system

Abstract

We provide the system. [Solution] Means for inputting and collecting users' personal information and preference information, A means for generating individual user profiles based on the input information, A means for automatically generating clothing designs based on generated profiles and fashion trend data, A means of presenting users with multiple design options and allowing them to choose, A means for processing orders and payments for designs selected by the user, By analyzing user feedback and fashion trend data, we can continuously improve the quality of our designs. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional clothing design and sales systems, it is difficult to customize according to individual user preferences and body types, and there are also limitations in providing designs that quickly reflect fashion trends. As a result, there has been a problem that users need time and effort to find clothes with the most suitable design for themselves. The purpose of this invention is to provide a system that can utilize a generative AI model to enable flexible and rapid customization based on individual user profiles and solve these problems.

Means for Solving the Problems

[0005] This invention provides a system comprising means for collecting personal information and preference information entered by a user to generate an individual profile, and means for an AI model to automatically generate designs based on the generated profile and fashion trend data. Furthermore, by combining means for presenting the user with multiple design options and enabling selection, and means for ordering and processing payment for the selected design, the system enables users to efficiently design and purchase clothing that meets their needs. In addition, by incorporating means for analyzing user feedback and fashion trend data to improve the quality of the designs, it is possible to provide optimal designs that always reflect the latest trends.

[0006] A "user" refers to an end-user who uses the system to input personal information and preferences, select clothing designs, and make purchases.

[0007] "Personal information" refers to information unique to a user, such as age, gender, height, and weight, which the user enters.

[0008] "Preference information" refers to information related to the user's fashion preferences, such as colors, styles, and patterns, which the user enters.

[0009] A "profile" is a dataset generated specifically for a user based on their personal information and preferences.

[0010] A "generative AI model" is an artificial intelligence technology used to automatically generate clothing designs based on individual user profiles and fashion trend data.

[0011] "Fashion trend data" refers to data on current fashions and trends, which is reflected in the design process.

[0012] "Design" refers to clothing design proposals created by a generative AI model.

[0013] "Ordering" refers to the process of ultimately purchasing clothing based on the design selected by the user.

[0014] "Payment processing" refers to the procedure for completing the payment required when a user places an order.

[0015] "User feedback" refers to opinions and impressions that users provide about the design and services of a system after using it. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a digital platform that utilizes generative AI models to provide clothing customization tailored to the individual needs of users. In implementing this system, the server, terminal, and user exchange data with each other, and the process proceeds sequentially.

[0038] First, the user accesses the system via their device and enters personal and preference information. This includes the user's age, gender, height, weight, and preferred colors and styles. The device collects this information and securely transmits it to the server using encryption technology. Based on the transmitted data, the server creates a unique profile for each user. This profile provides the foundational information necessary to enable the user's customized experience.

[0039] Next, the server utilizes a generative AI model to design clothing using collected profile information and fashion trend data. The server generates multiple design options and presents the selected design to the user. The user can then choose their preferred design from the presented options via their device.

[0040] Once the selected design is confirmed, the user proceeds with the order. The terminal displays an order confirmation interface, and the user enters their payment information. Payment is processed on the server through a secure process. The server then sends manufacturing instructions to partner factories and manufacturing departments, and the selected design is brought to market.

[0041] The key features of this system are personalized service tailored to each user and the provision of designs that reflect the latest fashion trends. The server regularly analyzes user feedback and new fashion trends, updating its AI model to provide more accurate and innovative suggestions. In this way, the user, device, and server interact together to provide users with a rich and comfortable customized experience.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users log in to the system via their device and access an interface to enter personal and preference information. Users enter their name, age, gender, size information, preferred colors, style, etc. The device then compiles this information on a confirmation screen and displays a message for the user to verify that the information is correct.

[0045] Step 2:

[0046] The device encrypts the information the user has confirmed and sends it to the server. The server receives this information and stores the user's individual profile in a database. Based on the stored information, the server creates a profile that reflects the user's preferences and physical characteristics.

[0047] Step 3:

[0048] The server runs a generative AI model to generate multiple clothing design options based on the user profile. Simultaneously, the server analyzes fashion trend data and provides designs that incorporate the latest trends.

[0049] Step 4:

[0050] The server sends the generated design proposals to the terminal. The user views the design proposals via the terminal and selects the one that best suits their preferences. The terminal displays the selected design as a confirmation screen and prompts the user for final confirmation.

[0051] Step 5:

[0052] The user enters their payment information to confirm their order for the selected design. The terminal securely transmits the user's payment information to the server in accordance with security protocols.

[0053] Step 6:

[0054] The server processes the payment and, if successful, sends an order confirmation email to the user. The server then instructs the manufacturing department or partner factory to produce and deliver the user's selected design.

[0055] Step 7:

[0056] The server collects user feedback and continuously updates fashion trend data. Using this information, the server aims to further improve user satisfaction by enhancing the accuracy of its generated AI models.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] There is a need for methods to meet users' desire to customize fashion based on their individual preferences and personalities, but conventional systems have problems with insufficient individual support and difficulty in providing designs that keep up with trends. Ensuring the security of user information is also a crucial issue.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for generating individual user profiles, means for automatically generating clothing designs based on trend data, and means for running a model to evaluate design proposals and provide the optimal choice. This enables safe and up-to-date fashion customization tailored to the individual needs of the user.

[0062] "User personal information" refers to data that enables a unique identification of a user, including, but not limited to, name, age, gender, and contact information.

[0063] "Preference information" refers to data that indicates a user's hobbies and fashion preferences, including, but not limited to, preferences regarding color, style, and material.

[0064] A "profile" is a dataset generated based on a user's individual characteristics and preferences, and serves as the foundation for providing customized fashion suggestions.

[0065] "Trend data" refers to information about fashion and market trends that change over time, and it reflects the latest styles and consumer preferences in the fashion industry.

[0066] A "generative AI model" is a system that uses artificial intelligence technology to generate new designs and options. It analyzes user profiles and behavioral data and automatically makes suggestions.

[0067] "Encryption technology" is a technology used to ensure the security of information by converting data into a format that cannot be deciphered by others.

[0068] "Design proposals" refer to the clothing design options generated by the generative AI model, which ultimately include a variety of styles that the user can choose from.

[0069] "Means of running the model" refers to methods for making a generative AI model executable and for carrying out the process of generating and evaluating design proposals.

[0070] The system of this invention operates through the cooperation of three parties: the user, the terminal, and the server. Specific details are described below.

[0071] First, the user accesses the system using their own device. During this process, the user enters personal and preference information into a dedicated interface. This information includes, for example, age, gender, favorite colors, and preferred styles. This information is encrypted using the SSL / TLS protocol by the device and securely transmitted to the server.

[0072] The server generates individual user profiles based on the received user information. These profiles serve as the foundational data for providing fashion suggestions tailored to each user's personality. A database system is used for profile generation.

[0073] The server then uses a generative AI model to generate clothing design proposals. This generative AI model runs on a machine learning platform such as TENSORFLOW® and analyzes trend and profile data. An example of a prompt used here is, "Generate a design for a casual jacket with a blue base color, suitable for men in their 30s."

[0074] The generated design proposals are sorted and evaluated by the server. Based on this evaluation, the top-rated design proposal is selected and sent to the user's device. The user can then review the design proposals displayed on the device and select their preferred design. This selection is optimized based on the user's past preferences and current trends.

[0075] Once the selection is complete, the user enters their payment information to proceed with the order. The entered information is encrypted again on the terminal and sent to the server. The server communicates with the payment service provider and processes the payment securely.

[0076] This series of processes provides a system that offers customized clothing tailored to individual user needs, along with a safe and efficient ordering process.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users access the system using a terminal and input personal and preference information into the interface. This data includes age, gender, favorite colors, and preferred styles. The terminal encrypts the entered data using the SSL / TLS protocol and securely transmits it to the server. Based on this input, basic data is prepared for generating a profile tailored to the user's individuality.

[0080] Step 2:

[0081] The server receives encrypted user information from the terminal and decodes it. It stores the received information in a database system and generates an individual user profile. At this time, the server uses a profile generation algorithm to construct a data structure based on the input personal information and preference information. The output is a user-specific profile object.

[0082] Step 3:

[0083] The server utilizes a generative AI model to analyze profile data and trend data to generate multiple clothing design options. This process uses machine learning platforms such as TensorFlow, and the latest trends are referenced as trend data. The prompt "Generate a casual jacket design for a man in his 30s, primarily in blue" is input to the AI ​​model, and the model generates multiple design options based on this. The output is a list of design options ready for evaluation.

[0084] Step 4:

[0085] The server scores the generated design proposals using an internal evaluation algorithm and selects the most suitable design proposal. User profile information and trend data are used as evaluation criteria. The selected design proposal is sent to the terminal as a suggestion that best reflects the user's preferences. The output is an optimized design proposal that can be presented to the user.

[0086] Step 5:

[0087] The user reviews the design options displayed on their device and selects a style they find appealing. After making their selection, the user proceeds to the order screen on their device and enters their payment details. The entered information is encrypted again and sent to the server. This output is generated as payment data for order confirmation.

[0088] Step 6:

[0089] The server transmits the received payment information to the payment service provider for secure processing. Once payment authorization is obtained, the server generates a manufacturing order and sends it to the management system of the partner manufacturing plant, initiating the product development process based on the design proposal. As an output, the product is ready for final manufacturing instructions.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] Modern customers demand personalized shopping experiences and real-time try-on experiences, but traditional online shopping fails to meet these needs. In particular, the difficulty in visually confirming clothing characteristics in a virtual space often leads to anxiety in purchasing decisions. Addressing this challenge requires improvements to the user interface and the adoption of new technologies.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for inputting and collecting user characteristic information and preference information, means for automatically generating clothing designs based on the generated information set and clothing trend data, and means for trying out clothing designs in a virtual space using a glasses-type device. This allows users to virtually try on clothes that fit their own body type, enabling a more confident and customized selection experience.

[0095] "User characteristic information" refers to a collection of basic personal information about the user, such as age, gender, height, and weight.

[0096] "Preference information" refers to individual preference data such as a user's tastes, style, and favorite colors.

[0097] An "information set" is a collection of user-specific information generated based on user characteristic information and preference information.

[0098] "Apparel trend data" refers to a collection of market data that shows the latest fashion and trends.

[0099] "Methods for automatically generating clothing designs" refers to a process in which an AI model generates appropriate clothing designs using an input set of information.

[0100] A "glasses-type device" is a device equipped with wearable technology that provides users with visual information using augmented reality or virtual reality.

[0101] "A means of trying out clothing designs in a virtual space" refers to a technology that allows users to virtually try on clothing and visualize designs through a glasses-type device.

[0102] The system for implementing this invention involves the coordinated functioning of a user, a terminal, and a server. The user first inputs characteristic and preference information through the terminal. The terminal collects this information and securely transmits it to the server using encryption technology. The server generates a unique information set for each user based on the transmitted information. This information set serves as the foundation for generating clothing designs suitable for the user.

[0103] The server utilizes a generative AI model to automatically generate clothing designs based on an information set and clothing trend data. Furthermore, the server generates multiple design options and presents them to the user. The user can virtually try on the designs using a glasses-type device. This device provides visual feedback to the user, enabling real-time design confirmation.

[0104] Users select their preferred designs through virtual try-ons and complete the transaction on their terminal. The selected designs, along with order information, are sent to the server, and the manufacturing process begins. Through this system, users can have a more accurate and secure clothing selection experience.

[0105] As a concrete example, consider a case where a user wears smart glasses and selects clothes in a virtual store while relaxing at home. This user can view several jacket designs adjusted based on their body shape data and confirm the style they prefer.

[0106] Examples of prompt messages include, "Generate a sporty casual jacket design based on the user profile and display it immediately on the 3D avatar."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] Users use a device to input characteristic information (age, gender, height, weight, etc.) and preference information (favorite colors, styles). This input data is collected by the device. The device encrypts the information and securely transmits it to the server.

[0110] Step 2:

[0111] The server decrypts the encrypted information received from the terminal and generates a unique information set for each user. To generate this profile information set, it performs data processing that integrates the received characteristic and preference information. The generated information set forms the basis for customizing the user's clothing design.

[0112] Step 3:

[0113] The server utilizes a generative AI model to automatically generate clothing designs based on an information set and clothing trend data. Prompts instruct the AI ​​model to generate appropriate designs. The output of this step is multiple clothing design proposals.

[0114] Step 4:

[0115] The server presents the user with multiple generated design options. The user can visualize and review these designs in a virtual space in real time through a glasses-type device. These designs are then applied to a 3D avatar in real time to obtain the user's visual feedback.

[0116] Step 5:

[0117] The user selects their preferred design through a virtual try-on. Information about the selected design is sent to the terminal for transaction processing. The terminal receives payment information from the user and securely transmits that information to the server.

[0118] Step 6:

[0119] The server processes the received order and payment information and issues manufacturing instructions based on the design. These manufacturing instructions are sent to partner factories and manufacturing departments, and production of the garments based on the user's selection begins.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] This invention, which incorporates an emotion engine, is a system that enables the customization of clothing designs by taking into account not only the user's input information but also their emotional state at any given time. This system involves collaboration between a server, a terminal, and the user to collect and analyze data.

[0122] Users access the system via their device and input basic information and style preferences. The emotion engine analyzes the user's facial expressions and voice tone through the camera and microphone, recognizing their emotional state in real time. This allows the system to accumulate not only temporary emotions but also long-term emotional data, contributing to improved profile accuracy.

[0123] The device encrypts the information obtained from the user and sends it to the server. The server creates a user profile and constructs a customized dataset that includes emotional states. Next, a generative AI model uses this data to generate multiple clothing design suggestions that best match the user's preferences and current emotions.

[0124] The server sends the generated design proposals to the terminal for the user to review. As the user selects a design from the presented options, the emotional response to that design is further analyzed by an emotion engine. Based on the design selected by the user, the terminal proceeds with the order process and instructs the server to complete the payment.

[0125] The server collects and analyzes user feedback and sentiment data to continuously improve the generative AI model. Through this feedback process, the user experience is enhanced while the design reflects the latest fashion trends.

[0126] Thus, this system, through a multidimensional profile that includes the user's emotional state, can suggest optimal clothing designs that reflect not only the user's preferences and body type, but also their mental state at that time. As a result, it can provide a personalized and highly satisfying service.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users log in to the system via their device and access screens where they can enter personal and preference information. In addition to basic information, users answer questionnaires about their current mood and preferred style. The emotion engine uses the camera and microphone to analyze the user's facial expressions and voice tone in real time and record their emotions at that time.

[0130] Step 2:

[0131] The device encrypts the information and sentiment data collected from the user and sends it to the server. The server receives this information and generates a user profile. The profile includes basic information, preference information, and sentiment information.

[0132] Step 3:

[0133] The server generates clothing design proposals by combining the generated user profile and accumulated fashion trend data based on the AI ​​model. This results in multiple design options that reflect the user's individual preferences and emotions at the time.

[0134] Step 4:

[0135] The server sends the generated design proposals to the terminal and presents them to the user. The user views the presented design proposals via the terminal and selects the one that best suits them. At this point, the emotion engine re-analyzes the user's response and records an evaluation based on the emotion data.

[0136] Step 5:

[0137] The user confirms their order with their selected design and enters their payment information. The terminal transmits this information to the server using a secure protocol.

[0138] Step 6:

[0139] The server processes the payment and, if successful, sends an order confirmation email to the user. The server then instructs the manufacturing department or partner factory to produce and deliver the selected design.

[0140] Step 7:

[0141] The server collects and analyzes user feedback and sentiment data, and updates the generated AI model based on this. This process allows the system to continuously improve the user experience and more effectively incorporate sentiment data into future suggestions.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] In today's world, there is a demand for personalized products that reflect the individual tastes and preferences of users. However, it is difficult to provide highly satisfying customized services because temporary and dynamic information such as the user's emotional state is not being fully utilized. This invention aims to realize the provision of automated and highly accurate product design based on a multidimensional profile that includes the user's real-time emotional state.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for analyzing the user's emotional state in real time and accumulating emotional data over a long period of time, means for generating an individual user profile based on the acquired information and emotional data, and means for generating multiple design proposals optimized for the user's tastes, preferences, and emotions using a generation AI model. This makes it possible to propose personalized clothing designs that take the user's emotional state into consideration.

[0147] "Personal information" refers to information about a user's individual characteristics, such as height, weight, age, and gender.

[0148] "Tastes and preferences" refers to information about a user's individual preferences, such as their favorite colors, styles, and fashion tastes.

[0149] "Emotional state" refers to information about a user's psychological state at a given time, obtained by analyzing their facial expressions and tone of voice.

[0150] A "profile" is a unique dataset that integrates a user's personal information, hobbies, preferences, emotional state, and other data.

[0151] "Trend data" refers to information about current fashion and design trends.

[0152] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate design proposals optimized for the user.

[0153] A "design proposal" refers to multiple clothing design plans suggested by a generative AI model.

[0154] "Encryption technology" is a technology that transforms data to ensure secure communication and storage, preventing third parties from deciphering it.

[0155] "Feedback" refers to opinions and comments from users after they have used a service or product, and is information that helps improve the service or product.

[0156] This system involves the user, terminal, and server working together to suggest personalized clothing designs that reflect the user's emotional state. Users access the system by inputting personal information and preferences using their terminal. The terminal is equipped with a camera and microphone, through which an emotion engine analyzes the user's facial expressions and voice tone in real time to recognize their emotional state.

[0157] The device encrypts the data obtained from the user and securely transmits it to the server. The server uses this data to create a user profile, building a detailed profile that reflects the user's emotional state. Combining this profile with trend data, a generative AI model is used to generate clothing designs optimized for the user.

[0158] For example, if a user inputs "I want casual, relaxed clothes," and the camera captures the user's smile, the system will prioritize the relaxed emotion and suggest casual designs. An example of a prompt to the generative AI model would be a specific instruction such as, "Generate a casual design that suits this user's preferences. The current emotion indicates happiness."

[0159] The generated design proposals are sent to the terminal for the user to review and select. Based on the user's selected design, the terminal then instructs the server to complete the ordering process and payment. The server also aggregates user feedback and emotional data, continuously improving the generating AI model to enhance the quality of the service. In this way, the system realizes design proposals based on a multidimensional profile that reflects the user's mind and emotional state.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] Users access the system via a terminal and input personal information and preferences. The terminal formats this information and sends it to the server as input. In this process, the terminal uses a form input window and receives personal information as text data.

[0163] Step 2:

[0164] The device uses its built-in camera and microphone to collect the user's real-time facial expressions and voice tone. This data is sent to the emotion engine and becomes input data for analyzing the user's emotional state. For example, if a smile is detected, it is output as "joy" in the emotion data.

[0165] Step 3:

[0166] The device encrypts the collected personal information, hobbies, preferences, and emotional data before sending it to the server. This encryption process uses secure communication protocols such as TLS to send the data, thereby enhancing the protection of the information.

[0167] Step 4:

[0168] The server generates a user profile using the received information. It takes personal information, hobbies, preferences, and emotional data as input, builds a database with this information, and outputs detailed user profile data.

[0169] Step 5:

[0170] The server activates the generative AI model and receives user profile and trend data as prompts. This allows the AI ​​to output highly personalized clothing design suggestions. For example, the AI ​​might be given a message such as, "Generate a casual design that suits this user's preferences. Their current mood is relaxed."

[0171] Step 6:

[0172] The server sends the generated clothing design proposals to the terminal. The terminal displays the design proposals to the user and provides a selectable interface. The user views these and selects their preferred design by touching or clicking.

[0173] Step 7:

[0174] Once the user has made their selection, the device analyzes the user's emotional response again and sends the data back to the server. This includes facial expression analysis after the selection, capturing a response, for example, as joy.

[0175] Step 8:

[0176] The terminal instructs the server on the order and payment process based on the design selected by the user. The server processes the order and subsequent payment, and sends the results to the terminal.

[0177] Step 9:

[0178] The server collects user feedback and sentiment data and analyzes it to improve the generated AI model. This allows the system to continuously improve the quality of its services and design.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] Traditionally, clothing designs suggested based solely on individual preferences and basic information failed to reflect the user's temporary emotional state, resulting in incomplete personalization. Furthermore, it was difficult to propose designs that took real-time emotions into account, posing a challenge to increasing purchase satisfaction. Additionally, there was a lack of emotional response-based support in the user's selection process. Therefore, there was a need for more accurate, personalized design suggestions that comprehensively considered the user's emotional state.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes means for inputting and collecting basic user information, preference information, and emotional state information; means for generating individual user profiles based on the input information and emotional state information; and means for analyzing the user's real-time emotional state using an emotion engine and integrating it into the profile. This makes it possible to propose clothing designs that comprehensively consider the user's preferences and emotional state.

[0184] "User basic information" refers to data that indicates the user's attributes, including information such as gender, age, and region.

[0185] "Preference information" refers to data that represents a user's preferences, including style and color preferences, past purchase history, and other relevant information.

[0186] "Emotional state information" refers to data that indicates a user's temporary or long-term emotional state, and is obtained through facial recognition and voice tone analysis.

[0187] A "user-specific profile" is a dataset that comprehensively integrates a user's basic information, preferences, and emotional state, forming a profile tailored to that user.

[0188] An "emotion engine" is a technology or system that analyzes a user's voice and video data to recognize their emotional state in real time.

[0189] A "generative AI model" refers to an artificial intelligence algorithm or system that generates new suggestions or designs based on input data.

[0190] "Trend data" refers to information about current fashion market and social trends, and is trend information that influences clothing design.

[0191] "Selection support" refers to functions and methods that assist users in making more appropriate choices when selecting from presented options.

[0192] "Encryption technology" is a technology that transforms the content of data into a form that cannot be recognized by third parties, and is a means of protecting privacy and security.

[0193] The system for implementing this invention is a platform for proposing clothing designs that take into account the user's real-time emotional state. This system is based on the user's terminal, a server, and a generative AI model, and comprehensively handles the user's basic information, preference information, and emotional state information.

[0194] The device inputs and collects the user's basic information and preferences, and uses an emotion engine via the camera and microphone to analyze the user's real-time emotional state. The device then encrypts this data before sending it to the server.

[0195] The server uses a generative AI model to build individual user profiles based on the transmitted data, and automatically generates clothing designs while also considering fashion trend data. In this process, the generative AI model produces multiple design options that match the user's current emotional state.

[0196] The generated design proposals are presented to the user via the device, and the user makes a selection based on their emotional response and other factors. The server provides selection assistance, helping the user's choice be based on more solid reasoning.

[0197] The design proposal selected by the user is ordered and billed via the device. User feedback and sentiment data are also collected and analyzed on the server to improve the accuracy of future suggestions. This optimizes the entire system for the user experience.

[0198] For example, if a user is in the mood to relax on a holiday, the application could analyze their emotional state using an emotion engine and suggest casual clothing designs that would promote relaxation. An example of a prompt to the generative AI model would be: "User's emotional data: Relaxed, comfortable. Preferred style: Casual. Provide design suggestions."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The user inputs basic information and preference information through the device. The device uses its camera and microphone to collect the user's facial expressions and voice, and supplies this data to an emotion engine for real-time emotional analysis. The input for this step is the user's basic information, preference information, and emotional state data, which is then analyzed and encrypted before outputting the data.

[0202] Step 2:

[0203] The terminal sends encrypted user information to the server. The server decrypts the received data and generates an individual user profile. The input for this step is encrypted user data, which is then analyzed to output profile information.

[0204] Step 3:

[0205] The server uses a generative AI model to generate multiple clothing designs based on the user's profile and fashion trend data. The input for this step is the user profile and trend data, which are used to output design proposals. The generative AI model generates designs based on prompt messages.

[0206] Step 4:

[0207] The server sends the generated clothing design proposals to the terminal. The terminal presents the design proposals to the user, who then provides an emotional response. The input for this step is the design proposals, which are then presented to the user and emotional data is collected.

[0208] Step 5:

[0209] Based on the design selected by the user, the terminal instructs the server to process the order and invoice. The server then completes the order based on the user's transaction information. The input for this step is the design selected by the user, and the terminal outputs the order and invoice data.

[0210] Step 6:

[0211] The server analyzes user feedback and sentiment data to continuously improve the generative AI model. The input for this step is feedback and sentiment data, and based on this, it outputs update information to improve the performance of the generative AI model.

[0212] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0219] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] This invention is a digital platform that utilizes generative AI models to provide clothing customization tailored to the individual needs of users. In implementing this system, the server, terminal, and user exchange data with each other, and the process proceeds sequentially.

[0229] First, the user accesses the system via their device and enters personal and preference information. This includes the user's age, gender, height, weight, and preferred colors and styles. The device collects this information and securely transmits it to the server using encryption technology. Based on the transmitted data, the server creates a unique profile for each user. This profile provides the foundational information necessary to enable the user's customized experience.

[0230] Next, the server utilizes a generative AI model to design clothing using collected profile information and fashion trend data. The server generates multiple design options and presents the selected design to the user. The user can then choose their preferred design from the presented options via their device.

[0231] Once the selected design is confirmed, the user proceeds with the order. The terminal displays an order confirmation interface, and the user enters their payment information. Payment is processed on the server through a secure process. The server then sends manufacturing instructions to partner factories and manufacturing departments, and the selected design is brought to market.

[0232] The key features of this system are personalized service tailored to each user and the provision of designs that reflect the latest fashion trends. The server regularly analyzes user feedback and new fashion trends, updating its AI model to provide more accurate and innovative suggestions. In this way, the user, device, and server interact together to provide users with a rich and comfortable customized experience.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] Users log in to the system via their device and access an interface to enter personal and preference information. Users enter their name, age, gender, size information, preferred colors, style, etc. The device then compiles this information on a confirmation screen and displays a message for the user to verify that the information is correct.

[0236] Step 2:

[0237] The device encrypts the information the user has confirmed and sends it to the server. The server receives this information and stores the user's individual profile in a database. Based on the stored information, the server creates a profile that reflects the user's preferences and physical characteristics.

[0238] Step 3:

[0239] The server runs a generative AI model to generate multiple clothing design options based on the user profile. Simultaneously, the server analyzes fashion trend data and provides designs that incorporate the latest trends.

[0240] Step 4:

[0241] The server sends the generated design proposals to the terminal. The user views the design proposals via the terminal and selects the one that best suits their preferences. The terminal displays the selected design as a confirmation screen and prompts the user for final confirmation.

[0242] Step 5:

[0243] The user enters their payment information to confirm their order for the selected design. The terminal securely transmits the user's payment information to the server in accordance with security protocols.

[0244] Step 6:

[0245] The server processes the payment and, if successful, sends an order confirmation email to the user. The server then instructs the manufacturing department or partner factory to produce and deliver the user's selected design.

[0246] Step 7:

[0247] The server collects user feedback and continuously updates fashion trend data. Using this information, the server aims to further improve user satisfaction by enhancing the accuracy of its generated AI models.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] There is a need for methods to meet users' desire to customize fashion based on their individual preferences and personalities, but conventional systems have problems with insufficient individual support and difficulty in providing designs that keep up with trends. Ensuring the security of user information is also a crucial issue.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes means for generating individual user profiles, means for automatically generating clothing designs based on trend data, and means for running a model to evaluate design proposals and provide the optimal choice. This enables safe and up-to-date fashion customization tailored to the individual needs of the user.

[0253] "User personal information" refers to data that enables a unique identification of a user, including, but not limited to, name, age, gender, and contact information.

[0254] "Preference information" refers to data that indicates a user's hobbies and fashion preferences, including, but not limited to, preferences regarding color, style, and material.

[0255] A "profile" is a dataset generated based on a user's individual characteristics and preferences, and serves as the foundation for providing customized fashion suggestions.

[0256] "Trend data" refers to information about fashion and market trends that change over time, and it reflects the latest styles and consumer preferences in the fashion industry.

[0257] A "generative AI model" is a system that uses artificial intelligence technology to generate new designs and options. It analyzes user profiles and behavioral data and automatically makes suggestions.

[0258] "Encryption technology" is a technology used to ensure the security of information by converting data into a format that cannot be deciphered by others.

[0259] "Design proposals" refer to the clothing design options generated by the generative AI model, which ultimately include a variety of styles that the user can choose from.

[0260] "Means of running the model" refers to methods for making a generative AI model executable and for carrying out the process of generating and evaluating design proposals.

[0261] The system of this invention operates through the cooperation of three parties: the user, the terminal, and the server. Specific details are described below.

[0262] First, the user accesses the system using their own device. During this process, the user enters personal and preference information into a dedicated interface. This information includes, for example, age, gender, favorite colors, and preferred styles. This information is encrypted using the SSL / TLS protocol by the device and securely transmitted to the server.

[0263] The server generates individual user profiles based on the received user information. These profiles serve as the foundational data for providing fashion suggestions tailored to each user's personality. A database system is used for profile generation.

[0264] The server then uses a generative AI model to generate clothing design proposals. This generative AI model runs on a machine learning platform such as TensorFlow and analyzes trend and profile data. An example of a prompt used here is "Generate a design for a casual jacket with a blue base color, suitable for a man in his 30s."

[0265] The generated design proposals are sorted and evaluated by the server. Based on this evaluation, the top-rated design proposal is selected and sent to the user's device. The user can then review the design proposals displayed on the device and select their preferred design. This selection is optimized based on the user's past preferences and current trends.

[0266] Once the selection is complete, the user enters their payment information to proceed with the order. The entered information is encrypted again on the terminal and sent to the server. The server communicates with the payment service provider and processes the payment securely.

[0267] This series of processes provides a system that offers customized clothing tailored to individual user needs, along with a safe and efficient ordering process.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] Users access the system using a terminal and input personal and preference information into the interface. This data includes age, gender, favorite colors, and preferred styles. The terminal encrypts the entered data using the SSL / TLS protocol and securely transmits it to the server. Based on this input, basic data is prepared for generating a profile tailored to the user's individuality.

[0271] Step 2:

[0272] The server receives encrypted user information from the terminal and decodes it. It stores the received information in a database system and generates an individual user profile. At this time, the server uses a profile generation algorithm to construct a data structure based on the input personal information and preference information. The output is a user-specific profile object.

[0273] Step 3:

[0274] The server utilizes a generative AI model to analyze profile data and trend data to generate multiple clothing design options. This process uses machine learning platforms such as TensorFlow, and the latest trends are referenced as trend data. The prompt "Generate a casual jacket design for a man in his 30s, primarily in blue" is input to the AI ​​model, and the model generates multiple design options based on this. The output is a list of design options ready for evaluation.

[0275] Step 4:

[0276] The server scores the generated design proposals using an internal evaluation algorithm and selects the most suitable design proposal. User profile information and trend data are used as evaluation criteria. The selected design proposal is sent to the terminal as a suggestion that best reflects the user's preferences. The output is an optimized design proposal that can be presented to the user.

[0277] Step 5:

[0278] The user checks the design proposals displayed on the terminal and selects a style that is acceptable. After selection, the user proceeds to the order screen on the terminal and enters the detailed payment information. The entered information is encrypted again and sent to the server. This output is generated as payment data for order confirmation.

[0279] Step 6:

[0280] The server sends the received payment information to the payment service provider for secure processing. Once payment approval is obtained, the server generates a manufacturing instruction and sends it to the management system of the partnering manufacturing factory, thereby starting the commercialization process based on the design proposal. As an output, preparations are made for the issuance of a confirmed instruction for product manufacturing.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] Modern customers seek individualized purchasing experiences and real-time fitting experiences, but conventional online shopping cannot meet these demands. In particular, it is difficult to visually confirm the characteristics of clothing in a virtual space, and customers often feel anxious when making purchase decisions. To solve this problem, improvements in the UI and the utilization of new technologies are required.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for inputting and collecting the user's characteristic information and preference information, means for automatically generating clothing designs based on the generated information set and clothing trend data, and means for trying on clothing designs in a virtual space using a glasses-type device. As a result, the user can virtually try on clothes that fit their own body shape, enabling a customized experience that allows for a more confident selection.

[0286] The "user's characteristic information" is a collection of basic personal information such as the user's age, gender, height, and weight.

[0287] The "preference information" is individual preference data such as the user's preferences, style, and favorite colors.

[0288] The "information set" is a collection of the user's individual information generated based on the user's characteristic information and preference information.

[0289] The "clothing trend data" is a collection of market data indicating the latest fashion and trends.

[0290] The "means for automatically generating clothing designs" is a process in which an appropriate clothing design is created by an AI model using the input information set.

[0291] The "glasses-type device" is a device equipped with wearable technology that provides visual information to the user using augmented reality or virtual reality.

[0292] The "means for trying on clothing designs in a virtual space" is a technology that enables the user to virtually try on clothes through a glasses-type device and visualize the design.

[0293] The system for implementing this invention involves the coordinated functioning of a user, a terminal, and a server. The user first inputs characteristic and preference information through the terminal. The terminal collects this information and securely transmits it to the server using encryption technology. The server generates a unique information set for each user based on the transmitted information. This information set serves as the foundation for generating clothing designs suitable for the user.

[0294] The server utilizes a generative AI model to automatically generate clothing designs based on an information set and clothing trend data. Furthermore, the server generates multiple design options and presents them to the user. The user can virtually try on the designs using a glasses-type device. This device provides visual feedback to the user, enabling real-time design confirmation.

[0295] Users select their preferred designs through virtual try-ons and complete the transaction on their terminal. The selected designs, along with order information, are sent to the server, and the manufacturing process begins. Through this system, users can have a more accurate and secure clothing selection experience.

[0296] As a concrete example, consider a case where a user wears smart glasses and selects clothes in a virtual store while relaxing at home. This user can view several jacket designs adjusted based on their body shape data and confirm the style they prefer.

[0297] Examples of prompt messages include, "Generate a sporty casual jacket design based on the user profile and display it immediately on the 3D avatar."

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The user uses the terminal to input characteristic information (age, gender, height, weight, etc.) and preference information (favorite color, style). This input data is collected by the terminal. The terminal encrypts the information and securely sends it to the server.

[0301] Step 2:

[0302] The server decrypts the encrypted information received from the terminal and generates an individual information set for each user. To generate this profile information set, data processing is performed to integrate the received characteristic information and preference information. The generated information set serves as the basis for customizing the user's clothing design.

[0303] Step 3:

[0304] The server utilizes the generated AI model to automatically generate clothing designs based on the information set and clothing trend data. Instructions are given to the AI model to generate appropriate designs through prompt sentences. The output of this step is multiple clothing design proposals.

[0305] Step 4:

[0306] The server presents the generated multiple design proposals to the user. The user can visualize and confirm these designs in the virtual space in real time through the glasses-type device. To obtain the user's visual feedback, these designs are applied to the 3D avatar in real time.

[0307] Step 5:

[0308] The user selects the preferred design through virtual try-on. The information of the selected design is sent to the terminal for transaction processing. The terminal receives payment information from the user and securely sends that information to the server.

[0309] Step 6:

[0310] The server processes the received order and payment information and issues manufacturing instructions based on the design. These manufacturing instructions are sent to partner factories and manufacturing departments, and production of the garments based on the user's selection begins.

[0311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0312] This invention, which incorporates an emotion engine, is a system that enables the customization of clothing designs by taking into account not only the user's input information but also their emotional state at any given time. This system involves collaboration between a server, a terminal, and the user to collect and analyze data.

[0313] Users access the system via their device and input basic information and style preferences. The emotion engine analyzes the user's facial expressions and voice tone through the camera and microphone, recognizing their emotional state in real time. This allows the system to accumulate not only temporary emotions but also long-term emotional data, contributing to improved profile accuracy.

[0314] The device encrypts the information obtained from the user and sends it to the server. The server creates a user profile and constructs a customized dataset that includes emotional states. Next, a generative AI model uses this data to generate multiple clothing design suggestions that best match the user's preferences and current emotions.

[0315] The server sends the generated design proposals to the terminal for the user to review. As the user selects a design from the presented options, the emotional response to that design is further analyzed by an emotion engine. Based on the design selected by the user, the terminal proceeds with the order process and instructs the server to complete the payment.

[0316] The server collects and analyzes user feedback and sentiment data to continuously improve the generative AI model. Through this feedback process, the user experience is enhanced while the design reflects the latest fashion trends.

[0317] Thus, this system, through a multidimensional profile that includes the user's emotional state, can suggest optimal clothing designs that reflect not only the user's preferences and body type, but also their mental state at that time. As a result, it can provide a personalized and highly satisfying service.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] Users log in to the system via their device and access screens where they can enter personal and preference information. In addition to basic information, users answer questionnaires about their current mood and preferred style. The emotion engine uses the camera and microphone to analyze the user's facial expressions and voice tone in real time and record their emotions at that time.

[0321] Step 2:

[0322] The device encrypts the information and sentiment data collected from the user and sends it to the server. The server receives this information and generates a user profile. The profile includes basic information, preference information, and sentiment information.

[0323] Step 3:

[0324] The server generates clothing design proposals by combining the generated user profile and accumulated fashion trend data based on the AI ​​model. This results in multiple design options that reflect the user's individual preferences and emotions at the time.

[0325] Step 4:

[0326] The server sends the generated design proposals to the terminal and presents them to the user. The user views the presented design proposals via the terminal and selects the one that best suits them. At this point, the emotion engine re-analyzes the user's response and records an evaluation based on the emotion data.

[0327] Step 5:

[0328] The user confirms their order with their selected design and enters their payment information. The terminal transmits this information to the server using a secure protocol.

[0329] Step 6:

[0330] The server processes the payment and, if successful, sends an order confirmation email to the user. The server then instructs the manufacturing department or partner factory to produce and deliver the selected design.

[0331] Step 7:

[0332] The server collects and analyzes user feedback and sentiment data, and updates the generated AI model based on this. This process allows the system to continuously improve the user experience and more effectively incorporate sentiment data into future suggestions.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] In today's world, there is a demand for personalized products that reflect the individual tastes and preferences of users. However, it is difficult to provide highly satisfying customized services because temporary and dynamic information such as the user's emotional state is not being fully utilized. This invention aims to realize the provision of automated and highly accurate product design based on a multidimensional profile that includes the user's real-time emotional state.

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

[0337] In this invention, the server includes means for analyzing the user's emotional state in real time and accumulating emotional data over a long period of time, means for generating an individual user profile based on the acquired information and emotional data, and means for generating multiple design proposals optimized for the user's tastes, preferences, and emotions using a generation AI model. This makes it possible to propose personalized clothing designs that take the user's emotional state into consideration.

[0338] "Personal information" refers to information about a user's individual characteristics, such as height, weight, age, and gender.

[0339] "Tastes and preferences" refers to information about a user's individual preferences, such as their favorite colors, styles, and fashion tastes.

[0340] "Emotional state" refers to information about a user's psychological state at a given time, obtained by analyzing their facial expressions and tone of voice.

[0341] A "profile" is a unique dataset that integrates a user's personal information, hobbies, preferences, emotional state, and other data.

[0342] "Trend data" refers to information about current fashion and design trends.

[0343] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate design proposals optimized for the user.

[0344] A "design proposal" refers to multiple clothing design plans suggested by a generative AI model.

[0345] "Encryption technology" is a technology that transforms data to ensure secure communication and storage, preventing third parties from deciphering it.

[0346] "Feedback" refers to opinions and comments from users after they have used a service or product, and is information that helps improve the service or product.

[0347] This system involves the user, terminal, and server working together to suggest personalized clothing designs that reflect the user's emotional state. Users access the system by inputting personal information and preferences using their terminal. The terminal is equipped with a camera and microphone, through which an emotion engine analyzes the user's facial expressions and voice tone in real time to recognize their emotional state.

[0348] The device encrypts the data obtained from the user and securely transmits it to the server. The server uses this data to create a user profile, building a detailed profile that reflects the user's emotional state. Combining this profile with trend data, a generative AI model is used to generate clothing designs optimized for the user.

[0349] For example, if a user inputs "I want casual, relaxed clothes," and the camera captures the user's smile, the system will prioritize the relaxed emotion and suggest casual designs. An example of a prompt to the generative AI model would be a specific instruction such as, "Generate a casual design that suits this user's preferences. The current emotion indicates happiness."

[0350] The generated design proposals are sent to the terminal for the user to review and select. Based on the user's selected design, the terminal then instructs the server to complete the ordering process and payment. The server also aggregates user feedback and emotional data, continuously improving the generating AI model to enhance the quality of the service. In this way, the system realizes design proposals based on a multidimensional profile that reflects the user's mind and emotional state.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] Users access the system via a terminal and input personal information and preferences. The terminal formats this information and sends it to the server as input. In this process, the terminal uses a form input window and receives personal information as text data.

[0354] Step 2:

[0355] The device uses its built-in camera and microphone to collect the user's real-time facial expressions and voice tone. This data is sent to the emotion engine and becomes input data for analyzing the user's emotional state. For example, if a smile is detected, it is output as "joy" in the emotion data.

[0356] Step 3:

[0357] The device encrypts the collected personal information, hobbies, preferences, and emotional data before sending it to the server. This encryption process uses secure communication protocols such as TLS to send the data, thereby enhancing the protection of the information.

[0358] Step 4:

[0359] The server generates a user profile using the received information. It takes personal information, hobbies, preferences, and emotional data as input, builds a database with this information, and outputs detailed user profile data.

[0360] Step 5:

[0361] The server activates the generative AI model and receives user profile and trend data as prompts. This allows the AI ​​to output highly personalized clothing design suggestions. For example, the AI ​​might be given a message such as, "Generate a casual design that suits this user's preferences. Their current mood is relaxed."

[0362] Step 6:

[0363] The server sends the generated clothing design proposals to the terminal. The terminal displays the design proposals to the user and provides a selectable interface. The user views these and selects their preferred design by touching or clicking.

[0364] Step 7:

[0365] Once the user has made their selection, the device analyzes the user's emotional response again and sends the data back to the server. This includes facial expression analysis after the selection, capturing a response, for example, as joy.

[0366] Step 8:

[0367] The terminal instructs the server on the order and payment process based on the design selected by the user. The server processes the order and subsequent payment, and sends the results to the terminal.

[0368] Step 9:

[0369] The server collects user feedback and sentiment data and analyzes it to improve the generated AI model. This allows the system to continuously improve the quality of its services and design.

[0370] (Application Example 2)

[0371] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0372] Traditionally, clothing designs suggested based solely on individual preferences and basic information failed to reflect the user's temporary emotional state, resulting in incomplete personalization. Furthermore, it was difficult to propose designs that took real-time emotions into account, posing a challenge to increasing purchase satisfaction. Additionally, there was a lack of emotional response-based support in the user's selection process. Therefore, there was a need for more accurate, personalized design suggestions that comprehensively considered the user's emotional state.

[0373] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0374] In this invention, the server includes means for inputting and collecting basic user information, preference information, and emotional state information; means for generating individual user profiles based on the input information and emotional state information; and means for analyzing the user's real-time emotional state using an emotion engine and integrating it into the profile. This makes it possible to propose clothing designs that comprehensively consider the user's preferences and emotional state.

[0375] "User basic information" refers to data that indicates the user's attributes, including information such as gender, age, and region.

[0376] "Preference information" refers to data that represents a user's preferences, including style and color preferences, past purchase history, and other relevant information.

[0377] "Emotional state information" refers to data that indicates a user's temporary or long-term emotional state, and is obtained through facial recognition and voice tone analysis.

[0378] A "user-specific profile" is a dataset that comprehensively integrates a user's basic information, preferences, and emotional state, forming a profile tailored to that user.

[0379] An "emotion engine" is a technology or system that analyzes a user's voice and video data to recognize their emotional state in real time.

[0380] A "generative AI model" refers to an artificial intelligence algorithm or system that generates new suggestions or designs based on input data.

[0381] "Trend data" refers to information about current fashion market and social trends, and is trend information that influences clothing design.

[0382] "Selection support" refers to functions and methods that assist users in making more appropriate choices when selecting from presented options.

[0383] "Encryption technology" is a technology that transforms the content of data into a form that cannot be recognized by third parties, and is a means of protecting privacy and security.

[0384] The system for implementing this invention is a platform for proposing clothing designs that take into account the user's real-time emotional state. This system is based on the user's terminal, a server, and a generative AI model, and comprehensively handles the user's basic information, preference information, and emotional state information.

[0385] The device inputs and collects the user's basic information and preferences, and uses an emotion engine via the camera and microphone to analyze the user's real-time emotional state. The device then encrypts this data before sending it to the server.

[0386] The server uses a generative AI model to build individual user profiles based on the transmitted data, and automatically generates clothing designs while also considering fashion trend data. In this process, the generative AI model produces multiple design options that match the user's current emotional state.

[0387] The generated design proposals are presented to the user via the device, and the user makes a selection based on their emotional response and other factors. The server provides selection assistance, helping the user's choice be based on more solid reasoning.

[0388] The design proposal selected by the user is ordered and billed via the device. User feedback and sentiment data are also collected and analyzed on the server to improve the accuracy of future suggestions. This optimizes the entire system for the user experience.

[0389] For example, if a user is in the mood to relax on a holiday, the application could analyze their emotional state using an emotion engine and suggest casual clothing designs that would promote relaxation. An example of a prompt to the generative AI model would be: "User's emotional data: Relaxed, comfortable. Preferred style: Casual. Provide design suggestions."

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The user inputs basic information and preference information through the device. The device uses its camera and microphone to collect the user's facial expressions and voice, and supplies this data to an emotion engine for real-time emotional analysis. The input for this step is the user's basic information, preference information, and emotional state data, which is then analyzed and encrypted before outputting the data.

[0393] Step 2:

[0394] The terminal sends encrypted user information to the server. The server decrypts the received data and generates an individual user profile. The input for this step is encrypted user data, which is then analyzed to output profile information.

[0395] Step 3:

[0396] The server uses a generative AI model to generate multiple clothing designs based on the user's profile and fashion trend data. The input for this step is the user profile and trend data, which are used to output design proposals. The generative AI model generates designs based on prompt messages.

[0397] Step 4:

[0398] The server sends the generated clothing design proposals to the terminal. The terminal presents the design proposals to the user, who then provides an emotional response. The input for this step is the design proposals, which are then presented to the user and emotional data is collected.

[0399] Step 5:

[0400] Based on the design selected by the user, the terminal instructs the server to process the order and invoice. The server then completes the order based on the user's transaction information. The input for this step is the design selected by the user, and the terminal outputs the order and invoice data.

[0401] Step 6:

[0402] The server analyzes user feedback and sentiment data to continuously improve the generative AI model. The input for this step is feedback and sentiment data, and based on this, it outputs update information to improve the performance of the generative AI model.

[0403] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0404] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0409] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0411] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0413] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0414] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0415] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0416] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0418] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0419] This invention is a digital platform that utilizes generative AI models to provide clothing customization tailored to the individual needs of users. In implementing this system, the server, terminal, and user exchange data with each other, and the process proceeds sequentially.

[0420] First, the user accesses the system via their device and enters personal and preference information. This includes the user's age, gender, height, weight, and preferred colors and styles. The device collects this information and securely transmits it to the server using encryption technology. Based on the transmitted data, the server creates a unique profile for each user. This profile provides the foundational information necessary to enable the user's customized experience.

[0421] Next, the server utilizes a generative AI model to design clothing using collected profile information and fashion trend data. The server generates multiple design options and presents the selected design to the user. The user can then choose their preferred design from the presented options via their device.

[0422] Once the selected design is confirmed, the user proceeds with the order. The terminal displays an order confirmation interface, and the user enters their payment information. Payment is processed on the server through a secure process. The server then sends manufacturing instructions to partner factories and manufacturing departments, and the selected design is brought to market.

[0423] The key features of this system are personalized service tailored to each user and the provision of designs that reflect the latest fashion trends. The server regularly analyzes user feedback and new fashion trends, updating its AI model to provide more accurate and innovative suggestions. In this way, the user, device, and server interact together to provide users with a rich and comfortable customized experience.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] Users log in to the system via their device and access an interface to enter personal and preference information. Users enter their name, age, gender, size information, preferred colors, style, etc. The device then compiles this information on a confirmation screen and displays a message for the user to verify that the information is correct.

[0427] Step 2:

[0428] The device encrypts the information the user has confirmed and sends it to the server. The server receives this information and stores the user's individual profile in a database. Based on the stored information, the server creates a profile that reflects the user's preferences and physical characteristics.

[0429] Step 3:

[0430] The server runs a generative AI model to generate multiple clothing design options based on the user profile. Simultaneously, the server analyzes fashion trend data and provides designs that incorporate the latest trends.

[0431] Step 4:

[0432] The server sends the generated design proposals to the terminal. The user views the design proposals via the terminal and selects the one that best suits their preferences. The terminal displays the selected design as a confirmation screen and prompts the user for final confirmation.

[0433] Step 5:

[0434] The user enters their payment information to confirm their order for the selected design. The terminal securely transmits the user's payment information to the server in accordance with security protocols.

[0435] Step 6:

[0436] The server processes the payment and, if successful, sends an order confirmation email to the user. The server then instructs the manufacturing department or partner factory to produce and deliver the user's selected design.

[0437] Step 7:

[0438] The server collects user feedback and continuously updates fashion trend data. Using this information, the server aims to further improve user satisfaction by enhancing the accuracy of its generated AI models.

[0439] (Example 1)

[0440] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0441] There is a need for methods to meet users' desire to customize fashion based on their individual preferences and personalities, but conventional systems have problems with insufficient individual support and difficulty in providing designs that keep up with trends. Ensuring the security of user information is also a crucial issue.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes means for generating individual user profiles, means for automatically generating clothing designs based on trend data, and means for running a model to evaluate design proposals and provide the optimal choice. This enables safe and up-to-date fashion customization tailored to the individual needs of the user.

[0444] "User personal information" refers to data that enables a unique identification of a user, including, but not limited to, name, age, gender, and contact information.

[0445] "Preference information" refers to data that indicates a user's hobbies and fashion preferences, including, but not limited to, preferences regarding color, style, and material.

[0446] A "profile" is a dataset generated based on a user's individual characteristics and preferences, and serves as the foundation for providing customized fashion suggestions.

[0447] "Trend data" refers to information about fashion and market trends that change over time, and it reflects the latest styles and consumer preferences in the fashion industry.

[0448] A "generative AI model" is a system that uses artificial intelligence technology to generate new designs and options. It analyzes user profiles and behavioral data and automatically makes suggestions.

[0449] "Encryption technology" is a technology used to ensure the security of information by converting data into a format that cannot be deciphered by others.

[0450] "Design proposals" refer to the clothing design options generated by the generative AI model, which ultimately include a variety of styles that the user can choose from.

[0451] "Means of running the model" refers to methods for making a generative AI model executable and for carrying out the process of generating and evaluating design proposals.

[0452] The system of this invention operates through the cooperation of three parties: the user, the terminal, and the server. Specific details are described below.

[0453] First, the user accesses the system using their own device. During this process, the user enters personal and preference information into a dedicated interface. This information includes, for example, age, gender, favorite colors, and preferred styles. This information is encrypted using the SSL / TLS protocol by the device and securely transmitted to the server.

[0454] The server generates individual user profiles based on the received user information. These profiles serve as the foundational data for providing fashion suggestions tailored to each user's personality. A database system is used for profile generation.

[0455] The server then uses a generative AI model to generate clothing design proposals. This generative AI model runs on a machine learning platform such as TensorFlow and analyzes trend and profile data. An example of a prompt used here is "Generate a design for a casual jacket with a blue base color, suitable for a man in his 30s."

[0456] The generated design proposals are sorted and evaluated by the server. Based on this evaluation, the top-rated design proposal is selected and sent to the user's device. The user can then review the design proposals displayed on the device and select their preferred design. This selection is optimized based on the user's past preferences and current trends.

[0457] Once the selection is complete, the user enters their payment information to proceed with the order. The entered information is encrypted again on the terminal and sent to the server. The server communicates with the payment service provider and processes the payment securely.

[0458] This series of processes provides a system that offers customized clothing tailored to individual user needs, along with a safe and efficient ordering process.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] Users access the system using a terminal and input personal and preference information into the interface. This data includes age, gender, favorite colors, and preferred styles. The terminal encrypts the entered data using the SSL / TLS protocol and securely transmits it to the server. Based on this input, basic data is prepared for generating a profile tailored to the user's individuality.

[0462] Step 2:

[0463] The server receives encrypted user information from the terminal and decodes it. It stores the received information in a database system and generates an individual user profile. At this time, the server uses a profile generation algorithm to construct a data structure based on the input personal information and preference information. The output is a user-specific profile object.

[0464] Step 3:

[0465] The server utilizes a generative AI model to analyze profile data and trend data to generate multiple clothing design options. This process uses machine learning platforms such as TensorFlow, and the latest trends are referenced as trend data. The prompt "Generate a casual jacket design for a man in his 30s, primarily in blue" is input to the AI ​​model, and the model generates multiple design options based on this. The output is a list of design options ready for evaluation.

[0466] Step 4:

[0467] The server scores the generated design proposals using an internal evaluation algorithm and selects the most suitable design proposal. User profile information and trend data are used as evaluation criteria. The selected design proposal is sent to the terminal as a suggestion that best reflects the user's preferences. The output is an optimized design proposal that can be presented to the user.

[0468] Step 5:

[0469] The user reviews the design options displayed on their device and selects a style they find appealing. After making their selection, the user proceeds to the order screen on their device and enters their payment details. The entered information is encrypted again and sent to the server. This output is generated as payment data for order confirmation.

[0470] Step 6:

[0471] The server transmits the received payment information to the payment service provider for secure processing. Once payment authorization is obtained, the server generates a manufacturing order and sends it to the management system of the partner manufacturing plant, initiating the product development process based on the design proposal. As an output, the product is ready for final manufacturing instructions.

[0472] (Application Example 1)

[0473] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] Modern customers demand personalized shopping experiences and real-time try-on experiences, but traditional online shopping fails to meet these needs. In particular, the difficulty in visually confirming clothing characteristics in a virtual space often leads to anxiety in purchasing decisions. Addressing this challenge requires improvements to the user interface and the adoption of new technologies.

[0475] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0476] In this invention, the server includes means for inputting and collecting user characteristic information and preference information, means for automatically generating clothing designs based on the generated information set and clothing trend data, and means for trying out clothing designs in a virtual space using a glasses-type device. This allows users to virtually try on clothes that fit their own body type, enabling a more confident and customized selection experience.

[0477] "User characteristic information" refers to a collection of basic personal information about the user, such as age, gender, height, and weight.

[0478] "Preference information" refers to individual preference data such as a user's tastes, style, and favorite colors.

[0479] An "information set" is a collection of user-specific information generated based on user characteristic information and preference information.

[0480] "Apparel trend data" refers to a collection of market data that shows the latest fashion and trends.

[0481] "Methods for automatically generating clothing designs" refers to a process in which an AI model generates appropriate clothing designs using an input set of information.

[0482] A "glasses-type device" is a device equipped with wearable technology that provides users with visual information using augmented reality or virtual reality.

[0483] "A means of trying out clothing designs in a virtual space" refers to a technology that allows users to virtually try on clothing and visualize designs through a glasses-type device.

[0484] The system for implementing this invention involves the coordinated functioning of a user, a terminal, and a server. The user first inputs characteristic and preference information through the terminal. The terminal collects this information and securely transmits it to the server using encryption technology. The server generates a unique information set for each user based on the transmitted information. This information set serves as the foundation for generating clothing designs suitable for the user.

[0485] The server utilizes a generative AI model to automatically generate clothing designs based on an information set and clothing trend data. Furthermore, the server generates multiple design options and presents them to the user. The user can virtually try on the designs using a glasses-type device. This device provides visual feedback to the user, enabling real-time design confirmation.

[0486] Users select their preferred designs through virtual try-ons and complete the transaction on their terminal. The selected designs, along with order information, are sent to the server, and the manufacturing process begins. Through this system, users can have a more accurate and secure clothing selection experience.

[0487] As a concrete example, consider a case where a user wears smart glasses and selects clothes in a virtual store while relaxing at home. This user can view several jacket designs adjusted based on their body shape data and confirm the style they prefer.

[0488] Examples of prompt messages include, "Generate a sporty casual jacket design based on the user profile and display it immediately on the 3D avatar."

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] Users use a device to input characteristic information (age, gender, height, weight, etc.) and preference information (favorite colors, styles). This input data is collected by the device. The device encrypts the information and securely transmits it to the server.

[0492] Step 2:

[0493] The server decrypts the encrypted information received from the terminal and generates a unique information set for each user. To generate this profile information set, it performs data processing that integrates the received characteristic and preference information. The generated information set forms the basis for customizing the user's clothing design.

[0494] Step 3:

[0495] The server utilizes a generative AI model to automatically generate clothing designs based on an information set and clothing trend data. Prompts instruct the AI ​​model to generate appropriate designs. The output of this step is multiple clothing design proposals.

[0496] Step 4:

[0497] The server presents the user with multiple generated design options. The user can visualize and review these designs in a virtual space in real time through a glasses-type device. These designs are then applied to a 3D avatar in real time to obtain the user's visual feedback.

[0498] Step 5:

[0499] The user selects their preferred design through a virtual try-on. Information about the selected design is sent to the terminal for transaction processing. The terminal receives payment information from the user and securely transmits that information to the server.

[0500] Step 6:

[0501] The server processes the received order and payment information and issues manufacturing instructions based on the design. These manufacturing instructions are sent to partner factories and manufacturing departments, and production of the garments based on the user's selection begins.

[0502] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0503] This invention, which incorporates an emotion engine, is a system that enables the customization of clothing designs by taking into account not only the user's input information but also their emotional state at any given time. This system involves collaboration between a server, a terminal, and the user to collect and analyze data.

[0504] Users access the system via their device and input basic information and style preferences. The emotion engine analyzes the user's facial expressions and voice tone through the camera and microphone, recognizing their emotional state in real time. This allows the system to accumulate not only temporary emotions but also long-term emotional data, contributing to improved profile accuracy.

[0505] The device encrypts the information obtained from the user and sends it to the server. The server creates a user profile and constructs a customized dataset that includes emotional states. Next, a generative AI model uses this data to generate multiple clothing design suggestions that best match the user's preferences and current emotions.

[0506] The server sends the generated design proposals to the terminal for the user to review. As the user selects a design from the presented options, the emotional response to that design is further analyzed by an emotion engine. Based on the design selected by the user, the terminal proceeds with the order process and instructs the server to complete the payment.

[0507] The server collects and analyzes user feedback and sentiment data to continuously improve the generative AI model. Through this feedback process, the user experience is enhanced while the design reflects the latest fashion trends.

[0508] Thus, this system, through a multidimensional profile that includes the user's emotional state, can suggest optimal clothing designs that reflect not only the user's preferences and body type, but also their mental state at that time. As a result, it can provide a personalized and highly satisfying service.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] Users log in to the system via their device and access screens where they can enter personal and preference information. In addition to basic information, users answer questionnaires about their current mood and preferred style. The emotion engine uses the camera and microphone to analyze the user's facial expressions and voice tone in real time and record their emotions at that time.

[0512] Step 2:

[0513] The device encrypts the information and sentiment data collected from the user and sends it to the server. The server receives this information and generates a user profile. The profile includes basic information, preference information, and sentiment information.

[0514] Step 3:

[0515] The server generates clothing design proposals by combining the generated user profile and accumulated fashion trend data based on the AI ​​model. This results in multiple design options that reflect the user's individual preferences and emotions at the time.

[0516] Step 4:

[0517] The server sends the generated design proposals to the terminal and presents them to the user. The user views the presented design proposals via the terminal and selects the one that best suits them. At this point, the emotion engine re-analyzes the user's response and records an evaluation based on the emotion data.

[0518] Step 5:

[0519] The user confirms their order with their selected design and enters their payment information. The terminal transmits this information to the server using a secure protocol.

[0520] Step 6:

[0521] The server processes the payment and, if successful, sends an order confirmation email to the user. The server then instructs the manufacturing department or partner factory to produce and deliver the selected design.

[0522] Step 7:

[0523] The server collects and analyzes user feedback and sentiment data, and updates the generated AI model based on this. This process allows the system to continuously improve the user experience and more effectively incorporate sentiment data into future suggestions.

[0524] (Example 2)

[0525] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0526] In today's world, there is a demand for personalized products that reflect the individual tastes and preferences of users. However, it is difficult to provide highly satisfying customized services because temporary and dynamic information such as the user's emotional state is not being fully utilized. This invention aims to realize the provision of automated and highly accurate product design based on a multidimensional profile that includes the user's real-time emotional state.

[0527] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0528] In this invention, the server includes means for analyzing the user's emotional state in real time and accumulating emotional data over a long period of time, means for generating an individual user profile based on the acquired information and emotional data, and means for generating multiple design proposals optimized for the user's tastes, preferences, and emotions using a generation AI model. This makes it possible to propose personalized clothing designs that take the user's emotional state into consideration.

[0529] "Personal information" refers to information about a user's individual characteristics, such as height, weight, age, and gender.

[0530] "Tastes and preferences" refers to information about a user's individual preferences, such as their favorite colors, styles, and fashion tastes.

[0531] "Emotional state" refers to information about a user's psychological state at a given time, obtained by analyzing their facial expressions and tone of voice.

[0532] A "profile" is a unique dataset that integrates a user's personal information, hobbies, preferences, emotional state, and other data.

[0533] "Trend data" refers to information about current fashion and design trends.

[0534] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate design proposals optimized for the user.

[0535] A "design proposal" refers to multiple clothing design plans suggested by a generative AI model.

[0536] "Encryption technology" is a technology that transforms data to ensure secure communication and storage, preventing third parties from deciphering it.

[0537] "Feedback" refers to opinions and comments from users after they have used a service or product, and is information that helps improve the service or product.

[0538] This system involves the user, terminal, and server working together to suggest personalized clothing designs that reflect the user's emotional state. Users access the system by inputting personal information and preferences using their terminal. The terminal is equipped with a camera and microphone, through which an emotion engine analyzes the user's facial expressions and voice tone in real time to recognize their emotional state.

[0539] The device encrypts the data obtained from the user and securely transmits it to the server. The server uses this data to create a user profile, building a detailed profile that reflects the user's emotional state. Combining this profile with trend data, a generative AI model is used to generate clothing designs optimized for the user.

[0540] For example, if a user inputs "I want casual, relaxed clothes," and the camera captures the user's smile, the system will prioritize the relaxed emotion and suggest casual designs. An example of a prompt to the generative AI model would be a specific instruction such as, "Generate a casual design that suits this user's preferences. The current emotion indicates happiness."

[0541] The generated design proposals are sent to the terminal for the user to review and select. Based on the user's selected design, the terminal then instructs the server to complete the ordering process and payment. The server also aggregates user feedback and emotional data, continuously improving the generating AI model to enhance the quality of the service. In this way, the system realizes design proposals based on a multidimensional profile that reflects the user's mind and emotional state.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] Users access the system via a terminal and input personal information and preferences. The terminal formats this information and sends it to the server as input. In this process, the terminal uses a form input window and receives personal information as text data.

[0545] Step 2:

[0546] The device uses its built-in camera and microphone to collect the user's real-time facial expressions and voice tone. This data is sent to the emotion engine and becomes input data for analyzing the user's emotional state. For example, if a smile is detected, it is output as "joy" in the emotion data.

[0547] Step 3:

[0548] The device encrypts the collected personal information, hobbies, preferences, and emotional data before sending it to the server. This encryption process uses secure communication protocols such as TLS to send the data, thereby enhancing the protection of the information.

[0549] Step 4:

[0550] The server generates a user profile using the received information. It takes personal information, hobbies, preferences, and emotional data as input, builds a database with this information, and outputs detailed user profile data.

[0551] Step 5:

[0552] The server activates the generative AI model and receives user profile and trend data as prompts. This allows the AI ​​to output highly personalized clothing design suggestions. For example, the AI ​​might be given a message such as, "Generate a casual design that suits this user's preferences. Their current mood is relaxed."

[0553] Step 6:

[0554] The server sends the generated clothing design proposals to the terminal. The terminal displays the design proposals to the user and provides a selectable interface. The user views these and selects their preferred design by touching or clicking.

[0555] Step 7:

[0556] Once the user has made their selection, the device analyzes the user's emotional response again and sends the data back to the server. This includes facial expression analysis after the selection, capturing a response, for example, as joy.

[0557] Step 8:

[0558] The terminal instructs the server on the order and payment process based on the design selected by the user. The server processes the order and subsequent payment, and sends the results to the terminal.

[0559] Step 9:

[0560] The server collects user feedback and sentiment data and analyzes it to improve the generated AI model. This allows the system to continuously improve the quality of its services and design.

[0561] (Application Example 2)

[0562] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0563] Traditionally, clothing designs suggested based solely on individual preferences and basic information failed to reflect the user's temporary emotional state, resulting in incomplete personalization. Furthermore, it was difficult to propose designs that took real-time emotions into account, posing a challenge to increasing purchase satisfaction. Additionally, there was a lack of emotional response-based support in the user's selection process. Therefore, there was a need for more accurate, personalized design suggestions that comprehensively considered the user's emotional state.

[0564] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0565] In this invention, the server includes means for inputting and collecting basic user information, preference information, and emotional state information; means for generating individual user profiles based on the input information and emotional state information; and means for analyzing the user's real-time emotional state using an emotion engine and integrating it into the profile. This makes it possible to propose clothing designs that comprehensively consider the user's preferences and emotional state.

[0566] "User basic information" refers to data that indicates the user's attributes, including information such as gender, age, and region.

[0567] "Preference information" refers to data that represents a user's preferences, including style and color preferences, past purchase history, and other relevant information.

[0568] "Emotional state information" refers to data that indicates a user's temporary or long-term emotional state, and is obtained through facial recognition and voice tone analysis.

[0569] A "user-specific profile" is a dataset that comprehensively integrates a user's basic information, preferences, and emotional state, forming a profile tailored to that user.

[0570] An "emotion engine" is a technology or system that analyzes a user's voice and video data to recognize their emotional state in real time.

[0571] A "generative AI model" refers to an artificial intelligence algorithm or system that generates new suggestions or designs based on input data.

[0572] "Trend data" refers to information about current fashion market and social trends, and is trend information that influences clothing design.

[0573] "Selection support" refers to functions and methods that assist users in making more appropriate choices when selecting from presented options.

[0574] "Encryption technology" is a technology that transforms the content of data into a form that cannot be recognized by third parties, and is a means of protecting privacy and security.

[0575] The system for implementing this invention is a platform for proposing clothing designs that take into account the user's real-time emotional state. This system is based on the user's terminal, a server, and a generative AI model, and comprehensively handles the user's basic information, preference information, and emotional state information.

[0576] The device inputs and collects the user's basic information and preferences, and uses an emotion engine via the camera and microphone to analyze the user's real-time emotional state. The device then encrypts this data before sending it to the server.

[0577] The server uses a generative AI model to build individual user profiles based on the transmitted data, and automatically generates clothing designs while also considering fashion trend data. In this process, the generative AI model produces multiple design options that match the user's current emotional state.

[0578] The generated design proposals are presented to the user via the device, and the user makes a selection based on their emotional response and other factors. The server provides selection assistance, helping the user's choice be based on more solid reasoning.

[0579] The design proposal selected by the user is ordered and billed via the device. User feedback and sentiment data are also collected and analyzed on the server to improve the accuracy of future suggestions. This optimizes the entire system for the user experience.

[0580] For example, if a user is in the mood to relax on a holiday, the application could analyze their emotional state using an emotion engine and suggest casual clothing designs that would promote relaxation. An example of a prompt to the generative AI model would be: "User's emotional data: Relaxed, comfortable. Preferred style: Casual. Provide design suggestions."

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The user inputs basic information and preference information through the device. The device uses its camera and microphone to collect the user's facial expressions and voice, and supplies this data to an emotion engine for real-time emotional analysis. The input for this step is the user's basic information, preference information, and emotional state data, which is then analyzed and encrypted before outputting the data.

[0584] Step 2:

[0585] The terminal sends encrypted user information to the server. The server decrypts the received data and generates an individual user profile. The input for this step is encrypted user data, which is then analyzed to output profile information.

[0586] Step 3:

[0587] The server uses a generative AI model to generate multiple clothing designs based on the user's profile and fashion trend data. The input for this step is the user profile and trend data, which are used to output design proposals. The generative AI model generates designs based on prompt messages.

[0588] Step 4:

[0589] The server sends the generated clothing design proposals to the terminal. The terminal presents the design proposals to the user, who then provides an emotional response. The input for this step is the design proposals, which are then presented to the user and emotional data is collected.

[0590] Step 5:

[0591] Based on the design selected by the user, the terminal instructs the server to process the order and invoice. The server then completes the order based on the user's transaction information. The input for this step is the design selected by the user, and the terminal outputs the order and invoice data.

[0592] Step 6:

[0593] The server analyzes user feedback and sentiment data to continuously improve the generative AI model. The input for this step is feedback and sentiment data, and based on this, it outputs update information to improve the performance of the generative AI model.

[0594] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0601] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0603] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0605] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0607] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] This invention is a digital platform that utilizes generative AI models to provide clothing customization tailored to the individual needs of users. In implementing this system, the server, terminal, and user exchange data with each other, and the process proceeds sequentially.

[0612] First, the user accesses the system via their device and enters personal and preference information. This includes the user's age, gender, height, weight, and preferred colors and styles. The device collects this information and securely transmits it to the server using encryption technology. Based on the transmitted data, the server creates a unique profile for each user. This profile provides the foundational information necessary to enable the user's customized experience.

[0613] Next, the server utilizes a generative AI model to design clothing using collected profile information and fashion trend data. The server generates multiple design options and presents the selected design to the user. The user can then choose their preferred design from the presented options via their device.

[0614] Once the selected design is confirmed, the user proceeds with the order. The terminal displays an order confirmation interface, and the user enters their payment information. Payment is processed on the server through a secure process. The server then sends manufacturing instructions to partner factories and manufacturing departments, and the selected design is brought to market.

[0615] The key features of this system are personalized service tailored to each user and the provision of designs that reflect the latest fashion trends. The server regularly analyzes user feedback and new fashion trends, updating its AI model to provide more accurate and innovative suggestions. In this way, the user, device, and server interact together to provide users with a rich and comfortable customized experience.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] Users log in to the system via their device and access an interface to enter personal and preference information. Users enter their name, age, gender, size information, preferred colors, style, etc. The device then compiles this information on a confirmation screen and displays a message for the user to verify that the information is correct.

[0619] Step 2:

[0620] The device encrypts the information the user has confirmed and sends it to the server. The server receives this information and stores the user's individual profile in a database. Based on the stored information, the server creates a profile that reflects the user's preferences and physical characteristics.

[0621] Step 3:

[0622] The server runs a generative AI model to generate multiple clothing design options based on the user profile. Simultaneously, the server analyzes fashion trend data and provides designs that incorporate the latest trends.

[0623] Step 4:

[0624] The server sends the generated design proposals to the terminal. The user views the design proposals via the terminal and selects the one that best suits their preferences. The terminal displays the selected design as a confirmation screen and prompts the user for final confirmation.

[0625] Step 5:

[0626] The user enters their payment information to confirm their order for the selected design. The terminal securely transmits the user's payment information to the server in accordance with security protocols.

[0627] Step 6:

[0628] The server processes the payment and, if successful, sends an order confirmation email to the user. The server then instructs the manufacturing department or partner factory to produce and deliver the user's selected design.

[0629] Step 7:

[0630] The server collects user feedback and continuously updates fashion trend data. Using this information, the server aims to further improve user satisfaction by enhancing the accuracy of its generated AI models.

[0631] (Example 1)

[0632] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] There is a need for methods to meet users' desire to customize fashion based on their individual preferences and personalities, but conventional systems have problems with insufficient individual support and difficulty in providing designs that keep up with trends. Ensuring the security of user information is also a crucial issue.

[0634] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0635] In this invention, the server includes means for generating individual user profiles, means for automatically generating clothing designs based on trend data, and means for running a model to evaluate design proposals and provide the optimal choice. This enables safe and up-to-date fashion customization tailored to the individual needs of the user.

[0636] "User personal information" refers to data that enables a unique identification of a user, including, but not limited to, name, age, gender, and contact information.

[0637] "Preference information" refers to data that indicates a user's hobbies and fashion preferences, including, but not limited to, preferences regarding color, style, and material.

[0638] A "profile" is a dataset generated based on a user's individual characteristics and preferences, and serves as the foundation for providing customized fashion suggestions.

[0639] "Trend data" refers to information about fashion and market trends that change over time, and it reflects the latest styles and consumer preferences in the fashion industry.

[0640] A "generative AI model" is a system that uses artificial intelligence technology to generate new designs and options. It analyzes user profiles and behavioral data and automatically makes suggestions.

[0641] "Encryption technology" is a technology used to ensure the security of information by converting data into a format that cannot be deciphered by others.

[0642] "Design proposals" refer to the clothing design options generated by the generative AI model, which ultimately include a variety of styles that the user can choose from.

[0643] "Means of running the model" refers to methods for making a generative AI model executable and for carrying out the process of generating and evaluating design proposals.

[0644] The system of this invention operates through the cooperation of three parties: the user, the terminal, and the server. Specific details are described below.

[0645] First, the user accesses the system using their own device. During this process, the user enters personal and preference information into a dedicated interface. This information includes, for example, age, gender, favorite colors, and preferred styles. This information is encrypted using the SSL / TLS protocol by the device and securely transmitted to the server.

[0646] The server generates individual user profiles based on the received user information. These profiles serve as the foundational data for providing fashion suggestions tailored to each user's personality. A database system is used for profile generation.

[0647] The server then uses a generative AI model to generate clothing design proposals. This generative AI model runs on a machine learning platform such as TensorFlow and analyzes trend and profile data. An example of a prompt used here is "Generate a design for a casual jacket with a blue base color, suitable for a man in his 30s."

[0648] The generated design proposals are sorted and evaluated by the server. Based on this evaluation, the top-rated design proposal is selected and sent to the user's device. The user can then review the design proposals displayed on the device and select their preferred design. This selection is optimized based on the user's past preferences and current trends.

[0649] Once the selection is complete, the user enters their payment information to proceed with the order. The entered information is encrypted again on the terminal and sent to the server. The server communicates with the payment service provider and processes the payment securely.

[0650] This series of processes provides a system that offers customized clothing tailored to individual user needs, along with a safe and efficient ordering process.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] Users access the system using a terminal and input personal and preference information into the interface. This data includes age, gender, favorite colors, and preferred styles. The terminal encrypts the entered data using the SSL / TLS protocol and securely transmits it to the server. Based on this input, basic data is prepared for generating a profile tailored to the user's individuality.

[0654] Step 2:

[0655] The server receives encrypted user information from the terminal and decodes it. It stores the received information in a database system and generates an individual user profile. At this time, the server uses a profile generation algorithm to construct a data structure based on the input personal information and preference information. The output is a user-specific profile object.

[0656] Step 3:

[0657] The server utilizes a generative AI model to analyze profile data and trend data to generate multiple clothing design options. This process uses machine learning platforms such as TensorFlow, and the latest trends are referenced as trend data. The prompt "Generate a casual jacket design for a man in his 30s, primarily in blue" is input to the AI ​​model, and the model generates multiple design options based on this. The output is a list of design options ready for evaluation.

[0658] Step 4:

[0659] The server scores the generated design proposals using an internal evaluation algorithm and selects the most suitable design proposal. User profile information and trend data are used as evaluation criteria. The selected design proposal is sent to the terminal as a suggestion that best reflects the user's preferences. The output is an optimized design proposal that can be presented to the user.

[0660] Step 5:

[0661] The user reviews the design options displayed on their device and selects a style they find appealing. After making their selection, the user proceeds to the order screen on their device and enters their payment details. The entered information is encrypted again and sent to the server. This output is generated as payment data for order confirmation.

[0662] Step 6:

[0663] The server transmits the received payment information to the payment service provider for secure processing. Once payment authorization is obtained, the server generates a manufacturing order and sends it to the management system of the partner manufacturing plant, initiating the product development process based on the design proposal. As an output, the product is ready for final manufacturing instructions.

[0664] (Application Example 1)

[0665] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] Modern customers demand personalized shopping experiences and real-time try-on experiences, but traditional online shopping fails to meet these needs. In particular, the difficulty in visually confirming clothing characteristics in a virtual space often leads to anxiety in purchasing decisions. Addressing this challenge requires improvements to the user interface and the adoption of new technologies.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0668] In this invention, the server includes means for inputting and collecting user characteristic information and preference information, means for automatically generating clothing designs based on the generated information set and clothing trend data, and means for trying out clothing designs in a virtual space using a glasses-type device. This allows users to virtually try on clothes that fit their own body type, enabling a more confident and customized selection experience.

[0669] "User characteristic information" refers to a collection of basic personal information about the user, such as age, gender, height, and weight.

[0670] "Preference information" refers to individual preference data such as a user's tastes, style, and favorite colors.

[0671] An "information set" is a collection of user-specific information generated based on user characteristic information and preference information.

[0672] "Apparel trend data" refers to a collection of market data that shows the latest fashion and trends.

[0673] "Methods for automatically generating clothing designs" refers to a process in which an AI model generates appropriate clothing designs using an input set of information.

[0674] A "glasses-type device" is a device equipped with wearable technology that provides users with visual information using augmented reality or virtual reality.

[0675] "A means of trying out clothing designs in a virtual space" refers to a technology that allows users to virtually try on clothing and visualize designs through a glasses-type device.

[0676] The system for implementing this invention involves the coordinated functioning of a user, a terminal, and a server. The user first inputs characteristic and preference information through the terminal. The terminal collects this information and securely transmits it to the server using encryption technology. The server generates a unique information set for each user based on the transmitted information. This information set serves as the foundation for generating clothing designs suitable for the user.

[0677] The server utilizes a generative AI model to automatically generate clothing designs based on an information set and clothing trend data. Furthermore, the server generates multiple design options and presents them to the user. The user can virtually try on the designs using a glasses-type device. This device provides visual feedback to the user, enabling real-time design confirmation.

[0678] Users select their preferred designs through virtual try-ons and complete the transaction on their terminal. The selected designs, along with order information, are sent to the server, and the manufacturing process begins. Through this system, users can have a more accurate and secure clothing selection experience.

[0679] As a concrete example, consider a case where a user wears smart glasses and selects clothes in a virtual store while relaxing at home. This user can view several jacket designs adjusted based on their body shape data and confirm the style they prefer.

[0680] Examples of prompt messages include, "Generate a sporty casual jacket design based on the user profile and display it immediately on the 3D avatar."

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] Users use a device to input characteristic information (age, gender, height, weight, etc.) and preference information (favorite colors, styles). This input data is collected by the device. The device encrypts the information and securely transmits it to the server.

[0684] Step 2:

[0685] The server decrypts the encrypted information received from the terminal and generates a unique information set for each user. To generate this profile information set, it performs data processing that integrates the received characteristic and preference information. The generated information set forms the basis for customizing the user's clothing design.

[0686] Step 3:

[0687] The server utilizes a generative AI model to automatically generate clothing designs based on an information set and clothing trend data. Prompts instruct the AI ​​model to generate appropriate designs. The output of this step is multiple clothing design proposals.

[0688] Step 4:

[0689] The server presents the user with multiple generated design options. The user can visualize and review these designs in a virtual space in real time through a glasses-type device. These designs are then applied to a 3D avatar in real time to obtain the user's visual feedback.

[0690] Step 5:

[0691] The user selects their preferred design through a virtual try-on. Information about the selected design is sent to the terminal for transaction processing. The terminal receives payment information from the user and securely transmits that information to the server.

[0692] Step 6:

[0693] The server processes the received order and payment information and issues manufacturing instructions based on the design. These manufacturing instructions are sent to partner factories and manufacturing departments, and production of the garments based on the user's selection begins.

[0694] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0695] This invention, which incorporates an emotion engine, is a system that enables the customization of clothing designs by taking into account not only the user's input information but also their emotional state at any given time. This system involves collaboration between a server, a terminal, and the user to collect and analyze data.

[0696] Users access the system via their device and input basic information and style preferences. The emotion engine analyzes the user's facial expressions and voice tone through the camera and microphone, recognizing their emotional state in real time. This allows the system to accumulate not only temporary emotions but also long-term emotional data, contributing to improved profile accuracy.

[0697] The device encrypts the information obtained from the user and sends it to the server. The server creates a user profile and constructs a customized dataset that includes emotional states. Next, a generative AI model uses this data to generate multiple clothing design suggestions that best match the user's preferences and current emotions.

[0698] The server sends the generated design proposals to the terminal for the user to review. As the user selects a design from the presented options, the emotional response to that design is further analyzed by an emotion engine. Based on the design selected by the user, the terminal proceeds with the order process and instructs the server to complete the payment.

[0699] The server collects and analyzes user feedback and sentiment data to continuously improve the generative AI model. Through this feedback process, the user experience is enhanced while the design reflects the latest fashion trends.

[0700] Thus, this system, through a multidimensional profile that includes the user's emotional state, can suggest optimal clothing designs that reflect not only the user's preferences and body type, but also their mental state at that time. As a result, it can provide a personalized and highly satisfying service.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] Users log in to the system via their device and access screens where they can enter personal and preference information. In addition to basic information, users answer questionnaires about their current mood and preferred style. The emotion engine uses the camera and microphone to analyze the user's facial expressions and voice tone in real time and record their emotions at that time.

[0704] Step 2:

[0705] The device encrypts the information and sentiment data collected from the user and sends it to the server. The server receives this information and generates a user profile. The profile includes basic information, preference information, and sentiment information.

[0706] Step 3:

[0707] The server generates clothing design proposals by combining the generated user profile and accumulated fashion trend data based on the AI ​​model. This results in multiple design options that reflect the user's individual preferences and emotions at the time.

[0708] Step 4:

[0709] The server sends the generated design proposals to the terminal and presents them to the user. The user views the presented design proposals via the terminal and selects the one that best suits them. At this point, the emotion engine re-analyzes the user's response and records an evaluation based on the emotion data.

[0710] Step 5:

[0711] The user confirms their order with their selected design and enters their payment information. The terminal transmits this information to the server using a secure protocol.

[0712] Step 6:

[0713] The server processes the payment and, if successful, sends an order confirmation email to the user. The server then instructs the manufacturing department or partner factory to produce and deliver the selected design.

[0714] Step 7:

[0715] The server collects and analyzes user feedback and sentiment data, and updates the generated AI model based on this. This process allows the system to continuously improve the user experience and more effectively incorporate sentiment data into future suggestions.

[0716] (Example 2)

[0717] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0718] In today's world, there is a demand for personalized products that reflect the individual tastes and preferences of users. However, it is difficult to provide highly satisfying customized services because temporary and dynamic information such as the user's emotional state is not being fully utilized. This invention aims to realize the provision of automated and highly accurate product design based on a multidimensional profile that includes the user's real-time emotional state.

[0719] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0720] In this invention, the server includes means for analyzing the user's emotional state in real time and accumulating emotional data over a long period of time, means for generating an individual user profile based on the acquired information and emotional data, and means for generating multiple design proposals optimized for the user's tastes, preferences, and emotions using a generation AI model. This makes it possible to propose personalized clothing designs that take the user's emotional state into consideration.

[0721] "Personal information" refers to information about a user's individual characteristics, such as height, weight, age, and gender.

[0722] "Tastes and preferences" refers to information about a user's individual preferences, such as their favorite colors, styles, and fashion tastes.

[0723] "Emotional state" refers to information about a user's psychological state at a given time, obtained by analyzing their facial expressions and tone of voice.

[0724] A "profile" is a unique dataset that integrates a user's personal information, hobbies, preferences, emotional state, and other data.

[0725] "Trend data" refers to information about current fashion and design trends.

[0726] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate design proposals optimized for the user.

[0727] A "design proposal" refers to multiple clothing design plans suggested by a generative AI model.

[0728] "Encryption technology" is a technology that transforms data to ensure secure communication and storage, preventing third parties from deciphering it.

[0729] "Feedback" refers to opinions and comments from users after they have used a service or product, and is information that helps improve the service or product.

[0730] This system involves the user, terminal, and server working together to suggest personalized clothing designs that reflect the user's emotional state. Users access the system by inputting personal information and preferences using their terminal. The terminal is equipped with a camera and microphone, through which an emotion engine analyzes the user's facial expressions and voice tone in real time to recognize their emotional state.

[0731] The device encrypts the data obtained from the user and securely transmits it to the server. The server uses this data to create a user profile, building a detailed profile that reflects the user's emotional state. Combining this profile with trend data, a generative AI model is used to generate clothing designs optimized for the user.

[0732] For example, if a user inputs "I want casual, relaxed clothes," and the camera captures the user's smile, the system will prioritize the relaxed emotion and suggest casual designs. An example of a prompt to the generative AI model would be a specific instruction such as, "Generate a casual design that suits this user's preferences. The current emotion indicates happiness."

[0733] The generated design proposals are sent to the terminal for the user to review and select. Based on the user's selected design, the terminal then instructs the server to complete the ordering process and payment. The server also aggregates user feedback and emotional data, continuously improving the generating AI model to enhance the quality of the service. In this way, the system realizes design proposals based on a multidimensional profile that reflects the user's mind and emotional state.

[0734] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0735] Step 1:

[0736] Users access the system via a terminal and input personal information and preferences. The terminal formats this information and sends it to the server as input. In this process, the terminal uses a form input window and receives personal information as text data.

[0737] Step 2:

[0738] The device uses its built-in camera and microphone to collect the user's real-time facial expressions and voice tone. This data is sent to the emotion engine and becomes input data for analyzing the user's emotional state. For example, if a smile is detected, it is output as "joy" in the emotion data.

[0739] Step 3:

[0740] The device encrypts the collected personal information, hobbies, preferences, and emotional data before sending it to the server. This encryption process uses secure communication protocols such as TLS to send the data, thereby enhancing the protection of the information.

[0741] Step 4:

[0742] The server generates a user profile using the received information. It takes personal information, hobbies, preferences, and emotional data as input, builds a database with this information, and outputs detailed user profile data.

[0743] Step 5:

[0744] The server activates the generative AI model and receives user profile and trend data as prompts. This allows the AI ​​to output highly personalized clothing design suggestions. For example, the AI ​​might be given a message such as, "Generate a casual design that suits this user's preferences. Their current mood is relaxed."

[0745] Step 6:

[0746] The server sends the generated clothing design proposals to the terminal. The terminal displays the design proposals to the user and provides a selectable interface. The user views these and selects their preferred design by touching or clicking.

[0747] Step 7:

[0748] Once the user has made their selection, the device analyzes the user's emotional response again and sends the data back to the server. This includes facial expression analysis after the selection, capturing a response, for example, as joy.

[0749] Step 8:

[0750] The terminal instructs the server on the order and payment process based on the design selected by the user. The server processes the order and subsequent payment, and sends the results to the terminal.

[0751] Step 9:

[0752] The server collects user feedback and sentiment data and analyzes it to improve the generated AI model. This allows the system to continuously improve the quality of its services and design.

[0753] (Application Example 2)

[0754] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0755] Traditionally, clothing designs suggested based solely on individual preferences and basic information failed to reflect the user's temporary emotional state, resulting in incomplete personalization. Furthermore, it was difficult to propose designs that took real-time emotions into account, posing a challenge to increasing purchase satisfaction. Additionally, there was a lack of emotional response-based support in the user's selection process. Therefore, there was a need for more accurate, personalized design suggestions that comprehensively considered the user's emotional state.

[0756] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0757] In this invention, the server includes means for inputting and collecting basic user information, preference information, and emotional state information; means for generating individual user profiles based on the input information and emotional state information; and means for analyzing the user's real-time emotional state using an emotion engine and integrating it into the profile. This makes it possible to propose clothing designs that comprehensively consider the user's preferences and emotional state.

[0758] "User basic information" refers to data that indicates the user's attributes, including information such as gender, age, and region.

[0759] "Preference information" refers to data that represents a user's preferences, including style and color preferences, past purchase history, and other relevant information.

[0760] "Emotional state information" refers to data that indicates a user's temporary or long-term emotional state, and is obtained through facial recognition and voice tone analysis.

[0761] A "user-specific profile" is a dataset that comprehensively integrates a user's basic information, preferences, and emotional state, forming a profile tailored to that user.

[0762] An "emotion engine" is a technology or system that analyzes a user's voice and video data to recognize their emotional state in real time.

[0763] A "generative AI model" refers to an artificial intelligence algorithm or system that generates new suggestions or designs based on input data.

[0764] "Trend data" refers to information about current fashion market and social trends, and is trend information that influences clothing design.

[0765] "Selection support" refers to functions and methods that assist users in making more appropriate choices when selecting from presented options.

[0766] "Encryption technology" is a technology that transforms the content of data into a form that cannot be recognized by third parties, and is a means of protecting privacy and security.

[0767] The system for implementing this invention is a platform for proposing clothing designs that take into account the user's real-time emotional state. This system is based on the user's terminal, a server, and a generative AI model, and comprehensively handles the user's basic information, preference information, and emotional state information.

[0768] The device inputs and collects the user's basic information and preferences, and uses an emotion engine via the camera and microphone to analyze the user's real-time emotional state. The device then encrypts this data before sending it to the server.

[0769] The server uses a generative AI model to build individual user profiles based on the transmitted data, and automatically generates clothing designs while also considering fashion trend data. In this process, the generative AI model produces multiple design options that match the user's current emotional state.

[0770] The generated design proposals are presented to the user via the device, and the user makes a selection based on their emotional response and other factors. The server provides selection assistance, helping the user's choice be based on more solid reasoning.

[0771] The design proposal selected by the user is ordered and billed via the device. User feedback and sentiment data are also collected and analyzed on the server to improve the accuracy of future suggestions. This optimizes the entire system for the user experience.

[0772] For example, if a user is in the mood to relax on a holiday, the application could analyze their emotional state using an emotion engine and suggest casual clothing designs that would promote relaxation. An example of a prompt to the generative AI model would be: "User's emotional data: Relaxed, comfortable. Preferred style: Casual. Provide design suggestions."

[0773] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0774] Step 1:

[0775] The user inputs basic information and preference information through the device. The device uses its camera and microphone to collect the user's facial expressions and voice, and supplies this data to an emotion engine for real-time emotional analysis. The input for this step is the user's basic information, preference information, and emotional state data, which is then analyzed and encrypted before outputting the data.

[0776] Step 2:

[0777] The terminal sends encrypted user information to the server. The server decrypts the received data and generates an individual user profile. The input for this step is encrypted user data, which is then analyzed to output profile information.

[0778] Step 3:

[0779] The server uses a generative AI model to generate multiple clothing designs based on the user's profile and fashion trend data. The input for this step is the user profile and trend data, which are used to output design proposals. The generative AI model generates designs based on prompt messages.

[0780] Step 4:

[0781] The server sends the generated clothing design proposals to the terminal. The terminal presents the design proposals to the user, who then provides an emotional response. The input for this step is the design proposals, which are then presented to the user and emotional data is collected.

[0782] Step 5:

[0783] Based on the design selected by the user, the terminal instructs the server to process the order and invoice. The server then completes the order based on the user's transaction information. The input for this step is the design selected by the user, and the terminal outputs the order and invoice data.

[0784] Step 6:

[0785] The server analyzes user feedback and sentiment data to continuously improve the generative AI model. The input for this step is feedback and sentiment data, and based on this, it outputs update information to improve the performance of the generative AI model.

[0786] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0787] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0788] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0789] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0790] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0791] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0792] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0793] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0794] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0795] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0796] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0797] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0798] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0799] 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.

[0800] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0801] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0802] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0803] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0804] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0805] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0806] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0807] The following is further disclosed regarding the embodiments described above.

[0808] (Claim 1)

[0809] Means for inputting and collecting users' personal information and preference information,

[0810] A means for generating individual user profiles based on the input information,

[0811] A means for automatically generating clothing designs based on generated profiles and fashion trend data,

[0812] A means of presenting users with multiple design options and allowing them to choose,

[0813] A means for processing orders and payments for designs selected by the user,

[0814] By analyzing user feedback and fashion trend data, we can continuously improve the quality of our designs.

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, further comprising means of using encryption technology to securely store information entered by a user.

[0818] (Claim 3)

[0819] The system according to claim 1, further comprising means for analyzing collected fashion trend data and reflecting the trends in the design of generated clothing.

[0820] "Example 1"

[0821] (Claim 1)

[0822] Means for inputting and collecting users' personal information and preference information,

[0823] A means for generating individual user profiles based on the input information,

[0824] A means for automatically generating clothing designs based on generated profiles and trend data,

[0825] A means of presenting users with multiple design options and allowing them to choose,

[0826] A means for processing orders and payments for designs selected by the user,

[0827] A means of continuously improving the quality of design by analyzing user feedback and trend data,

[0828] A means of running a model to evaluate the generated clothing design proposals and provide the optimal choice,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising means of using encryption technology to securely store information entered by a user.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising means for analyzing collected trend data and reflecting the trends in the design of generated clothing.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] Means for inputting and collecting user characteristic information and preference information,

[0837] A means for generating a user-specific information set based on the input information,

[0838] A means for automatically generating clothing designs based on the generated information set and clothing trend data,

[0839] A means of presenting users with multiple design options and allowing them to choose,

[0840] A means for processing orders and transactions based on designs selected by the user,

[0841] A means of continuously improving design quality by analyzing user feedback and clothing trend data,

[0842] A method for experimenting with clothing designs in a virtual space using glasses-type devices,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means of using encoding technology to securely store information entered by a user.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for analyzing collected clothing trend data and reflecting the trends in the design of generated garments.

[0848] "Example 2 of combining an emotion engine"

[0849] (Claim 1)

[0850] Means for inputting and obtaining users' personal information and hobbies / preferences,

[0851] A means to analyze users' emotional states in real time and accumulate emotional data over a long period of time,

[0852] A means of generating individual user profiles based on acquired information and emotional data,

[0853] A means for automatically generating clothing designs based on generated profiles and trend data,

[0854] A means for generating multiple design options optimized for the user's tastes, preferences, and emotions using a generative AI model,

[0855] A means of presenting users with multiple design options and allowing them to choose,

[0856] A means for processing orders and payments for designs selected by the user,

[0857] A means of continuously improving design quality by analyzing user feedback and trend data,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising means of using encryption technology to securely manage user-entered information and emotional data.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising means for analyzing collected fashion trend data and incorporating trends into the design of generated clothing.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] Means for inputting and collecting basic user information, preference information, and emotional state information,

[0866] A means for generating an individual user profile based on input information and emotional state information,

[0867] A means of analyzing a user's real-time emotional state using an emotion engine and integrating it into a profile,

[0868] A means for automatically generating clothing designs based on generated profiles and trend data,

[0869] A means of generating multiple design options that match the user's preferences and current emotional state using a generative AI model,

[0870] A means of presenting users with multiple design options and allowing them to choose,

[0871] A means of analyzing emotional responses on user devices and providing support for selecting proposed designs,

[0872] A means for processing orders and invoices for designs selected by the user,

[0873] A means to continuously improve the quality of generated AI models by analyzing user feedback and trend data,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means of using encryption technology to securely store user-entered information and emotional state information.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for analyzing collected trend data and reflecting those trends in the design of generated clothing. [Explanation of Symbols]

[0879] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for inputting and collecting users' personal information and preference information, A means for generating individual user profiles based on the input information, A means for automatically generating clothing designs based on generated profiles and fashion trend data, A means of presenting users with multiple design options and allowing them to choose, A means for processing orders and payments for designs selected by the user, By analyzing user feedback and fashion trend data, we can continuously improve the quality of our designs. A system that includes this.

2. The system according to claim 1, further comprising means of using encryption technology to securely store information entered by a user.

3. The system according to claim 1, further comprising means for analyzing collected fashion trend data and reflecting the trends in the design of generated clothing.

Images