System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting user information, a means for inputting weather information and a schedule, a means for proposing optimum clothes on the basis of the user information, the weather information and the schedule information, a means for performing virtual try-on, and a means for specifying lacking clothes and generating a purchase link from a linked online store.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] ---

[0005] Choosing clothes takes a lot of time, and it is especially difficult to choose the best outfit for the weather and schedule. Furthermore, it is difficult to find clothes that fit an individual's body type, preferences, and age, which leads many people to experience frustration and failure when choosing clothes. A reliable system to solve these problems is needed. [Means for solving the problem]

[0006] The present invention provides a system that includes a means for inputting user information, a means for inputting weather information and schedules, a means for suggesting optimal outfits based on the user information, weather information, and schedule information, a means for virtual try-on, and a means for identifying missing clothing and generating purchase links from linked online stores. This reduces the time spent selecting clothes, suggests optimal outfits for each individual user, and prevents mistakes in selecting clothes.

[0007] ---

[0008] "User information" is information that indicates the user's personal characteristics, and specifically includes body type, preferences, age, and the like.

[0009] "Weather information" is information indicating the weather conditions on a specified day, and specifically includes the temperature, probability of precipitation, wind speed, and the like.

[0010] "Schedule information" is information indicating the activities that the user has planned for a particular day, and specifically includes meetings, dates, trips, and the like.

[0011] "Optimal clothing" refers to a combination of clothing that is suitable for the user and is selected based on user information, weather information, and schedule information.

[0012] "Virtual try-on" refers to a function that combines a user's photo or avatar with an image of clothing to simulate how the user would actually look wearing the clothing.

[0013] "Missing clothing" refers to clothing that is required for the suggested optimal outfit but that the user does not own.

[0014] "Online store" refers to a website or service that sells products over the Internet.

[0015] "Purchase Link" means a hyperlink to an online store where a User can purchase the missing clothing item. [Brief explanation of the drawings]

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

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

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

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

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

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

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0037] ---

[0038] The present invention describes a system that suggests optimal clothing based on user information, weather information, and schedule information, and performs virtual try-on and suggests purchasing missing clothing.

[0039] Program processing overview

[0040] Entering user information

[0041] 1. The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[0042] 2. The terminal receives this information, formats it into a database, and sends it to the server.

[0043] Weather and schedule input

[0044] 3. The user enters the weather information and schedule for the day into the application. For example, the user enters that the weather is rainy and the schedule is an office meeting.

[0045] 4. The device receives the weather information and schedule information and sends it to the server.

[0046] Best clothing suggestions

[0047] 5. The server integrates the received user information, weather information, and schedule information.

[0048] 6. The server runs an AI algorithm on the integrated data to generate the best possible outfit, such as a navy blazer, a white shirt, and dark denim.

[0049] 7. The server creates image data of the generated clothing candidates and sends it to the terminal.

[0050] 8. The terminal displays the received image data of the clothing to the user.

[0051] Virtual try-on

[0052] 9. The user reviews the displayed outfit image and uploads a photo of themselves to virtually try on the outfit.

[0053] 10. The device sends the uploaded photos to the server.

[0054] 11. The server combines the user's photo with the generated clothing image to create a virtual try-on image.

[0055] 12. The device displays the virtual try-on image to the user.

[0056] Purchase suggestion

[0057] 13. The server checks the user's existing clothing information and identifies any missing clothing. For example, if the user does not own any dark denim, it will detect that this is missing.

[0058] 14. The server calls the API of the online store it is connected to and generates a purchase link for the missing clothing.

[0059] 15. The terminal displays the generated purchase link along with the purchase offer to the user.

[0060] ---

[0061] This completes the description of the embodiment of the present invention. With this, the user can receive suggestions for optimal clothing that matches their body type, preferences, age, weather, and schedule, check them out through virtual try-ons, and purchase any missing clothing from an online store.

[0062] The processing flow will be explained below.

[0063] ---

[0064] Step 1:

[0065] A user starts the application and enters their profile information (body type, preferences, age). For example, they enter their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[0066] Step 2:

[0067] The device receives the user's profile information, converts it into a database format, and sends it to the server. The data sent includes details such as "height: 175 cm, weight: 70 kg, preference: casual, age: 30s."

[0068] Step 3:

[0069] A user inputs the weather information and schedule for the day into the application. For example, the weather is "rainy" and the schedule is "a meeting at the office."

[0070] Step 4:

[0071] The device receives the weather and schedule information entered, converts it into a database format, and sends it to the server. The data sent includes information such as "Weather: Rain, Schedule: Meeting at the office."

[0072] Step 5:

[0073] The server integrates the received user information, weather information, and schedule information, providing the basic data for suggesting optimal clothing.

[0074] Step 6:

[0075] The server runs an AI algorithm on the integrated data to generate the best possible outfit, such as a navy blazer, white shirt, and dark denim.

[0076] Step 7:

[0077] The server creates image data of the clothing candidates and sends it to the terminal. This data includes images of the clothing that can be visually confirmed.

[0078] Step 8:

[0079] The terminal displays the received clothing image data to the user, allowing the user to check the clothing details.

[0080] Step 9:

[0081] The user checks the displayed clothing image and uploads a photo of himself / herself to virtually try on the clothing. For example, he / she selects and uploads a photo of his / her front view.

[0082] Step 10:

[0083] The device receives the uploaded user photos and sends them to the server. The data sent includes the user's photo data.

[0084] Step 11:

[0085] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image. This synthesis simulates how the user would actually look wearing the clothing.

[0086] Step 12:

[0087] The terminal receives the image of the virtual try-on and displays it to the user, allowing the user to check the results of the virtual try-on.

[0088] Step 13:

[0089] The server references the user's closet information and identifies any missing clothing items. For example, it determines that the user does not own any dark denim.

[0090] Step 14:

[0091] The server searches for the missing clothing through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[0092] Step 15:

[0093] The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[0094] ---

[0095] The above is the processing flow of the program divided into detailed steps.

[0096] Example 1

[0097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] Conventional clothing recommendation systems were unable to make recommendations that fully took into account the user's individual attributes or daily changing information (weather, schedules), and did not integrate virtual try-on, identification of missing clothing, and purchase recommendations. This meant that users had to spend time checking whether the suggested clothing suited them, which was inconvenient.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0100] In this invention, the server includes means for inputting user information, means for inputting weather information and schedule information, means for using a generative AI model to generate optimal outfit candidates based on the user information, weather information, and schedule information, means for displaying the generated outfit candidates, means for uploading a user's photo for virtual try-on, means for combining the user's photo with the outfit candidates, means for displaying virtual try-on images, and means for identifying missing clothing and generating purchase links from linked online stores. This allows the user to easily receive suggestions for optimal outfits tailored to their body type, preferences, age, weather, and schedule, check them through virtual try-on, and smoothly purchase the missing clothing.

[0101] "User information" refers to individual information such as the user's body type, preferences, age, etc.

[0102] "Weather information" refers to information about the weather for that day.

[0103] "Schedule information" refers to information about activities and events that the user has scheduled for that day.

[0104] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal outfit suggestions based on user information, weather information, and schedule information.

[0105] "Outfit candidates" refers to the set of optimal outfits suggested by the generative AI model.

[0106] "Virtual try-on" refers to a function that allows users to virtually try on clothes by combining a photo of themselves with suggested outfits.

[0107] "Online Store" refers to an e-commerce platform that allows you to purchase products over the Internet.

[0108] "Purchase Link" refers to a hyperlink to purchase the missing clothing from an online store.

[0109] "Server" refers to a centralized system for processing data and communicating with other devices.

[0110] "Terminal" refers to a device that is directly operated by a user (e.g., a smartphone or tablet).

[0111] The present invention will be described with reference to a system for suggesting optimal clothing based on user information, weather information, and schedule information, and for offering virtual try-on and purchasing suggestions for clothing that is in short supply. Specific embodiments will be described below.

[0112] Hardware and software used

[0113] Hardware:

[0114] Server: Use cloud servers such as Google Cloud Platform or Amazon Web Services.

[0115] Device: Smartphones include iPhones, Android devices, and tablets.

[0116] software:

[0117] AI algorithms: TensorFlow and PyTorch are used to provide outfit suggestions.

[0118] Database: MySQL and PostgreSQL are used to manage user information, weather information, and clothing information.

[0119] Front-end app: Build an application using React Native or Flutter that allows users to enter information and view results.

[0120] Overall system flow

[0121] 1. Entering user information: The user starts the application and enters personal information such as height, weight, preferences, and age. This allows the system to understand the user's body type and fashion preferences.

[0122] 2. Weather and schedule input: The user inputs the weather and schedule for the day. Based on this information, the system prepares to suggest appropriate clothing for the day.

[0123] 3. Optimal clothing suggestions: The server uses the generative AI model to generate optimal clothing suggestions based on the received user information, weather information, and schedule information. The generated clothing suggestions are sent to the user's device and displayed.

[0124] 4. Virtual Try-On: When a user uploads a photo of themselves to the application, the server combines this photo with the generated clothing image to create a virtual try-on image, which is also sent to the device and displayed to the user.

[0125] 5. Purchase Suggestion: The server compares the user's existing clothing information with the suggested clothing options and identifies any missing items. For any missing items, the server calls the API of the connected online store and generates a purchase link. This link is sent to the device and displayed to the user.

[0126] Specific examples

[0127] For example, a male user in his 30s, 175 cm tall, weighing 70 kg, with a casual style of clothing, enters into the application that he has a meeting in the office on a rainy day. The server uses a generative AI model to suggest a "navy blazer," a "white shirt," and "dark denim." When the user uploads a photo of himself and performs a virtual try-on, the server synthesizes this photo with the suggested outfit and provides an image of the virtual try-on. If the user does not own the "dark denim," the server generates a purchase link from a linked online store and displays it to the user.

[0128] Prompt Sentence Examples

[0129] "Please suggest some casual office meeting outfits for men in their 30s that can be worn on rainy days, and provide a link to purchase the outfits if they are not already available."

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] Entering user information

[0133] The user starts the application and enters personal information such as height, weight, fashion preferences, and age. The entered data is converted into a database format on the device and sent to the server. The input data includes information such as "height 175 cm," "weight 70 kg," "favorite clothing is casual," and "age 30s."

[0134] Step 2:

[0135] Weather and schedule input

[0136] The user inputs the weather forecast and schedule for the day. For example, "It's raining" and "I have a meeting at the office." The device formats this data and sends it to the server. The input data includes the weather conditions and specific schedule information.

[0137] Step 3:

[0138] Data integration

[0139] The server integrates the received user information, weather information, and schedule information. The result of this data integration becomes a reference data set for generating optimal outfit candidates using a generative AI model. The integrated data links information on each item (e.g., user information, weather, schedule) along a time axis and relationships.

[0140] Step 4:

[0141] Generating clothing candidates

[0142] The server runs a generative AI model based on the integrated data to generate optimal outfit suggestions. The input data includes integrated user information, weather information, and schedule information, and the output data includes outfit suggestions such as "navy blazer," "white shirt," and "dark denim." The generative AI model analyzes the data and selects the most suitable outfit.

[0143] Step 5:

[0144] Sending outfit suggestions

[0145] The server creates image data of the generated outfit candidates and sends it to the terminal. The input data includes the outfit candidates, and the output data includes a visual image of the outfit. This image data is used by the user to confirm what the outfit will look like.

[0146] Step 6:

[0147] Display of clothing images

[0148] The terminal displays the received clothing image data to the user. The input data includes the clothing image sent from the server, and the output is the displayed clothing image. This allows the user to visually confirm the proposed clothing.

[0149] Step 7:

[0150] Upload a photo

[0151] The user uploads a photo of themselves to perform a virtual try-on. The input data includes a full-body photo of the user, which the device sends to the server. The user must prepare a photo of themselves in the correct posture and from the front to reflect their actual appearance.

[0152] Step 8:

[0153] Generation of virtual try-on images

[0154] The server combines the received user photo with the generated clothing image to generate a virtual try-on image. The input data includes the user's photo and clothing image, and the output data includes a composite image of the virtual try-on. This process allows the user to virtually try on the clothes.

[0155] Step 9:

[0156] Displaying virtual try-on images

[0157] The terminal displays the virtual try-on image to the user. The input data includes the virtual try-on image sent from the server, and the output is the virtual try-on image displayed to the user. The user can check this and decide whether the proposed outfit suits them or not.

[0158] Step 10:

[0159] Checking existing clothing information

[0160] The server checks the user's existing clothing information stored in the database. The input data includes the user's existing clothing information, and the output data includes the check result. This step is performed to identify the clothing the user already owns.

[0161] Step 11:

[0162] Identifying missing clothing

[0163] The server compares the proposed outfit with existing outfit information to identify any missing items. For example, if the user does not own "dark denim," it will detect that the item is missing. The input data includes the proposed outfit information and existing outfit information, and the output data includes a list of missing items.

[0164] Step 12:

[0165] Generate purchase links

[0166] The server calls the API of the connected online store and generates a purchase link for the missing clothes. The input data includes a list of missing items, and the output data includes the purchase link. The link to the appropriate purchase page is automatically generated via the online store's API.

[0167] Step 13:

[0168] View purchase suggestions

[0169] The device displays the generated purchase link to the user. The input data includes the purchase link sent from the server, and the output is a purchase suggestion displayed to the user. The user can easily purchase the missing clothes by clicking this link.

[0170] (Application example 1)

[0171] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0172] Conventional systems have struggled to automate suggestions tailored to individual body types and preferences when users select appropriate clothing. They also lacked the ability to suggest clothing that takes into account weather and schedule information, or to facilitate purchasing decisions through virtual try-ons. Furthermore, their ability to identify missing clothing and provide appropriate purchasing links was also lacking.

[0173] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0174] In this invention, the server includes means for inputting user information, means for inputting weather information and schedule, means for suggesting optimal outfits based on the user information, weather information, and schedule information, means for virtual try-on that combines the user's photo with the suggested outfits, and means for identifying missing outfits and generating purchase links from linked online stores. This allows the user to not only receive optimal outfit suggestions based on their individual information, but also to check the outfits in the virtual try-on and easily purchase missing items.

[0175] "User information" refers to personal information such as the user's body type, preferences, age, etc.

[0176] "Weather Information" means information regarding current and forecast weather.

[0177] "Schedule information" refers to information about activities and events that a user has scheduled for that day.

[0178] "Means for suggesting optimal clothing" refers to a means for using an AI algorithm to select clothing that suits the user based on user information, weather information, and schedule information.

[0179] The "means for virtual try-on" refers to a means for combining a user's photograph with the suggested clothing and simulating the virtual try-on state.

[0180] The "means for identifying missing clothing" refers to a means for comparing information about clothing that the user already owns with the suggested clothing and identifying missing items.

[0181] "Means for generating purchase links from online stores" refers to means for generating purchase links for the identified missing clothing through the online store's API.

[0182] The present invention describes a system that suggests optimal clothing based on user information, weather information, and schedule information, and that offers virtual try-on and purchase suggestions for clothing that is in short supply. Specific embodiments are described below.

[0183] System configuration

[0184] This system mainly consists of a user's device (smartphone, tablet, etc.) and a server.

[0185] Hardware and Software

[0186] Hardware: smartphones, tablets, servers

[0187] Software: Mobile applications, databases, AI algorithms, image processing software

[0188] Program processing

[0189] 1. Enter your user information

[0190] The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s. The device receives this information, formats it into a database, and sends it to the server.

[0191] 2. Enter weather and schedule information

[0192] The user inputs the weather information and schedule for the day into the application. For example, the weather is rainy and the schedule is a meeting at the office. The device receives the weather information and schedule information and sends it to the server.

[0193] 3. Recommendations for the best outfit

[0194] The server integrates the received user information, weather information, and schedule information. Based on the integrated data, the server runs an AI algorithm using a generative AI model to generate optimal outfit candidates. For example, it might suggest a navy blazer, white shirt, and dark denim. It then creates image data of the generated outfit candidates and sends them to the device. The device then displays the received outfit image data to the user.

[0195] 4. Virtual try-on

[0196] The user checks the displayed clothing image and uploads their own photo to perform a virtual try-on. The device sends the uploaded photo to the server. The server combines the user's photo with the generated clothing image to create a virtual try-on image. The device displays the virtual try-on image to the user.

[0197] 5. Purchase Proposal

[0198] The server checks the user's existing clothing information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects the missing clothing. The server then calls the API of the online store it is connected to and generates a purchase link for the missing clothing. The device then displays the generated purchase link along with the purchase suggestion to the user.

[0199] Specific examples

[0200] For example, suppose a user inputs the following prompt sentence into a generative AI model:

[0201] Example prompt:

[0202] "I'm a 175cm tall, 70kg man in my 30s who likes a casual style. I own a navy blazer, a white shirt, and sneakers. It's raining today and I have a meeting at the office. What would be the best outfit for me?"

[0203] This allows the generative AI model to suggest the best outfits, output images for virtual try-on, and provide links to purchase the desired items.

[0204] The above is an embodiment of the present invention. This system allows users to receive suggestions for optimal clothing based on their body type, preferences, age, weather, and schedule, check the clothes through virtual try-on, and easily purchase any missing clothing from the online store.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] The user launches the application and enters their profile information (body type, preferences, age, and data on clothing they already own). The device receives this information, formats it into a database, and sends it to the server. In the process of formatting it into a database, numerical information such as height and weight and categorical information such as preferences and age are mapped to the appropriate fields. Input data: User profile information. Output data: User information in database format.

[0208] Step 2:

[0209] The user enters the weather information and schedule for the day into the application. The device receives the weather information and schedule information and sends it to the server. Weather information can also be obtained from an external weather API, and schedule information can be entered manually by the user. Input data: weather information, schedule information. Output data: weather information and schedule information sent to the server.

[0210] Step 3:

[0211] The server integrates the received user information, weather information, and schedule information. Based on this integrated data, it uses a generative AI model to run an AI algorithm and generate optimal outfit candidates. The generative AI model selects the outfit that best suits the user profile based on the input data. Input data: Integrated data (user information, weather information, schedule information). Output data: Data on optimal outfit candidates.

[0212] Step 4:

[0213] The server creates image data of the generated outfit candidates and sends it to the terminal. The image data is created in a format that the user can visually confirm. The terminal displays the received outfit image data to the user. Input data: optimal outfit candidate data. Output data: outfit image displayed on the terminal.

[0214] Step 5:

[0215] The user checks the displayed clothing image and uploads a photo of themselves to virtually try on the clothing. The device sends the uploaded photo to the server. Input data: User's photo. Output data: User's photo sent to the server.

[0216] Step 6:

[0217] The server combines the user's photo with the generated clothing image to create a virtual try-on image. Image processing software is used for this combination, and the clothing is positioned to fit the user's body shape. Input data: User's photo, clothing image data. Output data: Virtual try-on image data.

[0218] Step 7:

[0219] The server sends the image of the virtual try-on to the terminal, and the terminal displays the image to the user. Input data: Image data of the virtual try-on. Output data: The image of the virtual try-on displayed on the terminal.

[0220] Step 8:

[0221] The server checks the user's existing clothing information and identifies any missing clothing. The server then calls the API of the linked online store and generates a purchase link for the missing clothing. Input data: User's existing clothing information, weather information, and schedule information. Output data: Purchase link.

[0222] Step 9:

[0223] The terminal displays the generated purchase link along with the purchase suggestion to the user. Input data: Purchase link. Output data: Purchase link and purchase suggestion displayed to the user.

[0224] This allows users to receive suggestions for the best outfits based on their body type, preferences, age, weather, and schedule, and they can check them out through virtual try-ons, and easily purchase any clothes they are missing from the online store.

[0225] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0226] ---

[0227] The present invention describes a system that uses user information, weather information, schedule information, and an emotion engine to suggest optimal clothing, perform virtual try-on, and suggest purchasing missing clothing.

[0228] Program processing overview

[0229] Input of user information and emotions

[0230] 1. The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters height 175 cm, weight 70 kg, preference for casual clothing, and age 30s.

[0231] 2. The terminal receives this information, formats it into a database, and sends it to the server.

[0232] 3. The user inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice. For example, emotions are detected as "joy" or "stress."

[0233] Weather and schedule input

[0234] 4. The user enters the weather information and schedule for the day into the application. For example, the user enters "rain" for the weather and "a meeting at the office" for the schedule.

[0235] 5. The device receives the weather information and schedule information and sends it to the server.

[0236] Best clothing suggestions

[0237] 6. The server integrates the received user information, weather information, schedule information, and emotion information, providing the basic data for suggesting optimal clothing.

[0238] 7. The server runs an AI algorithm based on the integrated data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select light-colored clothing that evokes a positive impression. Specifically, it will select a navy blazer, a white shirt, and dark denim.

[0239] 8. The server creates image data of the generated clothing candidates and sends it to the terminal. This data includes visually verifiable images of the clothing.

[0240] Virtual try-on

[0241] 9. The user checks the displayed clothing image and uploads a photo of themselves to virtually try on the clothing. For example, they can select and upload a photo of their front view.

[0242] 10. The device receives the uploaded user photo and sends it to the server.

[0243] 11. The server combines the user's photo with the generated clothing image to create a virtual try-on image, simulating how the user would actually look wearing the clothing.

[0244] 12. The terminal receives the virtual try-on image and displays it to the user.

[0245] Purchase suggestion

[0246] 13. The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, it will detect that they are missing.

[0247] 14. The server searches for the missing clothing items through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[0248] 15. The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[0249] ---

[0250] This concludes the detailed embodiment. This system allows users to receive suggestions for optimal clothing tailored to their body type, preferences, age, weather, schedule, and emotions, and allows them to check the clothes through virtual try-on and purchase any missing clothing from an online store.

[0251] The processing flow will be explained below.

[0252] ---

[0253] Step 1:

[0254] A user starts the application and enters their profile information (body type, preferences, age). For example, they enter their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[0255] Step 2:

[0256] The device receives the user's profile information, converts it into a database format, and sends it to the server. The data sent includes details such as "height: 175 cm, weight: 70 kg, preference: casual, age: 30s."

[0257] Step 3:

[0258] A user inputs the weather information and schedule information for the day into the application. For example, the user inputs "rain" for the weather and "a meeting at the office" for the schedule.

[0259] Step 4:

[0260] The device receives the weather and schedule information entered, converts it into a database format, and sends it to the server. The data sent includes information such as "Weather: Rain, Schedule: Meeting at the office."

[0261] Step 5:

[0262] The user can input their emotional state for the day, or an emotion engine linked to the device can detect emotions from the user's facial expressions and voice. For example, emotions can be detected as "joy" or "stress."

[0263] Step 6:

[0264] The device converts the emotional information detected by the emotion engine into a database format and sends it to the server. The data sent includes information such as "Emotion: Joy."

[0265] Step 7:

[0266] The server integrates the received user information, weather information, schedule information, and emotion information, providing the basic data for suggesting optimal outfits.

[0267] Step 8:

[0268] The server runs an AI algorithm based on the integrated data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select light-colored clothing that evokes a positive impression. Specifically, it will select a navy blazer, a white shirt, and dark denim.

[0269] Step 9:

[0270] The server creates image data of the clothing candidates and sends it to the terminal. This data includes visually verifiable images of the clothing.

[0271] Step 10:

[0272] The terminal displays the received clothing image data to the user, allowing the user to check the clothing details.

[0273] Step 11:

[0274] The user checks the displayed clothing image and uploads a photo of himself / herself to virtually try on the clothing. For example, he / she selects and uploads a photo of his / her front view.

[0275] Step 12:

[0276] The device receives the uploaded user photos and sends them to the server. The data sent includes the user's photo data.

[0277] Step 13:

[0278] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image, simulating how the user would actually look wearing the clothing.

[0279] Step 14:

[0280] The terminal receives the image of the virtual try-on and displays it to the user.

[0281] Step 15:

[0282] The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects the lack of such clothing.

[0283] Step 16:

[0284] The server searches for the missing clothing through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[0285] Step 17:

[0286] The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[0287] ---

[0288] The above is the processing flow of the program divided into detailed steps.

[0289] Example 2

[0290] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0291] Conventional clothing suggestion systems can suggest optimal outfits by taking into account a user's body type, preferences, age, weather information, and schedule information, but they cannot suggest outfits that reflect the user's emotional state. Furthermore, they lack the functionality to simulate how the user would actually look wearing the outfit during virtual try-on. Furthermore, there was no seamless system that could identify the clothes a user was missing and quickly purchase them from an online store. These challenges made it difficult for users to select the best outfits that would satisfy them.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0293] In this invention, the server includes a means for integrating user information, weather information, schedule information, and emotional state to suggest optimal outfits, a means for uploading a user's photo for virtual try-on and combining it with the outfit suggestions, and a means for identifying missing clothes and generating a purchase link via the API of a linked online store. This allows the user to receive optimal outfit suggestions that take emotional state into consideration, check them in the virtual try-on, and seamlessly purchase the missing clothes.

[0294] ---

[0295] "User Information" refers to personal data such as a user's profile information, body type information, preferences, age, etc.

[0296] "Weather information" refers to data about the weather on that day, such as sunny, rainy, or snowy weather conditions.

[0297] "Schedule information" refers to data about the actions and events that the user has scheduled for that day.

[0298] "Emotional state" is information that indicates the user's current emotions, such as joy, sadness, stress, etc.

[0299] "Outfit candidates" refer to clothing options suitable for a user that are generated based on integrated user information, weather information, schedule information, and emotional state.

[0300] "Virtual try-on" refers to a technology that combines a user's photo with potential outfits to simulate how the user would actually look wearing those outfits.

[0301] "Uploading" refers to the act of a user sending data such as their own photos to a server via their device.

[0302] "Synthesis" refers to the act of combining a user's photo with image data of potential clothing items to generate a new image.

[0303] "Missing clothing" refers to clothing that is not in the user's closet but is necessary for the suggested outfit.

[0304] "Online store" refers to an e-commerce site that sells clothing and other products over the Internet.

[0305] "API" refers to a programming interface for exchanging functions and data between a terminal and a server or other external system.

[0306] "Purchase Link" refers to data containing a URL for direct access to the purchase page for a specific product.

[0307] This invention is a system that uses user information, weather information, schedule information, and an emotion engine to suggest optimal outfits, allows virtual fitting, and suggests purchasing clothes that are missing. This system is realized through cooperation between users, terminals, and a server.

[0308] 1. Input of user information and emotions

[0309] First, the user launches the application and enters their profile information, which includes the user's body type (e.g., height 175cm, weight 70kg), preferences (e.g., casual clothing), age (e.g., 30s), etc. The device receives the information entered by the user, formats it into a database, and then sends it to the server.

[0310] The user may input their emotional state for the day, or the emotion engine may detect emotions from the user's facial expressions and voice. If the user has an emotion such as "joy" or "stress," that information is sent to the server via the device.

[0311] 2. Weather and schedule input

[0312] Next, the user inputs the weather information for the day (e.g., rain) and schedule information (e.g., a meeting at the office) into the application. This information is also received by the device, formatted into a database, and sent to the server.

[0313] 3. Data integration and clothing candidate generation

[0314] The server integrates user information, weather information, schedule information, and emotional information. This creates the basic data for suggesting optimal outfits. The server uses this integrated data to generate optimal outfit suggestions using a generative AI model. For example, if the user's emotional state is "joy," the server selects bright-colored clothing that conveys a positive impression. In this case, it suggests a navy blazer, a white shirt, and dark denim.

[0315] 4. Displaying clothing options and virtually trying them on

[0316] Once the outfit candidates are generated, the server creates image data of them and sends it to the terminal. The terminal displays the received outfit candidate images to the user. The user then uploads a photo of themselves to virtually try on the outfits. The uploaded photo is sent from the terminal to the server, which then combines the user's photo with the outfit candidate image. Once this virtual try-on image is created, it is displayed to the user via the terminal.

[0317] 5. Identifying missing clothing and making purchasing suggestions

[0318] Finally, the server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects this. The server searches for the missing clothing through the API of a partner online store and generates a purchase link. This link provides direct access to the purchase page and is displayed to the user via their device. This allows the user to seamlessly purchase the missing clothing.

[0319] Specific examples

[0320] For example, suppose a user inputs that they are 175 cm tall, weigh 70 kg, prefer casual clothing, and are in their 30s. If the user also inputs their emotional state as "joy," the weather information as "rain," and their schedule information as "office meeting," the server will suggest a navy blazer, a white shirt, and dark jeans as the best outfit. Based on this image, a virtual try-on is performed, and if the user does not own the dark jeans, a purchase link to the online store is generated and displayed to the user.

[0321] Prompt Sentence Examples

[0322] "Suggest the best outfit for a user whose height is 175cm, weight is 70kg, preference is casual, age is 30s, emotional state is joy, weather is rainy, and scheduled appointment is an office meeting."

[0323] The above is a specific embodiment for carrying out the present invention. This system allows users to receive optimal clothing suggestions that take their emotional state into consideration, check them through virtual try-ons, and quickly purchase any clothes they are missing.

[0324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0325] Step 1: Enter your user information

[0326] Input: The user launches the application and enters profile information (body type, preferences, age, etc.).

[0327] Specific operation: The user enters his height as 175cm, weight as 70kg, preference as casual, and age as 30s.

[0328] Data processing and calculation: The terminal receives this information and formats it into a database.

[0329] Output: The formatted data is sent to the server.

[0330] Step 2: Input or detect emotional state

[0331] Input: The user manually inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice.

[0332] Specific operation: The user manually inputs the emotional state as "joy", or the emotion engine detects "joy" through the camera and microphone.

[0333] Data processing and calculation: The device formats the detected emotional information into a database.

[0334] Output: Formatted emotion data is sent to the server.

[0335] Step 3: Enter weather and schedule information

[0336] Input: The user enters today's weather and schedule information into the application.

[0337] Specific actions: Enter the weather information "Rain" and the schedule information "Meeting at the office."

[0338] Data processing and calculation: The device converts the weather and schedule information entered into a database format.

[0339] Output: Formatted weather and schedule information is sent to the server.

[0340] Step 4: Data integration and clothing candidate generation

[0341] Input: The server receives user information, emotional state, weather information, and schedule information.

[0342] How it works: The server aggregates all the information and uses a generative AI model to generate optimal outfit suggestions.

[0343] Data processing and calculation: The server inputs the integrated data into the generative AI model to generate optimal outfit suggestions. For example, if the emotion is "joy" and the weather is "rainy," a navy blazer, white shirt, and dark denim, which convey a positive impression, will be selected.

[0344] Output: Image data of the generated outfit candidates is generated.

[0345] Step 5: Viewing clothing suggestions

[0346] Input: The server sends image data of the generated outfit candidates.

[0347] Specific operation: The server sends image data of the clothing candidates generated to the terminal.

[0348] Data processing and calculation: The image data received by the terminal is processed to be displayed on the user interface.

[0349] Output: The user visually checks the images of the outfit candidates.

[0350] Step 6: Upload a photo for the virtual try-on

[0351] Input: A user uploads their photo to the application.

[0352] What it does: The user selects and uploads a photo of themselves taken from the front.

[0353] Data processing and calculation: The device sends the uploaded photo data to the server.

[0354] Output: User photo data sent to the server.

[0355] Step 7: Perform a virtual try-on

[0356] Input: The server receives the user's photo data and image data of clothing candidates.

[0357] How it works: The server synthesizes these data and creates an image of the virtual try-on.

[0358] Data processing and calculation: The server uses image processing technology to synthesize the user's photo with the clothing candidate image.

[0359] Output: Image data of the created virtual try-on.

[0360] Step 8: View the virtual try-on

[0361] Input: The server sends the image data of the created virtual try-on.

[0362] Specific operation: The server sends image data of the virtual try-on to the terminal.

[0363] Data processing and calculation: The terminal processes the image data of the virtual try-on to display it on the user interface.

[0364] Output: User sees the virtual try-on image.

[0365] Step 9: Identify missing clothing

[0366] Input: The server looks up the user's closet information.

[0367] What happens: The server checks the closet information in the database to identify the missing clothes.

[0368] Data processing and calculation: The server compares the clothes required for the suggested outfits with the closet information and identifies any missing clothes.

[0369] Output: A list of identified missing clothes.

[0370] Step 10: Generate a purchase link

[0371] Input: The server sends the missing clothing information to the online store's API.

[0372] What it does: The server uses the API to search for the missing clothing and generate a purchase link.

[0373] Data processing and calculation: The server searches for the missing clothes through the online store's API and obtains the corresponding purchase link.

[0374] Output: The generated purchase link.

[0375] Step 11: View purchase offers

[0376] Input: The server sends the generated purchase link to the device.

[0377] Specific operation: The server sends the generated purchase link to the device, which displays the link in its user interface.

[0378] Data processing and calculation: The terminal processes the purchase link to display it on the user interface.

[0379] Output: User can see the purchase link and easily purchase the missing clothes.

[0380] These are the processing steps of this system, which allows users to receive recommendations for optimal clothing based on their body type, preferences, age, emotional state, weather, and schedule, check them out through virtual try-ons, and quickly purchase any clothes they are missing.

[0381] (Application example 2)

[0382] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0383] Conventional clothing suggestion systems have difficulty responding flexibly based on a user's individual emotions and detailed schedule information, resulting in clothing suggestions that are often not optimal for the user. Furthermore, even with virtual try-on functions, it is difficult to provide a visual experience that is significantly different from a real try-on, which reduces user satisfaction. Furthermore, there is a need for a system that allows users to easily purchase clothing that they are running low on without any hassle.

[0384] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0385] In this invention, the server includes means for inputting user information and emotions, means for inputting weather information and schedule, means for suggesting optimal clothing based on user information, emotion information, weather information, and schedule information, means for virtual try-on, and means for identifying missing clothing and generating a purchase link from a linked online store. This makes it possible to suggest optimal clothing based on the user's detailed emotional state and individual schedule, providing a more realistic virtual try-on experience and enabling the user to easily purchase missing clothing.

[0386] "User information" is information about personal characteristics such as the user's body type, preferences, and age.

[0387] "Emotion information" is information that indicates the user's current emotional state, and is either detected by the emotion engine or directly input by the user.

[0388] "Weather information" is information about the weather conditions of the day, including temperature, precipitation, wind speed, and the like.

[0389] "Schedule information" is information about the activities and behaviors that the user plans for that day.

[0390] The "means for suggesting optimal clothing" is a function or algorithm that integrates user information, emotional information, weather information, and schedule information to select and suggest optimal clothing for the user.

[0391] "Means for virtual try-on" refers to a function or system that allows a user to virtually try on selected clothing, and includes a function that uses image processing technology to combine a photograph of the user with an image of the clothing.

[0392] The "means for identifying missing clothes" is a function that refers to the user's closet information and detects clothes among the suggested outfits that the user does not own.

[0393] The "means of generating purchase links" is a function that uses the online store's API to generate links to purchase pages for missing clothing.

[0394] The present invention describes a system that suggests optimal clothing based on user information, emotion information, weather information, and schedule information, and offers virtual try-on and purchase suggestions for clothing that is in short supply.

[0395] Input of user information and emotions

[0396] First, the user launches the application and enters their profile information (body type, preferences, age). For example, they might enter height 175cm, weight 70kg, preference casual, and age 30s. The device receives this information, formats it into a database, and sends it to the server. The user can then enter their emotional state for the day, or the emotion engine can detect emotions from the user's facial expressions and voice. For example, emotions might be detected as "joy" or "stress."

[0397] Weather and schedule input

[0398] Next, the user inputs the weather information and schedule for the day into the application. For example, the weather is "rain" and the schedule is "meeting at the office." The device receives the weather information and schedule information and sends it to the server.

[0399] Best clothing suggestions

[0400] The server integrates the received user information, emotional information, weather information, and schedule information. This provides the basic data for suggesting optimal outfits. The server then runs a generative AI model based on the integrated data to generate optimal outfit candidates. For example, if the emotional state is "joy," it selects light-colored clothing that conveys a positive impression. Specifically, it selects a navy blazer, white shirt, and dark denim. The server then creates image data of the generated outfit candidates and sends it to the device. This includes a visually verifiable image of the outfit.

[0401] Virtual try-on

[0402] The user checks the displayed clothing image and uploads a photo of themselves to perform a virtual try-on. For example, they can select and upload a photo of their front view. The device receives the uploaded user photo and sends it to the server. The server combines the user's photo with the generated clothing image to create a virtual try-on image. This simulates how the user would look if they were actually wearing the clothing. The device receives the virtual try-on image and displays it to the user.

[0403] Purchase suggestion

[0404] The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, it detects this. The server searches for the missing clothing through the API of the connected online store and generates a purchase link. The generated link includes direct access to the purchase page. The device displays the generated purchase link to the user along with a purchase suggestion, making it easy for the user to purchase the missing clothing.

[0405] Hardware and software used

[0406] Hardware: Smartphone, Head-Mounted Display (HMD)

[0407] Software: Python, Keras, OpenCV, Requests

[0408] Specific examples

[0409] For example, assume that User A is a man in his 30s who is 175 cm tall, weighs 70 kg, and likes casual clothing. It is raining that day, and he has a scheduled office meeting. If the emotion engine detects his emotional state as "joy," the server will suggest a navy blazer, a white shirt, and dark jeans. User A can visually check these outfits by combining them with his own photo using the virtual try-on function. Furthermore, if User A does not own dark jeans based on his closet information, an appropriate purchase link will be generated and provided to User A.

[0410] Prompt Sentence Examples

[0411] Examples of prompts include:

[0412] Generate a Python program to suggest clothes that the user likes, allow them to try them on virtually, and provide a purchase link. The AI ​​algorithm will select the most suitable outfit based on the user's profile information, the weather for the day, and their schedule. Synthesize the outfit into a virtual try-on image, and generate a purchase link for any missing items via the online store's API.

[0413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0414] Step 1:

[0415] The user starts the application and enters their profile information (body type, preferences, age). The entered information is converted into a database format by the device and sent to the server. Specifically, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s. The server then receives data that reflects the user's body type and preferences.

[0416] Step 2:

[0417] The user inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice. The input emotion information (e.g., "joy" or "stress") is acquired by the device and sent to the server. This information is analyzed by the emotion engine and provided to the server as data that accurately reflects the user's current emotions.

[0418] Step 3:

[0419] The user inputs the weather information and plans for the day into the application. For example, the weather is "rain" and the plan is "meeting at the office." The device converts this information into a database format and sends it to the server. The server then obtains data on the weather conditions for the day and the user's planned activities.

[0420] Step 4:

[0421] The server combines the received user information, emotional information, weather information, and schedule information to form the basic data for suggesting optimal outfits. It then runs a generative AI model based on the combined data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select bright-colored clothes that convey a positive impression.

[0422] Step 5:

[0423] The server creates image data of the generated outfit candidates and sends it to the terminal, which then provides the user with visually identifiable image data of the outfit. For example, an outfit image of a navy blazer, a white shirt, and dark denim is displayed.

[0424] Step 6:

[0425] The user checks the displayed clothing image and uploads their own photo to virtually try on the clothing. The device receives the uploaded user photo and sends it to the server, which then obtains the data needed to synthesize the user's photo with the generated clothing image.

[0426] Step 7:

[0427] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image. The generated virtual try-on image is sent to the terminal and displayed to the user, allowing the user to simulate how the clothing would actually look when worn.

[0428] Step 8:

[0429] The server references the user's closet information and identifies the missing clothing. For example, if the user does not own any dark denim, the server identifies the missing clothing. After identifying the missing clothing, the server searches the API of the relevant online store and generates a purchase link.

[0430] Step 9:

[0431] The generated purchase link is sent to the device and displayed to the user. The user can use the provided purchase link to easily purchase the missing clothing. For example, by searching for "dark denim," the purchase link is generated and displayed to the user, allowing the user to purchase the item directly.

[0432] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0433] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0435] [Second embodiment]

[0436] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0437] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0438] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0440] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0442] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0443] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0444] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0445] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0447] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0448] ---

[0449] The present invention describes a system that suggests optimal clothing based on user information, weather information, and schedule information, and performs virtual try-on and suggests purchasing missing clothing.

[0450] Program processing overview

[0451] Entering user information

[0452] 1. The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[0453] 2. The terminal receives this information, formats it into a database, and sends it to the server.

[0454] Weather and schedule input

[0455] 3. The user enters the weather information and schedule for the day into the application. For example, the user enters that the weather is rainy and the schedule is an office meeting.

[0456] 4. The device receives the weather information and schedule information and sends it to the server.

[0457] Best clothing suggestions

[0458] 5. The server integrates the received user information, weather information, and schedule information.

[0459] 6. The server runs an AI algorithm on the integrated data to generate the best possible outfit, such as a navy blazer, a white shirt, and dark denim.

[0460] 7. The server creates image data of the generated clothing candidates and sends it to the terminal.

[0461] 8. The terminal displays the received image data of the clothing to the user.

[0462] Virtual try-on

[0463] 9. The user reviews the displayed outfit image and uploads a photo of themselves to virtually try on the outfit.

[0464] 10. The device sends the uploaded photos to the server.

[0465] 11. The server combines the user's photo with the generated clothing image to create a virtual try-on image.

[0466] 12. The device displays the virtual try-on image to the user.

[0467] Purchase suggestion

[0468] 13. The server checks the user's existing clothing information and identifies any missing clothing. For example, if the user does not own any dark denim, it will detect that this is missing.

[0469] 14. The server calls the API of the online store it is connected to and generates a purchase link for the missing clothing.

[0470] 15. The terminal displays the generated purchase link along with the purchase offer to the user.

[0471] ---

[0472] This completes the description of the embodiment of the present invention. With this, the user can receive suggestions for optimal clothing that matches their body type, preferences, age, weather, and schedule, check them out through virtual try-ons, and purchase any missing clothing from an online store.

[0473] The processing flow will be explained below.

[0474] ---

[0475] Step 1:

[0476] A user starts the application and enters their profile information (body type, preferences, age). For example, they enter their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[0477] Step 2:

[0478] The device receives the user's profile information, converts it into a database format, and sends it to the server. The data sent includes details such as "height: 175 cm, weight: 70 kg, preference: casual, age: 30s."

[0479] Step 3:

[0480] A user inputs the weather information and schedule for the day into the application. For example, the weather is "rainy" and the schedule is "a meeting at the office."

[0481] Step 4:

[0482] The device receives the weather and schedule information entered, converts it into a database format, and sends it to the server. The data sent includes information such as "Weather: Rain, Schedule: Meeting at the office."

[0483] Step 5:

[0484] The server integrates the received user information, weather information, and schedule information, providing the basic data for suggesting optimal clothing.

[0485] Step 6:

[0486] The server runs an AI algorithm on the integrated data to generate the best possible outfit, such as a navy blazer, white shirt, and dark denim.

[0487] Step 7:

[0488] The server creates image data of the clothing candidates and sends it to the terminal. This data includes images of the clothing that can be visually confirmed.

[0489] Step 8:

[0490] The terminal displays the received clothing image data to the user, allowing the user to check the clothing details.

[0491] Step 9:

[0492] The user checks the displayed clothing image and uploads a photo of himself / herself to virtually try on the clothing. For example, he / she selects and uploads a photo of his / her front view.

[0493] Step 10:

[0494] The device receives the uploaded user photos and sends them to the server. The data sent includes the user's photo data.

[0495] Step 11:

[0496] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image. This synthesis simulates how the user would actually look wearing the clothing.

[0497] Step 12:

[0498] The terminal receives the image of the virtual try-on and displays it to the user, allowing the user to check the results of the virtual try-on.

[0499] Step 13:

[0500] The server references the user's closet information and identifies any missing clothing items. For example, it determines that the user does not own any dark denim.

[0501] Step 14:

[0502] The server searches for the missing clothing through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[0503] Step 15:

[0504] The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[0505] ---

[0506] The above is the processing flow of the program divided into detailed steps.

[0507] Example 1

[0508] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0509] Conventional clothing recommendation systems were unable to make recommendations that fully took into account the user's individual attributes or daily changing information (weather, schedules), and did not integrate virtual try-on, identification of missing clothing, and purchase recommendations. This meant that users had to spend time checking whether the suggested clothing suited them, which was inconvenient.

[0510] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0511] In this invention, the server includes means for inputting user information, means for inputting weather information and schedule information, means for using a generative AI model to generate optimal outfit candidates based on the user information, weather information, and schedule information, means for displaying the generated outfit candidates, means for uploading a user's photo for virtual try-on, means for combining the user's photo with the outfit candidates, means for displaying virtual try-on images, and means for identifying missing clothing and generating purchase links from linked online stores. This allows the user to easily receive suggestions for optimal outfits tailored to their body type, preferences, age, weather, and schedule, check them through virtual try-on, and smoothly purchase the missing clothing.

[0512] "User information" refers to individual information such as the user's body type, preferences, age, etc.

[0513] "Weather information" refers to information about the weather for that day.

[0514] "Schedule information" refers to information about activities and events that the user has scheduled for that day.

[0515] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal outfit suggestions based on user information, weather information, and schedule information.

[0516] "Outfit candidates" refers to the set of optimal outfits suggested by the generative AI model.

[0517] "Virtual try-on" refers to a function that allows users to virtually try on clothes by combining a photo of themselves with suggested outfits.

[0518] "Online Store" refers to an e-commerce platform that allows you to purchase products over the Internet.

[0519] "Purchase Link" refers to a hyperlink to purchase the missing clothing from an online store.

[0520] "Server" refers to a centralized system for processing data and communicating with other devices.

[0521] "Terminal" refers to a device that is directly operated by a user (e.g., a smartphone or tablet).

[0522] The present invention will be described with reference to a system for suggesting optimal clothing based on user information, weather information, and schedule information, and for offering virtual try-on and purchasing suggestions for clothing that is in short supply. Specific embodiments will be described below.

[0523] Hardware and software used

[0524] Hardware:

[0525] Server: Use cloud servers such as Google Cloud Platform or Amazon Web Services.

[0526] Device: Smartphones include iPhones, Android devices, and tablets.

[0527] software:

[0528] AI algorithms: TensorFlow and PyTorch are used to provide outfit suggestions.

[0529] Database: MySQL and PostgreSQL are used to manage user information, weather information, and clothing information.

[0530] Front-end app: Build an application using React Native or Flutter that allows users to enter information and view results.

[0531] Overall system flow

[0532] 1. Entering user information: The user starts the application and enters personal information such as height, weight, preferences, and age. This allows the system to understand the user's body type and fashion preferences.

[0533] 2. Weather and schedule input: The user inputs the weather and schedule for the day. Based on this information, the system prepares to suggest appropriate clothing for the day.

[0534] 3. Optimal clothing suggestions: The server uses the generative AI model to generate optimal clothing suggestions based on the received user information, weather information, and schedule information. The generated clothing suggestions are sent to the user's device and displayed.

[0535] 4. Virtual Try-On: When a user uploads a photo of themselves to the application, the server combines this photo with the generated clothing image to create a virtual try-on image, which is also sent to the device and displayed to the user.

[0536] 5. Purchase Suggestion: The server compares the user's existing clothing information with the suggested clothing options and identifies any missing items. For any missing items, the server calls the API of the connected online store and generates a purchase link. This link is sent to the device and displayed to the user.

[0537] Specific examples

[0538] For example, a male user in his 30s, 175 cm tall, weighing 70 kg, with a casual style of clothing, enters into the application that he has a meeting in the office on a rainy day. The server uses a generative AI model to suggest a "navy blazer," a "white shirt," and "dark denim." When the user uploads a photo of himself and performs a virtual try-on, the server synthesizes this photo with the suggested outfit and provides an image of the virtual try-on. If the user does not own the "dark denim," the server generates a purchase link from a linked online store and displays it to the user.

[0539] Prompt Sentence Examples

[0540] "Please suggest some casual office meeting outfits for men in their 30s that can be worn on rainy days, and provide a link to purchase the outfits if they are not already available."

[0541] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0542] Step 1:

[0543] Entering user information

[0544] The user starts the application and enters personal information such as height, weight, fashion preferences, and age. The entered data is converted into a database format on the device and sent to the server. The input data includes information such as "height 175 cm," "weight 70 kg," "favorite clothing is casual," and "age 30s."

[0545] Step 2:

[0546] Weather and schedule input

[0547] The user inputs the weather forecast and schedule for the day. For example, "It's raining" and "I have a meeting at the office." The device formats this data and sends it to the server. The input data includes the weather conditions and specific schedule information.

[0548] Step 3:

[0549] Data integration

[0550] The server integrates the received user information, weather information, and schedule information. The result of this data integration becomes a reference data set for generating optimal outfit candidates using a generative AI model. The integrated data links information on each item (e.g., user information, weather, schedule) along a time axis and relationships.

[0551] Step 4:

[0552] Generating clothing candidates

[0553] The server runs a generative AI model based on the integrated data to generate optimal outfit suggestions. The input data includes integrated user information, weather information, and schedule information, and the output data includes outfit suggestions such as "navy blazer," "white shirt," and "dark denim." The generative AI model analyzes the data and selects the most suitable outfit.

[0554] Step 5:

[0555] Sending outfit suggestions

[0556] The server creates image data of the generated outfit candidates and sends it to the terminal. The input data includes the outfit candidates, and the output data includes a visual image of the outfit. This image data is used by the user to confirm what the outfit will look like.

[0557] Step 6:

[0558] Display of clothing images

[0559] The terminal displays the received clothing image data to the user. The input data includes the clothing image sent from the server, and the output is the displayed clothing image. This allows the user to visually confirm the proposed clothing.

[0560] Step 7:

[0561] Upload a photo

[0562] The user uploads a photo of themselves to perform a virtual try-on. The input data includes a full-body photo of the user, which the device sends to the server. The user must prepare a photo of themselves in the correct posture and from the front to reflect their actual appearance.

[0563] Step 8:

[0564] Generation of virtual try-on images

[0565] The server combines the received user photo with the generated clothing image to generate a virtual try-on image. The input data includes the user's photo and clothing image, and the output data includes a composite image of the virtual try-on. This process allows the user to virtually try on the clothes.

[0566] Step 9:

[0567] Displaying virtual try-on images

[0568] The terminal displays the virtual try-on image to the user. The input data includes the virtual try-on image sent from the server, and the output is the virtual try-on image displayed to the user. The user can check this and decide whether the proposed outfit suits them or not.

[0569] Step 10:

[0570] Checking existing clothing information

[0571] The server checks the user's existing clothing information stored in the database. The input data includes the user's existing clothing information, and the output data includes the check result. This step is performed to identify the clothing the user already owns.

[0572] Step 11:

[0573] Identifying missing clothing

[0574] The server compares the proposed outfit with existing outfit information to identify any missing items. For example, if the user does not own "dark denim," it will detect that the item is missing. The input data includes the proposed outfit information and existing outfit information, and the output data includes a list of missing items.

[0575] Step 12:

[0576] Generate purchase links

[0577] The server calls the API of the connected online store and generates a purchase link for the missing clothes. The input data includes a list of missing items, and the output data includes the purchase link. The link to the appropriate purchase page is automatically generated via the online store's API.

[0578] Step 13:

[0579] View purchase suggestions

[0580] The device displays the generated purchase link to the user. The input data includes the purchase link sent from the server, and the output is a purchase suggestion displayed to the user. The user can easily purchase the missing clothes by clicking this link.

[0581] (Application example 1)

[0582] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0583] Conventional systems have struggled to automate suggestions tailored to individual body types and preferences when users select appropriate clothing. They also lacked the ability to suggest clothing that takes into account weather and schedule information, or to facilitate purchasing decisions through virtual try-ons. Furthermore, their ability to identify missing clothing and provide appropriate purchasing links was also lacking.

[0584] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0585] In this invention, the server includes means for inputting user information, means for inputting weather information and schedule, means for suggesting optimal outfits based on the user information, weather information, and schedule information, means for virtual try-on that combines the user's photo with the suggested outfits, and means for identifying missing outfits and generating purchase links from linked online stores. This allows the user to not only receive optimal outfit suggestions based on their individual information, but also to check the outfits in the virtual try-on and easily purchase missing items.

[0586] "User information" refers to personal information such as the user's body type, preferences, age, etc.

[0587] "Weather Information" means information regarding current and forecast weather.

[0588] "Schedule information" refers to information about activities and events that a user has scheduled for that day.

[0589] "Means for suggesting optimal clothing" refers to a means for using an AI algorithm to select clothing that suits the user based on user information, weather information, and schedule information.

[0590] The "means for virtual try-on" refers to a means for combining a user's photograph with the suggested clothing and simulating the virtual try-on state.

[0591] The "means for identifying missing clothing" refers to a means for comparing information about clothing that the user already owns with the suggested clothing and identifying missing items.

[0592] "Means for generating purchase links from online stores" refers to means for generating purchase links for the identified missing clothing through the online store's API.

[0593] The present invention describes a system that suggests optimal clothing based on user information, weather information, and schedule information, and that offers virtual try-on and purchase suggestions for clothing that is in short supply. Specific embodiments are described below.

[0594] System configuration

[0595] This system mainly consists of a user's device (smartphone, tablet, etc.) and a server.

[0596] Hardware and Software

[0597] Hardware: smartphones, tablets, servers

[0598] Software: Mobile applications, databases, AI algorithms, image processing software

[0599] Program processing

[0600] 1. Enter your user information

[0601] The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s. The device receives this information, formats it into a database, and sends it to the server.

[0602] 2. Enter weather and schedule information

[0603] The user inputs the weather information and schedule for the day into the application. For example, the weather is rainy and the schedule is a meeting at the office. The device receives the weather information and schedule information and sends it to the server.

[0604] 3. Recommendations for the best outfit

[0605] The server integrates the received user information, weather information, and schedule information. Based on the integrated data, the server runs an AI algorithm using a generative AI model to generate optimal outfit candidates. For example, it might suggest a navy blazer, white shirt, and dark denim. It then creates image data of the generated outfit candidates and sends them to the device. The device then displays the received outfit image data to the user.

[0606] 4. Virtual try-on

[0607] The user checks the displayed clothing image and uploads their own photo to perform a virtual try-on. The device sends the uploaded photo to the server. The server combines the user's photo with the generated clothing image to create a virtual try-on image. The device displays the virtual try-on image to the user.

[0608] 5. Purchase Proposal

[0609] The server checks the user's existing clothing information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects the missing clothing. The server then calls the API of the online store it is connected to and generates a purchase link for the missing clothing. The device then displays the generated purchase link along with the purchase suggestion to the user.

[0610] Specific examples

[0611] For example, suppose a user inputs the following prompt sentence into a generative AI model:

[0612] Example prompt:

[0613] "I'm a 175cm tall, 70kg man in my 30s who likes a casual style. I own a navy blazer, a white shirt, and sneakers. It's raining today and I have a meeting at the office. What would be the best outfit for me?"

[0614] This allows the generative AI model to suggest the best outfits, output images for virtual try-on, and provide links to purchase the desired items.

[0615] The above is an embodiment of the present invention. This system allows users to receive suggestions for optimal clothing based on their body type, preferences, age, weather, and schedule, check the clothes through virtual try-on, and easily purchase any missing clothing from the online store.

[0616] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0617] Step 1:

[0618] The user launches the application and enters their profile information (body type, preferences, age, and data on clothing they already own). The device receives this information, formats it into a database, and sends it to the server. In the process of formatting it into a database, numerical information such as height and weight and categorical information such as preferences and age are mapped to the appropriate fields. Input data: User profile information. Output data: User information in database format.

[0619] Step 2:

[0620] The user enters the weather information and schedule for the day into the application. The device receives the weather information and schedule information and sends it to the server. Weather information can also be obtained from an external weather API, and schedule information can be entered manually by the user. Input data: weather information, schedule information. Output data: weather information and schedule information sent to the server.

[0621] Step 3:

[0622] The server integrates the received user information, weather information, and schedule information. Based on this integrated data, it uses a generative AI model to run an AI algorithm and generate optimal outfit candidates. The generative AI model selects the outfit that best suits the user profile based on the input data. Input data: Integrated data (user information, weather information, schedule information). Output data: Data on optimal outfit candidates.

[0623] Step 4:

[0624] The server creates image data of the generated outfit candidates and sends it to the terminal. The image data is created in a format that the user can visually confirm. The terminal displays the received outfit image data to the user. Input data: optimal outfit candidate data. Output data: outfit image displayed on the terminal.

[0625] Step 5:

[0626] The user checks the displayed clothing image and uploads a photo of themselves to virtually try on the clothing. The device sends the uploaded photo to the server. Input data: User's photo. Output data: User's photo sent to the server.

[0627] Step 6:

[0628] The server combines the user's photo with the generated clothing image to create a virtual try-on image. Image processing software is used for this combination, and the clothing is positioned to fit the user's body shape. Input data: User's photo, clothing image data. Output data: Virtual try-on image data.

[0629] Step 7:

[0630] The server sends the image of the virtual try-on to the terminal, and the terminal displays the image to the user. Input data: Image data of the virtual try-on. Output data: The image of the virtual try-on displayed on the terminal.

[0631] Step 8:

[0632] The server checks the user's existing clothing information and identifies any missing clothing. The server then calls the API of the linked online store and generates a purchase link for the missing clothing. Input data: User's existing clothing information, weather information, and schedule information. Output data: Purchase link.

[0633] Step 9:

[0634] The terminal displays the generated purchase link along with the purchase suggestion to the user. Input data: Purchase link. Output data: Purchase link and purchase suggestion displayed to the user.

[0635] This allows users to receive suggestions for the best outfits based on their body type, preferences, age, weather, and schedule, and they can check them out through virtual try-ons, and easily purchase any clothes they are missing from the online store.

[0636] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0637] ---

[0638] The present invention describes a system that uses user information, weather information, schedule information, and an emotion engine to suggest optimal clothing, perform virtual try-on, and suggest purchasing missing clothing.

[0639] Program processing overview

[0640] Input of user information and emotions

[0641] 1. The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters height 175 cm, weight 70 kg, preference for casual clothing, and age 30s.

[0642] 2. The terminal receives this information, formats it into a database, and sends it to the server.

[0643] 3. The user inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice. For example, emotions are detected as "joy" or "stress."

[0644] Weather and schedule input

[0645] 4. The user enters the weather information and schedule for the day into the application. For example, the user enters "rain" for the weather and "a meeting at the office" for the schedule.

[0646] 5. The device receives the weather information and schedule information and sends it to the server.

[0647] Best clothing suggestions

[0648] 6. The server integrates the received user information, weather information, schedule information, and emotion information, providing the basic data for suggesting optimal clothing.

[0649] 7. The server runs an AI algorithm based on the integrated data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select light-colored clothing that evokes a positive impression. Specifically, it will select a navy blazer, a white shirt, and dark denim.

[0650] 8. The server creates image data of the generated clothing candidates and sends it to the terminal. This data includes visually verifiable images of the clothing.

[0651] Virtual try-on

[0652] 9. The user checks the displayed clothing image and uploads a photo of themselves to virtually try on the clothing. For example, they can select and upload a photo of their front view.

[0653] 10. The device receives the uploaded user photo and sends it to the server.

[0654] 11. The server combines the user's photo with the generated clothing image to create a virtual try-on image, simulating how the user would actually look wearing the clothing.

[0655] 12. The terminal receives the virtual try-on image and displays it to the user.

[0656] Purchase suggestion

[0657] 13. The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, it will detect that they are missing.

[0658] 14. The server searches for the missing clothing items through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[0659] 15. The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[0660] ---

[0661] This concludes the detailed embodiment. This system allows users to receive suggestions for optimal clothing tailored to their body type, preferences, age, weather, schedule, and emotions, and allows them to check the clothes through virtual try-on and purchase any missing clothing from an online store.

[0662] The processing flow will be explained below.

[0663] ---

[0664] Step 1:

[0665] A user starts the application and enters their profile information (body type, preferences, age). For example, they enter their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[0666] Step 2:

[0667] The device receives the user's profile information, converts it into a database format, and sends it to the server. The data sent includes details such as "height: 175 cm, weight: 70 kg, preference: casual, age: 30s."

[0668] Step 3:

[0669] A user inputs the weather information and schedule information for the day into the application. For example, the user inputs "rain" for the weather and "a meeting at the office" for the schedule.

[0670] Step 4:

[0671] The device receives the weather and schedule information entered, converts it into a database format, and sends it to the server. The data sent includes information such as "Weather: Rain, Schedule: Meeting at the office."

[0672] Step 5:

[0673] The user can input their emotional state for the day, or an emotion engine linked to the device can detect emotions from the user's facial expressions and voice. For example, emotions can be detected as "joy" or "stress."

[0674] Step 6:

[0675] The device converts the emotional information detected by the emotion engine into a database format and sends it to the server. The data sent includes information such as "Emotion: Joy."

[0676] Step 7:

[0677] The server integrates the received user information, weather information, schedule information, and emotion information, providing the basic data for suggesting optimal outfits.

[0678] Step 8:

[0679] The server runs an AI algorithm based on the integrated data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select light-colored clothing that evokes a positive impression. Specifically, it will select a navy blazer, a white shirt, and dark denim.

[0680] Step 9:

[0681] The server creates image data of the clothing candidates and sends it to the terminal. This data includes visually verifiable images of the clothing.

[0682] Step 10:

[0683] The terminal displays the received clothing image data to the user, allowing the user to check the clothing details.

[0684] Step 11:

[0685] The user checks the displayed clothing image and uploads a photo of himself / herself to virtually try on the clothing. For example, he / she selects and uploads a photo of his / her front view.

[0686] Step 12:

[0687] The device receives the uploaded user photos and sends them to the server. The data sent includes the user's photo data.

[0688] Step 13:

[0689] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image, simulating how the user would actually look wearing the clothing.

[0690] Step 14:

[0691] The terminal receives the image of the virtual try-on and displays it to the user.

[0692] Step 15:

[0693] The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects the lack of such clothing.

[0694] Step 16:

[0695] The server searches for the missing clothing through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[0696] Step 17:

[0697] The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[0698] ---

[0699] The above is the processing flow of the program divided into detailed steps.

[0700] Example 2

[0701] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0702] Conventional clothing suggestion systems can suggest optimal outfits by taking into account a user's body type, preferences, age, weather information, and schedule information, but they cannot suggest outfits that reflect the user's emotional state. Furthermore, they lack the functionality to simulate how the user would actually look wearing the outfit during virtual try-on. Furthermore, there was no seamless system that could identify the clothes a user was missing and quickly purchase them from an online store. These challenges made it difficult for users to select the best outfits that would satisfy them.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0704] In this invention, the server includes a means for integrating user information, weather information, schedule information, and emotional state to suggest optimal outfits, a means for uploading a user's photo for virtual try-on and combining it with the outfit suggestions, and a means for identifying missing clothes and generating a purchase link via the API of a linked online store. This allows the user to receive optimal outfit suggestions that take emotional state into consideration, check them in the virtual try-on, and seamlessly purchase the missing clothes.

[0705] ---

[0706] "User Information" refers to personal data such as a user's profile information, body type information, preferences, age, etc.

[0707] "Weather information" refers to data about the weather on that day, such as sunny, rainy, or snowy weather conditions.

[0708] "Schedule information" refers to data about the actions and events that the user has scheduled for that day.

[0709] "Emotional state" is information that indicates the user's current emotions, such as joy, sadness, stress, etc.

[0710] "Outfit candidates" refer to clothing options suitable for a user that are generated based on integrated user information, weather information, schedule information, and emotional state.

[0711] "Virtual try-on" refers to a technology that combines a user's photo with potential outfits to simulate how the user would actually look wearing those outfits.

[0712] "Uploading" refers to the act of a user sending data such as their own photos to a server via their device.

[0713] "Synthesis" refers to the act of combining a user's photo with image data of potential clothing items to generate a new image.

[0714] "Missing clothing" refers to clothing that is not in the user's closet but is necessary for the suggested outfit.

[0715] "Online store" refers to an e-commerce site that sells clothing and other products over the Internet.

[0716] "API" refers to a programming interface for exchanging functions and data between a terminal and a server or other external system.

[0717] "Purchase Link" refers to data containing a URL for direct access to the purchase page for a specific product.

[0718] This invention is a system that uses user information, weather information, schedule information, and an emotion engine to suggest optimal outfits, allows virtual fitting, and suggests purchasing clothes that are missing. This system is realized through cooperation between users, terminals, and a server.

[0719] 1. Input of user information and emotions

[0720] First, the user launches the application and enters their profile information, which includes the user's body type (e.g., height 175cm, weight 70kg), preferences (e.g., casual clothing), age (e.g., 30s), etc. The device receives the information entered by the user, formats it into a database, and then sends it to the server.

[0721] The user may input their emotional state for the day, or the emotion engine may detect emotions from the user's facial expressions and voice. If the user has an emotion such as "joy" or "stress," that information is sent to the server via the device.

[0722] 2. Weather and schedule input

[0723] Next, the user inputs the weather information for the day (e.g., rain) and schedule information (e.g., a meeting at the office) into the application. This information is also received by the device, formatted into a database, and sent to the server.

[0724] 3. Data integration and clothing candidate generation

[0725] The server integrates user information, weather information, schedule information, and emotional information. This creates the basic data for suggesting optimal outfits. The server uses this integrated data to generate optimal outfit suggestions using a generative AI model. For example, if the user's emotional state is "joy," the server selects bright-colored clothing that conveys a positive impression. In this case, it suggests a navy blazer, a white shirt, and dark denim.

[0726] 4. Displaying clothing options and virtually trying them on

[0727] Once the outfit candidates are generated, the server creates image data of them and sends it to the terminal. The terminal displays the received outfit candidate images to the user. The user then uploads a photo of themselves to virtually try on the outfits. The uploaded photo is sent from the terminal to the server, which then combines the user's photo with the outfit candidate image. Once this virtual try-on image is created, it is displayed to the user via the terminal.

[0728] 5. Identifying missing clothing and making purchasing suggestions

[0729] Finally, the server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects this. The server searches for the missing clothing through the API of a partner online store and generates a purchase link. This link provides direct access to the purchase page and is displayed to the user via their device. This allows the user to seamlessly purchase the missing clothing.

[0730] Specific examples

[0731] For example, suppose a user inputs that they are 175 cm tall, weigh 70 kg, prefer casual clothing, and are in their 30s. If the user also inputs their emotional state as "joy," the weather information as "rain," and their schedule information as "office meeting," the server will suggest a navy blazer, a white shirt, and dark jeans as the best outfit. Based on this image, a virtual try-on is performed, and if the user does not own the dark jeans, a purchase link to the online store is generated and displayed to the user.

[0732] Prompt Sentence Examples

[0733] "Suggest the best outfit for a user whose height is 175cm, weight is 70kg, preference is casual, age is 30s, emotional state is joy, weather is rainy, and scheduled appointment is an office meeting."

[0734] The above is a specific embodiment for carrying out the present invention. This system allows users to receive optimal clothing suggestions that take their emotional state into consideration, check them through virtual try-ons, and quickly purchase any clothes they are missing.

[0735] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0736] Step 1: Enter your user information

[0737] Input: The user launches the application and enters profile information (body type, preferences, age, etc.).

[0738] Specific operation: The user enters his height as 175cm, weight as 70kg, preference as casual, and age as 30s.

[0739] Data processing and calculation: The terminal receives this information and formats it into a database.

[0740] Output: The formatted data is sent to the server.

[0741] Step 2: Input or detect emotional state

[0742] Input: The user manually inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice.

[0743] Specific operation: The user manually inputs the emotional state as "joy", or the emotion engine detects "joy" through the camera and microphone.

[0744] Data processing and calculation: The device formats the detected emotional information into a database.

[0745] Output: Formatted emotion data is sent to the server.

[0746] Step 3: Enter weather and schedule information

[0747] Input: The user enters today's weather and schedule information into the application.

[0748] Specific actions: Enter the weather information "Rain" and the schedule information "Meeting at the office."

[0749] Data processing and calculation: The device converts the weather and schedule information entered into a database format.

[0750] Output: Formatted weather and schedule information is sent to the server.

[0751] Step 4: Data integration and clothing candidate generation

[0752] Input: The server receives user information, emotional state, weather information, and schedule information.

[0753] How it works: The server aggregates all the information and uses a generative AI model to generate optimal outfit suggestions.

[0754] Data processing and calculation: The server inputs the integrated data into the generative AI model to generate optimal outfit suggestions. For example, if the emotion is "joy" and the weather is "rainy," a navy blazer, white shirt, and dark denim, which convey a positive impression, will be selected.

[0755] Output: Image data of the generated outfit candidates is generated.

[0756] Step 5: Viewing clothing suggestions

[0757] Input: The server sends image data of the generated outfit candidates.

[0758] Specific operation: The server sends image data of the clothing candidates generated to the terminal.

[0759] Data processing and calculation: The image data received by the terminal is processed to be displayed on the user interface.

[0760] Output: The user visually checks the images of the outfit candidates.

[0761] Step 6: Upload a photo for the virtual try-on

[0762] Input: A user uploads their photo to the application.

[0763] What it does: The user selects and uploads a photo of themselves taken from the front.

[0764] Data processing and calculation: The device sends the uploaded photo data to the server.

[0765] Output: User photo data sent to the server.

[0766] Step 7: Perform a virtual try-on

[0767] Input: The server receives the user's photo data and image data of clothing candidates.

[0768] How it works: The server synthesizes these data and creates an image of the virtual try-on.

[0769] Data processing and calculation: The server uses image processing technology to synthesize the user's photo with the clothing candidate image.

[0770] Output: Image data of the created virtual try-on.

[0771] Step 8: View the virtual try-on

[0772] Input: The server sends the image data of the created virtual try-on.

[0773] Specific operation: The server sends image data of the virtual try-on to the terminal.

[0774] Data processing and calculation: The terminal processes the image data of the virtual try-on to display it on the user interface.

[0775] Output: User sees the virtual try-on image.

[0776] Step 9: Identify missing clothing

[0777] Input: The server looks up the user's closet information.

[0778] What happens: The server checks the closet information in the database to identify the missing clothes.

[0779] Data processing and calculation: The server compares the clothes required for the suggested outfits with the closet information and identifies any missing clothes.

[0780] Output: A list of identified missing clothes.

[0781] Step 10: Generate a purchase link

[0782] Input: The server sends the missing clothing information to the online store's API.

[0783] What it does: The server uses the API to search for the missing clothing and generate a purchase link.

[0784] Data processing and calculation: The server searches for the missing clothes through the online store's API and obtains the corresponding purchase link.

[0785] Output: The generated purchase link.

[0786] Step 11: View purchase offers

[0787] Input: The server sends the generated purchase link to the device.

[0788] Specific operation: The server sends the generated purchase link to the device, which displays the link in its user interface.

[0789] Data processing and calculation: The terminal processes the purchase link to display it on the user interface.

[0790] Output: User can see the purchase link and easily purchase the missing clothes.

[0791] These are the processing steps of this system, which allows users to receive recommendations for optimal clothing based on their body type, preferences, age, emotional state, weather, and schedule, check them out through virtual try-ons, and quickly purchase any clothes they are missing.

[0792] (Application example 2)

[0793] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0794] Conventional clothing suggestion systems have difficulty responding flexibly based on a user's individual emotions and detailed schedule information, resulting in clothing suggestions that are often not optimal for the user. Furthermore, even with virtual try-on functions, it is difficult to provide a visual experience that is significantly different from a real try-on, which reduces user satisfaction. Furthermore, there is a need for a system that allows users to easily purchase clothing that they are running low on without any hassle.

[0795] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0796] In this invention, the server includes means for inputting user information and emotions, means for inputting weather information and schedule, means for suggesting optimal clothing based on user information, emotion information, weather information, and schedule information, means for virtual try-on, and means for identifying missing clothing and generating a purchase link from a linked online store. This makes it possible to suggest optimal clothing based on the user's detailed emotional state and individual schedule, providing a more realistic virtual try-on experience and enabling the user to easily purchase missing clothing.

[0797] "User information" is information about personal characteristics such as the user's body type, preferences, and age.

[0798] "Emotion information" is information that indicates the user's current emotional state, and is either detected by the emotion engine or directly input by the user.

[0799] "Weather information" is information about the weather conditions of the day, including temperature, precipitation, wind speed, and the like.

[0800] "Schedule information" is information about the activities and behaviors that the user plans for that day.

[0801] The "means for suggesting optimal clothing" is a function or algorithm that integrates user information, emotional information, weather information, and schedule information to select and suggest optimal clothing for the user.

[0802] "Means for virtual try-on" refers to a function or system that allows a user to virtually try on selected clothing, and includes a function that uses image processing technology to combine a photograph of the user with an image of the clothing.

[0803] The "means for identifying missing clothes" is a function that refers to the user's closet information and detects clothes among the suggested outfits that the user does not own.

[0804] The "means of generating purchase links" is a function that uses the online store's API to generate links to purchase pages for missing clothing.

[0805] The present invention describes a system that suggests optimal clothing based on user information, emotion information, weather information, and schedule information, and offers virtual try-on and purchase suggestions for clothing that is in short supply.

[0806] Input of user information and emotions

[0807] First, the user launches the application and enters their profile information (body type, preferences, age). For example, they might enter height 175cm, weight 70kg, preference casual, and age 30s. The device receives this information, formats it into a database, and sends it to the server. The user can then enter their emotional state for the day, or the emotion engine can detect emotions from the user's facial expressions and voice. For example, emotions might be detected as "joy" or "stress."

[0808] Weather and schedule input

[0809] Next, the user inputs the weather information and schedule for the day into the application. For example, the weather is "rain" and the schedule is "meeting at the office." The device receives the weather information and schedule information and sends it to the server.

[0810] Best clothing suggestions

[0811] The server integrates the received user information, emotional information, weather information, and schedule information. This provides the basic data for suggesting optimal outfits. The server then runs a generative AI model based on the integrated data to generate optimal outfit candidates. For example, if the emotional state is "joy," it selects light-colored clothing that conveys a positive impression. Specifically, it selects a navy blazer, white shirt, and dark denim. The server then creates image data of the generated outfit candidates and sends it to the device. This includes a visually verifiable image of the outfit.

[0812] Virtual try-on

[0813] The user checks the displayed clothing image and uploads a photo of themselves to perform a virtual try-on. For example, they can select and upload a photo of their front view. The device receives the uploaded user photo and sends it to the server. The server combines the user's photo with the generated clothing image to create a virtual try-on image. This simulates how the user would look if they were actually wearing the clothing. The device receives the virtual try-on image and displays it to the user.

[0814] Purchase suggestion

[0815] The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, it detects this. The server searches for the missing clothing through the API of the connected online store and generates a purchase link. The generated link includes direct access to the purchase page. The device displays the generated purchase link to the user along with a purchase suggestion, making it easy for the user to purchase the missing clothing.

[0816] Hardware and software used

[0817] Hardware: Smartphone, Head-Mounted Display (HMD)

[0818] Software: Python, Keras, OpenCV, Requests

[0819] Specific examples

[0820] For example, assume that User A is a man in his 30s who is 175 cm tall, weighs 70 kg, and likes casual clothing. It is raining that day, and he has a scheduled office meeting. If the emotion engine detects his emotional state as "joy," the server will suggest a navy blazer, a white shirt, and dark jeans. User A can visually check these outfits by combining them with his own photo using the virtual try-on function. Furthermore, if User A does not own dark jeans based on his closet information, an appropriate purchase link will be generated and provided to User A.

[0821] Prompt Sentence Examples

[0822] Examples of prompts include:

[0823] Generate a Python program to suggest clothes that the user likes, allow them to try them on virtually, and provide a purchase link. The AI ​​algorithm will select the most suitable outfit based on the user's profile information, the weather for the day, and their schedule. Synthesize the outfit into a virtual try-on image, and generate a purchase link for any missing items via the online store's API.

[0824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0825] Step 1:

[0826] The user starts the application and enters their profile information (body type, preferences, age). The entered information is converted into a database format by the device and sent to the server. Specifically, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s. The server then receives data that reflects the user's body type and preferences.

[0827] Step 2:

[0828] The user inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice. The input emotion information (e.g., "joy" or "stress") is acquired by the device and sent to the server. This information is analyzed by the emotion engine and provided to the server as data that accurately reflects the user's current emotions.

[0829] Step 3:

[0830] The user inputs the weather information and plans for the day into the application. For example, the weather is "rain" and the plan is "meeting at the office." The device converts this information into a database format and sends it to the server. The server then obtains data on the weather conditions for the day and the user's planned activities.

[0831] Step 4:

[0832] The server combines the received user information, emotional information, weather information, and schedule information to form the basic data for suggesting optimal outfits. It then runs a generative AI model based on the combined data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select bright-colored clothes that convey a positive impression.

[0833] Step 5:

[0834] The server creates image data of the generated outfit candidates and sends it to the terminal, which then provides the user with visually identifiable image data of the outfit. For example, an outfit image of a navy blazer, a white shirt, and dark denim is displayed.

[0835] Step 6:

[0836] The user checks the displayed clothing image and uploads their own photo to virtually try on the clothing. The device receives the uploaded user photo and sends it to the server, which then obtains the data needed to synthesize the user's photo with the generated clothing image.

[0837] Step 7:

[0838] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image. The generated virtual try-on image is sent to the terminal and displayed to the user, allowing the user to simulate how the clothing would actually look when worn.

[0839] Step 8:

[0840] The server references the user's closet information and identifies the missing clothing. For example, if the user does not own any dark denim, the server identifies the missing clothing. After identifying the missing clothing, the server searches the API of the relevant online store and generates a purchase link.

[0841] Step 9:

[0842] The generated purchase link is sent to the device and displayed to the user. The user can use the provided purchase link to easily purchase the missing clothing. For example, by searching for "dark denim," the purchase link is generated and displayed to the user, allowing the user to purchase the item directly.

[0843] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0844] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0846] [Third embodiment]

[0847] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0848] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0849] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0850] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0851] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0852] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0853] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0854] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0855] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0856] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0857] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0858] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0859] ---

[0860] The present invention describes a system that suggests optimal clothing based on user information, weather information, and schedule information, and performs virtual try-on and suggests purchasing missing clothing.

[0861] Program processing overview

[0862] Entering user information

[0863] 1. The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[0864] 2. The terminal receives this information, formats it into a database, and sends it to the server.

[0865] Weather and schedule input

[0866] 3. The user enters the weather information and schedule for the day into the application. For example, the user enters that the weather is rainy and the schedule is an office meeting.

[0867] 4. The device receives the weather information and schedule information and sends it to the server.

[0868] Best clothing suggestions

[0869] 5. The server integrates the received user information, weather information, and schedule information.

[0870] 6. The server runs an AI algorithm on the integrated data to generate the best possible outfit, such as a navy blazer, a white shirt, and dark denim.

[0871] 7. The server creates image data of the generated clothing candidates and sends it to the terminal.

[0872] 8. The terminal displays the received image data of the clothing to the user.

[0873] Virtual try-on

[0874] 9. The user reviews the displayed outfit image and uploads a photo of themselves to virtually try on the outfit.

[0875] 10. The device sends the uploaded photos to the server.

[0876] 11. The server combines the user's photo with the generated clothing image to create a virtual try-on image.

[0877] 12. The device displays the virtual try-on image to the user.

[0878] Purchase suggestion

[0879] 13. The server checks the user's existing clothing information and identifies any missing clothing. For example, if the user does not own any dark denim, it will detect that this is missing.

[0880] 14. The server calls the API of the online store it is connected to and generates a purchase link for the missing clothing.

[0881] 15. The terminal displays the generated purchase link along with the purchase offer to the user.

[0882] ---

[0883] This completes the description of the embodiment of the present invention. With this, the user can receive suggestions for optimal clothing that matches their body type, preferences, age, weather, and schedule, check them out through virtual try-ons, and purchase any missing clothing from an online store.

[0884] The processing flow will be explained below.

[0885] ---

[0886] Step 1:

[0887] A user starts the application and enters their profile information (body type, preferences, age). For example, they enter their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[0888] Step 2:

[0889] The device receives the user's profile information, converts it into a database format, and sends it to the server. The data sent includes details such as "height: 175 cm, weight: 70 kg, preference: casual, age: 30s."

[0890] Step 3:

[0891] A user inputs the weather information and schedule for the day into the application. For example, the weather is "rainy" and the schedule is "a meeting at the office."

[0892] Step 4:

[0893] The device receives the weather and schedule information entered, converts it into a database format, and sends it to the server. The data sent includes information such as "Weather: Rain, Schedule: Meeting at the office."

[0894] Step 5:

[0895] The server integrates the received user information, weather information, and schedule information, providing the basic data for suggesting optimal clothing.

[0896] Step 6:

[0897] The server runs an AI algorithm on the integrated data to generate the best possible outfit, such as a navy blazer, white shirt, and dark denim.

[0898] Step 7:

[0899] The server creates image data of the clothing candidates and sends it to the terminal. This data includes images of the clothing that can be visually confirmed.

[0900] Step 8:

[0901] The terminal displays the received clothing image data to the user, allowing the user to check the clothing details.

[0902] Step 9:

[0903] The user checks the displayed clothing image and uploads a photo of himself / herself to virtually try on the clothing. For example, he / she selects and uploads a photo of his / her front view.

[0904] Step 10:

[0905] The device receives the uploaded user photos and sends them to the server. The data sent includes the user's photo data.

[0906] Step 11:

[0907] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image. This synthesis simulates how the user would actually look wearing the clothing.

[0908] Step 12:

[0909] The terminal receives the image of the virtual try-on and displays it to the user, allowing the user to check the results of the virtual try-on.

[0910] Step 13:

[0911] The server references the user's closet information and identifies any missing clothing items. For example, it determines that the user does not own any dark denim.

[0912] Step 14:

[0913] The server searches for the missing clothing through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[0914] Step 15:

[0915] The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[0916] ---

[0917] The above is the processing flow of the program divided into detailed steps.

[0918] Example 1

[0919] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0920] Conventional clothing recommendation systems were unable to make recommendations that fully took into account the user's individual attributes or daily changing information (weather, schedules), and did not integrate virtual try-on, identification of missing clothing, and purchase recommendations. This meant that users had to spend time checking whether the suggested clothing suited them, which was inconvenient.

[0921] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0922] In this invention, the server includes means for inputting user information, means for inputting weather information and schedule information, means for using a generative AI model to generate optimal outfit candidates based on the user information, weather information, and schedule information, means for displaying the generated outfit candidates, means for uploading a user's photo for virtual try-on, means for combining the user's photo with the outfit candidates, means for displaying virtual try-on images, and means for identifying missing clothing and generating purchase links from linked online stores. This allows the user to easily receive suggestions for optimal outfits tailored to their body type, preferences, age, weather, and schedule, check them through virtual try-on, and smoothly purchase the missing clothing.

[0923] "User information" refers to individual information such as the user's body type, preferences, age, etc.

[0924] "Weather information" refers to information about the weather for that day.

[0925] "Schedule information" refers to information about activities and events that the user has scheduled for that day.

[0926] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal outfit suggestions based on user information, weather information, and schedule information.

[0927] "Outfit candidates" refers to the set of optimal outfits suggested by the generative AI model.

[0928] "Virtual try-on" refers to a function that allows users to virtually try on clothes by combining a photo of themselves with suggested outfits.

[0929] "Online Store" refers to an e-commerce platform that allows you to purchase products over the Internet.

[0930] "Purchase Link" refers to a hyperlink to purchase the missing clothing from an online store.

[0931] "Server" refers to a centralized system for processing data and communicating with other devices.

[0932] "Terminal" refers to a device that is directly operated by a user (e.g., a smartphone or tablet).

[0933] The present invention will be described with reference to a system for suggesting optimal clothing based on user information, weather information, and schedule information, and for offering virtual try-on and purchasing suggestions for clothing that is in short supply. Specific embodiments will be described below.

[0934] Hardware and software used

[0935] Hardware:

[0936] Server: Use cloud servers such as Google Cloud Platform or Amazon Web Services.

[0937] Device: Smartphones include iPhones, Android devices, and tablets.

[0938] software:

[0939] AI algorithms: TensorFlow and PyTorch are used to provide outfit suggestions.

[0940] Database: MySQL and PostgreSQL are used to manage user information, weather information, and clothing information.

[0941] Front-end app: Build an application using React Native or Flutter that allows users to enter information and view results.

[0942] Overall system flow

[0943] 1. Entering user information: The user starts the application and enters personal information such as height, weight, preferences, and age. This allows the system to understand the user's body type and fashion preferences.

[0944] 2. Weather and schedule input: The user inputs the weather and schedule for the day. Based on this information, the system prepares to suggest appropriate clothing for the day.

[0945] 3. Optimal clothing suggestions: The server uses the generative AI model to generate optimal clothing suggestions based on the received user information, weather information, and schedule information. The generated clothing suggestions are sent to the user's device and displayed.

[0946] 4. Virtual Try-On: When a user uploads a photo of themselves to the application, the server combines this photo with the generated clothing image to create a virtual try-on image, which is also sent to the device and displayed to the user.

[0947] 5. Purchase Suggestion: The server compares the user's existing clothing information with the suggested clothing options and identifies any missing items. For any missing items, the server calls the API of the connected online store and generates a purchase link. This link is sent to the device and displayed to the user.

[0948] Specific examples

[0949] For example, a male user in his 30s, 175 cm tall, weighing 70 kg, with a casual style of clothing, enters into the application that he has a meeting in the office on a rainy day. The server uses a generative AI model to suggest a "navy blazer," a "white shirt," and "dark denim." When the user uploads a photo of himself and performs a virtual try-on, the server synthesizes this photo with the suggested outfit and provides an image of the virtual try-on. If the user does not own the "dark denim," the server generates a purchase link from a linked online store and displays it to the user.

[0950] Prompt Sentence Examples

[0951] "Please suggest some casual office meeting outfits for men in their 30s that can be worn on rainy days, and provide a link to purchase the outfits if they are not already available."

[0952] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0953] Step 1:

[0954] Entering user information

[0955] The user starts the application and enters personal information such as height, weight, fashion preferences, and age. The entered data is converted into a database format on the device and sent to the server. The input data includes information such as "height 175 cm," "weight 70 kg," "favorite clothing is casual," and "age 30s."

[0956] Step 2:

[0957] Weather and schedule input

[0958] The user inputs the weather forecast and schedule for the day. For example, "It's raining" and "I have a meeting at the office." The device formats this data and sends it to the server. The input data includes the weather conditions and specific schedule information.

[0959] Step 3:

[0960] Data integration

[0961] The server integrates the received user information, weather information, and schedule information. The result of this data integration becomes a reference data set for generating optimal outfit candidates using a generative AI model. The integrated data links information on each item (e.g., user information, weather, schedule) along a time axis and relationships.

[0962] Step 4:

[0963] Generating clothing candidates

[0964] The server runs a generative AI model based on the integrated data to generate optimal outfit suggestions. The input data includes integrated user information, weather information, and schedule information, and the output data includes outfit suggestions such as "navy blazer," "white shirt," and "dark denim." The generative AI model analyzes the data and selects the most suitable outfit.

[0965] Step 5:

[0966] Sending outfit suggestions

[0967] The server creates image data of the generated outfit candidates and sends it to the terminal. The input data includes the outfit candidates, and the output data includes a visual image of the outfit. This image data is used by the user to confirm what the outfit will look like.

[0968] Step 6:

[0969] Display of clothing images

[0970] The terminal displays the received clothing image data to the user. The input data includes the clothing image sent from the server, and the output is the displayed clothing image. This allows the user to visually confirm the proposed clothing.

[0971] Step 7:

[0972] Upload a photo

[0973] The user uploads a photo of themselves to perform a virtual try-on. The input data includes a full-body photo of the user, which the device sends to the server. The user must prepare a photo of themselves in the correct posture and from the front to reflect their actual appearance.

[0974] Step 8:

[0975] Generation of virtual try-on images

[0976] The server combines the received user photo with the generated clothing image to generate a virtual try-on image. The input data includes the user's photo and clothing image, and the output data includes a composite image of the virtual try-on. This process allows the user to virtually try on the clothes.

[0977] Step 9:

[0978] Displaying virtual try-on images

[0979] The terminal displays the virtual try-on image to the user. The input data includes the virtual try-on image sent from the server, and the output is the virtual try-on image displayed to the user. The user can check this and decide whether the proposed outfit suits them or not.

[0980] Step 10:

[0981] Checking existing clothing information

[0982] The server checks the user's existing clothing information stored in the database. The input data includes the user's existing clothing information, and the output data includes the check result. This step is performed to identify the clothing the user already owns.

[0983] Step 11:

[0984] Identifying missing clothing

[0985] The server compares the proposed outfit with existing outfit information to identify any missing items. For example, if the user does not own "dark denim," it will detect that the item is missing. The input data includes the proposed outfit information and existing outfit information, and the output data includes a list of missing items.

[0986] Step 12:

[0987] Generate purchase links

[0988] The server calls the API of the connected online store and generates a purchase link for the missing clothes. The input data includes a list of missing items, and the output data includes the purchase link. The link to the appropriate purchase page is automatically generated via the online store's API.

[0989] Step 13:

[0990] View purchase suggestions

[0991] The device displays the generated purchase link to the user. The input data includes the purchase link sent from the server, and the output is a purchase suggestion displayed to the user. The user can easily purchase the missing clothes by clicking this link.

[0992] (Application example 1)

[0993] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0994] Conventional systems have struggled to automate suggestions tailored to individual body types and preferences when users select appropriate clothing. They also lacked the ability to suggest clothing that takes into account weather and schedule information, or to facilitate purchasing decisions through virtual try-ons. Furthermore, their ability to identify missing clothing and provide appropriate purchasing links was also lacking.

[0995] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0996] In this invention, the server includes means for inputting user information, means for inputting weather information and schedule, means for suggesting optimal outfits based on the user information, weather information, and schedule information, means for virtual try-on that combines the user's photo with the suggested outfits, and means for identifying missing outfits and generating purchase links from linked online stores. This allows the user to not only receive optimal outfit suggestions based on their individual information, but also to check the outfits in the virtual try-on and easily purchase missing items.

[0997] "User information" refers to personal information such as the user's body type, preferences, age, etc.

[0998] "Weather Information" means information regarding current and forecast weather.

[0999] "Schedule information" refers to information about activities and events that a user has scheduled for that day.

[1000] "Means for suggesting optimal clothing" refers to a means for using an AI algorithm to select clothing that suits the user based on user information, weather information, and schedule information.

[1001] The "means for virtual try-on" refers to a means for combining a user's photograph with the suggested clothing and simulating the virtual try-on state.

[1002] The "means for identifying missing clothing" refers to a means for comparing information about clothing that the user already owns with the suggested clothing and identifying missing items.

[1003] "Means for generating purchase links from online stores" refers to means for generating purchase links for the identified missing clothing through the online store's API.

[1004] The present invention describes a system that suggests optimal clothing based on user information, weather information, and schedule information, and that offers virtual try-on and purchase suggestions for clothing that is in short supply. Specific embodiments are described below.

[1005] System configuration

[1006] This system mainly consists of a user's device (smartphone, tablet, etc.) and a server.

[1007] Hardware and Software

[1008] Hardware: smartphones, tablets, servers

[1009] Software: Mobile applications, databases, AI algorithms, image processing software

[1010] Program processing

[1011] 1. Enter your user information

[1012] The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s. The device receives this information, formats it into a database, and sends it to the server.

[1013] 2. Enter weather and schedule information

[1014] The user inputs the weather information and schedule for the day into the application. For example, the weather is rainy and the schedule is a meeting at the office. The device receives the weather information and schedule information and sends it to the server.

[1015] 3. Recommendations for the best outfit

[1016] The server integrates the received user information, weather information, and schedule information. Based on the integrated data, the server runs an AI algorithm using a generative AI model to generate optimal outfit candidates. For example, it might suggest a navy blazer, white shirt, and dark denim. It then creates image data of the generated outfit candidates and sends them to the device. The device then displays the received outfit image data to the user.

[1017] 4. Virtual try-on

[1018] The user checks the displayed clothing image and uploads their own photo to perform a virtual try-on. The device sends the uploaded photo to the server. The server combines the user's photo with the generated clothing image to create a virtual try-on image. The device displays the virtual try-on image to the user.

[1019] 5. Purchase Proposal

[1020] The server checks the user's existing clothing information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects the missing clothing. The server then calls the API of the online store it is connected to and generates a purchase link for the missing clothing. The device then displays the generated purchase link along with the purchase suggestion to the user.

[1021] Specific examples

[1022] For example, suppose a user inputs the following prompt sentence into a generative AI model:

[1023] Example prompt:

[1024] "I'm a 175cm tall, 70kg man in my 30s who likes a casual style. I own a navy blazer, a white shirt, and sneakers. It's raining today and I have a meeting at the office. What would be the best outfit for me?"

[1025] This allows the generative AI model to suggest the best outfits, output images for virtual try-on, and provide links to purchase the desired items.

[1026] The above is an embodiment of the present invention. This system allows users to receive suggestions for optimal clothing based on their body type, preferences, age, weather, and schedule, check the clothes through virtual try-on, and easily purchase any missing clothing from the online store.

[1027] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1028] Step 1:

[1029] The user launches the application and enters their profile information (body type, preferences, age, and data on clothing they already own). The device receives this information, formats it into a database, and sends it to the server. In the process of formatting it into a database, numerical information such as height and weight and categorical information such as preferences and age are mapped to the appropriate fields. Input data: User profile information. Output data: User information in database format.

[1030] Step 2:

[1031] The user enters the weather information and schedule for the day into the application. The device receives the weather information and schedule information and sends it to the server. Weather information can also be obtained from an external weather API, and schedule information can be entered manually by the user. Input data: weather information, schedule information. Output data: weather information and schedule information sent to the server.

[1032] Step 3:

[1033] The server integrates the received user information, weather information, and schedule information. Based on this integrated data, it uses a generative AI model to run an AI algorithm and generate optimal outfit candidates. The generative AI model selects the outfit that best suits the user profile based on the input data. Input data: Integrated data (user information, weather information, schedule information). Output data: Data on optimal outfit candidates.

[1034] Step 4:

[1035] The server creates image data of the generated outfit candidates and sends it to the terminal. The image data is created in a format that the user can visually confirm. The terminal displays the received outfit image data to the user. Input data: optimal outfit candidate data. Output data: outfit image displayed on the terminal.

[1036] Step 5:

[1037] The user checks the displayed clothing image and uploads a photo of themselves to virtually try on the clothing. The device sends the uploaded photo to the server. Input data: User's photo. Output data: User's photo sent to the server.

[1038] Step 6:

[1039] The server combines the user's photo with the generated clothing image to create a virtual try-on image. Image processing software is used for this combination, and the clothing is positioned to fit the user's body shape. Input data: User's photo, clothing image data. Output data: Virtual try-on image data.

[1040] Step 7:

[1041] The server sends the image of the virtual try-on to the terminal, and the terminal displays the image to the user. Input data: Image data of the virtual try-on. Output data: The image of the virtual try-on displayed on the terminal.

[1042] Step 8:

[1043] The server checks the user's existing clothing information and identifies any missing clothing. The server then calls the API of the linked online store and generates a purchase link for the missing clothing. Input data: User's existing clothing information, weather information, and schedule information. Output data: Purchase link.

[1044] Step 9:

[1045] The terminal displays the generated purchase link along with the purchase suggestion to the user. Input data: Purchase link. Output data: Purchase link and purchase suggestion displayed to the user.

[1046] This allows users to receive suggestions for the best outfits based on their body type, preferences, age, weather, and schedule, and they can check them out through virtual try-ons, and easily purchase any clothes they are missing from the online store.

[1047] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1048] ---

[1049] The present invention describes a system that uses user information, weather information, schedule information, and an emotion engine to suggest optimal clothing, perform virtual try-on, and suggest purchasing missing clothing.

[1050] Program processing overview

[1051] Input of user information and emotions

[1052] 1. The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters height 175 cm, weight 70 kg, preference for casual clothing, and age 30s.

[1053] 2. The terminal receives this information, formats it into a database, and sends it to the server.

[1054] 3. The user inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice. For example, emotions are detected as "joy" or "stress."

[1055] Weather and schedule input

[1056] 4. The user enters the weather information and schedule for the day into the application. For example, the user enters "rain" for the weather and "a meeting at the office" for the schedule.

[1057] 5. The device receives the weather information and schedule information and sends it to the server.

[1058] Best clothing suggestions

[1059] 6. The server integrates the received user information, weather information, schedule information, and emotion information, providing the basic data for suggesting optimal clothing.

[1060] 7. The server runs an AI algorithm based on the integrated data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select light-colored clothing that evokes a positive impression. Specifically, it will select a navy blazer, a white shirt, and dark denim.

[1061] 8. The server creates image data of the generated clothing candidates and sends it to the terminal. This data includes visually verifiable images of the clothing.

[1062] Virtual try-on

[1063] 9. The user checks the displayed clothing image and uploads a photo of themselves to virtually try on the clothing. For example, they can select and upload a photo of their front view.

[1064] 10. The device receives the uploaded user photo and sends it to the server.

[1065] 11. The server combines the user's photo with the generated clothing image to create a virtual try-on image, simulating how the user would actually look wearing the clothing.

[1066] 12. The terminal receives the virtual try-on image and displays it to the user.

[1067] Purchase suggestion

[1068] 13. The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, it will detect that they are missing.

[1069] 14. The server searches for the missing clothing items through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[1070] 15. The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[1071] ---

[1072] This concludes the detailed embodiment. This system allows users to receive suggestions for optimal clothing tailored to their body type, preferences, age, weather, schedule, and emotions, and allows them to check the clothes through virtual try-on and purchase any missing clothing from an online store.

[1073] The processing flow will be explained below.

[1074] ---

[1075] Step 1:

[1076] A user starts the application and enters their profile information (body type, preferences, age). For example, they enter their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[1077] Step 2:

[1078] The device receives the user's profile information, converts it into a database format, and sends it to the server. The data sent includes details such as "height: 175 cm, weight: 70 kg, preference: casual, age: 30s."

[1079] Step 3:

[1080] A user inputs the weather information and schedule information for the day into the application. For example, the user inputs "rain" for the weather and "a meeting at the office" for the schedule.

[1081] Step 4:

[1082] The device receives the weather and schedule information entered, converts it into a database format, and sends it to the server. The data sent includes information such as "Weather: Rain, Schedule: Meeting at the office."

[1083] Step 5:

[1084] The user can input their emotional state for the day, or an emotion engine linked to the device can detect emotions from the user's facial expressions and voice. For example, emotions can be detected as "joy" or "stress."

[1085] Step 6:

[1086] The device converts the emotional information detected by the emotion engine into a database format and sends it to the server. The data sent includes information such as "Emotion: Joy."

[1087] Step 7:

[1088] The server integrates the received user information, weather information, schedule information, and emotion information, providing the basic data for suggesting optimal outfits.

[1089] Step 8:

[1090] The server runs an AI algorithm based on the integrated data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select light-colored clothing that evokes a positive impression. Specifically, it will select a navy blazer, a white shirt, and dark denim.

[1091] Step 9:

[1092] The server creates image data of the clothing candidates and sends it to the terminal. This data includes visually verifiable images of the clothing.

[1093] Step 10:

[1094] The terminal displays the received clothing image data to the user, allowing the user to check the clothing details.

[1095] Step 11:

[1096] The user checks the displayed clothing image and uploads a photo of himself / herself to virtually try on the clothing. For example, he / she selects and uploads a photo of his / her front view.

[1097] Step 12:

[1098] The device receives the uploaded user photos and sends them to the server. The data sent includes the user's photo data.

[1099] Step 13:

[1100] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image, simulating how the user would actually look wearing the clothing.

[1101] Step 14:

[1102] The terminal receives the image of the virtual try-on and displays it to the user.

[1103] Step 15:

[1104] The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects the lack of such clothing.

[1105] Step 16:

[1106] The server searches for the missing clothing through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[1107] Step 17:

[1108] The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[1109] ---

[1110] The above is the processing flow of the program divided into detailed steps.

[1111] Example 2

[1112] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1113] Conventional clothing suggestion systems can suggest optimal outfits by taking into account a user's body type, preferences, age, weather information, and schedule information, but they cannot suggest outfits that reflect the user's emotional state. Furthermore, they lack the functionality to simulate how the user would actually look wearing the outfit during virtual try-on. Furthermore, there was no seamless system that could identify the clothes a user was missing and quickly purchase them from an online store. These challenges made it difficult for users to select the best outfits that would satisfy them.

[1114] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1115] In this invention, the server includes a means for integrating user information, weather information, schedule information, and emotional state to suggest optimal outfits, a means for uploading a user's photo for virtual try-on and combining it with the outfit suggestions, and a means for identifying missing clothes and generating a purchase link via the API of a linked online store. This allows the user to receive optimal outfit suggestions that take emotional state into consideration, check them in the virtual try-on, and seamlessly purchase the missing clothes.

[1116] ---

[1117] "User Information" refers to personal data such as a user's profile information, body type information, preferences, age, etc.

[1118] "Weather information" refers to data about the weather on that day, such as sunny, rainy, or snowy weather conditions.

[1119] "Schedule information" refers to data about the actions and events that the user has scheduled for that day.

[1120] "Emotional state" is information that indicates the user's current emotions, such as joy, sadness, stress, etc.

[1121] "Outfit candidates" refer to clothing options suitable for a user that are generated based on integrated user information, weather information, schedule information, and emotional state.

[1122] "Virtual try-on" refers to a technology that combines a user's photo with potential outfits to simulate how the user would actually look wearing those outfits.

[1123] "Uploading" refers to the act of a user sending data such as their own photos to a server via their device.

[1124] "Synthesis" refers to the act of combining a user's photo with image data of potential clothing items to generate a new image.

[1125] "Missing clothing" refers to clothing that is not in the user's closet but is necessary for the suggested outfit.

[1126] "Online store" refers to an e-commerce site that sells clothing and other products over the Internet.

[1127] "API" refers to a programming interface for exchanging functions and data between a terminal and a server or other external system.

[1128] "Purchase Link" refers to data containing a URL for direct access to the purchase page for a specific product.

[1129] This invention is a system that uses user information, weather information, schedule information, and an emotion engine to suggest optimal outfits, allows virtual fitting, and suggests purchasing clothes that are missing. This system is realized through cooperation between users, terminals, and a server.

[1130] 1. Input of user information and emotions

[1131] First, the user launches the application and enters their profile information, which includes the user's body type (e.g., height 175cm, weight 70kg), preferences (e.g., casual clothing), age (e.g., 30s), etc. The device receives the information entered by the user, formats it into a database, and then sends it to the server.

[1132] The user may input their emotional state for the day, or the emotion engine may detect emotions from the user's facial expressions and voice. If the user has an emotion such as "joy" or "stress," that information is sent to the server via the device.

[1133] 2. Weather and schedule input

[1134] Next, the user inputs the weather information for the day (e.g., rain) and schedule information (e.g., a meeting at the office) into the application. This information is also received by the device, formatted into a database, and sent to the server.

[1135] 3. Data integration and clothing candidate generation

[1136] The server integrates user information, weather information, schedule information, and emotional information. This creates the basic data for suggesting optimal outfits. The server uses this integrated data to generate optimal outfit suggestions using a generative AI model. For example, if the user's emotional state is "joy," the server selects bright-colored clothing that conveys a positive impression. In this case, it suggests a navy blazer, a white shirt, and dark denim.

[1137] 4. Displaying clothing options and virtually trying them on

[1138] Once the outfit candidates are generated, the server creates image data of them and sends it to the terminal. The terminal displays the received outfit candidate images to the user. The user then uploads a photo of themselves to virtually try on the outfits. The uploaded photo is sent from the terminal to the server, which then combines the user's photo with the outfit candidate image. Once this virtual try-on image is created, it is displayed to the user via the terminal.

[1139] 5. Identifying missing clothing and making purchasing suggestions

[1140] Finally, the server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects this. The server searches for the missing clothing through the API of a partner online store and generates a purchase link. This link provides direct access to the purchase page and is displayed to the user via their device. This allows the user to seamlessly purchase the missing clothing.

[1141] Specific examples

[1142] For example, suppose a user inputs that they are 175 cm tall, weigh 70 kg, prefer casual clothing, and are in their 30s. If the user also inputs their emotional state as "joy," the weather information as "rain," and their schedule information as "office meeting," the server will suggest a navy blazer, a white shirt, and dark jeans as the best outfit. Based on this image, a virtual try-on is performed, and if the user does not own the dark jeans, a purchase link to the online store is generated and displayed to the user.

[1143] Prompt Sentence Examples

[1144] "Suggest the best outfit for a user whose height is 175cm, weight is 70kg, preference is casual, age is 30s, emotional state is joy, weather is rainy, and scheduled appointment is an office meeting."

[1145] The above is a specific embodiment for carrying out the present invention. This system allows users to receive optimal clothing suggestions that take their emotional state into consideration, check them through virtual try-ons, and quickly purchase any clothes they are missing.

[1146] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1147] Step 1: Enter your user information

[1148] Input: The user launches the application and enters profile information (body type, preferences, age, etc.).

[1149] Specific operation: The user enters his height as 175cm, weight as 70kg, preference as casual, and age as 30s.

[1150] Data processing and calculation: The terminal receives this information and formats it into a database.

[1151] Output: The formatted data is sent to the server.

[1152] Step 2: Input or detect emotional state

[1153] Input: The user manually inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice.

[1154] Specific operation: The user manually inputs the emotional state as "joy", or the emotion engine detects "joy" through the camera and microphone.

[1155] Data processing and calculation: The device formats the detected emotional information into a database.

[1156] Output: Formatted emotion data is sent to the server.

[1157] Step 3: Enter weather and schedule information

[1158] Input: The user enters today's weather and schedule information into the application.

[1159] Specific actions: Enter the weather information "Rain" and the schedule information "Meeting at the office."

[1160] Data processing and calculation: The device converts the weather and schedule information entered into a database format.

[1161] Output: Formatted weather and schedule information is sent to the server.

[1162] Step 4: Data integration and clothing candidate generation

[1163] Input: The server receives user information, emotional state, weather information, and schedule information.

[1164] How it works: The server aggregates all the information and uses a generative AI model to generate optimal outfit suggestions.

[1165] Data processing and calculation: The server inputs the integrated data into the generative AI model to generate optimal outfit suggestions. For example, if the emotion is "joy" and the weather is "rainy," a navy blazer, white shirt, and dark denim, which convey a positive impression, will be selected.

[1166] Output: Image data of the generated outfit candidates is generated.

[1167] Step 5: Viewing clothing suggestions

[1168] Input: The server sends image data of the generated outfit candidates.

[1169] Specific operation: The server sends image data of the clothing candidates generated to the terminal.

[1170] Data processing and calculation: The image data received by the terminal is processed to be displayed on the user interface.

[1171] Output: The user visually checks the images of the outfit candidates.

[1172] Step 6: Upload a photo for the virtual try-on

[1173] Input: A user uploads their photo to the application.

[1174] What it does: The user selects and uploads a photo of themselves taken from the front.

[1175] Data processing and calculation: The device sends the uploaded photo data to the server.

[1176] Output: User photo data sent to the server.

[1177] Step 7: Perform a virtual try-on

[1178] Input: The server receives the user's photo data and image data of clothing candidates.

[1179] How it works: The server synthesizes these data and creates an image of the virtual try-on.

[1180] Data processing and calculation: The server uses image processing technology to synthesize the user's photo with the clothing candidate image.

[1181] Output: Image data of the created virtual try-on.

[1182] Step 8: View the virtual try-on

[1183] Input: The server sends the image data of the created virtual try-on.

[1184] Specific operation: The server sends image data of the virtual try-on to the terminal.

[1185] Data processing and calculation: The terminal processes the image data of the virtual try-on to display it on the user interface.

[1186] Output: User sees the virtual try-on image.

[1187] Step 9: Identify missing clothing

[1188] Input: The server looks up the user's closet information.

[1189] What happens: The server checks the closet information in the database to identify the missing clothes.

[1190] Data processing and calculation: The server compares the clothes required for the suggested outfits with the closet information and identifies any missing clothes.

[1191] Output: A list of identified missing clothes.

[1192] Step 10: Generate a purchase link

[1193] Input: The server sends the missing clothing information to the online store's API.

[1194] What it does: The server uses the API to search for the missing clothing and generate a purchase link.

[1195] Data processing and calculation: The server searches for the missing clothes through the online store's API and obtains the corresponding purchase link.

[1196] Output: The generated purchase link.

[1197] Step 11: View purchase offers

[1198] Input: The server sends the generated purchase link to the device.

[1199] Specific operation: The server sends the generated purchase link to the device, which displays the link in its user interface.

[1200] Data processing and calculation: The terminal processes the purchase link to display it on the user interface.

[1201] Output: User can see the purchase link and easily purchase the missing clothes.

[1202] These are the processing steps of this system, which allows users to receive recommendations for optimal clothing based on their body type, preferences, age, emotional state, weather, and schedule, check them out through virtual try-ons, and quickly purchase any clothes they are missing.

[1203] (Application example 2)

[1204] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1205] Conventional clothing suggestion systems have difficulty responding flexibly based on a user's individual emotions and detailed schedule information, resulting in clothing suggestions that are often not optimal for the user. Furthermore, even with virtual try-on functions, it is difficult to provide a visual experience that is significantly different from a real try-on, which reduces user satisfaction. Furthermore, there is a need for a system that allows users to easily purchase clothing that they are running low on without any hassle.

[1206] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1207] In this invention, the server includes means for inputting user information and emotions, means for inputting weather information and schedule, means for suggesting optimal clothing based on user information, emotion information, weather information, and schedule information, means for virtual try-on, and means for identifying missing clothing and generating a purchase link from a linked online store. This makes it possible to suggest optimal clothing based on the user's detailed emotional state and individual schedule, providing a more realistic virtual try-on experience and enabling the user to easily purchase missing clothing.

[1208] "User information" is information about personal characteristics such as the user's body type, preferences, and age.

[1209] "Emotion information" is information that indicates the user's current emotional state, and is either detected by the emotion engine or directly input by the user.

[1210] "Weather information" is information about the weather conditions of the day, including temperature, precipitation, wind speed, and the like.

[1211] "Schedule information" is information about the activities and behaviors that the user plans for that day.

[1212] The "means for suggesting optimal clothing" is a function or algorithm that integrates user information, emotional information, weather information, and schedule information to select and suggest optimal clothing for the user.

[1213] "Means for virtual try-on" refers to a function or system that allows a user to virtually try on selected clothing, and includes a function that uses image processing technology to combine a photograph of the user with an image of the clothing.

[1214] The "means for identifying missing clothes" is a function that refers to the user's closet information and detects clothes among the suggested outfits that the user does not own.

[1215] The "means of generating purchase links" is a function that uses the online store's API to generate links to purchase pages for missing clothing.

[1216] The present invention describes a system that suggests optimal clothing based on user information, emotion information, weather information, and schedule information, and offers virtual try-on and purchase suggestions for clothing that is in short supply.

[1217] Input of user information and emotions

[1218] First, the user launches the application and enters their profile information (body type, preferences, age). For example, they might enter height 175cm, weight 70kg, preference casual, and age 30s. The device receives this information, formats it into a database, and sends it to the server. The user can then enter their emotional state for the day, or the emotion engine can detect emotions from the user's facial expressions and voice. For example, emotions might be detected as "joy" or "stress."

[1219] Weather and schedule input

[1220] Next, the user inputs the weather information and schedule for the day into the application. For example, the weather is "rain" and the schedule is "meeting at the office." The device receives the weather information and schedule information and sends it to the server.

[1221] Best clothing suggestions

[1222] The server integrates the received user information, emotional information, weather information, and schedule information. This provides the basic data for suggesting optimal outfits. The server then runs a generative AI model based on the integrated data to generate optimal outfit candidates. For example, if the emotional state is "joy," it selects light-colored clothing that conveys a positive impression. Specifically, it selects a navy blazer, white shirt, and dark denim. The server then creates image data of the generated outfit candidates and sends it to the device. This includes a visually verifiable image of the outfit.

[1223] Virtual try-on

[1224] The user checks the displayed clothing image and uploads a photo of themselves to perform a virtual try-on. For example, they can select and upload a photo of their front view. The device receives the uploaded user photo and sends it to the server. The server combines the user's photo with the generated clothing image to create a virtual try-on image. This simulates how the user would look if they were actually wearing the clothing. The device receives the virtual try-on image and displays it to the user.

[1225] Purchase suggestion

[1226] The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, it detects this. The server searches for the missing clothing through the API of the connected online store and generates a purchase link. The generated link includes direct access to the purchase page. The device displays the generated purchase link to the user along with a purchase suggestion, making it easy for the user to purchase the missing clothing.

[1227] Hardware and software used

[1228] Hardware: Smartphone, Head-Mounted Display (HMD)

[1229] Software: Python, Keras, OpenCV, Requests

[1230] Specific examples

[1231] For example, assume that User A is a man in his 30s who is 175 cm tall, weighs 70 kg, and likes casual clothing. It is raining that day, and he has a scheduled office meeting. If the emotion engine detects his emotional state as "joy," the server will suggest a navy blazer, a white shirt, and dark jeans. User A can visually check these outfits by combining them with his own photo using the virtual try-on function. Furthermore, if User A does not own dark jeans based on his closet information, an appropriate purchase link will be generated and provided to User A.

[1232] Prompt Sentence Examples

[1233] Examples of prompts include:

[1234] Generate a Python program to suggest clothes that the user likes, allow them to try them on virtually, and provide a purchase link. The AI ​​algorithm will select the most suitable outfit based on the user's profile information, the weather for the day, and their schedule. Synthesize the outfit into a virtual try-on image, and generate a purchase link for any missing items via the online store's API.

[1235] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1236] Step 1:

[1237] The user starts the application and enters their profile information (body type, preferences, age). The entered information is converted into a database format by the device and sent to the server. Specifically, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s. The server then receives data that reflects the user's body type and preferences.

[1238] Step 2:

[1239] The user inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice. The input emotion information (e.g., "joy" or "stress") is acquired by the device and sent to the server. This information is analyzed by the emotion engine and provided to the server as data that accurately reflects the user's current emotions.

[1240] Step 3:

[1241] The user inputs the weather information and plans for the day into the application. For example, the weather is "rain" and the plan is "meeting at the office." The device converts this information into a database format and sends it to the server. The server then obtains data on the weather conditions for the day and the user's planned activities.

[1242] Step 4:

[1243] The server combines the received user information, emotional information, weather information, and schedule information to form the basic data for suggesting optimal outfits. It then runs a generative AI model based on the combined data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select bright-colored clothes that convey a positive impression.

[1244] Step 5:

[1245] The server creates image data of the generated outfit candidates and sends it to the terminal, which then provides the user with visually identifiable image data of the outfit. For example, an outfit image of a navy blazer, a white shirt, and dark denim is displayed.

[1246] Step 6:

[1247] The user checks the displayed clothing image and uploads their own photo to virtually try on the clothing. The device receives the uploaded user photo and sends it to the server, which then obtains the data needed to synthesize the user's photo with the generated clothing image.

[1248] Step 7:

[1249] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image. The generated virtual try-on image is sent to the terminal and displayed to the user, allowing the user to simulate how the clothing would actually look when worn.

[1250] Step 8:

[1251] The server references the user's closet information and identifies the missing clothing. For example, if the user does not own any dark denim, the server identifies the missing clothing. After identifying the missing clothing, the server searches the API of the relevant online store and generates a purchase link.

[1252] Step 9:

[1253] The generated purchase link is sent to the device and displayed to the user. The user can use the provided purchase link to easily purchase the missing clothing. For example, by searching for "dark denim," the purchase link is generated and displayed to the user, allowing the user to purchase the item directly.

[1254] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1255] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1256] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1257] [Fourth embodiment]

[1258] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1259] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1260] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1261] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1262] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1263] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1264] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1265] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1266] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1267] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1268] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1269] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1270] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1271] ---

[1272] The present invention describes a system that suggests optimal clothing based on user information, weather information, and schedule information, and performs virtual try-on and suggests purchasing missing clothing.

[1273] Program processing overview

[1274] Entering user information

[1275] 1. The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[1276] 2. The terminal receives this information, formats it into a database, and sends it to the server.

[1277] Weather and schedule input

[1278] 3. The user enters the weather information and schedule for the day into the application. For example, the user enters that the weather is rainy and the schedule is an office meeting.

[1279] 4. The device receives the weather information and schedule information and sends it to the server.

[1280] Best clothing suggestions

[1281] 5. The server integrates the received user information, weather information, and schedule information.

[1282] 6. The server runs an AI algorithm on the integrated data to generate the best possible outfit, such as a navy blazer, a white shirt, and dark denim.

[1283] 7. The server creates image data of the generated clothing candidates and sends it to the terminal.

[1284] 8. The terminal displays the received image data of the clothing to the user.

[1285] Virtual try-on

[1286] 9. The user reviews the displayed outfit image and uploads a photo of themselves to virtually try on the outfit.

[1287] 10. The device sends the uploaded photos to the server.

[1288] 11. The server combines the user's photo with the generated clothing image to create a virtual try-on image.

[1289] 12. The device displays the virtual try-on image to the user.

[1290] Purchase suggestion

[1291] 13. The server checks the user's existing clothing information and identifies any missing clothing. For example, if the user does not own any dark denim, it will detect that this is missing.

[1292] 14. The server calls the API of the online store it is connected to and generates a purchase link for the missing clothing.

[1293] 15. The terminal displays the generated purchase link along with the purchase offer to the user.

[1294] ---

[1295] This completes the description of the embodiment of the present invention. With this, the user can receive suggestions for optimal clothing that matches their body type, preferences, age, weather, and schedule, check them out through virtual try-ons, and purchase any missing clothing from an online store.

[1296] The processing flow will be explained below.

[1297] ---

[1298] Step 1:

[1299] A user starts the application and enters their profile information (body type, preferences, age). For example, they enter their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[1300] Step 2:

[1301] The device receives the user's profile information, converts it into a database format, and sends it to the server. The data sent includes details such as "height: 175 cm, weight: 70 kg, preference: casual, age: 30s."

[1302] Step 3:

[1303] A user inputs the weather information and schedule for the day into the application. For example, the weather is "rainy" and the schedule is "a meeting at the office."

[1304] Step 4:

[1305] The device receives the weather and schedule information entered, converts it into a database format, and sends it to the server. The data sent includes information such as "Weather: Rain, Schedule: Meeting at the office."

[1306] Step 5:

[1307] The server integrates the received user information, weather information, and schedule information, providing the basic data for suggesting optimal clothing.

[1308] Step 6:

[1309] The server runs an AI algorithm on the integrated data to generate the best possible outfit, such as a navy blazer, white shirt, and dark denim.

[1310] Step 7:

[1311] The server creates image data of the clothing candidates and sends it to the terminal. This data includes images of the clothing that can be visually confirmed.

[1312] Step 8:

[1313] The terminal displays the received clothing image data to the user, allowing the user to check the clothing details.

[1314] Step 9:

[1315] The user checks the displayed clothing image and uploads a photo of himself / herself to virtually try on the clothing. For example, he / she selects and uploads a photo of his / her front view.

[1316] Step 10:

[1317] The device receives the uploaded user photos and sends them to the server. The data sent includes the user's photo data.

[1318] Step 11:

[1319] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image. This synthesis simulates how the user would actually look wearing the clothing.

[1320] Step 12:

[1321] The terminal receives the image of the virtual try-on and displays it to the user, allowing the user to check the results of the virtual try-on.

[1322] Step 13:

[1323] The server references the user's closet information and identifies any missing clothing items. For example, it determines that the user does not own any dark denim.

[1324] Step 14:

[1325] The server searches for the missing clothing through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[1326] Step 15:

[1327] The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[1328] ---

[1329] The above is the processing flow of the program divided into detailed steps.

[1330] Example 1

[1331] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1332] Conventional clothing recommendation systems were unable to make recommendations that fully took into account the user's individual attributes or daily changing information (weather, schedules), and did not integrate virtual try-on, identification of missing clothing, and purchase recommendations. This meant that users had to spend time checking whether the suggested clothing suited them, which was inconvenient.

[1333] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1334] In this invention, the server includes means for inputting user information, means for inputting weather information and schedule information, means for using a generative AI model to generate optimal outfit candidates based on the user information, weather information, and schedule information, means for displaying the generated outfit candidates, means for uploading a user's photo for virtual try-on, means for combining the user's photo with the outfit candidates, means for displaying virtual try-on images, and means for identifying missing clothing and generating purchase links from linked online stores. This allows the user to easily receive suggestions for optimal outfits tailored to their body type, preferences, age, weather, and schedule, check them through virtual try-on, and smoothly purchase the missing clothing.

[1335] "User information" refers to individual information such as the user's body type, preferences, age, etc.

[1336] "Weather information" refers to information about the weather for that day.

[1337] "Schedule information" refers to information about activities and events that the user has scheduled for that day.

[1338] A "generative AI model" refers to an artificial intelligence algorithm that generates optimal outfit suggestions based on user information, weather information, and schedule information.

[1339] "Outfit candidates" refers to the set of optimal outfits suggested by the generative AI model.

[1340] "Virtual try-on" refers to a function that allows users to virtually try on clothes by combining a photo of themselves with suggested outfits.

[1341] "Online Store" refers to an e-commerce platform that allows you to purchase products over the Internet.

[1342] "Purchase Link" refers to a hyperlink to purchase the missing clothing from an online store.

[1343] "Server" refers to a centralized system for processing data and communicating with other devices.

[1344] "Terminal" refers to a device that is directly operated by a user (e.g., a smartphone or tablet).

[1345] The present invention will be described with reference to a system for suggesting optimal clothing based on user information, weather information, and schedule information, and for offering virtual try-on and purchasing suggestions for clothing that is in short supply. Specific embodiments will be described below.

[1346] Hardware and software used

[1347] Hardware:

[1348] Server: Use cloud servers such as Google Cloud Platform or Amazon Web Services.

[1349] Device: Smartphones include iPhones, Android devices, and tablets.

[1350] software:

[1351] AI algorithms: TensorFlow and PyTorch are used to provide outfit suggestions.

[1352] Database: MySQL and PostgreSQL are used to manage user information, weather information, and clothing information.

[1353] Front-end app: Build an application using React Native or Flutter that allows users to enter information and view results.

[1354] Overall system flow

[1355] 1. Entering user information: The user starts the application and enters personal information such as height, weight, preferences, and age. This allows the system to understand the user's body type and fashion preferences.

[1356] 2. Weather and schedule input: The user inputs the weather and schedule for the day. Based on this information, the system prepares to suggest appropriate clothing for the day.

[1357] 3. Optimal clothing suggestions: The server uses the generative AI model to generate optimal clothing suggestions based on the received user information, weather information, and schedule information. The generated clothing suggestions are sent to the user's device and displayed.

[1358] 4. Virtual Try-On: When a user uploads a photo of themselves to the application, the server combines this photo with the generated clothing image to create a virtual try-on image, which is also sent to the device and displayed to the user.

[1359] 5. Purchase Suggestion: The server compares the user's existing clothing information with the suggested clothing options and identifies any missing items. For any missing items, the server calls the API of the connected online store and generates a purchase link. This link is sent to the device and displayed to the user.

[1360] Specific examples

[1361] For example, a male user in his 30s, 175 cm tall, weighing 70 kg, with a casual style of clothing, enters into the application that he has a meeting in the office on a rainy day. The server uses a generative AI model to suggest a "navy blazer," a "white shirt," and "dark denim." When the user uploads a photo of himself and performs a virtual try-on, the server synthesizes this photo with the suggested outfit and provides an image of the virtual try-on. If the user does not own the "dark denim," the server generates a purchase link from a linked online store and displays it to the user.

[1362] Prompt Sentence Examples

[1363] "Please suggest some casual office meeting outfits for men in their 30s that can be worn on rainy days, and provide a link to purchase the outfits if they are not already available."

[1364] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1365] Step 1:

[1366] Entering user information

[1367] The user starts the application and enters personal information such as height, weight, fashion preferences, and age. The entered data is converted into a database format on the device and sent to the server. The input data includes information such as "height 175 cm," "weight 70 kg," "favorite clothing is casual," and "age 30s."

[1368] Step 2:

[1369] Weather and schedule input

[1370] The user inputs the weather forecast and schedule for the day. For example, "It's raining" and "I have a meeting at the office." The device formats this data and sends it to the server. The input data includes the weather conditions and specific schedule information.

[1371] Step 3:

[1372] Data integration

[1373] The server integrates the received user information, weather information, and schedule information. The result of this data integration becomes a reference data set for generating optimal outfit candidates using a generative AI model. The integrated data links information on each item (e.g., user information, weather, schedule) along a time axis and relationships.

[1374] Step 4:

[1375] Generating clothing candidates

[1376] The server runs a generative AI model based on the integrated data to generate optimal outfit suggestions. The input data includes integrated user information, weather information, and schedule information, and the output data includes outfit suggestions such as "navy blazer," "white shirt," and "dark denim." The generative AI model analyzes the data and selects the most suitable outfit.

[1377] Step 5:

[1378] Sending outfit suggestions

[1379] The server creates image data of the generated outfit candidates and sends it to the terminal. The input data includes the outfit candidates, and the output data includes a visual image of the outfit. This image data is used by the user to confirm what the outfit will look like.

[1380] Step 6:

[1381] Display of clothing images

[1382] The terminal displays the received clothing image data to the user. The input data includes the clothing image sent from the server, and the output is the displayed clothing image. This allows the user to visually confirm the proposed clothing.

[1383] Step 7:

[1384] Upload a photo

[1385] The user uploads a photo of themselves to perform a virtual try-on. The input data includes a full-body photo of the user, which the device sends to the server. The user must prepare a photo of themselves in the correct posture and from the front to reflect their actual appearance.

[1386] Step 8:

[1387] Generation of virtual try-on images

[1388] The server combines the received user photo with the generated clothing image to generate a virtual try-on image. The input data includes the user's photo and clothing image, and the output data includes a composite image of the virtual try-on. This process allows the user to virtually try on the clothes.

[1389] Step 9:

[1390] Displaying virtual try-on images

[1391] The terminal displays the virtual try-on image to the user. The input data includes the virtual try-on image sent from the server, and the output is the virtual try-on image displayed to the user. The user can check this and decide whether the proposed outfit suits them or not.

[1392] Step 10:

[1393] Checking existing clothing information

[1394] The server checks the user's existing clothing information stored in the database. The input data includes the user's existing clothing information, and the output data includes the check result. This step is performed to identify the clothing the user already owns.

[1395] Step 11:

[1396] Identifying missing clothing

[1397] The server compares the proposed outfit with existing outfit information to identify any missing items. For example, if the user does not own "dark denim," it will detect that the item is missing. The input data includes the proposed outfit information and existing outfit information, and the output data includes a list of missing items.

[1398] Step 12:

[1399] Generate purchase links

[1400] The server calls the API of the connected online store and generates a purchase link for the missing clothes. The input data includes a list of missing items, and the output data includes the purchase link. The link to the appropriate purchase page is automatically generated via the online store's API.

[1401] Step 13:

[1402] View purchase suggestions

[1403] The device displays the generated purchase link to the user. The input data includes the purchase link sent from the server, and the output is a purchase suggestion displayed to the user. The user can easily purchase the missing clothes by clicking this link.

[1404] (Application example 1)

[1405] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1406] Conventional systems have struggled to automate suggestions tailored to individual body types and preferences when users select appropriate clothing. They also lacked the ability to suggest clothing that takes into account weather and schedule information, or to facilitate purchasing decisions through virtual try-ons. Furthermore, their ability to identify missing clothing and provide appropriate purchasing links was also lacking.

[1407] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1408] In this invention, the server includes means for inputting user information, means for inputting weather information and schedule, means for suggesting optimal outfits based on the user information, weather information, and schedule information, means for virtual try-on that combines the user's photo with the suggested outfits, and means for identifying missing outfits and generating purchase links from linked online stores. This allows the user to not only receive optimal outfit suggestions based on their individual information, but also to check the outfits in the virtual try-on and easily purchase missing items.

[1409] "User information" refers to personal information such as the user's body type, preferences, age, etc.

[1410] "Weather Information" means information regarding current and forecast weather.

[1411] "Schedule information" refers to information about activities and events that a user has scheduled for that day.

[1412] "Means for suggesting optimal clothing" refers to a means for using an AI algorithm to select clothing that suits the user based on user information, weather information, and schedule information.

[1413] The "means for virtual try-on" refers to a means for combining a user's photograph with the suggested clothing and simulating the virtual try-on state.

[1414] The "means for identifying missing clothing" refers to a means for comparing information about clothing that the user already owns with the suggested clothing and identifying missing items.

[1415] "Means for generating purchase links from online stores" refers to means for generating purchase links for the identified missing clothing through the online store's API.

[1416] The present invention describes a system that suggests optimal clothing based on user information, weather information, and schedule information, and that offers virtual try-on and purchase suggestions for clothing that is in short supply. Specific embodiments are described below.

[1417] System configuration

[1418] This system mainly consists of a user's device (smartphone, tablet, etc.) and a server.

[1419] Hardware and Software

[1420] Hardware: smartphones, tablets, servers

[1421] Software: Mobile applications, databases, AI algorithms, image processing software

[1422] Program processing

[1423] 1. Enter your user information

[1424] The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s. The device receives this information, formats it into a database, and sends it to the server.

[1425] 2. Enter weather and schedule information

[1426] The user inputs the weather information and schedule for the day into the application. For example, the weather is rainy and the schedule is a meeting at the office. The device receives the weather information and schedule information and sends it to the server.

[1427] 3. Recommendations for the best outfit

[1428] The server integrates the received user information, weather information, and schedule information. Based on the integrated data, the server runs an AI algorithm using a generative AI model to generate optimal outfit candidates. For example, it might suggest a navy blazer, white shirt, and dark denim. It then creates image data of the generated outfit candidates and sends them to the device. The device then displays the received outfit image data to the user.

[1429] 4. Virtual try-on

[1430] The user checks the displayed clothing image and uploads their own photo to perform a virtual try-on. The device sends the uploaded photo to the server. The server combines the user's photo with the generated clothing image to create a virtual try-on image. The device displays the virtual try-on image to the user.

[1431] 5. Purchase Proposal

[1432] The server checks the user's existing clothing information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects the missing clothing. The server then calls the API of the online store it is connected to and generates a purchase link for the missing clothing. The device then displays the generated purchase link along with the purchase suggestion to the user.

[1433] Specific examples

[1434] For example, suppose a user inputs the following prompt sentence into a generative AI model:

[1435] Example prompt:

[1436] "I'm a 175cm tall, 70kg man in my 30s who likes a casual style. I own a navy blazer, a white shirt, and sneakers. It's raining today and I have a meeting at the office. What would be the best outfit for me?"

[1437] This allows the generative AI model to suggest the best outfits, output images for virtual try-on, and provide links to purchase the desired items.

[1438] The above is an embodiment of the present invention. This system allows users to receive suggestions for optimal clothing based on their body type, preferences, age, weather, and schedule, check the clothes through virtual try-on, and easily purchase any missing clothing from the online store.

[1439] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1440] Step 1:

[1441] The user launches the application and enters their profile information (body type, preferences, age, and data on clothing they already own). The device receives this information, formats it into a database, and sends it to the server. In the process of formatting it into a database, numerical information such as height and weight and categorical information such as preferences and age are mapped to the appropriate fields. Input data: User profile information. Output data: User information in database format.

[1442] Step 2:

[1443] The user enters the weather information and schedule for the day into the application. The device receives the weather information and schedule information and sends it to the server. Weather information can also be obtained from an external weather API, and schedule information can be entered manually by the user. Input data: weather information, schedule information. Output data: weather information and schedule information sent to the server.

[1444] Step 3:

[1445] The server integrates the received user information, weather information, and schedule information. Based on this integrated data, it uses a generative AI model to run an AI algorithm and generate optimal outfit candidates. The generative AI model selects the outfit that best suits the user profile based on the input data. Input data: Integrated data (user information, weather information, schedule information). Output data: Data on optimal outfit candidates.

[1446] Step 4:

[1447] The server creates image data of the generated outfit candidates and sends it to the terminal. The image data is created in a format that the user can visually confirm. The terminal displays the received outfit image data to the user. Input data: optimal outfit candidate data. Output data: outfit image displayed on the terminal.

[1448] Step 5:

[1449] The user checks the displayed clothing image and uploads a photo of themselves to virtually try on the clothing. The device sends the uploaded photo to the server. Input data: User's photo. Output data: User's photo sent to the server.

[1450] Step 6:

[1451] The server combines the user's photo with the generated clothing image to create a virtual try-on image. Image processing software is used for this combination, and the clothing is positioned to fit the user's body shape. Input data: User's photo, clothing image data. Output data: Virtual try-on image data.

[1452] Step 7:

[1453] The server sends the image of the virtual try-on to the terminal, and the terminal displays the image to the user. Input data: Image data of the virtual try-on. Output data: The image of the virtual try-on displayed on the terminal.

[1454] Step 8:

[1455] The server checks the user's existing clothing information and identifies any missing clothing. The server then calls the API of the linked online store and generates a purchase link for the missing clothing. Input data: User's existing clothing information, weather information, and schedule information. Output data: Purchase link.

[1456] Step 9:

[1457] The terminal displays the generated purchase link along with the purchase suggestion to the user. Input data: Purchase link. Output data: Purchase link and purchase suggestion displayed to the user.

[1458] This allows users to receive suggestions for the best outfits based on their body type, preferences, age, weather, and schedule, and they can check them out through virtual try-ons, and easily purchase any clothes they are missing from the online store.

[1459] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1460] ---

[1461] The present invention describes a system that uses user information, weather information, schedule information, and an emotion engine to suggest optimal clothing, perform virtual try-on, and suggest purchasing missing clothing.

[1462] Program processing overview

[1463] Input of user information and emotions

[1464] 1. The user starts the application and enters their profile information (body type, preferences, age). For example, the user enters height 175 cm, weight 70 kg, preference for casual clothing, and age 30s.

[1465] 2. The terminal receives this information, formats it into a database, and sends it to the server.

[1466] 3. The user inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice. For example, emotions are detected as "joy" or "stress."

[1467] Weather and schedule input

[1468] 4. The user enters the weather information and schedule for the day into the application. For example, the user enters "rain" for the weather and "a meeting at the office" for the schedule.

[1469] 5. The device receives the weather information and schedule information and sends it to the server.

[1470] Best clothing suggestions

[1471] 6. The server integrates the received user information, weather information, schedule information, and emotion information, providing the basic data for suggesting optimal clothing.

[1472] 7. The server runs an AI algorithm based on the integrated data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select light-colored clothing that evokes a positive impression. Specifically, it will select a navy blazer, a white shirt, and dark denim.

[1473] 8. The server creates image data of the generated clothing candidates and sends it to the terminal. This data includes visually verifiable images of the clothing.

[1474] Virtual try-on

[1475] 9. The user checks the displayed clothing image and uploads a photo of themselves to virtually try on the clothing. For example, they can select and upload a photo of their front view.

[1476] 10. The device receives the uploaded user photo and sends it to the server.

[1477] 11. The server combines the user's photo with the generated clothing image to create a virtual try-on image, simulating how the user would actually look wearing the clothing.

[1478] 12. The terminal receives the virtual try-on image and displays it to the user.

[1479] Purchase suggestion

[1480] 13. The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, it will detect that they are missing.

[1481] 14. The server searches for the missing clothing items through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[1482] 15. The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[1483] ---

[1484] This concludes the detailed embodiment. This system allows users to receive suggestions for optimal clothing tailored to their body type, preferences, age, weather, schedule, and emotions, and allows them to check the clothes through virtual try-on and purchase any missing clothing from an online store.

[1485] The processing flow will be explained below.

[1486] ---

[1487] Step 1:

[1488] A user starts the application and enters their profile information (body type, preferences, age). For example, they enter their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s.

[1489] Step 2:

[1490] The device receives the user's profile information, converts it into a database format, and sends it to the server. The data sent includes details such as "height: 175 cm, weight: 70 kg, preference: casual, age: 30s."

[1491] Step 3:

[1492] A user inputs the weather information and schedule information for the day into the application. For example, the user inputs "rain" for the weather and "a meeting at the office" for the schedule.

[1493] Step 4:

[1494] The device receives the weather and schedule information entered, converts it into a database format, and sends it to the server. The data sent includes information such as "Weather: Rain, Schedule: Meeting at the office."

[1495] Step 5:

[1496] The user can input their emotional state for the day, or an emotion engine linked to the device can detect emotions from the user's facial expressions and voice. For example, emotions can be detected as "joy" or "stress."

[1497] Step 6:

[1498] The device converts the emotional information detected by the emotion engine into a database format and sends it to the server. The data sent includes information such as "Emotion: Joy."

[1499] Step 7:

[1500] The server integrates the received user information, weather information, schedule information, and emotion information, providing the basic data for suggesting optimal outfits.

[1501] Step 8:

[1502] The server runs an AI algorithm based on the integrated data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select light-colored clothing that evokes a positive impression. Specifically, it will select a navy blazer, a white shirt, and dark denim.

[1503] Step 9:

[1504] The server creates image data of the clothing candidates and sends it to the terminal. This data includes visually verifiable images of the clothing.

[1505] Step 10:

[1506] The terminal displays the received clothing image data to the user, allowing the user to check the clothing details.

[1507] Step 11:

[1508] The user checks the displayed clothing image and uploads a photo of himself / herself to virtually try on the clothing. For example, he / she selects and uploads a photo of his / her front view.

[1509] Step 12:

[1510] The device receives the uploaded user photos and sends them to the server. The data sent includes the user's photo data.

[1511] Step 13:

[1512] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image, simulating how the user would actually look wearing the clothing.

[1513] Step 14:

[1514] The terminal receives the image of the virtual try-on and displays it to the user.

[1515] Step 15:

[1516] The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects the lack of such clothing.

[1517] Step 16:

[1518] The server searches for the missing clothing through the API of the connected online store and generates a purchase link, which includes direct access to the purchase page.

[1519] Step 17:

[1520] The device will display the generated purchase link along with the purchase suggestion to the user, allowing the user to easily purchase the missing clothing.

[1521] ---

[1522] The above is the processing flow of the program divided into detailed steps.

[1523] Example 2

[1524] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1525] Conventional clothing suggestion systems can suggest optimal outfits by taking into account a user's body type, preferences, age, weather information, and schedule information, but they cannot suggest outfits that reflect the user's emotional state. Furthermore, they lack the functionality to simulate how the user would actually look wearing the outfit during virtual try-on. Furthermore, there was no seamless system that could identify the clothes a user was missing and quickly purchase them from an online store. These challenges made it difficult for users to select the best outfits that would satisfy them.

[1526] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1527] In this invention, the server includes a means for integrating user information, weather information, schedule information, and emotional state to suggest optimal outfits, a means for uploading a user's photo for virtual try-on and combining it with the outfit suggestions, and a means for identifying missing clothes and generating a purchase link via the API of a linked online store. This allows the user to receive optimal outfit suggestions that take emotional state into consideration, check them in the virtual try-on, and seamlessly purchase the missing clothes.

[1528] ---

[1529] "User Information" refers to personal data such as a user's profile information, body type information, preferences, age, etc.

[1530] "Weather information" refers to data about the weather on that day, such as sunny, rainy, or snowy weather conditions.

[1531] "Schedule information" refers to data about the actions and events that the user has scheduled for that day.

[1532] "Emotional state" is information that indicates the user's current emotions, such as joy, sadness, stress, etc.

[1533] "Outfit candidates" refer to clothing options suitable for a user that are generated based on integrated user information, weather information, schedule information, and emotional state.

[1534] "Virtual try-on" refers to a technology that combines a user's photo with potential outfits to simulate how the user would actually look wearing those outfits.

[1535] "Uploading" refers to the act of a user sending data such as their own photos to a server via their device.

[1536] "Synthesis" refers to the act of combining a user's photo with image data of potential clothing items to generate a new image.

[1537] "Missing clothing" refers to clothing that is not in the user's closet but is necessary for the suggested outfit.

[1538] "Online store" refers to an e-commerce site that sells clothing and other products over the Internet.

[1539] "API" refers to a programming interface for exchanging functions and data between a terminal and a server or other external system.

[1540] "Purchase Link" refers to data containing a URL for direct access to the purchase page for a specific product.

[1541] This invention is a system that uses user information, weather information, schedule information, and an emotion engine to suggest optimal outfits, allows virtual fitting, and suggests purchasing clothes that are missing. This system is realized through cooperation between users, terminals, and a server.

[1542] 1. Input of user information and emotions

[1543] First, the user launches the application and enters their profile information, which includes the user's body type (e.g., height 175cm, weight 70kg), preferences (e.g., casual clothing), age (e.g., 30s), etc. The device receives the information entered by the user, formats it into a database, and then sends it to the server.

[1544] The user may input their emotional state for the day, or the emotion engine may detect emotions from the user's facial expressions and voice. If the user has an emotion such as "joy" or "stress," that information is sent to the server via the device.

[1545] 2. Weather and schedule input

[1546] Next, the user inputs the weather information for the day (e.g., rain) and schedule information (e.g., a meeting at the office) into the application. This information is also received by the device, formatted into a database, and sent to the server.

[1547] 3. Data integration and clothing candidate generation

[1548] The server integrates user information, weather information, schedule information, and emotional information. This creates the basic data for suggesting optimal outfits. The server uses this integrated data to generate optimal outfit suggestions using a generative AI model. For example, if the user's emotional state is "joy," the server selects bright-colored clothing that conveys a positive impression. In this case, it suggests a navy blazer, a white shirt, and dark denim.

[1549] 4. Displaying clothing options and virtually trying them on

[1550] Once the outfit candidates are generated, the server creates image data of them and sends it to the terminal. The terminal displays the received outfit candidate images to the user. The user then uploads a photo of themselves to virtually try on the outfits. The uploaded photo is sent from the terminal to the server, which then combines the user's photo with the outfit candidate image. Once this virtual try-on image is created, it is displayed to the user via the terminal.

[1551] 5. Identifying missing clothing and making purchasing suggestions

[1552] Finally, the server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, the server detects this. The server searches for the missing clothing through the API of a partner online store and generates a purchase link. This link provides direct access to the purchase page and is displayed to the user via their device. This allows the user to seamlessly purchase the missing clothing.

[1553] Specific examples

[1554] For example, suppose a user inputs that they are 175 cm tall, weigh 70 kg, prefer casual clothing, and are in their 30s. If the user also inputs their emotional state as "joy," the weather information as "rain," and their schedule information as "office meeting," the server will suggest a navy blazer, a white shirt, and dark jeans as the best outfit. Based on this image, a virtual try-on is performed, and if the user does not own the dark jeans, a purchase link to the online store is generated and displayed to the user.

[1555] Prompt Sentence Examples

[1556] "Suggest the best outfit for a user whose height is 175cm, weight is 70kg, preference is casual, age is 30s, emotional state is joy, weather is rainy, and scheduled appointment is an office meeting."

[1557] The above is a specific embodiment for carrying out the present invention. This system allows users to receive optimal clothing suggestions that take their emotional state into consideration, check them through virtual try-ons, and quickly purchase any clothes they are missing.

[1558] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1559] Step 1: Enter your user information

[1560] Input: The user launches the application and enters profile information (body type, preferences, age, etc.).

[1561] Specific operation: The user enters his height as 175cm, weight as 70kg, preference as casual, and age as 30s.

[1562] Data processing and calculation: The terminal receives this information and formats it into a database.

[1563] Output: The formatted data is sent to the server.

[1564] Step 2: Input or detect emotional state

[1565] Input: The user manually inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice.

[1566] Specific operation: The user manually inputs the emotional state as "joy", or the emotion engine detects "joy" through the camera and microphone.

[1567] Data processing and calculation: The device formats the detected emotional information into a database.

[1568] Output: Formatted emotion data is sent to the server.

[1569] Step 3: Enter weather and schedule information

[1570] Input: The user enters today's weather and schedule information into the application.

[1571] Specific actions: Enter the weather information "Rain" and the schedule information "Meeting at the office."

[1572] Data processing and calculation: The device converts the weather and schedule information entered into a database format.

[1573] Output: Formatted weather and schedule information is sent to the server.

[1574] Step 4: Data integration and clothing candidate generation

[1575] Input: The server receives user information, emotional state, weather information, and schedule information.

[1576] How it works: The server aggregates all the information and uses a generative AI model to generate optimal outfit suggestions.

[1577] Data processing and calculation: The server inputs the integrated data into the generative AI model to generate optimal outfit suggestions. For example, if the emotion is "joy" and the weather is "rainy," a navy blazer, white shirt, and dark denim, which convey a positive impression, will be selected.

[1578] Output: Image data of the generated outfit candidates is generated.

[1579] Step 5: Viewing clothing suggestions

[1580] Input: The server sends image data of the generated outfit candidates.

[1581] Specific operation: The server sends image data of the clothing candidates generated to the terminal.

[1582] Data processing and calculation: The image data received by the terminal is processed to be displayed on the user interface.

[1583] Output: The user visually checks the images of the outfit candidates.

[1584] Step 6: Upload a photo for the virtual try-on

[1585] Input: A user uploads their photo to the application.

[1586] What it does: The user selects and uploads a photo of themselves taken from the front.

[1587] Data processing and calculation: The device sends the uploaded photo data to the server.

[1588] Output: User photo data sent to the server.

[1589] Step 7: Perform a virtual try-on

[1590] Input: The server receives the user's photo data and image data of clothing candidates.

[1591] How it works: The server synthesizes these data and creates an image of the virtual try-on.

[1592] Data processing and calculation: The server uses image processing technology to synthesize the user's photo with the clothing candidate image.

[1593] Output: Image data of the created virtual try-on.

[1594] Step 8: View the virtual try-on

[1595] Input: The server sends the image data of the created virtual try-on.

[1596] Specific operation: The server sends image data of the virtual try-on to the terminal.

[1597] Data processing and calculation: The terminal processes the image data of the virtual try-on to display it on the user interface.

[1598] Output: User sees the virtual try-on image.

[1599] Step 9: Identify missing clothing

[1600] Input: The server looks up the user's closet information.

[1601] What happens: The server checks the closet information in the database to identify the missing clothes.

[1602] Data processing and calculation: The server compares the clothes required for the suggested outfits with the closet information and identifies any missing clothes.

[1603] Output: A list of identified missing clothes.

[1604] Step 10: Generate a purchase link

[1605] Input: The server sends the missing clothing information to the online store's API.

[1606] What it does: The server uses the API to search for the missing clothing and generate a purchase link.

[1607] Data processing and calculation: The server searches for the missing clothes through the online store's API and obtains the corresponding purchase link.

[1608] Output: The generated purchase link.

[1609] Step 11: View purchase offers

[1610] Input: The server sends the generated purchase link to the device.

[1611] Specific operation: The server sends the generated purchase link to the device, which displays the link in its user interface.

[1612] Data processing and calculation: The terminal processes the purchase link to display it on the user interface.

[1613] Output: User can see the purchase link and easily purchase the missing clothes.

[1614] These are the processing steps of this system, which allows users to receive recommendations for optimal clothing based on their body type, preferences, age, emotional state, weather, and schedule, check them out through virtual try-ons, and quickly purchase any clothes they are missing.

[1615] (Application example 2)

[1616] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1617] Conventional clothing suggestion systems have difficulty responding flexibly based on a user's individual emotions and detailed schedule information, resulting in clothing suggestions that are often not optimal for the user. Furthermore, even with virtual try-on functions, it is difficult to provide a visual experience that is significantly different from a real try-on, which reduces user satisfaction. Furthermore, there is a need for a system that allows users to easily purchase clothing that they are running low on without any hassle.

[1618] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1619] In this invention, the server includes means for inputting user information and emotions, means for inputting weather information and schedule, means for suggesting optimal clothing based on user information, emotion information, weather information, and schedule information, means for virtual try-on, and means for identifying missing clothing and generating a purchase link from a linked online store. This makes it possible to suggest optimal clothing based on the user's detailed emotional state and individual schedule, providing a more realistic virtual try-on experience and enabling the user to easily purchase missing clothing.

[1620] "User information" is information about personal characteristics such as the user's body type, preferences, and age.

[1621] "Emotion information" is information that indicates the user's current emotional state, and is either detected by the emotion engine or directly input by the user.

[1622] "Weather information" is information about the weather conditions of the day, including temperature, precipitation, wind speed, and the like.

[1623] "Schedule information" is information about the activities and behaviors that the user plans for that day.

[1624] The "means for suggesting optimal clothing" is a function or algorithm that integrates user information, emotional information, weather information, and schedule information to select and suggest optimal clothing for the user.

[1625] "Means for virtual try-on" refers to a function or system that allows a user to virtually try on selected clothing, and includes a function that uses image processing technology to combine a photograph of the user with an image of the clothing.

[1626] The "means for identifying missing clothes" is a function that refers to the user's closet information and detects clothes among the suggested outfits that the user does not own.

[1627] The "means of generating purchase links" is a function that uses the online store's API to generate links to purchase pages for missing clothing.

[1628] The present invention describes a system that suggests optimal clothing based on user information, emotion information, weather information, and schedule information, and offers virtual try-on and purchase suggestions for clothing that is in short supply.

[1629] Input of user information and emotions

[1630] First, the user launches the application and enters their profile information (body type, preferences, age). For example, they might enter height 175cm, weight 70kg, preference casual, and age 30s. The device receives this information, formats it into a database, and sends it to the server. The user can then enter their emotional state for the day, or the emotion engine can detect emotions from the user's facial expressions and voice. For example, emotions might be detected as "joy" or "stress."

[1631] Weather and schedule input

[1632] Next, the user inputs the weather information and schedule for the day into the application. For example, the weather is "rain" and the schedule is "meeting at the office." The device receives the weather information and schedule information and sends it to the server.

[1633] Best clothing suggestions

[1634] The server integrates the received user information, emotional information, weather information, and schedule information. This provides the basic data for suggesting optimal outfits. The server then runs a generative AI model based on the integrated data to generate optimal outfit candidates. For example, if the emotional state is "joy," it selects light-colored clothing that conveys a positive impression. Specifically, it selects a navy blazer, white shirt, and dark denim. The server then creates image data of the generated outfit candidates and sends it to the device. This includes a visually verifiable image of the outfit.

[1635] Virtual try-on

[1636] The user checks the displayed clothing image and uploads a photo of themselves to perform a virtual try-on. For example, they can select and upload a photo of their front view. The device receives the uploaded user photo and sends it to the server. The server combines the user's photo with the generated clothing image to create a virtual try-on image. This simulates how the user would look if they were actually wearing the clothing. The device receives the virtual try-on image and displays it to the user.

[1637] Purchase suggestion

[1638] The server references the user's closet information and identifies any missing clothing. For example, if the user does not own any dark denim, it detects this. The server searches for the missing clothing through the API of the connected online store and generates a purchase link. The generated link includes direct access to the purchase page. The device displays the generated purchase link to the user along with a purchase suggestion, making it easy for the user to purchase the missing clothing.

[1639] Hardware and software used

[1640] Hardware: Smartphone, Head-Mounted Display (HMD)

[1641] Software: Python, Keras, OpenCV, Requests

[1642] Specific examples

[1643] For example, assume that User A is a man in his 30s who is 175 cm tall, weighs 70 kg, and likes casual clothing. It is raining that day, and he has a scheduled office meeting. If the emotion engine detects his emotional state as "joy," the server will suggest a navy blazer, a white shirt, and dark jeans. User A can visually check these outfits by combining them with his own photo using the virtual try-on function. Furthermore, if User A does not own dark jeans based on his closet information, an appropriate purchase link will be generated and provided to User A.

[1644] Prompt Sentence Examples

[1645] Examples of prompts include:

[1646] Generate a Python program to suggest clothes that the user likes, allow them to try them on virtually, and provide a purchase link. The AI ​​algorithm will select the most suitable outfit based on the user's profile information, the weather for the day, and their schedule. Synthesize the outfit into a virtual try-on image, and generate a purchase link for any missing items via the online store's API.

[1647] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1648] Step 1:

[1649] The user starts the application and enters their profile information (body type, preferences, age). The entered information is converted into a database format by the device and sent to the server. Specifically, the user enters their height as 175 cm, weight as 70 kg, preference as casual, and age as 30s. The server then receives data that reflects the user's body type and preferences.

[1650] Step 2:

[1651] The user inputs their emotional state for the day, or the emotion engine detects emotions from the user's facial expressions and voice. The input emotion information (e.g., "joy" or "stress") is acquired by the device and sent to the server. This information is analyzed by the emotion engine and provided to the server as data that accurately reflects the user's current emotions.

[1652] Step 3:

[1653] The user inputs the weather information and plans for the day into the application. For example, the weather is "rain" and the plan is "meeting at the office." The device converts this information into a database format and sends it to the server. The server then obtains data on the weather conditions for the day and the user's planned activities.

[1654] Step 4:

[1655] The server combines the received user information, emotional information, weather information, and schedule information to form the basic data for suggesting optimal outfits. It then runs a generative AI model based on the combined data to generate optimal outfit suggestions. For example, if the emotional state is "joy," it will select bright-colored clothes that convey a positive impression.

[1656] Step 5:

[1657] The server creates image data of the generated outfit candidates and sends it to the terminal, which then provides the user with visually identifiable image data of the outfit. For example, an outfit image of a navy blazer, a white shirt, and dark denim is displayed.

[1658] Step 6:

[1659] The user checks the displayed clothing image and uploads their own photo to virtually try on the clothing. The device receives the uploaded user photo and sends it to the server, which then obtains the data needed to synthesize the user's photo with the generated clothing image.

[1660] Step 7:

[1661] The server synthesizes the user's photo with the generated clothing image to create a virtual try-on image. The generated virtual try-on image is sent to the terminal and displayed to the user, allowing the user to simulate how the clothing would actually look when worn.

[1662] Step 8:

[1663] The server references the user's closet information and identifies the missing clothing. For example, if the user does not own any dark denim, the server identifies the missing clothing. After identifying the missing clothing, the server searches the API of the relevant online store and generates a purchase link.

[1664] Step 9:

[1665] The generated purchase link is sent to the device and displayed to the user. The user can use the provided purchase link to easily purchase the missing clothing. For example, by searching for "dark denim," the purchase link is generated and displayed to the user, allowing the user to purchase the item directly.

[1666] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1667] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1668] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1669] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1670] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1671] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1672] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1673] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1674] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1675] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1676] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1677] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1678] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1679] 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.

[1680] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1681] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1682] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1683] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1684] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1685] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1686] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1687] The following is further disclosed regarding the above embodiment.

[1688] (Claim 1)

[1689] a means for inputting user information;

[1690] a means for inputting weather information and schedules;

[1691] A means for suggesting optimal clothing based on user information, weather information, and schedule information;

[1692] a means for conducting a virtual try-on;

[1693] A way to identify missing clothing and generate purchase links from affiliated online stores;

[1694] A system including:

[1695] (Claim 2)

[1696] The system of claim 1, wherein the system generates clothing suggestions based on an individual's body type, preferences, and age.

[1697] (Claim 3)

[1698] The system according to claim 1, which suggests weather-appropriate clothing based on weather information.

[1699] "Example 1"

[1700] (Claim 1)

[1701] a means for inputting user information;

[1702] means for inputting weather information and schedule information;

[1703] A means using a generative AI model that generates optimal outfit candidates based on user information, weather information, and schedule information;

[1704] a means for displaying the generated outfit candidates;

[1705] means for uploading a user's photo for virtual try-on;

[1706] A means for synthesizing a user's photo with clothing candidates;

[1707] a means for displaying a virtual try-on image;

[1708] A way to identify missing clothing and generate purchase links from affiliated online stores;

[1709] A system including:

[1710] (Claim 2)

[1711] The system of claim 1, wherein the system generates clothing suggestions based on an individual's body type, preferences, and age.

[1712] (Claim 3)

[1713] The system according to claim 1, which suggests weather-appropriate clothing based on weather information.

[1714] "Application Example 1"

[1715] (Claim 1)

[1716] a means for inputting user information;

[1717] a means for inputting weather information and schedules;

[1718] A means for suggesting optimal clothing based on user information, weather information, and schedule information;

[1719] A means for performing virtual try-on by combining a user's photograph with the suggested clothing;

[1720] A way to identify missing clothing and generate purchase links from affiliated online stores;

[1721] A system including:

[1722] (Claim 2)

[1723] The system of claim 1, wherein the system generates clothing suggestions based on an individual's body type, preferences, and age.

[1724] (Claim 3)

[1725] The system according to claim 1, which suggests weather-appropriate clothing based on weather information.

[1726] "Example 2: Combining Emotion Engines"

[1727] (Claim 1)

[1728] a means for inputting user information;

[1729] a means for inputting weather information and schedules;

[1730] A means for integrating user information, weather information, schedule information, and emotional state to suggest optimal clothing;

[1731] A means for uploading a user's photo for virtual try-on and combining it with candidate outfits;

[1732] A way to identify missing clothing and generate a purchase link through the API of a connected online store.

[1733] A system including:

[1734] (Claim 2)

[1735] The system of claim 1 , wherein the system generates outfit suggestions based on an individual's body type, preferences, age, and emotional state.

[1736] (Claim 3)

[1737] The system according to claim 1, which suggests optimal clothing based on weather information and schedule information.

[1738] "Application example 2 when combining emotion engines"

[1739] (Claim 1)

[1740] A means for inputting user information and emotions;

[1741] a means for inputting weather information and schedules;

[1742] A means for suggesting optimal clothing based on user information, emotional information, weather information, and schedule information;

[1743] a means for conducting a virtual try-on;

[1744] A way to identify missing clothing and generate purchase links from affiliated online stores;

[1745] A system including:

[1746] (Claim 2)

[1747] 10. The system of claim 1, wherein the system generates outfit suggestions based on an individual's body type, preferences, age, and emotions.

[1748] (Claim 3)

[1749] The system according to claim 1, which suggests weather-appropriate clothing based on weather information. [Explanation of symbols]

[1750] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting user information; a means for inputting weather information and schedules; A means for suggesting optimal clothing based on user information, weather information, and schedule information; a means for conducting a virtual try-on; A way to identify missing clothing and generate purchase links from affiliated online stores; A system including:

2. The system of claim 1 , wherein clothing candidates are generated based on an individual's body type, preferences, and age.

3. The system according to claim 1, wherein the system suggests clothing suitable for the weather based on weather information.

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