System

Abstract

A system is provided.SOLUTION: The fashion proposal system includes an input means for inputting personal basic information, a storage means for storing the basic information in a database, an analysis means for analyzing the basic information and generating personalized fashion proposals, and a display means for displaying the fashion proposals generated by the analysis means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The present invention aims to solve the following three problems. First, to provide a system that allows users to easily obtain fashion suggestions based on their basic information and preferences. Conventional fashion suggestion systems have the problem of providing many generic suggestions that do not adequately reflect the characteristics of individual users. Second, to save users time by automating the analysis process for generating fashion suggestions. Conventional methods require manual styling and complex question-and-answer process, which places a heavy burden on users. Third, to improve user satisfaction by incorporating the latest fashion trends while providing styles that suit individual preferences and body types. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, an input means is provided for inputting basic information of an individual. This allows the user to easily input information such as their name, age, gender, body type, preferred style, daily activities, and special events. Next, a storage means is provided for saving the basic information in a database. This allows the information entered by the user to be safely stored and used for later analysis. Furthermore, an analysis means is provided for analyzing the basic information and generating personalized fashion suggestions. In particular, the analysis means uses an artificial intelligence model to combine the user's basic information with the latest fashion trends to generate optimal fashion suggestions. Finally, a display means is provided for displaying the fashion suggestions generated by the analysis means. This allows the user to easily check the suggested fashion style. In this way, the present invention makes it possible to quickly and efficiently provide fashion suggestions optimized for individuals.

[0006] "Personal Basic Information" refers to information about a user's name, age, gender, body type, style preferences, daily activities, and special events.

[0007] "Input means" refers to the means by which a user inputs basic personal information into the system, and specifically includes forms and interfaces that operate via a website or application.

[0008] "Storage means" refers to a means for recording and storing basic personal information entered by a user in a database.

[0009] The "analysis means" is a means for generating optimal fashion suggestions for users based on stored basic personal information, and in particular, highly accurate analysis is performed by utilizing an artificial intelligence model.

[0010] An "artificial intelligence model" refers to an algorithm or program that uses technologies such as machine learning and neural networks to mimic human knowledge and experience, analyze data, and make predictions.

[0011] "Fashion suggestions" are suggestions that show the user the best combination of clothing and accessories based on the user's personal basic information.

[0012] The "display means" refers to a means for visually presenting the fashion suggestions generated by the analysis means to the user, and specifically includes the interface of a website or application. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. Specific embodiments for carrying out the present invention will be described below.

[0035] 1. Entering and submitting user data:

[0036] When a user accesses a website or application, an input form appears on the device, where the user enters basic information such as their name, age, gender, body type, preferred style, daily activities, special events, etc. Once completed, the device sends this information in JSON format to the server.

[0037] 2. Receiving and storing data:

[0038] The server receives the user's basic information sent from the device. This received data is saved in a database. Storing the data saves the user the trouble of having to enter the same information later, and the server can refer to past data to make more accurate fashion suggestions.

[0039] 3. Data Analysis:

[0040] The server then begins analyzing the user's basic information, using an artificial intelligence model to analyze the user's age, gender, body type, preferred style, daily activities, and special events. The model then combines this with the latest fashion trend data to generate optimal fashion recommendations.

[0041] 4. Fashion proposal generation:

[0042] The fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best suit the user's profile, making it easy for users to style their outfits for individual occasions. The generated fashion suggestions are then converted into an appropriate format for display to the user.

[0043] 5. Display of fashion suggestions:

[0044] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. The feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0045] Examples:

[0046] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." Based on this information, the AI ​​model generates fashion suggestions such as: "For casual style, a white blouse and denim jeans, and for office work days, a navy blazer and skirt," and "For a friend's wedding, an elegant dress and pearl accessories." This content is displayed on the user's device, and the user can choose their fashion based on the suggestions.

[0047] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style based on the user's basic information, thereby increasing the user's satisfaction.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] A user accesses a website or application. The device prompts the user with a login questionnaire, where the user enters basic information such as name, age, gender, body type, preferred style, daily activities, and special events.

[0051] Step 2:

[0052] The user clicks the button to submit the input form. The device converts the entered basic information into JSON format and sends it to the server.

[0053] Step 3:

[0054] The server receives the user data sent from the device, stores the received data in a database, and verifies that the data is properly recorded during this process.

[0055] Step 4:

[0056] The server retrieves basic user information from the database, prepares the retrieved information for analysis, and passes the data to the AI ​​model.

[0057] Step 5:

[0058] The AI ​​model analyzes the user's age, gender, body type, preferred style, daily activities, and special event information, and then compares it with the latest fashion trend data to generate optimal fashion suggestions.

[0059] Step 6:

[0060] The server formats the fashion suggestions obtained from the AI ​​model, making them easy for users to understand.

[0061] Step 7:

[0062] The server sends the formatted fashion suggestions to the terminal, and the sent data is immediately displayed on the user's terminal.

[0063] Step 8:

[0064] The user reviews the displayed fashion suggestions. If the user provides feedback on the suggestions, the data is sent back to the server. This feedback is used to improve the accuracy of future suggestions.

[0065] Example 1

[0066] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0067] Conventional fashion suggestion systems lacked efficient methods for collecting basic user information and generating and displaying personalized suggestions. In particular, they lacked a method for effectively utilizing user feedback to improve the accuracy of suggestions. As a result, there were issues with low user satisfaction and declining system utilization.

[0068] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0069] In this invention, the server includes means for inputting basic information of an individual, means for converting the basic information into JSON format and transmitting the converted information to the server, means for saving the basic information in a database, means for analyzing the basic information and using an artificial intelligence model to generate personalized fashion suggestions, means for formatting the fashion suggestions generated by the analysis means, means for displaying the formatted fashion suggestions, and means for analyzing feedback provided by the user via the display means to improve suggestion accuracy. This allows for the generation of highly accurate personalized suggestions based on the user's basic information, and further enables the accuracy of the suggestions to be continuously improved based on the feedback.

[0070] "Input means" refers to a device or software interface that allows a user to input basic personal information.

[0071] "Transmission means" refers to a device or software function that converts input data into JSON format and transmits it to the server.

[0072] "Storage means" refers to a device or software function for permanently recording data sent to the server in a database.

[0073] "Analysis Means" means a device or software function that utilizes artificial intelligence models to generate personalized fashion suggestions based on stored data.

[0074] An "artificial intelligence model" is a mathematical model that uses machine learning or deep learning algorithms to perform specific tasks.

[0075] "Formatting means" refers to the functionality of a device or software for converting the generated fashion suggestions into the format required for display to the user.

[0076] "Display means" refers to a device or software interface for visually presenting fashion suggestions to a user.

[0077] "Feedback means" refers to a device or software function that collects and analyzes user-provided evaluations and opinions to improve the accuracy of the system's suggestions.

[0078] The present invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. This system comprises an input means, a transmission means, a storage means, an analysis means, a formatting means, a display means, and a feedback means.

[0079] When a user accesses a website or application, a form for entering basic personal information is displayed on the device, where the user enters information such as name, age, gender, body type, preferred style, daily activities, special events, etc. Once the information is complete, the device uses JavaScript to convert the information into JSON format and sends it to the server via an HTTP POST request.

[0080] The server stores the received data in a database (e.g., MySQL or MongoDB). Based on the stored data, the server performs analysis using an artificial intelligence model (e.g., TensorFlow or PyTorch). This analysis combines the user's basic information with the latest fashion trend data to generate optimal fashion suggestions.

[0081] The generated fashion suggestions are returned in JSON format to the server, which formats this data into an appropriate format (e.g., HTML or JSON) for display to the user. The formatted data is then sent back to the device as an HTTP response, which receives it and visually displays it to the user.

[0082] The user reviews the displayed fashion suggestions and enters feedback if necessary. The device then converts this feedback back into JSON format and sends it to the server, where it analyzes the feedback and uses it to improve the accuracy of the suggestions.

[0083] As an example, a 25-year-old female user enters the following information:

[0084] Name: Hanako

[0085] Age: 25

[0086] Gender: Female

[0087] Build: Slim

[0088] Favorite style: Casual

[0089] Daily Activities: Office work

[0090] Special Event: Friend's Wedding

[0091] Based on this, the AI ​​model generates fashion suggestions like this:

[0092] "For casual style, I wear a white blouse and denim jeans. For office work, I wear a navy blazer and skirt. For a friend's wedding, I wear an elegant dress and pearl accessories."

[0093] An example of a prompt for a generative AI model might look like this:

[0094] "A 25-year-old woman with a slim figure, who likes casual styles, works in an office, and is attending a friend's wedding. What are some fashion suggestions that would be best for her?"

[0095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0096] Step 1:

[0097] A user visits a website or application.

[0098] Input: User access request

[0099] What it does: Displays a web form on the device asking users to enter basic personal information, including name, age, gender, body type, style preferences, daily activities, and special events.

[0100] Output: Display of form

[0101] Step 2:

[0102] The user enters basic information.

[0103] Input: Information such as name, age, gender, body type, preferred style, daily activities, special events, etc.

[0104] What it does: The terminal captures the user's input and temporarily stores it.

[0105] Output: Basic information is saved in the input form

[0106] Step 3:

[0107] Convert basic information into JSON format and send it to the server.

[0108] Input: Basic information entered by the user

[0109] How it works: The device uses JavaScript to convert the input information into JSON format, which is then sent to the server via an HTTP POST request.

[0110] Output: User data in JSON format is sent to the server

[0111] Step 4:

[0112] The server receives the JSON data and stores it in the database.

[0113] Input: User data in JSON format

[0114] How it works: The server extracts the user's JSON data from the incoming HTTP POST request and stores it in a database (e.g. MySQL or MongoDB).

[0115] Output: User data saved in database

[0116] Step 5:

[0117] The server analyzes the data and generates fashion suggestions.

[0118] Input: User basic information stored in the database

[0119] How it works: The server queries the database to retrieve stored data. The retrieved data is passed to an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate fashion suggestions. The model analyzes the user's basic information and the latest fashion trend data, and generates optimal fashion suggestions in JSON format.

[0120] Output: Fashion suggestion data in JSON format

[0121] Step 6:

[0122] The server formats the generated fashion suggestions.

[0123] Input: JSON format fashion proposal data

[0124] Behavior: The server formats the received JSON data into an appropriate format (e.g., HTML template, JSON structure modification).

[0125] Output: Formatted fashion suggestion data

[0126] Step 7:

[0127] The server sends the formatted data to the terminal.

[0128] Input: Formatted fashion suggestion data

[0129] How it works: The server sends data formatted as an HTTP response to the device.

[0130] Output: Fashion suggestion data is sent to the device

[0131] Step 8:

[0132] The terminal visually displays the fashion suggestions to the user.

[0133] Input: Fashion suggestion data sent from the server

[0134] What it does: The device parses the received data and displays it visually in a browser or application (e.g., dynamically embedding it in HTML using JavaScript).

[0135] Output: User is presented with fashion suggestions

[0136] Step 9:

[0137] The user provides feedback on the proposal.

[0138] Input: User feedback information (e.g., new requests and corrections)

[0139] How it works: The device converts the feedback information into JSON format and sends it back to the server.

[0140] Output: Feedback data in JSON format is sent to the server

[0141] Step 10:

[0142] The server analyzes the feedback and incorporates it into future suggestions.

[0143] Input: Feedback data in JSON format

[0144] How it works: The server analyzes the received feedback and stores it in a database. The analysis results are used as training data for the AI ​​model to improve the accuracy of future suggestions.

[0145] Output: AI model with improved proposal accuracy

[0146] (Application example 1)

[0147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] In virtual stores, the process of users finding the best fashion style for themselves and trying it on is very time-consuming. Furthermore, when shopping online, it is difficult to actually try on clothes, and the clothes purchased may not fit. To solve this problem, a system that provides personalized fashion suggestions and enables virtual try-on is needed.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0150] In this invention, the server includes an input means for inputting basic information of an individual, a storage means for storing the basic information in a database, an analysis means for analyzing the basic information and generating personalized fashion suggestions, a virtual try-on means for trying on the suggested outfits based on the user's avatar in a virtual space, and a display means for displaying the fashion suggestions generated by the analysis means and confirming them in a 3D environment, thereby enabling users to easily find the fashion style that best suits them and try them on in the virtual space.

[0151] "Basic personal information" refers to information such as the user's name, age, gender, body type, preferred style, daily activities, and special events.

[0152] "Input means" refers to a device or interface that allows a user to input basic personal information.

[0153] "Storage means" refers to a system or device for storing basic information about users in a database.

[0154] "Analysis means" refers to the processes and techniques used to generate personalized fashion suggestions based on the stored underlying information.

[0155] "Artificial intelligence model" refers to advanced algorithms such as machine learning and deep learning that are used to generate fashion suggestions.

[0156] "Virtual try-on means" refers to technology or devices that allow a user's avatar to try on suggested outfits in a virtual space.

[0157] "Display means" refers to a device or interface that allows the user to visually confirm the generated fashion suggestions.

[0158] This invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. This system generates optimal fashion suggestions based on the information entered by the user and allows the user to try on clothes in a virtual space.

[0159] The system is implemented in the following configuration.

[0160] Hardware and software used

[0161] Hardware: A smartphone or computer is used to input basic user information, and a head-mounted display (e.g., Oculus Rift) is used to display fashion suggestions.

[0162] Software: Web frameworks such as Flask and Django are used for server-side processing, and MySQL and PostgreSQL are used for databases. AI (artificial intelligence) models using TensorFlow and PyTorch are used to generate fashion suggestions. OpenGL is used for 3D display.

[0163] Data processing and calculation

[0164] 1. Entering and submitting user data

[0165] Users access the application using a smartphone or web browser and enter basic information such as their name, age, gender, body type, preferred style, daily activities, special events, etc. The entered information is sent to the server in JSON format.

[0166] 2. Receipt and storage of data

[0167] The server receives the user's basic information and stores this data in a database, which is then retained for future fashion suggestions.

[0168] 3. Data Analysis

[0169] The server analyzes the stored basic information using an AI model based on TensorFlow and PyTorch, taking in the user's age, gender, body type, preferred style, daily activities, and special event information as a dataset, and combining this with the latest fashion trend data to generate optimal fashion suggestions.

[0170] 4. Fashion Proposal Generation

[0171] Fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best suit the user's profile, and are then converted into an appropriate format for display to the user.

[0172] 5. Virtual try-on and viewing

[0173] Using the virtual try-on method, users can try on the proposed outfits on their avatar and check how they look in a 3D environment through a head-mounted display, allowing users to visually check the outfits in a virtual space without actually trying them on.

[0174] Specific examples

[0175] For example, if a 25-year-old female user enters information such as "Name: Hanako," "Age: 25," "Gender: Female," "Body Type: Slim," "Preferred Style: Casual," "Daily Activity: Office Work," and "Special Event: Friend's Wedding," the following fashion suggestions will be generated:

[0176] Here are some examples of prompts for a generative AI model:

[0177] User Information:

[0178] Name: Hanako,

[0179] Age: 25,

[0180] Gender: Female,

[0181] Body Type: Slim,

[0182] Favorite style: Casual,

[0183] Daily activities: office work,

[0184] Special Event: Friend's Wedding

[0185] Use this information to generate optimal outfit suggestions based on the latest fashion trends. The suggestions should include casual, office, and wedding styles.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] Users access the application using a smartphone or web browser and enter their basic information (such as name, age, gender, body type, preferred style, daily activities, special events, etc.) This input data is converted into JSON format, which is necessary for subsequent analysis and proposal generation.

[0189] Step 2:

[0190] The device sends the basic information entered by the user to the server. Specifically, it sends JSON format data to the server using an HTTP request. This information transmission is a prerequisite for performing the analysis process.

[0191] Step 3:

[0192] The server analyzes the received basic information in JSON format and stores it in a database. The database used is MySQL or PostgreSQL, and users' past data can also be referenced. The stored data is used to generate future proposals.

[0193] Step 4:

[0194] The server takes the basic information stored in the database and begins analyzing it with an AI model (using TensorFlow and PyTorch). Specifically, the AI ​​model receives input data such as the user's age, gender, body type, preferred style, daily activities, and special event information. This data is then combined with the latest fashion trend data to generate personalized fashion suggestions.

[0195] Step 5:

[0196] The resulting fashion suggestions are combinations of clothing and accessories that best fit the user's profile. These are then converted into an appropriate format (e.g., JSON or HTML) on the server side and sent to the user's device. This format conversion facilitates display in browsers and applications.

[0197] Step 6:

[0198] The device receives the fashion suggestions sent from the server and visually displays them to the user. A 3D model of an avatar wearing the suggested outfit is displayed on a head-mounted display or smartphone screen. This allows the user to try on the suggested outfit in a virtual space and check how it looks.

[0199] Step 7:

[0200] Users virtually try on clothes and provide feedback based on their satisfaction. This feedback information is also sent to the server and used to generate future recommendations. The AI ​​model retrains based on the feedback, enabling more accurate fashion recommendations.

[0201] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0202] The present invention provides fashion suggestions that take into account the emotional state of the user by combining an emotion engine with a system that inputs basic information about an individual and makes personalized fashion suggestions based on that information. Specific embodiments for carrying out the present invention are described below.

[0203] 1. Entering and submitting user data:

[0204] When a user accesses a website or application, an input form is displayed on the device. The user enters basic information such as their name, age, gender, body type, preferred style, daily activities, and special events into this form. As the user interacts with the input form, the device uses a camera and microphone to record the user's facial expressions and voice, collecting data for analysis by the emotion engine. Once the input is complete, the device sends this information in JSON format to the server.

[0205] 2. Receiving and storing data:

[0206] The server receives the user's basic information and emotional data sent from the device. The received data is stored in a database. Storing the data saves the user the trouble of having to enter the same information later, and also makes it possible to refer to past data to provide more accurate fashion suggestions.

[0207] 3. Analysis by emotion engine:

[0208] The server passes the user's emotional data to the emotion engine, which analyzes the user's facial expressions and voice to determine the user's current emotional state. This emotional state information is used to generate fashion suggestions.

[0209] 4. Data Analysis:

[0210] The server retrieves basic user information from the database and begins analysis by combining it with emotional state information from the emotion engine. This analysis is performed using an artificial intelligence model. The model incorporates the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state, and combines this with the latest fashion trend data to generate optimal fashion suggestions.

[0211] 5. Fashion proposal generation:

[0212] Fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best fit the user's profile and emotional state. The suggestions are then appropriately formatted and ready to be displayed to the user.

[0213] 6. Display of fashion suggestions:

[0214] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. The feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0215] Examples:

[0216] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." In addition to this information, the emotion engine analyzes the user's facial expressions and voice and identifies her current emotional state as "happy." The AI ​​model takes this emotional information into account and generates fashion suggestions such as the following: "A light-colored casual blouse and denim jeans to match a happy mood, and a navy blazer and skirt for office work days," and "An elegant dress and pearl accessories for a friend's wedding." This content is displayed on the user's device, and the user can choose an outfit based on the suggestions.

[0217] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style by taking into consideration the emotional state of the user in addition to basic information about the user, thereby increasing user satisfaction.

[0218] The processing flow will be explained below.

[0219] Step 1:

[0220] A user accesses a website or application. The device prompts the user with a login questionnaire, where the user enters basic information such as name, age, gender, body type, preferred style, daily activities, and special events.

[0221] Step 2:

[0222] The user clicks a button to submit the input form. The device converts the basic information entered into JSON format and sends it to the server. At this time, the device also records the user's facial expressions and voice using the camera and microphone.

[0223] Step 3:

[0224] The server receives the user data sent from the device. The received data includes basic information about the user, as well as recorded data of facial expressions and voice.

[0225] Step 4:

[0226] The server stores the received user basic information and emotion data in a database, allowing the same user's data to be reused in the future to improve analysis accuracy.

[0227] Step 5:

[0228] The server retrieves the user's basic information and emotional data from the database, and passes the retrieved data to the emotion engine, which analyzes the user's current emotional state.

[0229] Step 6:

[0230] The emotion engine analyzes the user's facial expressions and voice to identify the user's emotional state. Based on this information, the emotional state can be determined, for example, "the user is in a happy mood."

[0231] Step 7:

[0232] The server issues analytical instructions to the AI ​​model based on the user's basic information acquired by the server and the emotional state information obtained from the emotion engine. The AI ​​model performs analysis by combining the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state.

[0233] Step 8:

[0234] The AI ​​model analyzes the user's profile and generates fashion suggestions that best fit their emotional state, such as a light-colored casual blouse and denim jeans that complement a happy mood.

[0235] Step 9:

[0236] The server converts the fashion suggestions obtained from the AI ​​model into an appropriate format, which makes the suggestions easier for users to understand.

[0237] Step 10:

[0238] The server sends the formatted fashion suggestions to the terminal, which receives them and visually displays the fashion suggestions to the user.

[0239] Step 11:

[0240] The user reviews the displayed fashion suggestions. If the user provides feedback on the suggestions, the device again sends the data to the server. This feedback is used to improve future suggestions.

[0241] Through this series of processes, users can receive fashion suggestions based on their basic information and emotional state.

[0242] Example 2

[0243] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0244] Conventional fashion suggestion systems only consider basic user information when making suggestions, which means that the suggestions often do not match the user's emotional state. Furthermore, the convenience of websites and applications is low, making it difficult to improve the user experience.

[0245] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0246] In this invention, the server includes an input means for inputting basic information and emotional data of an individual, a storage means for storing the basic information and emotional data in a database, and an analysis means for analyzing the basic information and emotional data to generate personalized fashion suggestions, thereby enabling fashion suggestions that take into account the user's basic information and emotional state.

[0247] "Basic personal information" refers to basic information about a user, such as the user's name, age, gender, body type, preferred style, daily activities, and special events.

[0248] "Emotion data" refers to data that indicates the emotional state of the user, such as facial expressions and voice, and is information that is analyzed by the emotion engine.

[0249] "Input means" refers to an interface for users to input information, and refers to a device or software that operates via a website or application.

[0250] "Storage means" refers to a device or software that has the function of storing basic information and emotion data input by the user in a database.

[0251] "Analysis means" refers to a device or software that has the function of analyzing the basic information and emotional data stored by the storage means and generating personalized fashion suggestions.

[0252] A "generative AI model" refers to a model that uses artificial intelligence technology to analyze data and generate personalized suggestions and solutions.

[0253] The "display means" refers to a device or software for visually presenting the fashion suggestions generated by the analysis means to the user.

[0254] The present invention is a system that combines basic information and emotional state of a user to provide personalized fashion suggestions. Specific embodiments for carrying out the present invention are described below.

[0255] Entering and submitting user data

[0256] When a user accesses a website or application, an input form is displayed on the device. The user uses this form to enter basic information such as their name, age, gender, body type, preferred style, daily activities, and special events. In addition, the device uses a camera and microphone to record the user's facial expressions and voice, which are then collected as emotional data. The collected information is converted into JSON format and sent from the device to the server.

[0257] Receiving and storing data

[0258] The server receives the user's basic information and emotional data sent from the device. The received data is stored in a database. This storage method eliminates the need for the user to enter the same information twice, and more accurate fashion suggestions can be made by referencing past data.

[0259] Analysis by emotion engine

[0260] The server passes the user's emotional data to the emotion engine, which analyzes the user's facial expressions and voice to identify their current emotional state, which is then used as key information for generating fashion suggestions.

[0261] Data analysis

[0262] The server retrieves basic user information from the database and combines it with emotional state information from the emotion engine for analysis. This analysis is performed using a generative AI model. The generative AI model inputs the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state, and combines this with the latest fashion trend data to generate optimal fashion suggestions.

[0263] Fashion proposal generation

[0264] The fashion suggestions generated by the generative AI model show the combination of clothing and accessories that best suit the user's profile and emotional state, which are then appropriately formatted and ready to be displayed to the user.

[0265] Fashion suggestion display

[0266] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. This feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0267] Specific examples

[0268] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." In addition to this information, the emotion engine analyzes the user's facial expressions and voice and identifies their current emotional state as "happy." The generative AI model takes this emotional information into account and generates fashion suggestions such as: "A light-colored casual blouse and denim jeans to match a happy mood, and a navy blazer and skirt for office work days," and "An elegant dress and pearl accessories for a friend's wedding." This content is displayed on the user's device, allowing the user to choose an outfit based on the suggestions.

[0269] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style by taking into consideration the basic information and emotional state of the user, thereby increasing user satisfaction.

[0270] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0271] Step 1: Enter user data

[0272] When a user accesses a website or application, the terminal displays an input form.

[0273] Input: User basic information (e.g., name, age, gender, body type, preferred style, daily activities, special events)

[0274] How it works: The user enters basic information into the device's input form. The device detects this input in real time and performs input validation. The device then uses the camera and microphone to record the user's facial expressions and voice.

[0275] Step 2: Collect and send data

[0276] Once the input form is completed, the device converts the entered basic information and collected emotional data into JSON format and sends it to the server.

[0277] Input: Basic information and emotion data

[0278] Data processing: Convert the input information into JSON format

[0279] Specific operation: When the user presses the button to confirm the input, the device parses the basic information and recorded emotion data into JSON format and sends it to the server via a security layer. The data is then encrypted before being sent.

[0280] Step 3: Receiving and storing data

[0281] The server receives basic information and emotion data in JSON format sent from the device and stores them in a database.

[0282] Input: Basic information and emotion data in JSON format

[0283] Data processing: Parse the received data and save it in a database

[0284] Specific operation: The server checks the format of the received data and, if there are no errors, saves it to the database. When saving, each field is properly mapped and associated with the user ID.

[0285] Step 4: Analysis by the Emotion Engine

[0286] The server passes the emotion data to the emotion engine, which analyzes the user's facial expressions and voice.

[0287] Input: Emotion data

[0288] Data calculation: Identifying emotional states (e.g., happy, sad)

[0289] Specific operation: The emotion engine converts the received facial and voice data into multidimensional vectors and performs comparative analysis with existing emotion models. As a result, the emotional state is identified as "happy," for example.

[0290] Step 5: Analyze the data

[0291] The server retrieves basic information about the user from the database, combines it with emotional state information from the emotion engine, and inputs it into the generative AI model.

[0292] Input: Basic information and emotional state information

[0293] Data processing: Integrate basic information and emotional state and input it into a generative AI model

[0294] How it works: The generative AI model takes in the latest fashion trend data, as well as the basic information and emotional state provided, and generates the most suitable fashion suggestions for the user based on this.

[0295] Step 6: Generate fashion suggestions

[0296] The generative AI model generates optimal fashion suggestions based on the user's basic information and emotional state.

[0297] Input: Integrated dataset

[0298] Output: Fashion suggestions (e.g., a light-colored casual blouse with denim jeans, or an elegant dress with pearl accessories)

[0299] How it works: The generative AI model makes inferences based on the input data and generates the most suitable fashion style for the user. The generated suggestions are then properly formatted and ready to be sent to the device.

[0300] Step 7: Displaying fashion suggestions

[0301] The server transmits the generated fashion suggestions to the terminal, which receives them and displays them to the user.

[0302] Input: Fashion suggestions

[0303] Output: Fashion suggestions displayed to the user

[0304] How it works: The server sends fashion suggestions in JSON format to the device. The device receives them and displays them for the user to visually confirm. The user can then choose an outfit based on the suggestions and provide feedback. The feedback is then sent back to the server and used to improve the system's accuracy.

[0305] In this way, the system can provide personalized fashion suggestions that take into account the user's basic information and emotional state, thereby increasing user satisfaction.

[0306] (Application example 2)

[0307] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] While modern ad delivery systems commonly target ads based on users' personal data, personalized ad delivery that takes into account users' emotional state is not widely used. This makes it difficult to deliver appropriate ads based on users' real-time emotional state, resulting in limited advertising effectiveness. Furthermore, conventional systems are unable to fully incorporate user feedback, making it difficult to improve advertising accuracy. To address these issues, there is a need for the development of a personalized ad delivery system that takes into account both users' personal information and emotional data.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0310] In this invention, the server includes an input means for inputting basic information and emotional data of an individual, a storage means for storing the basic information and emotional data in a database, and an analysis means for analyzing the basic information and emotional data to generate personalized suggestions, thereby enabling highly accurate personalized advertisement delivery according to the user's real-time emotional state.

[0311] "Basic personal information" refers to individual identification information such as the user's name, age, sex, body type, preferred style, daily activities, and special events.

[0312] "Emotion data" is information about the current emotional state obtained by analyzing the user's facial expressions and voice.

[0313] "Input means" refers to an interface such as a website or application through which a user inputs basic personal information and emotional data.

[0314] The "storage means" is a system for storing the input basic information and emotion data in a database.

[0315] "Analysis Measure" means a system that uses generative AI models or other analytical methods to generate personalized recommendations using input background information and sentiment data.

[0316] "Display means" refers to a display or device for visually presenting the personalized suggestions generated by the analysis means to the user.

[0317] A "generative AI model" is an artificial intelligence model that analyzes a user's basic information and emotional data to generate optimal advertisements and fashion suggestions.

[0318] The present invention is a system for delivering personalized advertisements to users based on basic information and emotion data of individuals. Specific embodiments for carrying out the present invention will be described below.

[0319] 1. Entering user data

[0320] The server provides an interface through a website or application that allows users to input basic personal information (such as name, age, gender, body type, preferred style, daily activities, and special events). The device also uses a camera and microphone to record the user's facial expressions and voice, and obtains emotional data. This information is collected in real time.

[0321] 2. Data storage

[0322] The server receives the basic information and emotion data entered in JSON format and stores it in a database, which eliminates the need for re-entry the next time the user accesses the system, and enables more accurate analysis based on past data.

[0323] 3. Use of sentiment analysis engines

[0324] The server passes the collected emotional data to an emotion analysis engine to analyze the user's current emotional state, using software such as OpenCV and Google Cloud Speech-to-Text API.

[0325] 4. Data Analysis

[0326] The server uses a generative AI model to analyze the user's basic information and emotional state, and generates optimized ad suggestions for the user. This analysis is performed using an artificial intelligence framework such as TensorFlow.

[0327] 5. Generating Ad Proposals

[0328] The ad suggestions generated by the generative AI model are best suited to the user's profile and emotional state, and the server then formats these suggestions appropriately and prepares them for display to the user.

[0329] 6. Display of advertising suggestions

[0330] The server sends the analysis results to the device, which visually displays the ads to the user. The user can review the suggested ads and provide feedback, which is sent back to the server and used to improve future suggestions.

[0331] Specific examples

[0332] For example, if a 25-year-old female user enters basic information and the emotional data identifies her current emotional state as "happy," the ad suggestions generated by the server will be in the following format:

[0333] Example prompt sentence:

[0334] "Hi! You're attending a friend's wedding today, right? How about this new dress that reflects your fun spirit? Click here for more details."

[0335] This system enables more effective and personalized advertising based on the user's real-time emotional state.

[0336] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0337] Step 1: Enter user data

[0338] When a user accesses a website or application, they enter basic personal information (such as name, age, gender, body type, preferred style, daily activities, and special events) into a form. The device's camera and microphone also record the user's facial expressions and voice in real time, collecting emotional data. This information is sent to the server in JSON format.

[0339] Input: User basic information and emotional data

[0340] Output: User data in JSON format

[0341] Step 2: Save your data

[0342] The server analyzes the user data received in JSON format and saves it in a database. This saving process saves the user the trouble of having to enter the same information twice, and allows for more accurate analysis by referencing past data.

[0343] Input: User data in JSON format

[0344] Output: User data stored in the database

[0345] Step 3: Sentiment Analysis

[0346] The server passes the stored emotion data to an emotion analysis engine, which uses OpenCV and the Google Cloud Speech-to-Text API to analyze the user's facial expressions and voice to determine their current emotional state.

[0347] Input: Emotion data

[0348] Output: User's emotional state

[0349] Step 4: Analyze the data

[0350] The server retrieves the user's basic information and emotional state from the database and analyzes it using a generative AI model, which combines the user's personal data and emotional state to generate optimal advertising suggestions.

[0351] Input: User's basic information and emotional state

[0352] Output: Generated ad suggestions

[0353] Step 5: Generate advertising proposals

[0354] The ad suggestions generated by the generative AI model are best suited to the user's profile and emotional state, and the server then formats these suggestions and prepares them for transmission to the device.

[0355] Input: Generated ad proposals

[0356] Output: Formatted ad proposal

[0357] Step 6: View Ad Proposals

[0358] The server sends the analysis results to the device, which visually displays the ads to the user. The user can review the suggested ads and provide feedback, which is sent back to the server and used to improve future suggestions.

[0359] Input: Formatted ad proposal

[0360] Output: Display advertisement and feedback to the user

[0361] This system enables more effective and personalized advertising based on the user's real-time emotional state.

[0362] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0363] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0364] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0365] [Second embodiment]

[0366] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0367] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0368] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0369] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0370] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0371] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0372] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0373] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0374] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0375] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0376] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0377] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0378] The present invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. Specific embodiments for carrying out the present invention will be described below.

[0379] 1. Entering and submitting user data:

[0380] When a user accesses a website or application, an input form appears on the device, where the user enters basic information such as their name, age, gender, body type, preferred style, daily activities, special events, etc. Once completed, the device sends this information in JSON format to the server.

[0381] 2. Receiving and storing data:

[0382] The server receives the user's basic information sent from the device. This received data is saved in a database. Storing the data saves the user the trouble of having to enter the same information later, and the server can refer to past data to make more accurate fashion suggestions.

[0383] 3. Data Analysis:

[0384] The server then begins analyzing the user's basic information, using an artificial intelligence model to analyze the user's age, gender, body type, preferred style, daily activities, and special events. The model then combines this with the latest fashion trend data to generate optimal fashion recommendations.

[0385] 4. Fashion proposal generation:

[0386] The fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best suit the user's profile, making it easy for users to style their outfits for individual occasions. The generated fashion suggestions are then converted into an appropriate format for display to the user.

[0387] 5. Display of fashion suggestions:

[0388] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. The feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0389] Examples:

[0390] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." Based on this information, the AI ​​model generates fashion suggestions such as: "For casual style, a white blouse and denim jeans, and for office work days, a navy blazer and skirt," and "For a friend's wedding, an elegant dress and pearl accessories." This content is displayed on the user's device, and the user can choose their fashion based on the suggestions.

[0391] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style based on the user's basic information, thereby increasing the user's satisfaction.

[0392] The processing flow will be explained below.

[0393] Step 1:

[0394] A user accesses a website or application. The device prompts the user with a login questionnaire, where the user enters basic information such as name, age, gender, body type, preferred style, daily activities, and special events.

[0395] Step 2:

[0396] The user clicks the button to submit the input form. The device converts the entered basic information into JSON format and sends it to the server.

[0397] Step 3:

[0398] The server receives the user data sent from the device, stores the received data in a database, and verifies that the data is properly recorded during this process.

[0399] Step 4:

[0400] The server retrieves basic user information from the database, prepares the retrieved information for analysis, and passes the data to the AI ​​model.

[0401] Step 5:

[0402] The AI ​​model analyzes the user's age, gender, body type, preferred style, daily activities, and special event information, and then compares it with the latest fashion trend data to generate optimal fashion suggestions.

[0403] Step 6:

[0404] The server formats the fashion suggestions obtained from the AI ​​model, making them easy for users to understand.

[0405] Step 7:

[0406] The server sends the formatted fashion suggestions to the terminal, and the sent data is immediately displayed on the user's terminal.

[0407] Step 8:

[0408] The user reviews the displayed fashion suggestions. If the user provides feedback on the suggestions, the data is sent back to the server. This feedback is used to improve the accuracy of future suggestions.

[0409] Example 1

[0410] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0411] Conventional fashion suggestion systems lacked efficient methods for collecting basic user information and generating and displaying personalized suggestions. In particular, they lacked a method for effectively utilizing user feedback to improve the accuracy of suggestions. As a result, there were issues with low user satisfaction and declining system utilization.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0413] In this invention, the server includes means for inputting basic information of an individual, means for converting the basic information into JSON format and transmitting the converted information to the server, means for saving the basic information in a database, means for analyzing the basic information and using an artificial intelligence model to generate personalized fashion suggestions, means for formatting the fashion suggestions generated by the analysis means, means for displaying the formatted fashion suggestions, and means for analyzing feedback provided by the user via the display means to improve suggestion accuracy. This allows for the generation of highly accurate personalized suggestions based on the user's basic information, and further enables the accuracy of the suggestions to be continuously improved based on the feedback.

[0414] "Input means" refers to a device or software interface that allows a user to input basic personal information.

[0415] "Transmission means" refers to a device or software function that converts input data into JSON format and transmits it to the server.

[0416] "Storage means" refers to a device or software function for permanently recording data sent to the server in a database.

[0417] "Analysis Means" means a device or software function that utilizes artificial intelligence models to generate personalized fashion suggestions based on stored data.

[0418] An "artificial intelligence model" is a mathematical model that uses machine learning or deep learning algorithms to perform specific tasks.

[0419] "Formatting means" refers to the functionality of a device or software for converting the generated fashion suggestions into the format required for display to the user.

[0420] "Display means" refers to a device or software interface for visually presenting fashion suggestions to a user.

[0421] "Feedback means" refers to a device or software function that collects and analyzes user-provided evaluations and opinions to improve the accuracy of the system's suggestions.

[0422] The present invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. This system comprises an input means, a transmission means, a storage means, an analysis means, a formatting means, a display means, and a feedback means.

[0423] When a user accesses a website or application, a form for entering basic personal information is displayed on the device, where the user enters information such as name, age, gender, body type, preferred style, daily activities, special events, etc. Once the information is complete, the device uses JavaScript to convert the information into JSON format and sends it to the server via an HTTP POST request.

[0424] The server stores the received data in a database (e.g., MySQL or MongoDB). Based on the stored data, the server performs analysis using an artificial intelligence model (e.g., TensorFlow or PyTorch). This analysis combines the user's basic information with the latest fashion trend data to generate optimal fashion suggestions.

[0425] The generated fashion suggestions are returned in JSON format to the server, which formats this data into an appropriate format (e.g., HTML or JSON) for display to the user. The formatted data is then sent back to the device as an HTTP response, which receives it and visually displays it to the user.

[0426] The user reviews the displayed fashion suggestions and enters feedback if necessary. The device then converts this feedback back into JSON format and sends it to the server, where it analyzes the feedback and uses it to improve the accuracy of the suggestions.

[0427] As an example, a 25-year-old female user enters the following information:

[0428] Name: Hanako

[0429] Age: 25

[0430] Gender: Female

[0431] Build: Slim

[0432] Favorite style: Casual

[0433] Daily Activities: Office work

[0434] Special Event: Friend's Wedding

[0435] Based on this, the AI ​​model generates fashion suggestions like this:

[0436] "For casual style, I wear a white blouse and denim jeans. For office work, I wear a navy blazer and skirt. For a friend's wedding, I wear an elegant dress and pearl accessories."

[0437] An example of a prompt for a generative AI model might look like this:

[0438] "A 25-year-old woman with a slim figure, who likes casual styles, works in an office, and is attending a friend's wedding. What are some fashion suggestions that would be best for her?"

[0439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0440] Step 1:

[0441] A user visits a website or application.

[0442] Input: User access request

[0443] What it does: Displays a web form on the device asking users to enter basic personal information, including name, age, gender, body type, style preferences, daily activities, and special events.

[0444] Output: Display of form

[0445] Step 2:

[0446] The user enters basic information.

[0447] Input: Information such as name, age, gender, body type, preferred style, daily activities, special events, etc.

[0448] What it does: The terminal captures the user's input and temporarily stores it.

[0449] Output: Basic information is saved in the input form

[0450] Step 3:

[0451] Convert basic information into JSON format and send it to the server.

[0452] Input: Basic information entered by the user

[0453] How it works: The device uses JavaScript to convert the input information into JSON format, which is then sent to the server via an HTTP POST request.

[0454] Output: User data in JSON format is sent to the server

[0455] Step 4:

[0456] The server receives the JSON data and stores it in the database.

[0457] Input: User data in JSON format

[0458] How it works: The server extracts the user's JSON data from the incoming HTTP POST request and stores it in a database (e.g. MySQL or MongoDB).

[0459] Output: User data saved in database

[0460] Step 5:

[0461] The server analyzes the data and generates fashion suggestions.

[0462] Input: User basic information stored in the database

[0463] How it works: The server queries the database to retrieve stored data. The retrieved data is passed to an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate fashion suggestions. The model analyzes the user's basic information and the latest fashion trend data, and generates optimal fashion suggestions in JSON format.

[0464] Output: Fashion suggestion data in JSON format

[0465] Step 6:

[0466] The server formats the generated fashion suggestions.

[0467] Input: JSON format fashion proposal data

[0468] Behavior: The server formats the received JSON data into an appropriate format (e.g., HTML template, JSON structure modification).

[0469] Output: Formatted fashion suggestion data

[0470] Step 7:

[0471] The server sends the formatted data to the terminal.

[0472] Input: Formatted fashion suggestion data

[0473] How it works: The server sends data formatted as an HTTP response to the device.

[0474] Output: Fashion suggestion data is sent to the device

[0475] Step 8:

[0476] The terminal visually displays the fashion suggestions to the user.

[0477] Input: Fashion suggestion data sent from the server

[0478] What it does: The device parses the received data and displays it visually in a browser or application (e.g., dynamically embedding it in HTML using JavaScript).

[0479] Output: User is presented with fashion suggestions

[0480] Step 9:

[0481] The user provides feedback on the proposal.

[0482] Input: User feedback information (e.g., new requests and corrections)

[0483] How it works: The device converts the feedback information into JSON format and sends it back to the server.

[0484] Output: Feedback data in JSON format is sent to the server

[0485] Step 10:

[0486] The server analyzes the feedback and incorporates it into future suggestions.

[0487] Input: Feedback data in JSON format

[0488] How it works: The server analyzes the received feedback and stores it in a database. The analysis results are used as training data for the AI ​​model to improve the accuracy of future suggestions.

[0489] Output: AI model with improved proposal accuracy

[0490] (Application example 1)

[0491] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0492] In virtual stores, the process of users finding the best fashion style for themselves and trying it on is very time-consuming. Furthermore, when shopping online, it is difficult to actually try on clothes, and the clothes purchased may not fit. To solve this problem, a system that provides personalized fashion suggestions and enables virtual try-on is needed.

[0493] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0494] In this invention, the server includes an input means for inputting basic information of an individual, a storage means for storing the basic information in a database, an analysis means for analyzing the basic information and generating personalized fashion suggestions, a virtual try-on means for trying on the suggested outfits based on the user's avatar in a virtual space, and a display means for displaying the fashion suggestions generated by the analysis means and confirming them in a 3D environment, thereby enabling users to easily find the fashion style that best suits them and try them on in the virtual space.

[0495] "Basic personal information" refers to information such as the user's name, age, gender, body type, preferred style, daily activities, and special events.

[0496] "Input means" refers to a device or interface that allows a user to input basic personal information.

[0497] "Storage means" refers to a system or device for storing basic information about users in a database.

[0498] "Analysis means" refers to the processes and techniques used to generate personalized fashion suggestions based on the stored underlying information.

[0499] "Artificial intelligence model" refers to advanced algorithms such as machine learning and deep learning that are used to generate fashion suggestions.

[0500] "Virtual try-on means" refers to technology or devices that allow a user's avatar to try on suggested outfits in a virtual space.

[0501] "Display means" refers to a device or interface that allows the user to visually confirm the generated fashion suggestions.

[0502] This invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. This system generates optimal fashion suggestions based on the information entered by the user and allows the user to try on clothes in a virtual space.

[0503] The system is implemented in the following configuration.

[0504] Hardware and software used

[0505] Hardware: A smartphone or computer is used to input basic user information, and a head-mounted display (e.g., Oculus Rift) is used to display fashion suggestions.

[0506] Software: Web frameworks such as Flask and Django are used for server-side processing, and MySQL and PostgreSQL are used for databases. AI (artificial intelligence) models using TensorFlow and PyTorch are used to generate fashion suggestions. OpenGL is used for 3D display.

[0507] Data processing and calculation

[0508] 1. Entering and submitting user data

[0509] Users access the application using a smartphone or web browser and enter basic information such as their name, age, gender, body type, preferred style, daily activities, special events, etc. The entered information is sent to the server in JSON format.

[0510] 2. Receipt and storage of data

[0511] The server receives the user's basic information and stores this data in a database, which is then retained for future fashion suggestions.

[0512] 3. Data Analysis

[0513] The server analyzes the stored basic information using an AI model based on TensorFlow and PyTorch, taking in the user's age, gender, body type, preferred style, daily activities, and special event information as a dataset, and combining this with the latest fashion trend data to generate optimal fashion suggestions.

[0514] 4. Fashion Proposal Generation

[0515] Fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best suit the user's profile, and are then converted into an appropriate format for display to the user.

[0516] 5. Virtual try-on and viewing

[0517] Using the virtual try-on method, users can try on the proposed outfits on their avatar and check how they look in a 3D environment through a head-mounted display, allowing users to visually check the outfits in a virtual space without actually trying them on.

[0518] Specific examples

[0519] For example, if a 25-year-old female user enters information such as "Name: Hanako," "Age: 25," "Gender: Female," "Body Type: Slim," "Preferred Style: Casual," "Daily Activity: Office Work," and "Special Event: Friend's Wedding," the following fashion suggestions will be generated:

[0520] Here are some examples of prompts for a generative AI model:

[0521] User Information:

[0522] Name: Hanako,

[0523] Age: 25,

[0524] Gender: Female,

[0525] Body Type: Slim,

[0526] Favorite style: Casual,

[0527] Daily activities: office work,

[0528] Special Event: Friend's Wedding

[0529] Use this information to generate optimal outfit suggestions based on the latest fashion trends. The suggestions should include casual, office, and wedding styles.

[0530] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0531] Step 1:

[0532] Users access the application using a smartphone or web browser and enter their basic information (such as name, age, gender, body type, preferred style, daily activities, special events, etc.) This input data is converted into JSON format, which is necessary for subsequent analysis and proposal generation.

[0533] Step 2:

[0534] The device sends the basic information entered by the user to the server. Specifically, it sends JSON format data to the server using an HTTP request. This information transmission is a prerequisite for performing the analysis process.

[0535] Step 3:

[0536] The server analyzes the received basic information in JSON format and stores it in a database. The database used is MySQL or PostgreSQL, and users' past data can also be referenced. The stored data is used to generate future proposals.

[0537] Step 4:

[0538] The server takes the basic information stored in the database and begins analyzing it with an AI model (using TensorFlow and PyTorch). Specifically, the AI ​​model receives input data such as the user's age, gender, body type, preferred style, daily activities, and special event information. This data is then combined with the latest fashion trend data to generate personalized fashion suggestions.

[0539] Step 5:

[0540] The resulting fashion suggestions are combinations of clothing and accessories that best fit the user's profile. These are then converted into an appropriate format (e.g., JSON or HTML) on the server side and sent to the user's device. This format conversion facilitates display in browsers and applications.

[0541] Step 6:

[0542] The device receives the fashion suggestions sent from the server and visually displays them to the user. A 3D model of an avatar wearing the suggested outfit is displayed on a head-mounted display or smartphone screen. This allows the user to try on the suggested outfit in a virtual space and check how it looks.

[0543] Step 7:

[0544] Users virtually try on clothes and provide feedback based on their satisfaction. This feedback information is also sent to the server and used to generate future recommendations. The AI ​​model retrains based on the feedback, enabling more accurate fashion recommendations.

[0545] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0546] The present invention provides fashion suggestions that take into account the emotional state of the user by combining an emotion engine with a system that inputs basic information about an individual and makes personalized fashion suggestions based on that information. Specific embodiments for carrying out the present invention are described below.

[0547] 1. Entering and submitting user data:

[0548] When a user accesses a website or application, an input form is displayed on the device. The user enters basic information such as their name, age, gender, body type, preferred style, daily activities, and special events into this form. As the user interacts with the input form, the device uses a camera and microphone to record the user's facial expressions and voice, collecting data for analysis by the emotion engine. Once the input is complete, the device sends this information in JSON format to the server.

[0549] 2. Receiving and storing data:

[0550] The server receives the user's basic information and emotional data sent from the device. The received data is stored in a database. Storing the data saves the user the trouble of having to enter the same information later, and also makes it possible to refer to past data to provide more accurate fashion suggestions.

[0551] 3. Analysis by emotion engine:

[0552] The server passes the user's emotional data to the emotion engine, which analyzes the user's facial expressions and voice to determine the user's current emotional state. This emotional state information is used to generate fashion suggestions.

[0553] 4. Data Analysis:

[0554] The server retrieves basic user information from the database and begins analysis by combining it with emotional state information from the emotion engine. This analysis is performed using an artificial intelligence model. The model incorporates the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state, and combines this with the latest fashion trend data to generate optimal fashion suggestions.

[0555] 5. Fashion proposal generation:

[0556] Fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best fit the user's profile and emotional state. The suggestions are then appropriately formatted and ready to be displayed to the user.

[0557] 6. Display of fashion suggestions:

[0558] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. The feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0559] Examples:

[0560] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." In addition to this information, the emotion engine analyzes the user's facial expressions and voice and identifies her current emotional state as "happy." The AI ​​model takes this emotional information into account and generates fashion suggestions such as the following: "A light-colored casual blouse and denim jeans to match a happy mood, and a navy blazer and skirt for office work days," and "An elegant dress and pearl accessories for a friend's wedding." This content is displayed on the user's device, and the user can choose an outfit based on the suggestions.

[0561] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style by taking into consideration the emotional state of the user in addition to basic information about the user, thereby increasing user satisfaction.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] A user accesses a website or application. The device prompts the user with a login questionnaire, where the user enters basic information such as name, age, gender, body type, preferred style, daily activities, and special events.

[0565] Step 2:

[0566] The user clicks a button to submit the input form. The device converts the basic information entered into JSON format and sends it to the server. At this time, the device also records the user's facial expressions and voice using the camera and microphone.

[0567] Step 3:

[0568] The server receives the user data sent from the device. The received data includes basic information about the user, as well as recorded data of facial expressions and voice.

[0569] Step 4:

[0570] The server stores the received user basic information and emotion data in a database, allowing the same user's data to be reused in the future to improve analysis accuracy.

[0571] Step 5:

[0572] The server retrieves the user's basic information and emotional data from the database, and passes the retrieved data to the emotion engine, which analyzes the user's current emotional state.

[0573] Step 6:

[0574] The emotion engine analyzes the user's facial expressions and voice to identify the user's emotional state. Based on this information, the emotional state can be determined, for example, "the user is in a happy mood."

[0575] Step 7:

[0576] The server issues analytical instructions to the AI ​​model based on the user's basic information acquired by the server and the emotional state information obtained from the emotion engine. The AI ​​model performs analysis by combining the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state.

[0577] Step 8:

[0578] The AI ​​model analyzes the user's profile and generates fashion suggestions that best fit their emotional state, such as a light-colored casual blouse and denim jeans that complement a happy mood.

[0579] Step 9:

[0580] The server converts the fashion suggestions obtained from the AI ​​model into an appropriate format, which makes the suggestions easier for users to understand.

[0581] Step 10:

[0582] The server sends the formatted fashion suggestions to the terminal, which receives them and visually displays the fashion suggestions to the user.

[0583] Step 11:

[0584] The user reviews the displayed fashion suggestions. If the user provides feedback on the suggestions, the device again sends the data to the server. This feedback is used to improve future suggestions.

[0585] Through this series of processes, users can receive fashion suggestions based on their basic information and emotional state.

[0586] Example 2

[0587] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0588] Conventional fashion suggestion systems only consider basic user information when making suggestions, which means that the suggestions often do not match the user's emotional state. Furthermore, the convenience of websites and applications is low, making it difficult to improve the user experience.

[0589] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0590] In this invention, the server includes an input means for inputting basic information and emotional data of an individual, a storage means for storing the basic information and emotional data in a database, and an analysis means for analyzing the basic information and emotional data to generate personalized fashion suggestions, thereby enabling fashion suggestions that take into account the user's basic information and emotional state.

[0591] "Basic personal information" refers to basic information about a user, such as the user's name, age, gender, body type, preferred style, daily activities, and special events.

[0592] "Emotion data" refers to data that indicates the emotional state of the user, such as facial expressions and voice, and is information that is analyzed by the emotion engine.

[0593] "Input means" refers to an interface for users to input information, and refers to a device or software that operates via a website or application.

[0594] "Storage means" refers to a device or software that has the function of storing basic information and emotion data input by the user in a database.

[0595] "Analysis means" refers to a device or software that has the function of analyzing the basic information and emotional data stored by the storage means and generating personalized fashion suggestions.

[0596] A "generative AI model" refers to a model that uses artificial intelligence technology to analyze data and generate personalized suggestions and solutions.

[0597] The "display means" refers to a device or software for visually presenting the fashion suggestions generated by the analysis means to the user.

[0598] The present invention is a system that combines basic information and emotional state of a user to provide personalized fashion suggestions. Specific embodiments for carrying out the present invention are described below.

[0599] Entering and submitting user data

[0600] When a user accesses a website or application, an input form is displayed on the device. The user uses this form to enter basic information such as their name, age, gender, body type, preferred style, daily activities, and special events. In addition, the device uses a camera and microphone to record the user's facial expressions and voice, which are then collected as emotional data. The collected information is converted into JSON format and sent from the device to the server.

[0601] Receiving and storing data

[0602] The server receives the user's basic information and emotional data sent from the device. The received data is stored in a database. This storage method eliminates the need for the user to enter the same information twice, and more accurate fashion suggestions can be made by referencing past data.

[0603] Analysis by emotion engine

[0604] The server passes the user's emotional data to the emotion engine, which analyzes the user's facial expressions and voice to identify their current emotional state, which is then used as key information for generating fashion suggestions.

[0605] Data analysis

[0606] The server retrieves basic user information from the database and combines it with emotional state information from the emotion engine for analysis. This analysis is performed using a generative AI model. The generative AI model inputs the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state, and combines this with the latest fashion trend data to generate optimal fashion suggestions.

[0607] Fashion proposal generation

[0608] The fashion suggestions generated by the generative AI model show the combination of clothing and accessories that best suit the user's profile and emotional state, which are then appropriately formatted and ready to be displayed to the user.

[0609] Fashion suggestion display

[0610] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. This feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0611] Specific examples

[0612] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." In addition to this information, the emotion engine analyzes the user's facial expressions and voice and identifies their current emotional state as "happy." The generative AI model takes this emotional information into account and generates fashion suggestions such as: "A light-colored casual blouse and denim jeans to match a happy mood, and a navy blazer and skirt for office work days," and "An elegant dress and pearl accessories for a friend's wedding." This content is displayed on the user's device, allowing the user to choose an outfit based on the suggestions.

[0613] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style by taking into consideration the basic information and emotional state of the user, thereby increasing user satisfaction.

[0614] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0615] Step 1: Enter user data

[0616] When a user accesses a website or application, the terminal displays an input form.

[0617] Input: User basic information (e.g., name, age, gender, body type, preferred style, daily activities, special events)

[0618] How it works: The user enters basic information into the device's input form. The device detects this input in real time and performs input validation. The device then uses the camera and microphone to record the user's facial expressions and voice.

[0619] Step 2: Collect and send data

[0620] Once the input form is completed, the device converts the entered basic information and collected emotional data into JSON format and sends it to the server.

[0621] Input: Basic information and emotion data

[0622] Data processing: Convert the input information into JSON format

[0623] Specific operation: When the user presses the button to confirm the input, the device parses the basic information and recorded emotion data into JSON format and sends it to the server via a security layer. The data is then encrypted before being sent.

[0624] Step 3: Receiving and storing data

[0625] The server receives basic information and emotion data in JSON format sent from the device and stores them in a database.

[0626] Input: Basic information and emotion data in JSON format

[0627] Data processing: Parse the received data and save it in a database

[0628] Specific operation: The server checks the format of the received data and, if there are no errors, saves it to the database. When saving, each field is properly mapped and associated with the user ID.

[0629] Step 4: Analysis by the Emotion Engine

[0630] The server passes the emotion data to the emotion engine, which analyzes the user's facial expressions and voice.

[0631] Input: Emotion data

[0632] Data calculation: Identifying emotional states (e.g., happy, sad)

[0633] Specific operation: The emotion engine converts the received facial and voice data into multidimensional vectors and performs comparative analysis with existing emotion models. As a result, the emotional state is identified as "happy," for example.

[0634] Step 5: Analyze the data

[0635] The server retrieves basic information about the user from the database, combines it with emotional state information from the emotion engine, and inputs it into the generative AI model.

[0636] Input: Basic information and emotional state information

[0637] Data processing: Integrate basic information and emotional state and input it into a generative AI model

[0638] How it works: The generative AI model takes in the latest fashion trend data, as well as the basic information and emotional state provided, and generates the most suitable fashion suggestions for the user based on this.

[0639] Step 6: Generate fashion suggestions

[0640] The generative AI model generates optimal fashion suggestions based on the user's basic information and emotional state.

[0641] Input: Integrated dataset

[0642] Output: Fashion suggestions (e.g., a light-colored casual blouse with denim jeans, or an elegant dress with pearl accessories)

[0643] How it works: The generative AI model makes inferences based on the input data and generates the most suitable fashion style for the user. The generated suggestions are then properly formatted and ready to be sent to the device.

[0644] Step 7: Displaying fashion suggestions

[0645] The server transmits the generated fashion suggestions to the terminal, which receives them and displays them to the user.

[0646] Input: Fashion suggestions

[0647] Output: Fashion suggestions displayed to the user

[0648] How it works: The server sends fashion suggestions in JSON format to the device. The device receives them and displays them for the user to visually confirm. The user can then choose an outfit based on the suggestions and provide feedback. The feedback is then sent back to the server and used to improve the system's accuracy.

[0649] In this way, the system can provide personalized fashion suggestions that take into account the user's basic information and emotional state, thereby increasing user satisfaction.

[0650] (Application example 2)

[0651] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0652] While modern ad delivery systems commonly target ads based on users' personal data, personalized ad delivery that takes into account users' emotional state is not widely used. This makes it difficult to deliver appropriate ads based on users' real-time emotional state, resulting in limited advertising effectiveness. Furthermore, conventional systems are unable to fully incorporate user feedback, making it difficult to improve advertising accuracy. To address these issues, there is a need for the development of a personalized ad delivery system that takes into account both users' personal information and emotional data.

[0653] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0654] In this invention, the server includes an input means for inputting basic information and emotional data of an individual, a storage means for storing the basic information and emotional data in a database, and an analysis means for analyzing the basic information and emotional data to generate personalized suggestions, thereby enabling highly accurate personalized advertisement delivery according to the user's real-time emotional state.

[0655] "Basic personal information" refers to individual identification information such as the user's name, age, sex, body type, preferred style, daily activities, and special events.

[0656] "Emotion data" is information about the current emotional state obtained by analyzing the user's facial expressions and voice.

[0657] "Input means" refers to an interface such as a website or application through which a user inputs basic personal information and emotional data.

[0658] The "storage means" is a system for storing the input basic information and emotion data in a database.

[0659] "Analysis Measure" means a system that uses generative AI models or other analytical methods to generate personalized recommendations using input background information and sentiment data.

[0660] "Display means" refers to a display or device for visually presenting the personalized suggestions generated by the analysis means to the user.

[0661] A "generative AI model" is an artificial intelligence model that analyzes a user's basic information and emotional data to generate optimal advertisements and fashion suggestions.

[0662] The present invention is a system for delivering personalized advertisements to users based on basic information and emotion data of individuals. Specific embodiments for carrying out the present invention will be described below.

[0663] 1. Entering user data

[0664] The server provides an interface through a website or application that allows users to input basic personal information (such as name, age, gender, body type, preferred style, daily activities, and special events). The device also uses a camera and microphone to record the user's facial expressions and voice, and obtains emotional data. This information is collected in real time.

[0665] 2. Data storage

[0666] The server receives the basic information and emotion data entered in JSON format and stores it in a database, which eliminates the need for re-entry the next time the user accesses the system, and enables more accurate analysis based on past data.

[0667] 3. Use of sentiment analysis engines

[0668] The server passes the collected emotional data to an emotion analysis engine to analyze the user's current emotional state, using software such as OpenCV and Google Cloud Speech-to-Text API.

[0669] 4. Data Analysis

[0670] The server uses a generative AI model to analyze the user's basic information and emotional state, and generates optimized ad suggestions for the user. This analysis is performed using an artificial intelligence framework such as TensorFlow.

[0671] 5. Generating Ad Proposals

[0672] The ad suggestions generated by the generative AI model are best suited to the user's profile and emotional state, and the server then formats these suggestions appropriately and prepares them for display to the user.

[0673] 6. Display of advertising suggestions

[0674] The server sends the analysis results to the device, which visually displays the ads to the user. The user can review the suggested ads and provide feedback, which is sent back to the server and used to improve future suggestions.

[0675] Specific examples

[0676] For example, if a 25-year-old female user enters basic information and the emotional data identifies her current emotional state as "happy," the ad suggestions generated by the server will be in the following format:

[0677] Example prompt sentence:

[0678] "Hi! You're attending a friend's wedding today, right? How about this new dress that reflects your fun spirit? Click here for more details."

[0679] This system enables more effective and personalized advertising based on the user's real-time emotional state.

[0680] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0681] Step 1: Enter user data

[0682] When a user accesses a website or application, they enter basic personal information (such as name, age, gender, body type, preferred style, daily activities, and special events) into a form. The device's camera and microphone also record the user's facial expressions and voice in real time, collecting emotional data. This information is sent to the server in JSON format.

[0683] Input: User basic information and emotional data

[0684] Output: User data in JSON format

[0685] Step 2: Save your data

[0686] The server analyzes the user data received in JSON format and saves it in a database. This saving process saves the user the trouble of having to enter the same information twice, and allows for more accurate analysis by referencing past data.

[0687] Input: User data in JSON format

[0688] Output: User data stored in the database

[0689] Step 3: Sentiment Analysis

[0690] The server passes the stored emotion data to an emotion analysis engine, which uses OpenCV and the Google Cloud Speech-to-Text API to analyze the user's facial expressions and voice to determine their current emotional state.

[0691] Input: Emotion data

[0692] Output: User's emotional state

[0693] Step 4: Analyze the data

[0694] The server retrieves the user's basic information and emotional state from the database and analyzes it using a generative AI model, which combines the user's personal data and emotional state to generate optimal advertising suggestions.

[0695] Input: User's basic information and emotional state

[0696] Output: Generated ad suggestions

[0697] Step 5: Generate advertising proposals

[0698] The ad suggestions generated by the generative AI model are best suited to the user's profile and emotional state, and the server then formats these suggestions and prepares them for transmission to the device.

[0699] Input: Generated ad proposals

[0700] Output: Formatted ad proposal

[0701] Step 6: View Ad Proposals

[0702] The server sends the analysis results to the device, which visually displays the ads to the user. The user can review the suggested ads and provide feedback, which is sent back to the server and used to improve future suggestions.

[0703] Input: Formatted ad proposal

[0704] Output: Display advertisement and feedback to the user

[0705] This system enables more effective and personalized advertising based on the user's real-time emotional state.

[0706] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0707] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0708] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0709] [Third embodiment]

[0710] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0711] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0712] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0713] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0714] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0715] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0716] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0717] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0718] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0719] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0720] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0721] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0722] The present invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. Specific embodiments for carrying out the present invention will be described below.

[0723] 1. Entering and submitting user data:

[0724] When a user accesses a website or application, an input form appears on the device, where the user enters basic information such as their name, age, gender, body type, preferred style, daily activities, special events, etc. Once completed, the device sends this information in JSON format to the server.

[0725] 2. Receiving and storing data:

[0726] The server receives the user's basic information sent from the device. This received data is saved in a database. Storing the data saves the user the trouble of having to enter the same information later, and the server can refer to past data to make more accurate fashion suggestions.

[0727] 3. Data Analysis:

[0728] The server then begins analyzing the user's basic information, using an artificial intelligence model to analyze the user's age, gender, body type, preferred style, daily activities, and special events. The model then combines this with the latest fashion trend data to generate optimal fashion recommendations.

[0729] 4. Fashion proposal generation:

[0730] The fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best suit the user's profile, making it easy for users to style their outfits for individual occasions. The generated fashion suggestions are then converted into an appropriate format for display to the user.

[0731] 5. Display of fashion suggestions:

[0732] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. The feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0733] Examples:

[0734] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." Based on this information, the AI ​​model generates fashion suggestions such as: "For casual style, a white blouse and denim jeans, and for office work days, a navy blazer and skirt," and "For a friend's wedding, an elegant dress and pearl accessories." This content is displayed on the user's device, and the user can choose their fashion based on the suggestions.

[0735] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style based on the user's basic information, thereby increasing the user's satisfaction.

[0736] The processing flow will be explained below.

[0737] Step 1:

[0738] A user accesses a website or application. The device prompts the user with a login questionnaire, where the user enters basic information such as name, age, gender, body type, preferred style, daily activities, and special events.

[0739] Step 2:

[0740] The user clicks the button to submit the input form. The device converts the entered basic information into JSON format and sends it to the server.

[0741] Step 3:

[0742] The server receives the user data sent from the device, stores the received data in a database, and verifies that the data is properly recorded during this process.

[0743] Step 4:

[0744] The server retrieves basic user information from the database, prepares the retrieved information for analysis, and passes the data to the AI ​​model.

[0745] Step 5:

[0746] The AI ​​model analyzes the user's age, gender, body type, preferred style, daily activities, and special event information, and then compares it with the latest fashion trend data to generate optimal fashion suggestions.

[0747] Step 6:

[0748] The server formats the fashion suggestions obtained from the AI ​​model, making them easy for users to understand.

[0749] Step 7:

[0750] The server sends the formatted fashion suggestions to the terminal, and the sent data is immediately displayed on the user's terminal.

[0751] Step 8:

[0752] The user reviews the displayed fashion suggestions. If the user provides feedback on the suggestions, the data is sent back to the server. This feedback is used to improve the accuracy of future suggestions.

[0753] Example 1

[0754] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0755] Conventional fashion suggestion systems lacked efficient methods for collecting basic user information and generating and displaying personalized suggestions. In particular, they lacked a method for effectively utilizing user feedback to improve the accuracy of suggestions. As a result, there were issues with low user satisfaction and declining system utilization.

[0756] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0757] In this invention, the server includes means for inputting basic information of an individual, means for converting the basic information into JSON format and transmitting the converted information to the server, means for saving the basic information in a database, means for analyzing the basic information and using an artificial intelligence model to generate personalized fashion suggestions, means for formatting the fashion suggestions generated by the analysis means, means for displaying the formatted fashion suggestions, and means for analyzing feedback provided by the user via the display means to improve suggestion accuracy. This allows for the generation of highly accurate personalized suggestions based on the user's basic information, and further enables the accuracy of the suggestions to be continuously improved based on the feedback.

[0758] "Input means" refers to a device or software interface that allows a user to input basic personal information.

[0759] "Transmission means" refers to a device or software function that converts input data into JSON format and transmits it to the server.

[0760] "Storage means" refers to a device or software function for permanently recording data sent to the server in a database.

[0761] "Analysis Means" means a device or software function that utilizes artificial intelligence models to generate personalized fashion suggestions based on stored data.

[0762] An "artificial intelligence model" is a mathematical model that uses machine learning or deep learning algorithms to perform specific tasks.

[0763] "Formatting means" refers to the functionality of a device or software for converting the generated fashion suggestions into the format required for display to the user.

[0764] "Display means" refers to a device or software interface for visually presenting fashion suggestions to a user.

[0765] "Feedback means" refers to a device or software function that collects and analyzes user-provided evaluations and opinions to improve the accuracy of the system's suggestions.

[0766] The present invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. This system comprises an input means, a transmission means, a storage means, an analysis means, a formatting means, a display means, and a feedback means.

[0767] When a user accesses a website or application, a form for entering basic personal information is displayed on the device, where the user enters information such as name, age, gender, body type, preferred style, daily activities, special events, etc. Once the information is complete, the device uses JavaScript to convert the information into JSON format and sends it to the server via an HTTP POST request.

[0768] The server stores the received data in a database (e.g., MySQL or MongoDB). Based on the stored data, the server performs analysis using an artificial intelligence model (e.g., TensorFlow or PyTorch). This analysis combines the user's basic information with the latest fashion trend data to generate optimal fashion suggestions.

[0769] The generated fashion suggestions are returned in JSON format to the server, which formats this data into an appropriate format (e.g., HTML or JSON) for display to the user. The formatted data is then sent back to the device as an HTTP response, which receives it and visually displays it to the user.

[0770] The user reviews the displayed fashion suggestions and enters feedback if necessary. The device then converts this feedback back into JSON format and sends it to the server, where it analyzes the feedback and uses it to improve the accuracy of the suggestions.

[0771] As an example, a 25-year-old female user enters the following information:

[0772] Name: Hanako

[0773] Age: 25

[0774] Gender: Female

[0775] Build: Slim

[0776] Favorite style: Casual

[0777] Daily Activities: Office work

[0778] Special Event: Friend's Wedding

[0779] Based on this, the AI ​​model generates fashion suggestions like this:

[0780] "For casual style, I wear a white blouse and denim jeans. For office work, I wear a navy blazer and skirt. For a friend's wedding, I wear an elegant dress and pearl accessories."

[0781] An example of a prompt for a generative AI model might look like this:

[0782] "A 25-year-old woman with a slim figure, who likes casual styles, works in an office, and is attending a friend's wedding. What are some fashion suggestions that would be best for her?"

[0783] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0784] Step 1:

[0785] A user visits a website or application.

[0786] Input: User access request

[0787] What it does: Displays a web form on the device asking users to enter basic personal information, including name, age, gender, body type, style preferences, daily activities, and special events.

[0788] Output: Display of form

[0789] Step 2:

[0790] The user enters basic information.

[0791] Input: Information such as name, age, gender, body type, preferred style, daily activities, special events, etc.

[0792] What it does: The terminal captures the user's input and temporarily stores it.

[0793] Output: Basic information is saved in the input form

[0794] Step 3:

[0795] Convert basic information into JSON format and send it to the server.

[0796] Input: Basic information entered by the user

[0797] How it works: The device uses JavaScript to convert the input information into JSON format, which is then sent to the server via an HTTP POST request.

[0798] Output: User data in JSON format is sent to the server

[0799] Step 4:

[0800] The server receives the JSON data and stores it in the database.

[0801] Input: User data in JSON format

[0802] How it works: The server extracts the user's JSON data from the incoming HTTP POST request and stores it in a database (e.g. MySQL or MongoDB).

[0803] Output: User data saved in database

[0804] Step 5:

[0805] The server analyzes the data and generates fashion suggestions.

[0806] Input: User basic information stored in the database

[0807] How it works: The server queries the database to retrieve stored data. The retrieved data is passed to an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate fashion suggestions. The model analyzes the user's basic information and the latest fashion trend data, and generates optimal fashion suggestions in JSON format.

[0808] Output: Fashion suggestion data in JSON format

[0809] Step 6:

[0810] The server formats the generated fashion suggestions.

[0811] Input: JSON format fashion proposal data

[0812] Behavior: The server formats the received JSON data into an appropriate format (e.g., HTML template, JSON structure modification).

[0813] Output: Formatted fashion suggestion data

[0814] Step 7:

[0815] The server sends the formatted data to the terminal.

[0816] Input: Formatted fashion suggestion data

[0817] How it works: The server sends data formatted as an HTTP response to the device.

[0818] Output: Fashion suggestion data is sent to the device

[0819] Step 8:

[0820] The terminal visually displays the fashion suggestions to the user.

[0821] Input: Fashion suggestion data sent from the server

[0822] What it does: The device parses the received data and displays it visually in a browser or application (e.g., dynamically embedding it in HTML using JavaScript).

[0823] Output: User is presented with fashion suggestions

[0824] Step 9:

[0825] The user provides feedback on the proposal.

[0826] Input: User feedback information (e.g., new requests and corrections)

[0827] How it works: The device converts the feedback information into JSON format and sends it back to the server.

[0828] Output: Feedback data in JSON format is sent to the server

[0829] Step 10:

[0830] The server analyzes the feedback and incorporates it into future suggestions.

[0831] Input: Feedback data in JSON format

[0832] How it works: The server analyzes the received feedback and stores it in a database. The analysis results are used as training data for the AI ​​model to improve the accuracy of future suggestions.

[0833] Output: AI model with improved proposal accuracy

[0834] (Application example 1)

[0835] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0836] In virtual stores, the process of users finding the best fashion style for themselves and trying it on is very time-consuming. Furthermore, when shopping online, it is difficult to actually try on clothes, and the clothes purchased may not fit. To solve this problem, a system that provides personalized fashion suggestions and enables virtual try-on is needed.

[0837] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0838] In this invention, the server includes an input means for inputting basic information of an individual, a storage means for storing the basic information in a database, an analysis means for analyzing the basic information and generating personalized fashion suggestions, a virtual try-on means for trying on the suggested outfits based on the user's avatar in a virtual space, and a display means for displaying the fashion suggestions generated by the analysis means and confirming them in a 3D environment, thereby enabling users to easily find the fashion style that best suits them and try them on in the virtual space.

[0839] "Basic personal information" refers to information such as the user's name, age, gender, body type, preferred style, daily activities, and special events.

[0840] "Input means" refers to a device or interface that allows a user to input basic personal information.

[0841] "Storage means" refers to a system or device for storing basic information about users in a database.

[0842] "Analysis means" refers to the processes and techniques used to generate personalized fashion suggestions based on the stored underlying information.

[0843] "Artificial intelligence model" refers to advanced algorithms such as machine learning and deep learning that are used to generate fashion suggestions.

[0844] "Virtual try-on means" refers to technology or devices that allow a user's avatar to try on suggested outfits in a virtual space.

[0845] "Display means" refers to a device or interface that allows the user to visually confirm the generated fashion suggestions.

[0846] This invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. This system generates optimal fashion suggestions based on the information entered by the user and allows the user to try on clothes in a virtual space.

[0847] The system is implemented in the following configuration.

[0848] Hardware and software used

[0849] Hardware: A smartphone or computer is used to input basic user information, and a head-mounted display (e.g., Oculus Rift) is used to display fashion suggestions.

[0850] Software: Web frameworks such as Flask and Django are used for server-side processing, and MySQL and PostgreSQL are used for databases. AI (artificial intelligence) models using TensorFlow and PyTorch are used to generate fashion suggestions. OpenGL is used for 3D display.

[0851] Data processing and calculation

[0852] 1. Entering and submitting user data

[0853] Users access the application using a smartphone or web browser and enter basic information such as their name, age, gender, body type, preferred style, daily activities, special events, etc. The entered information is sent to the server in JSON format.

[0854] 2. Receipt and storage of data

[0855] The server receives the user's basic information and stores this data in a database, which is then retained for future fashion suggestions.

[0856] 3. Data Analysis

[0857] The server analyzes the stored basic information using an AI model based on TensorFlow and PyTorch, taking in the user's age, gender, body type, preferred style, daily activities, and special event information as a dataset, and combining this with the latest fashion trend data to generate optimal fashion suggestions.

[0858] 4. Fashion Proposal Generation

[0859] Fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best suit the user's profile, and are then converted into an appropriate format for display to the user.

[0860] 5. Virtual try-on and viewing

[0861] Using the virtual try-on method, users can try on the proposed outfits on their avatar and check how they look in a 3D environment through a head-mounted display, allowing users to visually check the outfits in a virtual space without actually trying them on.

[0862] Specific examples

[0863] For example, if a 25-year-old female user enters information such as "Name: Hanako," "Age: 25," "Gender: Female," "Body Type: Slim," "Preferred Style: Casual," "Daily Activity: Office Work," and "Special Event: Friend's Wedding," the following fashion suggestions will be generated:

[0864] Here are some examples of prompts for a generative AI model:

[0865] User Information:

[0866] Name: Hanako,

[0867] Age: 25,

[0868] Gender: Female,

[0869] Body Type: Slim,

[0870] Favorite style: Casual,

[0871] Daily activities: office work,

[0872] Special Event: Friend's Wedding

[0873] Use this information to generate optimal outfit suggestions based on the latest fashion trends. The suggestions should include casual, office, and wedding styles.

[0874] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0875] Step 1:

[0876] Users access the application using a smartphone or web browser and enter their basic information (such as name, age, gender, body type, preferred style, daily activities, special events, etc.) This input data is converted into JSON format, which is necessary for subsequent analysis and proposal generation.

[0877] Step 2:

[0878] The device sends the basic information entered by the user to the server. Specifically, it sends JSON format data to the server using an HTTP request. This information transmission is a prerequisite for performing the analysis process.

[0879] Step 3:

[0880] The server analyzes the received basic information in JSON format and stores it in a database. The database used is MySQL or PostgreSQL, and users' past data can also be referenced. The stored data is used to generate future proposals.

[0881] Step 4:

[0882] The server takes the basic information stored in the database and begins analyzing it with an AI model (using TensorFlow and PyTorch). Specifically, the AI ​​model receives input data such as the user's age, gender, body type, preferred style, daily activities, and special event information. This data is then combined with the latest fashion trend data to generate personalized fashion suggestions.

[0883] Step 5:

[0884] The resulting fashion suggestions are combinations of clothing and accessories that best fit the user's profile. These are then converted into an appropriate format (e.g., JSON or HTML) on the server side and sent to the user's device. This format conversion facilitates display in browsers and applications.

[0885] Step 6:

[0886] The device receives the fashion suggestions sent from the server and visually displays them to the user. A 3D model of an avatar wearing the suggested outfit is displayed on a head-mounted display or smartphone screen. This allows the user to try on the suggested outfit in a virtual space and check how it looks.

[0887] Step 7:

[0888] Users virtually try on clothes and provide feedback based on their satisfaction. This feedback information is also sent to the server and used to generate future recommendations. The AI ​​model retrains based on the feedback, enabling more accurate fashion recommendations.

[0889] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0890] The present invention provides fashion suggestions that take into account the emotional state of the user by combining an emotion engine with a system that inputs basic information about an individual and makes personalized fashion suggestions based on that information. Specific embodiments for carrying out the present invention are described below.

[0891] 1. Entering and submitting user data:

[0892] When a user accesses a website or application, an input form is displayed on the device. The user enters basic information such as their name, age, gender, body type, preferred style, daily activities, and special events into this form. As the user interacts with the input form, the device uses a camera and microphone to record the user's facial expressions and voice, collecting data for analysis by the emotion engine. Once the input is complete, the device sends this information in JSON format to the server.

[0893] 2. Receiving and storing data:

[0894] The server receives the user's basic information and emotional data sent from the device. The received data is stored in a database. Storing the data saves the user the trouble of having to enter the same information later, and also makes it possible to refer to past data to provide more accurate fashion suggestions.

[0895] 3. Analysis by emotion engine:

[0896] The server passes the user's emotional data to the emotion engine, which analyzes the user's facial expressions and voice to determine the user's current emotional state. This emotional state information is used to generate fashion suggestions.

[0897] 4. Data Analysis:

[0898] The server retrieves basic user information from the database and begins analysis by combining it with emotional state information from the emotion engine. This analysis is performed using an artificial intelligence model. The model incorporates the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state, and combines this with the latest fashion trend data to generate optimal fashion suggestions.

[0899] 5. Fashion proposal generation:

[0900] Fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best fit the user's profile and emotional state. The suggestions are then appropriately formatted and ready to be displayed to the user.

[0901] 6. Display of fashion suggestions:

[0902] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. The feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0903] Examples:

[0904] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." In addition to this information, the emotion engine analyzes the user's facial expressions and voice and identifies her current emotional state as "happy." The AI ​​model takes this emotional information into account and generates fashion suggestions such as the following: "A light-colored casual blouse and denim jeans to match a happy mood, and a navy blazer and skirt for office work days," and "An elegant dress and pearl accessories for a friend's wedding." This content is displayed on the user's device, and the user can choose an outfit based on the suggestions.

[0905] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style by taking into consideration the emotional state of the user in addition to basic information about the user, thereby increasing user satisfaction.

[0906] The processing flow will be explained below.

[0907] Step 1:

[0908] A user accesses a website or application. The device prompts the user with a login questionnaire, where the user enters basic information such as name, age, gender, body type, preferred style, daily activities, and special events.

[0909] Step 2:

[0910] The user clicks a button to submit the input form. The device converts the basic information entered into JSON format and sends it to the server. At this time, the device also records the user's facial expressions and voice using the camera and microphone.

[0911] Step 3:

[0912] The server receives the user data sent from the device. The received data includes basic information about the user, as well as recorded data of facial expressions and voice.

[0913] Step 4:

[0914] The server stores the received user basic information and emotion data in a database, allowing the same user's data to be reused in the future to improve analysis accuracy.

[0915] Step 5:

[0916] The server retrieves the user's basic information and emotional data from the database, and passes the retrieved data to the emotion engine, which analyzes the user's current emotional state.

[0917] Step 6:

[0918] The emotion engine analyzes the user's facial expressions and voice to identify the user's emotional state. Based on this information, the emotional state can be determined, for example, "the user is in a happy mood."

[0919] Step 7:

[0920] The server issues analytical instructions to the AI ​​model based on the user's basic information acquired by the server and the emotional state information obtained from the emotion engine. The AI ​​model performs analysis by combining the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state.

[0921] Step 8:

[0922] The AI ​​model analyzes the user's profile and generates fashion suggestions that best fit their emotional state, such as a light-colored casual blouse and denim jeans that complement a happy mood.

[0923] Step 9:

[0924] The server converts the fashion suggestions obtained from the AI ​​model into an appropriate format, which makes the suggestions easier for users to understand.

[0925] Step 10:

[0926] The server sends the formatted fashion suggestions to the terminal, which receives them and visually displays the fashion suggestions to the user.

[0927] Step 11:

[0928] The user reviews the displayed fashion suggestions. If the user provides feedback on the suggestions, the device again sends the data to the server. This feedback is used to improve future suggestions.

[0929] Through this series of processes, users can receive fashion suggestions based on their basic information and emotional state.

[0930] Example 2

[0931] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0932] Conventional fashion suggestion systems only consider basic user information when making suggestions, which means that the suggestions often do not match the user's emotional state. Furthermore, the convenience of websites and applications is low, making it difficult to improve the user experience.

[0933] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0934] In this invention, the server includes an input means for inputting basic information and emotional data of an individual, a storage means for storing the basic information and emotional data in a database, and an analysis means for analyzing the basic information and emotional data to generate personalized fashion suggestions, thereby enabling fashion suggestions that take into account the user's basic information and emotional state.

[0935] "Basic personal information" refers to basic information about a user, such as the user's name, age, gender, body type, preferred style, daily activities, and special events.

[0936] "Emotion data" refers to data that indicates the emotional state of the user, such as facial expressions and voice, and is information that is analyzed by the emotion engine.

[0937] "Input means" refers to an interface for users to input information, and refers to a device or software that operates via a website or application.

[0938] "Storage means" refers to a device or software that has the function of storing basic information and emotion data input by the user in a database.

[0939] "Analysis means" refers to a device or software that has the function of analyzing the basic information and emotional data stored by the storage means and generating personalized fashion suggestions.

[0940] A "generative AI model" refers to a model that uses artificial intelligence technology to analyze data and generate personalized suggestions and solutions.

[0941] The "display means" refers to a device or software for visually presenting the fashion suggestions generated by the analysis means to the user.

[0942] The present invention is a system that combines basic information and emotional state of a user to provide personalized fashion suggestions. Specific embodiments for carrying out the present invention are described below.

[0943] Entering and submitting user data

[0944] When a user accesses a website or application, an input form is displayed on the device. The user uses this form to enter basic information such as their name, age, gender, body type, preferred style, daily activities, and special events. In addition, the device uses a camera and microphone to record the user's facial expressions and voice, which are then collected as emotional data. The collected information is converted into JSON format and sent from the device to the server.

[0945] Receiving and storing data

[0946] The server receives the user's basic information and emotional data sent from the device. The received data is stored in a database. This storage method eliminates the need for the user to enter the same information twice, and more accurate fashion suggestions can be made by referencing past data.

[0947] Analysis by emotion engine

[0948] The server passes the user's emotional data to the emotion engine, which analyzes the user's facial expressions and voice to identify their current emotional state, which is then used as key information for generating fashion suggestions.

[0949] Data analysis

[0950] The server retrieves basic user information from the database and combines it with emotional state information from the emotion engine for analysis. This analysis is performed using a generative AI model. The generative AI model inputs the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state, and combines this with the latest fashion trend data to generate optimal fashion suggestions.

[0951] Fashion proposal generation

[0952] The fashion suggestions generated by the generative AI model show the combination of clothing and accessories that best suit the user's profile and emotional state, which are then appropriately formatted and ready to be displayed to the user.

[0953] Fashion suggestion display

[0954] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. This feedback is sent back to the server and used to improve the accuracy of future suggestions.

[0955] Specific examples

[0956] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." In addition to this information, the emotion engine analyzes the user's facial expressions and voice and identifies their current emotional state as "happy." The generative AI model takes this emotional information into account and generates fashion suggestions such as: "A light-colored casual blouse and denim jeans to match a happy mood, and a navy blazer and skirt for office work days," and "An elegant dress and pearl accessories for a friend's wedding." This content is displayed on the user's device, allowing the user to choose an outfit based on the suggestions.

[0957] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style by taking into consideration the basic information and emotional state of the user, thereby increasing user satisfaction.

[0958] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0959] Step 1: Enter user data

[0960] When a user accesses a website or application, the terminal displays an input form.

[0961] Input: User basic information (e.g., name, age, gender, body type, preferred style, daily activities, special events)

[0962] How it works: The user enters basic information into the device's input form. The device detects this input in real time and performs input validation. The device then uses the camera and microphone to record the user's facial expressions and voice.

[0963] Step 2: Collect and send data

[0964] Once the input form is completed, the device converts the entered basic information and collected emotional data into JSON format and sends it to the server.

[0965] Input: Basic information and emotion data

[0966] Data processing: Convert the input information into JSON format

[0967] Specific operation: When the user presses the button to confirm the input, the device parses the basic information and recorded emotion data into JSON format and sends it to the server via a security layer. The data is then encrypted before being sent.

[0968] Step 3: Receiving and storing data

[0969] The server receives basic information and emotion data in JSON format sent from the device and stores them in a database.

[0970] Input: Basic information and emotion data in JSON format

[0971] Data processing: Parse the received data and save it in a database

[0972] Specific operation: The server checks the format of the received data and, if there are no errors, saves it to the database. When saving, each field is properly mapped and associated with the user ID.

[0973] Step 4: Analysis by the Emotion Engine

[0974] The server passes the emotion data to the emotion engine, which analyzes the user's facial expressions and voice.

[0975] Input: Emotion data

[0976] Data calculation: Identifying emotional states (e.g., happy, sad)

[0977] Specific operation: The emotion engine converts the received facial and voice data into multidimensional vectors and performs comparative analysis with existing emotion models. As a result, the emotional state is identified as "happy," for example.

[0978] Step 5: Analyze the data

[0979] The server retrieves basic information about the user from the database, combines it with emotional state information from the emotion engine, and inputs it into the generative AI model.

[0980] Input: Basic information and emotional state information

[0981] Data processing: Integrate basic information and emotional state and input it into a generative AI model

[0982] How it works: The generative AI model takes in the latest fashion trend data, as well as the basic information and emotional state provided, and generates the most suitable fashion suggestions for the user based on this.

[0983] Step 6: Generate fashion suggestions

[0984] The generative AI model generates optimal fashion suggestions based on the user's basic information and emotional state.

[0985] Input: Integrated dataset

[0986] Output: Fashion suggestions (e.g., a light-colored casual blouse with denim jeans, or an elegant dress with pearl accessories)

[0987] How it works: The generative AI model makes inferences based on the input data and generates the most suitable fashion style for the user. The generated suggestions are then properly formatted and ready to be sent to the device.

[0988] Step 7: Displaying fashion suggestions

[0989] The server transmits the generated fashion suggestions to the terminal, which receives them and displays them to the user.

[0990] Input: Fashion suggestions

[0991] Output: Fashion suggestions displayed to the user

[0992] How it works: The server sends fashion suggestions in JSON format to the device. The device receives them and displays them for the user to visually confirm. The user can then choose an outfit based on the suggestions and provide feedback. The feedback is then sent back to the server and used to improve the system's accuracy.

[0993] In this way, the system can provide personalized fashion suggestions that take into account the user's basic information and emotional state, thereby increasing user satisfaction.

[0994] (Application example 2)

[0995] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0996] While modern ad delivery systems commonly target ads based on users' personal data, personalized ad delivery that takes into account users' emotional state is not widely used. This makes it difficult to deliver appropriate ads based on users' real-time emotional state, resulting in limited advertising effectiveness. Furthermore, conventional systems are unable to fully incorporate user feedback, making it difficult to improve advertising accuracy. To address these issues, there is a need for the development of a personalized ad delivery system that takes into account both users' personal information and emotional data.

[0997] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0998] In this invention, the server includes an input means for inputting basic information and emotional data of an individual, a storage means for storing the basic information and emotional data in a database, and an analysis means for analyzing the basic information and emotional data to generate personalized suggestions, thereby enabling highly accurate personalized advertisement delivery according to the user's real-time emotional state.

[0999] "Basic personal information" refers to individual identification information such as the user's name, age, sex, body type, preferred style, daily activities, and special events.

[1000] "Emotion data" is information about the current emotional state obtained by analyzing the user's facial expressions and voice.

[1001] "Input means" refers to an interface such as a website or application through which a user inputs basic personal information and emotional data.

[1002] The "storage means" is a system for storing the input basic information and emotion data in a database.

[1003] "Analysis Measure" means a system that uses generative AI models or other analytical methods to generate personalized recommendations using input background information and sentiment data.

[1004] "Display means" refers to a display or device for visually presenting the personalized suggestions generated by the analysis means to the user.

[1005] A "generative AI model" is an artificial intelligence model that analyzes a user's basic information and emotional data to generate optimal advertisements and fashion suggestions.

[1006] The present invention is a system for delivering personalized advertisements to users based on basic information and emotion data of individuals. Specific embodiments for carrying out the present invention will be described below.

[1007] 1. Entering user data

[1008] The server provides an interface through a website or application that allows users to input basic personal information (such as name, age, gender, body type, preferred style, daily activities, and special events). The device also uses a camera and microphone to record the user's facial expressions and voice, and obtains emotional data. This information is collected in real time.

[1009] 2. Data storage

[1010] The server receives the basic information and emotion data entered in JSON format and stores it in a database, which eliminates the need for re-entry the next time the user accesses the system, and enables more accurate analysis based on past data.

[1011] 3. Use of sentiment analysis engines

[1012] The server passes the collected emotional data to an emotion analysis engine to analyze the user's current emotional state, using software such as OpenCV and Google Cloud Speech-to-Text API.

[1013] 4. Data Analysis

[1014] The server uses a generative AI model to analyze the user's basic information and emotional state, and generates optimized ad suggestions for the user. This analysis is performed using an artificial intelligence framework such as TensorFlow.

[1015] 5. Generating Ad Proposals

[1016] The ad suggestions generated by the generative AI model are best suited to the user's profile and emotional state, and the server then formats these suggestions appropriately and prepares them for display to the user.

[1017] 6. Display of advertising suggestions

[1018] The server sends the analysis results to the device, which visually displays the ads to the user. The user can review the suggested ads and provide feedback, which is sent back to the server and used to improve future suggestions.

[1019] Specific examples

[1020] For example, if a 25-year-old female user enters basic information and the emotional data identifies her current emotional state as "happy," the ad suggestions generated by the server will be in the following format:

[1021] Example prompt sentence:

[1022] "Hi! You're attending a friend's wedding today, right? How about this new dress that reflects your fun spirit? Click here for more details."

[1023] This system enables more effective and personalized advertising based on the user's real-time emotional state.

[1024] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1025] Step 1: Enter user data

[1026] When a user accesses a website or application, they enter basic personal information (such as name, age, gender, body type, preferred style, daily activities, and special events) into a form. The device's camera and microphone also record the user's facial expressions and voice in real time, collecting emotional data. This information is sent to the server in JSON format.

[1027] Input: User basic information and emotional data

[1028] Output: User data in JSON format

[1029] Step 2: Save your data

[1030] The server analyzes the user data received in JSON format and saves it in a database. This saving process saves the user the trouble of having to enter the same information twice, and allows for more accurate analysis by referencing past data.

[1031] Input: User data in JSON format

[1032] Output: User data stored in the database

[1033] Step 3: Sentiment Analysis

[1034] The server passes the stored emotion data to an emotion analysis engine, which uses OpenCV and the Google Cloud Speech-to-Text API to analyze the user's facial expressions and voice to determine their current emotional state.

[1035] Input: Emotion data

[1036] Output: User's emotional state

[1037] Step 4: Analyze the data

[1038] The server retrieves the user's basic information and emotional state from the database and analyzes it using a generative AI model, which combines the user's personal data and emotional state to generate optimal advertising suggestions.

[1039] Input: User's basic information and emotional state

[1040] Output: Generated ad suggestions

[1041] Step 5: Generate advertising proposals

[1042] The ad suggestions generated by the generative AI model are best suited to the user's profile and emotional state, and the server then formats these suggestions and prepares them for transmission to the device.

[1043] Input: Generated ad proposals

[1044] Output: Formatted ad proposal

[1045] Step 6: View Ad Proposals

[1046] The server sends the analysis results to the device, which visually displays the ads to the user. The user can review the suggested ads and provide feedback, which is sent back to the server and used to improve future suggestions.

[1047] Input: Formatted ad proposal

[1048] Output: Display advertisement and feedback to the user

[1049] This system enables more effective and personalized advertising based on the user's real-time emotional state.

[1050] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1051] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1052] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1053] [Fourth embodiment]

[1054] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1055] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1056] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1057] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1058] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1059] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1060] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1061] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1062] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1063] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1064] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1065] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1066] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1067] The present invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. Specific embodiments for carrying out the present invention will be described below.

[1068] 1. Entering and submitting user data:

[1069] When a user accesses a website or application, an input form appears on the device, where the user enters basic information such as their name, age, gender, body type, preferred style, daily activities, special events, etc. Once completed, the device sends this information in JSON format to the server.

[1070] 2. Receiving and storing data:

[1071] The server receives the user's basic information sent from the device. This received data is saved in a database. Storing the data saves the user the trouble of having to enter the same information later, and the server can refer to past data to make more accurate fashion suggestions.

[1072] 3. Data Analysis:

[1073] The server then begins analyzing the user's basic information, using an artificial intelligence model to analyze the user's age, gender, body type, preferred style, daily activities, and special events. The model then combines this with the latest fashion trend data to generate optimal fashion recommendations.

[1074] 4. Fashion proposal generation:

[1075] The fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best suit the user's profile, making it easy for users to style their outfits for individual occasions. The generated fashion suggestions are then converted into an appropriate format for display to the user.

[1076] 5. Display of fashion suggestions:

[1077] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. The feedback is sent back to the server and used to improve the accuracy of future suggestions.

[1078] Examples:

[1079] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." Based on this information, the AI ​​model generates fashion suggestions such as: "For casual style, a white blouse and denim jeans, and for office work days, a navy blazer and skirt," and "For a friend's wedding, an elegant dress and pearl accessories." This content is displayed on the user's device, and the user can choose their fashion based on the suggestions.

[1080] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style based on the user's basic information, thereby increasing the user's satisfaction.

[1081] The processing flow will be explained below.

[1082] Step 1:

[1083] A user accesses a website or application. The device prompts the user with a login questionnaire, where the user enters basic information such as name, age, gender, body type, preferred style, daily activities, and special events.

[1084] Step 2:

[1085] The user clicks the button to submit the input form. The device converts the entered basic information into JSON format and sends it to the server.

[1086] Step 3:

[1087] The server receives the user data sent from the device, stores the received data in a database, and verifies that the data is properly recorded during this process.

[1088] Step 4:

[1089] The server retrieves basic user information from the database, prepares the retrieved information for analysis, and passes the data to the AI ​​model.

[1090] Step 5:

[1091] The AI ​​model analyzes the user's age, gender, body type, preferred style, daily activities, and special event information, and then compares it with the latest fashion trend data to generate optimal fashion suggestions.

[1092] Step 6:

[1093] The server formats the fashion suggestions obtained from the AI ​​model, making them easy for users to understand.

[1094] Step 7:

[1095] The server sends the formatted fashion suggestions to the terminal, and the sent data is immediately displayed on the user's terminal.

[1096] Step 8:

[1097] The user reviews the displayed fashion suggestions. If the user provides feedback on the suggestions, the data is sent back to the server. This feedback is used to improve the accuracy of future suggestions.

[1098] Example 1

[1099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1100] Conventional fashion suggestion systems lacked efficient methods for collecting basic user information and generating and displaying personalized suggestions. In particular, they lacked a method for effectively utilizing user feedback to improve the accuracy of suggestions. As a result, there were issues with low user satisfaction and declining system utilization.

[1101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1102] In this invention, the server includes means for inputting basic information of an individual, means for converting the basic information into JSON format and transmitting the converted information to the server, means for saving the basic information in a database, means for analyzing the basic information and using an artificial intelligence model to generate personalized fashion suggestions, means for formatting the fashion suggestions generated by the analysis means, means for displaying the formatted fashion suggestions, and means for analyzing feedback provided by the user via the display means to improve suggestion accuracy. This allows for the generation of highly accurate personalized suggestions based on the user's basic information, and further enables the accuracy of the suggestions to be continuously improved based on the feedback.

[1103] "Input means" refers to a device or software interface that allows a user to input basic personal information.

[1104] "Transmission means" refers to a device or software function that converts input data into JSON format and transmits it to the server.

[1105] "Storage means" refers to a device or software function for permanently recording data sent to the server in a database.

[1106] "Analysis Means" means a device or software function that utilizes artificial intelligence models to generate personalized fashion suggestions based on stored data.

[1107] An "artificial intelligence model" is a mathematical model that uses machine learning or deep learning algorithms to perform specific tasks.

[1108] "Formatting means" refers to the functionality of a device or software for converting the generated fashion suggestions into the format required for display to the user.

[1109] "Display means" refers to a device or software interface for visually presenting fashion suggestions to a user.

[1110] "Feedback means" refers to a device or software function that collects and analyzes user-provided evaluations and opinions to improve the accuracy of the system's suggestions.

[1111] The present invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. This system comprises an input means, a transmission means, a storage means, an analysis means, a formatting means, a display means, and a feedback means.

[1112] When a user accesses a website or application, a form for entering basic personal information is displayed on the device, where the user enters information such as name, age, gender, body type, preferred style, daily activities, special events, etc. Once the information is complete, the device uses JavaScript to convert the information into JSON format and sends it to the server via an HTTP POST request.

[1113] The server stores the received data in a database (e.g., MySQL or MongoDB). Based on the stored data, the server performs analysis using an artificial intelligence model (e.g., TensorFlow or PyTorch). This analysis combines the user's basic information with the latest fashion trend data to generate optimal fashion suggestions.

[1114] The generated fashion suggestions are returned in JSON format to the server, which formats this data into an appropriate format (e.g., HTML or JSON) for display to the user. The formatted data is then sent back to the device as an HTTP response, which receives it and visually displays it to the user.

[1115] The user reviews the displayed fashion suggestions and enters feedback if necessary. The device then converts this feedback back into JSON format and sends it to the server, where it analyzes the feedback and uses it to improve the accuracy of the suggestions.

[1116] As an example, a 25-year-old female user enters the following information:

[1117] Name: Hanako

[1118] Age: 25

[1119] Gender: Female

[1120] Build: Slim

[1121] Favorite style: Casual

[1122] Daily Activities: Office work

[1123] Special Event: Friend's Wedding

[1124] Based on this, the AI ​​model generates fashion suggestions like this:

[1125] "For casual style, I wear a white blouse and denim jeans. For office work, I wear a navy blazer and skirt. For a friend's wedding, I wear an elegant dress and pearl accessories."

[1126] An example of a prompt for a generative AI model might look like this:

[1127] "A 25-year-old woman with a slim figure, who likes casual styles, works in an office, and is attending a friend's wedding. What are some fashion suggestions that would be best for her?"

[1128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1129] Step 1:

[1130] A user visits a website or application.

[1131] Input: User access request

[1132] What it does: Displays a web form on the device asking users to enter basic personal information, including name, age, gender, body type, style preferences, daily activities, and special events.

[1133] Output: Display of form

[1134] Step 2:

[1135] The user enters basic information.

[1136] Input: Information such as name, age, gender, body type, preferred style, daily activities, special events, etc.

[1137] What it does: The terminal captures the user's input and temporarily stores it.

[1138] Output: Basic information is saved in the input form

[1139] Step 3:

[1140] Convert basic information into JSON format and send it to the server.

[1141] Input: Basic information entered by the user

[1142] How it works: The device uses JavaScript to convert the input information into JSON format, which is then sent to the server via an HTTP POST request.

[1143] Output: User data in JSON format is sent to the server

[1144] Step 4:

[1145] The server receives the JSON data and stores it in the database.

[1146] Input: User data in JSON format

[1147] How it works: The server extracts the user's JSON data from the incoming HTTP POST request and stores it in a database (e.g. MySQL or MongoDB).

[1148] Output: User data saved in database

[1149] Step 5:

[1150] The server analyzes the data and generates fashion suggestions.

[1151] Input: User basic information stored in the database

[1152] How it works: The server queries the database to retrieve stored data. The retrieved data is passed to an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate fashion suggestions. The model analyzes the user's basic information and the latest fashion trend data, and generates optimal fashion suggestions in JSON format.

[1153] Output: Fashion suggestion data in JSON format

[1154] Step 6:

[1155] The server formats the generated fashion suggestions.

[1156] Input: JSON format fashion proposal data

[1157] Behavior: The server formats the received JSON data into an appropriate format (e.g., HTML template, JSON structure modification).

[1158] Output: Formatted fashion suggestion data

[1159] Step 7:

[1160] The server sends the formatted data to the terminal.

[1161] Input: Formatted fashion suggestion data

[1162] How it works: The server sends data formatted as an HTTP response to the device.

[1163] Output: Fashion suggestion data is sent to the device

[1164] Step 8:

[1165] The terminal visually displays the fashion suggestions to the user.

[1166] Input: Fashion suggestion data sent from the server

[1167] What it does: The device parses the received data and displays it visually in a browser or application (e.g., dynamically embedding it in HTML using JavaScript).

[1168] Output: User is presented with fashion suggestions

[1169] Step 9:

[1170] The user provides feedback on the proposal.

[1171] Input: User feedback information (e.g., new requests and corrections)

[1172] How it works: The device converts the feedback information into JSON format and sends it back to the server.

[1173] Output: Feedback data in JSON format is sent to the server

[1174] Step 10:

[1175] The server analyzes the feedback and incorporates it into future suggestions.

[1176] Input: Feedback data in JSON format

[1177] How it works: The server analyzes the received feedback and stores it in a database. The analysis results are used as training data for the AI ​​model to improve the accuracy of future suggestions.

[1178] Output: AI model with improved proposal accuracy

[1179] (Application example 1)

[1180] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1181] In virtual stores, the process of users finding the best fashion style for themselves and trying it on is very time-consuming. Furthermore, when shopping online, it is difficult to actually try on clothes, and the clothes purchased may not fit. To solve this problem, a system that provides personalized fashion suggestions and enables virtual try-on is needed.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1183] In this invention, the server includes an input means for inputting basic information of an individual, a storage means for storing the basic information in a database, an analysis means for analyzing the basic information and generating personalized fashion suggestions, a virtual try-on means for trying on the suggested outfits based on the user's avatar in a virtual space, and a display means for displaying the fashion suggestions generated by the analysis means and confirming them in a 3D environment, thereby enabling users to easily find the fashion style that best suits them and try them on in the virtual space.

[1184] "Basic personal information" refers to information such as the user's name, age, gender, body type, preferred style, daily activities, and special events.

[1185] "Input means" refers to a device or interface that allows a user to input basic personal information.

[1186] "Storage means" refers to a system or device for storing basic information about users in a database.

[1187] "Analysis means" refers to the processes and techniques used to generate personalized fashion suggestions based on the stored underlying information.

[1188] "Artificial intelligence model" refers to advanced algorithms such as machine learning and deep learning that are used to generate fashion suggestions.

[1189] "Virtual try-on means" refers to technology or devices that allow a user's avatar to try on suggested outfits in a virtual space.

[1190] "Display means" refers to a device or interface that allows the user to visually confirm the generated fashion suggestions.

[1191] This invention relates to a system that inputs basic information about an individual and provides personalized fashion suggestions based on that information. This system generates optimal fashion suggestions based on the information entered by the user and allows the user to try on clothes in a virtual space.

[1192] The system is implemented in the following configuration.

[1193] Hardware and software used

[1194] Hardware: A smartphone or computer is used to input basic user information, and a head-mounted display (e.g., Oculus Rift) is used to display fashion suggestions.

[1195] Software: Web frameworks such as Flask and Django are used for server-side processing, and MySQL and PostgreSQL are used for databases. AI (artificial intelligence) models using TensorFlow and PyTorch are used to generate fashion suggestions. OpenGL is used for 3D display.

[1196] Data processing and calculation

[1197] 1. Entering and submitting user data

[1198] Users access the application using a smartphone or web browser and enter basic information such as their name, age, gender, body type, preferred style, daily activities, special events, etc. The entered information is sent to the server in JSON format.

[1199] 2. Receipt and storage of data

[1200] The server receives the user's basic information and stores this data in a database, which is then retained for future fashion suggestions.

[1201] 3. Data Analysis

[1202] The server analyzes the stored basic information using an AI model based on TensorFlow and PyTorch, taking in the user's age, gender, body type, preferred style, daily activities, and special event information as a dataset, and combining this with the latest fashion trend data to generate optimal fashion suggestions.

[1203] 4. Fashion Proposal Generation

[1204] Fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best suit the user's profile, and are then converted into an appropriate format for display to the user.

[1205] 5. Virtual try-on and viewing

[1206] Using the virtual try-on method, users can try on the proposed outfits on their avatar and check how they look in a 3D environment through a head-mounted display, allowing users to visually check the outfits in a virtual space without actually trying them on.

[1207] Specific examples

[1208] For example, if a 25-year-old female user enters information such as "Name: Hanako," "Age: 25," "Gender: Female," "Body Type: Slim," "Preferred Style: Casual," "Daily Activity: Office Work," and "Special Event: Friend's Wedding," the following fashion suggestions will be generated:

[1209] Here are some examples of prompts for a generative AI model:

[1210] User Information:

[1211] Name: Hanako,

[1212] Age: 25,

[1213] Gender: Female,

[1214] Body Type: Slim,

[1215] Favorite style: Casual,

[1216] Daily activities: office work,

[1217] Special Event: Friend's Wedding

[1218] Use this information to generate optimal outfit suggestions based on the latest fashion trends. The suggestions should include casual, office, and wedding styles.

[1219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1220] Step 1:

[1221] Users access the application using a smartphone or web browser and enter their basic information (such as name, age, gender, body type, preferred style, daily activities, special events, etc.) This input data is converted into JSON format, which is necessary for subsequent analysis and proposal generation.

[1222] Step 2:

[1223] The device sends the basic information entered by the user to the server. Specifically, it sends JSON format data to the server using an HTTP request. This information transmission is a prerequisite for performing the analysis process.

[1224] Step 3:

[1225] The server analyzes the received basic information in JSON format and stores it in a database. The database used is MySQL or PostgreSQL, and users' past data can also be referenced. The stored data is used to generate future proposals.

[1226] Step 4:

[1227] The server takes the basic information stored in the database and begins analyzing it with an AI model (using TensorFlow and PyTorch). Specifically, the AI ​​model receives input data such as the user's age, gender, body type, preferred style, daily activities, and special event information. This data is then combined with the latest fashion trend data to generate personalized fashion suggestions.

[1228] Step 5:

[1229] The resulting fashion suggestions are combinations of clothing and accessories that best fit the user's profile. These are then converted into an appropriate format (e.g., JSON or HTML) on the server side and sent to the user's device. This format conversion facilitates display in browsers and applications.

[1230] Step 6:

[1231] The device receives the fashion suggestions sent from the server and visually displays them to the user. A 3D model of an avatar wearing the suggested outfit is displayed on a head-mounted display or smartphone screen. This allows the user to try on the suggested outfit in a virtual space and check how it looks.

[1232] Step 7:

[1233] Users virtually try on clothes and provide feedback based on their satisfaction. This feedback information is also sent to the server and used to generate future recommendations. The AI ​​model retrains based on the feedback, enabling more accurate fashion recommendations.

[1234] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1235] The present invention provides fashion suggestions that take into account the emotional state of the user by combining an emotion engine with a system that inputs basic information about an individual and makes personalized fashion suggestions based on that information. Specific embodiments for carrying out the present invention are described below.

[1236] 1. Entering and submitting user data:

[1237] When a user accesses a website or application, an input form is displayed on the device. The user enters basic information such as their name, age, gender, body type, preferred style, daily activities, and special events into this form. As the user interacts with the input form, the device uses a camera and microphone to record the user's facial expressions and voice, collecting data for analysis by the emotion engine. Once the input is complete, the device sends this information in JSON format to the server.

[1238] 2. Receiving and storing data:

[1239] The server receives the user's basic information and emotional data sent from the device. The received data is stored in a database. Storing the data saves the user the trouble of having to enter the same information later, and also makes it possible to refer to past data to provide more accurate fashion suggestions.

[1240] 3. Analysis by emotion engine:

[1241] The server passes the user's emotional data to the emotion engine, which analyzes the user's facial expressions and voice to determine the user's current emotional state. This emotional state information is used to generate fashion suggestions.

[1242] 4. Data Analysis:

[1243] The server retrieves basic user information from the database and begins analysis by combining it with emotional state information from the emotion engine. This analysis is performed using an artificial intelligence model. The model incorporates the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state, and combines this with the latest fashion trend data to generate optimal fashion suggestions.

[1244] 5. Fashion proposal generation:

[1245] Fashion suggestions generated by the AI ​​model show the combination of clothing and accessories that best fit the user's profile and emotional state. The suggestions are then appropriately formatted and ready to be displayed to the user.

[1246] 6. Display of fashion suggestions:

[1247] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. The feedback is sent back to the server and used to improve the accuracy of future suggestions.

[1248] Examples:

[1249] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." In addition to this information, the emotion engine analyzes the user's facial expressions and voice and identifies her current emotional state as "happy." The AI ​​model takes this emotional information into account and generates fashion suggestions such as the following: "A light-colored casual blouse and denim jeans to match a happy mood, and a navy blazer and skirt for office work days," and "An elegant dress and pearl accessories for a friend's wedding." This content is displayed on the user's device, and the user can choose an outfit based on the suggestions.

[1250] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style by taking into consideration the emotional state of the user in addition to basic information about the user, thereby increasing user satisfaction.

[1251] The processing flow will be explained below.

[1252] Step 1:

[1253] A user accesses a website or application. The device prompts the user with a login questionnaire, where the user enters basic information such as name, age, gender, body type, preferred style, daily activities, and special events.

[1254] Step 2:

[1255] The user clicks a button to submit the input form. The device converts the basic information entered into JSON format and sends it to the server. At this time, the device also records the user's facial expressions and voice using the camera and microphone.

[1256] Step 3:

[1257] The server receives the user data sent from the device. The received data includes basic information about the user, as well as recorded data of facial expressions and voice.

[1258] Step 4:

[1259] The server stores the received user basic information and emotion data in a database, allowing the same user's data to be reused in the future to improve analysis accuracy.

[1260] Step 5:

[1261] The server retrieves the user's basic information and emotional data from the database, and passes the retrieved data to the emotion engine, which analyzes the user's current emotional state.

[1262] Step 6:

[1263] The emotion engine analyzes the user's facial expressions and voice to identify the user's emotional state. Based on this information, the emotional state can be determined, for example, "the user is in a happy mood."

[1264] Step 7:

[1265] The server issues analytical instructions to the AI ​​model based on the user's basic information acquired by the server and the emotional state information obtained from the emotion engine. The AI ​​model performs analysis by combining the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state.

[1266] Step 8:

[1267] The AI ​​model analyzes the user's profile and generates fashion suggestions that best fit their emotional state, such as a light-colored casual blouse and denim jeans that complement a happy mood.

[1268] Step 9:

[1269] The server converts the fashion suggestions obtained from the AI ​​model into an appropriate format, which makes the suggestions easier for users to understand.

[1270] Step 10:

[1271] The server sends the formatted fashion suggestions to the terminal, which receives them and visually displays the fashion suggestions to the user.

[1272] Step 11:

[1273] The user reviews the displayed fashion suggestions. If the user provides feedback on the suggestions, the device again sends the data to the server. This feedback is used to improve future suggestions.

[1274] Through this series of processes, users can receive fashion suggestions based on their basic information and emotional state.

[1275] Example 2

[1276] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1277] Conventional fashion suggestion systems only consider basic user information when making suggestions, which means that the suggestions often do not match the user's emotional state. Furthermore, the convenience of websites and applications is low, making it difficult to improve the user experience.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1279] In this invention, the server includes an input means for inputting basic information and emotional data of an individual, a storage means for storing the basic information and emotional data in a database, and an analysis means for analyzing the basic information and emotional data to generate personalized fashion suggestions, thereby enabling fashion suggestions that take into account the user's basic information and emotional state.

[1280] "Basic personal information" refers to basic information about a user, such as the user's name, age, gender, body type, preferred style, daily activities, and special events.

[1281] "Emotion data" refers to data that indicates the emotional state of the user, such as facial expressions and voice, and is information that is analyzed by the emotion engine.

[1282] "Input means" refers to an interface for users to input information, and refers to a device or software that operates via a website or application.

[1283] "Storage means" refers to a device or software that has the function of storing basic information and emotion data input by the user in a database.

[1284] "Analysis means" refers to a device or software that has the function of analyzing the basic information and emotional data stored by the storage means and generating personalized fashion suggestions.

[1285] A "generative AI model" refers to a model that uses artificial intelligence technology to analyze data and generate personalized suggestions and solutions.

[1286] The "display means" refers to a device or software for visually presenting the fashion suggestions generated by the analysis means to the user.

[1287] The present invention is a system that combines basic information and emotional state of a user to provide personalized fashion suggestions. Specific embodiments for carrying out the present invention are described below.

[1288] Entering and submitting user data

[1289] When a user accesses a website or application, an input form is displayed on the device. The user uses this form to enter basic information such as their name, age, gender, body type, preferred style, daily activities, and special events. In addition, the device uses a camera and microphone to record the user's facial expressions and voice, which are then collected as emotional data. The collected information is converted into JSON format and sent from the device to the server.

[1290] Receiving and storing data

[1291] The server receives the user's basic information and emotional data sent from the device. The received data is stored in a database. This storage method eliminates the need for the user to enter the same information twice, and more accurate fashion suggestions can be made by referencing past data.

[1292] Analysis by emotion engine

[1293] The server passes the user's emotional data to the emotion engine, which analyzes the user's facial expressions and voice to identify their current emotional state, which is then used as key information for generating fashion suggestions.

[1294] Data analysis

[1295] The server retrieves basic user information from the database and combines it with emotional state information from the emotion engine for analysis. This analysis is performed using a generative AI model. The generative AI model inputs the user's age, gender, body type, preferred style, daily activities, special event information, and emotional state, and combines this with the latest fashion trend data to generate optimal fashion suggestions.

[1296] Fashion proposal generation

[1297] The fashion suggestions generated by the generative AI model show the combination of clothing and accessories that best suit the user's profile and emotional state, which are then appropriately formatted and ready to be displayed to the user.

[1298] Fashion suggestion display

[1299] The server sends the analysis results to the device, which receives them and displays visual fashion suggestions to the user. The user can review the suggested styling and provide feedback if necessary. This feedback is sent back to the server and used to improve the accuracy of future suggestions.

[1300] Specific examples

[1301] For example, a 25-year-old female user enters the following information into an input form: "Name: Hanako," "Age: 25," "Gender: Female," "Body type: Slim," "Preferred style: Casual," "Daily activity: Office work," "Special event: Friend's wedding." In addition to this information, the emotion engine analyzes the user's facial expressions and voice and identifies their current emotional state as "happy." The generative AI model takes this emotional information into account and generates fashion suggestions such as: "A light-colored casual blouse and denim jeans to match a happy mood, and a navy blazer and skirt for office work days," and "An elegant dress and pearl accessories for a friend's wedding." This content is displayed on the user's device, allowing the user to choose an outfit based on the suggestions.

[1302] As described above, the fashion suggestion system of the present invention can provide an optimal fashion style by taking into consideration the basic information and emotional state of the user, thereby increasing user satisfaction.

[1303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1304] Step 1: Enter user data

[1305] When a user accesses a website or application, the terminal displays an input form.

[1306] Input: User basic information (e.g., name, age, gender, body type, preferred style, daily activities, special events)

[1307] How it works: The user enters basic information into the device's input form. The device detects this input in real time and performs input validation. The device then uses the camera and microphone to record the user's facial expressions and voice.

[1308] Step 2: Collect and send data

[1309] Once the input form is completed, the device converts the entered basic information and collected emotional data into JSON format and sends it to the server.

[1310] Input: Basic information and emotion data

[1311] Data processing: Convert the input information into JSON format

[1312] Specific operation: When the user presses the button to confirm the input, the device parses the basic information and recorded emotion data into JSON format and sends it to the server via a security layer. The data is then encrypted before being sent.

[1313] Step 3: Receiving and storing data

[1314] The server receives basic information and emotion data in JSON format sent from the device and stores them in a database.

[1315] Input: Basic information and emotion data in JSON format

[1316] Data processing: Parse the received data and save it in a database

[1317] Specific operation: The server checks the format of the received data and, if there are no errors, saves it to the database. When saving, each field is properly mapped and associated with the user ID.

[1318] Step 4: Analysis by the Emotion Engine

[1319] The server passes the emotion data to the emotion engine, which analyzes the user's facial expressions and voice.

[1320] Input: Emotion data

[1321] Data calculation: Identifying emotional states (e.g., happy, sad)

[1322] Specific operation: The emotion engine converts the received facial and voice data into multidimensional vectors and performs comparative analysis with existing emotion models. As a result, the emotional state is identified as "happy," for example.

[1323] Step 5: Analyze the data

[1324] The server retrieves basic information about the user from the database, combines it with emotional state information from the emotion engine, and inputs it into the generative AI model.

[1325] Input: Basic information and emotional state information

[1326] Data processing: Integrate basic information and emotional state and input it into a generative AI model

[1327] How it works: The generative AI model takes in the latest fashion trend data, as well as the basic information and emotional state provided, and generates the most suitable fashion suggestions for the user based on this.

[1328] Step 6: Generate fashion suggestions

[1329] The generative AI model generates optimal fashion suggestions based on the user's basic information and emotional state.

[1330] Input: Integrated dataset

[1331] Output: Fashion suggestions (e.g., a light-colored casual blouse with denim jeans, or an elegant dress with pearl accessories)

[1332] How it works: The generative AI model makes inferences based on the input data and generates the most suitable fashion style for the user. The generated suggestions are then properly formatted and ready to be sent to the device.

[1333] Step 7: Displaying fashion suggestions

[1334] The server transmits the generated fashion suggestions to the terminal, which receives them and displays them to the user.

[1335] Input: Fashion suggestions

[1336] Output: Fashion suggestions displayed to the user

[1337] How it works: The server sends fashion suggestions in JSON format to the device. The device receives them and displays them for the user to visually confirm. The user can then choose an outfit based on the suggestions and provide feedback. The feedback is then sent back to the server and used to improve the system's accuracy.

[1338] In this way, the system can provide personalized fashion suggestions that take into account the user's basic information and emotional state, thereby increasing user satisfaction.

[1339] (Application example 2)

[1340] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1341] While modern ad delivery systems commonly target ads based on users' personal data, personalized ad delivery that takes into account users' emotional state is not widely used. This makes it difficult to deliver appropriate ads based on users' real-time emotional state, resulting in limited advertising effectiveness. Furthermore, conventional systems are unable to fully incorporate user feedback, making it difficult to improve advertising accuracy. To address these issues, there is a need for the development of a personalized ad delivery system that takes into account both users' personal information and emotional data.

[1342] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1343] In this invention, the server includes an input means for inputting basic information and emotional data of an individual, a storage means for storing the basic information and emotional data in a database, and an analysis means for analyzing the basic information and emotional data to generate personalized suggestions, thereby enabling highly accurate personalized advertisement delivery according to the user's real-time emotional state.

[1344] "Basic personal information" refers to individual identification information such as the user's name, age, sex, body type, preferred style, daily activities, and special events.

[1345] "Emotion data" is information about the current emotional state obtained by analyzing the user's facial expressions and voice.

[1346] "Input means" refers to an interface such as a website or application through which a user inputs basic personal information and emotional data.

[1347] The "storage means" is a system for storing the input basic information and emotion data in a database.

[1348] "Analysis Measure" means a system that uses generative AI models or other analytical methods to generate personalized recommendations using input background information and sentiment data.

[1349] "Display means" refers to a display or device for visually presenting the personalized suggestions generated by the analysis means to the user.

[1350] A "generative AI model" is an artificial intelligence model that analyzes a user's basic information and emotional data to generate optimal advertisements and fashion suggestions.

[1351] The present invention is a system for delivering personalized advertisements to users based on basic information and emotion data of individuals. Specific embodiments for carrying out the present invention will be described below.

[1352] 1. Entering user data

[1353] The server provides an interface through a website or application that allows users to input basic personal information (such as name, age, gender, body type, preferred style, daily activities, and special events). The device also uses a camera and microphone to record the user's facial expressions and voice, and obtains emotional data. This information is collected in real time.

[1354] 2. Data storage

[1355] The server receives the basic information and emotion data entered in JSON format and stores it in a database, which eliminates the need for re-entry the next time the user accesses the system, and enables more accurate analysis based on past data.

[1356] 3. Use of sentiment analysis engines

[1357] The server passes the collected emotional data to an emotion analysis engine to analyze the user's current emotional state, using software such as OpenCV and Google Cloud Speech-to-Text API.

[1358] 4. Data Analysis

[1359] The server uses a generative AI model to analyze the user's basic information and emotional state, and generates optimized ad suggestions for the user. This analysis is performed using an artificial intelligence framework such as TensorFlow.

[1360] 5. Generating Ad Proposals

[1361] The ad suggestions generated by the generative AI model are best suited to the user's profile and emotional state, and the server then formats these suggestions appropriately and prepares them for display to the user.

[1362] 6. Display of advertising suggestions

[1363] The server sends the analysis results to the device, which visually displays the ads to the user. The user can review the suggested ads and provide feedback, which is sent back to the server and used to improve future suggestions.

[1364] Specific examples

[1365] For example, if a 25-year-old female user enters basic information and the emotional data identifies her current emotional state as "happy," the ad suggestions generated by the server will be in the following format:

[1366] Example prompt sentence:

[1367] "Hi! You're attending a friend's wedding today, right? How about this new dress that reflects your fun spirit? Click here for more details."

[1368] This system enables more effective and personalized advertising based on the user's real-time emotional state.

[1369] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1370] Step 1: Enter user data

[1371] When a user accesses a website or application, they enter basic personal information (such as name, age, gender, body type, preferred style, daily activities, and special events) into a form. The device's camera and microphone also record the user's facial expressions and voice in real time, collecting emotional data. This information is sent to the server in JSON format.

[1372] Input: User basic information and emotional data

[1373] Output: User data in JSON format

[1374] Step 2: Save your data

[1375] The server analyzes the user data received in JSON format and saves it in a database. This saving process saves the user the trouble of having to enter the same information twice, and allows for more accurate analysis by referencing past data.

[1376] Input: User data in JSON format

[1377] Output: User data stored in the database

[1378] Step 3: Sentiment Analysis

[1379] The server passes the stored emotion data to an emotion analysis engine, which uses OpenCV and the Google Cloud Speech-to-Text API to analyze the user's facial expressions and voice to determine their current emotional state.

[1380] Input: Emotion data

[1381] Output: User's emotional state

[1382] Step 4: Analyze the data

[1383] The server retrieves the user's basic information and emotional state from the database and analyzes it using a generative AI model, which combines the user's personal data and emotional state to generate optimal advertising suggestions.

[1384] Input: User's basic information and emotional state

[1385] Output: Generated ad suggestions

[1386] Step 5: Generate advertising proposals

[1387] The ad suggestions generated by the generative AI model are best suited to the user's profile and emotional state, and the server then formats these suggestions and prepares them for transmission to the device.

[1388] Input: Generated ad proposals

[1389] Output: Formatted ad proposal

[1390] Step 6: View Ad Proposals

[1391] The server sends the analysis results to the device, which visually displays the ads to the user. The user can review the suggested ads and provide feedback, which is sent back to the server and used to improve future suggestions.

[1392] Input: Formatted ad proposal

[1393] Output: Display advertisement and feedback to the user

[1394] This system enables more effective and personalized advertising based on the user's real-time emotional state.

[1395] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1396] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1397] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1398] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1399] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1400] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1401] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1402] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1403] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1404] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1405] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1406] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1407] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1408] 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.

[1409] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1410] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1411] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1412] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1413] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1414] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1415] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1416] The following is further disclosed regarding the above embodiment.

[1417] (Claim 1)

[1418] an input means for inputting basic information of an individual;

[1419] a storage means for storing the basic information in a database;

[1420] an analysis means for analyzing the basic information and generating personalized fashion suggestions;

[1421] a display means for displaying fashion suggestions generated by the analysis means;

[1422] A fashion suggestion system including:

[1423] (Claim 2)

[1424] 10. The fashion suggestion system of claim 1, wherein the analysis means utilizes an artificial intelligence model to generate fashion suggestions.

[1425] (Claim 3)

[1426] 2. The fashion suggestion system according to claim 1, wherein the input means operates via a website or an application.

[1427] "Example 1"

[1428] (Claim 1)

[1429] an input means for inputting basic information of an individual;

[1430] means for converting the basic information into a JSON format and transmitting the JSON format to a server;

[1431] a storage means for storing the basic information in a database;

[1432] analysis means that utilizes an artificial intelligence model to analyze the basic information and generate personalized fashion suggestions;

[1433] means for formatting the fashion suggestions generated by said analyzing means;

[1434] display means for displaying said formatted fashion suggestions;

[1435] means for analyzing feedback provided by a user through said display means and improving the accuracy of suggestions;

[1436] A system including:

[1437] (Claim 2)

[1438] 10. The system of claim 1, wherein the analysis means utilizes an artificial intelligence model (e.g., TensorFlow or PyTorch) to generate fashion suggestions.

[1439] (Claim 3)

[1440] 2. The system according to claim 1, wherein the input means operates via a website or an application and displays an input form for inputting basic information of the user.

[1441] "Application Example 1"

[1442] (Claim 1)

[1443] an input means for inputting basic information of an individual;

[1444] a storage means for storing the basic information in a database;

[1445] an analysis means for analyzing the basic information and generating personalized fashion suggestions;

[1446] a virtual try-on means for trying on the outfits suggested based on the user's avatar in a virtual space;

[1447] a display means for displaying the fashion proposals generated by the analysis means and confirming them in a 3D environment;

[1448] A system including:

[1449] (Claim 2)

[1450] The system of claim 1 , wherein the analysis means utilizes an artificial intelligence model to generate fashion suggestions.

[1451] (Claim 3)

[1452] The system of claim 1 , wherein the input means operates via a website or an application.

[1453] "Example 2: Combining Emotion Engines"

[1454] (Claim 1)

[1455] an input means for inputting basic information of an individual;

[1456] a storage means for storing the basic information and emotion data in a database;

[1457] an analysis means for analyzing the basic information and emotion data to generate personalized fashion suggestions;

[1458] a display means for displaying fashion suggestions generated by the analysis means;

[1459] A system including:

[1460] (Claim 2)

[1461] The system of claim 1 , wherein the analysis means utilizes a generative AI model to generate fashion suggestions.

[1462] (Claim 3)

[1463] The system of claim 1 , wherein the input means operates via a website or an application.

[1464] "Application example 2 when combining emotion engines"

[1465] (Claim 1)

[1466] an input means for inputting basic information and emotional data of an individual;

[1467] a storage means for storing the basic information and emotion data in a database;

[1468] analysis means for analyzing the basic information and emotion data to generate personalized suggestions;

[1469] a display means for displaying the proposal generated by the analysis means;

[1470] A system including:

[1471] (Claim 2)

[1472] 10. The system of claim 1, wherein the analysis means utilizes a generative AI model to generate the recommendations.

[1473] (Claim 3)

[1474] The system of claim 1 , wherein the input means operates via a website or an application. [Explanation of symbols]

[1475] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting basic information of an individual; a storage means for storing the basic information in a database; an analysis means for analyzing the basic information and generating personalized fashion suggestions; a display means for displaying fashion suggestions generated by the analysis means; A fashion suggestion system including:

2. The fashion suggestion system of claim 1 , wherein the analysis means utilizes an artificial intelligence model to generate fashion suggestions.

3. The fashion suggestion system according to claim 1 , wherein the input means operates via a website or an application.

Images