system

Abstract

We provide the system. [Solution] A means for generating personalized clothing suggestions based on information entered by the user, A means of generating new clothing suggestions by analyzing images of items owned by the user, A means of using virtual reality to try on clothing and provide feedback on suitability, A means of sharing clothing suggestions with other users within the community, and exchanging evaluations and opinions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional fashion coordination, consumers often cannot easily find clothing that suits their individual preferences. Especially when it is necessary to consider budget constraints and compatibility with the existing wardrobe, it has been a problem that it requires time and effort. In addition, the place where fashion ideas can be shared and evaluated through communication between consumers is limited, and it has been difficult for consumers to demonstrate their uniqueness. Furthermore, when purchasing clothes online without actually trying them on, problems such as size and style incompatibility have also occurred.

Means for Solving the Problems

[0005] This invention provides a means for generating personalized style suggestions using user-inputted information and image analysis of belongings, thereby enabling the suggestion of optimal clothing tailored to each consumer's specific preferences. Furthermore, by utilizing virtual try-on technology to provide feedback on size and style suitability, it supports appropriate purchases without the need for actual try-ons. In addition, through community functions, users can share fashion suggestions with other users, promoting interaction among consumers, enabling them to express their individuality and acquire new fashion ideas.

[0006] "A means of generating personalized clothing suggestions based on user input" refers to a function that analyzes user-inputted information such as preferences, age, gender, and budget, and presents the most suitable clothing combinations.

[0007] "A means of generating new clothing suggestions by analyzing images of items owned by the user" refers to a function that uses image analysis technology to identify the characteristics of items based on image data uploaded by the user, and then makes new style suggestions based on that.

[0008] "A means of trying on clothing using virtual reality and providing feedback on suitability" refers to a function that utilizes AR technology to allow users to try on clothing in a virtual space, providing information on the size suitability to the user's body type and the overall style.

[0009] "A means of sharing clothing suggestions with other users within the community and exchanging ratings and opinions" refers to a function that facilitates communication by allowing users to post fashion suggestions through the system and receive ratings and opinions from other users.

[0010] "A means of recommending lower-priced items with a similar appearance, taking into account the user's budget constraints," refers to a function that presents more economical options while maintaining a visually similar style, while ensuring that the user does not exceed their set budget.

[0011] "A means of optimizing the product recommendation algorithm based on feedback collected from users" refers to a function that collects evaluations and opinions obtained from users as data, improves the algorithm based on that information, and makes more accurate recommendations. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a 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), etc.

[0016] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system designed to generate and promote user-specific fashion styles, specifically enabling personalized clothing suggestions based on information entered by the user. To this end, the user can access a styling generation module using a terminal. The user inputs information about their preferences and budget, and the terminal sends this information to a server. An AI engine on the server analyzes the received data, generates the optimal styling for each individual user, and sends the results back to the terminal.

[0034] Furthermore, the app includes a feature that allows users to upload images of items they own. Users take photos of their items using their device and send them to the server. The server analyzes the images and generates new outfit suggestions based on those items. This information is displayed on the device, allowing users to make the most of their existing wardrobe.

[0035] Furthermore, the virtual try-on function allows users to virtually try on selected clothing. When a user selects clothing through their device, the device sends this information to a server. The server uses AR technology to generate a virtual try-on image and returns the result to the device. This allows the user to receive feedback on the fit of the size and style.

[0036] In addition, this system integrates community-based features, allowing users to share their outfits with other users and exchange ratings and opinions. Outfits proposed by users are posted to the server via their devices, and this information is presented to other users. This allows users to gain new inspiration while also incorporating the perspectives of others.

[0037] For example, if a male user in his 30s is looking for a casual business style, the system uses an AI engine to suggest the most suitable jacket and shoes based on photos of the shirt and jeans the user owns. In this process, AR technology allows the user to virtually try on the clothes, and feedback is provided that the suggested jacket is slightly too large. Based on this information, the user can seek opinions from other users in the community and use that information to make a final purchase decision.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the app on their device and selects the styling generation option. The user enters information such as their preferences, budget, age group, and the occasion for which they want to wear the clothes, and prepares to submit the information.

[0041] Step 2:

[0042] The terminal receives user input data and sends it to the server as formatted data. The server then obtains the input for data analysis.

[0043] Step 3:

[0044] The server passes the received data to an AI model, which then generates personalized clothing suggestions based on the information provided. The AI ​​analyzes data based on past trends and the behavior of users with similar preferences.

[0045] Step 4:

[0046] The server sends the generated styling results to the terminal. These results include clothing combinations and recommended brands tailored to the user's preferences.

[0047] Step 5:

[0048] If a user wants to take photos of items they own with their device and receive suggestions that take their existing wardrobe into consideration, the device sends those photos to the server as image data.

[0049] Step 6:

[0050] The server processes photos using image analysis technology, detects the characteristics of the items, and the AI ​​generates new outfit combinations based on the analysis results.

[0051] Step 7:

[0052] The server sends new outfit suggestions back to the user's device. The user can review the suggested styles and select items they like.

[0053] Step 8:

[0054] If a user requests a virtual try-on, the device transfers data on the selected items and the user's body measurements to the server.

[0055] Step 9:

[0056] The server uses AR technology to generate virtual try-on images and provides the user with a try-on simulation. Style and size feedback is also generated during this process.

[0057] Step 10:

[0058] The server sends the results of the virtual try-on to the user's device for review. Based on the feedback, the user can determine the optimal size and style.

[0059] Step 11:

[0060] When users share their generated outfits with the community, their devices post the data to the server, allowing them to receive feedback and ratings from other users.

[0061] Step 12:

[0062] The server presents the user's outfit as a new post within the community, giving other users the opportunity to rate and comment on it.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] In today's fashion scene, there is a demand for services that automatically generate styling suggestions tailored to individual users and provide an interactive experience through virtual try-ons and community feedback during the selection process. However, conventional technologies have struggled to efficiently provide styling suggestions that match individual user preferences and have not been able to fully meet users' needs for virtual try-ons and community features. Furthermore, there are challenges in optimizing pricing and continuously improving based on user feedback.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for generating individual styling suggestions based on user-inputted preferences and constraints, means for analyzing photos of clothing owned by the user to generate additional coordination suggestions, and means for virtually trying on clothing in a virtual environment and providing feedback on the fit of dimensions and style. This not only enables fashion suggestions tailored to each user's preferences, but also allows for the use of a more effective and user-friendly styling assistant by leveraging realistic feedback from virtual try-ons and interaction through community features.

[0068] "A means of generating styling suggestions" refers to a technology that automatically lists appropriate fashion coordinates based on the user's preferences and conditions.

[0069] "A method for analyzing clothing photos and generating suggestions" refers to an algorithm that recognizes images of clothing uploaded by users and devises new outfit combinations based on that image information.

[0070] "A means of trying on clothes and providing feedback using a virtual environment" refers to a system that allows users to virtually try on clothes they have selected and check the fit of the size and style on the screen.

[0071] A "community platform" is an online service where users can share their fashion suggestions with other users, and exchange ratings and opinions.

[0072] A "generative AI model" is a general term for algorithms and models that use machine learning techniques to generate fashion suggestions.

[0073] "Improving the recommendation algorithm" refers to a technical method that utilizes user feedback to improve the accuracy and quality of styling suggestions.

[0074] This invention is a system that automatically provides styling suggestions tailored to the user's fashion needs. Users can access the system via a terminal and input their preferences and constraints to obtain personalized fashion coordinates.

[0075] Users input information such as their fashion preferences, budget, colors, and materials using a dedicated application installed on their device or a web interface. The device sends this data to a server. Based on the received data, the server uses a generative AI model to suggest styling options that match the user's preferences. The suggested options are visualized on the device and illustrated for easy review by the user.

[0076] Furthermore, users can take pictures of their clothing with their devices and upload them to the server. The server uses image analysis algorithms (for example, deep learning technology) to obtain clothing information from the uploaded images and incorporates it into the suggestions. This allows users to receive new outfits that utilize their existing wardrobe.

[0077] The virtual try-on feature works by having the user select clothing items and send them to a server, which then uses AR technology to generate a virtual try-on image. This image is then sent back to the user via their device, allowing them to check the fit and style. Specifically, the server utilizes technologies such as Unity and ARKit to perform this process.

[0078] This system also includes a community feature that allows users to share suggested outfits with other users and exchange opinions. Users can post their outfits within the community from their devices and receive feedback and ratings from other users. Through this process, users can gain new fashion inspiration and improve their own style.

[0079] As a concrete example, the prompt message for a male user in his 30s requesting a casual business style would be as follows:

[0080] "A man in his 30s, casual business attire. Please suggest a jacket and shoes that would match the shirts and jeans he owns."

[0081] Thus, the present invention flexibly responds to the individual needs of users and provides a user-friendly fashion experience.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users access a dedicated application or web interface using their device and input information such as their fashion preferences, budget, colors, and materials. The device then sends this information received from the user as data to the server. In this step, the input is user preference data, and the output is the transmission of data to the server. The user interface is intuitive, using pull-down menus and checkboxes when inputting information.

[0085] Step 2:

[0086] The server receives user data sent from the terminal and analyzes the data using a generating AI model. During this process, it compares fashion information in the database with the user's input data to generate optimal styling. The input for this step is the user's preference data, and the output is the generated styling suggestions. The AI ​​engine lists multiple outfit suggestions within seconds.

[0087] Step 3:

[0088] The server structures the generated styling suggestions in JSON format and sends the data back to the terminal. Based on the received data, the terminal visually displays the suggested styling to the user. The input for this step is styling suggestion data, and the output is the fashion suggestions displayed to the user. On the interface, the suggestions are presented in card format, allowing for comparison and consideration by scrolling.

[0089] Step 4:

[0090] Users use their device's camera function to take pictures of their clothing and upload them. The device then sends the image data to the server. The input for this step is the image of the clothing, and the output is the transmission of the image data to the server. The user interface is designed to allow users to easily complete the upload process with just a tap.

[0091] Step 5:

[0092] The server processes the received images using image analysis technology to extract clothing features. Based on the analysis results, it generates additional outfit suggestions and sends them back to the terminal. The input for this step is image data, and the output is outfit suggestions generated by image analysis. Computer vision technology recognizes color, material, and design patterns from the captured images.

[0093] Step 6:

[0094] When a user selects virtual try-on, the device sends the selected clothing information to the server. The server uses AR technology to generate a virtual try-on image and sends the result back to the device. The input for this step is the data of the clothing the user wishes to try on, and the output is the try-on image generated by AR. The user can view the virtual try-on image on the device screen and visually evaluate the size and style.

[0095] Step 7:

[0096] Users share their generated outfits with other users using the community feature. The device sends the posted content to the server, which displays it within the community. The input for this step is the user's posted data, and the output is its presentation to other users in the community. Users receive comments and ratings from other users and utilize the feedback.

[0097] In this way, the entire system's processing works in coordination to provide users with personalized fashion suggestions and realize a rich fashion experience.

[0098] (Application Example 1)

[0099] 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."

[0100] Conventional fashion suggestion systems failed to adequately provide personalized suggestions based on user preferences and budgets, and lacked accuracy and real-time capabilities in virtual try-on, making it difficult to improve user satisfaction. Furthermore, there were limitations in effective means for gaining new inspiration through opinion exchange within communities.

[0101] 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.

[0102] In this invention, the server includes means for generating individual clothing suggestions based on information entered by the user, means for generating new clothing suggestions by analyzing images of items owned by the user, means for virtually trying on clothing using virtual reality and providing feedback on suitability, means for managing information including the user's wardrobe data and providing optimal suggestions in real time, and means for visualizing the results of virtually trying on clothes using augmented reality technology and providing feedback on how the clothes feel when worn. This makes it possible for users to receive highly accurate fashion suggestions based on their preferences and budget in real time, and also enables effective sharing of new inspiration and information through the activation of a community among users.

[0103] "A means of generating personalized clothing suggestions based on user input" refers to a process for suggesting the optimal clothing combination based on input data such as the user's preferences and budget.

[0104] "A means of analyzing images of items owned by a user to generate new clothing suggestions" refers to a technology that identifies images of clothing and accessories provided by a user and suggests new outfits that match them.

[0105] "A means of trying on clothing using virtual reality and providing feedback on suitability" refers to a technology that provides users with a virtual fitting experience and evaluates suitability in terms of size and style.

[0106] "A means of sharing outfit suggestions with other users within a community and exchanging evaluations and opinions" refers to a platform where users can share their outfit ideas with other users and receive feedback and suggestions.

[0107] "A means of managing information including user wardrobe data and providing optimal suggestions in real time" refers to a function that organizes data about the user's belongings and immediately suggests the most suitable styling.

[0108] "A means of visualizing the results of virtually trying on clothes using augmented reality technology and providing feedback on the feeling of wearing them" refers to a process that uses AR technology to visually present how the user would virtually wear the clothes they have selected, and then provides feedback on the fit and design.

[0109] This system begins with the user sending information about their fashion preferences to a server using a terminal. The user inputs their preferences, budget, and images of clothing items they own, and all this information is aggregated on a cloud server. The server uses deep learning technologies such as TENSORFLOW® to analyze the user's data and generate personalized outfit suggestions. This utilizes a data structure based on the user's input data and a generative AI model learned from past fashion data.

[0110] Furthermore, the server uses image recognition technology to analyze images of items uploaded by the user and generates new outfit suggestions that utilize the user's existing wardrobe. Users receive these suggestions via their device and can obtain real-time feedback. This feedback includes specific comments on size and style suitability.

[0111] The virtual try-on feature utilizes augmented reality technologies such as Unity and ARCore to virtually display selected clothing items, providing users with visual feedback. During this process, users can overlay suggested clothing items onto their own images to evaluate style suitability.

[0112] Furthermore, the system integrates a community-based interface. Users can share suggested outfits with other users, evaluate them, and exchange opinions. Users can post their own outfits, receive feedback from other members, and gain new ideas. This feature includes a platform that facilitates two-way communication among users.

[0113] As a concrete example, a prompt such as, "Based on a photo of a summer shirt the user owns, please suggest a casual outfit for going out. I would like an outfit that is as color-coordinated as possible," prompts the AI ​​engine to generate a personalized outfit. This is then used to provide feedback through virtual try-on, along with opinions from the community, to help users choose the optimal styling. This system provides users with the opportunity to constantly improve their fashion sense and discover styles they hadn't considered before.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] Users use their devices to input information such as their fashion preferences, budget, and images of their owned items. The devices then send this user data to a cloud server. The input data consists of text (preferences, budget) and image data (owned items).

[0117] Step 2:

[0118] The server preprocesses the received data. Text data related to preferences is analyzed using natural language processing, and image data is analyzed using computer vision technology to extract item features. As a result, it outputs string data and feature data as analyzed data.

[0119] Step 3:

[0120] The server uses a generative AI model to generate individual clothing suggestions based on pre-processed data. Specifically, it considers the user's preferences and budget, and outputs optimal outfit suggestions using analyzed clothing features. The output is a list of suggested fashion items.

[0121] Step 4:

[0122] The user receives AI-generated clothing suggestions via their device. After reviewing the suggestions displayed on the device, the user selects specific items and requests a virtual try-on.

[0123] Step 5:

[0124] The device sends selection information to the server, which uses augmented reality technology to overlay the items onto the user's image. It generates and returns a feedback image regarding the fit of size and style to the device. This results in a visual feedback image being output.

[0125] Step 6:

[0126] Users view feedback on their devices and post their opinions on the coordination proposals to the community. The device sends the posted content to the server for sharing and exchange of opinions. The input data is the user's opinion, and the output is community feedback and evaluation.

[0127] 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.

[0128] This invention is a system built to provide a fashion experience while taking into account the user's emotional state. In addition to general fashion suggestions, style generation based on possessions, virtual try-on, and community interaction, this system achieves a more personalized experience by integrating an emotion engine.

[0129] The user first inputs information about their fashion choices via a terminal and uses a sensor device to read their emotions. The terminal sends the user's input and collected biometric information to a server. An emotion engine on the server analyzes the biometric data and identifies the user's current emotional state. Based on this, the server uses an AI engine to generate clothing suggestions appropriate to the user's emotional state and send them back to the terminal.

[0130] Furthermore, by uploading images of items the user already owns, new outfit suggestions are provided based on those images. Throughout this process, the emotion engine also detects the user's emotions and adjusts the outfit suggestions to match their state.

[0131] During virtual try-on, the emotion engine analyzes user reactions in real time and generates feedback on the server. Based on this feedback, the server optimizes the size and style to provide a better user experience.

[0132] Furthermore, users can use the community feature to share the outfits they create with other users. The emotion engine analyzes other users' emotional reactions to the shared outfits, and users can receive feedback based on the results.

[0133] For example, if a user is feeling depressed, the system uses an emotion engine to detect this emotional state and suggests clothing in bright, cheerful colors. When the user virtually tries on the clothes and experiences happiness or a change in mood, the server records this emotional response and uses it to improve future suggestions. This emotion-based adjustment allows users to have a psychologically satisfying fashion experience.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user launches the app using their device and enters information about their fashion. Additionally, a sensor device is connected to the device to read the user's emotions.

[0137] Step 2:

[0138] The terminal transmits user input information and biometric data acquired from sensor devices to the server. This includes data necessary for emotion recognition.

[0139] Step 3:

[0140] The server uses an emotion engine to analyze the received biometric information and identify the user's current emotional state. Based on these results, it generates initial data to select the optimal fashion style.

[0141] Step 4:

[0142] The server's AI engine combines user preferences, emotional states, and trend data to generate individually optimized clothing suggestions, which are then sent back to the device.

[0143] Step 5:

[0144] If a user takes pictures of items they own with their device and wants new style suggestions based on their existing wardrobe, the device uploads those images to the server.

[0145] Step 6:

[0146] The server analyzes the image and passes the results to the AI ​​engine. The AI ​​engine then generates a personalized outfit for the user based on the analysis results and their emotional state.

[0147] Step 7:

[0148] A new outfit suggestion is sent from the server to the terminal, allowing the user to review the proposed content.

[0149] Step 8:

[0150] If a user requests a virtual try-on, the device sends data on the try-on items and the user's body shape information to the server. The server, via an emotion engine, senses the user's reactions in real time and generates a virtual try-on image.

[0151] Step 9:

[0152] The server sends back an image of the virtual try-on to the terminal, allowing the user to experience the try-on process. If the user's emotions change during the try-on, that information is also reported to the server.

[0153] Step 10:

[0154] Users access communities within the system and share their outfits. When doing so, the device posts its emotional state along with the suggested data to the server.

[0155] Step 11:

[0156] When other users in the community react, the server analyzes it, generates feedback, and sends it back to the user's device. This allows users to choose their clothing while also considering the opinions of others.

[0157] (Example 2)

[0158] 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".

[0159] Traditional fashion recommendation systems did not take into account the individual emotional state of users, and as a result, often made suggestions that did not necessarily match the user's feelings or preferences. Furthermore, it was difficult to provide optimal suggestions that took into account the user's assets and budget constraints. This meant that users could potentially have an unsatisfactory experience with fashion choices.

[0160] 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.

[0161] In this invention, the server includes means for generating individual clothing suggestions based on information and biological status entered by the user, means for generating new clothing suggestions by analyzing images of assets owned by the user, and means for sharing clothing suggestions with other users and exchanging evaluations and opinions. This enables personalized suggestions that take into account the user's emotional state and budget conditions, resulting in a more satisfying fashion experience.

[0162] A "user" refers to an individual who uses a fashion suggestion system to request personalized clothing suggestions.

[0163] "Information" refers to user input data related to fashion and data related to biological status.

[0164] "Biological state" refers to information that indicates an individual's current situation, such as their emotional state, based on the user's biometric data.

[0165] "Assets" refer to image data of clothing and accessories owned by the user.

[0166] A "virtual environment" refers to a virtual fitting room created using a computer.

[0167] "Opinions" refers to feedback and evaluations of the user's clothing suggestions.

[0168] "Emotional state" refers to the emotional condition determined by the user's biometric data and the analysis based on that data.

[0169] "Personalization" refers to adjusting suggestions to take into account the user's emotional state and budget constraints.

[0170] As a form of carrying out the invention, the system of the present invention provides a more personalized fashion experience by allowing the user to provide fashion information and receive clothing suggestions based on their emotional state.

[0171] The user first inputs information about their fashion preferences via a device. This information includes their preferred colors and styles, as well as their current fashion interests. Furthermore, the user wears a biometric sensor device designed to read their emotions. This device is typically a wearable device that measures things like heart rate and skin electrical activity.

[0172] The terminal transmits user input information and biometric data collected from sensor devices to the server. The server is equipped with an emotion engine that analyzes the user's emotional state using, for example, a common emotion recognition API. This analysis makes it possible to determine whether the user is experiencing an emotional state such as "happiness" or "depression."

[0173] Next, the server uses a generative AI model to provide fashion suggestions tailored to the user's emotional state. The AI ​​engine used is specialized in natural language processing and generates suggestions by taking prompts such as "Generate a fashion coordinate that is appropriate for the user's current emotional state."

[0174] Furthermore, when a user uploads image data of their clothing and accessories, the server analyzes these images and provides new clothing suggestions based on their possessions. Even in this process, the emotion engine evaluates the user's emotional state and adjusts the suggestions accordingly, thus achieving personalization.

[0175] This system allows users to receive personalized outfit suggestions based on their emotions, resulting in a psychologically satisfying fashion experience.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The user inputs fashion-related information using a terminal and wears a biometric sensor device. This information includes favorite colors and styles, as well as current fashion interests. The sensor device measures heart rate, skin electrical activity, and other parameters in real time and transmits this data to the terminal as biometric data.

[0179] Step 2:

[0180] The device transmits fashion information and biometric data received from the user to the server. This data is typically transferred securely over the internet. This input data is then analyzed by the server's emotion engine.

[0181] Step 3:

[0182] The server uses an emotion engine to analyze the user's biometric data and identify their emotional state. For example, based on changes in heart rate and patterns of skin electrical activity, the AI ​​determines emotions such as "happy" or "depressed." The output of this step is the analyzed emotional state of the user.

[0183] Step 4:

[0184] The server uses a generative AI model to generate personalized clothing suggestions based on the user's emotional state. To achieve this, the server receives a prompt message, "Generate a fashion coordinate suitable for the user's current emotional state," and the AI ​​model performs natural language processing. The output of this step is a personalized fashion suggestion.

[0185] Step 5:

[0186] The server sends the generated fashion suggestions back to the device. The device visually presents the suggestions to the user. Through a mobile app or webpage, the user can review the suggested outfits and virtually try them on.

[0187] Step 6:

[0188] Users upload images of their clothing and accessories via their device. The submitted image data is sent to a server for analysis.

[0189] Step 7:

[0190] The server uses image analysis technology to analyze uploaded images and generate new clothing suggestions. These suggestions are then adjusted based on the user's emotional state, which has already been analyzed. The output is the adjusted new clothing suggestion.

[0191] Step 8:

[0192] Users can share the generated outfits within the community and receive ratings and feedback from other users. This allows for the collection of data to further personalize and improve suggestions.

[0193] (Application Example 2)

[0194] 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".

[0195] In modern e-commerce, a challenge exists in that users find it difficult to make product choices that take their emotional state into account when selecting fashion items online. Furthermore, the inability to provide dynamic suggestions that respond to the user's changing psychological state leads to decreased purchase satisfaction. Improving the user experience through feedback and communication features via virtual try-on is also needed.

[0196] 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.

[0197] In this invention, the server includes means for detecting the user's emotional state and generating appropriate clothing suggestions, means for suggesting a fashion style suitable for the user's emotional state using a generative AI model, and means for analyzing the user's emotional data to generate prompt sentences and optimize input to the generative AI model. This makes it possible to improve the quality of the user experience through personalized clothing suggestions and virtual try-ons based on the user's emotional state.

[0198] "User emotional state" refers to the psychological or emotional state that the user is currently experiencing, and includes data measured in real time by emotion sensors.

[0199] "Appropriate clothing suggestions" refer to recommendations for clothing and styling that enhance the user's psychological satisfaction, generated by an AI system based on the user's emotional state.

[0200] A "generative AI model" is a mathematical or computational model that uses artificial intelligence technology to generate and analyze various possibilities based on input data.

[0201] "Emotional data" refers to data that includes biometric information and psychological indicators necessary to identify a user's emotional state.

[0202] A "prompt sentence" is a natural language sentence that is input to explicitly indicate the intention for running a generative AI model, and is used to prompt a specific generation result.

[0203] "Virtual try-on" refers to an experience where users can evaluate the fit of clothing in an online environment without actually trying it on, using virtual reality technology.

[0204] This invention is a system that provides real-time fashion suggestions that take into account the user's emotional state. The system is configured as follows: The user uses a device equipped with an emotion sensor, such as smart glasses, to collect biometric data such as heart rate and skin electrical activity. This data is transmitted to a server via Wi-Fi or Bluetooth.

[0205] The server uses an emotion analysis engine based on Python and TensorFlow to analyze biometric data and identify the user's emotional state. Based on this analysis, a generative AI model powered by Amazon SageMaker generates clothing suggestions that are appropriate for the user's emotions.

[0206] For example, if a user is feeling stressed, the generative AI model will suggest clothing in relaxing colors and materials. Following this, a data platform such as Cisco Kinetic generates a prompt message for the user, providing feedback to the AI ​​model. This prompt message includes an instruction such as, "Suggest clothing that will help the user relax."

[0207] The generated suggestions are displayed on the smart glasses' screen, allowing the user to visually confirm them. In one embodiment, the user can enjoy a virtual try-on experience using virtual reality technology in a virtual store. This system can improve the user's psychological satisfaction and enrich the purchasing experience.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The user puts on smart glasses and begins collecting biometric data. An emotion sensor built into the smart glasses collects the user's heart rate and skin electrical activity in real time. The input is biometric data, and the output is a biosignal measured by the emotion sensor.

[0211] Step 2:

[0212] The device collects biometric data and transmits it to the server via Bluetooth or Wi-Fi. The input is biometric data obtained from smart glasses, and the output is the digital data that is transmitted from it. The server receives this data.

[0213] Step 3:

[0214] The server uses Python and TensorFlow to analyze biometric data and estimate the user's emotional state. The input is biometric data sent to the server, and the output is the emotion determination result from the emotion analysis engine. Data processing involves filtering and normalization of the biometric data before calculations are performed using an emotion model.

[0215] Step 4:

[0216] The server uses Amazon SageMaker to generate clothing suggestions based on the user's emotional state. The input is the result of an emotional analysis, and the output is clothing suggestions that enhance the user's psychological satisfaction. A generative AI model is used to select the most suitable fashion items from the data.

[0217] Step 5:

[0218] The server formats the generated clothing suggestions as prompt messages and provides feedback to the user via Cisco Kinetic. The input is the clothing suggestion data, and the output is visual feedback information for the user. Specifically, the suggestions are displayed on the smart glasses' display.

[0219] Step 6:

[0220] The user views suggested outfits through the display of smart glasses and tries them on in virtual reality. The input is the outfit suggestions displayed on the screen, and the output is visual information and a virtual try-on experience. This allows the user to visually evaluate the suggested outfits.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention is a system designed to generate and promote user-specific fashion styles, specifically enabling personalized clothing suggestions based on information entered by the user. To this end, the user can access a styling generation module using a terminal. The user inputs information about their preferences and budget, and the terminal sends this information to a server. An AI engine on the server analyzes the received data, generates the optimal styling for each individual user, and sends the results back to the terminal.

[0238] Furthermore, the app includes a feature that allows users to upload images of items they own. Users take photos of their items using their device and send them to the server. The server analyzes the images and generates new outfit suggestions based on those items. This information is displayed on the device, allowing users to make the most of their existing wardrobe.

[0239] Furthermore, the virtual try-on function allows users to virtually try on selected clothing. When a user selects clothing through their device, the device sends this information to a server. The server uses AR technology to generate a virtual try-on image and returns the result to the device. This allows the user to receive feedback on the fit of the size and style.

[0240] In addition, this system integrates community-based features, allowing users to share their outfits with other users and exchange ratings and opinions. Outfits proposed by users are posted to the server via their devices, and this information is presented to other users. This allows users to gain new inspiration while also incorporating the perspectives of others.

[0241] For example, if a male user in his 30s is looking for a casual business style, the system uses an AI engine to suggest the most suitable jacket and shoes based on photos of the shirt and jeans the user owns. In this process, AR technology allows the user to virtually try on the clothes, and feedback is provided that the suggested jacket is slightly too large. Based on this information, the user can seek opinions from other users in the community and use that information to make a final purchase decision.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The user launches the app on their device and selects the styling generation option. The user enters information such as their preferences, budget, age group, and the occasion for which they want to wear the clothes, and prepares to submit the information.

[0245] Step 2:

[0246] The terminal receives user input data and sends it to the server as formatted data. The server then obtains the input for data analysis.

[0247] Step 3:

[0248] The server passes the received data to an AI model, which then generates personalized clothing suggestions based on the information provided. The AI ​​analyzes data based on past trends and the behavior of users with similar preferences.

[0249] Step 4:

[0250] The server sends the generated styling results to the terminal. These results include clothing combinations and recommended brands tailored to the user's preferences.

[0251] Step 5:

[0252] If a user wants to take photos of items they own with their device and receive suggestions that take their existing wardrobe into consideration, the device sends those photos to the server as image data.

[0253] Step 6:

[0254] The server processes photos using image analysis technology, detects the characteristics of the items, and the AI ​​generates new outfit combinations based on the analysis results.

[0255] Step 7:

[0256] The server sends new outfit suggestions back to the user's device. The user can review the suggested styles and select items they like.

[0257] Step 8:

[0258] If a user requests a virtual try-on, the device transfers data on the selected items and the user's body measurements to the server.

[0259] Step 9:

[0260] The server uses AR technology to generate virtual try-on images and provides the user with a try-on simulation. Style and size feedback is also generated during this process.

[0261] Step 10:

[0262] The server sends the results of the virtual try-on to the user's device for review. Based on the feedback, the user can determine the optimal size and style.

[0263] Step 11:

[0264] When users share their generated outfits with the community, their devices post the data to the server, allowing them to receive feedback and ratings from other users.

[0265] Step 12:

[0266] The server presents the user's outfit as a new post within the community, giving other users the opportunity to rate and comment on it.

[0267] (Example 1)

[0268] 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."

[0269] In today's fashion scene, there is a demand for services that automatically generate styling suggestions tailored to individual users and provide an interactive experience through virtual try-ons and community feedback during the selection process. However, conventional technologies have struggled to efficiently provide styling suggestions that match individual user preferences and have not been able to fully meet users' needs for virtual try-ons and community features. Furthermore, there are challenges in optimizing pricing and continuously improving based on user feedback.

[0270] 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.

[0271] In this invention, the server includes means for generating individual styling suggestions based on user-inputted preferences and constraints, means for analyzing photos of clothing owned by the user to generate additional coordination suggestions, and means for virtually trying on clothing in a virtual environment and providing feedback on the fit of dimensions and style. This not only enables fashion suggestions tailored to each user's preferences, but also allows for the use of a more effective and user-friendly styling assistant by leveraging realistic feedback from virtual try-ons and interaction through community features.

[0272] "A means of generating styling suggestions" refers to a technology that automatically lists appropriate fashion coordinates based on the user's preferences and conditions.

[0273] "A method for analyzing clothing photos and generating suggestions" refers to an algorithm that recognizes images of clothing uploaded by users and devises new outfit combinations based on that image information.

[0274] "A means of trying on clothes and providing feedback using a virtual environment" refers to a system that allows users to virtually try on clothes they have selected and check the fit of the size and style on the screen.

[0275] A "community platform" is an online service where users can share their fashion suggestions with other users, and exchange ratings and opinions.

[0276] A "generative AI model" is a general term for algorithms and models that use machine learning techniques to generate fashion suggestions.

[0277] "Improving the recommendation algorithm" refers to a technical method that utilizes user feedback to improve the accuracy and quality of styling suggestions.

[0278] This invention is a system that automatically provides styling suggestions tailored to the user's fashion needs. Users can access the system via a terminal and input their preferences and constraints to obtain personalized fashion coordinates.

[0279] Users input information such as their fashion preferences, budget, colors, and materials using a dedicated application installed on their device or a web interface. The device sends this data to a server. Based on the received data, the server uses a generative AI model to suggest styling options that match the user's preferences. The suggested options are visualized on the device and illustrated for easy review by the user.

[0280] Furthermore, users can take pictures of their clothing with their devices and upload them to the server. The server uses image analysis algorithms (for example, deep learning technology) to obtain clothing information from the uploaded images and incorporates it into the suggestions. This allows users to receive new outfits that utilize their existing wardrobe.

[0281] In the virtual try-on function, the user sends the selected clothing to the server, and the server uses AR technology to generate an image of virtual try-on. The generated try-on image is sent back to the user through the terminal, and the user can check the fit and style. Specifically, the server uses technologies such as Unity and ARKit to perform this process.

[0282] This system also has a community function that allows users to share the coordinates proposed by other users and exchange opinions. Users can post their own coordinates in the community from the terminal and get feedback and evaluations from other users. Through this process, users can obtain new fashion inspiration and improve their own styles.

[0283] As a specific example, the prompt text when a male user in his 30s seeks a casual business style is as follows.

[0284] "Male in his 30s, casual business style. Please propose a jacket and shoes that match the shirts and jeans I own."

[0285] In this way, the present invention flexibly responds to the individual needs of users and provides a user-friendly fashion experience.

[0286] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0287] Step 1:

[0288] Users access a dedicated application or web interface using their device and input information such as their fashion preferences, budget, colors, and materials. The device then sends this information received from the user as data to the server. In this step, the input is user preference data, and the output is the transmission of data to the server. The user interface is intuitive, using pull-down menus and checkboxes when inputting information.

[0289] Step 2:

[0290] The server receives user data sent from the terminal and analyzes the data using a generating AI model. During this process, it compares fashion information in the database with the user's input data to generate optimal styling. The input for this step is the user's preference data, and the output is the generated styling suggestions. The AI ​​engine lists multiple outfit suggestions within seconds.

[0291] Step 3:

[0292] The server structures the generated styling suggestions in JSON format and sends the data back to the terminal. Based on the received data, the terminal visually displays the suggested styling to the user. The input for this step is styling suggestion data, and the output is the fashion suggestions displayed to the user. On the interface, the suggestions are presented in card format, allowing for comparison and consideration by scrolling.

[0293] Step 4:

[0294] Users use their device's camera function to take pictures of their clothing and upload them. The device then sends the image data to the server. The input for this step is the image of the clothing, and the output is the transmission of the image data to the server. The user interface is designed to allow users to easily complete the upload process with just a tap.

[0295] Step 5:

[0296] The server processes the received images using image analysis technology to extract clothing features. Based on the analysis results, it generates additional outfit suggestions and sends them back to the terminal. The input for this step is image data, and the output is outfit suggestions generated by image analysis. Computer vision technology recognizes color, material, and design patterns from the captured images.

[0297] Step 6:

[0298] When a user selects virtual try-on, the device sends the selected clothing information to the server. The server uses AR technology to generate a virtual try-on image and sends the result back to the device. The input for this step is the data of the clothing the user wishes to try on, and the output is the try-on image generated by AR. The user can view the virtual try-on image on the device screen and visually evaluate the size and style.

[0299] Step 7:

[0300] Users share their generated outfits with other users using the community feature. The device sends the posted content to the server, which displays it within the community. The input for this step is the user's posted data, and the output is its presentation to other users in the community. Users receive comments and ratings from other users and utilize the feedback.

[0301] In this way, the entire system's processing works in coordination to provide users with personalized fashion suggestions and realize a rich fashion experience.

[0302] (Application Example 1)

[0303] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0304] In conventional fashion recommendation systems, personalized recommendations based on users' preferences and budgets were not made sufficiently, and there were deficiencies in the accuracy and real-time performance of virtual fitting, making it difficult to improve user satisfaction. Also, there was a problem that effective means were limited for obtaining new inspiration through opinion exchange via a community.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0306] In this invention, the server includes means for generating personalized clothing recommendations based on information input by the user, means for analyzing images of items owned by the user to generate new clothing recommendations, means for using virtual reality to try on clothing and provide feedback regarding suitability, means for managing information including the user's wardrobe data and providing optimal recommendations in real time, and means for visualizing the results of virtual try-on using augmented reality technology and providing feedback regarding the wearing sensation. As a result, it becomes possible to receive highly accurate fashion recommendations in real time based on the user's preferences and budget, and it also becomes possible to effectively carry out new inspiration and information sharing through the activation of the community among users.

[0307] The "means for generating personalized clothing recommendations based on information input by the user" is a process for proposing an optimal combination of clothes based on input data such as the user's preferences and budget.

[0308] The "means for analyzing images of items owned by the user to generate new clothing recommendations" is a technology for identifying images of clothes and accessories provided by the user and proposing new coordinates suitable for them.

[0309] The "means for using virtual reality to try on clothing and provide feedback regarding suitability" is a technology for providing the user with a virtual fitting experience and evaluating the suitability regarding size and style.

[0310] "A means of sharing outfit suggestions with other users within a community and exchanging evaluations and opinions" refers to a platform where users can share their outfit ideas with other users and receive feedback and suggestions.

[0311] "A means of managing information including user wardrobe data and providing optimal suggestions in real time" refers to a function that organizes data about the user's belongings and immediately suggests the most suitable styling.

[0312] "A means of visualizing the results of virtually trying on clothes using augmented reality technology and providing feedback on the feeling of wearing them" refers to a process that uses AR technology to visually present how the user would virtually wear the clothes they have selected, and then provides feedback on the fit and design.

[0313] This system begins with the user sending information about their fashion preferences to a server using a device. The user inputs their preferences, budget, and images of clothing items they own, and all this information is aggregated on a cloud server. The server uses deep learning technologies such as TensorFlow to analyze the user's data and generate personalized outfit suggestions. This utilizes a data structure based on the user's input data and a generative AI model trained on past fashion data.

[0314] Furthermore, the server uses image recognition technology to analyze images of items uploaded by the user and generates new outfit suggestions that utilize the user's existing wardrobe. Users receive these suggestions via their device and can obtain real-time feedback. This feedback includes specific comments on size and style suitability.

[0315] The virtual try-on feature utilizes augmented reality technologies such as Unity and ARCore to virtually display selected clothing items, providing users with visual feedback. During this process, users can overlay suggested clothing items onto their own images to evaluate style suitability.

[0316] Furthermore, the system integrates a community-based interface. Users can share suggested outfits with other users, evaluate them, and exchange opinions. Users can post their own outfits, receive feedback from other members, and gain new ideas. This feature includes a platform that facilitates two-way communication among users.

[0317] As a concrete example, a prompt such as, "Based on a photo of a summer shirt the user owns, please suggest a casual outfit for going out. I would like an outfit that is as color-coordinated as possible," prompts the AI ​​engine to generate a personalized outfit. This is then used to provide feedback through virtual try-on, along with opinions from the community, to help users choose the optimal styling. This system provides users with the opportunity to constantly improve their fashion sense and discover styles they hadn't considered before.

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] Users use their devices to input information such as their fashion preferences, budget, and images of their owned items. The devices then send this user data to a cloud server. The input data consists of text (preferences, budget) and image data (owned items).

[0321] Step 2:

[0322] The server preprocesses the received data. Text data related to preferences is analyzed using natural language processing, and image data is analyzed using computer vision technology to extract item features. As a result, it outputs string data and feature data as analyzed data.

[0323] Step 3:

[0324] The server uses a generative AI model to generate individual clothing suggestions based on pre-processed data. Specifically, it considers the user's preferences and budget, and outputs optimal outfit suggestions using analyzed clothing features. The output is a list of suggested fashion items.

[0325] Step 4:

[0326] The user receives AI-generated clothing suggestions via their device. After reviewing the suggestions displayed on the device, the user selects specific items and requests a virtual try-on.

[0327] Step 5:

[0328] The device sends selection information to the server, which uses augmented reality technology to overlay the items onto the user's image. It generates and returns a feedback image regarding the fit of size and style to the device. This results in a visual feedback image being output.

[0329] Step 6:

[0330] Users view feedback on their devices and post their opinions on the coordination proposals to the community. The device sends the posted content to the server for sharing and exchange of opinions. The input data is the user's opinion, and the output is community feedback and evaluation.

[0331] 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.

[0332] This invention is a system built to provide a fashion experience while taking into account the user's emotional state. In addition to general fashion suggestions, style generation based on possessions, virtual try-on, and community interaction, this system achieves a more personalized experience by integrating an emotion engine.

[0333] The user first inputs information about their fashion choices via a terminal and uses a sensor device to read their emotions. The terminal sends the user's input and collected biometric information to a server. An emotion engine on the server analyzes the biometric data and identifies the user's current emotional state. Based on this, the server uses an AI engine to generate clothing suggestions appropriate to the user's emotional state and send them back to the terminal.

[0334] Furthermore, by uploading images of items the user already owns, new outfit suggestions are provided based on those images. Throughout this process, the emotion engine also detects the user's emotions and adjusts the outfit suggestions to match their state.

[0335] During virtual try-on, the emotion engine analyzes user reactions in real time and generates feedback on the server. Based on this feedback, the server optimizes the size and style to provide a better user experience.

[0336] Furthermore, users can use the community feature to share the outfits they create with other users. The emotion engine analyzes other users' emotional reactions to the shared outfits, and users can receive feedback based on the results.

[0337] For example, if a user is feeling depressed, the system uses an emotion engine to detect this emotional state and suggests clothing in bright, cheerful colors. When the user virtually tries on the clothes and experiences happiness or a change in mood, the server records this emotional response and uses it to improve future suggestions. This emotion-based adjustment allows users to have a psychologically satisfying fashion experience.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The user launches the app using their device and enters information about their fashion. Additionally, a sensor device is connected to the device to read the user's emotions.

[0341] Step 2:

[0342] The terminal transmits user input information and biometric data acquired from sensor devices to the server. This includes data necessary for emotion recognition.

[0343] Step 3:

[0344] The server uses an emotion engine to analyze the received biometric information and identify the user's current emotional state. Based on these results, it generates initial data to select the optimal fashion style.

[0345] Step 4:

[0346] The server's AI engine combines user preferences, emotional states, and trend data to generate individually optimized clothing suggestions, which are then sent back to the device.

[0347] Step 5:

[0348] If a user takes pictures of items they own with their device and wants new style suggestions based on their existing wardrobe, the device uploads those images to the server.

[0349] Step 6:

[0350] The server analyzes the image and passes the results to the AI ​​engine. The AI ​​engine then generates a personalized outfit for the user based on the analysis results and their emotional state.

[0351] Step 7:

[0352] A new outfit suggestion is sent from the server to the terminal, allowing the user to review the proposed content.

[0353] Step 8:

[0354] If a user requests a virtual try-on, the device sends data on the try-on items and the user's body shape information to the server. The server, via an emotion engine, senses the user's reactions in real time and generates a virtual try-on image.

[0355] Step 9:

[0356] The server sends back an image of the virtual try-on to the terminal, allowing the user to experience the try-on process. If the user's emotions change during the try-on, that information is also reported to the server.

[0357] Step 10:

[0358] Users access communities within the system and share their outfits. When doing so, the device posts its emotional state along with the suggested data to the server.

[0359] Step 11:

[0360] When other users in the community react, the server analyzes it, generates feedback, and sends it back to the user's device. This allows users to choose their clothing while also considering the opinions of others.

[0361] (Example 2)

[0362] 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".

[0363] Traditional fashion recommendation systems did not take into account the individual emotional state of users, and as a result, often made suggestions that did not necessarily match the user's feelings or preferences. Furthermore, it was difficult to provide optimal suggestions that took into account the user's assets and budget constraints. This meant that users could potentially have an unsatisfactory experience with fashion choices.

[0364] 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.

[0365] In this invention, the server includes means for generating individual clothing suggestions based on information and biological status entered by the user, means for generating new clothing suggestions by analyzing images of assets owned by the user, and means for sharing clothing suggestions with other users and exchanging evaluations and opinions. This enables personalized suggestions that take into account the user's emotional state and budget conditions, resulting in a more satisfying fashion experience.

[0366] A "user" refers to an individual who uses a fashion suggestion system to request personalized clothing suggestions.

[0367] "Information" refers to user input data related to fashion and data related to biological status.

[0368] "Biological state" refers to information that indicates an individual's current situation, such as their emotional state, based on the user's biometric data.

[0369] "Assets" refer to image data of clothing and accessories owned by the user.

[0370] A "virtual environment" refers to a virtual fitting room created using a computer.

[0371] "Opinions" refers to feedback and evaluations of the user's clothing suggestions.

[0372] "Emotional state" refers to the emotional condition determined by the user's biometric data and the analysis based on that data.

[0373] "Personalization" refers to adjusting suggestions to take into account the user's emotional state and budget constraints.

[0374] As a form of carrying out the invention, the system of the present invention provides a more personalized fashion experience by allowing the user to provide fashion information and receive clothing suggestions based on their emotional state.

[0375] The user first inputs information about fashion via a device. This information includes preferred colors and styles, and current fashion interests. Furthermore, the user wears a biometric sensor device designed to read emotions. This device is typically a wearable device that measures heart rate, skin electrical activity, and other parameters.

[0376] The terminal transmits user input information and biometric data collected from sensor devices to the server. The server is equipped with an emotion engine that analyzes the user's emotional state using, for example, a common emotion recognition API. This analysis makes it possible to determine whether the user is experiencing an emotional state such as "happiness" or "depression."

[0377] Next, the server uses a generative AI model to provide fashion suggestions tailored to the user's emotional state. The AI ​​engine used is specialized in natural language processing and generates suggestions by taking prompts such as "Generate a fashion coordinate that is appropriate for the user's current emotional state."

[0378] Furthermore, when a user uploads image data of their clothing and accessories, the server analyzes these images and provides new clothing suggestions based on their possessions. Even in this process, the emotion engine evaluates the user's emotional state and adjusts the suggestions accordingly, thus achieving personalization.

[0379] This system allows users to receive personalized outfit suggestions based on their emotions, resulting in a psychologically satisfying fashion experience.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The user inputs fashion-related information using a terminal and wears a biometric sensor device. This information includes favorite colors and styles, as well as current fashion interests. The sensor device measures heart rate, skin electrical activity, and other parameters in real time and transmits this data to the terminal as biometric data.

[0383] Step 2:

[0384] The device transmits fashion information and biometric data received from the user to the server. This data is typically transferred securely over the internet. This input data is then analyzed by the server's emotion engine.

[0385] Step 3:

[0386] The server uses an emotion engine to analyze the user's biometric data and identify their emotional state. For example, based on changes in heart rate and patterns of skin electrical activity, the AI ​​determines emotions such as "happy" or "depressed." The output of this step is the analyzed emotional state of the user.

[0387] Step 4:

[0388] The server uses a generative AI model to generate personalized clothing suggestions based on the user's emotional state. To achieve this, the server receives a prompt message, "Generate a fashion coordinate suitable for the user's current emotional state," and the AI ​​model performs natural language processing. The output of this step is a personalized fashion suggestion.

[0389] Step 5:

[0390] The server sends the generated fashion suggestions back to the device. The device visually presents the suggestions to the user. Through a mobile app or webpage, the user can review the suggested outfits and virtually try them on.

[0391] Step 6:

[0392] Users upload images of their clothing and accessories via their device. The submitted image data is sent to a server for analysis.

[0393] Step 7:

[0394] The server uses image analysis technology to analyze uploaded images and generate new clothing suggestions. These suggestions are then adjusted based on the user's emotional state, which has already been analyzed. The output is the adjusted new clothing suggestion.

[0395] Step 8:

[0396] Users can share the generated outfits within the community and receive ratings and feedback from other users. This allows for the collection of data to further personalize and improve suggestions.

[0397] (Application Example 2)

[0398] 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 as the "terminal".

[0399] In modern e-commerce, a challenge exists in that users find it difficult to make product choices that take their emotional state into account when selecting fashion items online. Furthermore, the inability to provide dynamic suggestions that respond to the user's changing psychological state leads to decreased purchase satisfaction. Improving the user experience through feedback and communication features via virtual try-on is also needed.

[0400] 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.

[0401] In this invention, the server includes means for detecting the user's emotional state and generating appropriate clothing suggestions, means for suggesting a fashion style suitable for the user's emotional state using a generative AI model, and means for analyzing the user's emotional data to generate prompt sentences and optimize input to the generative AI model. This makes it possible to improve the quality of the user experience through personalized clothing suggestions and virtual try-ons based on the user's emotional state.

[0402] "User emotional state" refers to the psychological or emotional state that the user is currently experiencing, and includes data measured in real time by emotion sensors.

[0403] "Appropriate clothing suggestions" refer to recommendations for clothing and styling that enhance the user's psychological satisfaction, generated by an AI system based on the user's emotional state.

[0404] A "generative AI model" is a mathematical or computational model that uses artificial intelligence technology to generate and analyze various possibilities based on input data.

[0405] "Emotional data" refers to data that includes biometric information and psychological indicators necessary to identify a user's emotional state.

[0406] A "prompt sentence" is a natural language sentence that is input to explicitly indicate the intention for operating a generative AI model, and is used to prompt a specific generation result.

[0407] "Virtual try-on" refers to an experience where users can evaluate the fit of clothing in an online environment without actually trying it on, using virtual reality technology.

[0408] This invention is a system that provides real-time fashion suggestions that take into account the user's emotional state. The system is configured as follows: The user uses a device equipped with an emotion sensor, such as smart glasses, to collect biometric data such as heart rate and skin electrical activity. This data is transmitted to a server via Wi-Fi or Bluetooth.

[0409] The server uses an emotion analysis engine based on Python and TensorFlow to analyze biometric data and identify the user's emotional state. Based on this analysis, a generative AI model powered by Amazon SageMaker generates clothing suggestions that are appropriate for the user's emotions.

[0410] For example, if a user is feeling stressed, the generative AI model will suggest clothing in relaxing colors and materials. Following this, a data platform such as Cisco Kinetic generates a prompt message for the user, providing feedback to the AI ​​model. This prompt message includes an instruction such as, "Suggest clothing that will help the user relax."

[0411] The generated suggestions are displayed on the smart glasses' screen, allowing the user to visually confirm them. In one embodiment, the user can enjoy a virtual try-on experience using virtual reality technology in a virtual store. This system can improve the user's psychological satisfaction and enrich the purchasing experience.

[0412] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0413] Step 1:

[0414] The user puts on smart glasses and begins collecting biometric data. An emotion sensor built into the smart glasses collects the user's heart rate and skin electrical activity in real time. The input is biometric data, and the output is a biosignal measured by the emotion sensor.

[0415] Step 2:

[0416] The device collects biometric data and transmits it to the server via Bluetooth or Wi-Fi. The input is biometric data obtained from smart glasses, and the output is the digital data that is transmitted from it. The server receives this data.

[0417] Step 3:

[0418] The server uses Python and TensorFlow to analyze biometric data and estimate the user's emotional state. The input is biometric data sent to the server, and the output is the emotion determination result from the emotion analysis engine. Data processing involves filtering and normalization of the biometric data before calculations are performed using an emotion model.

[0419] Step 4:

[0420] The server uses Amazon SageMaker to generate clothing suggestions based on the user's emotional state. The input is the result of an emotional analysis, and the output is clothing suggestions that enhance the user's psychological satisfaction. A generative AI model is used to select the most suitable fashion items from the data.

[0421] Step 5:

[0422] The server formats the generated clothing suggestions as prompt messages and provides feedback to the user via Cisco Kinetic. The input is the clothing suggestion data, and the output is visual feedback information for the user. Specifically, the suggestions are displayed on the smart glasses' display.

[0423] Step 6:

[0424] The user views suggested outfits through the display of smart glasses and tries them on in virtual reality. The input is the outfit suggestions displayed on the screen, and the output is visual information and a virtual try-on experience. This allows the user to visually evaluate the suggested outfits.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] This invention is a system designed to generate and promote user-specific fashion styles, specifically enabling personalized clothing suggestions based on information entered by the user. To this end, the user can access a styling generation module using a terminal. The user inputs information about their preferences and budget, and the terminal sends this information to a server. An AI engine on the server analyzes the received data, generates the optimal styling for each individual user, and sends the results back to the terminal.

[0442] Furthermore, the app includes a feature that allows users to upload images of items they own. Users take photos of their items using their device and send them to the server. The server analyzes the images and generates new outfit suggestions based on those items. This information is displayed on the device, allowing users to make the most of their existing wardrobe.

[0443] Furthermore, the virtual try-on function allows users to virtually try on selected clothing. When a user selects clothing through their device, the device sends this information to a server. The server uses AR technology to generate a virtual try-on image and returns the result to the device. This allows the user to receive feedback on the fit of the size and style.

[0444] In addition, this system integrates community-based features, allowing users to share their outfits with other users and exchange ratings and opinions. Outfits proposed by users are posted to the server via their devices, and this information is presented to other users. This allows users to gain new inspiration while also incorporating the perspectives of others.

[0445] For example, if a male user in his 30s is looking for a casual business style, the system uses an AI engine to suggest the most suitable jacket and shoes based on photos of the shirt and jeans the user owns. In this process, AR technology allows the user to virtually try on the clothes, and feedback is provided that the suggested jacket is slightly too large. Based on this information, the user can seek opinions from other users in the community and use that information to make a final purchase decision.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] The user launches the app on their device and selects the styling generation option. The user enters information such as their preferences, budget, age group, and the occasion for which they want to wear the clothes, and prepares to submit the information.

[0449] Step 2:

[0450] The terminal receives user input data and sends it to the server as formatted data. The server then obtains the input for data analysis.

[0451] Step 3:

[0452] The server passes the received data to an AI model, which then generates personalized clothing suggestions based on the information provided. The AI ​​analyzes data based on past trends and the behavior of users with similar preferences.

[0453] Step 4:

[0454] The server sends the generated styling results to the terminal. These results include clothing combinations and recommended brands tailored to the user's preferences.

[0455] Step 5:

[0456] If a user wants to take photos of items they own with their device and receive suggestions that take their existing wardrobe into consideration, the device sends those photos to the server as image data.

[0457] Step 6:

[0458] The server processes photos using image analysis technology, detects the characteristics of the items, and the AI ​​generates new outfit combinations based on the analysis results.

[0459] Step 7:

[0460] The server sends new outfit suggestions back to the user's device. The user can review the suggested styles and select items they like.

[0461] Step 8:

[0462] If a user requests a virtual try-on, the device transfers data on the selected items and the user's body measurements to the server.

[0463] Step 9:

[0464] The server uses AR technology to generate virtual try-on images and provides the user with a try-on simulation. Style and size feedback is also generated during this process.

[0465] Step 10:

[0466] The server sends the results of the virtual try-on to the user's device for review. Based on the feedback, the user can determine the optimal size and style.

[0467] Step 11:

[0468] When users share their generated outfits with the community, their devices post the data to the server, allowing them to receive feedback and ratings from other users.

[0469] Step 12:

[0470] The server presents the user's outfit as a new post within the community, giving other users the opportunity to rate and comment on it.

[0471] (Example 1)

[0472] 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."

[0473] In today's fashion scene, there is a demand for services that automatically generate styling suggestions tailored to individual users and provide an interactive experience through virtual try-ons and community feedback during the selection process. However, conventional technologies have struggled to efficiently provide styling suggestions that match individual user preferences and have not been able to fully meet users' needs for virtual try-ons and community features. Furthermore, there are challenges in optimizing pricing and continuously improving based on user feedback.

[0474] 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.

[0475] In this invention, the server includes means for generating individual styling suggestions based on user-inputted preferences and constraints, means for analyzing photos of clothing owned by the user to generate additional coordination suggestions, and means for virtually trying on clothing in a virtual environment and providing feedback on the fit of dimensions and style. This not only enables fashion suggestions tailored to each user's preferences, but also allows for the use of a more effective and user-friendly styling assistant by leveraging realistic feedback from virtual try-ons and interaction through community features.

[0476] "A means of generating styling suggestions" refers to a technology that automatically lists appropriate fashion coordinates based on the user's preferences and conditions.

[0477] "A method for analyzing clothing photos and generating suggestions" refers to an algorithm that recognizes images of clothing uploaded by users and devises new outfit combinations based on that image information.

[0478] "A means of trying on clothes and providing feedback using a virtual environment" refers to a system that allows users to virtually try on clothes they have selected and check the fit of the size and style on the screen.

[0479] A "community platform" is an online service where users can share their fashion suggestions with other users, and exchange ratings and opinions.

[0480] A "generative AI model" is a general term for algorithms and models that use machine learning techniques to generate fashion suggestions.

[0481] "Improving the recommendation algorithm" refers to a technical method that utilizes user feedback to improve the accuracy and quality of styling suggestions.

[0482] This invention is a system that automatically provides styling suggestions tailored to the user's fashion needs. Users can access the system via a terminal and input their preferences and constraints to obtain personalized fashion coordinates.

[0483] Users input information such as their fashion preferences, budget, colors, and materials using a dedicated application installed on their device or a web interface. The device sends this data to a server. Based on the received data, the server uses a generative AI model to suggest styling options that match the user's preferences. The suggested options are visualized on the device and illustrated for easy review by the user.

[0484] Furthermore, users can take pictures of their clothing with their devices and upload them to the server. The server uses image analysis algorithms (for example, deep learning technology) to obtain clothing information from the uploaded images and incorporates it into the suggestions. This allows users to receive new outfits that utilize their existing wardrobe.

[0485] The virtual try-on feature works by having the user select clothing items and send them to a server, which then uses AR technology to generate a virtual try-on image. This image is then sent back to the user via their device, allowing them to check the fit and style. Specifically, the server utilizes technologies such as Unity and ARKit to perform this process.

[0486] This system also includes a community feature that allows users to share suggested outfits with other users and exchange opinions. Users can post their outfits within the community from their devices and receive feedback and ratings from other users. Through this process, users can gain new fashion inspiration and improve their own style.

[0487] As a concrete example, the prompt message for a male user in his 30s requesting a casual business style would be as follows:

[0488] "A man in his 30s, casual business attire. Please suggest a jacket and shoes that would match the shirts and jeans he owns."

[0489] Thus, the present invention flexibly responds to the individual needs of users and provides a user-friendly fashion experience.

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] Users access a dedicated application or web interface using their device and input information such as their fashion preferences, budget, colors, and materials. The device then sends this information received from the user as data to the server. In this step, the input is user preference data, and the output is the transmission of data to the server. The user interface is intuitive, using pull-down menus and checkboxes when inputting information.

[0493] Step 2:

[0494] The server receives user data sent from the terminal and analyzes the data using a generating AI model. During this process, it compares fashion information in the database with the user's input data to generate optimal styling. The input for this step is the user's preference data, and the output is the generated styling suggestions. The AI ​​engine lists multiple outfit suggestions within seconds.

[0495] Step 3:

[0496] The server structures the generated styling suggestions in JSON format and sends the data back to the terminal. Based on the received data, the terminal visually displays the suggested styling to the user. The input for this step is styling suggestion data, and the output is the fashion suggestions displayed to the user. On the interface, the suggestions are presented in card format, allowing for comparison and consideration by scrolling.

[0497] Step 4:

[0498] Users use their device's camera function to take pictures of their clothing and upload them. The device then sends the image data to the server. The input for this step is the image of the clothing, and the output is the transmission of the image data to the server. The user interface is designed to allow users to easily complete the upload process with just a tap.

[0499] Step 5:

[0500] The server processes the received images using image analysis technology to extract clothing features. Based on the analysis results, it generates additional outfit suggestions and sends them back to the terminal. The input for this step is image data, and the output is outfit suggestions generated by image analysis. Computer vision technology recognizes color, material, and design patterns from the captured images.

[0501] Step 6:

[0502] When a user selects virtual try-on, the device sends the selected clothing information to the server. The server uses AR technology to generate a virtual try-on image and sends the result back to the device. The input for this step is the data of the clothing the user wishes to try on, and the output is the try-on image generated by AR. The user can view the virtual try-on image on the device screen and visually evaluate the size and style.

[0503] Step 7:

[0504] Users share their generated outfits with other users using the community feature. The device sends the posted content to the server, which displays it within the community. The input for this step is the user's posted data, and the output is its presentation to other users in the community. Users receive comments and ratings from other users and utilize the feedback.

[0505] In this way, the entire system's processing works in coordination to provide users with personalized fashion suggestions and realize a rich fashion experience.

[0506] (Application Example 1)

[0507] 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."

[0508] Conventional fashion suggestion systems failed to adequately provide personalized suggestions based on user preferences and budgets, and lacked accuracy and real-time capabilities in virtual try-on, making it difficult to improve user satisfaction. Furthermore, there were limitations in effective means for gaining new inspiration through opinion exchange within communities.

[0509] 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.

[0510] In this invention, the server includes means for generating individual clothing suggestions based on information entered by the user, means for generating new clothing suggestions by analyzing images of items owned by the user, means for virtually trying on clothing using virtual reality and providing feedback on suitability, means for managing information including the user's wardrobe data and providing optimal suggestions in real time, and means for visualizing the results of virtually trying on clothes using augmented reality technology and providing feedback on how the clothes feel when worn. This makes it possible for users to receive highly accurate fashion suggestions based on their preferences and budget in real time, and also enables effective sharing of new inspiration and information through the activation of a community among users.

[0511] "A means of generating personalized clothing suggestions based on user input" refers to a process for suggesting the optimal clothing combination based on input data such as the user's preferences and budget.

[0512] "A means of analyzing images of items owned by a user to generate new clothing suggestions" refers to a technology that identifies images of clothing and accessories provided by a user and suggests new outfits that match them.

[0513] "A means of trying on clothing using virtual reality and providing feedback on suitability" refers to a technology that provides users with a virtual fitting experience and evaluates suitability in terms of size and style.

[0514] "A means of sharing outfit suggestions with other users within a community and exchanging evaluations and opinions" refers to a platform where users can share their outfit ideas with other users and receive feedback and suggestions.

[0515] "A means of managing information including user wardrobe data and providing optimal suggestions in real time" refers to a function that organizes data about the user's belongings and immediately suggests the most suitable styling.

[0516] "A means of visualizing the results of virtually trying on clothes using augmented reality technology and providing feedback on the feeling of wearing them" refers to a process that uses AR technology to visually present how the user would virtually wear the clothes they have selected, and then provides feedback on the fit and design.

[0517] This system begins with the user sending information about their fashion preferences to a server using a device. The user inputs their preferences, budget, and images of clothing items they own, and all this information is aggregated on a cloud server. The server uses deep learning technologies such as TensorFlow to analyze the user's data and generate personalized outfit suggestions. This utilizes a data structure based on the user's input data and a generative AI model trained on past fashion data.

[0518] Furthermore, the server uses image recognition technology to analyze images of items uploaded by the user and generates new outfit suggestions that utilize the user's existing wardrobe. Users receive these suggestions via their device and can obtain real-time feedback. This feedback includes specific comments on size and style suitability.

[0519] The virtual try-on feature utilizes augmented reality technologies such as Unity and ARCore to virtually display selected clothing items, providing users with visual feedback. During this process, users can overlay suggested clothing items onto their own images to evaluate style suitability.

[0520] Furthermore, the system integrates a community-based interface. Users can share suggested outfits with other users, evaluate them, and exchange opinions. Users can post their own outfits, receive feedback from other members, and gain new ideas. This feature includes a platform that facilitates two-way communication among users.

[0521] As a concrete example, a prompt such as, "Based on a photo of a summer shirt the user owns, please suggest a casual outfit for going out. I would like an outfit that is as color-coordinated as possible," prompts the AI ​​engine to generate a personalized outfit. This is then used to provide feedback through virtual try-on, along with opinions from the community, to help users choose the optimal styling. This system provides users with the opportunity to constantly improve their fashion sense and discover styles they hadn't considered before.

[0522] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0523] Step 1:

[0524] Users use their devices to input information such as their fashion preferences, budget, and images of their owned items. The devices then send this user data to a cloud server. The input data consists of text (preferences, budget) and image data (owned items).

[0525] Step 2:

[0526] The server preprocesses the received data. Text data related to preferences is analyzed using natural language processing, and image data is analyzed using computer vision technology to extract item features. As a result, it outputs string data and feature data as analyzed data.

[0527] Step 3:

[0528] The server uses a generative AI model to generate individual clothing suggestions based on pre-processed data. Specifically, it considers the user's preferences and budget, and outputs optimal outfit suggestions using analyzed clothing features. The output is a list of suggested fashion items.

[0529] Step 4:

[0530] The user receives AI-generated clothing suggestions via their device. After reviewing the suggestions displayed on the device, the user selects specific items and requests a virtual try-on.

[0531] Step 5:

[0532] The device sends selection information to the server, which uses augmented reality technology to overlay the items onto the user's image. It generates and returns a feedback image regarding the fit of size and style to the device. This results in a visual feedback image being output.

[0533] Step 6:

[0534] Users view feedback on their devices and post their opinions on the coordination proposals to the community. The device sends the posted content to the server for sharing and exchange of opinions. The input data is the user's opinion, and the output is community feedback and evaluation.

[0535] 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.

[0536] This invention is a system built to provide a fashion experience while taking into account the user's emotional state. In addition to general fashion suggestions, style generation based on possessions, virtual try-on, and community interaction, this system achieves a more personalized experience by integrating an emotion engine.

[0537] The user first inputs information about their fashion choices via a terminal and uses a sensor device to read their emotions. The terminal sends the user's input and collected biometric information to a server. An emotion engine on the server analyzes the biometric data and identifies the user's current emotional state. Based on this, the server uses an AI engine to generate clothing suggestions appropriate to the user's emotional state and send them back to the terminal.

[0538] Furthermore, by uploading images of items the user already owns, new outfit suggestions are provided based on those images. Throughout this process, the emotion engine also detects the user's emotions and adjusts the outfit suggestions to match their state.

[0539] During virtual try-on, the emotion engine analyzes user reactions in real time and generates feedback on the server. Based on this feedback, the server optimizes the size and style to provide a better user experience.

[0540] Furthermore, users can use the community feature to share the outfits they create with other users. The emotion engine analyzes other users' emotional reactions to the shared outfits, and users can receive feedback based on the results.

[0541] For example, if a user is feeling depressed, the system uses an emotion engine to detect this emotional state and suggests clothing in bright, cheerful colors. When the user virtually tries on the clothes and experiences happiness or a change in mood, the server records this emotional response and uses it to improve future suggestions. This emotion-based adjustment allows users to have a psychologically satisfying fashion experience.

[0542] The following describes the processing flow.

[0543] Step 1:

[0544] The user launches the app using their device and enters information about their fashion. Additionally, a sensor device is connected to the device to read the user's emotions.

[0545] Step 2:

[0546] The terminal transmits user input information and biometric data acquired from sensor devices to the server. This includes data necessary for emotion recognition.

[0547] Step 3:

[0548] The server uses an emotion engine to analyze the received biometric information and identify the user's current emotional state. Based on these results, it generates initial data to select the optimal fashion style.

[0549] Step 4:

[0550] The server's AI engine combines user preferences, emotional states, and trend data to generate individually optimized clothing suggestions, which are then sent back to the device.

[0551] Step 5:

[0552] If a user takes pictures of items they own with their device and wants new style suggestions based on their existing wardrobe, the device uploads those images to the server.

[0553] Step 6:

[0554] The server analyzes the image and passes the results to the AI ​​engine. The AI ​​engine then generates a personalized outfit for the user based on the analysis results and their emotional state.

[0555] Step 7:

[0556] A new outfit suggestion is sent from the server to the terminal, allowing the user to review the proposed content.

[0557] Step 8:

[0558] If a user requests a virtual try-on, the device sends data on the try-on items and the user's body shape information to the server. The server, via an emotion engine, senses the user's reactions in real time and generates a virtual try-on image.

[0559] Step 9:

[0560] The server sends back an image of the virtual try-on to the terminal, allowing the user to experience the try-on process. If the user's emotions change during the try-on, that information is also reported to the server.

[0561] Step 10:

[0562] Users access communities within the system and share their outfits. When doing so, the device posts its emotional state along with the suggested data to the server.

[0563] Step 11:

[0564] When other users in the community react, the server analyzes it, generates feedback, and sends it back to the user's device. This allows users to choose their clothing while also considering the opinions of others.

[0565] (Example 2)

[0566] 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."

[0567] Traditional fashion recommendation systems did not take into account the individual emotional state of users, and as a result, often made suggestions that did not necessarily match the user's feelings or preferences. Furthermore, it was difficult to provide optimal suggestions that took into account the user's assets and budget constraints. This meant that users could potentially have an unsatisfactory experience with fashion choices.

[0568] 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.

[0569] In this invention, the server includes means for generating individual clothing suggestions based on information and biological status entered by the user, means for generating new clothing suggestions by analyzing images of assets owned by the user, and means for sharing clothing suggestions with other users and exchanging evaluations and opinions. This enables personalized suggestions that take into account the user's emotional state and budget conditions, resulting in a more satisfying fashion experience.

[0570] A "user" refers to an individual who uses a fashion suggestion system to request personalized clothing suggestions.

[0571] "Information" refers to user input data related to fashion and data related to biological status.

[0572] "Biological state" refers to information that indicates an individual's current situation, such as their emotional state, based on the user's biometric data.

[0573] "Assets" refer to image data of clothing and accessories owned by the user.

[0574] A "virtual environment" refers to a virtual fitting room created using a computer.

[0575] "Opinions" refers to feedback and evaluations of the user's clothing suggestions.

[0576] "Emotional state" refers to the emotional condition determined by the user's biometric data and the analysis based on that data.

[0577] "Personalization" refers to adjusting suggestions to take into account the user's emotional state and budget constraints.

[0578] As a form of carrying out the invention, the system of the present invention provides a more personalized fashion experience by allowing the user to provide fashion information and receive clothing suggestions based on their emotional state.

[0579] The user first inputs information about fashion via a device. This information includes preferred colors and styles, and current fashion interests. Furthermore, the user wears a biometric sensor device designed to read emotions. This device is typically a wearable device that measures heart rate, skin electrical activity, and other parameters.

[0580] The terminal transmits user input information and biometric data collected from sensor devices to the server. The server is equipped with an emotion engine that analyzes the user's emotional state using, for example, a common emotion recognition API. This analysis makes it possible to determine whether the user is experiencing an emotional state such as "happiness" or "depression."

[0581] Next, the server uses a generative AI model to provide fashion suggestions tailored to the user's emotional state. The AI ​​engine used is specialized in natural language processing and generates suggestions by taking prompts such as "Generate a fashion coordinate that is appropriate for the user's current emotional state."

[0582] Furthermore, when a user uploads image data of their clothing and accessories, the server analyzes these images and provides new clothing suggestions based on their possessions. Even in this process, the emotion engine evaluates the user's emotional state and adjusts the suggestions accordingly, thus achieving personalization.

[0583] This system allows users to receive personalized outfit suggestions based on their emotions, resulting in a psychologically satisfying fashion experience.

[0584] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0585] Step 1:

[0586] The user inputs fashion-related information using a terminal and wears a biometric sensor device. This information includes favorite colors and styles, as well as current fashion interests. The sensor device measures heart rate, skin electrical activity, and other parameters in real time and transmits this data to the terminal as biometric data.

[0587] Step 2:

[0588] The device transmits fashion information and biometric data received from the user to the server. This data is typically transferred securely over the internet. This input data is then analyzed by the server's emotion engine.

[0589] Step 3:

[0590] The server uses an emotion engine to analyze the user's biometric data and identify their emotional state. For example, based on changes in heart rate and patterns of skin electrical activity, the AI ​​determines emotions such as "happy" or "depressed." The output of this step is the analyzed emotional state of the user.

[0591] Step 4:

[0592] The server uses a generative AI model to generate personalized clothing suggestions based on the user's emotional state. To achieve this, the server receives a prompt message, "Generate a fashion coordinate suitable for the user's current emotional state," and the AI ​​model performs natural language processing. The output of this step is a personalized fashion suggestion.

[0593] Step 5:

[0594] The server sends the generated fashion suggestions back to the device. The device visually presents the suggestions to the user. Through a mobile app or webpage, the user can review the suggested outfits and virtually try them on.

[0595] Step 6:

[0596] Users upload images of their clothing and accessories via their device. The submitted image data is sent to a server for analysis.

[0597] Step 7:

[0598] The server uses image analysis technology to analyze uploaded images and generate new clothing suggestions. These suggestions are then adjusted based on the user's emotional state, which has already been analyzed. The output is the adjusted new clothing suggestion.

[0599] Step 8:

[0600] Users can share the generated outfits within the community and receive ratings and feedback from other users. This allows for the collection of data to further personalize and improve suggestions.

[0601] (Application Example 2)

[0602] 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."

[0603] In modern e-commerce, a challenge exists in that users find it difficult to make product choices that take their emotional state into account when selecting fashion items online. Furthermore, the inability to provide dynamic suggestions that respond to the user's changing psychological state leads to decreased purchase satisfaction. Improving the user experience through feedback and communication features via virtual try-on is also needed.

[0604] 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.

[0605] In this invention, the server includes means for detecting the user's emotional state and generating appropriate clothing suggestions, means for suggesting a fashion style suitable for the user's emotional state using a generative AI model, and means for analyzing the user's emotional data to generate prompt sentences and optimize input to the generative AI model. This makes it possible to improve the quality of the user experience through personalized clothing suggestions and virtual try-ons based on the user's emotional state.

[0606] "User emotional state" refers to the psychological or emotional state that the user is currently experiencing, and includes data measured in real time by emotion sensors.

[0607] "Appropriate clothing suggestions" refer to recommendations for clothing and styling that enhance the user's psychological satisfaction, generated by an AI system based on the user's emotional state.

[0608] A "generative AI model" is a mathematical or computational model that uses artificial intelligence technology to generate and analyze various possibilities based on input data.

[0609] "Emotional data" refers to data that includes biometric information and psychological indicators necessary to identify a user's emotional state.

[0610] A "prompt sentence" is a natural language sentence that is input to explicitly indicate the intention for operating a generative AI model, and is used to prompt a specific generation result.

[0611] "Virtual try-on" refers to an experience where users can evaluate the fit of clothing in an online environment without actually trying it on, using virtual reality technology.

[0612] This invention is a system that provides real-time fashion suggestions that take into account the user's emotional state. The system is configured as follows: The user uses a device equipped with an emotion sensor, such as smart glasses, to collect biometric data such as heart rate and skin electrical activity. This data is transmitted to a server via Wi-Fi or Bluetooth.

[0613] The server uses an emotion analysis engine based on Python and TensorFlow to analyze biometric data and identify the user's emotional state. Based on this analysis, a generative AI model powered by Amazon SageMaker generates clothing suggestions that are appropriate for the user's emotions.

[0614] For example, if a user is feeling stressed, the generative AI model will suggest clothing in relaxing colors and materials. Following this, a data platform such as Cisco Kinetic generates a prompt message for the user, providing feedback to the AI ​​model. This prompt message includes an instruction such as, "Suggest clothing that will help the user relax."

[0615] The generated suggestions are displayed on the smart glasses' screen, allowing the user to visually confirm them. In one embodiment, the user can enjoy a virtual try-on experience using virtual reality technology in a virtual store. This system can improve the user's psychological satisfaction and enrich the purchasing experience.

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] The user puts on smart glasses and begins collecting biometric data. An emotion sensor built into the smart glasses collects the user's heart rate and skin electrical activity in real time. The input is biometric data, and the output is a biosignal measured by the emotion sensor.

[0619] Step 2:

[0620] The device collects biometric data and transmits it to the server via Bluetooth or Wi-Fi. The input is biometric data obtained from smart glasses, and the output is the digital data that is transmitted from it. The server receives this data.

[0621] Step 3:

[0622] The server uses Python and TensorFlow to analyze biometric data and estimate the user's emotional state. The input is biometric data sent to the server, and the output is the emotion determination result from the emotion analysis engine. Data processing involves filtering and normalization of the biometric data before calculations are performed using an emotion model.

[0623] Step 4:

[0624] The server uses Amazon SageMaker to generate clothing suggestions based on the user's emotional state. The input is the result of an emotional analysis, and the output is clothing suggestions that enhance the user's psychological satisfaction. A generative AI model is used to select the most suitable fashion items from the data.

[0625] Step 5:

[0626] The server formats the generated clothing suggestions as prompt messages and provides feedback to the user via Cisco Kinetic. The input is the clothing suggestion data, and the output is visual feedback information for the user. Specifically, the suggestions are displayed on the smart glasses' display.

[0627] Step 6:

[0628] The user views suggested outfits through the display of smart glasses and tries them on in virtual reality. The input is the outfit suggestions displayed on the screen, and the output is visual information and a virtual try-on experience. This allows the user to visually evaluate the suggested outfits.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] [Fourth Embodiment]

[0633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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".

[0646] This invention is a system designed to generate and promote user-specific fashion styles, specifically enabling personalized clothing suggestions based on information entered by the user. To this end, the user can access a styling generation module using a terminal. The user inputs information about their preferences and budget, and the terminal sends this information to a server. An AI engine on the server analyzes the received data, generates the optimal styling for each individual user, and sends the results back to the terminal.

[0647] Furthermore, the app includes a feature that allows users to upload images of items they own. Users take photos of their items using their device and send them to the server. The server analyzes the images and generates new outfit suggestions based on those items. This information is displayed on the device, allowing users to make the most of their existing wardrobe.

[0648] Furthermore, the virtual try-on function allows users to virtually try on selected clothing. When a user selects clothing through their device, the device sends this information to a server. The server uses AR technology to generate a virtual try-on image and returns the result to the device. This allows the user to receive feedback on the fit of the size and style.

[0649] In addition, this system integrates community-based features, allowing users to share their outfits with other users and exchange ratings and opinions. Outfits proposed by users are posted to the server via their devices, and this information is presented to other users. This allows users to gain new inspiration while also incorporating the perspectives of others.

[0650] For example, if a male user in his 30s is looking for a casual business style, the system uses an AI engine to suggest the most suitable jacket and shoes based on photos of the shirt and jeans the user owns. In this process, AR technology allows the user to virtually try on the clothes, and feedback is provided that the suggested jacket is slightly too large. Based on this information, the user can seek opinions from other users in the community and use that information to make a final purchase decision.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The user launches the app on their device and selects the styling generation option. The user enters information such as their preferences, budget, age group, and the occasion for which they want to wear the clothes, and prepares to submit the information.

[0654] Step 2:

[0655] The terminal receives user input data and sends it to the server as formatted data. The server then obtains the input for data analysis.

[0656] Step 3:

[0657] The server passes the received data to an AI model, which then generates personalized clothing suggestions based on the information provided. The AI ​​analyzes data based on past trends and the behavior of users with similar preferences.

[0658] Step 4:

[0659] The server sends the generated styling results to the terminal. These results include clothing combinations and recommended brands tailored to the user's preferences.

[0660] Step 5:

[0661] If a user wants to take photos of items they own with their device and receive suggestions that take their existing wardrobe into consideration, the device sends those photos to the server as image data.

[0662] Step 6:

[0663] The server processes photos using image analysis technology, detects the characteristics of the items, and the AI ​​generates new outfit combinations based on the analysis results.

[0664] Step 7:

[0665] The server sends new outfit suggestions back to the user's device. The user can review the suggested styles and select items they like.

[0666] Step 8:

[0667] If a user requests a virtual try-on, the device transfers data on the selected items and the user's body measurements to the server.

[0668] Step 9:

[0669] The server uses AR technology to generate virtual try-on images and provides the user with a try-on simulation. Style and size feedback is also generated during this process.

[0670] Step 10:

[0671] The server sends the results of the virtual try-on to the user's device for review. Based on the feedback, the user can determine the optimal size and style.

[0672] Step 11:

[0673] When users share their generated outfits with the community, their devices post the data to the server, allowing them to receive feedback and ratings from other users.

[0674] Step 12:

[0675] The server presents the user's outfit as a new post within the community, giving other users the opportunity to rate and comment on it.

[0676] (Example 1)

[0677] 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".

[0678] In today's fashion scene, there is a demand for services that automatically generate styling suggestions tailored to individual users and provide an interactive experience through virtual try-ons and community feedback during the selection process. However, conventional technologies have struggled to efficiently provide styling suggestions that match individual user preferences and have not been able to fully meet users' needs for virtual try-ons and community features. Furthermore, there are challenges in optimizing pricing and continuously improving based on user feedback.

[0679] 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.

[0680] In this invention, the server includes means for generating individual styling suggestions based on user-inputted preferences and constraints, means for analyzing photos of clothing owned by the user to generate additional coordination suggestions, and means for virtually trying on clothing in a virtual environment and providing feedback on the fit of dimensions and style. This not only enables fashion suggestions tailored to each user's preferences, but also allows for the use of a more effective and user-friendly styling assistant by leveraging realistic feedback from virtual try-ons and interaction through community features.

[0681] "A means of generating styling suggestions" refers to a technology that automatically lists appropriate fashion coordinates based on the user's preferences and conditions.

[0682] "A method for analyzing clothing photos and generating suggestions" refers to an algorithm that recognizes images of clothing uploaded by users and devises new outfit combinations based on that image information.

[0683] "A means of trying on clothes and providing feedback using a virtual environment" refers to a system that allows users to virtually try on clothes they have selected and check the fit of the size and style on the screen.

[0684] A "community platform" is an online service where users can share their fashion suggestions with other users, and exchange ratings and opinions.

[0685] A "generative AI model" is a general term for algorithms and models that use machine learning techniques to generate fashion suggestions.

[0686] "Improving the recommendation algorithm" refers to a technical method that utilizes user feedback to improve the accuracy and quality of styling suggestions.

[0687] This invention is a system that automatically provides styling suggestions tailored to the user's fashion needs. Users can access the system via a terminal and input their preferences and constraints to obtain personalized fashion coordinates.

[0688] Users input information such as their fashion preferences, budget, colors, and materials using a dedicated application installed on their device or a web interface. The device sends this data to a server. Based on the received data, the server uses a generative AI model to suggest styling options that match the user's preferences. The suggested options are visualized on the device and illustrated for easy review by the user.

[0689] Furthermore, users can take pictures of their clothing with their devices and upload them to the server. The server uses image analysis algorithms (for example, deep learning technology) to obtain clothing information from the uploaded images and incorporates it into the suggestions. This allows users to receive new outfits that utilize their existing wardrobe.

[0690] The virtual try-on feature works by having the user select clothing items and send them to a server, which then uses AR technology to generate a virtual try-on image. This image is then sent back to the user via their device, allowing them to check the fit and style. Specifically, the server utilizes technologies such as Unity and ARKit to perform this process.

[0691] This system also includes a community feature that allows users to share suggested outfits with other users and exchange opinions. Users can post their outfits within the community from their devices and receive feedback and ratings from other users. Through this process, users can gain new fashion inspiration and improve their own style.

[0692] As a concrete example, the prompt message for a male user in his 30s requesting a casual business style would be as follows:

[0693] "A man in his 30s, casual business attire. Please suggest a jacket and shoes that would match the shirts and jeans he owns."

[0694] Thus, the present invention flexibly responds to the individual needs of users and provides a user-friendly fashion experience.

[0695] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0696] Step 1:

[0697] Users access a dedicated application or web interface using their device and input information such as their fashion preferences, budget, colors, and materials. The device then sends this information received from the user as data to the server. In this step, the input is user preference data, and the output is the transmission of data to the server. The user interface is intuitive, using pull-down menus and checkboxes when inputting information.

[0698] Step 2:

[0699] The server receives user data sent from the terminal and analyzes the data using a generating AI model. During this process, it compares fashion information in the database with the user's input data to generate optimal styling. The input for this step is the user's preference data, and the output is the generated styling suggestions. The AI ​​engine lists multiple outfit suggestions within seconds.

[0700] Step 3:

[0701] The server structures the generated styling suggestions in JSON format and sends the data back to the terminal. Based on the received data, the terminal visually displays the suggested styling to the user. The input for this step is styling suggestion data, and the output is the fashion suggestions displayed to the user. On the interface, the suggestions are presented in card format, allowing for comparison and consideration by scrolling.

[0702] Step 4:

[0703] Users use their device's camera function to take pictures of their clothing and upload them. The device then sends the image data to the server. The input for this step is the image of the clothing, and the output is the transmission of the image data to the server. The user interface is designed to allow users to easily complete the upload process with just a tap.

[0704] Step 5:

[0705] The server processes the received images using image analysis technology to extract clothing features. Based on the analysis results, it generates additional outfit suggestions and sends them back to the terminal. The input for this step is image data, and the output is outfit suggestions generated by image analysis. Computer vision technology recognizes color, material, and design patterns from the captured images.

[0706] Step 6:

[0707] When a user selects virtual try-on, the device sends the selected clothing information to the server. The server uses AR technology to generate a virtual try-on image and sends the result back to the device. The input for this step is the data of the clothing the user wishes to try on, and the output is the try-on image generated by AR. The user can view the virtual try-on image on the device screen and visually evaluate the size and style.

[0708] Step 7:

[0709] Users share their generated outfits with other users using the community feature. The device sends the posted content to the server, which displays it within the community. The input for this step is the user's posted data, and the output is its presentation to other users in the community. Users receive comments and ratings from other users and utilize the feedback.

[0710] In this way, the entire system's processing works in coordination to provide users with personalized fashion suggestions and realize a rich fashion experience.

[0711] (Application Example 1)

[0712] 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".

[0713] Conventional fashion suggestion systems failed to adequately provide personalized suggestions based on user preferences and budgets, and lacked accuracy and real-time capabilities in virtual try-on, making it difficult to improve user satisfaction. Furthermore, there were limitations in effective means for gaining new inspiration through opinion exchange within communities.

[0714] 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.

[0715] In this invention, the server includes means for generating individual clothing suggestions based on information entered by the user, means for generating new clothing suggestions by analyzing images of items owned by the user, means for virtually trying on clothing using virtual reality and providing feedback on suitability, means for managing information including the user's wardrobe data and providing optimal suggestions in real time, and means for visualizing the results of virtually trying on clothes using augmented reality technology and providing feedback on how the clothes feel when worn. This makes it possible for users to receive highly accurate fashion suggestions based on their preferences and budget in real time, and also enables effective sharing of new inspiration and information through the activation of a community among users.

[0716] "A means of generating personalized clothing suggestions based on user input" refers to a process for suggesting the optimal clothing combination based on input data such as the user's preferences and budget.

[0717] "A means of analyzing images of items owned by a user to generate new clothing suggestions" refers to a technology that identifies images of clothing and accessories provided by a user and suggests new outfits that match them.

[0718] "A means of trying on clothing using virtual reality and providing feedback on suitability" refers to a technology that provides users with a virtual fitting experience and evaluates suitability in terms of size and style.

[0719] "A means of sharing outfit suggestions with other users within a community and exchanging evaluations and opinions" refers to a platform where users can share their outfit ideas with other users and receive feedback and suggestions.

[0720] "A means of managing information including user wardrobe data and providing optimal suggestions in real time" refers to a function that organizes data about the user's belongings and immediately suggests the most suitable styling.

[0721] "A means of visualizing the results of virtually trying on clothes using augmented reality technology and providing feedback on the feeling of wearing them" refers to a process that uses AR technology to visually present how the user would virtually wear the clothes they have selected, and then provides feedback on the fit and design.

[0722] This system begins with the user sending information about their fashion preferences to a server using a device. The user inputs their preferences, budget, and images of clothing items they own, and all this information is aggregated on a cloud server. The server uses deep learning technologies such as TensorFlow to analyze the user's data and generate personalized outfit suggestions. This utilizes a data structure based on the user's input data and a generative AI model trained on past fashion data.

[0723] Furthermore, the server uses image recognition technology to analyze images of items uploaded by the user and generates new outfit suggestions that utilize the user's existing wardrobe. Users receive these suggestions via their device and can obtain real-time feedback. This feedback includes specific comments on size and style suitability.

[0724] The virtual try-on feature utilizes augmented reality technologies such as Unity and ARCore to virtually display selected clothing items, providing users with visual feedback. During this process, users can overlay suggested clothing items onto their own images to evaluate style suitability.

[0725] Furthermore, the system integrates a community-based interface. Users can share suggested outfits with other users, evaluate them, and exchange opinions. Users can post their own outfits, receive feedback from other members, and gain new ideas. This feature includes a platform that facilitates two-way communication among users.

[0726] As a concrete example, a prompt such as, "Based on a photo of a summer shirt the user owns, please suggest a casual outfit for going out. I would like an outfit that is as color-coordinated as possible," prompts the AI ​​engine to generate a personalized outfit. This is then used to provide feedback through virtual try-on, along with opinions from the community, to help users choose the optimal styling. This system provides users with the opportunity to constantly improve their fashion sense and discover styles they hadn't considered before.

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] Users use their devices to input information such as their fashion preferences, budget, and images of their owned items. The devices then send this user data to a cloud server. The input data consists of text (preferences, budget) and image data (owned items).

[0730] Step 2:

[0731] The server preprocesses the received data. Text data related to preferences is analyzed using natural language processing, and image data is analyzed using computer vision technology to extract item features. As a result, it outputs string data and feature data as analyzed data.

[0732] Step 3:

[0733] The server uses a generative AI model to generate individual clothing suggestions based on pre-processed data. Specifically, it considers the user's preferences and budget, and outputs optimal outfit suggestions using analyzed clothing features. The output is a list of suggested fashion items.

[0734] Step 4:

[0735] The user receives AI-generated clothing suggestions via their device. After reviewing the suggestions displayed on the device, the user selects specific items and requests a virtual try-on.

[0736] Step 5:

[0737] The device sends selection information to the server, which uses augmented reality technology to overlay the items onto the user's image. It generates and returns a feedback image regarding the fit of size and style to the device. This results in a visual feedback image being output.

[0738] Step 6:

[0739] Users view feedback on their devices and post their opinions on the coordination proposals to the community. The device sends the posted content to the server for sharing and exchange of opinions. The input data is the user's opinion, and the output is community feedback and evaluation.

[0740] 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.

[0741] This invention is a system built to provide a fashion experience while taking into account the user's emotional state. In addition to general fashion suggestions, style generation based on possessions, virtual try-on, and community interaction, this system achieves a more personalized experience by integrating an emotion engine.

[0742] The user first inputs information about their fashion choices via a terminal and uses a sensor device to read their emotions. The terminal sends the user's input and collected biometric information to a server. An emotion engine on the server analyzes the biometric data and identifies the user's current emotional state. Based on this, the server uses an AI engine to generate clothing suggestions appropriate to the user's emotional state and send them back to the terminal.

[0743] Furthermore, by uploading images of items the user already owns, new outfit suggestions are provided based on those images. Throughout this process, the emotion engine also detects the user's emotions and adjusts the outfit suggestions to match their state.

[0744] During virtual try-on, the emotion engine analyzes user reactions in real time and generates feedback on the server. Based on this feedback, the server optimizes the size and style to provide a better user experience.

[0745] Furthermore, users can use the community feature to share the outfits they create with other users. The emotion engine analyzes other users' emotional reactions to the shared outfits, and users can receive feedback based on the results.

[0746] For example, if a user is feeling depressed, the system uses an emotion engine to detect this emotional state and suggests clothing in bright, cheerful colors. When the user virtually tries on the clothes and experiences happiness or a change in mood, the server records this emotional response and uses it to improve future suggestions. This emotion-based adjustment allows users to have a psychologically satisfying fashion experience.

[0747] The following describes the processing flow.

[0748] Step 1:

[0749] The user launches the app using their device and enters information about their fashion. Additionally, a sensor device is connected to the device to read the user's emotions.

[0750] Step 2:

[0751] The terminal transmits user input information and biometric data acquired from sensor devices to the server. This includes data necessary for emotion recognition.

[0752] Step 3:

[0753] The server uses an emotion engine to analyze the received biometric information and identify the user's current emotional state. Based on these results, it generates initial data to select the optimal fashion style.

[0754] Step 4:

[0755] The server's AI engine combines user preferences, emotional states, and trend data to generate individually optimized clothing suggestions, which are then sent back to the device.

[0756] Step 5:

[0757] If a user takes pictures of items they own with their device and wants new style suggestions based on their existing wardrobe, the device uploads those images to the server.

[0758] Step 6:

[0759] The server analyzes the image and passes the results to the AI ​​engine. The AI ​​engine then generates a personalized outfit for the user based on the analysis results and their emotional state.

[0760] Step 7:

[0761] A new outfit suggestion is sent from the server to the terminal, allowing the user to review the proposed content.

[0762] Step 8:

[0763] If a user requests a virtual try-on, the device sends data on the try-on items and the user's body shape information to the server. The server, via an emotion engine, senses the user's reactions in real time and generates a virtual try-on image.

[0764] Step 9:

[0765] The server sends back an image of the virtual try-on to the terminal, allowing the user to experience the try-on process. If the user's emotions change during the try-on, that information is also reported to the server.

[0766] Step 10:

[0767] Users access communities within the system and share their outfits. When doing so, the device posts its emotional state along with the suggested data to the server.

[0768] Step 11:

[0769] When other users in the community react, the server analyzes it, generates feedback, and sends it back to the user's device. This allows users to choose their clothing while also considering the opinions of others.

[0770] (Example 2)

[0771] 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".

[0772] Traditional fashion recommendation systems did not take into account the individual emotional state of users, and as a result, often made suggestions that did not necessarily match the user's feelings or preferences. Furthermore, it was difficult to provide optimal suggestions that took into account the user's assets and budget constraints. This meant that users could potentially have an unsatisfactory experience with fashion choices.

[0773] 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.

[0774] In this invention, the server includes means for generating individual clothing suggestions based on information and biological status entered by the user, means for generating new clothing suggestions by analyzing images of assets owned by the user, and means for sharing clothing suggestions with other users and exchanging evaluations and opinions. This enables personalized suggestions that take into account the user's emotional state and budget conditions, resulting in a more satisfying fashion experience.

[0775] A "user" refers to an individual who uses a fashion suggestion system to request personalized clothing suggestions.

[0776] "Information" refers to user input data related to fashion and data related to biological status.

[0777] "Biological state" refers to information that indicates an individual's current situation, such as their emotional state, based on the user's biometric data.

[0778] "Assets" refer to image data of clothing and accessories owned by the user.

[0779] A "virtual environment" refers to a virtual fitting room created using a computer.

[0780] "Opinions" refers to feedback and evaluations of the user's clothing suggestions.

[0781] "Emotional state" refers to the emotional condition determined by the user's biometric data and the analysis based on that data.

[0782] "Personalization" refers to adjusting suggestions to take into account the user's emotional state and budget constraints.

[0783] As a form of carrying out the invention, the system of the present invention provides a more personalized fashion experience by allowing the user to provide fashion information and receive clothing suggestions based on their emotional state.

[0784] The user first inputs information about fashion via a device. This information includes preferred colors and styles, and current fashion interests. Furthermore, the user wears a biometric sensor device designed to read emotions. This device is typically a wearable device that measures heart rate, skin electrical activity, and other parameters.

[0785] The terminal transmits user input information and biometric data collected from sensor devices to the server. The server is equipped with an emotion engine that analyzes the user's emotional state using, for example, a common emotion recognition API. This analysis makes it possible to determine whether the user is experiencing an emotional state such as "happiness" or "depression."

[0786] Next, the server uses a generative AI model to provide fashion suggestions tailored to the user's emotional state. The AI ​​engine used is specialized in natural language processing and generates suggestions by taking prompts such as "Generate a fashion coordinate that is appropriate for the user's current emotional state."

[0787] Furthermore, when a user uploads image data of their clothing and accessories, the server analyzes these images and provides new clothing suggestions based on their possessions. Even in this process, the emotion engine evaluates the user's emotional state and adjusts the suggestions accordingly, thus achieving personalization.

[0788] This system allows users to receive personalized outfit suggestions based on their emotions, resulting in a psychologically satisfying fashion experience.

[0789] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0790] Step 1:

[0791] The user inputs fashion-related information using a terminal and wears a biometric sensor device. This information includes favorite colors and styles, as well as current fashion interests. The sensor device measures heart rate, skin electrical activity, and other parameters in real time and transmits this data to the terminal as biometric data.

[0792] Step 2:

[0793] The device transmits fashion information and biometric data received from the user to the server. This data is typically transferred securely over the internet. This input data is then analyzed by the server's emotion engine.

[0794] Step 3:

[0795] The server uses an emotion engine to analyze the user's biometric data and identify their emotional state. For example, based on changes in heart rate and patterns of skin electrical activity, the AI ​​determines emotions such as "happy" or "depressed." The output of this step is the analyzed emotional state of the user.

[0796] Step 4:

[0797] The server uses a generative AI model to generate personalized clothing suggestions based on the user's emotional state. To achieve this, the server receives a prompt message, "Generate a fashion coordinate suitable for the user's current emotional state," and the AI ​​model performs natural language processing. The output of this step is a personalized fashion suggestion.

[0798] Step 5:

[0799] The server sends the generated fashion suggestions back to the device. The device visually presents the suggestions to the user. Through a mobile app or webpage, the user can review the suggested outfits and virtually try them on.

[0800] Step 6:

[0801] Users upload images of their clothing and accessories via their device. The submitted image data is sent to a server for analysis.

[0802] Step 7:

[0803] The server uses image analysis technology to analyze uploaded images and generate new clothing suggestions. These suggestions are then adjusted based on the user's emotional state, which has already been analyzed. The output is the adjusted new clothing suggestion.

[0804] Step 8:

[0805] Users can share the generated outfits within the community and receive ratings and feedback from other users. This allows for the collection of data to further personalize and improve suggestions.

[0806] (Application Example 2)

[0807] 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".

[0808] In modern e-commerce, a challenge exists in that users find it difficult to make product choices that take their emotional state into account when selecting fashion items online. Furthermore, the inability to provide dynamic suggestions that respond to the user's changing psychological state leads to decreased purchase satisfaction. Improving the user experience through feedback and communication features via virtual try-on is also needed.

[0809] 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.

[0810] In this invention, the server includes means for detecting the user's emotional state and generating appropriate clothing suggestions, means for suggesting a fashion style suitable for the user's emotional state using a generative AI model, and means for analyzing the user's emotional data to generate prompt sentences and optimize input to the generative AI model. This makes it possible to improve the quality of the user experience through personalized clothing suggestions and virtual try-ons based on the user's emotional state.

[0811] "User emotional state" refers to the psychological or emotional state that the user is currently experiencing, and includes data measured in real time by emotion sensors.

[0812] "Appropriate clothing suggestions" refer to recommendations for clothing and styling that enhance the user's psychological satisfaction, generated by an AI system based on the user's emotional state.

[0813] A "generative AI model" is a mathematical or computational model that uses artificial intelligence technology to generate and analyze various possibilities based on input data.

[0814] "Emotional data" refers to data that includes biometric information and psychological indicators necessary to identify a user's emotional state.

[0815] A "prompt sentence" is a natural language sentence that is input to explicitly indicate the intention for operating a generative AI model, and is used to prompt a specific generation result.

[0816] "Virtual try-on" refers to an experience where users can evaluate the fit of clothing in an online environment without actually trying it on, using virtual reality technology.

[0817] This invention is a system that provides real-time fashion suggestions that take into account the user's emotional state. The system is configured as follows: The user uses a device equipped with an emotion sensor, such as smart glasses, to collect biometric data such as heart rate and skin electrical activity. This data is transmitted to a server via Wi-Fi or Bluetooth.

[0818] The server uses an emotion analysis engine based on Python and TensorFlow to analyze biometric data and identify the user's emotional state. Based on this analysis, a generative AI model powered by Amazon SageMaker generates clothing suggestions that are appropriate for the user's emotions.

[0819] For example, if a user is feeling stressed, the generative AI model will suggest clothing in relaxing colors and materials. Following this, a data platform such as Cisco Kinetic generates a prompt message for the user, providing feedback to the AI ​​model. This prompt message includes an instruction such as, "Suggest clothing that will help the user relax."

[0820] The generated suggestions are displayed on the smart glasses' screen, allowing the user to visually confirm them. In one embodiment, the user can enjoy a virtual try-on experience using virtual reality technology in a virtual store. This system can improve the user's psychological satisfaction and enrich the purchasing experience.

[0821] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0822] Step 1:

[0823] The user puts on smart glasses and begins collecting biometric data. An emotion sensor built into the smart glasses collects the user's heart rate and skin electrical activity in real time. The input is biometric data, and the output is a biosignal measured by the emotion sensor.

[0824] Step 2:

[0825] The device collects biometric data and transmits it to the server via Bluetooth or Wi-Fi. The input is biometric data obtained from smart glasses, and the output is the digital data that is transmitted from it. The server receives this data.

[0826] Step 3:

[0827] The server uses Python and TensorFlow to analyze biometric data and estimate the user's emotional state. The input is biometric data sent to the server, and the output is the emotion determination result from the emotion analysis engine. Data processing involves filtering and normalization of the biometric data before calculations are performed using an emotion model.

[0828] Step 4:

[0829] The server uses Amazon SageMaker to generate clothing suggestions based on the user's emotional state. The input is the result of an emotional analysis, and the output is clothing suggestions that enhance the user's psychological satisfaction. A generative AI model is used to select the most suitable fashion items from the data.

[0830] Step 5:

[0831] The server formats the generated clothing suggestions as prompt messages and provides feedback to the user via Cisco Kinetic. The input is the clothing suggestion data, and the output is visual feedback information for the user. Specifically, the suggestions are displayed on the smart glasses' display.

[0832] Step 6:

[0833] The user views suggested outfits through the display of smart glasses and tries them on in virtual reality. The input is the outfit suggestions displayed on the screen, and the output is visual information and a virtual try-on experience. This allows the user to visually evaluate the suggested outfits.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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."

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] The following is further disclosed regarding the embodiments described above.

[0856] (Claim 1)

[0857] A means for generating personalized clothing suggestions based on information entered by the user,

[0858] A means of generating new clothing suggestions by analyzing images of items owned by the user,

[0859] A means of using virtual reality to try on clothing and provide feedback on suitability,

[0860] A means of sharing clothing suggestions with other users within the community, and exchanging evaluations and opinions,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, comprising means for recommending similar-looking but lower-priced items, taking into account the user's budget constraints.

[0864] (Claim 3)

[0865] The system according to claim 1, comprising means for optimizing an item recommendation algorithm based on feedback collected from users.

[0866] "Example 1"

[0867] (Claim 1)

[0868] A means of generating individual styling suggestions based on user input preferences and constraints,

[0869] A means of analyzing photos of clothing owned by the user to generate additional outfit suggestions,

[0870] A means of trying on clothes using a virtual environment and providing feedback on the fit of dimensions and style,

[0871] A means of sharing clothing suggestions on a community platform and exchanging opinions with other users,

[0872] A method for deriving the optimal coordination using a data analysis function and a generated AI model,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, comprising means for recommending a more economical alternative product with a similar appearance, taking into account the user's price range constraints.

[0876] (Claim 3)

[0877] The system according to claim 1, comprising means for collecting user feedback and improving the recommendation algorithm based on that feedback.

[0878] "Application Example 1"

[0879] (Claim 1)

[0880] A means for generating individual clothing suggestions based on information entered by the user,

[0881] A means of generating new clothing suggestions by analyzing images of items owned by the user,

[0882] A means of using virtual reality to try on clothing and provide feedback on suitability,

[0883] A means of sharing clothing suggestions with other users within the community, and exchanging evaluations and opinions,

[0884] A means of managing information including user wardrobe data and providing optimal suggestions in real time,

[0885] A means of visualizing the results of virtually trying on clothes using augmented reality technology and providing feedback on the feeling of wearing them,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, comprising means for recommending similar-looking but lower-priced items, taking into account the user's budget constraints.

[0889] (Claim 3)

[0890] The system according to claim 1, comprising means for optimizing an item recommendation algorithm based on feedback collected from users.

[0891] "Example 2 of combining an emotion engine"

[0892] (Claim 1)

[0893] A means for generating personalized clothing suggestions based on user input and biological status,

[0894] A means of analyzing images of assets owned by the user to generate new clothing suggestions,

[0895] A means of trying on clothing in a virtual environment and providing feedback on its suitability,

[0896] A means of sharing clothing suggestions with other users, evaluating them, and exchanging opinions,

[0897] A means of analyzing the user's biological information and identifying their emotional state,

[0898] A means of adjusting and individualizing proposals based on emotional state,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, comprising means for recommending assets with a similar appearance but lower price, taking into account the user's budget requirements.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising means for optimizing an asset recommendation algorithm based on opinions collected from users.

[0904] "Application example 2 when combining with an emotional engine"

[0905] (Claim 1)

[0906] A means for detecting the user's emotional state and generating appropriate clothing suggestions,

[0907] A means for generating personalized clothing suggestions based on information entered by the user,

[0908] A means of generating new clothing suggestions by analyzing images of items owned by the user,

[0909] A means for trying on clothing using a virtual environment and providing a response regarding suitability,

[0910] A means of sharing clothing suggestions with other users within the community, and exchanging evaluations and opinions,

[0911] A means of adjusting clothing to change emotions by taking into account the user's emotional state,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, comprising means for suggesting a fashion style suitable for the user's emotional state using a generative AI model.

[0915] (Claim 3)

[0916] The system according to claim 1, comprising means for analyzing user emotion data to generate prompt sentences and optimizing input to a generation AI model. [Explanation of Symbols]

[0917] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating personalized clothing suggestions based on information entered by the user, A means of generating new clothing suggestions by analyzing images of items owned by the user, A means of using virtual reality to try on clothing and provide feedback on suitability, A means of sharing clothing suggestions with other users within the community, and exchanging evaluations and opinions, A system that includes this.

2. The system according to claim 1, comprising means for recommending similar-looking but lower-priced items, taking into account the user's budget constraints.

3. The system according to claim 1, comprising means for optimizing an item recommendation algorithm based on feedback collected from users.

Images