system

Abstract

We provide the system. [Solution] An analysis means that receives an image of clothing from a user and analyzes the attributes of the clothing from the image, A suggestion generation means that uses a generative model to propose the most suitable clothing combination for the user based on the user's daily schedule and hairstyle information, A display means that transmits and displays the generated clothing combinations to the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern life, many people spend a lot of time choosing their daily outfits and often have anxiety and dissatisfaction with styling. In particular, it is not easy to figure out how to make the best use of the clothes an individual has and find the optimal combination according to the day's schedule and fashion trends. Also, existing methods have the problem that it is difficult to immediately reflect the user's preferences and trends, and it is impossible to obtain optimal suggestions.

Means for Solving the Problems

[0005] This invention provides a system that photographs the clothes owned by the user and clarifies their characteristics using an analysis method. Based on this information, along with the user's daily schedule and hairstyle information, it uses a generative model to suggest the optimal combination. Furthermore, the system collects trendy clothing information from the internet and utilizes it in the suggestion generation process to achieve instantaneous and trend-reflecting coordination. In addition, by collecting user feedback and improving the accuracy of the generative model, it provides suggestions that match the user's preferences in a short amount of time.

[0006] A "user" refers to an individual who uses this system to receive clothing coordination suggestions.

[0007] "Clothing images" are visual information captured by a user to show the characteristics and design of their clothing.

[0008] "Analysis means" refers to software or hardware functions for automatically extracting attributes from images of clothing.

[0009] "Schedule" refers to information that indicates the activities or events a user has planned for that day.

[0010] "Hair style" refers to information that describes the design of a user's hairstyle on a specific day.

[0011] A "generative model" is an algorithm or system used to suggest the optimal clothing combination based on user information.

[0012] "Suggestion generation means" refers to a process or function that uses a generative model to construct clothing combinations that are optimal for the user.

[0013] "Display means" refers to the functions and processes for displaying the generated clothing combinations on the user's device.

[0014] "Feedback" refers to opinions and evaluations that users use to communicate their satisfaction level and suggestions for improvement regarding the suggested outfits.

[0015] "Learning methods" refer to functions and processes that improve the performance of generative models based on feedback obtained from users.

[0016] "Information gathering means" refers to functions and systems for obtaining information about trendy clothing from the internet and utilizing that information in the proposal generation process. [Brief explanation of the drawing]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] 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.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention provides a system that streamlines the user's daily clothing selection process. The user first takes photos of their clothes with their smartphone camera and uploads these images to the system using a terminal. The server automatically extracts clothing attributes from the received images using an image analysis algorithm and stores data such as color, design, and category in a database.

[0039] Next, the user sends information about their schedule for the day (TPO: time, place, occasion) and hairstyle to the server via their device. The server uses this information, along with pre-configured fashion data, to suggest the most suitable clothing combination for the user using a generative model.

[0040] The suggestion generation method uses an algorithm on the server to derive stylish and appropriate outfits that meet the requirements of the time, place, and occasion (TPO), by referencing clothing information stored in the user's existing database and the latest fashion trend data.

[0041] The server sends the generated outfit information to the user's device, which then displays and notifies the user. The user can evaluate the suggested outfit and input feedback into the device.

[0042] Feedback information is sent to the server. The server analyzes this feedback and incorporates it into the training of the generative model, improving the accuracy of future outfit suggestions. Through this process, users can receive suggestions tailored to their individual preferences and daily trends.

[0043] As described above, the present invention embodies a system that simplifies the user's clothing selection process and provides everyday outfits efficiently and in line with current trends.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users use their smartphone cameras to take pictures of each item of clothing they own. The captured images are uploaded to the application via the device.

[0047] Step 2:

[0048] The device sends the uploaded clothing image data to the server. The server uses an image analysis algorithm to automatically extract clothing attributes such as color, shape, pattern, and category (shirt, pants, etc.), and stores them in a database for each user.

[0049] Step 3:

[0050] The user enters information about their daily schedule (TPO) and hairstyle into their device and sends it to the server. The schedule includes information about the type and location of the event.

[0051] Step 4:

[0052] The server receives the entered schedule and hairstyle information and references the latest fashion trends collected from the internet. At the same time, it checks the clothing information in the user's database.

[0053] Step 5:

[0054] The server-based generation model generates the optimal clothing combination for the user based on aggregated information. It creates several clothing options that meet the requirements of the time, place, and occasion (TPO), and then selects the best combination from among them.

[0055] Step 6:

[0056] The server sends the generated outfit information to the user's device. The device displays the suggested outfit on the screen and notifies the user.

[0057] Step 7:

[0058] Users review the displayed outfits and rate their satisfaction level and areas for improvement. This rating is entered as feedback using their device.

[0059] Step 8:

[0060] Feedback information sent from the terminal is sent to the server, where it is analyzed. The analysis results are used to improve the generative model and enhance the accuracy of coordination suggestions.

[0061] (Example 1)

[0062] 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."

[0063] In modern times, many users spend time and effort choosing everyday accessories. However, finding combinations that suit individual preferences, current trends, and appropriate occasions is not easy. Against this backdrop, improving the efficiency and accuracy of accessory selection is a crucial challenge.

[0064] 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.

[0065] In this invention, the server includes an analysis means for receiving images of clothing from a user and analyzing the characteristics of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination to the user using generative artificial intelligence based on information about the user's schedule and appearance; and an information collection means for collecting information on trendy accessories through an information and communication network and using this information as reference data for the suggestion generation means. This makes it possible for users to easily obtain the optimal combination of accessories based on their individual preferences and external trend information.

[0066] A "user" is an individual who uses this system to receive clothing coordination suggestions.

[0067] "Clothing images" refer to visual digital data of clothing owned by the user.

[0068] "Characteristics" refer to various elements that indicate the attributes of clothing, including data such as color, design, and category.

[0069] "Analysis means" refers to a process or apparatus used to extract features from images of received clothing.

[0070] "Information regarding schedules and appearance" refers to data that users provide to the system regarding their schedules for the day and information about their appearance.

[0071] "Generative artificial intelligence" refers to a machine learning algorithm or model used to generate the optimal clothing combination for a user.

[0072] "Suggestion generation means" refers to an operation or device that derives a suitable clothing combination for the user based on analyzed features and other information.

[0073] An "information and communication network" is a communication framework for exchanging digital information, usually referring to the internet.

[0074] "Information on trendy accessories" refers to data on currently popular designs and styles.

[0075] "Information gathering means" refers to the operations or techniques used to obtain information about trends from sources such as the internet.

[0076] This invention is a system designed to streamline the user's daily clothing selection process. The user uses the device to photograph their clothing and uploads the image to the system. Upon receiving this image, the server extracts clothing features using an image analysis algorithm. Specifically, it automatically analyzes important attributes such as color, design, and category from the image using libraries such as OpenCV and TENSORFLOW®.

[0077] The server stores the analysis results in a database and receives information from the user regarding their daily schedule and appearance. This information is input via the user's device. Next, the server generates clothing combinations suitable for the user using a generative AI model. This AI model is typically built using, for example, PyTorch or Keras. The server also collects information on the latest trends through information and communication networks and uses this as reference data for the generation process.

[0078] The generated coordination information is sent to the user's device and displayed. The user evaluates the received suggestions and provides feedback. This feedback is analyzed by the server and incorporated into the training of the generating AI model. This allows for improvements to be made so that more accurate suggestions are provided.

[0079] For example, if a user uploads an image of themselves wearing a blue jacket and black pants and plans to go out casually that day, the system might suggest an outfit combining a white T-shirt and sneakers. An example of a prompt that could be input to the generative AI model in this system would be, "Please suggest a stylish outfit for a casual outing using a blue jacket and black pants."

[0080] As described above, this invention makes it possible to suggest optimal clothing based on each user's individual preferences and fashion trends.

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

[0082] Step 1:

[0083] The user takes a picture of their clothing using their smartphone camera. The captured image file becomes the input. The device provides an interface for uploading this image to the system, and the user sends the image to the server. The output is the image arriving at the server.

[0084] Step 2:

[0085] The server analyzes the received images. The input is images of clothing sent by the user. The server uses libraries such as OpenCV and TensorFlow to execute image analysis algorithms and performs data processing to extract features such as color, design, and category from the images. The output is this feature data, which the server stores in a database.

[0086] Step 3:

[0087] The user inputs information about their daily schedule and appearance, such as their schedule and hairstyle, through a terminal. This constitutes the input information. The terminal sends the input information to the server. The output is that this information arrives at the server.

[0088] Step 4:

[0089] The server uses stored feature data and information about the user's schedule and appearance to generate the optimal clothing combination using a generative AI model. The input is the analyzed feature data and user information. The generative AI model generates prompt sentences based on this information and creates suggestions through data calculations within the model. The output is the outfit data suggested to the user.

[0090] Step 5:

[0091] The server sends the generated coordination data to the user's terminal. The input is the generated coordination information. The terminal displays the received information to the user and sends a notification. The output at this time is that the user can confirm the suggestion on the terminal.

[0092] Step 6:

[0093] The user evaluates the presented outfit and enters feedback into the terminal. The input is the user's evaluation data. The terminal sends this information to the server. The output is the server receiving the evaluation data.

[0094] Step 7:

[0095] The server analyzes the received evaluation data and uses it to improve the generative AI model. The input is user feedback data. Based on this data, the server adjusts the model parameters and performs online learning to improve the accuracy of the suggestions. The output is the generative AI model that reflects the learning process.

[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] There is a need for a system that streamlines the process of choosing everyday clothes while simultaneously allowing users to virtually try on clothing. However, conventional systems have the challenge of making it difficult for users to experience the feeling of actually trying on new clothes in real time. Furthermore, it is difficult for users to intuitively understand the suggested outfits and immediately decide whether they suit them.

[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 an analysis means for acquiring images of clothing from a user and analyzing the characteristics of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination to the user using a generation method based on information about the user's planned activities and hairstyle for the day; and a virtual try-on means for visualizing the suggested clothing combination using a virtual reality device. This allows the user to intuitively grasp the suitability of the suggested outfit through a try-on experience in a virtual space.

[0101] A "user" is an individual who utilizes the system and is the entity that provides information such as clothing images, activity plans, and hairstyles.

[0102] "Clothing" refers to clothing owned by the user and provided to the system as image data.

[0103] "Images" are visual information data that users capture or acquire and send to the system for analysis.

[0104] "Characteristics" refer to characteristic information such as color, design, and category extracted from images of clothing using analytical methods.

[0105] "Analysis means" refers to a method or apparatus for analyzing images sent by a user and identifying the characteristics of clothing.

[0106] "Planned activities" refers to information such as the activities, locations, and times that the user has planned for the day.

[0107] "Hairstyle" refers to information about the hairstyle the user has specified for that day.

[0108] A "generation method" is a process that uses a pre-set algorithm to suggest the optimal clothing combination based on the user's information.

[0109] A "suggestion generation method" is a system that uses a generation method to derive clothing coordination based on information received from the user.

[0110] A "virtual reality device" is a device used by users to virtually try on clothes.

[0111] A "virtual fitting method" is a method or device that visualizes proposed clothing combinations using a virtual reality device, allowing the user to experience them as if they were actually trying them on.

[0112] The system for implementing this invention begins with the user taking an image of clothing using their own image acquisition device (e.g., a smartphone) and uploading that image to the user's terminal. The server receives the uploaded image and uses an image analysis library (e.g., OpenCV) to extract the characteristics of the clothing. This includes information such as color, design, and category.

[0113] Next, the user sends information about their daily schedule and hairstyle to the server via their device. The server then uses a generative method to suggest appropriate clothing combinations based on all the collected information and pre-configured fashion data. This generative method can utilize machine learning models or generative AI models.

[0114] The suggested clothing combinations are visualized to the user using a virtual reality device (e.g., smart glasses). The server sends the generated coordination information to the user's terminal, and the suggested clothing is visually overlaid on the virtual reality device through a virtual try-on mechanism. As a result, the user can have an experience as if they were trying on clothes in a virtual space. The user can also evaluate the suggested coordination and submit their feedback.

[0115] The feedback is analyzed on the server side and used to train the suggestion generation system. This further improves the accuracy of future suggestions. Through this entire process, users can efficiently choose clothes that match their preferences and the latest trends.

[0116] As a concrete example, when a user plans a casual outing with friends, the server suggests an outfit that includes a red jacket based on relevant information. The AI ​​model then uses a prompt message such as, "Generate a virtual try-on image of the outfit including a red jacket, tailored to the user's casual outing," to provide optimized suggestions. This allows the user to receive advice that suits their own style while enjoying a new fashion experience.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The user takes pictures of their clothing using an image acquisition device such as a smartphone. The input is image data of the clothing, which the device receives and sends to the server. The output is the image data of the clothing received by the server.

[0120] Step 2:

[0121] The server analyzes the received clothing image data using an image analysis library (e.g., OpenCV). The input for this step is the clothing image received by the server. The server performs image analysis and extracts clothing characteristic data such as color, design, and category. The output is a clothing characteristic dataset.

[0122] Step 3:

[0123] The user sends information about their daily schedule and hairstyle to the server via their device. The input for this step is the user's scheduled activities and hairstyle information. The output is the user's daily information data sent to the server.

[0124] Step 4:

[0125] The server combines received clothing characteristic data, user daily information, and pre-configured trending fashion data to suggest the optimal clothing combination using a generative AI model. The inputs for this step are characteristic data, planned activities, hairstyle, and trending fashion data. Through data calculation, appropriate clothing combinations are generated, and the output is coordinated outfit data suggested to the user.

[0126] Step 5:

[0127] The generated outfit data is sent to the user's terminal and visualized by the user through a virtual reality device (such as smart glasses). The input for this step is the generated outfit data. The terminal uses the virtual reality device to visualize the outfit, and the user experiences a virtual try-on. The output is the user's virtual try-on video.

[0128] Step 6:

[0129] The user provides feedback on the proposed outfit and sends it to the server via their device. The input for this step is the user's feedback information. The output is the evaluation data sent to the server.

[0130] Step 7:

[0131] The server analyzes the feedback received from the user and uses it to train the generative AI model of the suggestion generation system to improve its accuracy. The input for this step is the user's feedback data. The server updates the model using a learning algorithm and provides an improved generative AI model as output.

[0132] 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.

[0133] This invention provides a coordinate suggestion system incorporating an emotion engine to enable users to experience emotional satisfaction when choosing clothes. First, the user takes a picture of their own clothes with their smartphone and uploads the image to the system via the terminal. The server receives the image of the clothes, analyzes the characteristics of the clothes using an image analysis algorithm, and stores the category, color, design, etc. in a database.

[0134] Next, the user enters information about their schedule for the day and their hairstyle, and sends the data from their device to the server. Furthermore, the system uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice input to determine their current emotional state.

[0135] Based on this data, the server uses a generative model to suggest outfits suitable for the user. The generative model takes into account the user's analyzed emotional state and suggests a fashion style that matches it. Furthermore, it improves the accuracy of its suggestions by referring to previously recorded user feedback and emotional history.

[0136] For example, if a user has planned an "outdoor event on a sunny day" and the emotion engine detects a "positive emotion," the server will prioritize and recommend outfits with bright colors and cheerful designs. The suggestion generation system incorporates this information to generate the optimal clothing combination for the user.

[0137] The user's device can receive the generated coordination information and display it to the user via notification. The user can evaluate the presented coordination and provide feedback. This feedback is used by the server to improve the accuracy of the emotion engine and suggestion generation means.

[0138] Thus, the present invention provides a system that takes into account emotional states and, based on personal preferences and the latest trends, makes the selection of clothing in individuals' daily lives more efficient and satisfying.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] Users take pictures of their clothes with their smartphone camera and upload the images of their clothes to the system via their device.

[0142] Step 2:

[0143] The device sends the uploaded image of the clothing to the server. The server uses an image analysis algorithm to analyze the attributes of the clothing, such as its color, shape, and pattern, and stores this information in a user-specific database.

[0144] Step 3:

[0145] The user enters information about their schedule for the day and their hairstyle into the device. In addition, the device's camera takes a picture of the user's current facial expression and sends it to the server.

[0146] Step 4:

[0147] The server uses an emotion engine to analyze the user's current emotional state based on the user's schedule, hairstyle, and facial expression images. This analysis result is recorded and used as reference information in subsequent processes.

[0148] Step 5:

[0149] The server uses a generative model to combine the user's saved clothing data with analyzed emotional states, schedules, and hairstyle information to generate optimal outfit suggestions. The model also references current fashion trends to select clothing that matches the user's emotions.

[0150] Step 6:

[0151] The server sends the generated outfit suggestions to the user's terminal. The terminal notifies the user of the suggestions and displays the details on the screen.

[0152] Step 7:

[0153] The user reviews the suggested outfit and provides feedback on their satisfaction level and areas for improvement via their device. This feedback is then sent to the server.

[0154] Step 8:

[0155] The server analyzes the feedback it receives and uses it as training data for its generative models and emotion engine. This improves the accuracy of future outfit suggestions, enabling suggestions that better suit the user's preferences.

[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] Conventional clothing selection systems struggle to suggest the optimal clothing combination based on the user's mood and individual schedule. Furthermore, a lack of effective means to utilize user feedback and improve the accuracy of suggestions is a significant challenge.

[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 analysis means for receiving images of clothing from a user and analyzing the attributes of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination for the user using a generative model based on the user's schedule and appearance information received from the user; and an emotion recognition means for analyzing the user's facial expression images and voice input to identify their emotional state. This makes it possible to suggest the optimal clothing according to emotions and schedules.

[0161] "Analysis means" refers to a device or method for extracting attributes from clothing images received from a user and storing them in a database.

[0162] A "proposal generation means" is a device or method that, based on user input information, uses a generation model to create the optimal clothing combination and proposes it to the user.

[0163] "Emotion recognition means" refers to a device or method for analyzing data such as a user's facial expression image or voice input to determine the user's current emotional state.

[0164] "Display means" refers to a device or method that transmits the generated clothing combination to the user's device and displays it visually to the user.

[0165] A "learning method" refers to a process or algorithm that accumulates and analyzes information based on user feedback to improve the accuracy of the suggestion generation method.

[0166] "Information gathering means" refers to a device or method that collects information on fashionable clothing through communication networks such as the internet and utilizes it as reference data for proposal generation means.

[0167] This system is designed to allow users to receive optimal suggestions regarding clothing choices based on their individual feelings and schedules. An embodiment of this system is shown below.

[0168] The user first takes a picture of their clothing with a smartphone or other device and uploads the image to the system. The device then sends this image data to the server. The server uses image analysis software to analyze attributes such as category, color, and design from the uploaded clothing image. This analyzed data is stored in a database on the server.

[0169] Next, the user enters information about their schedule for the day and their hairstyle into the terminal and sends it to the server. At this time, the system also recognizes the user's current emotional state using an emotion engine. The emotion engine identifies emotions by analyzing the user's facial expressions and voice input.

[0170] The server integrates all data from the user and generates outfits using a generative AI model. This generative AI model takes into account the user's emotional state, schedule, and past feedback to suggest the most suitable clothing combination.

[0171] The suggested outfits are sent from the server to the terminal and displayed to the user. The user can review the presented outfits and provide feedback. This feedback is sent back to the server and used to improve the accuracy of the suggestion generation system.

[0172] As a concrete example, if a user plans to attend an "outdoor event on a sunny day," and the emotion engine recognizes a "positive emotion," the server will select and suggest clothing with a bright and cheerful design. An example of a prompt in this case would be something like, "The user's plan for today is an outdoor picnic. Their emotional state is excited. What kind of outfit would be good?" which would be input into the generative model.

[0173] This system allows users to make efficient and satisfying clothing choices that match their schedule and mood for the day.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The user takes a picture of their own clothing using their smartphone camera. The captured image is uploaded from the device to the server. At this point, the input data is the image of the clothing taken by the user. The server receives the image file and prepares for data processing.

[0177] Step 2:

[0178] The server performs image analysis based on the received images of clothing. Using an image analysis algorithm, it extracts attributes such as the clothing's category, color, and design. The input is the clothing image received in step 1, and the output is the analyzed clothing attribute data. This data is stored in a database.

[0179] Step 3:

[0180] The user enters information about their schedule and hairstyle into the terminal. This information is sent to the server as part of the user's context. The input data, which consists of the user's schedule and hairstyle information, is stored on the server to proceed to the next step.

[0181] Step 4:

[0182] The server uses emotion recognition to understand the user's emotional state. The user sends facial images and audio from their terminal to the server. The input is the user's facial images and audio data, which the server analyzes to identify the user's emotional state. At this point, the output is the analyzed emotional state.

[0183] Step 5:

[0184] The server uses the data collected so far to run a generative AI model. This model considers the user's emotional state and schedule, and generates outfit suggestions based on prompt text. The input consists of analyzed clothing attributes, schedule, hairstyle, and emotional state, and the output is outfit suggestions for the user.

[0185] Step 6:

[0186] The generated outfit suggestions are sent from the server to the user's terminal. The terminal displays the received information on its screen and notifies the user. This allows the user to view the suggested outfits. The input data is the outfit suggestions from the server, and the output is a visual display on the terminal.

[0187] Step 7:

[0188] Users evaluate the suggested outfits via their terminal and provide feedback. This feedback is sent from the terminal to the server. The input is the user's evaluation data, and the output is stored on the server as training data to improve the accuracy of the suggestion generation system.

[0189] (Application Example 2)

[0190] 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".

[0191] Modern consumers face the challenge of choosing clothing that suits their style from a vast array of options. In particular, there is no system that automatically suggests outfits based on a consumer's mood or daily schedule, often leaving consumers wasting time and effort without making a satisfactory choice. Furthermore, virtual stores struggle to provide real-time fashion suggestions that respond to consumers' emotions.

[0192] 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.

[0193] In this invention, the server includes a processing unit that receives images of clothing from a user and analyzes the attributes of the clothing from the images; a suggestion generation unit that proposes the optimal clothing combination for the user using a generation model based on the user's daily activity plan and hair style information; a display unit that transmits and displays the generated clothing combination to the user's information processing unit; and an emotion analysis unit that acquires the user's emotional state and adjusts the suggestion content based on the acquired emotion. This makes it possible to propose the optimal fashion coordination according to the individual user's emotions and schedule.

[0194] A "user" is the entity that provides input to the system and receives the suggested fashion coordinates.

[0195] "Clothing images" refer to digital image data of clothing owned by the user.

[0196] "Attributes" refer to characteristic information such as the color, design, material, and category of clothing.

[0197] A "processing device" is a computer device used to analyze attributes from images of received clothing.

[0198] An "action plan" is the user's schedule for the day, including information such as time, location, and activities.

[0199] "Hair style" refers to information about the hairstyle the user will have on that day, as entered by the user.

[0200] A "generative model" is an artificial intelligence model used to derive the optimal clothing combination for a user.

[0201] A "proposal generation device" is a device that uses a generation model based on input information to suggest clothing combinations.

[0202] An "information processing device" is a terminal used by users to review proposed outfits.

[0203] A "display device" is a device that visually presents the generated clothing combinations to the user.

[0204] "Emotional state" refers to the psychological state that can be inferred from the user's facial expressions, voice, and other factors.

[0205] An "emotion analysis device" is a device that acquires the user's emotional state and uses that information to adjust the content of the suggestions.

[0206] A "virtual store" is an online commercial environment that provides products to consumers through electronic means without displaying physical goods.

[0207] The system implementing this invention automatically suggests clothing coordinates based on the user's wardrobe. The user first takes images of their clothing using a smartphone or other information processing device and uploads these images to the system. The server analyzes the received clothing images using a processing device, identifies the attributes of the clothing, and records this information in a database. The software used can utilize OpenCV as an image processing library.

[0208] The user further inputs information about their daily activity plan and hairstyle via a terminal and sends it to the server. Based on this information, the server uses a generative model to generate a suggestion generator for the optimal clothing combination for the user. At this time, the suggestion generator uses an emotion analysis device to detect the user's emotional state from their facial expressions and voice, and prioritizes selecting an outfit that matches this emotional state. Emotion analysis APIs such as Microsoft® Cognitive Services can be used.

[0209] The generated coordinates are transmitted to an information processing terminal and visually presented to the user by a display device. The user can review the presented coordinates and provide feedback to the system. This feedback contributes to improving the accuracy of the suggestion generation device through a learning device.

[0210] For example, if a user plans a "sports event in the park on a sunny day" and the emotion analysis device determines that they are feeling "refreshed," the server will suggest clothing with a light design and cool colors.

[0211] An example of a prompt statement is as follows:

[0212] User's emotion: Refreshing

[0213] Planned: Sports event in the park on a sunny day

[0214] Suggested outfit: Clothing with cool colors and a light, airy design.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] Users take pictures of their clothes using information processing devices such as smartphones and tablets and upload them to the system. The input data is digital image data of the clothes and is sent directly from the device to the server.

[0218] Step 2:

[0219] The server analyzes the received image data of clothing using OpenCV and extracts clothing attributes such as color, design, and category. These attributes are stored in a database and used later to generate suggestions. The input is image data, and the output is clothing attribute information.

[0220] Step 3:

[0221] Users input their daily activity plan and hairstyle via an information processing terminal and send this information to the server. The activity plan includes the scheduled date, time, location, and activities. The input consists of the activity plan and hairstyle information, which are stored on the server as output.

[0222] Step 4:

[0223] The server uses facial expression and voice data transmitted from the terminal to execute an emotion analysis API (e.g., Microsoft Cognitive Services) to identify the user's emotional state. This analysis outputs the type and intensity of the emotion. The input is the user's facial expression and voice data, and the output is the emotional state.

[0224] Step 5:

[0225] The server combines the user's action plan, hair style, emotional state, and analyzed clothing attributes to generate optimal outfit suggestions using a generative AI model. In this process, the generative AI model is run using prompt text as input, and optimal outfit data is output.

[0226] Step 6:

[0227] The generated coordination information is transmitted to an information processing terminal and notified and displayed to the user via a display device. The user visually confirms the generated coordination and, if necessary, enters feedback and sends it to the server.

[0228] Step 7:

[0229] The server analyzes user feedback and trains a generative AI model via a learning device to improve the proposal generation process. It uses the data obtained from the feedback as input to update model parameters to improve the accuracy of proposal generation.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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".

[0246] This invention provides a system that streamlines the user's daily clothing selection process. The user first takes photos of their clothes with their smartphone camera and uploads these images to the system using a terminal. The server automatically extracts clothing attributes from the received images using an image analysis algorithm and stores data such as color, design, and category in a database.

[0247] Next, the user sends information about their schedule for the day (TPO: time, place, occasion) and hairstyle to the server via their device. The server uses this information, along with pre-configured fashion data, to suggest the most suitable clothing combination for the user using a generative model.

[0248] The suggestion generation method uses an algorithm on the server to derive stylish and appropriate outfits that meet the requirements of the time, place, and occasion (TPO), by referencing clothing information stored in the user's existing database and the latest fashion trend data.

[0249] The server sends the generated outfit information to the user's device, which then displays and notifies the user. The user can evaluate the suggested outfit and input feedback into the device.

[0250] Feedback information is sent to the server. The server analyzes this feedback and incorporates it into the training of the generative model, improving the accuracy of future outfit suggestions. Through this process, users can receive suggestions tailored to their individual preferences and daily trends.

[0251] As described above, the present invention embodies a system that simplifies the user's clothing selection process and provides everyday outfits efficiently and in line with current trends.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] Users use their smartphone cameras to take pictures of each item of clothing they own. The captured images are uploaded to the application via the device.

[0255] Step 2:

[0256] The device sends the uploaded clothing image data to the server. The server uses an image analysis algorithm to automatically extract clothing attributes such as color, shape, pattern, and category (shirt, pants, etc.), and stores them in a database for each user.

[0257] Step 3:

[0258] The user enters information about their daily schedule (TPO) and hairstyle into their device and sends it to the server. The schedule includes information about the type and location of the event.

[0259] Step 4:

[0260] The server receives the entered schedule and hairstyle information and references the latest fashion trends collected from the internet. At the same time, it checks the clothing information in the user's database.

[0261] Step 5:

[0262] The server-based generation model generates the optimal clothing combination for the user based on aggregated information. It creates several clothing options that meet the requirements of the time, place, and occasion (TPO), and then selects the best combination from among them.

[0263] Step 6:

[0264] The server sends the generated outfit information to the user's device. The device displays the suggested outfit on the screen and notifies the user.

[0265] Step 7:

[0266] Users review the displayed outfits and rate their satisfaction level and areas for improvement. This rating is entered as feedback using their device.

[0267] Step 8:

[0268] Feedback information sent from the terminal is sent to the server, where it is analyzed. The analysis results are used to improve the generative model and enhance the accuracy of coordination suggestions.

[0269] (Example 1)

[0270] 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."

[0271] In modern times, many users spend time and effort choosing everyday accessories. However, finding combinations that suit individual preferences, current trends, and appropriate occasions is not easy. Against this backdrop, improving the efficiency and accuracy of accessory selection is a crucial challenge.

[0272] 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.

[0273] In this invention, the server includes an analysis means for receiving images of clothing from a user and analyzing the characteristics of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination to the user using generative artificial intelligence based on information about the user's schedule and appearance; and an information collection means for collecting information on trendy accessories through an information and communication network and using this information as reference data for the suggestion generation means. This makes it possible for users to easily obtain the optimal combination of accessories based on their individual preferences and external trend information.

[0274] A "user" is an individual who uses this system to receive clothing coordination suggestions.

[0275] "Clothing images" refer to visual digital data of clothing owned by the user.

[0276] "Characteristics" refer to various elements that indicate the attributes of clothing, including data such as color, design, and category.

[0277] The "analysis means" is a process or device used to extract the characteristics from the received image of clothing.

[0278] The "information on schedule and appearance" is data in which the user provides information on the schedule for the day and their own appearance to the system.

[0279] The "generative artificial intelligence" is a machine learning algorithm or model used to generate the optimal combination of clothing for the user.

[0280] The "proposal generation means" is an operation or device that derives a combination of clothing suitable for the user based on the analyzed characteristics and other information.

[0281] The "information and communication network" is a communication framework for exchanging digital information, usually referring to the Internet.

[0282] The "information on popular accessories" is data on currently popular designs and styles.

[0283] The "information collection means" is an operation or technology for obtaining information on trends from the Internet or the like.

[0284] This invention is a system for improving the efficiency of clothing selection in the user's daily life. The user uses the device to take a picture of their clothing and upload the image to the system. After receiving this image, the server uses an image analysis algorithm to extract the characteristics of the clothing. Specifically, by using libraries such as OpenCV and TensorFlow, important attributes such as color, design, and category are automatically analyzed from the image.

[0285] The server stores the analysis results in a database and receives information about the user's schedule and appearance for the day provided by the user. This information is input via the user's device. Next, the server uses a generative AI model to generate clothing combinations suitable for the user. This AI model is generally constructed using, for example, PyTorch or Keras. The server collects information about the latest fashion trends through an information communication network and uses this as reference data for the generation process.

[0286] The generated coordination information is sent to the user's device and displayed. The user evaluates the received proposal and provides feedback. This feedback is analyzed by the server and reflected in the learning of the generative AI model. As a result, improvements are made so that more accurate proposals are provided.

[0287] As a specific example, if the user uploads an image of their blue jacket and black pants and plans a casual outing that day, the system might propose a coordination combining a white T-shirt and sneakers. An example of a prompt sentence input into the generative AI model in this system is "Please propose a stylish coordination for a casual outing using a blue jacket and black pants."

[0288] As described above, this invention enables the provision of optimal clothing proposals based on individual user preferences and fashion information.

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

[0290] Step 1:

[0291] The user takes a picture of their clothing using their smartphone camera. The captured image file becomes the input. The device provides an interface for uploading this image to the system, and the user sends the image to the server. The output is the image arriving at the server.

[0292] Step 2:

[0293] The server analyzes the received images. The input is images of clothing sent by the user. The server uses libraries such as OpenCV and TensorFlow to execute image analysis algorithms and performs data processing to extract features such as color, design, and category from the images. The output is this feature data, which the server stores in a database.

[0294] Step 3:

[0295] The user inputs information about their daily schedule and appearance, such as their schedule and hairstyle, through a terminal. This constitutes the input information. The terminal sends the input information to the server. The output is the arrival of this information at the server.

[0296] Step 4:

[0297] The server uses stored feature data and information about the user's schedule and appearance to generate the optimal clothing combination using a generative AI model. The input is the analyzed feature data and user information. The generative AI model generates prompt sentences based on this information and creates suggestions through data calculations within the model. The output is the outfit data suggested to the user.

[0298] Step 5:

[0299] The server sends the generated coordination data to the user's terminal. The input is the generated coordination information. The terminal displays the received information to the user and sends a notification. The output at this time is that the user can confirm the suggestion on the terminal.

[0300] Step 6:

[0301] The user evaluates the presented coordinates and inputs feedback to the terminal. The input is the user's evaluation data. The terminal transmits this information to the server. The output is that the evaluation data reaches the server.

[0302] Step 7:

[0303] The server analyzes the received evaluation data and uses it to improve the generated AI model. The input is the feedback data from the user. The server adjusts the model parameters based on this data and performs online learning to improve the proposed accuracy. The output is the generated AI model in which the learning is reflected.

[0304] (Application Example 1)

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

[0306] There is a demand for a system that can improve the efficiency of daily clothing selection and at the same time enable the user to experience a clothing try-on experience in a virtual space. However, in the conventional system, there is a problem that it is difficult for the user to experience the feeling of actually trying on new clothes in real time. Also, it is difficult for the user to intuitively understand the proposed coordinates and immediately judge whether they suit themselves.

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

[0308] In this invention, the server includes an analysis means for acquiring images of clothing from a user and analyzing the characteristics of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination to the user using a generation method based on information about the user's planned activities and hairstyle for the day; and a virtual try-on means for visualizing the suggested clothing combination using a virtual reality device. This allows the user to intuitively grasp the suitability of the suggested outfit through a try-on experience in a virtual space.

[0309] A "user" is an individual who utilizes the system and is the entity that provides information such as clothing images, activity plans, and hairstyles.

[0310] "Clothing" refers to clothing owned by the user and provided to the system as image data.

[0311] "Images" are visual information data that users capture or acquire and send to the system for analysis.

[0312] "Characteristics" refer to characteristic information such as color, design, and category extracted from images of clothing using analytical methods.

[0313] "Analysis means" refers to a method or apparatus for analyzing images sent by a user and identifying the characteristics of clothing.

[0314] "Planned activities" refers to information such as the activities, locations, and times that the user has planned for the day.

[0315] "Hairstyle" refers to information about the hairstyle the user has specified for that day.

[0316] A "generation method" is a process that uses a pre-set algorithm to suggest the optimal clothing combination based on the user's information.

[0317] A "suggestion generation method" is a system that uses a generation method to derive clothing coordination based on information received from the user.

[0318] A "virtual reality device" is a device used by users to virtually try on clothes.

[0319] A "virtual fitting method" is a method or device that visualizes proposed clothing combinations using a virtual reality device, allowing the user to experience them as if they were actually trying them on.

[0320] The system for implementing this invention begins with the user taking an image of clothing using their own image acquisition device (e.g., a smartphone) and uploading that image to the user's terminal. The server receives the uploaded image and uses an image analysis library (e.g., OpenCV) to extract the characteristics of the clothing. This includes information such as color, design, and category.

[0321] Next, the user sends information about their daily schedule and hairstyle to the server via their device. The server then uses a generative method to suggest appropriate clothing combinations based on all the collected information and pre-configured fashion data. This generative method can utilize machine learning models or generative AI models.

[0322] The suggested clothing combinations are visualized to the user using a virtual reality device (e.g., smart glasses). The server sends the generated coordination information to the user's terminal, and the suggested clothing is visually overlaid on the virtual reality device through a virtual try-on mechanism. As a result, the user can have an experience as if they were trying on clothes in a virtual space. The user can also evaluate the suggested coordination and submit their feedback.

[0323] The feedback is analyzed on the server side and used to train the suggestion generation system. This further improves the accuracy of future suggestions. Through this entire process, users can efficiently choose clothes that match their preferences and the latest trends.

[0324] As a concrete example, when a user plans a casual outing with friends, the server suggests an outfit that includes a red jacket based on relevant information. The AI ​​model then uses a prompt message such as, "Generate a virtual try-on image of the outfit including a red jacket, tailored to the user's casual outing," to provide optimized suggestions. This allows the user to receive advice that suits their own style while enjoying a new fashion experience.

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The user takes pictures of their clothing using an image acquisition device such as a smartphone. The input is image data of the clothing, which the device receives and sends to the server. The output is the image data of the clothing received by the server.

[0328] Step 2:

[0329] The server analyzes the received clothing image data using an image analysis library (e.g., OpenCV). The input for this step is the clothing image received by the server. The server performs image analysis and extracts clothing characteristic data such as color, design, and category. The output is a clothing characteristic dataset.

[0330] Step 3:

[0331] The user sends information about their daily schedule and hairstyle to the server via their device. The input for this step is the user's scheduled activities and hairstyle information. The output is the user's daily information data sent to the server.

[0332] Step 4:

[0333] The server combines received clothing characteristic data, user daily information, and pre-configured trending fashion data to suggest the optimal clothing combination using a generative AI model. The inputs for this step are characteristic data, planned activities, hairstyle, and trending fashion data. Through data calculation, appropriate clothing combinations are generated, and the output is coordinated outfit data suggested to the user.

[0334] Step 5:

[0335] The generated outfit data is sent to the user's terminal and visualized by the user through a virtual reality device (such as smart glasses). The input for this step is the generated outfit data. The terminal uses the virtual reality device to visualize the outfit, and the user experiences a virtual try-on. The output is the user's virtual try-on video.

[0336] Step 6:

[0337] The user provides feedback on the proposed outfit and sends it to the server via their device. The input for this step is the user's feedback information. The output is the evaluation data sent to the server.

[0338] Step 7:

[0339] The server analyzes the feedback received from the user and uses it to train the generative AI model of the suggestion generation system to improve its accuracy. The input for this step is the user's feedback data. The server updates the model using a learning algorithm and provides an improved generative AI model as output.

[0340] 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.

[0341] This invention provides a coordinate suggestion system incorporating an emotion engine to enable users to experience emotional satisfaction when choosing clothes. First, the user takes a picture of their own clothes with their smartphone and uploads the image to the system via the terminal. The server receives the image of the clothes, analyzes the characteristics of the clothes using an image analysis algorithm, and stores the category, color, design, etc. in a database.

[0342] Next, the user enters information about their schedule for the day and their hairstyle, and sends the data from their device to the server. Furthermore, the system uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice input to determine their current emotional state.

[0343] Based on this data, the server uses a generative model to suggest outfits suitable for the user. The generative model takes into account the user's analyzed emotional state and suggests a fashion style that matches it. Furthermore, it improves the accuracy of its suggestions by referring to previously recorded user feedback and emotional history.

[0344] For example, if a user has planned an "outdoor event on a sunny day" and the emotion engine detects a "positive emotion," the server will prioritize and recommend outfits with bright colors and cheerful designs. The suggestion generation system incorporates this information to generate the optimal clothing combination for the user.

[0345] The user's device can receive the generated coordination information and display it to the user via notification. The user can evaluate the presented coordination and provide feedback. This feedback is used by the server to improve the accuracy of the emotion engine and suggestion generation means.

[0346] Thus, the present invention provides a system that takes into account emotional states and, based on personal preferences and the latest trends, makes the selection of clothing in individuals' daily lives more efficient and satisfying.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] Users take pictures of their clothes with their smartphone camera and upload the images of their clothes to the system via their device.

[0350] Step 2:

[0351] The device sends the uploaded image of the clothing to the server. The server uses an image analysis algorithm to analyze the attributes of the clothing, such as its color, shape, and pattern, and stores this information in a user-specific database.

[0352] Step 3:

[0353] The user enters information about their schedule for the day and their hairstyle into the device. In addition, the device's camera takes a picture of the user's current facial expression and sends it to the server.

[0354] Step 4:

[0355] The server uses an emotion engine to analyze the user's current emotional state based on the user's schedule, hairstyle, and facial expression images. This analysis result is recorded and used as reference information in subsequent processes.

[0356] Step 5:

[0357] The server uses a generative model to combine the user's saved clothing data with analyzed emotional states, schedules, and hairstyle information to generate optimal outfit suggestions. The model also references current fashion trends to select clothing that matches the user's emotions.

[0358] Step 6:

[0359] The server sends the generated outfit suggestions to the user's terminal. The terminal notifies the user of the suggestions and displays the details on the screen.

[0360] Step 7:

[0361] The user reviews the suggested outfit and provides feedback on their satisfaction level and areas for improvement via their device. This feedback is then sent to the server.

[0362] Step 8:

[0363] The server analyzes the feedback it receives and uses it as training data for its generative models and emotion engine. This improves the accuracy of future outfit suggestions, enabling suggestions that better suit the user's preferences.

[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] Conventional clothing selection systems struggle to suggest the optimal clothing combination based on the user's mood and individual schedule. Furthermore, a lack of effective means to utilize user feedback and improve the accuracy of suggestions is a significant challenge.

[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 analysis means for receiving images of clothing from a user and analyzing the attributes of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination for the user using a generative model based on the user's schedule and appearance information received from the user; and an emotion recognition means for analyzing the user's facial expression images and voice input to identify their emotional state. This makes it possible to suggest the optimal clothing according to emotions and schedules.

[0369] "Analysis means" refers to a device or method for extracting attributes from clothing images received from a user and storing them in a database.

[0370] A "proposal generation means" is a device or method that, based on user input information, uses a generation model to create the optimal clothing combination and proposes it to the user.

[0371] "Emotion recognition means" refers to a device or method for analyzing data such as a user's facial expression image or voice input to determine the user's current emotional state.

[0372] "Display means" refers to a device or method that transmits the generated clothing combination to the user's device and displays it visually to the user.

[0373] A "learning method" refers to a process or algorithm that accumulates and analyzes information based on user feedback to improve the accuracy of the suggestion generation method.

[0374] "Information gathering means" refers to a device or method that collects information on fashionable clothing through communication networks such as the internet and utilizes it as reference data for proposal generation means.

[0375] This system is designed to allow users to receive optimal suggestions regarding clothing choices based on their individual feelings and schedules. An embodiment of this system is shown below.

[0376] The user first takes a picture of their clothing with a smartphone or other device and uploads the image to the system. The device then sends this image data to the server. The server uses image analysis software to analyze attributes such as category, color, and design from the uploaded clothing image. This analyzed data is stored in a database on the server.

[0377] Next, the user enters information about their schedule for the day and their hairstyle into the terminal and sends it to the server. At this time, the system also recognizes the user's current emotional state using an emotion engine. The emotion engine identifies emotions by analyzing the user's facial expressions and voice input.

[0378] The server integrates all data from the user and generates outfits using a generative AI model. This generative AI model takes into account the user's emotional state, schedule, and past feedback to suggest the most suitable clothing combination.

[0379] The suggested outfits are sent from the server to the terminal and displayed to the user. The user can review the presented outfits and provide feedback. This feedback is sent back to the server and used to improve the accuracy of the suggestion generation system.

[0380] As a concrete example, if a user plans to attend an "outdoor event on a sunny day," and the emotion engine recognizes a "positive emotion," the server will select and suggest clothing with a bright and cheerful design. An example of a prompt in this case would be something like, "The user's plan for today is an outdoor picnic. Their emotional state is excited. What kind of outfit would be good?" which would be input into the generative model.

[0381] This system allows users to make efficient and satisfying clothing choices that match their schedule and mood for the day.

[0382] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0383] Step 1:

[0384] The user takes a picture of their own clothing using their smartphone camera. The captured image is uploaded from the device to the server. At this point, the input data is the image of the clothing taken by the user. The server receives the image file and prepares for data processing.

[0385] Step 2:

[0386] The server performs image analysis based on the received images of clothing. Using an image analysis algorithm, it extracts attributes such as the clothing's category, color, and design. The input is the clothing image received in step 1, and the output is the analyzed clothing attribute data. This data is stored in a database.

[0387] Step 3:

[0388] The user enters information about their schedule and hairstyle into the terminal. This information is sent to the server as part of the user's context. The input data, which consists of the user's schedule and hairstyle information, is stored on the server to proceed to the next step.

[0389] Step 4:

[0390] The server uses emotion recognition to understand the user's emotional state. The user sends facial images and audio from their terminal to the server. The input is the user's facial images and audio data, which the server analyzes to identify the user's emotional state. At this point, the output is the analyzed emotional state.

[0391] Step 5:

[0392] The server uses the data collected so far to run a generative AI model. This model considers the user's emotional state and schedule, and generates outfit suggestions based on prompt text. The input consists of analyzed clothing attributes, schedule, hairstyle, and emotional state, and the output is outfit suggestions for the user.

[0393] Step 6:

[0394] The generated outfit suggestions are sent from the server to the user's terminal. The terminal displays the received information on its screen and notifies the user. This allows the user to view the suggested outfits. The input data is the outfit suggestions from the server, and the output is a visual display on the terminal.

[0395] Step 7:

[0396] Users evaluate the suggested outfits via their terminal and provide feedback. This feedback is sent from the terminal to the server. The input is the user's evaluation data, and the output is stored on the server as training data to improve the accuracy of the suggestion generation system.

[0397] (Application Example 2)

[0398] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0399] Modern consumers face the challenge of choosing clothing that suits their style from a vast array of options. In particular, there is no system that automatically suggests outfits based on a consumer's mood or daily schedule, often leaving consumers wasting time and effort without making a satisfactory choice. Furthermore, virtual stores struggle to provide real-time fashion suggestions that respond to consumers' emotions.

[0400] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0401] In this invention, the server includes a processing unit that receives images of clothing from a user and analyzes the attributes of the clothing from the images; a suggestion generation unit that proposes the optimal clothing combination for the user using a generation model based on the user's daily activity plan and hair style information; a display unit that transmits and displays the generated clothing combination to the user's information processing unit; and an emotion analysis unit that acquires the user's emotional state and adjusts the suggestion content based on the acquired emotion. This makes it possible to propose the optimal fashion coordination according to the individual user's emotions and schedule.

[0402] A "user" is the entity that provides input to the system and receives the suggested fashion coordinates.

[0403] "Clothing images" refer to digital image data of clothing owned by the user.

[0404] "Attributes" refer to characteristic information such as the color, design, material, and category of clothing.

[0405] A "processing device" is a computer device used to analyze attributes from images of received clothing.

[0406] An "action plan" is the user's schedule for the day, including information such as time, location, and activities.

[0407] "Hair style" refers to information about the hairstyle the user will have on that day, as entered by the user.

[0408] A "generative model" is an artificial intelligence model used to derive the optimal clothing combination for a user.

[0409] A "proposal generation device" is a device that uses a generation model based on input information to suggest clothing combinations.

[0410] An "information processing device" is a terminal used by users to review proposed outfits.

[0411] A "display device" is a device that visually presents the generated clothing combinations to the user.

[0412] "Emotional state" refers to the psychological state that can be inferred from the user's facial expressions, voice, and other factors.

[0413] An "emotion analysis device" is a device that acquires the user's emotional state and uses that information to adjust the content of the suggestions.

[0414] A "virtual store" is an online commercial environment that provides products to consumers through electronic means without displaying physical goods.

[0415] The system implementing this invention automatically suggests clothing coordinates based on the user's wardrobe. The user first takes images of their clothing using a smartphone or other information processing device and uploads these images to the system. The server analyzes the received clothing images using a processing device, identifies the attributes of the clothing, and records this information in a database. The software used can utilize OpenCV as an image processing library.

[0416] The user then inputs information about their daily activity plan and hairstyle via a terminal and sends it to the server. Based on this information, the server uses a generative model to generate a suggestion generator for the optimal clothing combination for the user. At this time, the suggestion generator uses an emotion analysis device to detect the user's emotional state from their facial expressions and voice, and prioritizes selecting an outfit that matches this emotional state. Emotion analysis APIs such as Microsoft Cognitive Services can be used.

[0417] The generated coordinates are transmitted to an information processing terminal and visually presented to the user by a display device. The user can review the presented coordinates and provide feedback to the system. This feedback contributes to improving the accuracy of the suggestion generation device through a learning device.

[0418] For example, if a user plans a "sports event in the park on a sunny day" and the emotion analysis device determines that they are feeling "refreshed," the server will suggest clothing with a light design and cool colors.

[0419] An example of a prompt statement is as follows:

[0420] User's emotion: Refreshing

[0421] Planned: Sports event in the park on a sunny day

[0422] Suggested outfit: Clothing with cool colors and a light, airy design.

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] Users take pictures of their clothes using information processing devices such as smartphones and tablets and upload them to the system. The input data is digital image data of the clothes and is sent directly from the device to the server.

[0426] Step 2:

[0427] The server analyzes the received image data of clothing using OpenCV and extracts clothing attributes such as color, design, and category. These attributes are stored in a database and used later to generate suggestions. The input is image data, and the output is clothing attribute information.

[0428] Step 3:

[0429] Users input their daily activity plan and hairstyle via an information processing terminal and send this information to the server. The activity plan includes the scheduled date, time, location, and activities. The input consists of the activity plan and hairstyle information, which are stored on the server as output.

[0430] Step 4:

[0431] The server uses facial expression and voice data transmitted from the terminal to execute an emotion analysis API (e.g., Microsoft Cognitive Services) to identify the user's emotional state. This analysis outputs the type and intensity of the emotion. The input is the user's facial expression and voice data, and the output is the emotional state.

[0432] Step 5:

[0433] The server combines the user's action plan, hair style, emotional state, and analyzed clothing attributes to generate optimal outfit suggestions using a generative AI model. In this process, the generative AI model is run using prompt text as input, and optimal outfit data is output.

[0434] Step 6:

[0435] The generated coordination information is transmitted to an information processing terminal and notified and displayed to the user via a display device. The user visually confirms the generated coordination and, if necessary, inputs feedback and sends it to the server.

[0436] Step 7:

[0437] The server analyzes user feedback and trains a generative AI model via a learning device to improve the proposal generation process. It uses the data obtained from the feedback as input to update model parameters to improve the accuracy of proposal generation.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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".

[0454] This invention provides a system that streamlines the user's daily clothing selection process. The user first takes photos of their clothes with their smartphone camera and uploads these images to the system using a terminal. The server automatically extracts clothing attributes from the received images using an image analysis algorithm and stores data such as color, design, and category in a database.

[0455] Next, the user sends information about their schedule for the day (TPO: time, place, occasion) and hairstyle to the server via their device. The server uses this information, along with pre-configured fashion data, to suggest the most suitable clothing combination for the user using a generative model.

[0456] The suggestion generation method uses an algorithm on the server to derive stylish and appropriate outfits that meet the requirements of the time, place, and occasion (TPO), by referencing clothing information stored in the user's existing database and the latest fashion trend data.

[0457] The server sends the generated outfit information to the user's device, which then displays and notifies the user. The user can evaluate the suggested outfit and input feedback into the device.

[0458] Feedback information is sent to the server. The server analyzes this feedback and incorporates it into the training of the generative model, improving the accuracy of future outfit suggestions. Through this process, users can receive suggestions tailored to their individual preferences and daily trends.

[0459] As described above, the present invention embodies a system that simplifies the user's clothing selection process and provides everyday outfits efficiently and in line with current trends.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] Users use their smartphone cameras to take pictures of each item of clothing they own. The captured images are uploaded to the application via the device.

[0463] Step 2:

[0464] The device sends the uploaded clothing image data to the server. The server uses an image analysis algorithm to automatically extract clothing attributes such as color, shape, pattern, and category (shirt, pants, etc.), and stores them in a database for each user.

[0465] Step 3:

[0466] The user enters information about their daily schedule (TPO) and hairstyle into their device and sends it to the server. The schedule includes information about the type and location of the event.

[0467] Step 4:

[0468] The server receives the entered schedule and hairstyle information and references the latest fashion trends collected from the internet. At the same time, it checks the clothing information in the user's database.

[0469] Step 5:

[0470] The server-based generation model generates the optimal clothing combination for the user based on aggregated information. It creates several clothing options that meet the requirements of the time, place, and occasion (TPO), and then selects the best combination from among them.

[0471] Step 6:

[0472] The server sends the generated outfit information to the user's device. The device displays the suggested outfit on the screen and notifies the user.

[0473] Step 7:

[0474] Users review the displayed outfits and rate their satisfaction level and areas for improvement. This rating is entered as feedback using their device.

[0475] Step 8:

[0476] Feedback information sent from the terminal is sent to the server, where it is analyzed. The analysis results are used to improve the generative model and enhance the accuracy of coordination suggestions.

[0477] (Example 1)

[0478] 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."

[0479] In modern times, many users spend time and effort choosing everyday accessories. However, finding combinations that suit individual preferences, current trends, and appropriate occasions is not easy. Against this backdrop, improving the efficiency and accuracy of accessory selection is a crucial challenge.

[0480] 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.

[0481] In this invention, the server includes an analysis means for receiving images of clothing from a user and analyzing the characteristics of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination to the user using generative artificial intelligence based on information about the user's schedule and appearance; and an information collection means for collecting information on trendy accessories through an information and communication network and using this information as reference data for the suggestion generation means. This makes it possible for users to easily obtain the optimal combination of accessories based on their individual preferences and external trend information.

[0482] A "user" is an individual who uses this system to receive clothing coordination suggestions.

[0483] "Clothing images" refer to visual digital data of clothing owned by the user.

[0484] "Characteristics" refer to various elements that indicate the attributes of clothing, including data such as color, design, and category.

[0485] "Analysis means" refers to a process or apparatus used to extract features from images of received clothing.

[0486] "Information regarding schedules and appearance" refers to data that users provide to the system regarding their schedules for the day and information about their appearance.

[0487] "Generative artificial intelligence" refers to a machine learning algorithm or model used to generate the optimal clothing combination for a user.

[0488] "Suggestion generation means" refers to an operation or device that derives a suitable clothing combination for the user based on analyzed features and other information.

[0489] An "information and communication network" is a communication framework for exchanging digital information, usually referring to the internet.

[0490] "Information on trendy accessories" refers to data on currently popular designs and styles.

[0491] "Information gathering means" refers to the operations or techniques used to obtain information about trends from sources such as the internet.

[0492] This invention is a system designed to streamline the user's daily clothing selection process. The user uses a device to photograph their clothing and uploads the image to the system. Upon receiving this image, the server extracts clothing features using an image analysis algorithm. Specifically, it automatically analyzes important attributes such as color, design, and category from the image using libraries like OpenCV and TensorFlow.

[0493] The server stores the analysis results in a database and receives information from the user regarding their daily schedule and appearance. This information is input via the user's device. Next, the server generates clothing combinations suitable for the user using a generative AI model. This AI model is typically built using, for example, PyTorch or Keras. The server also collects information on the latest trends through information and communication networks and uses this as reference data for the generation process.

[0494] The generated coordination information is sent to the user's device and displayed. The user evaluates the received suggestions and provides feedback. This feedback is analyzed by the server and incorporated into the training of the generating AI model. This allows for improvements to be made so that more accurate suggestions are provided.

[0495] For example, if a user uploads an image of themselves wearing a blue jacket and black pants and plans to go out casually that day, the system might suggest an outfit combining a white T-shirt and sneakers. An example of a prompt that could be input to the generative AI model in this system would be, "Please suggest a stylish outfit for a casual outing using a blue jacket and black pants."

[0496] As described above, this invention makes it possible to suggest optimal clothing based on each user's individual preferences and fashion trends.

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

[0498] Step 1:

[0499] The user takes a picture of their clothing using their smartphone camera. The captured image file becomes the input. The device provides an interface for uploading this image to the system, and the user sends the image to the server. The output is the image arriving at the server.

[0500] Step 2:

[0501] The server analyzes the received images. The input is images of clothing sent by the user. The server uses libraries such as OpenCV and TensorFlow to execute image analysis algorithms and performs data processing to extract features such as color, design, and category from the images. The output is this feature data, which the server stores in a database.

[0502] Step 3:

[0503] The user inputs information about their daily schedule and appearance, such as their schedule and hairstyle, through a terminal. This constitutes the input information. The terminal sends the input information to the server. The output is the arrival of this information at the server.

[0504] Step 4:

[0505] The server uses stored feature data and information about the user's schedule and appearance to generate the optimal clothing combination using a generative AI model. The input is the analyzed feature data and user information. The generative AI model generates prompt sentences based on this information and creates suggestions through data calculations within the model. The output is the outfit data suggested to the user.

[0506] Step 5:

[0507] The server sends the generated coordination data to the user's terminal. The input is the generated coordination information. The terminal displays the received information to the user and sends a notification. The output at this time is that the user can confirm the suggestion on the terminal.

[0508] Step 6:

[0509] The user evaluates the presented outfit and enters feedback into the terminal. The input is the user's evaluation data. The terminal sends this information to the server. The output is the server receiving the evaluation data.

[0510] Step 7:

[0511] The server analyzes the received evaluation data and uses it to improve the generative AI model. The input is user feedback data. Based on this data, the server adjusts the model parameters and performs online learning to improve the accuracy of the suggestions. The output is the generative AI model that reflects the learning process.

[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] There is a need for a system that streamlines the process of choosing everyday clothes while simultaneously allowing users to virtually try on clothing. However, conventional systems have the challenge of making it difficult for users to experience the feeling of actually trying on new clothes in real time. Furthermore, it is difficult for users to intuitively understand the suggested outfits and immediately decide whether they suit them.

[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 an analysis means for acquiring images of clothing from a user and analyzing the characteristics of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination to the user using a generation method based on information about the user's planned activities and hairstyle for the day; and a virtual try-on means for visualizing the suggested clothing combination using a virtual reality device. This allows the user to intuitively grasp the suitability of the suggested outfit through a try-on experience in a virtual space.

[0517] A "user" is an individual who utilizes the system and is the entity that provides information such as clothing images, activity plans, and hairstyles.

[0518] "Clothing" refers to clothing owned by the user and provided to the system as image data.

[0519] "Images" are visual information data that users capture or acquire and send to the system for analysis.

[0520] "Characteristics" refer to characteristic information such as color, design, and category extracted from images of clothing using analytical methods.

[0521] "Analysis means" refers to a method or apparatus for analyzing images sent by a user and identifying the characteristics of clothing.

[0522] "Planned activities" refers to information such as the activities, locations, and times that the user has planned for the day.

[0523] "Hairstyle" refers to information about the hairstyle the user has specified for that day.

[0524] A "generation method" is a process that uses a pre-set algorithm to suggest the optimal clothing combination based on the user's information.

[0525] A "suggestion generation method" is a system that uses a generation method to derive clothing coordination based on information received from the user.

[0526] A "virtual reality device" is a device used by users to virtually try on clothes.

[0527] A "virtual fitting method" is a method or device that visualizes proposed clothing combinations using a virtual reality device, allowing the user to experience them as if they were actually trying them on.

[0528] The system for implementing this invention begins with the user taking an image of clothing using their own image acquisition device (e.g., a smartphone) and uploading that image to the user's terminal. The server receives the uploaded image and uses an image analysis library (e.g., OpenCV) to extract the characteristics of the clothing. This includes information such as color, design, and category.

[0529] Next, the user sends information about their daily schedule and hairstyle to the server via their device. The server then uses a generative method to suggest appropriate clothing combinations based on all the collected information and pre-configured fashion data. This generative method can utilize machine learning models or generative AI models.

[0530] The suggested clothing combinations are visualized to the user using a virtual reality device (e.g., smart glasses). The server sends the generated coordination information to the user's terminal, and the suggested clothing is visually overlaid on the virtual reality device through a virtual try-on mechanism. As a result, the user can have an experience as if they were trying on clothes in a virtual space. The user can also evaluate the suggested coordination and submit their feedback.

[0531] The feedback is analyzed on the server side and used to train the suggestion generation system. This further improves the accuracy of future suggestions. Through this entire process, users can efficiently choose clothes that match their preferences and the latest trends.

[0532] As a concrete example, when a user plans a casual outing with friends, the server suggests an outfit that includes a red jacket based on relevant information. The AI ​​model then uses a prompt message such as, "Generate a virtual try-on image of the outfit including a red jacket, tailored to the user's casual outing," to provide optimized suggestions. This allows the user to receive advice that suits their own style while enjoying a new fashion experience.

[0533] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0534] Step 1:

[0535] The user takes pictures of their clothing using an image acquisition device such as a smartphone. The input is image data of the clothing, which the device receives and sends to the server. The output is the image data of the clothing received by the server.

[0536] Step 2:

[0537] The server analyzes the received clothing image data using an image analysis library (e.g., OpenCV). The input for this step is the clothing image received by the server. The server performs image analysis and extracts clothing characteristic data such as color, design, and category. The output is a clothing characteristic dataset.

[0538] Step 3:

[0539] The user sends information about their daily schedule and hairstyle to the server via their device. The input for this step is the user's scheduled activities and hairstyle information. The output is the user's daily information data sent to the server.

[0540] Step 4:

[0541] The server combines received clothing characteristic data, user daily information, and pre-configured trending fashion data to suggest the optimal clothing combination using a generative AI model. The inputs for this step are characteristic data, planned activities, hairstyle, and trending fashion data. Through data calculation, appropriate clothing combinations are generated, and the output is coordinated outfit data suggested to the user.

[0542] Step 5:

[0543] The generated outfit data is sent to the user's terminal and visualized by the user through a virtual reality device (such as smart glasses). The input for this step is the generated outfit data. The terminal uses the virtual reality device to visualize the outfit, and the user experiences a virtual try-on. The output is the user's virtual try-on video.

[0544] Step 6:

[0545] The user provides feedback on the proposed outfit and sends it to the server via their device. The input for this step is the user's feedback information. The output is the evaluation data sent to the server.

[0546] Step 7:

[0547] The server analyzes the feedback received from the user and uses it to train the generative AI model of the suggestion generation system to improve its accuracy. The input for this step is the user's feedback data. The server updates the model using a learning algorithm and provides an improved generative AI model as output.

[0548] 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.

[0549] This invention provides a coordinate suggestion system incorporating an emotion engine to enable users to experience emotional satisfaction when choosing clothes. First, the user takes a picture of their own clothes with their smartphone and uploads the image to the system via the terminal. The server receives the image of the clothes, analyzes the characteristics of the clothes using an image analysis algorithm, and stores the category, color, design, etc. in a database.

[0550] Next, the user enters information about their schedule for the day and their hairstyle, and sends the data from their device to the server. Furthermore, the system uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice input to determine their current emotional state.

[0551] Based on this data, the server uses a generative model to suggest outfits suitable for the user. The generative model takes into account the user's analyzed emotional state and suggests a fashion style that matches it. Furthermore, it improves the accuracy of its suggestions by referring to previously recorded user feedback and emotional history.

[0552] For example, if a user has planned an "outdoor event on a sunny day" and the emotion engine detects a "positive emotion," the server will prioritize and recommend outfits with bright colors and cheerful designs. The suggestion generation system incorporates this information to generate the optimal clothing combination for the user.

[0553] The user's device can receive the generated coordination information and display it to the user via notification. The user can evaluate the presented coordination and provide feedback. This feedback is used by the server to improve the accuracy of the emotion engine and suggestion generation means.

[0554] Thus, the present invention provides a system that takes into account emotional states and, based on personal preferences and the latest trends, makes the selection of clothing in individuals' daily lives more efficient and satisfying.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] Users take pictures of their clothes with their smartphone camera and upload the images of their clothes to the system via their device.

[0558] Step 2:

[0559] The device sends the uploaded image of the clothing to the server. The server uses an image analysis algorithm to analyze the attributes of the clothing, such as its color, shape, and pattern, and stores this information in a user-specific database.

[0560] Step 3:

[0561] The user enters information about their schedule for the day and their hairstyle into the device. In addition, the device's camera takes a picture of the user's current facial expression and sends it to the server.

[0562] Step 4:

[0563] The server uses an emotion engine to analyze the user's current emotional state based on the user's schedule, hairstyle, and facial expression images. This analysis result is recorded and used as reference information in subsequent processes.

[0564] Step 5:

[0565] The server uses a generative model to combine the user's saved clothing data with analyzed emotional states, schedules, and hairstyle information to generate optimal outfit suggestions. The model also references current fashion trends to select clothing that matches the user's emotions.

[0566] Step 6:

[0567] The server sends the generated outfit suggestions to the user's terminal. The terminal notifies the user of the suggestions and displays the details on the screen.

[0568] Step 7:

[0569] The user reviews the suggested outfit and provides feedback on their satisfaction level and areas for improvement via their device. This feedback is then sent to the server.

[0570] Step 8:

[0571] The server analyzes the feedback it receives and uses it as training data for its generative models and emotion engine. This improves the accuracy of future outfit suggestions, enabling suggestions that better suit the user's preferences.

[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] Conventional clothing selection systems struggle to suggest the optimal clothing combination based on the user's mood and individual schedule. Furthermore, a lack of effective means to utilize user feedback and improve the accuracy of suggestions is a significant challenge.

[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 analysis means for receiving images of clothing from a user and analyzing the attributes of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination for the user using a generative model based on the user's schedule and appearance information received from the user; and an emotion recognition means for analyzing the user's facial expression images and voice input to identify their emotional state. This makes it possible to suggest the optimal clothing according to emotions and schedules.

[0577] "Analysis means" refers to a device or method for extracting attributes from clothing images received from a user and storing them in a database.

[0578] A "proposal generation means" is a device or method that, based on user input information, uses a generation model to create the optimal clothing combination and proposes it to the user.

[0579] "Emotion recognition means" refers to a device or method for analyzing data such as a user's facial expression image or voice input to determine the user's current emotional state.

[0580] "Display means" refers to a device or method that transmits the generated clothing combination to the user's device and displays it visually to the user.

[0581] A "learning method" refers to a process or algorithm that accumulates and analyzes information based on user feedback to improve the accuracy of the suggestion generation method.

[0582] "Information gathering means" refers to a device or method that collects information on fashionable clothing through communication networks such as the internet and utilizes it as reference data for proposal generation means.

[0583] This system is designed to allow users to receive optimal suggestions regarding clothing choices based on their individual feelings and schedules. An embodiment of this system is shown below.

[0584] The user first takes a picture of their clothing with a smartphone or other device and uploads the image to the system. The device then sends this image data to the server. The server uses image analysis software to analyze attributes such as category, color, and design from the uploaded clothing image. This analyzed data is stored in a database on the server.

[0585] Next, the user enters information about their schedule for the day and their hairstyle into the terminal and sends it to the server. At this time, the system also recognizes the user's current emotional state using an emotion engine. The emotion engine identifies emotions by analyzing the user's facial expressions and voice input.

[0586] The server integrates all data from the user and generates outfits using a generative AI model. This generative AI model takes into account the user's emotional state, schedule, and past feedback to suggest the most suitable clothing combination.

[0587] The suggested outfits are sent from the server to the terminal and displayed to the user. The user can review the presented outfits and provide feedback. This feedback is sent back to the server and used to improve the accuracy of the suggestion generation system.

[0588] As a concrete example, if a user plans to attend an "outdoor event on a sunny day," and the emotion engine recognizes a "positive emotion," the server will select and suggest clothing with a bright and cheerful design. An example of a prompt in this case would be something like, "The user's plan for today is an outdoor picnic. Their emotional state is excited. What kind of outfit would be good?" which would be input into the generative model.

[0589] This system allows users to make efficient and satisfying clothing choices that match their schedule and mood for the day.

[0590] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0591] Step 1:

[0592] The user takes a picture of their own clothing using their smartphone camera. The captured image is uploaded from the device to the server. At this point, the input data is the image of the clothing taken by the user. The server receives the image file and prepares for data processing.

[0593] Step 2:

[0594] The server performs image analysis based on the received images of clothing. Using an image analysis algorithm, it extracts attributes such as the clothing's category, color, and design. The input is the clothing image received in step 1, and the output is the analyzed clothing attribute data. This data is stored in a database.

[0595] Step 3:

[0596] The user enters information about their schedule and hairstyle into the terminal. This information is sent to the server as part of the user's context. The input data, which consists of the user's schedule and hairstyle information, is stored on the server to proceed to the next step.

[0597] Step 4:

[0598] The server uses emotion recognition to understand the user's emotional state. The user sends facial images and audio from their terminal to the server. The input is the user's facial images and audio data, which the server analyzes to identify the user's emotional state. At this point, the output is the analyzed emotional state.

[0599] Step 5:

[0600] The server uses the data collected so far to run a generative AI model. This model considers the user's emotional state and schedule, and generates outfit suggestions based on prompt text. The input consists of analyzed clothing attributes, schedule, hairstyle, and emotional state, and the output is outfit suggestions for the user.

[0601] Step 6:

[0602] The generated outfit suggestions are sent from the server to the user's terminal. The terminal displays the received information on its screen and notifies the user. This allows the user to view the suggested outfits. The input data is the outfit suggestions from the server, and the output is a visual display on the terminal.

[0603] Step 7:

[0604] Users evaluate the suggested outfits via their terminal and provide feedback. This feedback is sent from the terminal to the server. The input is the user's evaluation data, and the output is stored on the server as training data to improve the accuracy of the suggestion generation system.

[0605] (Application Example 2)

[0606] 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."

[0607] Modern consumers face the challenge of choosing clothing that suits their style from a vast array of options. In particular, there is no system that automatically suggests outfits based on a consumer's mood or daily schedule, often leaving consumers wasting time and effort without making a satisfactory choice. Furthermore, virtual stores struggle to provide real-time fashion suggestions that respond to consumers' emotions.

[0608] 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.

[0609] In this invention, the server includes a processing unit that receives images of clothing from a user and analyzes the attributes of the clothing from the images; a suggestion generation unit that proposes the optimal clothing combination for the user using a generation model based on the user's daily activity plan and hair style information; a display unit that transmits and displays the generated clothing combination to the user's information processing unit; and an emotion analysis unit that acquires the user's emotional state and adjusts the suggestion content based on the acquired emotion. This makes it possible to propose the optimal fashion coordination according to the individual user's emotions and schedule.

[0610] A "user" is the entity that provides input to the system and receives the suggested fashion coordinates.

[0611] "Clothing images" refer to digital image data of clothing owned by the user.

[0612] "Attributes" refer to characteristic information such as the color, design, material, and category of clothing.

[0613] A "processing device" is a computer device used to analyze attributes from images of received clothing.

[0614] An "action plan" is the user's schedule for the day, including information such as time, location, and activities.

[0615] "Hair style" refers to information about the hairstyle the user will have on that day, as entered by the user.

[0616] A "generative model" is an artificial intelligence model used to derive the optimal clothing combination for a user.

[0617] A "proposal generation device" is a device that uses a generation model based on input information to suggest clothing combinations.

[0618] An "information processing device" is a terminal used by users to review proposed outfits.

[0619] A "display device" is a device that visually presents the generated clothing combinations to the user.

[0620] "Emotional state" refers to the psychological state that can be inferred from the user's facial expressions, voice, and other factors.

[0621] An "emotion analysis device" is a device that acquires the user's emotional state and uses that information to adjust the content of the suggestions.

[0622] A "virtual store" is an online commercial environment that provides products to consumers through electronic means without displaying physical goods.

[0623] The system implementing this invention automatically suggests clothing coordinates based on the user's wardrobe. The user first takes images of their clothing using a smartphone or other information processing device and uploads these images to the system. The server analyzes the received clothing images using a processing device, identifies the attributes of the clothing, and records this information in a database. The software used can utilize OpenCV as an image processing library.

[0624] The user then inputs information about their daily activity plan and hairstyle via a terminal and sends it to the server. Based on this information, the server uses a generative model to generate a suggestion generator for the optimal clothing combination for the user. At this time, the suggestion generator uses an emotion analysis device to detect the user's emotional state from their facial expressions and voice, and prioritizes selecting an outfit that matches this emotional state. Emotion analysis APIs such as Microsoft Cognitive Services can be used.

[0625] The generated coordinates are transmitted to an information processing terminal and visually presented to the user by a display device. The user can review the presented coordinates and provide feedback to the system. This feedback contributes to improving the accuracy of the suggestion generation device through a learning device.

[0626] For example, if a user plans a "sports event in the park on a sunny day" and the emotion analysis device determines that they are feeling "refreshed," the server will suggest clothing with a light design and cool colors.

[0627] An example of a prompt statement is as follows:

[0628] User's emotion: Refreshing

[0629] Planned: Sports event in the park on a sunny day

[0630] Suggested outfit: Clothing with cool colors and a light, airy design.

[0631] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0632] Step 1:

[0633] Users take pictures of their clothes using information processing devices such as smartphones and tablets and upload them to the system. The input data is digital image data of the clothes and is sent directly from the device to the server.

[0634] Step 2:

[0635] The server analyzes the received image data of clothing using OpenCV and extracts clothing attributes such as color, design, and category. These attributes are stored in a database and used later to generate suggestions. The input is image data, and the output is clothing attribute information.

[0636] Step 3:

[0637] Users input their daily activity plan and hairstyle via an information processing terminal and send this information to the server. The activity plan includes the scheduled date, time, location, and activities. The input consists of the activity plan and hairstyle information, which are stored on the server as output.

[0638] Step 4:

[0639] The server uses facial expression and voice data transmitted from the terminal to execute an emotion analysis API (e.g., Microsoft Cognitive Services) to identify the user's emotional state. This analysis outputs the type and intensity of the emotion. The input is the user's facial expression and voice data, and the output is the emotional state.

[0640] Step 5:

[0641] The server combines the user's action plan, hair style, emotional state, and analyzed clothing attributes to generate optimal outfit suggestions using a generative AI model. In this process, the generative AI model is run using prompt text as input, and optimal outfit data is output.

[0642] Step 6:

[0643] The generated coordination information is transmitted to an information processing terminal and notified and displayed to the user via a display device. The user visually confirms the generated coordination and, if necessary, inputs feedback and sends it to the server.

[0644] Step 7:

[0645] The server analyzes user feedback and trains a generative AI model via a learning device to improve the proposal generation process. It uses the data obtained from the feedback as input to update model parameters to improve the accuracy of proposal generation.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] [Fourth Embodiment]

[0650] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0651] 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.

[0652] 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).

[0653] 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.

[0654] 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.

[0655] 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).

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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".

[0663] This invention provides a system that streamlines the user's daily clothing selection process. The user first takes photos of their clothes with their smartphone camera and uploads these images to the system using a terminal. The server automatically extracts clothing attributes from the received images using an image analysis algorithm and stores data such as color, design, and category in a database.

[0664] Next, the user sends information about their schedule for the day (TPO: time, place, occasion) and hairstyle to the server via their device. The server uses this information, along with pre-configured fashion data, to suggest the most suitable clothing combination for the user using a generative model.

[0665] The suggestion generation method uses an algorithm on the server to derive stylish and appropriate outfits that meet the requirements of the time, place, and occasion (TPO), by referencing clothing information stored in the user's existing database and the latest fashion trend data.

[0666] The server sends the generated outfit information to the user's device, which then displays and notifies the user. The user can evaluate the suggested outfit and input feedback into the device.

[0667] Feedback information is sent to the server. The server analyzes this feedback and incorporates it into the training of the generative model, improving the accuracy of future outfit suggestions. Through this process, users can receive suggestions tailored to their individual preferences and daily trends.

[0668] As described above, the present invention embodies a system that simplifies the user's clothing selection process and provides everyday outfits efficiently and in line with current trends.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] Users use their smartphone cameras to take pictures of each item of clothing they own. The captured images are uploaded to the application via the device.

[0672] Step 2:

[0673] The device sends the uploaded clothing image data to the server. The server uses an image analysis algorithm to automatically extract clothing attributes such as color, shape, pattern, and category (shirt, pants, etc.), and stores them in a database for each user.

[0674] Step 3:

[0675] The user enters information about their daily schedule (TPO) and hairstyle into their device and sends it to the server. The schedule includes information about the type and location of the event.

[0676] Step 4:

[0677] The server receives the entered schedule and hairstyle information and references the latest fashion trends collected from the internet. At the same time, it checks the clothing information in the user's database.

[0678] Step 5:

[0679] The server-based generation model generates the optimal clothing combination for the user based on aggregated information. It creates several clothing options that meet the requirements of the time, place, and occasion (TPO), and then selects the best combination from among them.

[0680] Step 6:

[0681] The server sends the generated outfit information to the user's device. The device displays the suggested outfit on the screen and notifies the user.

[0682] Step 7:

[0683] Users review the displayed outfits and rate their satisfaction level and areas for improvement. This rating is entered as feedback using their device.

[0684] Step 8:

[0685] Feedback information sent from the terminal is sent to the server, where it is analyzed. The analysis results are used to improve the generative model and enhance the accuracy of coordination suggestions.

[0686] (Example 1)

[0687] 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".

[0688] In modern times, many users spend time and effort choosing everyday accessories. However, finding combinations that suit individual preferences, current trends, and appropriate occasions is not easy. Against this backdrop, improving the efficiency and accuracy of accessory selection is a crucial challenge.

[0689] 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.

[0690] In this invention, the server includes an analysis means for receiving images of clothing from a user and analyzing the characteristics of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination to the user using generative artificial intelligence based on information about the user's schedule and appearance; and an information collection means for collecting information on trendy accessories through an information and communication network and using this information as reference data for the suggestion generation means. This makes it possible for users to easily obtain the optimal combination of accessories based on their individual preferences and external trend information.

[0691] A "user" is an individual who uses this system to receive clothing coordination suggestions.

[0692] "Clothing images" refer to visual digital data of clothing owned by the user.

[0693] "Characteristics" refer to various elements that indicate the attributes of clothing, including data such as color, design, and category.

[0694] "Analysis means" refers to a process or apparatus used to extract features from images of received clothing.

[0695] "Information regarding schedules and appearance" refers to data that users provide to the system regarding their schedules for the day and information about their appearance.

[0696] "Generative artificial intelligence" refers to a machine learning algorithm or model used to generate the optimal clothing combination for a user.

[0697] "Suggestion generation means" refers to an operation or device that derives a suitable clothing combination for the user based on analyzed features and other information.

[0698] An "information and communication network" is a communication framework for exchanging digital information, usually referring to the internet.

[0699] "Information on trendy accessories" refers to data on currently popular designs and styles.

[0700] "Information gathering means" refers to the operations or techniques used to obtain information about trends from sources such as the internet.

[0701] This invention is a system designed to streamline the user's daily clothing selection process. The user uses a device to photograph their clothing and uploads the image to the system. Upon receiving this image, the server extracts clothing features using an image analysis algorithm. Specifically, it automatically analyzes important attributes such as color, design, and category from the image using libraries like OpenCV and TensorFlow.

[0702] The server stores the analysis results in a database and receives information from the user regarding their daily schedule and appearance. This information is input via the user's device. Next, the server generates clothing combinations suitable for the user using a generative AI model. This AI model is typically built using, for example, PyTorch or Keras. The server also collects information on the latest trends through information and communication networks and uses this as reference data for the generation process.

[0703] The generated coordination information is sent to the user's device and displayed. The user evaluates the received suggestions and provides feedback. This feedback is analyzed by the server and incorporated into the training of the generating AI model. This allows for improvements to be made so that more accurate suggestions are provided.

[0704] For example, if a user uploads an image of themselves wearing a blue jacket and black pants and plans to go out casually that day, the system might suggest an outfit combining a white T-shirt and sneakers. An example of a prompt that could be input to the generative AI model in this system would be, "Please suggest a stylish outfit for a casual outing using a blue jacket and black pants."

[0705] As described above, this invention makes it possible to suggest optimal clothing based on each user's individual preferences and fashion trends.

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

[0707] Step 1:

[0708] The user takes a picture of their clothing using their smartphone camera. The captured image file becomes the input. The device provides an interface for uploading this image to the system, and the user sends the image to the server. The output is the image arriving at the server.

[0709] Step 2:

[0710] The server analyzes the received images. The input is images of clothing sent by the user. The server uses libraries such as OpenCV and TensorFlow to execute image analysis algorithms and performs data processing to extract features such as color, design, and category from the images. The output is this feature data, which the server stores in a database.

[0711] Step 3:

[0712] The user inputs information about their daily schedule and appearance, such as their schedule and hairstyle, through a terminal. This constitutes the input information. The terminal sends the input information to the server. The output is the arrival of this information at the server.

[0713] Step 4:

[0714] The server uses stored feature data and information about the user's schedule and appearance to generate the optimal clothing combination using a generative AI model. The input is the analyzed feature data and user information. The generative AI model generates prompt sentences based on this information and creates suggestions through data calculations within the model. The output is the outfit data suggested to the user.

[0715] Step 5:

[0716] The server sends the generated coordination data to the user's terminal. The input is the generated coordination information. The terminal displays the received information to the user and sends a notification. The output at this time is that the user can confirm the suggestion on the terminal.

[0717] Step 6:

[0718] The user evaluates the presented outfit and enters feedback into the terminal. The input is the user's evaluation data. The terminal sends this information to the server. The output is the server receiving the evaluation data.

[0719] Step 7:

[0720] The server analyzes the received evaluation data and uses it to improve the generative AI model. The input is user feedback data. Based on this data, the server adjusts the model parameters and performs online learning to improve the accuracy of the suggestions. The output is the generative AI model that reflects the learning process.

[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] There is a need for a system that streamlines the process of choosing everyday clothes while simultaneously allowing users to virtually try on clothing. However, conventional systems have the challenge of making it difficult for users to experience the feeling of actually trying on new clothes in real time. Furthermore, it is difficult for users to intuitively understand the suggested outfits and immediately decide whether they suit them.

[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 an analysis means for acquiring images of clothing from a user and analyzing the characteristics of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination to the user using a generation method based on information about the user's planned activities and hairstyle for the day; and a virtual try-on means for visualizing the suggested clothing combination using a virtual reality device. This allows the user to intuitively grasp the suitability of the suggested outfit through a try-on experience in a virtual space.

[0726] A "user" is an individual who utilizes the system and is the entity that provides information such as clothing images, activity plans, and hairstyles.

[0727] "Clothing" refers to clothing owned by the user and provided to the system as image data.

[0728] "Images" are visual information data that users capture or acquire and send to the system for analysis.

[0729] "Characteristics" refer to characteristic information such as color, design, and category extracted from images of clothing using analytical methods.

[0730] "Analysis means" refers to a method or apparatus for analyzing images sent by a user and identifying the characteristics of clothing.

[0731] "Planned activities" refers to information such as the activities, locations, and times that the user has planned for the day.

[0732] "Hairstyle" refers to information about the hairstyle the user has specified for that day.

[0733] A "generation method" is a process that uses a pre-set algorithm to suggest the optimal clothing combination based on the user's information.

[0734] A "suggestion generation method" is a system that uses a generation method to derive clothing coordination based on information received from the user.

[0735] A "virtual reality device" is a device used by users to virtually try on clothes.

[0736] A "virtual fitting method" is a method or device that visualizes proposed clothing combinations using a virtual reality device, allowing the user to experience them as if they were actually trying them on.

[0737] The system for implementing this invention begins with the user taking an image of clothing using their own image acquisition device (e.g., a smartphone) and uploading that image to the user's terminal. The server receives the uploaded image and uses an image analysis library (e.g., OpenCV) to extract the characteristics of the clothing. This includes information such as color, design, and category.

[0738] Next, the user sends information about their daily schedule and hairstyle to the server via their device. The server then uses a generative method to suggest appropriate clothing combinations based on all the collected information and pre-configured fashion data. This generative method can utilize machine learning models or generative AI models.

[0739] The suggested clothing combinations are visualized to the user using a virtual reality device (e.g., smart glasses). The server sends the generated coordination information to the user's terminal, and the suggested clothing is visually overlaid on the virtual reality device through a virtual try-on mechanism. As a result, the user can have an experience as if they were trying on clothes in a virtual space. The user can also evaluate the suggested coordination and submit their feedback.

[0740] The feedback is analyzed on the server side and used to train the suggestion generation system. This further improves the accuracy of future suggestions. Through this entire process, users can efficiently choose clothes that match their preferences and the latest trends.

[0741] As a concrete example, when a user plans a casual outing with friends, the server suggests an outfit that includes a red jacket based on relevant information. The AI ​​model then uses a prompt message such as, "Generate a virtual try-on image of the outfit including a red jacket, tailored to the user's casual outing," to provide optimized suggestions. This allows the user to receive advice that suits their own style while enjoying a new fashion experience.

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] The user takes pictures of their clothing using an image acquisition device such as a smartphone. The input is image data of the clothing, which the device receives and sends to the server. The output is the image data of the clothing received by the server.

[0745] Step 2:

[0746] The server analyzes the received clothing image data using an image analysis library (e.g., OpenCV). The input for this step is the clothing image received by the server. The server performs image analysis and extracts clothing characteristic data such as color, design, and category. The output is a clothing characteristic dataset.

[0747] Step 3:

[0748] The user sends information about their daily schedule and hairstyle to the server via their device. The input for this step is the user's scheduled activities and hairstyle information. The output is the user's daily information data sent to the server.

[0749] Step 4:

[0750] The server combines received clothing characteristic data, user daily information, and pre-configured trending fashion data to suggest the optimal clothing combination using a generative AI model. The inputs for this step are characteristic data, planned activities, hairstyle, and trending fashion data. Through data calculation, appropriate clothing combinations are generated, and the output is coordinated outfit data suggested to the user.

[0751] Step 5:

[0752] The generated outfit data is sent to the user's terminal and visualized by the user through a virtual reality device (such as smart glasses). The input for this step is the generated outfit data. The terminal uses the virtual reality device to visualize the outfit, and the user experiences a virtual try-on. The output is the user's virtual try-on video.

[0753] Step 6:

[0754] The user provides feedback on the proposed outfit and sends it to the server via their device. The input for this step is the user's feedback information. The output is the evaluation data sent to the server.

[0755] Step 7:

[0756] The server analyzes the feedback received from the user and uses it to train the generative AI model of the suggestion generation system to improve its accuracy. The input for this step is the user's feedback data. The server updates the model using a learning algorithm and provides an improved generative AI model as output.

[0757] 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.

[0758] This invention provides a coordinate suggestion system incorporating an emotion engine to enable users to experience emotional satisfaction when choosing clothes. First, the user takes a picture of their own clothes with their smartphone and uploads the image to the system via the terminal. The server receives the image of the clothes, analyzes the characteristics of the clothes using an image analysis algorithm, and stores the category, color, design, etc. in a database.

[0759] Next, the user enters information about their schedule for the day and their hairstyle, and sends the data from their device to the server. Furthermore, the system uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice input to determine their current emotional state.

[0760] Based on this data, the server uses a generative model to suggest outfits suitable for the user. The generative model takes into account the user's analyzed emotional state and suggests a fashion style that matches it. Furthermore, it improves the accuracy of its suggestions by referring to previously recorded user feedback and emotional history.

[0761] For example, if a user has planned an "outdoor event on a sunny day" and the emotion engine detects a "positive emotion," the server will prioritize and recommend outfits with bright colors and cheerful designs. The suggestion generation system incorporates this information to generate the optimal clothing combination for the user.

[0762] The user's device can receive the generated coordination information and display it to the user via notification. The user can evaluate the presented coordination and provide feedback. This feedback is used by the server to improve the accuracy of the emotion engine and suggestion generation means.

[0763] Thus, the present invention provides a system that takes into account emotional states and, based on personal preferences and the latest trends, makes the selection of clothing in individuals' daily lives more efficient and satisfying.

[0764] The following describes the processing flow.

[0765] Step 1:

[0766] Users take pictures of their clothes with their smartphone camera and upload the images of their clothes to the system via their device.

[0767] Step 2:

[0768] The device sends the uploaded image of the clothing to the server. The server uses an image analysis algorithm to analyze the attributes of the clothing, such as its color, shape, and pattern, and stores this information in a user-specific database.

[0769] Step 3:

[0770] The user enters information about their schedule for the day and their hairstyle into the device. In addition, the device's camera takes a picture of the user's current facial expression and sends it to the server.

[0771] Step 4:

[0772] The server uses an emotion engine to analyze the user's current emotional state based on the user's schedule, hairstyle, and facial expression images. This analysis result is recorded and used as reference information in subsequent processes.

[0773] Step 5:

[0774] The server uses a generative model to combine the user's saved clothing data with analyzed emotional states, schedules, and hairstyle information to generate optimal outfit suggestions. The model also references current fashion trends to select clothing that matches the user's emotions.

[0775] Step 6:

[0776] The server sends the generated outfit suggestions to the user's terminal. The terminal notifies the user of the suggestions and displays the details on the screen.

[0777] Step 7:

[0778] The user reviews the suggested outfit and provides feedback on their satisfaction level and areas for improvement via their device. This feedback is then sent to the server.

[0779] Step 8:

[0780] The server analyzes the feedback it receives and uses it as training data for its generative models and emotion engine. This improves the accuracy of future outfit suggestions, enabling suggestions that better suit the user's preferences.

[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] Conventional clothing selection systems struggle to suggest the optimal clothing combination based on the user's mood and individual schedule. Furthermore, a lack of effective means to utilize user feedback and improve the accuracy of suggestions is a significant challenge.

[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 analysis means for receiving images of clothing from a user and analyzing the attributes of the clothing from the images; a suggestion generation means for proposing the optimal clothing combination for the user using a generative model based on the user's schedule and appearance information received from the user; and an emotion recognition means for analyzing the user's facial expression images and voice input to identify their emotional state. This makes it possible to suggest the optimal clothing according to emotions and schedules.

[0786] "Analysis means" refers to a device or method for extracting attributes from clothing images received from a user and storing them in a database.

[0787] A "proposal generation means" is a device or method that, based on user input information, uses a generation model to create the optimal clothing combination and proposes it to the user.

[0788] "Emotion recognition means" refers to a device or method for analyzing data such as a user's facial expression image or voice input to determine the user's current emotional state.

[0789] "Display means" refers to a device or method that transmits the generated clothing combination to the user's device and displays it visually to the user.

[0790] A "learning method" refers to a process or algorithm that accumulates and analyzes information based on user feedback to improve the accuracy of the suggestion generation method.

[0791] "Information gathering means" refers to a device or method that collects information on fashionable clothing through communication networks such as the internet and utilizes it as reference data for proposal generation means.

[0792] This system is designed to allow users to receive optimal suggestions regarding clothing choices based on their individual feelings and schedules. An embodiment of this system is shown below.

[0793] The user first takes a picture of their clothing with a smartphone or other device and uploads the image to the system. The device then sends this image data to the server. The server uses image analysis software to analyze attributes such as category, color, and design from the uploaded clothing image. This analyzed data is stored in a database on the server.

[0794] Next, the user enters information about their schedule for the day and their hairstyle into the terminal and sends it to the server. At this time, the system also recognizes the user's current emotional state using an emotion engine. The emotion engine identifies emotions by analyzing the user's facial expressions and voice input.

[0795] The server integrates all data from the user and generates outfits using a generative AI model. This generative AI model takes into account the user's emotional state, schedule, and past feedback to suggest the most suitable clothing combination.

[0796] The suggested outfits are sent from the server to the terminal and displayed to the user. The user can review the presented outfits and provide feedback. This feedback is sent back to the server and used to improve the accuracy of the suggestion generation system.

[0797] As a concrete example, if a user plans to attend an "outdoor event on a sunny day," and the emotion engine recognizes a "positive emotion," the server will select and suggest clothing with a bright and cheerful design. An example of a prompt in this case would be something like, "The user's plan for today is an outdoor picnic. Their emotional state is excited. What kind of outfit would be good?" which would be input into the generative model.

[0798] This system allows users to make efficient and satisfying clothing choices that match their schedule and mood for the day.

[0799] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0800] Step 1:

[0801] The user takes a picture of their own clothing using their smartphone camera. The captured image is uploaded from the device to the server. At this point, the input data is the image of the clothing taken by the user. The server receives the image file and prepares for data processing.

[0802] Step 2:

[0803] The server performs image analysis based on the received images of clothing. Using an image analysis algorithm, it extracts attributes such as the clothing's category, color, and design. The input is the clothing image received in step 1, and the output is the analyzed clothing attribute data. This data is stored in a database.

[0804] Step 3:

[0805] The user enters information about their schedule and hairstyle into the terminal. This information is sent to the server as part of the user's context. The input data, which consists of the user's schedule and hairstyle information, is stored on the server to proceed to the next step.

[0806] Step 4:

[0807] The server uses emotion recognition to understand the user's emotional state. The user sends facial images and audio from their terminal to the server. The input is the user's facial images and audio data, which the server analyzes to identify the user's emotional state. At this point, the output is the analyzed emotional state.

[0808] Step 5:

[0809] The server uses the data collected so far to run a generative AI model. This model considers the user's emotional state and schedule, and generates outfit suggestions based on prompt text. The input consists of analyzed clothing attributes, schedule, hairstyle, and emotional state, and the output is outfit suggestions for the user.

[0810] Step 6:

[0811] The generated outfit suggestions are sent from the server to the user's terminal. The terminal displays the received information on its screen and notifies the user. This allows the user to view the suggested outfits. The input data is the outfit suggestions from the server, and the output is a visual display on the terminal.

[0812] Step 7:

[0813] Users evaluate the suggested outfits via their terminal and provide feedback. This feedback is sent from the terminal to the server. The input is the user's evaluation data, and the output is stored on the server as training data to improve the accuracy of the suggestion generation system.

[0814] (Application Example 2)

[0815] 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".

[0816] Modern consumers face the challenge of choosing clothing that suits their style from a vast array of options. In particular, there is no system that automatically suggests outfits based on a consumer's mood or daily schedule, often leaving consumers wasting time and effort without making a satisfactory choice. Furthermore, virtual stores struggle to provide real-time fashion suggestions that respond to consumers' emotions.

[0817] 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.

[0818] In this invention, the server includes a processing unit that receives images of clothing from a user and analyzes the attributes of the clothing from the images; a suggestion generation unit that proposes the optimal clothing combination for the user using a generation model based on the user's daily activity plan and hair style information; a display unit that transmits and displays the generated clothing combination to the user's information processing unit; and an emotion analysis unit that acquires the user's emotional state and adjusts the suggestion content based on the acquired emotion. This makes it possible to propose the optimal fashion coordination according to the individual user's emotions and schedule.

[0819] A "user" is the entity that provides input to the system and receives the suggested fashion coordinates.

[0820] "Clothing images" refer to digital image data of clothing owned by the user.

[0821] "Attributes" refer to characteristic information such as the color, design, material, and category of clothing.

[0822] A "processing device" is a computer device used to analyze attributes from images of received clothing.

[0823] An "action plan" is the user's schedule for the day, including information such as time, location, and activities.

[0824] "Hair style" refers to information about the hairstyle the user will have on that day, as entered by the user.

[0825] A "generative model" is an artificial intelligence model used to derive the optimal clothing combination for a user.

[0826] A "proposal generation device" is a device that uses a generation model based on input information to suggest clothing combinations.

[0827] An "information processing device" is a terminal used by users to review proposed outfits.

[0828] A "display device" is a device that visually presents the generated clothing combinations to the user.

[0829] "Emotional state" refers to the psychological state that can be inferred from the user's facial expressions, voice, and other factors.

[0830] An "emotion analysis device" is a device that acquires the user's emotional state and uses that information to adjust the content of the suggestions.

[0831] A "virtual store" is an online commercial environment that provides products to consumers through electronic means without displaying physical goods.

[0832] The system implementing this invention automatically suggests clothing coordinates based on the user's wardrobe. The user first takes images of their clothing using a smartphone or other information processing device and uploads these images to the system. The server analyzes the received clothing images using a processing device, identifies the attributes of the clothing, and records this information in a database. The software used can utilize OpenCV as an image processing library.

[0833] The user then inputs information about their daily activity plan and hairstyle via a terminal and sends it to the server. Based on this information, the server uses a generative model to generate a suggestion generator for the optimal clothing combination for the user. At this time, the suggestion generator uses an emotion analysis device to detect the user's emotional state from their facial expressions and voice, and prioritizes selecting an outfit that matches this emotional state. Emotion analysis APIs such as Microsoft Cognitive Services can be used.

[0834] The generated coordinates are transmitted to an information processing terminal and visually presented to the user by a display device. The user can review the presented coordinates and provide feedback to the system. This feedback contributes to improving the accuracy of the suggestion generation device through a learning device.

[0835] For example, if a user plans a "sports event in the park on a sunny day" and the emotion analysis device determines that they are feeling "refreshed," the server will suggest clothing with a light design and cool colors.

[0836] An example of a prompt statement is as follows:

[0837] User's emotion: Refreshing

[0838] Planned: Sports event in the park on a sunny day

[0839] Suggested outfit: Clothing with cool colors and a light, airy design.

[0840] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0841] Step 1:

[0842] Users take pictures of their clothes using information processing devices such as smartphones and tablets and upload them to the system. The input data is digital image data of the clothes and is sent directly from the device to the server.

[0843] Step 2:

[0844] The server analyzes the received image data of clothing using OpenCV and extracts clothing attributes such as color, design, and category. These attributes are stored in a database and used later to generate suggestions. The input is image data, and the output is clothing attribute information.

[0845] Step 3:

[0846] Users input their daily activity plan and hairstyle via an information processing terminal and send this information to the server. The activity plan includes the scheduled date, time, location, and activities. The input consists of the activity plan and hairstyle information, which are stored on the server as output.

[0847] Step 4:

[0848] The server uses facial expression and voice data transmitted from the terminal to execute an emotion analysis API (e.g., Microsoft Cognitive Services) to identify the user's emotional state. This analysis outputs the type and intensity of the emotion. The input is the user's facial expression and voice data, and the output is the emotional state.

[0849] Step 5:

[0850] The server combines the user's action plan, hair style, emotional state, and analyzed clothing attributes to generate optimal outfit suggestions using a generative AI model. In this process, the generative AI model is run using prompt text as input, and optimal outfit data is output.

[0851] Step 6:

[0852] The generated coordination information is transmitted to an information processing terminal and notified and displayed to the user via a display device. The user visually confirms the generated coordination and, if necessary, inputs feedback and sends it to the server.

[0853] Step 7:

[0854] The server analyzes user feedback and trains a generative AI model via a learning device to improve the proposal generation process. It uses the data obtained from the feedback as input to update model parameters to improve the accuracy of proposal generation.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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 as being incorporated by reference.

[0876] The following is further disclosed regarding the embodiments described above.

[0877] (Claim 1)

[0878] An analysis means that receives an image of clothing from a user and analyzes the attributes of the clothing from the image,

[0879] A suggestion generation means that uses a generative model to propose the most suitable clothing combination for the user based on the user's daily schedule and hairstyle information,

[0880] A display means that transmits and displays the generated clothing combinations to the user terminal,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, further comprising a learning means for improving the accuracy of the suggestion generation means based on feedback received from users.

[0884] (Claim 3)

[0885] The system according to claim 1, further comprising an information gathering means for collecting information on trendy clothing from the internet and utilizing said information as reference data for a suggestion generation means.

[0886] "Example 1"

[0887] (Claim 1)

[0888] An analysis means that receives images of clothing from a user and analyzes the characteristics of the clothing from those images,

[0889] A suggestion generation means that uses artificial intelligence to propose the most suitable clothing combination for the user based on information about the user's schedule and appearance,

[0890] A display means that transmits and displays the generated clothing combinations to a user device,

[0891] Information gathering means that collects information on trendy accessories through an information and communication network and uses said information as reference data for a proposal generation means,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, further comprising a learning means for improving the accuracy of the proposal generation means based on evaluation information received from the user.

[0895] (Claim 3)

[0896] The system according to claim 1, wherein the suggestion generation means combines clothing information and fashion information from the user's existing database to calculate the optimal clothing combination.

[0897] "Application Example 1"

[0898] (Claim 1)

[0899] An analytical means for obtaining images of clothing from a user and analyzing the characteristics of the clothing from those images,

[0900] A suggestion generation means that proposes the most suitable clothing combination for the user using a generation method based on information about the user's planned activities and hairstyle for the day,

[0901] A visualization means that transmits the generated clothing combinations to the user's device for visualization,

[0902] A virtual try-on method that visualizes proposed clothing combinations using a virtual reality device,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, further comprising a learning means for improving the accuracy of the proposal generation means based on evaluation information obtained from users.

[0906] (Claim 3)

[0907] The system according to claim 1, further comprising an information gathering means for collecting information on fashionable clothing from an information network and utilizing said information as reference data for a suggestion generation means.

[0908] "Example 2 of combining an emotion engine"

[0909] (Claim 1)

[0910] An analysis means that receives an image of clothing from a user and analyzes the attributes of the clothing from the image,

[0911] A suggestion generation means that, based on the user's daily schedule and physical appearance information, uses a generative model to propose the most suitable clothing combination for the user.

[0912] An emotion recognition means that analyzes the user's facial expression image and voice input to identify their emotional state,

[0913] A display means that transmits and displays the generated clothing combinations to the user's device,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, further comprising a learning means for improving the accuracy of the suggestion generation means based on evaluations received from users.

[0917] (Claim 3)

[0918] The system according to claim 1, further comprising an information gathering means for collecting information on fashionable clothing from a communication network and utilizing said information as reference data for a suggestion generation means.

[0919] "Application example 2 when combining with an emotional engine"

[0920] (Claim 1)

[0921] A processing device that receives an image of clothing from a user and analyzes the attributes of the clothing from the image,

[0922] A suggestion generation device that uses a generative model to propose the most suitable clothing combination for a user based on the user's daily activity plan and hair style information,

[0923] A display device that transmits and displays the generated clothing combinations to the user's information processing device,

[0924] An emotion analysis device that acquires the user's emotional state and adjusts the suggested content based on the acquired emotions,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, further comprising a learning device that improves the accuracy of the suggestion generation device based on evaluation information received from the user.

[0928] (Claim 3)

[0929] The system according to claim 1, further comprising an information gathering device that collects information on clothing trends from an electronic information network and applies said information as basic data for a proposal generation device. [Explanation of Symbols]

[0930] 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. An analysis means that receives an image of clothing from a user and analyzes the attributes of the clothing from the image, A suggestion generation means that uses a generative model to propose the most suitable clothing combination for the user based on the user's daily schedule and hairstyle information, A display means that transmits and displays the generated clothing combinations to the user terminal, A system that includes this.

2. The system according to claim 1, further comprising a learning means for improving the accuracy of the suggestion generation means based on feedback received from users.

3. The system according to claim 1, further comprising an information gathering means for collecting information on trendy clothing from the internet and utilizing said information as reference data for a suggestion generation means.

Images