system

Abstract

We provide the system. [Solution] Image recognition means for identifying users, A clothing recognition means for recording the user's clothing data for the day, A transmission means for collecting the aforementioned clothing data and sending it to a server, A proposal means that uses a generative model to propose outfits based on clothing data received by the server, A display means for displaying the proposed coordination to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In busy modern society, there is a problem that many individuals spend a lot of time and energy on daily clothing selection. As a result, the resulting decision fatigue can affect productivity in the workplace. In addition, the stereotyped fashion due to the bias of choices may also damage a professional impression. Furthermore, the limited means of accessing new fashion items also causes a narrowing of the range of self-expression. There is a need for a system that comprehensively solves these problems.

Means for Solving the Problems

[0005] This invention provides a system comprising an image recognition means for identifying a user and a clothing recognition means for recording the user's clothing data. This enables the automatic recording of the user's daily clothing choices and efficient data collection. Furthermore, based on the clothing data, a suggestion means using a generative model proposes the optimal outfit tailored to the day's weather and the user's schedule. The system also includes a learning means that improves the accuracy of the suggestion means based on feedback on the proposed outfits, providing continuously optimized fashion suggestions to the user. In addition, a purchase support means allows the user to easily acquire the suggested items, improving access to new fashion options. This invention makes it possible to reduce user decision fatigue and provide a personalized fashion experience.

[0006] A "user" refers to an individual who uses the system, and is a specific, individual customer.

[0007] "Image recognition means" refers to technology that uses devices such as cameras to identify a user's face or body and to identify that individual.

[0008] "Clothing recognition means" refers to a system function that automatically detects the clothing worn by a user and analyzes its characteristics and details.

[0009] "Transmission means" refers to communication technology used to move data from a terminal to a server.

[0010] A "generative model" is a type of AI algorithm that uses collected data to create new proposals.

[0011] "Proposal methods" refer to the process of concretizing the coordination and ideas derived from the generative model and presenting them to the user.

[0012] "Display means" refers to displays and monitors used to visually convey information to users.

[0013] "Feedback" refers to the opinions and impressions that users give regarding the suggested outfits, and this information is used to improve future suggestions.

[0014] A "learning method" is an adaptive process in which AI improves the accuracy of its suggestions based on feedback.

[0015] A "purchase support tool" is a system function that provides users with the necessary information and links when purchasing suggested items. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] This invention provides a personal fashion assistant system that users can use on a daily basis. This system consists of a stand mirror-type terminal equipped with a camera and a display, and assists the user in the process of choosing their daily clothes.

[0038] First, when a user stands in front of a full-length mirror, the device's camera recognizes the user's face and identifies them as an individual. Next, the device automatically records the user's clothing and extracts data on color, design, and brand. This data is used as basic information to understand the user's wearing patterns and preferred style.

[0039] Next, the device sends the collected clothing data to the server. The server uses a generative AI model to analyze the received data and, taking into account the user's preferences and past history, suggests outfits suitable for the weather and schedule. This process is automated, allowing for real-time optimal suggestions to be returned to the user.

[0040] The suggestions are displayed on the device's screen, allowing the user to visually check the outfit. Multiple options are provided for the suggestions, and the user can choose their preferred style. Furthermore, when feedback is entered into the device, the server learns from this information and uses it to improve the accuracy of future suggestions.

[0041] Furthermore, the device supports access to purchase new items included in the suggested outfits. Users can easily access the online store through the provided purchase links, making it convenient to acquire new fashion items.

[0042] This system allows users to reduce the time and effort spent on choosing their daily outfits while expanding their range of self-expression. In this way, the present invention improves the efficiency and quality of style selection in the user's daily life.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] When a user stands in front of a full-length mirror, the camera automatically activates and the device captures the user's face. Facial recognition technology is used to identify the user, and this information is then used to refer to a separate database.

[0046] Step 2:

[0047] The device collects image data of the user's current clothing. Using clothing recognition technology, it analyzes the color, design, and brand information of the clothing to generate detailed clothing data.

[0048] Step 3:

[0049] The device sends the analyzed clothing data to the server. The data is structured and sent to the server in the format required for analysis.

[0050] Step 4:

[0051] Based on the clothing data received by the server, an AI model is used to suggest outfit combinations. Weather forecast data, schedule information, and past user history are used in conjunction to select the most optimal style.

[0052] Step 5:

[0053] The server sends the generated outfit suggestions to the terminal. The terminal displays the suggested outfits on its screen, providing the user with visual information.

[0054] Step 6:

[0055] Users review the displayed outfits and provide feedback based on their preferences and suitability. This feedback includes both positive aspects and areas for improvement.

[0056] Step 7:

[0057] The device collects user feedback and sends it to the server. The server analyzes the feedback data and uses it to improve the accuracy of the proposed algorithm.

[0058] Step 8:

[0059] If the suggested outfit includes a new item, the device will display a purchase link to the online store to the user. The user can easily purchase the item by clicking the link.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In today's busy lifestyle, choosing what to wear each day is a time-consuming and laborious task. Finding the perfect outfit based on personal preferences and various circumstances is also difficult. Furthermore, online clothing purchases can be overwhelming due to the sheer number of options available. There is a need to address these challenges and provide a more efficient and personalized clothing selection experience.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes a person identification means for identifying the user, a clothing identification means for recording the user's clothing data for the day, and a display means for displaying suggested outfits to the user. This enables the automatic suggestion of optimal outfits based on the user's individual preferences and conditions.

[0065] "Personal identification means" refers to technology that detects a user's face or other physical characteristics to identify them as an individual.

[0066] "Clothing identification means" refers to technology that analyzes the type and characteristics of the clothing a user is wearing and records it as data.

[0067] "Transmission means" refers to network communication technology for transmitting data from a terminal to an information processing device.

[0068] "Proposal methods using generative models" refer to artificial intelligence technology that proposes the most suitable attire to the user based on collected data.

[0069] "Display means" refers to display technology for visually showing the proposed design to the user.

[0070] "Evaluation information" refers to feedback data provided by users regarding the suggested outfits.

[0071] "Purchase options" refer to the options for purchasing new items included in the suggested outfit.

[0072] An "e-commerce site" is an online platform where goods can be purchased via the internet.

[0073] This invention is a personal fashion assistant system that assists users in choosing their everyday clothing. This system uses a stand mirror-type terminal and proposes the optimal outfit by utilizing the user's personal style data.

[0074] First, the device is equipped with a camera and a display. The camera recognizes the user's face when they stand in front of a full-length mirror and identifies them as an individual. This face recognition can utilize OpenCV, a common image recognition library. This ensures the system's ability to identify each user uniquely.

[0075] Next, the device uses clothing identification to photograph the user's clothing and automatically analyzes the color, design, and brand information of the clothes being worn. In this step, a deep learning model such as YOLOv3 is used to extract clothing features in real time.

[0076] The extracted data is sent from the terminal to the server. The HTTPS protocol is used for transmission, and the data is encoded in JSON format. The server analyzes the received data using a generative AI model and generates appropriate outfits considering the user's history, weather information, schedule, etc. Specific generative AI models include fashion-specific machine learning models using tools like PyTorch.

[0077] The generated outfit suggestions are immediately displayed on the device's screen, allowing the user to choose from multiple style options. When the user provides feedback on their chosen outfit, it is sent back to the server for the AI ​​model to learn from. This feedback loop improves the accuracy of future suggestions.

[0078] The device also offers the option to purchase new clothing items included in the suggestions. A link to the online store is displayed, making it easily accessible to the user.

[0079] This system allows users to reduce the time and effort spent choosing their daily outfits, enabling them to live a more individual and stylish life.

[0080] A concrete example of a prompt message would be, "Generate a new outfit suggestion based on User A's outfit data from the past week and today's weather. Please make it as casual as possible." This prompt helps the system generate an appropriate outfit.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] When a user stands in front of the device, the device's camera automatically activates. The camera captures the user's face, and an image recognition algorithm analyzes it to identify the user. The input is the image data acquired by the camera, and the output is the user's ID. Specifically, a face recognition library (e.g., OpenCV) is used to compare the user's facial characteristics with a database.

[0084] Step 2:

[0085] For identified users, the device uses clothing identification to photograph the user's clothing with its camera. The captured image data is input into a deep learning model (e.g., YOLOv3) to extract features such as the color, design, and brand of the clothing. The input is the captured image, and the output is the analyzed clothing feature data. This data is used to understand the user's fashion style.

[0086] Step 3:

[0087] The terminal sends the extracted clothing feature data to the server. A secure protocol (e.g., HTTPS) is used for this communication. The input is the parsed clothing data, and the output is a notification that the data has been successfully sent to the server. Specifically, this involves converting the data to JSON format and sending it to the server via an HTTP POST request.

[0088] Step 4:

[0089] The server analyzes clothing data received using a generative AI model. The input consists of clothing data sent from the terminal and user history data, while the output is a suggested outfit. Here, machine learning frameworks such as PyTorch are utilized to automatically generate outfits that take into account user preferences, weather, and schedule. A specific prompt used during generation is, "Generate a new outfit suggestion based on user A's outfit data from the past week and today's weather."

[0090] Step 5:

[0091] The server sends the generated outfit information to the terminal. The terminal displays the received outfit on its screen, providing the user with visual feedback. The input is the outfit information sent from the server, and the output is the visual presentation to the user. The user can scroll through and view multiple outfits.

[0092] Step 6:

[0093] The user selects an outfit from the suggested options and enters their evaluation information into the device. This evaluation information is then sent back to the server and used as training data for the generated AI model. The input is user feedback data, and the output is an update to the server's AI model. A simple form is displayed on the device's screen when providing feedback.

[0094] Step 7:

[0095] Based on the suggested outfit, the terminal presents new clothing purchase options. Here, a link to an e-commerce site is displayed on the screen, allowing the user to directly access the purchase page. The input is the purchase link data sent from the server, and the output is the purchase page accessed by the user. When the user clicks the link, the browser opens and displays the details of the selected item.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] Traditional fashion assistant systems made it difficult for users to receive clothing selection and coordination suggestions outside of stores, and to incorporate individual feedback. Furthermore, they lacked the functionality to allow users to try on suggested outfits on the spot and then purchase them. As a result, users were unable to choose clothes efficiently, and there was a need to improve the shopping experience.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes a person recognition means for identifying the user, a decoration recognition means for recording the user's decoration data for the day, a communication means for collecting the decoration data and transmitting it to a computer, and a suggestion means using a generation mechanism. This allows the user to confirm the suggested adjustments in the store using a visualization means and make choices about trying on or purchasing the items on the spot.

[0101] A "user" refers to an individual who uses the system, and is particularly the subject who receives suggestions for clothing selection and coordination.

[0102] A "person recognition system" is a mechanism that uses video information to recognize an individual in order to identify the user's identity.

[0103] A "decorative recognition means" is a system that identifies decorative items such as clothing and accessories worn by a user and collects data on them.

[0104] "Communication means" refers to a mechanism for transmitting collected decorative data to a server or computer.

[0105] A "processing machine" refers to a computer or server that receives, analyzes, and processes data.

[0106] "Generative mechanism" refers to the process of utilizing generative AI models to suggest the most suitable outfits for users.

[0107] A "suggestion mechanism" is a system that suggests appropriate decoration combinations to the user, taking into account the user's past history and preferences.

[0108] "Visualization means" refers to displays or screens that visually show proposed adjustments or coordinations to the user.

[0109] "Collaboration methods" refer to a series of mechanisms that enable users to try on or purchase the proposed adjustments within the sales space.

[0110] To realize this invention, the following system is necessary: ​​The server acquires image data of the user through a person recognition means and identifies the individual. Next, the accessory recognition means analyzes the clothing the user is wearing and transmits that data to the server via a communication means. At this time, detailed information such as the color, design, and brand of the accessory items is collected.

[0111] The server utilizes a generation mechanism based on received data to suggest outfits tailored to the user. Specifically, it uses a generation AI model to take into account the user's past selection history and preferences to generate the optimal adjustment plan. This suggestion is displayed to the user in real time through visualization means.

[0112] Furthermore, if a user tries on suggested adjustments on the spot or considers purchasing them at a virtual store, the system can be integrated to provide immediate feedback. This system utilizes sensor technology (e.g., cameras) and communication technology (e.g., Wi-Fi, cloud services).

[0113] As a concrete example, a user might use a stand-up mirror-type terminal in a fitting room within a store to take a picture of themselves using their smartphone. The server instantly analyzes the image and displays suggested suit styles on the terminal's screen. An example of a prompt to the generating AI model would be, "Please suggest an outfit that suits the person in this image. Please suggest something casual, based on items available in the store."

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

[0115] Step 1:

[0116] The user stands in front of the terminal, and their image data is acquired using a person recognition system. This image data is then input. The terminal uses this image data to identify the user and generate personal identification information.

[0117] Step 2:

[0118] The device uses decorative recognition technology to analyze the user's clothing and accessories. During this process, information such as color, design, and brand is extracted as decorative data. The device then formats this information into structured data and outputs it.

[0119] Step 3:

[0120] The terminal uses a communication method to send the decoration data collected in the previous step to the server. The server receives this data and analyzes it along with relevant information, including the user's preferences and past history.

[0121] Step 4:

[0122] The server uses a generative AI model to generate optimal outfit suggestions for the user based on the collected data. This process utilizes prompt messages (e.g., "Please suggest an outfit for today that suits the person in this image. Please suggest something casual and based on items available in-store.") and sends the generated suggestions to the terminal as data output.

[0123] Step 5:

[0124] The terminal uses visualization tools to display coordination suggestions sent from the server in real time. The user can visually review the suggestions and select adjustments as needed.

[0125] Step 6:

[0126] When a user tries on or purchases a suggested outfit, they send their selection to the server using a linked system. The server accepts the user's selection and stores the information as training data to use for future suggestions.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention is a personal fashion assistant system designed to provide users with a more comfortable fashion experience. The system operates a camera, display, and emotion engine mounted on a stand-up mirror-type terminal, utilizing the user's emotions and clothing data to provide more accurate style suggestions.

[0129] When a user stands in front of a full-length mirror, the device uses its camera to recognize the user's face and emotions. The emotion engine identifies the user's emotional state (e.g., joy, surprise, sadness) through facial expression analysis. This information enables personalized suggestions based on the user's current emotions.

[0130] Next, the device records the user's clothing and extracts data on color, design, and brand. This data is sent to a server in real time and analyzed along with sentiment data. The server uses a generative AI model to integrate weather, schedule information, historical data, and sentiment-based information to generate data-driven, sentiment-perceptual outfit suggestions.

[0131] The suggested outfits are displayed on the device's screen. The presented outfits are adjusted in color and style based on the user's mood, visually showing the most appealing options. This allows the user to easily choose from a variety of styles that suit their mood.

[0132] Furthermore, users can provide feedback on the suggested outfits. The device collects this feedback and sends it to the server, enabling further improvements to the suggestion algorithm.

[0133] Furthermore, the device provides a link to the online store through purchase support mechanisms when the user wishes to purchase new items. This makes discovering and purchasing fashion items intuitive and seamless.

[0134] This invention, by using an emotion engine, can provide personalized style suggestions that meet the user's psychological needs, thereby improving the quality of everyday clothing choices and enriching the user experience.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user stands in front of a standing mirror, and the system starts. The camera automatically activates and captures the user's image.

[0138] Step 2:

[0139] The device uses facial recognition technology to identify the user's identity from the captured image. This then allows access to a corresponding database.

[0140] Step 3:

[0141] The device inputs the user's facial image into an emotion engine, which analyzes the user's current emotional state. The emotion engine extracts emotional information from the user's facial expressions and uses it to make subsequent outfit suggestions.

[0142] Step 4:

[0143] The device photographs the user's clothing and analyzes the data using clothing recognition technology. Information such as the color, design, and brand of the clothing is collected and compiled into clothing data.

[0144] Step 5:

[0145] The device sends the clothing data and emotion data obtained above to the server. The server then begins processing the received data.

[0146] Step 6:

[0147] The server utilizes an AI model to generate outfit suggestions, taking into account the user's emotional state, clothing data, weather information, schedule information, and past style history.

[0148] Step 7:

[0149] The server sends the generated outfit suggestions to the terminal. The terminal displays them on the screen, emphasizing colors and styles that take the user's emotions into consideration.

[0150] Step 8:

[0151] Users review the suggested outfits, select their favorite styles, or provide feedback. This feedback is recorded to further improve the suggestions.

[0152] Step 9:

[0153] The device collects user feedback and sends it to the server. The server uses the feedback as training data to improve the accuracy of future suggestions.

[0154] Step 10:

[0155] The device provides the user with purchase options related to the suggested outfit and displays a link to the online store, allowing the user to easily acquire new items.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] In modern times, fashion is an important element that reflects an individual's lifestyle and emotions. However, choosing the appropriate style to match ever-changing emotional states and environments is not easy. This requires time and effort, and it is difficult to obtain suggestions that directly address the user's interests and emotions. Therefore, there is a need for a system that automatically provides personalized fashion suggestions tailored to an individual's emotional state and preferences.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes an image recognition means for identifying the user's emotional state, a clothing recognition means for extracting the user's clothing feature data, a transmission means for sending the emotional state and clothing feature data to the server, a suggestion means for suggesting outfits using a generative AI model, and a display means for adjusting and displaying the suggestions according to the user's emotions. This enables personalized style suggestions that effectively respond to the user's changing emotional state.

[0161] "Image recognition means" refers to technology that uses cameras and sensors to analyze a user's face and facial expressions, and then identifies their emotional state based on the results.

[0162] "Clothing recognition means" refers to a technology that analyzes the characteristics of the clothing a user is wearing, such as its color, design, and brand, and extracts them as specific data.

[0163] "Transmission means" refers to technology that has the function of transmitting collected emotional data and clothing data to a server via a communication network.

[0164] A "generative AI model" refers to an artificial intelligence algorithm that generates style suggestions based on received data, taking into account the user's emotional state and external factors.

[0165] "Suggestion method" refers to a technology that presents users with style options created by a generative AI model.

[0166] "Display means" refers to displays and projection technologies used to visually present proposed fashion styles to users.

[0167] "Purchase support means" refers to technology that provides users with a link to relevant sales platforms so that they can easily purchase the fashion items suggested to them.

[0168] The system of the present invention uses a stand-up mirror-type terminal and a server connected to it to realize personalized fashion suggestions for the user. The terminal is equipped with a camera for capturing the user's face and entire body, and accompanying image analysis software. These hardware components play a role in quickly acquiring the user's emotional state and clothing characteristics.

[0169] The device first takes a picture of the user's face with its camera when the user stands in front of a full-length mirror. This facial image is then analyzed by an emotion engine to identify the user's current emotional state. For example, if the user is smiling, the emotion "joy" is recognized; if they are frowning, the emotion "surprise" is recognized.

[0170] Next, the device's camera captures the user's entire body, extracting detailed information such as the color, design, and brand of their clothing. This information is then recorded in the system as digital data.

[0171] Based on this data, the device sends emotion data and clothing data to a server via the internet. The server uses a generative AI model to analyze the received information in detail and generate style suggestions optimized for each individual user. This generative AI model enables highly personalized suggestions by considering the user's emotional state, past history, weather information, and other factors.

[0172] As a concrete example, if a user stands in front of a mirror on a cold morning and shows a slightly tired expression, the AI ​​model might suggest a scarf in a cheerful color. This suggestion would be visually displayed on the system's screen, allowing the user to confirm it.

[0173] An example of a prompt message is, "How are you feeling today? What colors and styles do you usually use?" By answering this prompt, the user can receive further customization suggestions.

[0174] Ultimately, users can provide feedback on the suggested outfits. The device records this feedback and sends it to the server to help improve the accuracy of the suggestion algorithm. Furthermore, if a user wishes to purchase a specific item, the device provides a link to the relevant online store, supporting a seamless purchasing experience.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user stands in front of a full-length mirror and activates the system. The terminal's built-in camera activates and takes a picture of the user's face. The input is the user's facial image data. The terminal passes the facial image to analysis software for facial expression analysis. The output is the analyzed emotional state data of the user (e.g., "joy," "sadness," etc.).

[0178] Step 2:

[0179] The device continues to use its camera to capture a full-body image of the user. The input is the user's full-body image data. From this image, the clothing recognition system extracts feature data such as the color, design, and brand of the clothing. The output is obtained as the user's clothing feature data.

[0180] Step 3:

[0181] The terminal collects the emotional state data and clothing feature data obtained in steps 1 and 2, encrypts them, and transmits them to the server over the network. The inputs are emotional data and clothing data. The server receives this data and records it in its database. The output is the integrated data stored on the server.

[0182] Step 4:

[0183] The server runs a generative AI model based on the received data. The input consists of user emotional state and clothing characteristic data stored on the server. The generative AI model considers additional database information such as weather information and past history to generate style suggestions suitable for the user. The output is a personalized style suggestion.

[0184] Step 5:

[0185] The terminal displays style suggestions received from the server and presents them visually to the user. The input is style suggestion data sent from the server. The suggestions, visualized on the terminal's display, are provided in a format that the user can easily understand and select. The output is the adjusted style suggestion displayed to the user.

[0186] Step 6:

[0187] The user provides feedback on the displayed suggestions. The input is the user's feedback. The terminal records this feedback and sends it back to the server. The output is the feedback data used to improve the suggestion algorithm.

[0188] Step 7:

[0189] The terminal provides links to relevant online stores to support the purchase process if the user wishes to buy the suggested items. The input is the user's purchase request. With a single click, the user can access the purchase page and buy the desired items. The output is the online purchase link accessible to the user.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] Conventional fashion suggestion systems often fail to adequately consider the user's mood and emotions, making it difficult to provide the desired style and coordination in real time. The objective of this invention is to improve the user experience by considering the user's emotional state and providing more personalized fashion coordination.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes image analysis means for identifying the user, data acquisition means for acquiring data on the clothing the user is wearing, data communication means for transmitting emotional state and clothing data to the server, and suggestion means for utilizing a generative model that takes into account the emotional state and comprehensive conditions. This enables optimal fashion suggestions based on the user's emotional state.

[0195] A "user" is an individual person who uses the system, and is the subject of image analysis and emotion recognition.

[0196] "Image analysis means" refers to a device or program that identifies a user and further determines their emotional state by analyzing their facial expressions.

[0197] "Clothing data" refers to information about the clothing a user is currently wearing, including characteristics such as color, design, and brand.

[0198] "Emotion analysis means" refers to a technology that analyzes a user's facial expressions through image analysis and identifies their emotional state.

[0199] "Data communication means" refers to communication technology or devices used to transmit emotional state and clothing data obtained from users to a server.

[0200] A "generative model" refers to an algorithm or AI model that generates the optimal outfit for a user based on data.

[0201] A "proposal method" is a mechanism that presents the coordinated results obtained by the generative model to the user.

[0202] "Display means" refers to a display or interface that visually shows the proposed fashion coordination to the user.

[0203] "Purchase support means" refers to technology that provides an e-commerce environment for users to acquire items related to the suggested outfit.

[0204] "Emotional state" refers to the psychological state based on the user's facial expression analysis, and includes states such as joy, surprise, and sadness.

[0205] "Comprehensive conditions" refer to the various factors considered when making a proposal, specifically including weather, schedule, and past preference history.

[0206] The system for implementing this invention utilizes multiple hardware and software components to provide users with optimal fashion suggestions.

[0207] The user captures their face and clothing through a camera using a device such as a smartphone or smart glasses. The device then uses image analysis technology (e.g., OpenCV) to analyze facial expressions and identifies the emotional state using emotion analysis software called EmotionRecognizer.

[0208] This information is transmitted to the server in real time via data communication. The server is equipped with a generative AI model that runs a fashion suggestion algorithm, and based on the received emotional state and clothing data, it suggests the optimal outfit while considering comprehensive conditions (e.g., weather, past preferences, schedule information, etc.).

[0209] The generated outfit is displayed on the device's screen, allowing the user to visually confirm it. Based on this suggestion, the user can purchase the necessary fashion items, and the purchase assistance system seamlessly integrates with the e-commerce platform.

[0210] As a concrete example, if a user is wearing smart glasses, emotion analysis may detect a state of "joy," and past data may reveal a tendency to prefer "casual summer clothes." The system would then suggest a colorful shirt and light shorts suitable for a sunny day picnic.

[0211] An example of a prompt message would be, "Analyze the user's facial expressions and emotions, and suggest the most suitable fashion items for them."

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The device captures the user's face and clothing using its camera. The input is the captured image data, and the output is image data for analysis. The device preprocesses this data using image analysis technology to extract facial feature points.

[0215] Step 2:

[0216] The device uses EmotionRecognizer to analyze the user's emotional state from image data. The input is facial feature data acquired in the previous step, and the output is the identified emotion label (e.g., joy, surprise). The device analyzes facial expression patterns to identify emotions.

[0217] Step 3:

[0218] The device uses clothing recognition technology to identify the user's clothing data and extract color, design, and brand information. The input is clothing image data, and the output is clothing attribute data. The device analyzes various clothing attributes from the image and formats the data.

[0219] Step 4:

[0220] The terminal transmits emotion information and clothing data to the server via data communication technology. The input is emotion labels and clothing attribute data, and the output is the completion of data transmission to the server.

[0221] Step 5:

[0222] The server analyzes emotion and clothing data received from each user using a generative AI model. The input is a dataset of emotions and clothing, and the output is a suggested outfit. The server performs comprehensive data processing, taking into account weather information and the user's past history, to generate fashion suggestions.

[0223] Step 6:

[0224] The server sends the proposed coordinate to the terminal. The input is the proposed data, and the output is the completion of data transmission to the terminal.

[0225] Step 7:

[0226] The terminal displays the received coordination information on its screen and presents it to the user. The input is the suggested coordination, and the output is a visual presentation to the user.

[0227] Step 8:

[0228] The user selects the most suitable fashion items based on the suggestions. The user's selection is fed back to the device, and a purchase support system provides links to facilitate the purchase of related items. The input is the user's selection data, and the output is purchase support information.

[0229] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] This invention provides a personal fashion assistant system that users can use on a daily basis. This system consists of a stand mirror-type terminal equipped with a camera and a display, and assists the user in the process of choosing their daily clothes.

[0246] First, when a user stands in front of a full-length mirror, the device's camera recognizes the user's face and identifies them as an individual. Next, the device automatically records the user's clothing and extracts data on color, design, and brand. This data is used as basic information to understand the user's wearing patterns and preferred style.

[0247] Next, the device sends the collected clothing data to the server. The server uses a generative AI model to analyze the received data and, taking into account the user's preferences and past history, suggests outfits suitable for the weather and schedule. This process is automated, allowing for real-time optimal suggestions to be returned to the user.

[0248] The suggestions are displayed on the device's screen, allowing the user to visually check the outfit. Multiple options are provided for the suggestions, and the user can choose their preferred style. Furthermore, when feedback is entered into the device, the server learns from this information and uses it to improve the accuracy of future suggestions.

[0249] Furthermore, the device supports access to purchase new items included in the suggested outfits. Users can easily access the online store through the provided purchase links, making it convenient to acquire new fashion items.

[0250] This system allows users to reduce the time and effort spent on choosing their daily outfits while expanding their range of self-expression. In this way, the present invention improves the efficiency and quality of style selection in the user's daily life.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] When a user stands in front of a full-length mirror, the camera automatically activates and the device captures the user's face. Facial recognition technology is used to identify the user, and this information is then used to refer to a separate database.

[0254] Step 2:

[0255] The device collects image data of the user's current clothing. Using clothing recognition technology, it analyzes the color, design, and brand information of the clothing to generate detailed clothing data.

[0256] Step 3:

[0257] The device sends the analyzed clothing data to the server. The data is structured and sent to the server in the format required for analysis.

[0258] Step 4:

[0259] Based on the clothing data received by the server, an AI model is used to suggest outfit combinations. Weather forecast data, schedule information, and past user history are used in conjunction to select the most optimal style.

[0260] Step 5:

[0261] The server sends the generated outfit suggestions to the terminal. The terminal displays the suggested outfits on its screen, providing the user with visual information.

[0262] Step 6:

[0263] Users review the displayed outfits and provide feedback based on their preferences and suitability. This feedback includes both positive aspects and areas for improvement.

[0264] Step 7:

[0265] The device collects user feedback and sends it to the server. The server analyzes the feedback data and uses it to improve the accuracy of the proposed algorithm.

[0266] Step 8:

[0267] If the suggested outfit includes a new item, the device will display a purchase link to the online store to the user. The user can easily purchase the item by clicking the link.

[0268] (Example 1)

[0269] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] In today's busy lifestyle, choosing what to wear each day is a time-consuming and laborious task. Finding the perfect outfit based on personal preferences and various circumstances is also difficult. Furthermore, online clothing purchases can be overwhelming due to the sheer number of options available. There is a need to address these challenges and provide a more efficient and personalized clothing selection experience.

[0271] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0272] In this invention, the server includes a person identification means for identifying the user, a clothing identification means for recording the user's clothing data for the day, and a display means for displaying suggested outfits to the user. This enables the automatic suggestion of optimal outfits based on the user's individual preferences and conditions.

[0273] "Personal identification means" refers to technology that detects a user's face or other physical characteristics to identify them as an individual.

[0274] "Clothing identification means" refers to technology that analyzes the type and characteristics of the clothing a user is wearing and records it as data.

[0275] "Transmission means" refers to network communication technology for transmitting data from a terminal to an information processing device.

[0276] "Proposal methods using generative models" refer to artificial intelligence technology that proposes the most suitable attire to the user based on collected data.

[0277] "Display means" refers to display technology for visually showing the proposed design to the user.

[0278] "Evaluation information" refers to feedback data provided by users regarding the suggested outfits.

[0279] "Purchase options" refer to the options for purchasing new items included in the suggested outfit.

[0280] An "e-commerce site" is an online platform where goods can be purchased via the internet.

[0281] This invention is a personal fashion assistant system that assists users in choosing their everyday clothing. This system uses a stand mirror-type terminal and proposes the optimal outfit by utilizing the user's personal style data.

[0282] First, the device is equipped with a camera and a display. The camera recognizes the user's face when they stand in front of a full-length mirror and identifies them as an individual. This face recognition can utilize OpenCV, a common image recognition library. This ensures the system's ability to identify each user uniquely.

[0283] Next, the device uses clothing identification to photograph the user's clothing and automatically analyzes the color, design, and brand information of the clothes being worn. In this step, a deep learning model such as YOLOv3 is used to extract clothing features in real time.

[0284] The extracted data is sent to the server by the terminal. Here, the HTTPS protocol is used for transmission, and the data is encoded in JSON format. The server analyzes the received data using a generative AI model and generates appropriate coordination considering the user's history, weather information, schedule, etc. Specific generative AI models include fashion-specialized machine learning models using PyTorch, etc.

[0285] The proposed coordination is immediately displayed on the terminal's display. As a result, the user can select from multiple style options. When the user inputs feedback on the selected coordination, it is sent to the server again and used by the AI model for learning. This feedback loop improves the proposal accuracy for subsequent times.

[0286] The terminal also provides purchase options for new clothing items included in the proposal. At this time, a link to the online store is displayed and the user can easily access it.

[0287] With this system, the user can reduce the time and effort spent on daily clothing selection and can lead a more personalized and stylish life.

[0288] Specific examples of the prompt text include "Based on the coordination data of User A in the past week and today's weather, please generate new clothing proposals. Please make it as casual as possible." This prompt helps the system generate appropriate coordination.

[0289] The flow of the specific process in Example 1 will be described using FIG. 11.

[0290] Step 1:

[0291] When a user stands in front of the device, the device's camera automatically activates. The camera captures the user's face, and an image recognition algorithm analyzes it to identify the user. The input is the image data acquired by the camera, and the output is the user's ID. Specifically, a face recognition library (e.g., OpenCV) is used to compare the user's facial characteristics with a database.

[0292] Step 2:

[0293] For identified users, the device uses clothing identification to photograph the user's clothing with its camera. The captured image data is input into a deep learning model (e.g., YOLOv3) to extract features such as the color, design, and brand of the clothing. The input is the captured image, and the output is the analyzed clothing feature data. This data is used to understand the user's fashion style.

[0294] Step 3:

[0295] The terminal sends the extracted clothing feature data to the server. A secure protocol (e.g., HTTPS) is used for this communication. The input is the parsed clothing data, and the output is a notification that the data has been successfully sent to the server. Specifically, this involves converting the data to JSON format and sending it to the server via an HTTP POST request.

[0296] Step 4:

[0297] The server analyzes clothing data received using a generative AI model. The input consists of clothing data sent from the terminal and user history data, while the output is a suggested outfit. Here, machine learning frameworks such as PyTorch are utilized to automatically generate outfits that take into account user preferences, weather, and schedule. A specific prompt used during generation is, "Generate a new outfit suggestion based on user A's outfit data from the past week and today's weather."

[0298] Step 5:

[0299] The server sends the generated outfit information to the terminal. The terminal displays the received outfit on its screen, providing the user with visual feedback. The input is the outfit information sent from the server, and the output is the visual presentation to the user. The user can scroll through and view multiple outfits.

[0300] Step 6:

[0301] The user selects an outfit from the suggested options and enters their evaluation information into the device. This evaluation information is then sent back to the server and used as training data for the generated AI model. The input is user feedback data, and the output is an update to the server's AI model. A simple form is displayed on the device's screen when providing feedback.

[0302] Step 7:

[0303] Based on the suggested outfit, the terminal presents new clothing purchase options. Here, a link to an e-commerce site is displayed on the screen, allowing the user to directly access the purchase page. The input is the purchase link data sent from the server, and the output is the purchase page accessed by the user. When the user clicks the link, the browser opens and displays the details of the selected item.

[0304] (Application Example 1)

[0305] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0306] In conventional fashion assistant systems, it was difficult for users to utilize them outside of stores or reflect individual feedback when receiving clothing selection or coordination proposals. Additionally, there was a lack of a function to try on the proposed coordination immediately and link it to a purchase. As a result, users were unable to efficiently select clothing, and an improvement in the shopping experience was desired.

[0307] Specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0308] In this invention, the server includes a person recognition means for identifying the user, a decoration recognition means for recording the user's decoration data for the day, a communication means for collecting the decoration data and transmitting it to a computing machine, and a proposal means using a generation mechanism. As a result, the user can confirm the proposed adjustment plan in the store by means of visualization means and make a choice of trying on or purchasing immediately.

[0309] The "user" refers to an individual who uses the system, and particularly the subject who receives clothing selection or coordination proposals.

[0310] The "person recognition means" is a mechanism for recognizing an individual using video information to identify the identity of the user.

[0311] The "decoration recognition means" is a mechanism for identifying decoration items such as clothes and accessories worn by the user and collecting their data.

[0312] The "communication means" is a mechanism for transmitting the collected decoration data to a server or a computing machine.

[0313] The "computing machine" refers to a computer or a server that receives data and performs analysis and processing.

[0314] The "generation mechanism" refers to a process that utilizes a generation AI model to propose an optimal coordination for the user.

[0315] A "suggestion mechanism" is a system that suggests appropriate decoration combinations to the user, taking into account the user's past history and preferences.

[0316] "Visualization means" refers to displays or screens that visually show proposed adjustments or coordinations to the user.

[0317] "Collaboration methods" refer to a series of mechanisms that enable users to try on or purchase the proposed adjustments within the sales space.

[0318] To realize this invention, the following system is necessary: ​​The server acquires image data of the user through a person recognition means and identifies the individual. Next, the accessory recognition means analyzes the clothing the user is wearing and transmits that data to the server via a communication means. At this time, detailed information such as the color, design, and brand of the accessory items is collected.

[0319] The server utilizes a generation mechanism based on received data to suggest outfits tailored to the user. Specifically, it uses a generation AI model to take into account the user's past selection history and preferences to generate the optimal adjustment plan. This suggestion is displayed to the user in real time through visualization means.

[0320] Furthermore, if a user tries on suggested adjustments on the spot or considers purchasing them at a virtual store, the system can be integrated to provide immediate feedback. This system utilizes sensor technology (e.g., cameras) and communication technology (e.g., Wi-Fi, cloud services).

[0321] As a concrete example, a user might use a stand-up mirror-type terminal in a fitting room within a store to take a picture of themselves using their smartphone. The server instantly analyzes the image and displays suggested suit styles on the terminal's screen. An example of a prompt to the generating AI model would be, "Please suggest an outfit that suits the person in this image. Please suggest something casual, based on items available in the store."

[0322] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0323] Step 1:

[0324] The user stands in front of the terminal, and their image data is acquired using a person recognition system. This image data is then input. The terminal uses this image data to identify the user and generate personal identification information.

[0325] Step 2:

[0326] The device uses decorative recognition technology to analyze the user's clothing and accessories. During this process, information such as color, design, and brand is extracted as decorative data. The device then formats this information into structured data and outputs it.

[0327] Step 3:

[0328] The terminal uses a communication method to send the decoration data collected in the previous step to the server. The server receives this data and analyzes it along with relevant information, including the user's preferences and past history.

[0329] Step 4:

[0330] The server uses a generative AI model to generate optimal outfit suggestions for the user based on the collected data. This process utilizes prompt messages (e.g., "Please suggest an outfit for today that suits the person in this image. Please suggest something casual and based on items available in-store.") and sends the generated suggestions to the terminal as data output.

[0331] Step 5:

[0332] The terminal uses visualization tools to display coordination suggestions sent from the server in real time. The user can visually review the suggestions and select adjustments as needed.

[0333] Step 6:

[0334] When a user tries on or purchases a suggested outfit, they send their selection to the server using a linked system. The server accepts the user's selection and stores the information as training data to use for future suggestions.

[0335] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0336] This invention is a personal fashion assistant system designed to provide users with a more comfortable fashion experience. The system operates a camera, display, and emotion engine mounted on a stand-up mirror-type terminal, utilizing the user's emotions and clothing data to provide more accurate style suggestions.

[0337] When a user stands in front of a full-length mirror, the device uses its camera to recognize the user's face and emotions. The emotion engine identifies the user's emotional state (e.g., joy, surprise, sadness) through facial expression analysis. This information enables personalized suggestions based on the user's current emotions.

[0338] Next, the device records the user's clothing and extracts data on color, design, and brand. This data is sent to a server in real time and analyzed along with sentiment data. The server uses a generative AI model to integrate weather, schedule information, historical data, and sentiment-based information to generate data-driven, sentiment-perceptual outfit suggestions.

[0339] The suggested outfits are displayed on the device's screen. The presented outfits are adjusted in color and style based on the user's mood, visually showing the most appealing options. This allows the user to easily choose from a variety of styles that suit their mood.

[0340] Furthermore, users can provide feedback on the suggested outfits. The device collects this feedback and sends it to the server, enabling further improvements to the suggestion algorithm.

[0341] Furthermore, the device provides a link to the online store through purchase support mechanisms when the user wishes to purchase new items. This makes discovering and purchasing fashion items intuitive and seamless.

[0342] This invention, by using an emotion engine, can provide personalized style suggestions that meet the user's psychological needs, thereby improving the quality of everyday clothing choices and enriching the user experience.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] The user stands in front of a standing mirror, and the system starts. The camera automatically activates and captures the user's image.

[0346] Step 2:

[0347] The device uses facial recognition technology to identify the user's identity from the captured image. This then allows access to a corresponding database.

[0348] Step 3:

[0349] The device inputs the user's facial image into an emotion engine, which analyzes the user's current emotional state. The emotion engine extracts emotional information from the user's facial expressions and uses it to make subsequent outfit suggestions.

[0350] Step 4:

[0351] The device photographs the user's clothing and analyzes the data using clothing recognition technology. Information such as the color, design, and brand of the clothing is collected and compiled into clothing data.

[0352] Step 5:

[0353] The device sends the clothing data and emotion data obtained above to the server. The server then begins processing the received data.

[0354] Step 6:

[0355] The server utilizes an AI model to generate outfit suggestions, taking into account the user's emotional state, clothing data, weather information, schedule information, and past style history.

[0356] Step 7:

[0357] The server sends the generated outfit suggestions to the terminal. The terminal displays them on the screen, emphasizing colors and styles that take the user's emotions into consideration.

[0358] Step 8:

[0359] Users review the suggested outfits, select their favorite styles, or provide feedback. This feedback is recorded to further improve the suggestions.

[0360] Step 9:

[0361] The device collects user feedback and sends it to the server. The server uses the feedback as training data to improve the accuracy of future suggestions.

[0362] Step 10:

[0363] The device provides the user with purchase options related to the suggested outfit and displays a link to the online store, allowing the user to easily acquire new items.

[0364] (Example 2)

[0365] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0366] In modern times, fashion is an important element that reflects an individual's lifestyle and emotions. However, choosing the appropriate style to match ever-changing emotional states and environments is not easy. This requires time and effort, and it is difficult to obtain suggestions that directly address the user's interests and emotions. Therefore, there is a need for a system that automatically provides personalized fashion suggestions tailored to an individual's emotional state and preferences.

[0367] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0368] In this invention, the server includes an image recognition means for identifying the user's emotional state, a clothing recognition means for extracting the user's clothing feature data, a transmission means for sending the emotional state and clothing feature data to the server, a suggestion means for suggesting outfits using a generative AI model, and a display means for adjusting and displaying the suggestions according to the user's emotions. This enables personalized style suggestions that effectively respond to the user's changing emotional state.

[0369] "Image recognition means" refers to technology that uses cameras and sensors to analyze a user's face and facial expressions, and then identifies their emotional state based on the results.

[0370] "Clothing recognition means" refers to technology that analyzes the characteristics of the clothing a user is wearing, such as its color, design, and brand, and extracts them as specific data.

[0371] "Transmission means" refers to technology that has the function of transmitting collected emotional data and clothing data to a server via a communication network.

[0372] A "generative AI model" refers to an artificial intelligence algorithm that generates style suggestions based on received data, taking into account the user's emotional state and external factors.

[0373] "Suggestion method" refers to a technology that presents users with style options created by a generative AI model.

[0374] "Display means" refers to displays and projection technologies used to visually present proposed fashion styles to users.

[0375] "Purchase support means" refers to technology that provides users with a link to relevant sales platforms so that they can easily purchase the fashion items suggested to them.

[0376] The system of the present invention uses a stand-up mirror-type terminal and a server connected to it to realize personalized fashion suggestions for the user. The terminal is equipped with a camera for capturing the user's face and entire body, and accompanying image analysis software. These hardware components play a role in quickly acquiring the user's emotional state and clothing characteristics.

[0377] The device first takes a picture of the user's face with its camera when the user stands in front of a full-length mirror. This facial image is then analyzed by an emotion engine to identify the user's current emotional state. For example, if the user is smiling, the emotion "joy" is recognized; if they are frowning, the emotion "surprise" is recognized.

[0378] Next, the device's camera captures the user's entire body, extracting detailed information such as the color, design, and brand of their clothing. This information is then recorded in the system as digital data.

[0379] Based on this data, the device sends emotion data and clothing data to a server via the internet. The server uses a generative AI model to analyze the received information in detail and generate style suggestions optimized for each individual user. This generative AI model enables highly personalized suggestions by considering the user's emotional state, past history, weather information, and other factors.

[0380] As a concrete example, if a user stands in front of a mirror on a cold morning and shows a slightly tired expression, the AI ​​model might suggest a scarf in a cheerful color. This suggestion would be visually displayed on the system's screen, allowing the user to confirm it.

[0381] An example of a prompt message is, "How are you feeling today? What colors and styles do you usually use?" By answering this prompt, the user can receive further customization suggestions.

[0382] Ultimately, users can provide feedback on the suggested outfits. The device records this feedback and sends it to the server to help improve the accuracy of the suggestion algorithm. Furthermore, if a user wishes to purchase a specific item, the device provides a link to the relevant online store, supporting a seamless purchasing experience.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The user stands in front of a full-length mirror and activates the system. The terminal's built-in camera activates and takes a picture of the user's face. The input is the user's facial image data. The terminal passes the facial image to analysis software for facial expression analysis. The output is the analyzed emotional state data of the user (e.g., "joy," "sadness," etc.).

[0386] Step 2:

[0387] The device continues to use its camera to capture a full-body image of the user. The input is the user's full-body image data. From this image, the clothing recognition system extracts feature data such as the color, design, and brand of the clothing. The output is obtained as the user's clothing feature data.

[0388] Step 3:

[0389] The terminal collects the emotional state data and clothing feature data obtained in steps 1 and 2, encrypts them, and transmits them to the server over the network. The inputs are emotional data and clothing data. The server receives this data and records it in its database. The output is the integrated data stored on the server.

[0390] Step 4:

[0391] The server runs a generative AI model based on the received data. The input consists of user emotional state and clothing characteristic data stored on the server. The generative AI model considers additional database information such as weather information and past history to generate style suggestions suitable for the user. The output is a personalized style suggestion.

[0392] Step 5:

[0393] The terminal displays style suggestions received from the server and presents them visually to the user. The input is style suggestion data sent from the server. The suggestions, visualized on the terminal's display, are provided in a format that the user can easily understand and select. The output is the adjusted style suggestion displayed to the user.

[0394] Step 6:

[0395] The user provides feedback on the displayed suggestions. The input is the user's feedback. The terminal records this feedback and sends it back to the server. The output is the feedback data used to improve the suggestion algorithm.

[0396] Step 7:

[0397] The terminal provides links to relevant online stores to support the purchase process if the user wishes to buy the suggested items. The input is the user's purchase request. With a single click, the user can access the purchase page and buy the desired items. The output is the online purchase link accessible to the user.

[0398] (Application Example 2)

[0399] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0400] Conventional fashion suggestion systems often fail to adequately consider the user's mood and emotions, making it difficult to provide the desired style and coordination in real time. The objective of this invention is to improve the user experience by considering the user's emotional state and providing more personalized fashion coordination.

[0401] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0402] In this invention, the server includes image analysis means for identifying the user, data acquisition means for acquiring data on the clothing the user is wearing, data communication means for transmitting emotional state and clothing data to the server, and suggestion means for utilizing a generative model that takes into account the emotional state and comprehensive conditions. This enables optimal fashion suggestions based on the user's emotional state.

[0403] A "user" is an individual person who uses the system, and is the subject of image analysis and emotion recognition.

[0404] "Image analysis means" refers to a device or program that identifies a user and further determines their emotional state by analyzing their facial expressions.

[0405] "Clothing data" refers to information about the clothing a user is currently wearing, including characteristics such as color, design, and brand.

[0406] "Emotion analysis means" refers to a technology that analyzes a user's facial expressions through image analysis and identifies their emotional state.

[0407] "Data communication means" refers to communication technology or devices used to transmit emotional state and clothing data obtained from users to a server.

[0408] A "generative model" refers to an algorithm or AI model that generates the optimal outfit for a user based on data.

[0409] A "proposal method" is a mechanism that presents the coordinated results obtained by the generative model to the user.

[0410] "Display means" refers to a display or interface that visually shows the proposed fashion coordination to the user.

[0411] "Purchase support means" refers to technology that provides an e-commerce environment for users to acquire items related to the suggested outfit.

[0412] "Emotional state" refers to the psychological state based on the user's facial expression analysis, and includes states such as joy, surprise, and sadness.

[0413] "Comprehensive conditions" refer to the various factors considered when making a proposal, specifically including weather, schedule, and past preference history.

[0414] The system for implementing this invention utilizes multiple hardware and software components to provide users with optimal fashion suggestions.

[0415] The user captures their face and clothing through a camera using a device such as a smartphone or smart glasses. The device then uses image analysis technology (e.g., OpenCV) to analyze facial expressions and identifies the emotional state using emotion analysis software called EmotionRecognizer.

[0416] This information is transmitted to the server in real time via data communication. The server is equipped with a generative AI model that runs a fashion suggestion algorithm, and based on the received emotional state and clothing data, it suggests the optimal outfit while considering comprehensive conditions (e.g., weather, past preferences, schedule information, etc.).

[0417] The generated outfit is displayed on the device's screen, allowing the user to visually confirm it. Based on this suggestion, the user can purchase the necessary fashion items, and the purchase assistance system seamlessly integrates with the e-commerce platform.

[0418] As a concrete example, if a user is wearing smart glasses, emotion analysis may detect a state of "joy," and past data may reveal a tendency to prefer "casual summer clothes." The system would then suggest a colorful shirt and light shorts suitable for a sunny day picnic.

[0419] An example of a prompt message would be, "Analyze the user's facial expressions and emotions, and suggest the most suitable fashion items for them."

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The device captures the user's face and clothing using its camera. The input is the captured image data, and the output is image data for analysis. The device preprocesses this data using image analysis technology to extract facial feature points.

[0423] Step 2:

[0424] The device uses EmotionRecognizer to analyze the user's emotional state from image data. The input is facial feature data acquired in the previous step, and the output is the identified emotion label (e.g., joy, surprise). The device analyzes facial expression patterns to identify emotions.

[0425] Step 3:

[0426] The device uses clothing recognition technology to identify the user's clothing data and extract color, design, and brand information. The input is clothing image data, and the output is clothing attribute data. The device analyzes various clothing attributes from the image and formats the data.

[0427] Step 4:

[0428] The terminal transmits emotion information and clothing data to the server via data communication technology. The input is emotion labels and clothing attribute data, and the output is the completion of data transmission to the server.

[0429] Step 5:

[0430] The server analyzes emotion and clothing data received from each user using a generative AI model. The input is a dataset of emotions and clothing, and the output is a suggested outfit. The server performs comprehensive data processing, taking into account weather information and the user's past history, to generate fashion suggestions.

[0431] Step 6:

[0432] The server sends the proposed coordinate to the terminal. The input is the proposed data, and the output is the completion of data transmission to the terminal.

[0433] Step 7:

[0434] The terminal displays the received coordination information on its screen and presents it to the user. The input is the suggested coordination, and the output is a visual presentation to the user.

[0435] Step 8:

[0436] The user selects the most suitable fashion items based on the suggestions. The user's selection is fed back to the device, and a purchase support system provides links to facilitate the purchase of related items. The input is the user's selection data, and the output is purchase support information.

[0437] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0438] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0439] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0443] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0444] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0445] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0446] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0447] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0448] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0449] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0450] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0451] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0452] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0453] This invention provides a personal fashion assistant system that users can use on a daily basis. This system consists of a stand mirror-type terminal equipped with a camera and a display, and assists the user in the process of choosing their daily clothes.

[0454] First, when a user stands in front of a full-length mirror, the device's camera recognizes the user's face and identifies them as an individual. Next, the device automatically records the user's clothing and extracts data on color, design, and brand. This data is used as basic information to understand the user's wearing patterns and preferred style.

[0455] Next, the device sends the collected clothing data to the server. The server uses a generative AI model to analyze the received data and, taking into account the user's preferences and past history, suggests outfits suitable for the weather and schedule. This process is automated, allowing for real-time optimal suggestions to be returned to the user.

[0456] The suggestions are displayed on the device's screen, allowing the user to visually check the outfit. Multiple options are provided for the suggestions, and the user can choose their preferred style. Furthermore, when feedback is entered into the device, the server learns from this information and uses it to improve the accuracy of future suggestions.

[0457] Furthermore, the device supports access to purchase new items included in the suggested outfits. Users can easily access the online store through the provided purchase links, making it convenient to acquire new fashion items.

[0458] This system allows users to reduce the time and effort spent on choosing their daily outfits while expanding their range of self-expression. In this way, the present invention improves the efficiency and quality of style selection in the user's daily life.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] When a user stands in front of a full-length mirror, the camera automatically activates and the device captures the user's face. Facial recognition technology is used to identify the user, and this information is then used to refer to a separate database.

[0462] Step 2:

[0463] The device collects image data of the user's current clothing. Using clothing recognition technology, it analyzes the color, design, and brand information of the clothing to generate detailed clothing data.

[0464] Step 3:

[0465] The device sends the analyzed clothing data to the server. The data is structured and sent to the server in the format required for analysis.

[0466] Step 4:

[0467] Based on the clothing data received by the server, an AI model is used to suggest outfit combinations. Weather forecast data, schedule information, and past user history are used in conjunction to select the most optimal style.

[0468] Step 5:

[0469] The server sends the generated outfit suggestions to the terminal. The terminal displays the suggested outfits on its screen, providing the user with visual information.

[0470] Step 6:

[0471] Users review the displayed outfits and provide feedback based on their preferences and suitability. This feedback includes both positive aspects and areas for improvement.

[0472] Step 7:

[0473] The device collects user feedback and sends it to the server. The server analyzes the feedback data and uses it to improve the accuracy of the proposed algorithm.

[0474] Step 8:

[0475] If the suggested outfit includes a new item, the device will display a purchase link to the online store to the user. The user can easily purchase the item by clicking the link.

[0476] (Example 1)

[0477] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0478] In today's busy lifestyle, choosing what to wear each day is a time-consuming and laborious task. Finding the perfect outfit based on personal preferences and various circumstances is also difficult. Furthermore, online clothing purchases can be overwhelming due to the sheer number of options available. There is a need to address these challenges and provide a more efficient and personalized clothing selection experience.

[0479] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0480] In this invention, the server includes a person identification means for identifying the user, a clothing identification means for recording the user's clothing data for the day, and a display means for displaying suggested outfits to the user. This enables the automatic suggestion of optimal outfits based on the user's individual preferences and conditions.

[0481] "Personal identification means" refers to technology that detects a user's face or other physical characteristics to identify them as an individual.

[0482] "Clothing identification means" refers to technology that analyzes the type and characteristics of the clothing a user is wearing and records it as data.

[0483] "Transmission means" refers to network communication technology for transmitting data from a terminal to an information processing device.

[0484] "Proposal methods using generative models" refer to artificial intelligence technology that proposes the most suitable attire to the user based on collected data.

[0485] "Display means" refers to display technology for visually showing the proposed design to the user.

[0486] "Evaluation information" refers to feedback data provided by users regarding the suggested outfits.

[0487] "Purchase options" refer to the options for purchasing new items included in the suggested outfit.

[0488] An "e-commerce site" is an online platform where goods can be purchased via the internet.

[0489] This invention is a personal fashion assistant system that assists users in choosing their everyday clothing. This system uses a stand mirror-type terminal and proposes the optimal outfit by utilizing the user's personal style data.

[0490] First, the device is equipped with a camera and a display. The camera recognizes the user's face when they stand in front of a full-length mirror and identifies them as an individual. This face recognition can utilize OpenCV, a common image recognition library. This ensures the system's ability to identify each user uniquely.

[0491] Next, the device uses clothing identification to photograph the user's clothing and automatically analyzes the color, design, and brand information of the clothes being worn. In this step, a deep learning model such as YOLOv3 is used to extract clothing features in real time.

[0492] The extracted data is sent from the terminal to the server. The HTTPS protocol is used for transmission, and the data is encoded in JSON format. The server analyzes the received data using a generative AI model and generates appropriate outfits considering the user's history, weather information, schedule, etc. Specific generative AI models include fashion-specific machine learning models using tools like PyTorch.

[0493] The generated outfit suggestions are immediately displayed on the device's screen, allowing the user to choose from multiple style options. When the user provides feedback on their chosen outfit, it is sent back to the server for the AI ​​model to learn from. This feedback loop improves the accuracy of future suggestions.

[0494] The device also offers the option to purchase new clothing items included in the suggestions. A link to the online store is displayed, making it easily accessible to the user.

[0495] This system allows users to reduce the time and effort spent choosing their daily outfits, enabling them to live a more individual and stylish life.

[0496] A concrete example of a prompt message would be, "Generate a new outfit suggestion based on User A's outfit data from the past week and today's weather. Please make it as casual as possible." This prompt helps the system generate an appropriate outfit.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] When a user stands in front of the device, the device's camera automatically activates. The camera captures the user's face, and an image recognition algorithm analyzes it to identify the user. The input is the image data acquired by the camera, and the output is the user's ID. Specifically, a face recognition library (e.g., OpenCV) is used to compare the user's facial characteristics with a database.

[0500] Step 2:

[0501] For identified users, the device uses clothing identification to photograph the user's clothing with its camera. The captured image data is input into a deep learning model (e.g., YOLOv3) to extract features such as the color, design, and brand of the clothing. The input is the captured image, and the output is the analyzed clothing feature data. This data is used to understand the user's fashion style.

[0502] Step 3:

[0503] The terminal sends the extracted clothing feature data to the server. A secure protocol (e.g., HTTPS) is used for this communication. The input is the parsed clothing data, and the output is a notification that the data has been successfully sent to the server. Specifically, this involves converting the data to JSON format and sending it to the server via an HTTP POST request.

[0504] Step 4:

[0505] The server analyzes clothing data received using a generative AI model. The input consists of clothing data sent from the terminal and user history data, while the output is a suggested outfit. Here, machine learning frameworks such as PyTorch are utilized to automatically generate outfits that take into account user preferences, weather, and schedule. A specific prompt used during generation is, "Generate a new outfit suggestion based on user A's outfit data from the past week and today's weather."

[0506] Step 5:

[0507] The server sends the generated outfit information to the terminal. The terminal displays the received outfit on its screen, providing the user with visual feedback. The input is the outfit information sent from the server, and the output is the visual presentation to the user. The user can scroll through and view multiple outfits.

[0508] Step 6:

[0509] The user selects an outfit from the suggested options and enters their evaluation information into the device. This evaluation information is then sent back to the server and used as training data for the generated AI model. The input is user feedback data, and the output is an update to the server's AI model. A simple form is displayed on the device's screen when providing feedback.

[0510] Step 7:

[0511] Based on the suggested outfit, the terminal presents new clothing purchase options. Here, a link to an e-commerce site is displayed on the screen, allowing the user to directly access the purchase page. The input is the purchase link data sent from the server, and the output is the purchase page accessed by the user. When the user clicks the link, the browser opens and displays the details of the selected item.

[0512] (Application Example 1)

[0513] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] Traditional fashion assistant systems made it difficult for users to receive clothing selection and coordination suggestions outside of stores, and to incorporate individual feedback. Furthermore, they lacked the functionality to allow users to try on suggested outfits on the spot and then purchase them. As a result, users were unable to choose clothes efficiently, and there was a need to improve the shopping experience.

[0515] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0516] In this invention, the server includes a person recognition means for identifying the user, a decoration recognition means for recording the user's decoration data for the day, a communication means for collecting the decoration data and transmitting it to a computer, and a suggestion means using a generation mechanism. This allows the user to confirm the suggested adjustments in the store using a visualization means and make choices about trying on or purchasing the items on the spot.

[0517] A "user" refers to an individual who uses the system, and is particularly the subject who receives suggestions for clothing selection and coordination.

[0518] A "person recognition system" is a mechanism that uses video information to recognize an individual in order to identify the user's identity.

[0519] A "decorative recognition means" is a system that identifies decorative items such as clothing and accessories worn by a user and collects data on them.

[0520] "Communication means" refers to a mechanism for transmitting collected decorative data to a server or computer.

[0521] A "processing machine" refers to a computer or server that receives, analyzes, and processes data.

[0522] "Generative mechanism" refers to the process of utilizing generative AI models to suggest the most suitable outfits for users.

[0523] A "suggestion mechanism" is a system that suggests appropriate decoration combinations to the user, taking into account the user's past history and preferences.

[0524] "Visualization means" refers to displays or screens that visually show proposed adjustments or coordinations to the user.

[0525] "Collaboration methods" refer to a series of mechanisms that enable users to try on or purchase the proposed adjustments within the sales space.

[0526] To realize this invention, the following system is necessary: ​​The server acquires image data of the user through a person recognition means and identifies the individual. Next, the accessory recognition means analyzes the clothing the user is wearing and transmits that data to the server via a communication means. At this time, detailed information such as the color, design, and brand of the accessory items is collected.

[0527] The server utilizes a generation mechanism based on received data to suggest outfits tailored to the user. Specifically, it uses a generation AI model to take into account the user's past selection history and preferences to generate the optimal adjustment plan. This suggestion is displayed to the user in real time through visualization means.

[0528] Furthermore, if a user tries on suggested adjustments on the spot or considers purchasing them at a virtual store, the system can be integrated to provide immediate feedback. This system utilizes sensor technology (e.g., cameras) and communication technology (e.g., Wi-Fi, cloud services).

[0529] As a concrete example, a user might use a stand-up mirror-type terminal in a fitting room within a store to take a picture of themselves using their smartphone. The server instantly analyzes the image and displays suggested suit styles on the terminal's screen. An example of a prompt to the generating AI model would be, "Please suggest an outfit that suits the person in this image. Please suggest something casual, based on items available in the store."

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] The user stands in front of the terminal, and their image data is acquired using a person recognition system. This image data is then input. The terminal uses this image data to identify the user and generate personal identification information.

[0533] Step 2:

[0534] The device uses decorative recognition technology to analyze the user's clothing and accessories. During this process, information such as color, design, and brand is extracted as decorative data. The device then formats this information into structured data and outputs it.

[0535] Step 3:

[0536] The terminal uses a communication method to send the decoration data collected in the previous step to the server. The server receives this data and analyzes it along with relevant information, including the user's preferences and past history.

[0537] Step 4:

[0538] The server uses a generative AI model to generate optimal outfit suggestions for the user based on the collected data. This process utilizes prompt messages (e.g., "Please suggest an outfit for today that suits the person in this image. Please suggest something casual and based on items available in-store.") and sends the generated suggestions to the terminal as data output.

[0539] Step 5:

[0540] The terminal uses visualization tools to display coordination suggestions sent from the server in real time. The user can visually review the suggestions and select adjustments as needed.

[0541] Step 6:

[0542] When a user tries on or purchases a suggested outfit, they send their selection to the server using a linked system. The server accepts the user's selection and stores the information as training data to use for future suggestions.

[0543] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0544] This invention is a personal fashion assistant system designed to provide users with a more comfortable fashion experience. The system operates a camera, display, and emotion engine mounted on a stand-up mirror-type terminal, utilizing the user's emotions and clothing data to provide more accurate style suggestions.

[0545] When a user stands in front of a full-length mirror, the device uses its camera to recognize the user's face and emotions. The emotion engine identifies the user's emotional state (e.g., joy, surprise, sadness) through facial expression analysis. This information enables personalized suggestions based on the user's current emotions.

[0546] Next, the device records the user's clothing and extracts data on color, design, and brand. This data is sent to a server in real time and analyzed along with sentiment data. The server uses a generative AI model to integrate weather, schedule information, historical data, and sentiment-based information to generate data-driven, sentiment-perceptual outfit suggestions.

[0547] The suggested outfits are displayed on the device's screen. The presented outfits are adjusted in color and style based on the user's mood, visually showing the most appealing options. This allows the user to easily choose from a variety of styles that suit their mood.

[0548] Furthermore, users can provide feedback on the suggested outfits. The device collects this feedback and sends it to the server, enabling further improvements to the suggestion algorithm.

[0549] Furthermore, the device provides a link to the online store through purchase support mechanisms when the user wishes to purchase new items. This makes discovering and purchasing fashion items intuitive and seamless.

[0550] This invention, by using an emotion engine, can provide personalized style suggestions that meet the user's psychological needs, thereby improving the quality of everyday clothing choices and enriching the user experience.

[0551] The following describes the processing flow.

[0552] Step 1:

[0553] The user stands in front of a standing mirror, and the system starts. The camera automatically activates and captures the user's image.

[0554] Step 2:

[0555] The device uses facial recognition technology to identify the user's identity from the captured image. This then allows access to a corresponding database.

[0556] Step 3:

[0557] The device inputs the user's facial image into an emotion engine, which analyzes the user's current emotional state. The emotion engine extracts emotional information from the user's facial expressions and uses it to make subsequent outfit suggestions.

[0558] Step 4:

[0559] The device photographs the user's clothing and analyzes the data using clothing recognition technology. Information such as the color, design, and brand of the clothing is collected and compiled into clothing data.

[0560] Step 5:

[0561] The device sends the clothing data and emotion data obtained above to the server. The server then begins processing the received data.

[0562] Step 6:

[0563] The server utilizes an AI model to generate outfit suggestions, taking into account the user's emotional state, clothing data, weather information, schedule information, and past style history.

[0564] Step 7:

[0565] The server sends the generated outfit suggestions to the terminal. The terminal displays them on the screen, emphasizing colors and styles that take the user's emotions into consideration.

[0566] Step 8:

[0567] Users review the suggested outfits, select their favorite styles, or provide feedback. This feedback is recorded to further improve the suggestions.

[0568] Step 9:

[0569] The device collects user feedback and sends it to the server. The server uses the feedback as training data to improve the accuracy of future suggestions.

[0570] Step 10:

[0571] The device provides the user with purchase options related to the suggested outfit and displays a link to the online store, allowing the user to easily acquire new items.

[0572] (Example 2)

[0573] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0574] In modern times, fashion is an important element that reflects an individual's lifestyle and emotions. However, choosing the appropriate style to match ever-changing emotional states and environments is not easy. This requires time and effort, and it is difficult to obtain suggestions that directly address the user's interests and emotions. Therefore, there is a need for a system that automatically provides personalized fashion suggestions tailored to an individual's emotional state and preferences.

[0575] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0576] In this invention, the server includes an image recognition means for identifying the user's emotional state, a clothing recognition means for extracting the user's clothing feature data, a transmission means for sending the emotional state and clothing feature data to the server, a suggestion means for suggesting outfits using a generative AI model, and a display means for adjusting and displaying the suggestions according to the user's emotions. This enables personalized style suggestions that effectively respond to the user's changing emotional state.

[0577] "Image recognition means" refers to technology that uses cameras and sensors to analyze a user's face and facial expressions, and then identifies their emotional state based on the results.

[0578] "Clothing recognition means" refers to technology that analyzes the characteristics of the clothing a user is wearing, such as its color, design, and brand, and extracts them as specific data.

[0579] "Transmission means" refers to technology that has the function of transmitting collected emotional data and clothing data to a server via a communication network.

[0580] A "generative AI model" refers to an artificial intelligence algorithm that generates style suggestions based on received data, taking into account the user's emotional state and external factors.

[0581] "Suggestion method" refers to a technology that presents users with style options created by a generative AI model.

[0582] "Display means" refers to displays and projection technologies used to visually present proposed fashion styles to users.

[0583] "Purchase support means" refers to technology that provides users with a link to relevant sales platforms so that they can easily purchase the fashion items suggested to them.

[0584] The system of the present invention uses a stand-up mirror-type terminal and a server connected to it to realize personalized fashion suggestions for the user. The terminal is equipped with a camera for capturing the user's face and entire body, and accompanying image analysis software. These hardware components play a role in quickly acquiring the user's emotional state and clothing characteristics.

[0585] The device first takes a picture of the user's face with its camera when the user stands in front of a full-length mirror. This facial image is then analyzed by an emotion engine to identify the user's current emotional state. For example, if the user is smiling, the emotion "joy" is recognized; if they are frowning, the emotion "surprise" is recognized.

[0586] Next, the device's camera captures the user's entire body, extracting detailed information such as the color, design, and brand of their clothing. This information is then recorded in the system as digital data.

[0587] Based on this data, the device sends emotion data and clothing data to a server via the internet. The server uses a generative AI model to analyze the received information in detail and generate style suggestions optimized for each individual user. This generative AI model enables highly personalized suggestions by considering the user's emotional state, past history, weather information, and other factors.

[0588] As a concrete example, if a user stands in front of a mirror on a cold morning and shows a slightly tired expression, the AI ​​model might suggest a scarf in a cheerful color. This suggestion would be visually displayed on the system's screen, allowing the user to confirm it.

[0589] An example of a prompt message is, "How are you feeling today? What colors and styles do you usually use?" By answering this prompt, the user can receive further customization suggestions.

[0590] Ultimately, users can provide feedback on the suggested outfits. The device records this feedback and sends it to the server to help improve the accuracy of the suggestion algorithm. Furthermore, if a user wishes to purchase a specific item, the device provides a link to the relevant online store, supporting a seamless purchasing experience.

[0591] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0592] Step 1:

[0593] The user stands in front of a full-length mirror and activates the system. The terminal's built-in camera activates and takes a picture of the user's face. The input is the user's facial image data. The terminal passes the facial image to analysis software for facial expression analysis. The output is the analyzed emotional state data of the user (e.g., "joy," "sadness," etc.).

[0594] Step 2:

[0595] The device continues to use its camera to capture a full-body image of the user. The input is the user's full-body image data. From this image, the clothing recognition system extracts feature data such as the color, design, and brand of the clothing. The output is obtained as the user's clothing feature data.

[0596] Step 3:

[0597] The terminal collects the emotional state data and clothing feature data obtained in steps 1 and 2, encrypts them, and transmits them to the server over the network. The inputs are emotional data and clothing data. The server receives this data and records it in its database. The output is the integrated data stored on the server.

[0598] Step 4:

[0599] The server runs a generative AI model based on the received data. The input consists of user emotional state and clothing characteristic data stored on the server. The generative AI model considers additional database information such as weather information and past history to generate style suggestions suitable for the user. The output is a personalized style suggestion.

[0600] Step 5:

[0601] The terminal displays style suggestions received from the server and presents them visually to the user. The input is style suggestion data sent from the server. The suggestions, visualized on the terminal's display, are provided in a format that the user can easily understand and select. The output is the adjusted style suggestion displayed to the user.

[0602] Step 6:

[0603] The user provides feedback on the displayed suggestions. The input is the user's feedback. The terminal records this feedback and sends it back to the server. The output is the feedback data used to improve the suggestion algorithm.

[0604] Step 7:

[0605] The terminal provides links to relevant online stores to support the purchase process if the user wishes to buy the suggested items. The input is the user's purchase request. With a single click, the user can access the purchase page and buy the desired items. The output is the online purchase link accessible to the user.

[0606] (Application Example 2)

[0607] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0608] Conventional fashion suggestion systems often fail to adequately consider the user's mood and emotions, making it difficult to provide the desired style and coordination in real time. The objective of this invention is to improve the user experience by considering the user's emotional state and providing more personalized fashion coordination.

[0609] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0610] In this invention, the server includes image analysis means for identifying the user, data acquisition means for acquiring data on the clothing the user is wearing, data communication means for transmitting emotional state and clothing data to the server, and suggestion means for utilizing a generative model that takes into account the emotional state and comprehensive conditions. This enables optimal fashion suggestions based on the user's emotional state.

[0611] A "user" is an individual person who uses the system, and is the subject of image analysis and emotion recognition.

[0612] "Image analysis means" refers to a device or program that identifies a user and further determines their emotional state by analyzing their facial expressions.

[0613] "Clothing data" refers to information about the clothing a user is currently wearing, including characteristics such as color, design, and brand.

[0614] "Emotion analysis means" refers to a technology that analyzes a user's facial expressions through image analysis and identifies their emotional state.

[0615] "Data communication means" refers to communication technology or devices used to transmit emotional state and clothing data obtained from users to a server.

[0616] A "generative model" refers to an algorithm or AI model that generates the optimal outfit for a user based on data.

[0617] A "proposal method" is a mechanism that presents the coordinated results obtained by the generative model to the user.

[0618] "Display means" refers to a display or interface that visually shows the proposed fashion coordination to the user.

[0619] "Purchase support means" refers to technology that provides an e-commerce environment for users to acquire items related to the suggested outfit.

[0620] "Emotional state" refers to the psychological state based on the user's facial expression analysis, and includes states such as joy, surprise, and sadness.

[0621] "Comprehensive conditions" refer to the various factors considered when making a proposal, specifically including weather, schedule, and past preference history.

[0622] The system for implementing this invention utilizes multiple hardware and software components to provide users with optimal fashion suggestions.

[0623] The user captures their face and clothing through a camera using a device such as a smartphone or smart glasses. The device then uses image analysis technology (e.g., OpenCV) to analyze facial expressions and identifies the emotional state using emotion analysis software called EmotionRecognizer.

[0624] This information is transmitted to the server in real time via data communication. The server is equipped with a generative AI model that runs a fashion suggestion algorithm, and based on the received emotional state and clothing data, it suggests the optimal outfit while considering comprehensive conditions (e.g., weather, past preferences, schedule information, etc.).

[0625] The generated outfit is displayed on the device's screen, allowing the user to visually confirm it. Based on this suggestion, the user can purchase the necessary fashion items, and the purchase assistance system seamlessly integrates with the e-commerce platform.

[0626] As a concrete example, if a user is wearing smart glasses, emotion analysis may detect a state of "joy," and past data may reveal a tendency to prefer "casual summer clothes." The system would then suggest a colorful shirt and light shorts suitable for a sunny day picnic.

[0627] An example of a prompt message would be, "Analyze the user's facial expressions and emotions, and suggest the most suitable fashion items for them."

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The device captures the user's face and clothing using its camera. The input is the captured image data, and the output is image data for analysis. The device preprocesses this data using image analysis technology to extract facial feature points.

[0631] Step 2:

[0632] The device uses EmotionRecognizer to analyze the user's emotional state from image data. The input is facial feature data acquired in the previous step, and the output is the identified emotion label (e.g., joy, surprise). The device analyzes facial expression patterns to identify emotions.

[0633] Step 3:

[0634] The device uses clothing recognition technology to identify the user's clothing data and extract color, design, and brand information. The input is clothing image data, and the output is clothing attribute data. The device analyzes various clothing attributes from the image and formats the data.

[0635] Step 4:

[0636] The terminal transmits emotion information and clothing data to the server via data communication technology. The input is emotion labels and clothing attribute data, and the output is the completion of data transmission to the server.

[0637] Step 5:

[0638] The server analyzes emotion and clothing data received from each user using a generative AI model. The input is a dataset of emotions and clothing, and the output is a suggested outfit. The server performs comprehensive data processing, taking into account weather information and the user's past history, to generate fashion suggestions.

[0639] Step 6:

[0640] The server sends the proposed coordinate to the terminal. The input is the proposed data, and the output is the completion of data transmission to the terminal.

[0641] Step 7:

[0642] The terminal displays the received coordination information on its screen and presents it to the user. The input is the suggested coordination, and the output is a visual presentation to the user.

[0643] Step 8:

[0644] The user selects the most suitable fashion items based on the suggestions. The user's selection is fed back to the device, and a purchase support system provides links to facilitate the purchase of related items. The input is the user's selection data, and the output is purchase support information.

[0645] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0646] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0647] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0651] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0652] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0653] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0654] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0655] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0656] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0657] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0658] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0659] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0660] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0661] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] This invention provides a personal fashion assistant system that users can use on a daily basis. This system consists of a stand mirror-type terminal equipped with a camera and a display, and assists the user in the process of choosing their daily clothes.

[0663] First, when a user stands in front of a full-length mirror, the device's camera recognizes the user's face and identifies them as an individual. Next, the device automatically records the user's clothing and extracts data on color, design, and brand. This data is used as basic information to understand the user's wearing patterns and preferred style.

[0664] Next, the device sends the collected clothing data to the server. The server uses a generative AI model to analyze the received data and, taking into account the user's preferences and past history, suggests outfits suitable for the weather and schedule. This process is automated, allowing for real-time optimal suggestions to be returned to the user.

[0665] The suggestions are displayed on the device's screen, allowing the user to visually check the outfit. Multiple options are provided for the suggestions, and the user can choose their preferred style. Furthermore, when feedback is entered into the device, the server learns from this information and uses it to improve the accuracy of future suggestions.

[0666] Furthermore, the device supports access to purchase new items included in the suggested outfits. Users can easily access the online store through the provided purchase links, making it convenient to acquire new fashion items.

[0667] This system allows users to reduce the time and effort spent on choosing their daily outfits while expanding their range of self-expression. In this way, the present invention improves the efficiency and quality of style selection in the user's daily life.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] When a user stands in front of a full-length mirror, the camera automatically activates and the device captures the user's face. Facial recognition technology is used to identify the user, and this information is then used to refer to a separate database.

[0671] Step 2:

[0672] The device collects image data of the user's current clothing. Using clothing recognition technology, it analyzes the color, design, and brand information of the clothing to generate detailed clothing data.

[0673] Step 3:

[0674] The device sends the analyzed clothing data to the server. The data is structured and sent to the server in the format required for analysis.

[0675] Step 4:

[0676] Based on the clothing data received by the server, an AI model is used to suggest outfit combinations. Weather forecast data, schedule information, and past user history are used in conjunction to select the most optimal style.

[0677] Step 5:

[0678] The server sends the generated outfit suggestions to the terminal. The terminal displays the suggested outfits on its screen, providing the user with visual information.

[0679] Step 6:

[0680] Users review the displayed outfits and provide feedback based on their preferences and suitability. This feedback includes both positive aspects and areas for improvement.

[0681] Step 7:

[0682] The device collects user feedback and sends it to the server. The server analyzes the feedback data and uses it to improve the accuracy of the proposed algorithm.

[0683] Step 8:

[0684] If the suggested outfit includes a new item, the device will display a purchase link to the online store to the user. The user can easily purchase the item by clicking the link.

[0685] (Example 1)

[0686] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] In today's busy lifestyle, choosing what to wear each day is a time-consuming and laborious task. Finding the perfect outfit based on personal preferences and various circumstances is also difficult. Furthermore, online clothing purchases can be overwhelming due to the sheer number of options available. There is a need to address these challenges and provide a more efficient and personalized clothing selection experience.

[0688] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0689] In this invention, the server includes a person identification means for identifying the user, a clothing identification means for recording the user's clothing data for the day, and a display means for displaying suggested outfits to the user. This enables the automatic suggestion of optimal outfits based on the user's individual preferences and conditions.

[0690] "Personal identification means" refers to technology that detects a user's face or other physical characteristics to identify them as an individual.

[0691] "Clothing identification means" refers to technology that analyzes the type and characteristics of the clothing a user is wearing and records it as data.

[0692] "Transmission means" refers to network communication technology for transmitting data from a terminal to an information processing device.

[0693] "Proposal methods using generative models" refer to artificial intelligence technology that proposes the most suitable attire to the user based on collected data.

[0694] "Display means" refers to display technology for visually showing the proposed design to the user.

[0695] "Evaluation information" refers to feedback data provided by users regarding the suggested outfits.

[0696] "Purchase options" refer to the options for purchasing new items included in the suggested outfit.

[0697] An "e-commerce site" is an online platform where goods can be purchased via the internet.

[0698] This invention is a personal fashion assistant system that assists users in choosing their everyday clothing. This system uses a stand mirror-type terminal and proposes the optimal outfit by utilizing the user's personal style data.

[0699] First, the device is equipped with a camera and a display. The camera recognizes the user's face when they stand in front of a full-length mirror and identifies them as an individual. This face recognition can utilize OpenCV, a common image recognition library. This ensures the system's ability to identify each user uniquely.

[0700] Next, the device uses clothing identification to photograph the user's clothing and automatically analyzes the color, design, and brand information of the clothes being worn. In this step, a deep learning model such as YOLOv3 is used to extract clothing features in real time.

[0701] The extracted data is sent from the terminal to the server. The HTTPS protocol is used for transmission, and the data is encoded in JSON format. The server analyzes the received data using a generative AI model and generates appropriate outfits considering the user's history, weather information, schedule, etc. Specific generative AI models include fashion-specific machine learning models using tools like PyTorch.

[0702] The generated outfit suggestions are immediately displayed on the device's screen, allowing the user to choose from multiple style options. When the user provides feedback on their chosen outfit, it is sent back to the server for the AI ​​model to learn from. This feedback loop improves the accuracy of future suggestions.

[0703] The device also offers the option to purchase new clothing items included in the suggestions. A link to the online store is displayed, making it easily accessible to the user.

[0704] This system allows users to reduce the time and effort spent choosing their daily outfits, enabling them to live a more individual and stylish life.

[0705] A concrete example of a prompt message would be, "Generate a new outfit suggestion based on User A's outfit data from the past week and today's weather. Please make it as casual as possible." This prompt helps the system generate an appropriate outfit.

[0706] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0707] Step 1:

[0708] When a user stands in front of the device, the device's camera automatically activates. The camera captures the user's face, and an image recognition algorithm analyzes it to identify the user. The input is the image data acquired by the camera, and the output is the user's ID. Specifically, a face recognition library (e.g., OpenCV) is used to compare the user's facial characteristics with a database.

[0709] Step 2:

[0710] For identified users, the device uses clothing identification to photograph the user's clothing with its camera. The captured image data is input into a deep learning model (e.g., YOLOv3) to extract features such as the color, design, and brand of the clothing. The input is the captured image, and the output is the analyzed clothing feature data. This data is used to understand the user's fashion style.

[0711] Step 3:

[0712] The terminal sends the extracted clothing feature data to the server. A secure protocol (e.g., HTTPS) is used for this communication. The input is the parsed clothing data, and the output is a notification that the data has been successfully sent to the server. Specifically, this involves converting the data to JSON format and sending it to the server via an HTTP POST request.

[0713] Step 4:

[0714] The server analyzes clothing data received using a generative AI model. The input consists of clothing data sent from the terminal and user history data, while the output is a suggested outfit. Here, machine learning frameworks such as PyTorch are utilized to automatically generate outfits that take into account user preferences, weather, and schedule. A specific prompt used during generation is, "Generate a new outfit suggestion based on user A's outfit data from the past week and today's weather."

[0715] Step 5:

[0716] The server sends the generated outfit information to the terminal. The terminal displays the received outfit on its screen, providing the user with visual feedback. The input is the outfit information sent from the server, and the output is the visual presentation to the user. The user can scroll through and view multiple outfits.

[0717] Step 6:

[0718] The user selects an outfit from the suggested options and enters their evaluation information into the device. This evaluation information is then sent back to the server and used as training data for the generated AI model. The input is user feedback data, and the output is an update to the server's AI model. A simple form is displayed on the device's screen when providing feedback.

[0719] Step 7:

[0720] Based on the suggested outfit, the terminal presents new clothing purchase options. Here, a link to an e-commerce site is displayed on the screen, allowing the user to directly access the purchase page. The input is the purchase link data sent from the server, and the output is the purchase page accessed by the user. When the user clicks the link, the browser opens and displays the details of the selected item.

[0721] (Application Example 1)

[0722] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] Traditional fashion assistant systems made it difficult for users to receive clothing selection and coordination suggestions outside of stores, and to incorporate individual feedback. Furthermore, they lacked the functionality to allow users to try on suggested outfits on the spot and then purchase them. As a result, users were unable to choose clothes efficiently, and there was a need to improve the shopping experience.

[0724] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0725] In this invention, the server includes a person recognition means for identifying the user, a decoration recognition means for recording the user's decoration data for the day, a communication means for collecting the decoration data and transmitting it to a computer, and a suggestion means using a generation mechanism. This allows the user to confirm the suggested adjustments in the store using a visualization means and make choices about trying on or purchasing the items on the spot.

[0726] A "user" refers to an individual who uses the system, and is particularly the subject who receives suggestions for clothing selection and coordination.

[0727] A "person recognition system" is a mechanism that uses video information to recognize an individual in order to identify the user's identity.

[0728] A "decorative recognition means" is a system that identifies decorative items such as clothing and accessories worn by a user and collects data on them.

[0729] "Communication means" refers to a mechanism for transmitting collected decorative data to a server or computer.

[0730] A "processing machine" refers to a computer or server that receives, analyzes, and processes data.

[0731] "Generative mechanism" refers to the process of utilizing generative AI models to suggest the most suitable outfits for users.

[0732] A "suggestion mechanism" is a system that suggests appropriate decoration combinations to the user, taking into account the user's past history and preferences.

[0733] "Visualization means" refers to displays or screens that visually show proposed adjustments or coordinations to the user.

[0734] "Collaboration methods" refer to a series of mechanisms that enable users to try on or purchase the proposed adjustments within the sales space.

[0735] To realize this invention, the following system is necessary: ​​The server acquires image data of the user through a person recognition means and identifies the individual. Next, the accessory recognition means analyzes the clothing the user is wearing and transmits that data to the server via a communication means. At this time, detailed information such as the color, design, and brand of the accessory items is collected.

[0736] The server utilizes a generation mechanism based on received data to suggest outfits tailored to the user. Specifically, it uses a generation AI model to take into account the user's past selection history and preferences to generate the optimal adjustment plan. This suggestion is displayed to the user in real time through visualization means.

[0737] Furthermore, if a user tries on suggested adjustments on the spot or considers purchasing them at a virtual store, the system can be integrated to provide immediate feedback. This system utilizes sensor technology (e.g., cameras) and communication technology (e.g., Wi-Fi, cloud services).

[0738] As a concrete example, a user might use a stand-up mirror-type terminal in a fitting room within a store to take a picture of themselves using their smartphone. The server instantly analyzes the image and displays suggested suit styles on the terminal's screen. An example of a prompt to the generating AI model would be, "Please suggest an outfit that suits the person in this image. Please suggest something casual, based on items available in the store."

[0739] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0740] Step 1:

[0741] The user stands in front of the terminal, and their image data is acquired using a person recognition system. This image data is then input. The terminal uses this image data to identify the user and generate personal identification information.

[0742] Step 2:

[0743] The device uses decorative recognition technology to analyze the user's clothing and accessories. During this process, information such as color, design, and brand is extracted as decorative data. The device then formats this information into structured data and outputs it.

[0744] Step 3:

[0745] The terminal uses a communication method to send the decoration data collected in the previous step to the server. The server receives this data and analyzes it along with relevant information, including the user's preferences and past history.

[0746] Step 4:

[0747] The server uses a generative AI model to generate optimal outfit suggestions for the user based on the collected data. This process utilizes prompt messages (e.g., "Please suggest an outfit for today that suits the person in this image. Please suggest something casual and based on items available in-store.") and sends the generated suggestions to the terminal as data output.

[0748] Step 5:

[0749] The terminal uses visualization tools to display coordination suggestions sent from the server in real time. The user can visually review the suggestions and select adjustments as needed.

[0750] Step 6:

[0751] When a user tries on or purchases a suggested outfit, they send their selection to the server using a linked system. The server accepts the user's selection and stores the information as training data to use for future suggestions.

[0752] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0753] This invention is a personal fashion assistant system designed to provide users with a more comfortable fashion experience. The system operates a camera, display, and emotion engine mounted on a stand-up mirror-type terminal, utilizing the user's emotions and clothing data to provide more accurate style suggestions.

[0754] When a user stands in front of a full-length mirror, the device uses its camera to recognize the user's face and emotions. The emotion engine identifies the user's emotional state (e.g., joy, surprise, sadness) through facial expression analysis. This information enables personalized suggestions based on the user's current emotions.

[0755] Next, the device records the user's clothing and extracts data on color, design, and brand. This data is sent to a server in real time and analyzed along with sentiment data. The server uses a generative AI model to integrate weather, schedule information, historical data, and sentiment-based information to generate data-driven, sentiment-perceptual outfit suggestions.

[0756] The suggested outfits are displayed on the device's screen. The presented outfits are adjusted in color and style based on the user's mood, visually showing the most appealing options. This allows the user to easily choose from a variety of styles that suit their mood.

[0757] Furthermore, users can provide feedback on the suggested outfits. The device collects this feedback and sends it to the server, enabling further improvements to the suggestion algorithm.

[0758] Furthermore, the device provides a link to the online store through purchase support mechanisms when the user wishes to purchase new items. This makes discovering and purchasing fashion items intuitive and seamless.

[0759] This invention, by using an emotion engine, can provide personalized style suggestions that meet the user's psychological needs, thereby improving the quality of everyday clothing choices and enriching the user experience.

[0760] The following describes the processing flow.

[0761] Step 1:

[0762] The user stands in front of a standing mirror, and the system starts. The camera automatically activates and captures the user's image.

[0763] Step 2:

[0764] The device uses facial recognition technology to identify the user's identity from the captured image. This then allows access to a corresponding database.

[0765] Step 3:

[0766] The device inputs the user's facial image into an emotion engine, which analyzes the user's current emotional state. The emotion engine extracts emotional information from the user's facial expressions and uses it to make subsequent outfit suggestions.

[0767] Step 4:

[0768] The device photographs the user's clothing and analyzes the data using clothing recognition technology. Information such as the color, design, and brand of the clothing is collected and compiled into clothing data.

[0769] Step 5:

[0770] The device sends the clothing data and emotion data obtained above to the server. The server then begins processing the received data.

[0771] Step 6:

[0772] The server utilizes an AI model to generate outfit suggestions, taking into account the user's emotional state, clothing data, weather information, schedule information, and past style history.

[0773] Step 7:

[0774] The server sends the generated outfit suggestions to the terminal. The terminal displays them on the screen, emphasizing colors and styles that take the user's emotions into consideration.

[0775] Step 8:

[0776] Users review the suggested outfits, select their favorite styles, or provide feedback. This feedback is recorded to further improve the suggestions.

[0777] Step 9:

[0778] The device collects user feedback and sends it to the server. The server uses the feedback as training data to improve the accuracy of future suggestions.

[0779] Step 10:

[0780] The device provides the user with purchase options related to the suggested outfit and displays a link to the online store, allowing the user to easily acquire new items.

[0781] (Example 2)

[0782] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] In modern times, fashion is an important element that reflects an individual's lifestyle and emotions. However, choosing the appropriate style to match ever-changing emotional states and environments is not easy. This requires time and effort, and it is difficult to obtain suggestions that directly address the user's interests and emotions. Therefore, there is a need for a system that automatically provides personalized fashion suggestions tailored to an individual's emotional state and preferences.

[0784] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0785] In this invention, the server includes an image recognition means for identifying the user's emotional state, a clothing recognition means for extracting the user's clothing feature data, a transmission means for sending the emotional state and clothing feature data to the server, a suggestion means for suggesting outfits using a generative AI model, and a display means for adjusting and displaying the suggestions according to the user's emotions. This enables personalized style suggestions that effectively respond to the user's changing emotional state.

[0786] "Image recognition means" refers to technology that uses cameras and sensors to analyze a user's face and facial expressions, and then identifies their emotional state based on the results.

[0787] "Clothing recognition means" refers to technology that analyzes the characteristics of the clothing a user is wearing, such as its color, design, and brand, and extracts them as specific data.

[0788] "Transmission means" refers to technology that has the function of transmitting collected emotional data and clothing data to a server via a communication network.

[0789] A "generative AI model" refers to an artificial intelligence algorithm that generates style suggestions based on received data, taking into account the user's emotional state and external factors.

[0790] "Suggestion method" refers to a technology that presents users with style options created by a generative AI model.

[0791] "Display means" refers to displays and projection technologies used to visually present proposed fashion styles to users.

[0792] "Purchase support means" refers to technology that provides users with a link to relevant sales platforms so that they can easily purchase the fashion items suggested to them.

[0793] The system of the present invention uses a stand-up mirror-type terminal and a server connected to it to realize personalized fashion suggestions for the user. The terminal is equipped with a camera for capturing the user's face and entire body, and accompanying image analysis software. These hardware components play a role in quickly acquiring the user's emotional state and clothing characteristics.

[0794] The device first takes a picture of the user's face with its camera when the user stands in front of a full-length mirror. This facial image is then analyzed by an emotion engine to identify the user's current emotional state. For example, if the user is smiling, the emotion "joy" is recognized; if they are frowning, the emotion "surprise" is recognized.

[0795] Next, the device's camera captures the user's entire body, extracting detailed information such as the color, design, and brand of their clothing. This information is then recorded in the system as digital data.

[0796] Based on this data, the device sends emotion data and clothing data to a server via the internet. The server uses a generative AI model to analyze the received information in detail and generate style suggestions optimized for each individual user. This generative AI model enables highly personalized suggestions by considering the user's emotional state, past history, weather information, and other factors.

[0797] As a concrete example, if a user stands in front of a mirror on a cold morning and shows a slightly tired expression, the AI ​​model might suggest a scarf in a cheerful color. This suggestion would be visually displayed on the system's screen, allowing the user to confirm it.

[0798] An example of a prompt message is, "How are you feeling today? What colors and styles do you usually use?" By answering this prompt, the user can receive further customization suggestions.

[0799] Ultimately, users can provide feedback on the suggested outfits. The device records this feedback and sends it to the server to help improve the accuracy of the suggestion algorithm. Furthermore, if a user wishes to purchase a specific item, the device provides a link to the relevant online store, supporting a seamless purchasing experience.

[0800] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0801] Step 1:

[0802] The user stands in front of a full-length mirror and activates the system. The terminal's built-in camera activates and takes a picture of the user's face. The input is the user's facial image data. The terminal passes the facial image to analysis software for facial expression analysis. The output is the analyzed emotional state data of the user (e.g., "joy," "sadness," etc.).

[0803] Step 2:

[0804] The device continues to use its camera to capture a full-body image of the user. The input is the user's full-body image data. From this image, the clothing recognition system extracts feature data such as the color, design, and brand of the clothing. The output is obtained as the user's clothing feature data.

[0805] Step 3:

[0806] The terminal collects the emotional state data and clothing feature data obtained in steps 1 and 2, encrypts them, and transmits them to the server over the network. The inputs are emotional data and clothing data. The server receives this data and records it in its database. The output is the integrated data stored on the server.

[0807] Step 4:

[0808] The server runs a generative AI model based on the received data. The input consists of user emotional state and clothing characteristic data stored on the server. The generative AI model considers additional database information such as weather information and past history to generate style suggestions suitable for the user. The output is a personalized style suggestion.

[0809] Step 5:

[0810] The terminal displays style suggestions received from the server and presents them visually to the user. The input is style suggestion data sent from the server. The suggestions, visualized on the terminal's display, are provided in a format that the user can easily understand and select. The output is the adjusted style suggestion displayed to the user.

[0811] Step 6:

[0812] The user provides feedback on the displayed suggestions. The input is the user's feedback. The terminal records this feedback and sends it back to the server. The output is the feedback data used to improve the suggestion algorithm.

[0813] Step 7:

[0814] The terminal provides links to relevant online stores to support the purchase process if the user wishes to buy the suggested items. The input is the user's purchase request. With a single click, the user can access the purchase page and buy the desired items. The output is the online purchase link accessible to the user.

[0815] (Application Example 2)

[0816] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0817] Conventional fashion suggestion systems often fail to adequately consider the user's mood and emotions, making it difficult to provide the desired style and coordination in real time. The objective of this invention is to improve the user experience by considering the user's emotional state and providing more personalized fashion coordination.

[0818] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0819] In this invention, the server includes image analysis means for identifying the user, data acquisition means for acquiring data on the clothing the user is wearing, data communication means for transmitting emotional state and clothing data to the server, and suggestion means for utilizing a generative model that takes into account the emotional state and comprehensive conditions. This enables optimal fashion suggestions based on the user's emotional state.

[0820] A "user" is an individual person who uses the system, and is the subject of image analysis and emotion recognition.

[0821] "Image analysis means" refers to a device or program that identifies a user and further determines their emotional state by analyzing their facial expressions.

[0822] "Clothing data" refers to information about the clothing a user is currently wearing, including characteristics such as color, design, and brand.

[0823] "Emotion analysis means" refers to a technology that analyzes a user's facial expressions through image analysis and identifies their emotional state.

[0824] "Data communication means" refers to communication technology or devices used to transmit emotional state and clothing data obtained from users to a server.

[0825] A "generative model" refers to an algorithm or AI model that generates the optimal outfit for a user based on data.

[0826] A "proposal method" is a mechanism that presents the coordinated results obtained by the generative model to the user.

[0827] "Display means" refers to a display or interface that visually shows the proposed fashion coordination to the user.

[0828] "Purchase support means" refers to technology that provides an e-commerce environment for users to acquire items related to the suggested outfit.

[0829] "Emotional state" refers to the psychological state based on the user's facial expression analysis, and includes states such as joy, surprise, and sadness.

[0830] "Comprehensive conditions" refer to the various factors considered when making a proposal, specifically including weather, schedule, and past preference history.

[0831] The system for implementing this invention utilizes multiple hardware and software components to provide users with optimal fashion suggestions.

[0832] The user captures their face and clothing through a camera using a device such as a smartphone or smart glasses. The device then uses image analysis technology (e.g., OpenCV) to analyze facial expressions and identifies the emotional state using emotion analysis software called EmotionRecognizer.

[0833] This information is transmitted to the server in real time via data communication. The server is equipped with a generative AI model that runs a fashion suggestion algorithm, and based on the received emotional state and clothing data, it suggests the optimal outfit while considering comprehensive conditions (e.g., weather, past preferences, schedule information, etc.).

[0834] The generated outfit is displayed on the device's screen, allowing the user to visually confirm it. Based on this suggestion, the user can purchase the necessary fashion items, and the purchase assistance system seamlessly integrates with the e-commerce platform.

[0835] As a concrete example, if a user is wearing smart glasses, emotion analysis may detect a state of "joy," and past data may reveal a tendency to prefer "casual summer clothes." The system would then suggest a colorful shirt and light shorts suitable for a sunny day picnic.

[0836] An example of a prompt message would be, "Analyze the user's facial expressions and emotions, and suggest the most suitable fashion items for them."

[0837] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0838] Step 1:

[0839] The device captures the user's face and clothing using its camera. The input is the captured image data, and the output is image data for analysis. The device preprocesses this data using image analysis technology to extract facial feature points.

[0840] Step 2:

[0841] The device uses EmotionRecognizer to analyze the user's emotional state from image data. The input is facial feature data acquired in the previous step, and the output is the identified emotion label (e.g., joy, surprise). The device analyzes facial expression patterns to identify emotions.

[0842] Step 3:

[0843] The device uses clothing recognition technology to identify the user's clothing data and extract color, design, and brand information. The input is clothing image data, and the output is clothing attribute data. The device analyzes various clothing attributes from the image and formats the data.

[0844] Step 4:

[0845] The terminal transmits emotion information and clothing data to the server via data communication technology. The input is emotion labels and clothing attribute data, and the output is the completion of data transmission to the server.

[0846] Step 5:

[0847] The server analyzes emotion and clothing data received from each user using a generative AI model. The input is a dataset of emotions and clothing, and the output is a suggested outfit. The server performs comprehensive data processing, taking into account weather information and the user's past history, to generate fashion suggestions.

[0848] Step 6:

[0849] The server sends the proposed coordinate to the terminal. The input is the proposed data, and the output is the completion of data transmission to the terminal.

[0850] Step 7:

[0851] The terminal displays the received coordination information on its screen and presents it to the user. The input is the suggested coordination, and the output is a visual presentation to the user.

[0852] Step 8:

[0853] The user selects the most suitable fashion items based on the suggestions. The user's selection is fed back to the device, and a purchase support system provides links to facilitate the purchase of related items. The input is the user's selection data, and the output is purchase support information.

[0854] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0855] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0856] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0857] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0858] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0859] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0860] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0861] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0862] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0863] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0864] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0865] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0866] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0867] 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.

[0868] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0869] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0870] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0871] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0872] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0873] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0874] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0875] The following is further disclosed regarding the embodiments described above.

[0876] (Claim 1)

[0877] Image recognition means for identifying users,

[0878] A clothing recognition means for recording the user's clothing data for the day,

[0879] A transmission means for collecting the aforementioned clothing data and sending it to a server,

[0880] A proposal means that uses a generative model to propose outfits based on clothing data received by the server,

[0881] A display means for displaying the proposed coordination to the user,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, further comprising means for recording user feedback and for a server to perform learning in order to improve the accuracy of the proposed means.

[0885] (Claim 3)

[0886] The system according to claim 1, comprising a purchase support means that provides the user with purchase options based on suggested outfits and enables access to an online store.

[0887] "Example 1"

[0888] (Claim 1)

[0889] A means of identifying a person to identify a user,

[0890] A clothing identification means for recording the user's clothing data for the day,

[0891] A transmission means for collecting the aforementioned clothing data and transmitting it to an information processing device,

[0892] The aforementioned information processing device uses a generative model to suggest outfits based on clothing data received by the information processing device,

[0893] A display means for displaying the proposed outfit to the user,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising means for recording user evaluation information and for an information processing device to perform learning in order to improve the accuracy of the proposed means.

[0897] (Claim 3)

[0898] The system according to claim 1, comprising a purchase support means that provides the user with purchase options based on suggested attire and enables access to an e-commerce site.

[0899] "Application Example 1"

[0900] (Claim 1)

[0901] A means of identifying a person for identifying a user,

[0902] A decoration recognition means for recording the decoration data of the user on that day,

[0903] A communication means for collecting the aforementioned decorative data and transmitting it to a computer,

[0904] A proposal means that uses a generation mechanism to propose adjustment plans based on the decorative data received by the aforementioned computing machine,

[0905] A visualization means for displaying the proposed adjustment plan to the user,

[0906] A means of collaboration for confirming and selecting the adjustment plan within the sales space based on the proposed adjustment plan,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, further comprising means for recording user feedback and for a computer to perform learning in order to improve the accuracy of the proposed means.

[0910] (Claim 3)

[0911] The system according to claim 1, comprising a purchase support means that provides a purchase function based on adjustment proposals made to the user and enables connection to a virtual store.

[0912] "Example 2 of combining an emotion engine"

[0913] (Claim 1)

[0914] An image recognition means for identifying the user's emotional state,

[0915] A clothing recognition means for extracting the user's clothing characteristic data,

[0916] A transmission means for transmitting the aforementioned emotional state and clothing characteristic data to a server,

[0917] A proposal means for suggesting outfits using a generated AI model based on emotional state and clothing characteristic data received by the server,

[0918] A display means that adjusts and displays the proposed coordination according to the user's emotions,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, further comprising means for recording user feedback and for the server to perform learning in order to improve the accuracy of the suggestion means.

[0922] (Claim 3)

[0923] The system according to claim 1, comprising a purchase support means that provides the user with purchase options based on suggested outfits and enables access to relevant online sales platforms.

[0924] "Application example 2 when combining with an emotional engine"

[0925] (Claim 1)

[0926] Image analysis means for user identification,

[0927] A data acquisition means for acquiring data on the clothing worn by the user,

[0928] An emotion analysis means that determines the user's emotional state using the image analysis means,

[0929] A data communication means for transmitting the aforementioned emotional state and clothing data to a server,

[0930] A proposal means that utilizes a generative model that proposes a data-driven and sentiment-recognizing coordination, taking into account comprehensive conditions (weather, historical data, schedule, etc.) using the data received by the server,

[0931] A display means for presenting the proposed coordination to the user,

[0932] An assistive mechanism that enables the selection of fashion items optimized based on emotions,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, further comprising means for recording user responses and for a server to learn in order to optimize the performance of the proposed means.

[0936] (Claim 3)

[0937] The system according to claim 1, comprising a purchase support means that presents purchase options based on the suggested outfit to the user and enables integration with an e-commerce platform. [Explanation of Symbols]

[0938] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Image recognition means for identifying users, A clothing recognition means for recording the user's clothing data for the day, A transmission means for collecting the aforementioned clothing data and sending it to a server, A proposal means that uses a generative model to propose outfits based on clothing data received by the server, A display means for displaying the proposed coordination to the user, A system that includes this.

2. The system according to claim 1, further comprising means for recording user feedback and for a server to perform learning in order to improve the accuracy of the proposed means.

3. The system according to claim 1, comprising a purchase support means that provides the user with purchase options based on suggested outfits and enables access to an online store.

Images