the appropriate format and stores it.

[0414] Step 3:

[0415] The server analyzes the biometric data it receives. Specifically, it determines recommended colors based on the user's skin tone. For example, if the skin tone is "light," light blue, pink, and white might be recommended.

[0416] Step 4:

[0417] The server suggests styles based on the user's height and body type. For example, it recommends long coats and bootcut pants for taller users, and high-waisted pants and A-line skirts for users of average or shorter height.

[0418] Step 5:

[0419] The server generates and sends recommended fashion styles to the device. The device receives this data and presents it visually to the user.

[0420] Step 6:

[0421] Users can review the suggested fashion styles presented and use them to help them make their own fashion choices. Specific item images and style combinations are shown, making it easy for users to understand.

[0422] Through the above processing steps, users can find their optimal fashion style based on their biometric data and enjoy fashion with confidence.

[0423] (Example 1)

[0424] 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."

[0425] Conventional fashion style suggestion systems could only offer suggestions based on general styles and trends, making it difficult to provide suggestions optimized for the individual characteristics of each user. Furthermore, because users needed specialized knowledge and experience to choose fashion items based on their biometric data, finding the optimal style was not easy. This resulted in users being unable to enjoy fashion with confidence.

[0426] 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.

[0427] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recommending a fashion style based on the analysis results, and means for presenting the recommendation to the user. This makes it possible for the user to easily find a fashion style that is individually optimized for them based on their own biometric data.

[0428] "Biometric data" refers to data that describes the physical characteristics of individual users, including height, weight, waist circumference, skin color, and facial features.

[0429] "Analysis means" refers to a system component that processes acquired biometric data to determine the optimal fashion style for the user.

[0430] A "means for recommending fashion styles" is a system component that suggests optimal colors and clothing styles to users based on data derived from analytical methods.

[0431] "Means of presenting recommendations to users" refer to system components that visually display fashion style recommendations to users, and include images of specific fashion items, color lists, and examples of style combinations.

[0432] The system of this invention acquires and analyzes the user's biometric data and proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0433] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends it to the server.

[0434] Next, the server analyzes the received data. The server has an algorithm to recommend suitable colors based on the user's skin tone. For example, if the user's skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. For tall users, it recommends long coats and bootcut pants, while for users below average height, it recommends high-waisted pants and A-line skirts.

[0435] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0436] The following hardware and software are required to implement this system.

[0437] Hardware:

[0438] User devices such as smartphones and tablets

[0439] Server for data analysis and recommendation generation

[0440] software:

[0441] Mobile application for user data collection and display

[0442] Algorithms for data analysis and fashion style suggestions

[0443] As a concrete example, suppose a user enters the following data:

[0444] Height: 165 cm

[0445] Weight: 60 kg

[0446] Waist circumference: 75 cm

[0447] Skin tone: Light

[0448] Face shape: Oval

[0449] Based on this data, the server makes the following recommendations:

[0450] Recommended colors: Light blue, pink, white

[0451] Style suggestions: High-waisted pants, A-line skirts, etc.

[0452] Examples of prompt statements:

[0453] "User data: Height 165 cm, weight 60 kg, waist circumference 75 cm, skin tone light, face shape oval. Please tell me your recommended fashion colors and styles."

[0454] The above recommendations are sent to the device, which visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style. This system allows users to easily find individually optimized fashion styles based on their biometric data, thereby boosting their confidence.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The user launches the application.

[0458] Specific actions: The device displays the application's initial screen. An interface prompting the user to enter biometric data appears.

[0459] Input: None

[0460] Output: Displays the data entry screen.

[0461] Step 2:

[0462] The user enters biometric data such as height, weight, waist circumference, skin color, and facial features.

[0463] Specific operation: The terminal provides text fields and dropdown menus, and the user enters their data into these fields.

[0464] Input: User's biometric data (height, weight, waist circumference, skin color, face shape)

[0465] Output: The user's biometric data is stored on the device.

[0466] Step 3:

[0467] The terminal sends the entered biometric data to the server.

[0468] Specific operation: The terminal converts the input data into JSON format and securely sends it to the server using the HTTPS protocol.

[0469] Input: Biometric data entered by the user

[0470] Output: The data, converted to JSON format, is sent to the server.

[0471] Step 4:

[0472] The server analyzes the received data.

[0473] Specific operation: The server uses Python scripts and TensorFlow models to execute algorithms that analyze biometric data. For example, it can recommend suitable colors based on skin tone or suggest styles based on height and weight.

[0474] Input: Biometric data in JSON format

[0475] Output: Analysis results (suggestions for recommended colors and styles)

[0476] Step 5:

[0477] The server generates fashion styles based on the analysis results.

[0478] Specific operation: The server generates fashion style data, including recommended colors and style suggestions, and converts it into JSON format.

[0479] Input: Analysis results (suggestions for recommended colors and styles)

[0480] Output: Fashion style data in JSON format

[0481] Step 6:

[0482] The server sends the generated recommended fashion style data to the device.

[0483] Specific operation: The server uses the HTTPS protocol to send data in JSON format to the terminal.

[0484] Input: Fashion style data in JSON format

[0485] Output: Fashion style data is sent to the terminal.

[0486] Step 7:

[0487] The device visually displays recommended fashion styles received from the server to the user.

[0488] Specific operation: The device uses a UI framework (e.g., React Native or Flutter) to display the received data to the user in the form of a list of recommended colors, images of specific fashion items, and examples of style combinations.

[0489] Input: Fashion style data in JSON format

[0490] Output: Visual display of recommended fashion styles

[0491] Step 8:

[0492] Users review the suggested fashion styles and select the one that suits them best.

[0493] Specific operation: The user selects a style based on the recommendations displayed on the device.

[0494] Input: Visual display of recommended fashion styles

[0495] Output: User-selected fashion style

[0496] (Application Example 1)

[0497] 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."

[0498] Traditional fashion style suggestion systems recommended appropriate fashion styles based on the user's biometric data, but they lacked a way for users to actually try on and verify those styles. Therefore, it was difficult for users to visualize how the items they selected based on the suggestions would actually look. To solve this inconvenience, a system is needed that allows users to visually check the suggested fashion styles using a virtual fitting room.

[0499] 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.

[0500] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recommending a fashion style based on the analysis results, and means for the user to check the suggested fashion style in a virtual fitting room. This allows the user to visually confirm the fashion style that is best suited to them and actually try it on.

[0501] "Biometric data" refers to information about a user's physical characteristics, specifically including height, weight, waist circumference, skin color, and facial features.

[0502] "Analysis means" refers to algorithms and software that process acquired biometric data to determine fashion styles and colors suitable for the user.

[0503] "Fashion style recommendation methods" refer to functions and processes that suggest the most suitable fashion items and color combinations to users based on analyzed data.

[0504] "Means of presenting to the user" refers to user interfaces and display devices that visually show the user the analysis results and recommendations.

[0505] A "virtual fitting room" refers to a virtual space or technology that allows users to visually check suggested fashion items using 3D avatars or similar displays.

[0506] This invention provides a system that suggests the optimal fashion style for a user based on biometric data and allows them to visually confirm the style using a virtual fitting room.

[0507] First, when a user launches the application, the device prompts the user to input biometric data or capture it using the camera. Biometric data refers to information about the user's physical characteristics, specifically height, weight, waist circumference, skin color, and facial features. This data can be manually entered by the user or automatically acquired using the device's camera function.

[0508] Next, the acquired biometric data is transmitted from the terminal to the server. The server uses analytical means to analyze the received data. Analytical means refer to algorithms and software that process the acquired biometric data and determine fashion styles and colors suitable for the user. Specifically, this includes means for determining recommended colors based on the user's skin tone.

[0509] The server has a fashion style recommendation system that suggests fashion styles based on the analysis results. This system proposes the most suitable fashion items and color combinations to the user. The suggestions are then sent back to the terminal, which presents them in a visually easy-to-understand format. The presentation system refers to the user interface and display device, which show details of the suggested fashion items and combinations.

[0510] Furthermore, the suggested fashion styles can be viewed in a virtual fitting room. A virtual fitting room refers to a virtual space or technology that allows users to visually check suggested fashion items using 3D avatars or other visual aids. This allows users to see exactly how the fashion items fit them.

[0511] The hardware and software used to implement this system include an iOS or Android smartphone, Python (server-side), Flask (API), Unity (3D rendering), and SQLite (database). The program processes as follows:

[0512] 1. Input data acquisition: The user enters biometric data into the app or takes a picture with the camera.

[0513] 2. Server Sending: An HTTP POST request is used to send this data to the server.

[0514] 3. Data Analysis: Data analysis is performed on the server using algorithms written in Python.

[0515] 4. Result reception: Receive the analysis results and display them on the terminal.

[0516] 5. 3D Rendering: The fashion style selected by the user is applied to a 3D avatar in Unity and displayed in a virtual fitting room.

[0517] For example, if a user enters data such as height 165cm, weight 60kg, waist circumference 75cm, light skin tone, and oval face shape, the system will recommend colors such as light blue, pink, and white, and suggest styles such as high-waisted pants and A-line skirts. These suggestions can be visually confirmed in a virtual fitting room.

[0518] Example of a prompt:

[0519] "Please suggest the best fashion items for a user who is 165cm tall, weighs 60kg, has a waist circumference of 75cm, has a light skin tone, and an oval face shape."

[0520] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0521] Step 1:

[0522] When a user launches the application, the device prompts the user to input biometric data. The user can manually enter biometric data such as height, weight, waist circumference, skin color, and face shape, or the device can automatically acquire this data using its camera function. Once the device receives the data, it stores this biometric data in a variable.

[0523] Input: User's biometric data (height, weight, waist circumference, skin color, face shape)

[0524] Output: Biometric data stored on the device

[0525] Step 2:

[0526] The device sends the acquired biometric data to the server. Specifically, it sends the data to the server in JSON format using an HTTP POST request.

[0527] Input: Biometric data stored in the device

[0528] Output: Biometric data sent to the server

[0529] Step 3:

[0530] The server analyzes the biometric data it receives. An algorithm written in Python is executed on the server to recommend the optimal fashion style and colors based on the user's characteristics. For example, if the user's skin tone is light, colors such as light blue, pink, and white will be recommended.

[0531] Input: Biometric data sent to the server

[0532] Output: List of recommended colors and fashion styles

[0533] Step 4:

[0534] The server then sends the list of recommended colors and fashion styles, which are the results of the analysis, back to the terminal.

[0535] Input: List of recommended colors and fashion styles

[0536] Output: A list of recommended colors and fashion styles sent to the terminal.

[0537] Step 5:

[0538] The device displays the analysis results it receives to the user. The interface is designed to allow users to visually check recommended colors and fashion styles, making it easy for them to view the suggestions.

[0539] Input: A list of recommended colors and fashion styles sent to the device.

[0540] Output: Display of fashion style suggestions that the user can visually confirm.

[0541] Step 6:

[0542] Users can view suggested fashion styles in a virtual fitting room. The fashion items selected by the user on their device are applied to a 3D avatar using Unity's 3D rendering technology. This allows users to visually see how the suggested styles fit them.

[0543] Input: Fashion style suggestions that users can visually confirm.

[0544] Output: Visual confirmation of the fashion style applied to the 3D avatar.

[0545] Through the above processing steps, users are offered optimal fashion styles based on their biometric data, which they can then visually confirm in a virtual fitting room. This makes it easier for users to choose the fashion items that best suit them.

[0546] 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.

[0547] Patent Specification

[0548] The present invention provides a system that acquires and analyzes a user's biometric data and emotional state, and then suggests the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0549] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends that data to the server.

[0550] Next, the server uses the device's camera to scan the user's face in order to analyze their facial expressions. This process detects the user's current emotional state. The emotion engine recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[0551] Furthermore, the server analyzes the received biometric and emotional data. It also has an algorithm that determines recommended colors based on the user's skin tone. For example, if the skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. Long coats and bootcut pants are recommended for tall users, while high-waisted pants and A-line skirts are recommended for users of average height or shorter.

[0552] Based on emotional data, recommended fashion styles are further refined. For example, if a user indicates "joy," bright colors and casual styles are recommended. Conversely, if a user indicates "sadness," calm colors and relaxing styles are recommended.

[0553] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is done in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0554] As a concrete example, suppose a user enters the following data:

[0555] Height: 165 cm

[0556] Weight: 60 kg

[0557] Waist circumference: 75 cm

[0558] Skin tone: Light

[0559] Face shape: Oval

[0560] Then, if the emotion engine recognizes the user's current emotional state as joy, the server will make the following recommendation based on this data:

[0561] Recommended colors: Light blue, pink, white

[0562] Style suggestion: High-waisted pants or A-line skirt

[0563] Emotion-based adjustment: Casual style in bright colors

[0564] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style and enjoy fashion with confidence.

[0565] This system allows users to easily find the optimal fashion style based on their biometric data and emotional state, thereby improving their quality of life.

[0566] The following describes the processing flow.

[0567] Program processing steps

[0568] Step 1:

[0569] The device prompts the user to enter biometric data (height, weight, waist circumference, skin color, face shape). The user then enters the required information.

[0570] Step 2:

[0571] The device sends the entered biometric data to the server. The server stores the received data in an appropriate format.

[0572] Step 3:

[0573] The system uses the device's camera to scan the user's face and collect facial expression data. The system automatically acquires data when the user faces the camera.

[0574] Step 4:

[0575] The server analyzes facial expression data and recognizes the user's emotional state (e.g., joy, sadness, anger, surprise, etc.). The emotion engine is responsible for this.

[0576] Step 5:

[0577] The server analyzes the biometric data it receives. Specifically, it uses an algorithm that determines recommended colors based on the user's skin tone. For example, light blue, pink, and white are recommended for a "light" skin tone.

[0578] Step 6:

[0579] The server suggests styles based on the user's height and body type. For example, it recommends long coats and bootcut pants for taller users, and high-waisted pants and A-line skirts for users of average height or shorter.

[0580] Step 7:

[0581] The server further adjusts recommended fashion styles based on emotional data. For example, if a user indicates an emotion of "joy," it recommends bright colors and casual styles. Conversely, if a user indicates an emotion of "sadness," it recommends calm colors and relaxing styles.

[0582] Step 8:

[0583] The server sends the generated recommended fashion style to the device. The device receives this data and presents it visually to the user.

[0584] Step 9:

[0585] The device displays recommended fashion styles to the user. These recommendations include a list of suggested colors and styles, images of specific fashion items, and explanations on how to combine them.

[0586] Step 10:

[0587] Users review the suggested fashion styles presented and use them as a basis for their own fashion choices. They can refer to the system's recommendations when selecting specific items.

[0588] Through the processing steps described above, users can easily find the optimal fashion style based on their biometric data and emotional state. This allows users to enjoy fashion with confidence.

[0589] (Example 2)

[0590] 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".

[0591] Conventional fashion style suggestion systems could make suggestions based on the user's biometric data, but they could not make suggestions that took the user's emotional state into account. Therefore, it was difficult to provide fashion styles that matched the user's current mood and emotions. This invention aims to solve this problem and provide a system that suggests the optimal fashion style by simultaneously considering the user's biometric data and emotional state.

[0592] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recognizing the user's emotional state, means for recommending a fashion style based on the analysis results and the recognized emotional state, and means for presenting the recommendation to the user. This makes it possible to comprehensively analyze the user's biometric data and emotional state and provide an optimal fashion style based on them.

[0593] "User biometric data" refers to information that represents an individual user's physical characteristics, such as height, weight, waist circumference, skin color, and facial features.

[0594] "Analysis methods" refer to means of analyzing each data item based on biometric data obtained from users, and deriving characteristics and trends.

[0595] "Emotional state" refers to feelings such as joy, sadness, anger, and surprise, which can be recognized from the user's facial expressions and other indicators.

[0596] "Recognition means" refers to methods for evaluating a user's facial expressions and actions and identifying their emotional state.

[0597] "Fashion style recommendation methods" refer to methods for suggesting fashion items and styles suitable for the user based on analysis results and recognized emotional states.

[0598] "Presentation methods" refer to means of visually showing a recommended fashion style to the user, and are primarily carried out through displays or application interfaces.

[0599] Modes for carrying out the invention

[0600] The present invention provides a system that acquires and analyzes a user's biometric data and emotional state, and then proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0601] First, when the user launches the mobile application, the device prompts the user to enter biometric data. The user enters the following information within the application:

[0602] height

[0603] body weight

[0604] waist area

[0605] skin color

[0606] Face shape

[0607] Once the user provides this information, the device sends that data to the server. This allows the server to obtain the user's biometric data.

[0608] Next, the server uses the device's camera to scan the user's face and acquires the image data. Through this process, the server analyzes the user's emotional state. The emotion engine recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[0609] Furthermore, the server analyzes the received biometric and emotional data. The analysis includes the following steps:

[0610] Recommended colors are determined based on skin tone. (For example, if your skin tone is light, then light blue, pink, white, etc.)

[0611] We will suggest suitable styles based on your height and body type. (For example, for taller individuals, we might suggest a long coat and bootcut pants.)

[0612] The recommended fashion style is adjusted based on emotional data. (For example, a bright, casual style for joyful occasions, and a calm, relaxed style for sad occasions.)

[0613] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is done in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0614] As a concrete example, suppose a user enters the following data:

[0615] Height: 165 cm

[0616] Weight: 60 kg

[0617] Waist circumference: 75 cm

[0618] Skin tone: Light

[0619] Face shape: Oval

[0620] Then, if the emotion engine recognizes the user's current emotional state as "joy," the server will make the following recommendation based on this data:

[0621] Recommended colors: Light blue, pink, white

[0622] Style suggestion: High-waisted pants or A-line skirt

[0623] Emotion-based adjustment: Casual style in bright colors

[0624] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style and enjoy fashion with confidence.

[0625] An example of a prompt statement is as follows:

[0626] "Please suggest the best fashion styles for a user who is 165 cm tall, weighs 60 kg, has a waist circumference of 75 cm, and has a light skin tone, so that they can feel happy."

[0627] This system allows users to easily find the optimal fashion style based on their biometric data and emotional state, thereby improving their quality of life.

[0628] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0629] System program processing steps

[0630] Step 1: User data entry

[0631] The user launches the mobile application.

[0632] The user enters biometric data (height, weight, waist circumference, skin color, face shape) into the application.

[0633] Input data: height, weight, waist circumference, skin color, face shape

[0634] Output data: Biometric data entered by the user

[0635] Specific operation: The user enters data into each input field and presses the complete button, at which point the entered data is saved to the device.

[0636] Step 2: Send to the server

[0637] The device sends the user's entered biometric data to the server.

[0638] Input data: Biometric data entered by the user.

[0639] Output data: Biometric data sent to the server

[0640] Specific operation: The device sends biometric data to the server via the internet. After transmission, a success message is displayed on the device.

[0641] Step 3: User facial scan and emotion recognition

[0642] The device's camera scans the user's face and sends the image data to the server.

[0643] Input data: Image data of the user's face

[0644] Output data: Facial image data sent to the server

[0645] Specific operation: The user faces the camera and a scan is performed, displaying the message "Scanning." After the scan is complete, the message "Analyzing emotional state" is displayed.

[0646] Step 4: Server-based analysis of biometric and emotional data

[0647] The server analyzes the user's emotional state using biometric data and facial image data sent from the user.

[0648] Input data: biometric data, facial image data

[0649] Output data: Analyzed user emotional state (e.g., joy, sadness, anger, surprise)

[0650] Specific operation: The server-side emotion recognition engine analyzes image data to identify the user's emotions. This data is then integrated with biometric data for further analysis.

[0651] Step 5: Generate Recommended Fashion Styles

[0652] The server uses the biometric and emotional data it receives to generate recommended fashion styles.

[0653] Input data: biometric data, emotional data

[0654] Output data: Recommended fashion style (recommended colors, style suggestions, emotion-based adjustments)

[0655] Specific operation: The server's algorithm calculates the optimal fashion style based on the user's skin color, height, body type, and emotional state, and generates it as text and image data.

[0656] Step 6: Sending from server to terminal in the recommended style

[0657] The server sends the recommended fashion style it generates to the device.

[0658] Input data: Recommended fashion style

[0659] Output data: Recommended fashion styles sent to the user's device.

[0660] Specific operation: The server converts the recommended content into an appropriate data format such as JSON and sends it to the terminal. Once the terminal receives the data, a confirmation message is displayed.

[0661] Step 7: Recommended style displayed by the device

[0662] The device visually presents the user with recommended fashion styles it has received.

[0663] Input data: Recommended fashion style

[0664] Output data: Visual presentation to the user (recommended colors, style suggestions, images of specific fashion items)

[0665] Specific operation: The application displays a list of recommended fashion items and styles. Users can tap each item to view detailed information.

[0666] Through these steps, the system enables the suggestion of the optimal fashion style based on the user's biometric data and emotional state.

[0667] (Application Example 2)

[0668] 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."

[0669] Conventional fashion recommendation systems recommend fashion styles based solely on the user's static biometric data, which means they cannot provide appropriate suggestions that take into account the user's current emotional state. As a result, users may not receive fashion advice that resonates with their emotions, leading to decreased satisfaction.

[0670] 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.

[0671] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for acquiring the user's emotional state in real time, means for analyzing emotional data based on the emotional state, means for recommending a fashion style based on the analysis results of the biometric data and emotional data, and means for presenting the recommendation to the user. This makes it possible to propose individually optimized fashion that takes into account not only the user's biometric data but also their emotional state.

[0672] "User biometric data" refers to data about a user's physical characteristics, such as height, weight, waist circumference, skin color, and facial features.

[0673] "Means for analyzing biometric data" refers to methods for analyzing acquired biometric data to determine a fashion style suitable for the user.

[0674] "Means for acquiring a user's emotional state in real time" refers to methods of recognizing a user's facial expressions and actions using cameras and sensors in order to acquire the user's emotions in real time.

[0675] "Means for analyzing emotional data based on emotional state" refers to methods for analyzing the emotional state of acquired users and using the analysis results to suggest fashion styles.

[0676] "A method for recommending fashion styles based on the analysis results of biometric and emotional data" refers to a method for recommending the optimal fashion style based on the analysis results.

[0677] "Means of presenting recommendations to users" refers to methods of visually presenting recommended fashion styles so that users can confirm them.

[0678] Modes for carrying out the invention

[0679] This invention is a system that acquires and analyzes a user's biometric data and emotional state in real time, and recommends the optimal fashion style based on the results. This system is mainly realized by exchanging information between a server, a terminal, and the user, and by comprehensively analyzing various types of data.

[0680] Hardware and software usage

[0681] Hardware: Utilizes the camera, microphone, and internet connection built into the smartphone or tablet.

[0682] software:

[0683] I will build a program in Python and use it for data analysis.

[0684] Perform real-time face recognition using OpenCV.

[0685] We will use Keras to build an emotion recognition model and analyze the user's emotional state.

[0686] Data flow and analysis

[0687] 1. Collection of user biometric data:

[0688] Users input biometric data such as height, weight, waist circumference, skin color, and face shape into the application. This data is then sent from the device to the server.

[0689] 2. Real-time acquisition of emotional state:

[0690] The device's built-in camera is used to capture the user's facial expressions. This facial expression data is sent to a server and analyzed in real time using an emotion recognition model.

[0691] 3. Data Analysis:

[0692] The server analyzes the acquired biometric and emotional data. This analysis determines recommended colors based on the user's skin tone and fashion styles adjusted according to the user's current emotional state.

[0693] 4. Recommended fashion styles:

[0694] Based on the analysis results, the system suggests the optimal fashion style to the user. The suggestions include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0695] Specific example

[0696] Consider a user who is 165 cm tall, weighs 60 kg, has a waist circumference of 75 cm, has light skin tone, and an oval face shape. The emotion recognition model evaluates the user's current emotional state as "joyful." Based on this data, the server makes the following recommendations.

[0697] Recommended colors: Light blue, pink, white

[0698] Style suggestion: High-waisted pants or A-line skirt

[0699] Emotion-based adjustment: Casual style in bright colors

[0700] These recommendations are sent to the device and presented to the user visually. Based on these recommendations, the user can enjoy self-optimized fashion choices.

[0701] Example of a prompt

[0702] "Please tell me your fashion style, given that you are 165 cm tall, weigh 60 kg, have light skin tone, an oval face shape, and your emotional state is joyful."

[0703] This allows the server to suggest the most suitable fashion style to the user. Through this system, users can intuitively receive optimal fashion advice based on biometric data and emotional state, thereby improving their quality of life.

[0704] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0705] Step 1:

[0706] The user enters biometric data (height, weight, waist circumference, skin color, face shape) into the application on their device.

[0707] Input: Height, weight, waist circumference, skin color, face shape

[0708] Output: This data is sent to the server.

[0709] Specific operation: The user launches the application and enters various biometric data according to the instructions. Once the input is complete, they press the submit button to send the data to the server.

[0710] Step 2:

[0711] The device uses its built-in camera to capture the user's facial expressions.

[0712] Input: User's face image

[0713] Output: The facial image is sent to the server.

[0714] Specific operation: The device's camera activates and detects the user's face. When the user makes an appropriate facial expression towards the camera, the image is captured and sent to the server.

[0715] Step 3:

[0716] The server uses the received facial images to analyze the user's emotional state in real time.

[0717] Input: User's face image

[0718] Output: Emotional state (e.g., joy, sadness, anger, surprise, etc.)

[0719] Specific operation: The server inputs a facial image into an emotion recognition model, which analyzes facial features to identify the emotional state. The emotion recognition model is built using a pre-trained Keras model. The analysis result provides the emotional state, which is then used for subsequent processing.

[0720] Step 4:

[0721] The server analyzes both biometric data and emotional state, and based on that, recommends the optimal fashion style.

[0722] Input: Biometric data, emotional state

[0723] Output: Fashion style recommendations (recommended colors, styles)

[0724] Specific operation: The server analyzes biometric data (e.g., skin color, height, weight) to determine suitable colors and styles. Simultaneously, it adjusts recommendations based on emotional state (e.g., bright colors and casual styles for happy moods). This analysis result is compiled into fashion style recommendations.

[0725] Step 5:

[0726] The server sends the recommended fashion style to the device.

[0727] Input: Fashion style recommendations

[0728] Output: Information on recommended fashion styles

[0729] Specific operation: The server compiles data on recommended fashion styles (recommended colors, styles, specific item images, etc.) and sends it to the terminal.

[0730] Step 6:

[0731] The device visually presents the user with recommended fashion styles received from the server.

[0732] Input: Information on recommended fashion styles

[0733] Output: Images and descriptions of fashion styles presented to the user.

[0734] Specific operation: The application on the device displays the received data, and the user can see the recommended fashion style on the screen. Specifically, a list of recommended colors and styles, images of specific fashion items, and explanations of how to combine them are displayed.

[0735] This process allows users to receive personalized fashion style suggestions in real time.

[0736] 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.

[0737] 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 the following. 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 indicated 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.

[0738] 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.

[0739] [Third Embodiment]

[0740] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0741] 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.

[0742] 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).

[0743] 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.

[0744] 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.

[0745] 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).

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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".

[0752] Patent Specification

[0753] The system of this invention acquires and analyzes the user's biometric data and proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0754] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends it to the server.

[0755] Next, the server analyzes the received data. The server has an algorithm to recommend suitable colors based on the user's skin tone. For example, if the user's skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. For tall users, it recommends long coats and bootcut pants, while for users below average height, it recommends high-waisted pants and A-line skirts.

[0756] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0757] As a concrete example, suppose a user enters the following data:

[0758] Height: 165 cm

[0759] Weight: 60 kg

[0760] Waist circumference: 75 cm

[0761] Skin tone: Light

[0762] Face shape: Oval

[0763] Based on this data, the server makes the following recommendations:

[0764] Recommended colors: Light blue, pink, white

[0765] Style suggestions: High-waisted pants, A-line skirts, etc.

[0766] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the most suitable fashion style.

[0767] This system allows users to easily find individually optimized fashion styles based on their biometric data, thereby boosting their confidence.

[0768] The following describes the processing flow.

[0769] Program processing steps

[0770] Step 1:

[0771] The device prompts the user to enter biometric data. The user enters information such as height, weight, waist circumference, skin color, and face shape.

[0772] Step 2:

[0773] The terminal sends the entered biometric data to the server. The server formats the received data into the appropriate format and stores it.

[0774] Step 3:

[0775] The server analyzes the biometric data it receives. Specifically, it determines recommended colors based on the user's skin tone. For example, if the skin tone is "light," light blue, pink, and white might be recommended.

[0776] Step 4:

[0777] The server suggests styles based on the user's height and body type. For example, it recommends long coats and bootcut pants for taller users, and high-waisted pants and A-line skirts for users of average or shorter height.

[0778] Step 5:

[0779] The server generates and sends recommended fashion styles to the device. The device receives this data and presents it visually to the user.

[0780] Step 6:

[0781] Users can review the suggested fashion styles presented and use them to help them make their own fashion choices. Specific item images and style combinations are shown, making it easy for users to understand.

[0782] Through the above processing steps, users can find their optimal fashion style based on their biometric data and enjoy fashion with confidence.

[0783] (Example 1)

[0784] 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."

[0785] Conventional fashion style suggestion systems could only offer suggestions based on general styles and trends, making it difficult to provide suggestions optimized for the individual characteristics of each user. Furthermore, because users needed specialized knowledge and experience to choose fashion items based on their biometric data, finding the optimal style was not easy. This resulted in users being unable to enjoy fashion with confidence.

[0786] 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.

[0787] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recommending a fashion style based on the analysis results, and means for presenting the recommendation to the user. This makes it possible for the user to easily find a fashion style that is individually optimized for them based on their own biometric data.

[0788] "Biometric data" refers to data that describes the physical characteristics of individual users, including height, weight, waist circumference, skin color, and facial features.

[0789] "Analysis means" refers to a system component that processes acquired biometric data to determine the optimal fashion style for the user.

[0790] A "means for recommending fashion styles" is a system component that suggests optimal colors and clothing styles to users based on data derived from analytical methods.

[0791] "Means of presenting recommendations to users" refer to system components that visually display fashion style recommendations to users, and include images of specific fashion items, color lists, and examples of style combinations.

[0792] The system of this invention acquires and analyzes the user's biometric data and proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0793] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends it to the server.

[0794] Next, the server analyzes the received data. The server has an algorithm to recommend suitable colors based on the user's skin tone. For example, if the user's skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. For tall users, it recommends long coats and bootcut pants, while for users below average height, it recommends high-waisted pants and A-line skirts.

[0795] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0796] The following hardware and software are required to implement this system.

[0797] Hardware:

[0798] User devices such as smartphones and tablets

[0799] Server for data analysis and recommendation generation

[0800] software:

[0801] Mobile application for user data collection and display

[0802] Algorithms for data analysis and fashion style suggestions

[0803] As a concrete example, suppose a user enters the following data:

[0804] Height: 165 cm

[0805] Weight: 60 kg

[0806] Waist circumference: 75 cm

[0807] Skin tone: Light

[0808] Face shape: Oval

[0809] Based on this data, the server makes the following recommendations:

[0810] Recommended colors: Light blue, pink, white

[0811] Style suggestions: High-waisted pants, A-line skirts, etc.

[0812] Examples of prompt statements:

[0813] "User data: Height 165 cm, weight 60 kg, waist circumference 75 cm, skin tone light, face shape oval. Please tell me your recommended fashion colors and styles."

[0814] The above recommendations are sent to the device, which visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style. This system allows users to easily find individually optimized fashion styles based on their biometric data, thereby boosting their confidence.

[0815] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0816] Step 1:

[0817] The user launches the application.

[0818] Specific actions: The device displays the application's initial screen. An interface prompting the user to enter biometric data appears.

[0819] Input: None

[0820] Output: Displays the data entry screen.

[0821] Step 2:

[0822] The user enters biometric data such as height, weight, waist circumference, skin color, and facial features.

[0823] Specific operation: The terminal provides text fields and dropdown menus, and the user enters their data into these fields.

[0824] Input: User's biometric data (height, weight, waist circumference, skin color, face shape)

[0825] Output: The user's biometric data is stored on the device.

[0826] Step 3:

[0827] The terminal sends the entered biometric data to the server.

[0828] Specific operation: The terminal converts the input data into JSON format and securely sends it to the server using the HTTPS protocol.

[0829] Input: Biometric data entered by the user

[0830] Output: The data, converted to JSON format, is sent to the server.

[0831] Step 4:

[0832] The server analyzes the received data.

[0833] Specific operation: The server uses Python scripts and TensorFlow models to execute algorithms that analyze biometric data. For example, it can recommend suitable colors based on skin tone or suggest styles based on height and weight.

[0834] Input: Biometric data in JSON format

[0835] Output: Analysis results (suggestions for recommended colors and styles)

[0836] Step 5:

[0837] The server generates fashion styles based on the analysis results.

[0838] Specific operation: The server generates fashion style data, including recommended colors and style suggestions, and converts it into JSON format.

[0839] Input: Analysis results (suggestions for recommended colors and styles)

[0840] Output: Fashion style data in JSON format

[0841] Step 6:

[0842] The server sends the generated recommended fashion style data to the device.

[0843] Specific operation: The server uses the HTTPS protocol to send data in JSON format to the terminal.

[0844] Input: Fashion style data in JSON format

[0845] Output: Fashion style data is sent to the terminal.

[0846] Step 7:

[0847] The device visually displays recommended fashion styles received from the server to the user.

[0848] Specific operation: The device uses a UI framework (e.g., React Native or Flutter) to display the received data to the user in the form of a list of recommended colors, images of specific fashion items, and examples of style combinations.

[0849] Input: Fashion style data in JSON format

[0850] Output: Visual display of recommended fashion styles

[0851] Step 8:

[0852] Users review the suggested fashion styles and select the one that suits them best.

[0853] Specific operation: The user selects a style based on the recommendations displayed on the device.

[0854] Input: Visual display of recommended fashion styles

[0855] Output: User-selected fashion style

[0856] (Application Example 1)

[0857] 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."

[0858] Traditional fashion style suggestion systems recommended appropriate fashion styles based on the user's biometric data, but they lacked a way for users to actually try on and verify those styles. Therefore, it was difficult for users to visualize how the items they selected based on the suggestions would actually look. To solve this inconvenience, a system is needed that allows users to visually check the suggested fashion styles using a virtual fitting room.

[0859] 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.

[0860] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recommending a fashion style based on the analysis results, and means for the user to check the suggested fashion style in a virtual fitting room. This allows the user to visually confirm the fashion style that is best suited to them and actually try it on.

[0861] "Biometric data" refers to information about a user's physical characteristics, specifically including height, weight, waist circumference, skin color, and facial features.

[0862] "Analysis means" refers to algorithms and software that process acquired biometric data to determine fashion styles and colors suitable for the user.

[0863] "Fashion style recommendation methods" refer to functions and processes that suggest the most suitable fashion items and color combinations to users based on analyzed data.

[0864] "Means of presenting to the user" refers to user interfaces and display devices that visually show the user the analysis results and recommendations.

[0865] A "virtual fitting room" refers to a virtual space or technology that allows users to visually check suggested fashion items using 3D avatars or similar displays.

[0866] This invention provides a system that suggests the optimal fashion style for a user based on biometric data and allows them to visually confirm the style using a virtual fitting room.

[0867] First, when a user launches the application, the device prompts the user to input biometric data or capture it using the camera. Biometric data refers to information about the user's physical characteristics, specifically height, weight, waist circumference, skin color, and facial features. This data can be manually entered by the user or automatically acquired using the device's camera function.

[0868] Next, the acquired biometric data is transmitted from the terminal to the server. The server uses analytical means to analyze the received data. Analytical means refer to algorithms and software that process the acquired biometric data and determine fashion styles and colors suitable for the user. Specifically, this includes means for determining recommended colors based on the user's skin tone.

[0869] The server has a fashion style recommendation system that suggests fashion styles based on the analysis results. This system proposes the most suitable fashion items and color combinations to the user. The suggestions are then sent back to the terminal, which presents them in a visually easy-to-understand format. The presentation system refers to the user interface and display device, which show details of the suggested fashion items and combinations.

[0870] Furthermore, the suggested fashion styles can be viewed in a virtual fitting room. A virtual fitting room refers to a virtual space or technology that allows users to visually check suggested fashion items using 3D avatars or other visual aids. This allows users to see exactly how the fashion items fit them.

[0871] The hardware and software used to implement this system include an iOS or Android smartphone, Python (server-side), Flask (API), Unity (3D rendering), and SQLite (database). The program processes as follows:

[0872] 1. Input data acquisition: The user enters biometric data into the app or takes a picture with the camera.

[0873] 2. Server Sending: An HTTP POST request is used to send this data to the server.

[0874] 3. Data Analysis: Data analysis is performed on the server using algorithms written in Python.

[0875] 4. Result reception: Receive the analysis results and display them on the terminal.

[0876] 5. 3D Rendering: The fashion style selected by the user is applied to a 3D avatar in Unity and displayed in a virtual fitting room.

[0877] For example, if a user enters data such as height 165cm, weight 60kg, waist circumference 75cm, light skin tone, and oval face shape, the system will recommend colors such as light blue, pink, and white, and suggest styles such as high-waisted pants and A-line skirts. These suggestions can be visually confirmed in a virtual fitting room.

[0878] Example of a prompt:

[0879] "Please suggest the best fashion items for a user who is 165cm tall, weighs 60kg, has a waist circumference of 75cm, has a light skin tone, and an oval face shape."

[0880] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0881] Step 1:

[0882] When a user launches the application, the device prompts the user to input biometric data. The user can manually enter biometric data such as height, weight, waist circumference, skin color, and face shape, or the device can automatically acquire this data using its camera function. Once the device receives the data, it stores this biometric data in a variable.

[0883] Input: User's biometric data (height, weight, waist circumference, skin color, face shape)

[0884] Output: Biometric data stored on the device

[0885] Step 2:

[0886] The device sends the acquired biometric data to the server. Specifically, it sends the data to the server in JSON format using an HTTP POST request.

[0887] Input: Biometric data stored in the device

[0888] Output: Biometric data sent to the server

[0889] Step 3:

[0890] The server analyzes the biometric data it receives. An algorithm written in Python is executed on the server to recommend the optimal fashion style and colors based on the user's characteristics. For example, if the user's skin tone is light, colors such as light blue, pink, and white will be recommended.

[0891] Input: Biometric data sent to the server

[0892] Output: List of recommended colors and fashion styles

[0893] Step 4:

[0894] The server then sends the list of recommended colors and fashion styles, which are the results of the analysis, back to the terminal.

[0895] Input: List of recommended colors and fashion styles

[0896] Output: A list of recommended colors and fashion styles sent to the terminal.

[0897] Step 5:

[0898] The device displays the analysis results it receives to the user. The interface is designed to allow users to visually check recommended colors and fashion styles, making it easy for them to view the suggestions.

[0899] Input: A list of recommended colors and fashion styles sent to the device.

[0900] Output: Display of fashion style suggestions that the user can visually confirm.

[0901] Step 6:

[0902] Users can view suggested fashion styles in a virtual fitting room. The fashion items selected by the user on their device are applied to a 3D avatar using Unity's 3D rendering technology. This allows users to visually see how the suggested styles fit them.

[0903] Input: Fashion style suggestions that users can visually confirm.

[0904] Output: Visual confirmation of the fashion style applied to the 3D avatar.

[0905] Through the above processing steps, users are offered optimal fashion styles based on their biometric data, which they can then visually confirm in a virtual fitting room. This makes it easier for users to choose the fashion items that best suit them.

[0906] 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.

[0907] Patent Specification

[0908] The present invention provides a system that acquires and analyzes a user's biometric data and emotional state, and then suggests the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0909] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends that data to the server.

[0910] Next, the server uses the device's camera to scan the user's face in order to analyze their facial expressions. This process detects the user's current emotional state. The emotion engine recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[0911] Furthermore, the server analyzes the received biometric and emotional data. It also has an algorithm that determines recommended colors based on the user's skin tone. For example, if the skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. Long coats and bootcut pants are recommended for tall users, while high-waisted pants and A-line skirts are recommended for users of average height or shorter.

[0912] Based on emotional data, recommended fashion styles are further refined. For example, if a user indicates "joy," bright colors and casual styles are recommended. Conversely, if a user indicates "sadness," calm colors and relaxing styles are recommended.

[0913] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is done in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0914] As a concrete example, suppose a user enters the following data:

[0915] Height: 165 cm

[0916] Weight: 60 kg

[0917] Waist circumference: 75 cm

[0918] Skin tone: Light

[0919] Face shape: Oval

[0920] Then, if the emotion engine recognizes the user's current emotional state as joy, the server will make the following recommendation based on this data:

[0921] Recommended colors: Light blue, pink, white

[0922] Style suggestion: High-waisted pants or A-line skirt

[0923] Emotion-based adjustment: Casual style in bright colors

[0924] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style and enjoy fashion with confidence.

[0925] This system allows users to easily find the optimal fashion style based on their biometric data and emotional state, thereby improving their quality of life.

[0926] The following describes the processing flow.

[0927] Program processing steps

[0928] Step 1:

[0929] The device prompts the user to enter biometric data (height, weight, waist circumference, skin color, face shape). The user then enters the required information.

[0930] Step 2:

[0931] The device sends the entered biometric data to the server. The server stores the received data in an appropriate format.

[0932] Step 3:

[0933] The system uses the device's camera to scan the user's face and collect facial expression data. The system automatically acquires data when the user faces the camera.

[0934] Step 4:

[0935] The server analyzes facial expression data and recognizes the user's emotional state (e.g., joy, sadness, anger, surprise, etc.). The emotion engine is responsible for this.

[0936] Step 5:

[0937] The server analyzes the biometric data it receives. Specifically, it uses an algorithm that determines recommended colors based on the user's skin tone. For example, light blue, pink, and white are recommended for a "light" skin tone.

[0938] Step 6:

[0939] The server suggests styles based on the user's height and body type. For example, it recommends long coats and bootcut pants for taller users, and high-waisted pants and A-line skirts for users of average height or shorter.

[0940] Step 7:

[0941] The server further adjusts recommended fashion styles based on emotional data. For example, if a user indicates an emotion of "joy," it recommends bright colors and casual styles. Conversely, if a user indicates an emotion of "sadness," it recommends calm colors and relaxing styles.

[0942] Step 8:

[0943] The server sends the generated recommended fashion style to the device. The device receives this data and presents it visually to the user.

[0944] Step 9:

[0945] The device displays recommended fashion styles to the user. These recommendations include a list of suggested colors and styles, images of specific fashion items, and explanations on how to combine them.

[0946] Step 10:

[0947] Users review the suggested fashion styles presented and use them as a basis for their own fashion choices. They can refer to the system's recommendations when selecting specific items.

[0948] Through the processing steps described above, users can easily find the optimal fashion style based on their biometric data and emotional state. This allows users to enjoy fashion with confidence.

[0949] (Example 2)

[0950] 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."

[0951] Conventional fashion style suggestion systems could make suggestions based on the user's biometric data, but they could not make suggestions that took the user's emotional state into account. Therefore, it was difficult to provide fashion styles that matched the user's current mood and emotions. This invention aims to solve this problem and provide a system that suggests the optimal fashion style by simultaneously considering the user's biometric data and emotional state.

[0952] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recognizing the user's emotional state, means for recommending a fashion style based on the analysis results and the recognized emotional state, and means for presenting the recommendation to the user. This makes it possible to comprehensively analyze the user's biometric data and emotional state and provide an optimal fashion style based on them.

[0953] "User biometric data" refers to information that represents an individual user's physical characteristics, such as height, weight, waist circumference, skin color, and facial features.

[0954] "Analysis methods" refer to means of analyzing each data item based on biometric data obtained from users, and deriving characteristics and trends.

[0955] "Emotional state" refers to feelings such as joy, sadness, anger, and surprise, which can be recognized from the user's facial expressions and other indicators.

[0956] "Recognition means" refers to methods for evaluating a user's facial expressions and actions and identifying their emotional state.

[0957] "Fashion style recommendation methods" refer to methods for suggesting fashion items and styles suitable for the user based on analysis results and recognized emotional states.

[0958] "Presentation methods" refer to means of visually showing a recommended fashion style to the user, and are primarily carried out through displays or application interfaces.

[0959] Modes for carrying out the invention

[0960] The present invention provides a system that acquires and analyzes a user's biometric data and emotional state, and then proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[0961] First, when the user launches the mobile application, the device prompts the user to enter biometric data. The user enters the following information within the application:

[0962] height

[0963] body weight

[0964] waist area

[0965] skin color

[0966] Face shape

[0967] Once the user provides this information, the device sends that data to the server. This allows the server to obtain the user's biometric data.

[0968] Next, the server uses the device's camera to scan the user's face and acquires the image data. Through this process, the server analyzes the user's emotional state. The emotion engine recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[0969] Furthermore, the server analyzes the received biometric and emotional data. The analysis includes the following steps:

[0970] Recommended colors are determined based on skin tone. (For example, if your skin tone is light, then light blue, pink, white, etc.)

[0971] We will suggest suitable styles based on your height and body type. (For example, for taller individuals, we might suggest a long coat and bootcut pants.)

[0972] The recommended fashion style is adjusted based on emotional data. (For example, a bright, casual style for joyful occasions, and a calm, relaxed style for sad occasions.)

[0973] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is done in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[0974] As a concrete example, suppose a user enters the following data:

[0975] Height: 165 cm

[0976] Weight: 60 kg

[0977] Waist circumference: 75 cm

[0978] Skin tone: Light

[0979] Face shape: Oval

[0980] Then, if the emotion engine recognizes the user's current emotional state as "joy," the server will make the following recommendation based on this data:

[0981] Recommended colors: Light blue, pink, white

[0982] Style suggestion: High-waisted pants or A-line skirt

[0983] Emotion-based adjustment: Casual style in bright colors

[0984] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style and enjoy fashion with confidence.

[0985] An example of a prompt statement is as follows:

[0986] "Please suggest the best fashion styles for a user who is 165 cm tall, weighs 60 kg, has a waist circumference of 75 cm, and has a light skin tone, so that they can feel happy."

[0987] This system allows users to easily find the optimal fashion style based on their biometric data and emotional state, thereby improving their quality of life.

[0988] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0989] System program processing steps

[0990] Step 1: User data entry

[0991] The user launches the mobile application.

[0992] The user enters biometric data (height, weight, waist circumference, skin color, face shape) into the application.

[0993] Input data: height, weight, waist circumference, skin color, face shape

[0994] Output data: Biometric data entered by the user

[0995] Specific operation: The user enters data into each input field and presses the complete button, at which point the entered data is saved to the device.

[0996] Step 2: Send to the server

[0997] The device sends the user's entered biometric data to the server.

[0998] Input data: Biometric data entered by the user.

[0999] Output data: Biometric data sent to the server

[1000] Specific operation: The device sends biometric data to the server via the internet. After transmission, a success message is displayed on the device.

[1001] Step 3: User facial scan and emotion recognition

[1002] The device's camera scans the user's face and sends the image data to the server.

[1003] Input data: Image data of the user's face

[1004] Output data: Facial image data sent to the server

[1005] Specific operation: The user faces the camera and a scan is performed, displaying the message "Scanning." After the scan is complete, the message "Analyzing emotional state" is displayed.

[1006] Step 4: Server-based analysis of biometric and emotional data

[1007] The server analyzes the user's emotional state using biometric data and facial image data sent from the user.

[1008] Input data: biometric data, facial image data

[1009] Output data: Analyzed user emotional state (e.g., joy, sadness, anger, surprise)

[1010] Specific operation: The server-side emotion recognition engine analyzes image data to identify the user's emotions. This data is then integrated with biometric data for further analysis.

[1011] Step 5: Generate Recommended Fashion Styles

[1012] The server uses the biometric and emotional data it receives to generate recommended fashion styles.

[1013] Input data: biometric data, emotional data

[1014] Output data: Recommended fashion style (recommended colors, style suggestions, emotion-based adjustments)

[1015] Specific operation: The server's algorithm calculates the optimal fashion style based on the user's skin color, height, body type, and emotional state, and generates it as text and image data.

[1016] Step 6: Sending from server to terminal in the recommended style

[1017] The server sends the recommended fashion style it generates to the device.

[1018] Input data: Recommended fashion style

[1019] Output data: Recommended fashion styles sent to the user's device.

[1020] Specific operation: The server converts the recommended content into an appropriate data format such as JSON and sends it to the terminal. Once the terminal receives the data, a confirmation message is displayed.

[1021] Step 7: Recommended style displayed by the device

[1022] The device visually presents the user with recommended fashion styles it has received.

[1023] Input data: Recommended fashion style

[1024] Output data: Visual presentation to the user (recommended colors, style suggestions, images of specific fashion items)

[1025] Specific operation: The application displays a list of recommended fashion items and styles. Users can tap each item to view detailed information.

[1026] Through these steps, the system enables the suggestion of the optimal fashion style based on the user's biometric data and emotional state.

[1027] (Application Example 2)

[1028] 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."

[1029] Conventional fashion recommendation systems recommend fashion styles based solely on the user's static biometric data, which means they cannot provide appropriate suggestions that take into account the user's current emotional state. As a result, users may not receive fashion advice that resonates with their emotions, leading to decreased satisfaction.

[1030] 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.

[1031] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for acquiring the user's emotional state in real time, means for analyzing emotional data based on the emotional state, means for recommending a fashion style based on the analysis results of the biometric data and emotional data, and means for presenting the recommendation to the user. This makes it possible to propose individually optimized fashion that takes into account not only the user's biometric data but also their emotional state.

[1032] "User biometric data" refers to data about a user's physical characteristics, such as height, weight, waist circumference, skin color, and facial features.

[1033] "Means for analyzing biometric data" refers to methods for analyzing acquired biometric data to determine a fashion style suitable for the user.

[1034] "Means for acquiring a user's emotional state in real time" refers to methods of recognizing a user's facial expressions and actions using cameras and sensors in order to acquire the user's emotions in real time.

[1035] "Means for analyzing emotional data based on emotional state" refers to methods for analyzing the emotional state of acquired users and using the analysis results to suggest fashion styles.

[1036] "A method for recommending fashion styles based on the analysis results of biometric and emotional data" refers to a method for recommending the optimal fashion style based on the analysis results.

[1037] "Means of presenting recommendations to users" refers to methods of visually presenting recommended fashion styles so that users can confirm them.

[1038] Modes for carrying out the invention

[1039] This invention is a system that acquires and analyzes a user's biometric data and emotional state in real time, and recommends the optimal fashion style based on the results. This system is mainly realized by exchanging information between a server, a terminal, and the user, and by comprehensively analyzing various types of data.

[1040] Hardware and software usage

[1041] Hardware: Utilizes the camera, microphone, and internet connection built into the smartphone or tablet.

[1042] software:

[1043] I will build a program in Python and use it for data analysis.

[1044] Perform real-time face recognition using OpenCV.

[1045] We will use Keras to build an emotion recognition model and analyze the user's emotional state.

[1046] Data flow and analysis

[1047] 1. Collection of user biometric data:

[1048] Users input biometric data such as height, weight, waist circumference, skin color, and face shape into the application. This data is then sent from the device to the server.

[1049] 2. Real-time acquisition of emotional state:

[1050] The device's built-in camera is used to capture the user's facial expressions. This facial expression data is sent to a server and analyzed in real time using an emotion recognition model.

[1051] 3. Data Analysis:

[1052] The server analyzes the acquired biometric and emotional data. This analysis determines recommended colors based on the user's skin tone and fashion styles adjusted according to the user's current emotional state.

[1053] 4. Recommended fashion styles:

[1054] Based on the analysis results, the system suggests the optimal fashion style to the user. The suggestions include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[1055] Specific example

[1056] Consider a user who is 165 cm tall, weighs 60 kg, has a waist circumference of 75 cm, has light skin tone, and an oval face shape. The emotion recognition model evaluates the user's current emotional state as "joyful." Based on this data, the server makes the following recommendations.

[1057] Recommended colors: Light blue, pink, white

[1058] Style suggestion: High-waisted pants or A-line skirt

[1059] Emotion-based adjustment: Casual style in bright colors

[1060] These recommendations are sent to the device and presented to the user visually. Based on these recommendations, the user can enjoy self-optimized fashion choices.

[1061] Example of a prompt

[1062] "Please tell me your fashion style, given that you are 165 cm tall, weigh 60 kg, have light skin tone, an oval face shape, and your emotional state is joyful."

[1063] This allows the server to suggest the most suitable fashion style to the user. Through this system, users can intuitively receive optimal fashion advice based on biometric data and emotional state, thereby improving their quality of life.

[1064] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1065] Step 1:

[1066] The user enters biometric data (height, weight, waist circumference, skin color, face shape) into the application on their device.

[1067] Input: Height, weight, waist circumference, skin color, face shape

[1068] Output: This data is sent to the server.

[1069] Specific operation: The user launches the application and enters various biometric data according to the instructions. Once the input is complete, they press the submit button to send the data to the server.

[1070] Step 2:

[1071] The device uses its built-in camera to capture the user's facial expressions.

[1072] Input: User's face image

[1073] Output: The facial image is sent to the server.

[1074] Specific operation: The device's camera activates and detects the user's face. When the user makes an appropriate facial expression towards the camera, the image is captured and sent to the server.

[1075] Step 3:

[1076] The server uses the received facial images to analyze the user's emotional state in real time.

[1077] Input: User's face image

[1078] Output: Emotional state (e.g., joy, sadness, anger, surprise, etc.)

[1079] Specific operation: The server inputs a facial image into an emotion recognition model, which analyzes facial features to identify the emotional state. The emotion recognition model is built using a pre-trained Keras model. The analysis result provides the emotional state, which is then used for subsequent processing.

[1080] Step 4:

[1081] The server analyzes both biometric data and emotional state, and based on that, recommends the optimal fashion style.

[1082] Input: Biometric data, emotional state

[1083] Output: Fashion style recommendations (recommended colors, styles)

[1084] Specific operation: The server analyzes biometric data (e.g., skin color, height, weight) to determine suitable colors and styles. Simultaneously, it adjusts recommendations based on emotional state (e.g., bright colors and casual styles for happy moods). This analysis result is compiled into fashion style recommendations.

[1085] Step 5:

[1086] The server sends the recommended fashion style to the device.

[1087] Input: Fashion style recommendations

[1088] Output: Information on recommended fashion styles

[1089] Specific operation: The server compiles data on recommended fashion styles (recommended colors, styles, specific item images, etc.) and sends it to the terminal.

[1090] Step 6:

[1091] The device visually presents the user with recommended fashion styles received from the server.

[1092] Input: Information on recommended fashion styles

[1093] Output: Images and descriptions of fashion styles presented to the user.

[1094] Specific operation: The application on the device displays the received data, and the user can see the recommended fashion style on the screen. Specifically, a list of recommended colors and styles, images of specific fashion items, and explanations of how to combine them are displayed.

[1095] This process allows users to receive personalized fashion style suggestions in real time.

[1096] 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.

[1097] 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 the following. 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 indicated 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.

[1098] 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.

[1099] [Fourth Embodiment]

[1100] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1101] 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.

[1102] 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).

[1103] 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.

[1104] 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.

[1105] 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).

[1106] 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.

[1107] 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.

[1108] 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.

[1109] 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.

[1110] 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.

[1111] 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.

[1112] 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".

[1113] Patent Specification

[1114] The system of this invention acquires and analyzes the user's biometric data and proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[1115] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends it to the server.

[1116] Next, the server analyzes the received data. The server has an algorithm to recommend suitable colors based on the user's skin tone. For example, if the user's skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. For tall users, it recommends long coats and bootcut pants, while for users below average height, it recommends high-waisted pants and A-line skirts.

[1117] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[1118] As a concrete example, suppose a user enters the following data:

[1119] Height: 165 cm

[1120] Weight: 60 kg

[1121] Waist circumference: 75 cm

[1122] Skin tone: Light

[1123] Face shape: Oval

[1124] Based on this data, the server makes the following recommendations:

[1125] Recommended colors: Light blue, pink, white

[1126] Style suggestions: High-waisted pants, A-line skirts, etc.

[1127] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the most suitable fashion style.

[1128] This system allows users to easily find individually optimized fashion styles based on their biometric data, thereby boosting their confidence.

[1129] The following describes the processing flow.

[1130] Program processing steps

[1131] Step 1:

[1132] The device prompts the user to enter biometric data. The user enters information such as height, weight, waist circumference, skin color, and face shape.

[1133] Step 2:

[1134] The terminal sends the entered biometric data to the server. The server formats the received data into the appropriate format and stores it.

[1135] Step 3:

[1136] The server analyzes the biometric data it receives. Specifically, it determines recommended colors based on the user's skin tone. For example, if the skin tone is "light," light blue, pink, and white might be recommended.

[1137] Step 4:

[1138] The server suggests styles based on the user's height and body type. For example, it recommends long coats and bootcut pants for taller users, and high-waisted pants and A-line skirts for users of average or shorter height.

[1139] Step 5:

[1140] The server generates and sends recommended fashion styles to the device. The device receives this data and presents it visually to the user.

[1141] Step 6:

[1142] Users can review the suggested fashion styles presented and use them to help them make their own fashion choices. Specific item images and style combinations are shown, making it easy for users to understand.

[1143] Through the above processing steps, users can find their optimal fashion style based on their biometric data and enjoy fashion with confidence.

[1144] (Example 1)

[1145] 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".

[1146] Conventional fashion style suggestion systems could only offer suggestions based on general styles and trends, making it difficult to provide suggestions optimized for the individual characteristics of each user. Furthermore, because users needed specialized knowledge and experience to choose fashion items based on their biometric data, finding the optimal style was not easy. This resulted in users being unable to enjoy fashion with confidence.

[1147] 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.

[1148] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recommending a fashion style based on the analysis results, and means for presenting the recommendation to the user. This makes it possible for the user to easily find a fashion style that is individually optimized for them based on their own biometric data.

[1149] "Biometric data" refers to data that describes the physical characteristics of individual users, including height, weight, waist circumference, skin color, and facial features.

[1150] "Analysis means" refers to a system component that processes acquired biometric data to determine the optimal fashion style for the user.

[1151] A "means for recommending fashion styles" is a system component that suggests optimal colors and clothing styles to users based on data derived from analytical methods.

[1152] "Means of presenting recommendations to users" refer to system components that visually display fashion style recommendations to users, and include images of specific fashion items, color lists, and examples of style combinations.

[1153] The system of this invention acquires and analyzes the user's biometric data and proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[1154] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends it to the server.

[1155] Next, the server analyzes the received data. The server has an algorithm to recommend suitable colors based on the user's skin tone. For example, if the user's skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. For tall users, it recommends long coats and bootcut pants, while for users below average height, it recommends high-waisted pants and A-line skirts.

[1156] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[1157] The following hardware and software are required to implement this system.

[1158] Hardware:

[1159] User devices such as smartphones and tablets

[1160] Server for data analysis and recommendation generation

[1161] software:

[1162] Mobile application for user data collection and display

[1163] Algorithms for data analysis and fashion style suggestions

[1164] As a concrete example, suppose a user enters the following data:

[1165] Height: 165 cm

[1166] Weight: 60 kg

[1167] Waist circumference: 75 cm

[1168] Skin tone: Light

[1169] Face shape: Oval

[1170] Based on this data, the server makes the following recommendations:

[1171] Recommended colors: Light blue, pink, white

[1172] Style suggestions: High-waisted pants, A-line skirts, etc.

[1173] Examples of prompt statements:

[1174] "User data: Height 165 cm, weight 60 kg, waist circumference 75 cm, skin tone light, face shape oval. Please tell me your recommended fashion colors and styles."

[1175] The above recommendations are sent to the device, which visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style. This system allows users to easily find individually optimized fashion styles based on their biometric data, thereby boosting their confidence.

[1176] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1177] Step 1:

[1178] The user launches the application.

[1179] Specific actions: The device displays the application's initial screen. An interface prompting the user to enter biometric data appears.

[1180] Input: None

[1181] Output: Displays the data entry screen.

[1182] Step 2:

[1183] The user enters biometric data such as height, weight, waist circumference, skin color, and facial features.

[1184] Specific operation: The terminal provides text fields and dropdown menus, and the user enters their data into these fields.

[1185] Input: User's biometric data (height, weight, waist circumference, skin color, face shape)

[1186] Output: The user's biometric data is stored on the device.

[1187] Step 3:

[1188] The terminal sends the entered biometric data to the server.

[1189] Specific operation: The terminal converts the input data into JSON format and securely sends it to the server using the HTTPS protocol.

[1190] Input: Biometric data entered by the user

[1191] Output: The data, converted to JSON format, is sent to the server.

[1192] Step 4:

[1193] The server analyzes the received data.

[1194] Specific operation: The server uses Python scripts and TensorFlow models to execute algorithms that analyze biometric data. For example, it can recommend suitable colors based on skin tone or suggest styles based on height and weight.

[1195] Input: Biometric data in JSON format

[1196] Output: Analysis results (suggestions for recommended colors and styles)

[1197] Step 5:

[1198] The server generates fashion styles based on the analysis results.

[1199] Specific operation: The server generates fashion style data, including recommended colors and style suggestions, and converts it into JSON format.

[1200] Input: Analysis results (suggestions for recommended colors and styles)

[1201] Output: Fashion style data in JSON format

[1202] Step 6:

[1203] The server sends the generated recommended fashion style data to the device.

[1204] Specific operation: The server uses the HTTPS protocol to send data in JSON format to the terminal.

[1205] Input: Fashion style data in JSON format

[1206] Output: Fashion style data is sent to the terminal.

[1207] Step 7:

[1208] The device visually displays recommended fashion styles received from the server to the user.

[1209] Specific operation: The device uses a UI framework (e.g., React Native or Flutter) to display the received data to the user in the form of a list of recommended colors, images of specific fashion items, and examples of style combinations.

[1210] Input: Fashion style data in JSON format

[1211] Output: Visual display of recommended fashion styles

[1212] Step 8:

[1213] Users review the suggested fashion styles and select the one that suits them best.

[1214] Specific operation: The user selects a style based on the recommendations displayed on the device.

[1215] Input: Visual display of recommended fashion styles

[1216] Output: User-selected fashion style

[1217] (Application Example 1)

[1218] 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".

[1219] Traditional fashion style suggestion systems recommended appropriate fashion styles based on the user's biometric data, but they lacked a way for users to actually try on and verify those styles. Therefore, it was difficult for users to visualize how the items they selected based on the suggestions would actually look. To solve this inconvenience, a system is needed that allows users to visually check the suggested fashion styles using a virtual fitting room.

[1220] 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.

[1221] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recommending a fashion style based on the analysis results, and means for the user to check the suggested fashion style in a virtual fitting room. This allows the user to visually confirm the fashion style that is best suited to them and actually try it on.

[1222] "Biometric data" refers to information about a user's physical characteristics, specifically including height, weight, waist circumference, skin color, and facial features.

[1223] "Analysis means" refers to algorithms and software that process acquired biometric data to determine fashion styles and colors suitable for the user.

[1224] "Fashion style recommendation methods" refer to functions and processes that suggest the most suitable fashion items and color combinations to users based on analyzed data.

[1225] "Means of presenting to the user" refers to user interfaces and display devices that visually show the user the analysis results and recommendations.

[1226] A "virtual fitting room" refers to a virtual space or technology that allows users to visually check suggested fashion items using 3D avatars or similar displays.

[1227] This invention provides a system that suggests the optimal fashion style for a user based on biometric data and allows them to visually confirm the style using a virtual fitting room.

[1228] First, when a user launches the application, the device prompts the user to input biometric data or capture it using the camera. Biometric data refers to information about the user's physical characteristics, specifically height, weight, waist circumference, skin color, and facial features. This data can be manually entered by the user or automatically acquired using the device's camera function.

[1229] Next, the acquired biometric data is transmitted from the terminal to the server. The server uses analytical means to analyze the received data. Analytical means refer to algorithms and software that process the acquired biometric data and determine fashion styles and colors suitable for the user. Specifically, this includes means for determining recommended colors based on the user's skin tone.

[1230] The server has a fashion style recommendation system that suggests fashion styles based on the analysis results. This system proposes the most suitable fashion items and color combinations to the user. The suggestions are then sent back to the terminal, which presents them in a visually easy-to-understand format. The presentation system refers to the user interface and display device, which show details of the suggested fashion items and combinations.

[1231] Furthermore, the suggested fashion styles can be viewed in a virtual fitting room. A virtual fitting room refers to a virtual space or technology that allows users to visually check suggested fashion items using 3D avatars or other visual aids. This allows users to see exactly how the fashion items fit them.

[1232] The hardware and software used to implement this system include an iOS or Android smartphone, Python (server-side), Flask (API), Unity (3D rendering), and SQLite (database). The program processes as follows:

[1233] 1. Input data acquisition: The user enters biometric data into the app or takes a picture with the camera.

[1234] 2. Server Sending: An HTTP POST request is used to send this data to the server.

[1235] 3. Data Analysis: Data analysis is performed on the server using algorithms written in Python.

[1236] 4. Result reception: Receive the analysis results and display them on the terminal.

[1237] 5. 3D Rendering: The fashion style selected by the user is applied to a 3D avatar in Unity and displayed in a virtual fitting room.

[1238] For example, if a user enters data such as height 165cm, weight 60kg, waist circumference 75cm, light skin tone, and oval face shape, the system will recommend colors such as light blue, pink, and white, and suggest styles such as high-waisted pants and A-line skirts. These suggestions can be visually confirmed in a virtual fitting room.

[1239] Example of a prompt:

[1240] "Please suggest the best fashion items for a user who is 165cm tall, weighs 60kg, has a waist circumference of 75cm, has a light skin tone, and an oval face shape."

[1241] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1242] Step 1:

[1243] When a user launches the application, the device prompts the user to input biometric data. The user can manually enter biometric data such as height, weight, waist circumference, skin color, and face shape, or the device can automatically acquire this data using its camera function. Once the device receives the data, it stores this biometric data in a variable.

[1244] Input: User's biometric data (height, weight, waist circumference, skin color, face shape)

[1245] Output: Biometric data stored on the device

[1246] Step 2:

[1247] The device sends the acquired biometric data to the server. Specifically, it sends the data to the server in JSON format using an HTTP POST request.

[1248] Input: Biometric data stored in the device

[1249] Output: Biometric data sent to the server

[1250] Step 3:

[1251] The server analyzes the biometric data it receives. An algorithm written in Python is executed on the server to recommend the optimal fashion style and colors based on the user's characteristics. For example, if the user's skin tone is light, colors such as light blue, pink, and white will be recommended.

[1252] Input: Biometric data sent to the server

[1253] Output: List of recommended colors and fashion styles

[1254] Step 4:

[1255] The server then sends the list of recommended colors and fashion styles, which are the results of the analysis, back to the terminal.

[1256] Input: List of recommended colors and fashion styles

[1257] Output: A list of recommended colors and fashion styles sent to the terminal.

[1258] Step 5:

[1259] The device displays the analysis results it receives to the user. The interface is designed to allow users to visually check recommended colors and fashion styles, making it easy for them to view the suggestions.

[1260] Input: A list of recommended colors and fashion styles sent to the device.

[1261] Output: Display of fashion style suggestions that the user can visually confirm.

[1262] Step 6:

[1263] Users can view suggested fashion styles in a virtual fitting room. The fashion items selected by the user on their device are applied to a 3D avatar using Unity's 3D rendering technology. This allows users to visually see how the suggested styles fit them.

[1264] Input: Fashion style suggestions that users can visually confirm.

[1265] Output: Visual confirmation of the fashion style applied to the 3D avatar.

[1266] Through the above processing steps, users are offered optimal fashion styles based on their biometric data, which they can then visually confirm in a virtual fitting room. This makes it easier for users to choose the fashion items that best suit them.

[1267] 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.

[1268] Patent Specification

[1269] The present invention provides a system that acquires and analyzes a user's biometric data and emotional state, and then suggests the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[1270] First, when a user launches the application, the device prompts the user to enter biometric data. Specifically, it asks for information such as height, weight, waist circumference, skin color, and facial features. Once the user provides this information, the device sends that data to the server.

[1271] Next, the server uses the device's camera to scan the user's face in order to analyze their facial expressions. This process detects the user's current emotional state. The emotion engine recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[1272] Furthermore, the server analyzes the received biometric and emotional data. It also has an algorithm that determines recommended colors based on the user's skin tone. For example, if the skin tone is "light," recommended colors include light blue, pink, and white. It also suggests suitable styles based on the user's height and body type. Long coats and bootcut pants are recommended for tall users, while high-waisted pants and A-line skirts are recommended for users of average height or shorter.

[1273] Based on emotional data, recommended fashion styles are further refined. For example, if a user indicates "joy," bright colors and casual styles are recommended. Conversely, if a user indicates "sadness," calm colors and relaxing styles are recommended.

[1274] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is done in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[1275] As a concrete example, suppose a user enters the following data:

[1276] Height: 165 cm

[1277] Weight: 60 kg

[1278] Waist circumference: 75 cm

[1279] Skin tone: Light

[1280] Face shape: Oval

[1281] Then, if the emotion engine recognizes the user's current emotional state as joy, the server will make the following recommendation based on this data:

[1282] Recommended colors: Light blue, pink, white

[1283] Style suggestion: High-waisted pants or A-line skirt

[1284] Emotion-based adjustment: Casual style in bright colors

[1285] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style and enjoy fashion with confidence.

[1286] This system allows users to easily find the optimal fashion style based on their biometric data and emotional state, thereby improving their quality of life.

[1287] The following describes the processing flow.

[1288] Program processing steps

[1289] Step 1:

[1290] The device prompts the user to enter biometric data (height, weight, waist circumference, skin color, face shape). The user then enters the required information.

[1291] Step 2:

[1292] The device sends the entered biometric data to the server. The server stores the received data in an appropriate format.

[1293] Step 3:

[1294] The system uses the device's camera to scan the user's face and collect facial expression data. The system automatically acquires data when the user faces the camera.

[1295] Step 4:

[1296] The server analyzes facial expression data and recognizes the user's emotional state (e.g., joy, sadness, anger, surprise, etc.). The emotion engine is responsible for this.

[1297] Step 5:

[1298] The server analyzes the biometric data it receives. Specifically, it uses an algorithm that determines recommended colors based on the user's skin tone. For example, light blue, pink, and white are recommended for a "light" skin tone.

[1299] Step 6:

[1300] The server suggests styles based on the user's height and body type. For example, it recommends long coats and bootcut pants for taller users, and high-waisted pants and A-line skirts for users of average height or shorter.

[1301] Step 7:

[1302] The server further adjusts recommended fashion styles based on emotional data. For example, if a user indicates an emotion of "joy," it recommends bright colors and casual styles. Conversely, if a user indicates an emotion of "sadness," it recommends calm colors and relaxing styles.

[1303] Step 8:

[1304] The server sends the generated recommended fashion style to the device. The device receives this data and presents it visually to the user.

[1305] Step 9:

[1306] The device displays recommended fashion styles to the user. These recommendations include a list of suggested colors and styles, images of specific fashion items, and explanations on how to combine them.

[1307] Step 10:

[1308] Users review the suggested fashion styles presented and use them as a basis for their own fashion choices. They can refer to the system's recommendations when selecting specific items.

[1309] Through the processing steps described above, users can easily find the optimal fashion style based on their biometric data and emotional state. This allows users to enjoy fashion with confidence.

[1310] (Example 2)

[1311] 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".

[1312] Conventional fashion style suggestion systems could make suggestions based on the user's biometric data, but they could not make suggestions that took the user's emotional state into account. Therefore, it was difficult to provide fashion styles that matched the user's current mood and emotions. This invention aims to solve this problem and provide a system that suggests the optimal fashion style by simultaneously considering the user's biometric data and emotional state.

[1313] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for recognizing the user's emotional state, means for recommending a fashion style based on the analysis results and the recognized emotional state, and means for presenting the recommendation to the user. This makes it possible to comprehensively analyze the user's biometric data and emotional state and provide an optimal fashion style based on them.

[1314] "User biometric data" refers to information that represents an individual user's physical characteristics, such as height, weight, waist circumference, skin color, and facial features.

[1315] "Analysis methods" refer to means of analyzing each data item based on biometric data obtained from users, and deriving characteristics and trends.

[1316] "Emotional state" refers to feelings such as joy, sadness, anger, and surprise, which can be recognized from the user's facial expressions and other indicators.

[1317] "Recognition means" refers to methods for evaluating a user's facial expressions and actions and identifying their emotional state.

[1318] "Fashion style recommendation methods" refer to methods for suggesting fashion items and styles suitable for the user based on analysis results and recognized emotional states.

[1319] "Presentation methods" refer to means of visually showing a recommended fashion style to the user, and are primarily carried out through displays or application interfaces.

[1320] Modes for carrying out the invention

[1321] The present invention provides a system that acquires and analyzes a user's biometric data and emotional state, and then proposes the optimal fashion style based on the results. This system is provided in the form of software and a mobile application.

[1322] First, when the user launches the mobile application, the device prompts the user to enter biometric data. The user enters the following information within the application:

[1323] height

[1324] body weight

[1325] waist area

[1326] skin color

[1327] Face shape

[1328] Once the user provides this information, the device sends that data to the server. This allows the server to obtain the user's biometric data.

[1329] Next, the server uses the device's camera to scan the user's face and acquires the image data. Through this process, the server analyzes the user's emotional state. The emotion engine recognizes emotions such as joy, sadness, anger, and surprise from the user's facial expressions.

[1330] Furthermore, the server analyzes the received biometric and emotional data. The analysis includes the following steps:

[1331] Recommended colors are determined based on skin tone. (For example, if your skin tone is light, then light blue, pink, white, etc.)

[1332] We will suggest suitable styles based on your height and body type. (For example, for taller individuals, we might suggest a long coat and bootcut pants.)

[1333] The recommended fashion style is adjusted based on emotional data. (For example, a bright, casual style for joyful occasions, and a calm, relaxed style for sad occasions.)

[1334] The recommended fashion styles generated by the server are sent back to the device, which then presents them to the user. This presentation is done in a visually easy-to-understand format. For example, it may include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[1335] As a concrete example, suppose a user enters the following data:

[1336] Height: 165 cm

[1337] Weight: 60 kg

[1338] Waist circumference: 75 cm

[1339] Skin tone: Light

[1340] Face shape: Oval

[1341] Then, if the emotion engine recognizes the user's current emotional state as "joy," the server will make the following recommendation based on this data:

[1342] Recommended colors: Light blue, pink, white

[1343] Style suggestion: High-waisted pants or A-line skirt

[1344] Emotion-based adjustment: Casual style in bright colors

[1345] The above recommendations are sent to the device, which then visually presents them to the user. Based on these recommendations, the user can select the optimal fashion style and enjoy fashion with confidence.

[1346] An example of a prompt statement is as follows:

[1347] "Please suggest the best fashion styles for a user who is 165 cm tall, weighs 60 kg, has a waist circumference of 75 cm, and has a light skin tone, so that they can feel happy."

[1348] This system allows users to easily find the optimal fashion style based on their biometric data and emotional state, thereby improving their quality of life.

[1349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1350] System program processing steps

[1351] Step 1: User data entry

[1352] The user launches the mobile application.

[1353] The user enters biometric data (height, weight, waist circumference, skin color, face shape) into the application.

[1354] Input data: height, weight, waist circumference, skin color, face shape

[1355] Output data: Biometric data entered by the user

[1356] Specific operation: The user enters data into each input field and presses the complete button, at which point the entered data is saved to the device.

[1357] Step 2: Send to the server

[1358] The device sends the user's entered biometric data to the server.

[1359] Input data: Biometric data entered by the user.

[1360] Output data: Biometric data sent to the server

[1361] Specific operation: The device sends biometric data to the server via the internet. After transmission, a success message is displayed on the device.

[1362] Step 3: User facial scan and emotion recognition

[1363] The device's camera scans the user's face and sends the image data to the server.

[1364] Input data: Image data of the user's face

[1365] Output data: Facial image data sent to the server

[1366] Specific operation: The user faces the camera and a scan is performed, displaying the message "Scanning." After the scan is complete, the message "Analyzing emotional state" is displayed.

[1367] Step 4: Server-based analysis of biometric and emotional data

[1368] The server analyzes the user's emotional state using biometric data and facial image data sent from the user.

[1369] Input data: biometric data, facial image data

[1370] Output data: Analyzed user emotional state (e.g., joy, sadness, anger, surprise)

[1371] Specific operation: The server-side emotion recognition engine analyzes image data to identify the user's emotions. This data is then integrated with biometric data for further analysis.

[1372] Step 5: Generate Recommended Fashion Styles

[1373] The server uses the biometric and emotional data it receives to generate recommended fashion styles.

[1374] Input data: biometric data, emotional data

[1375] Output data: Recommended fashion style (recommended colors, style suggestions, emotion-based adjustments)

[1376] Specific operation: The server's algorithm calculates the optimal fashion style based on the user's skin color, height, body type, and emotional state, and generates it as text and image data.

[1377] Step 6: Sending from server to terminal in the recommended style

[1378] The server sends the recommended fashion style it generates to the device.

[1379] Input data: Recommended fashion style

[1380] Output data: Recommended fashion styles sent to the user's device.

[1381] Specific operation: The server converts the recommended content into an appropriate data format such as JSON and sends it to the terminal. Once the terminal receives the data, a confirmation message is displayed.

[1382] Step 7: Recommended style displayed by the device

[1383] The device visually presents the user with recommended fashion styles it has received.

[1384] Input data: Recommended fashion style

[1385] Output data: Visual presentation to the user (recommended colors, style suggestions, images of specific fashion items)

[1386] Specific operation: The application displays a list of recommended fashion items and styles. Users can tap each item to view detailed information.

[1387] Through these steps, the system enables the suggestion of the optimal fashion style based on the user's biometric data and emotional state.

[1388] (Application Example 2)

[1389] 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".

[1390] Conventional fashion recommendation systems recommend fashion styles based solely on the user's static biometric data, which means they cannot provide appropriate suggestions that take into account the user's current emotional state. As a result, users may not receive fashion advice that resonates with their emotions, leading to decreased satisfaction.

[1391] 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.

[1392] In this invention, the server includes means for acquiring the user's biometric data, means for analyzing the biometric data, means for acquiring the user's emotional state in real time, means for analyzing emotional data based on the emotional state, means for recommending a fashion style based on the analysis results of the biometric data and emotional data, and means for presenting the recommendation to the user. This makes it possible to propose individually optimized fashion that takes into account not only the user's biometric data but also their emotional state.

[1393] "User biometric data" refers to data about a user's physical characteristics, such as height, weight, waist circumference, skin color, and facial features.

[1394] "Means for analyzing biometric data" refers to methods for analyzing acquired biometric data to determine a fashion style suitable for the user.

[1395] "Means for acquiring a user's emotional state in real time" refers to methods of recognizing a user's facial expressions and actions using cameras and sensors in order to acquire the user's emotions in real time.

[1396] "Means for analyzing emotional data based on emotional state" refers to methods for analyzing the emotional state of acquired users and using the analysis results to suggest fashion styles.

[1397] "A method for recommending fashion styles based on the analysis results of biometric and emotional data" refers to a method for recommending the optimal fashion style based on the analysis results.

[1398] "Means of presenting recommendations to users" refers to methods of visually presenting recommended fashion styles so that users can confirm them.

[1399] Modes for carrying out the invention

[1400] This invention is a system that acquires and analyzes a user's biometric data and emotional state in real time, and recommends the optimal fashion style based on the results. This system is mainly realized by exchanging information between a server, a terminal, and the user, and by comprehensively analyzing various types of data.

[1401] Hardware and software usage

[1402] Hardware: Utilizes the camera, microphone, and internet connection built into the smartphone or tablet.

[1403] software:

[1404] I will build a program in Python and use it for data analysis.

[1405] Perform real-time face recognition using OpenCV.

[1406] We will use Keras to build an emotion recognition model and analyze the user's emotional state.

[1407] Data flow and analysis

[1408] 1. Collection of user biometric data:

[1409] Users input biometric data such as height, weight, waist circumference, skin color, and face shape into the application. This data is then sent from the device to the server.

[1410] 2. Real-time acquisition of emotional state:

[1411] The device's built-in camera is used to capture the user's facial expressions. This facial expression data is sent to a server and analyzed in real time using an emotion recognition model.

[1412] 3. Data Analysis:

[1413] The server analyzes the acquired biometric and emotional data. This analysis determines recommended colors based on the user's skin tone and fashion styles adjusted according to the user's current emotional state.

[1414] 4. Recommended fashion styles:

[1415] Based on the analysis results, the system suggests the optimal fashion style to the user. The suggestions include a list of recommended colors and styles, images of specific fashion items, and explanations on how to combine them.

[1416] Specific example

[1417] Consider a user who is 165 cm tall, weighs 60 kg, has a waist circumference of 75 cm, has light skin tone, and an oval face shape. The emotion recognition model evaluates the user's current emotional state as "joyful." Based on this data, the server makes the following recommendations.

[1418] Recommended colors: Light blue, pink, white

[1419] Style suggestion: High-waisted pants or A-line skirt

[1420] Emotion-based adjustment: Casual style in bright colors

[1421] These recommendations are sent to the device and presented to the user visually. Based on these recommendations, the user can enjoy self-optimized fashion choices.

[1422] Example of a prompt

[1423] "Please tell me your fashion style, given that you are 165 cm tall, weigh 60 kg, have light skin tone, an oval face shape, and your emotional state is joyful."

[1424] This allows the server to suggest the most suitable fashion style to the user. Through this system, users can intuitively receive optimal fashion advice based on biometric data and emotional state, thereby improving their quality of life.

[1425] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1426] Step 1:

[1427] The user enters biometric data (height, weight, waist circumference, skin color, face shape) into the application on their device.

[1428] Input: Height, weight, waist circumference, skin color, face shape

[1429] Output: This data is sent to the server.

[1430] Specific operation: The user launches the application and enters various biometric data according to the instructions. Once the input is complete, they press the submit button to send the data to the server.

[1431] Step 2:

[1432] The device uses its built-in camera to capture the user's facial expressions.

[1433] Input: User's face image

[1434] Output: The facial image is sent to the server.

[1435] Specific operation: The device's camera activates and detects the user's face. When the user makes an appropriate facial expression towards the camera, the image is captured and sent to the server.

[1436] Step 3:

[1437] The server uses the received facial images to analyze the user's emotional state in real time.

[1438] Input: User's face image

[1439] Output: Emotional state (e.g., joy, sadness, anger, surprise, etc.)

[1440] Specific operation: The server inputs a facial image into an emotion recognition model, which analyzes facial features to identify the emotional state. The emotion recognition model is built using a pre-trained Keras model. The analysis result provides the emotional state, which is then used for subsequent processing.

[1441] Step 4:

[1442] The server analyzes both biometric data and emotional state, and based on that, recommends the optimal fashion style.

[1443] Input: Biometric data, emotional state

[1444] Output: Fashion style recommendations (recommended colors, styles)

[1445] Specific operation: The server analyzes biometric data (e.g., skin color, height, weight) to determine suitable colors and styles. Simultaneously, it adjusts recommendations based on emotional state (e.g., bright colors and casual styles for happy moods). This analysis result is compiled into fashion style recommendations.

[1446] Step 5:

[1447] The server sends the recommended fashion style to the device.

[1448] Input: Fashion style recommendations

[1449] Output: Information on recommended fashion styles

[1450] Specific operation: The server compiles data on recommended fashion styles (recommended colors, styles, specific item images, etc.) and sends it to the terminal.

[1451] Step 6:

[1452] The device visually presents the user with recommended fashion styles received from the server.

[1453] Input: Information on recommended fashion styles

[1454] Output: Images and descriptions of fashion styles presented to the user.

[1455] Specific operation: The application on the device displays the received data, and the user can see the recommended fashion style on the screen. Specifically, a list of recommended colors and styles, images of specific fashion items, and explanations of how to combine them are displayed.

[1456] This process allows users to receive personalized fashion style suggestions in real time.

[1457] 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.

[1458] 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 the following. 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 indicated 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.

[1459] 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.

[1460] 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.

[1461] 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.

[1462] 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.

[1463] 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.

[1464] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1465] 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."

[1466] 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.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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.

[1477] 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.

[1478] The following is further disclosed regarding the embodiments described above.

[1479] (Claim 1)

[1480] Means for obtaining user biometric data,

[1481] Means for analyzing the aforementioned biometric data,

[1482] A means of recommending fashion styles based on analysis results,

[1483] A means of presenting the aforementioned recommendation to the user,

[1484] A system that includes this.

[1485] (Claim 2)

[1486] The system according to claim 1, wherein the biometric data includes height, weight, waist circumference, skin color, and facial features.

[1487] (Claim 3)

[1488] The system according to claim 1, wherein the analysis means includes means for determining a recommended color based on the user's skin tone.

[1489] "Example 1"

[1490] (Claim 1)

[1491] Means for acquiring user biometric data,

[1492] The means for analyzing the aforementioned biological data,

[1493] A means of recommending fashion styles based on analysis results,

[1494] A means of presenting the aforementioned recommendation to the user,

[1495] A system that includes this.

[1496] (Claim 2)

[1497] The system according to claim 1, wherein the biometric data includes height, weight, waist circumference, skin color, and facial shape.

[1498] (Claim 3)

[1499] The system according to claim 1, wherein the analysis means includes means for determining a recommended color based on the user's skin color.

[1500] "Application Example 1"

[1501] (Claim 1)

[1502] Means for obtaining user biometric data,

[1503] Means for analyzing the aforementioned biometric data,

[1504] A means of recommending fashion styles based on analysis results,

[1505] A means of presenting the aforementioned recommendation to the user,

[1506] A means for users to check suggested fashion styles in a virtual fitting room,

[1507] A system that includes this.

[1508] (Claim 2)

[1509] The aforementioned biometric data includes height, weight, waist circumference, skin color, and facial features.

[1510] The system according to claim 1.

[1511] (Claim 3)

[1512] The analysis means includes means for determining a recommended color based on the user's skin tone.

[1513] The system according to claim 1.

[1514] "Example 2 of combining an emotion engine"

[1515] (Claim 1)

[1516] Means for acquiring user biometric data,

[1517] The means for analyzing the aforementioned biological data,

[1518] A means of recognizing the user's emotional state,

[1519] A means of recommending fashion styles based on analysis results and perceived emotional states,

[1520] A means of presenting the aforementioned recommendation to the user,

[1521] A system that includes this.

[1522] (Claim 2)

[1523] The system according to claim 1, wherein the biometric data includes height, weight, waist circumference, skin color, and facial shape.

[1524] (Claim 3)

[1525] The system according to claim 1, wherein the analysis means includes means for determining a recommended color based on the user's skin tone.

[1526] (Claim 4)

[1527] The system according to claim 1, wherein the aforementioned emotional state includes at least one of joy, sadness, anger, and surprise.

[1528] (Claim 5)

[1529] The system according to claim 1, further comprising means for adjusting the recommended style based on emotional state.

[1530] "Application example 2 when combining with an emotional engine"

[1531] (Claim 1)

[1532] Means for obtaining user biometric data,

[1533] Means for analyzing the aforementioned biometric data,

[1534] A means of acquiring the user's emotional state in real time,

[1535] A means for analyzing emotional data based on the aforementioned emotional state,

[1536] A means for recommending fashion styles based on the analysis results of the aforementioned biometric data and emotional data,

[1537] A means of presenting the aforementioned recommendation to the user,

[1538] A system that includes this.

[1539] (Claim 2)

[1540] The system according to claim 1, wherein the biometric data includes height, weight, waist circumference, skin color, and facial features.

[1541] (Claim 3)

[1542] The system according to claim 1, wherein the analysis means includes means for determining a recommended color based on the user's skin tone and means for adjusting a fashion style based on the user's emotional state. [Explanation of Symbols]

[1543] 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. Means for obtaining user biometric data, Means for analyzing the aforementioned biometric data, A means of recommending fashion styles based on analysis results, A means of presenting the aforementioned recommendation to the user, A system that includes this.

2. The system according to claim 1, wherein the biometric data includes height, weight, waist circumference, skin color, and facial features.

3. The system according to claim 1, wherein the analysis means includes means for determining a recommended color based on the user's skin tone.

Images