system

Abstract

We provide the system. [Solution] A means for receiving image data and analyzing the characteristics of a person from said image data, Based on the analyzed features, a means of selecting a suitable style from past databases, A means of integrating and presenting the selected style's purchase information to the user, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Modern consumers are faced with a very diverse range of fashion options, and it is not easy to choose a style that suits their body shape and personality. Also, conventional styling services are generally based on face-to-face advice and have limitations in efficiently and quickly providing specific fashion proposals according to individual needs. Furthermore, there may be no clear guidance for efficiently purchasing the proposed fashion items. It is required to solve these problems and provide users with optimal fashion selection and purchasing convenience. 【Means for Solving the Problems】 【0005】 This invention includes means for receiving image data and analyzing the characteristics of a person from that image. Based on the analyzed characteristics, it includes means for statistically selecting a style suitable for that person from a past database, thereby enabling fashion suggestions tailored to the user's individual needs. Furthermore, by integrating and presenting purchase information related to the selected style to the user, it makes it easy to acquire the suggested items. As a result, the user can efficiently select a style that suits them and quickly complete the purchase process. 【0006】 "Image data" refers to electronic data containing visual information about a person, and is the object that the system uses for analysis. 【0007】 "Personal characteristics" refer to attributes extracted from image data, such as facial features, body shape, hair color, and skin tone, that are used to identify or classify an individual. 【0008】 "Analysis" is the process of extracting human characteristics from image data and identifying and understanding those characteristics using technical means. 【0009】 A "database" is an electronic collection of information containing past fashion-related data, used for selecting styles. 【0010】 "Style" is a concept that refers to the combinations and choices of fashion that are deemed appropriate based on the analyzed characteristics of a person. 【0011】 "Purchase information" refers to purchasable information related to items in the suggested style, and is obtained from online stores. 【0012】 A "user" refers to a person or individual who provides image data to the system and, as a result, receives fashion suggestions. [Brief explanation of the drawing] 【0013】 [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 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. 【0017】 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. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0019】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc. 【0020】 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." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 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. 【0024】 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). 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 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". 【0034】 This invention relates to a comprehensive system for fashion proposals. This system starts with the user providing their own image data and operates in the following manner. 【0035】 Users upload their own image data to the system using their device. This image data includes the user's face and entire body. The uploaded image data is received by the server and immediately analyzed. 【0036】 The server uses image processing algorithms to analyze the received image data. This extracts features such as the user's facial shape, body shape, hair color, and skin tone. This feature information is then compared against a vast fashion database managed by the server. The database contains historical data on numerous fashion styles, and the style best suited to the analyzed features is selected. 【0037】 After this selection process is complete, the server integrates the details of the resulting style and provides them to the user. Specifically, style suggestions include color harmony, clothing material selection, and accessory recommendations. Furthermore, the server generates purchasing information related to the suggested style. This includes purchase links obtained from online stores and is optimized to allow the user to easily purchase the suggested items. 【0038】 The device displays all of these suggestions and purchase information, allowing users to select according to their preferences and providing a clear path to purchase the suggested styles online. 【0039】 For example, when a user uploads an image of themselves to the system, the server analyzes this image and detects features such as "round face, broad shoulders, brown hair." Comparing this with past data, the server suggests that a style like "light blue jeans, white T-shirt, and navy blue jacket" would be optimal for the user. This suggestion includes purchase links for each item, allowing the user to easily access the designated online store and complete the purchase process by clicking on them. 【0040】 In this way, the present invention achieves a seamless integration of data analysis and online purchasing to support users in making individualized and appropriate fashion choices. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 Users access the system's platform using their own devices, select image data of their face or full body, and upload it. 【0044】 Step 2: 【0045】 The server receives the image data sent from the user's terminal and prepares to begin the analysis. 【0046】 Step 3: 【0047】 The server uses the received image data to execute image processing algorithms, analyzing and extracting detailed characteristics of the person, such as facial features, body shape, hair color, and skin tone. 【0048】 Step 4: 【0049】 Based on the characteristics obtained through analysis, the server accesses an internal fashion database and uses statistical methods to select the past fashion style that best matches these characteristics. 【0050】 Step 5: 【0051】 Based on the selected style, the server generates specific fashion suggestions, taking into account factors such as color harmony and clothing material selection. 【0052】 Step 6: 【0053】 The server collects purchase links for items related to the suggested style from online stores and organizes this information for the user to receive. 【0054】 Step 7: 【0055】 The terminal displays fashion suggestions and associated purchase links received from the server to the user, helping the user easily purchase the suggested items. 【0056】 (Example 1) 【0057】 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." 【0058】 Modern consumers face the challenge of choosing the perfect fashion style from a vast array of options. This challenge extends beyond simply selecting products; it encompasses the entire process of finding a style that suits their individual physical characteristics and purchasing the right items. Therefore, there is a demand for fashion suggestions tailored to each user's characteristics, along with an efficient and seamless purchasing experience based on those suggestions. 【0059】 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. 【0060】 In this invention, the server includes means for receiving image data and analyzing a person's physical characteristics from the image data; means for selecting the most suitable outfit from a past fashion database based on the analyzed characteristics; and means for integrating purchase information related to the selected outfit and presenting it to the user. This allows the user to easily find a fashion style that suits their physical characteristics and to quickly and efficiently purchase the suggested items. 【0061】 "Image data" refers to still images and videos of people provided by the user, and is fundamental information that forms the basis for analysis. 【0062】 "Physical characteristics" refer to individual outward features such as facial features, body shape, hair color, and skin tone. 【0063】 A "fashion database" is a collection of data that aggregates historical information about past fashion styles, serving as a basis for selecting styles. 【0064】 "Purchase information" refers to information that includes links and details necessary for purchasing selected fashion items. 【0065】 An "online platform" refers to a website or application that enables users to access information and purchase products via the internet. 【0066】 "Method of selection" refers to a computer process that uses analyzed physical characteristics to find the most suitable clothing and style from a database. 【0067】 "Means of integration" refers to the technical process of combining selected styles and purchase information and presenting them to the user in a single, integrated format. 【0068】 This invention is a system primarily composed of a server, a terminal, and a user, each working together to generate fashion suggestions and support the user's purchasing activities. The following details each of the main components and how the system functions. 【0069】 First, the user uploads their image data using their device. Ideally, this image data should include the user's face and full body. The device can be any internet-connected device, such as a smartphone or personal computer. 【0070】 The server receives image data sent by the user and analyzes it using image processing algorithms. This analysis utilizes machine learning libraries and computer vision technologies, specifically OpenCV and TENSORFLOW®. Through this process, physical characteristics such as the user's face shape, body shape, hair color, and skin tone are extracted. 【0071】 The extracted feature information is compared against a large fashion database managed by the server. The database contains information on a diverse range of fashion styles collected in the past, and the server selects the most suitable style based on the analysis results. This selection process uses sophisticated algorithms to determine the style best suited to be suggested to the user. 【0072】 Once the selection is complete, the server generates purchase information based on that style. This purchase information includes purchase links to online stores associated with the suggested items. The server integrates this information and sends it to the user's terminal in a format for them to enjoy. 【0073】 The device will display suggestions and purchase information received from the server. Users can review the options presented on the screen and select their preferred items and styles. The device has a feature that allows users to connect directly to the online store by clicking a link, making the purchasing process smooth. 【0074】 For example, if a user uploads an image with features such as "brown hair, round face, and broad shoulders," the server will analyze it and suggest a style such as "light blue jeans, white T-shirt, and navy blue jacket." An example of a prompt would be, "Suggest a suitable fashion style based on the features in the user's image and generate a purchase link." This prompt initiates a series of processes for the AI ​​model to provide the most suitable suggestions to the user. 【0075】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0076】 Step 1: 【0077】 The user uploads their own image data from their device. The input is an image file selected by the user using their device. The device sends this file to the server via an HTTP request. The output is the image data arriving on the server. Specifically, the user selects a photo from a file browser on their smartphone or PC and presses the system's upload button. 【0078】 Step 2: 【0079】 The server receives image data sent by the user and begins analysis using image processing algorithms. The input is the image data received from the user. Based on this data, the server extracts physical features such as facial shape, body shape, hair color, and skin tone. The output is this feature information. Specifically, it uses libraries such as OpenCV and TensorFlow to apply machine learning algorithms and perform image analysis. 【0080】 Step 3: 【0081】 The server compares extracted physical characteristic information with a fashion database. The inputs are the analyzed characteristic information and the fashion database. The server compares it with past fashion styles in the database and selects the optimal style. The output is the fashion style information deemed most suitable for the user. Specifically, it uses SQL queries or NoSQL searches to access the database and perform similarity evaluations. 【0082】 Step 4: 【0083】 The server generates relevant purchase information based on the selected fashion style. The input is the selected style information. The server uses the APIs of relevant online stores to obtain links to purchasable clothing items and integrates the purchase information. The output is an integrated fashion suggestion and purchase link information to be presented to the user. Specifically, it collects real-time data from each online store and creates purchase options. 【0084】 Step 5: 【0085】 The terminal displays fashion suggestions and purchase information from the server to the user. The input is integrated suggestion information received from the server. The terminal arranges this information on a user interface that is easy to view, allowing the user to check and purchase items. The output is the user's browsing and purchase actions via clicks. Specifically, the suggested items are displayed on the screen, and when the user clicks the purchase link for an item they like, they are directly connected to that store. 【0086】 (Application Example 1) 【0087】 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." 【0088】 Traditional fashion recommendation systems presented a cumbersome process for users to find and quickly purchase the style that best suited them. Furthermore, the accuracy and suitability of the recommendations were insufficient, and the purchase process was complicated. Additionally, there was a lack of utilization of interactive devices to enhance the user experience. 【0089】 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. 【0090】 In this invention, the server includes means for receiving image information and analyzing human characteristics from the image information; means for selecting a suitable style from a past information database based on the analyzed characteristics; and means for presenting the selected style on a display device and providing purchase procedure information. This allows users to easily find a fashion style that suits their characteristics and proceed to purchase quickly and seamlessly. As a result, the user experience is improved and sales are promoted. 【0091】 "Image information" refers to data about a person's appearance acquired using devices such as cameras. 【0092】 "Methods for analyzing human characteristics" refer to methods that utilize algorithms and processes to identify features such as facial shape, body shape, hair color, and skin tone from received image information. 【0093】 An "information repository" is a database that stores historical data on numerous fashion styles, providing information from past data that is useful for current choices. 【0094】 "Style" in fashion refers to a specific style or coordination, encompassing a comprehensive proposal that includes color harmony and material selection. 【0095】 A "display device" is an interface that allows users to visually confirm information, and includes, for example, smart mirrors and displays. 【0096】 "Means of providing purchase procedure information" refers to methods of presenting links or purchase options to allow users to easily purchase suggested fashion items. 【0097】 The system realizing this invention consists of a server for receiving and analyzing user image information, a terminal for displaying suggestions to the user, and an interactive device for the user's use. The server analyzes the image information and extracts human features. This utilizes image processing algorithms such as OpenCV and TensorFlow. The analyzed feature information is used to select the appropriate format using a cloud-based information repository such as AWS® DynamoDB. 【0098】 The terminal presents the user with a format selected by the server. This utilizes display devices such as smart mirrors and displays. The displayed format also includes purchase procedure information, allowing the user to easily purchase items. This information provision is achieved through integration with an automated e-commerce platform. 【0099】 As a concrete example, when a user stands in front of a smart mirror, the mirror automatically captures the user's appearance, and a server analyzes this data. As a result, the mirror displays the optimal fashion style in real time, along with relevant purchase links. The user can then smoothly proceed with the purchase process by clicking on these links. This invention provides a more personalized shopping experience. 【0100】 An example of a prompt would be: "Analyze the user's image and suggest the most suitable fashion style based on the results. Include a purchase link in the suggestion to facilitate the buying process." Using this prompt, the generative AI model can make effective suggestions and provide the user with a satisfying purchasing experience. 【0101】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0102】 Step 1: 【0103】 The user stands in front of the smart mirror, and their appearance is captured by the smart mirror's camera. The image captured by this camera is the input for this step. This image information is then sent to the server for analysis in the next step. 【0104】 Step 2: 【0105】 The server acquires the received image information and uses image processing algorithms such as OpenCV and TensorFlow to analyze features such as human face shape, body shape, hair color, and skin tone. Based on this analysis, specific feature data of the user is output. This creates a foundation for selecting a fashion style that suits the user. 【0106】 Step 3: 【0107】 The server compares the analyzed feature data with an information repository such as AWS DynamoDB and selects the style best suited to the user. This comparison identifies a fashion style that takes color adjustments and material selection into account, and the server generates that style as output. 【0108】 Step 4: 【0109】 The server sends the selected fashion style to the device. This transmitted style information also includes related purchase procedure information, and a purchase link is attached to the user in conjunction with the e-commerce platform. This information is used as input for the next step. 【0110】 Step 5: 【0111】 The device uses a smart mirror display to show the user fashion styles and purchase links received from the server. This allows the user to visually confirm the suggested styles and potentially develop a purchase intent. 【0112】 Step 6: 【0113】 Users can easily purchase recommended items by interacting with the provided purchase link. This interaction results in the output, the order is sent to the e-commerce platform, and the actual purchase process is completed. This allows users to enjoy a seamless shopping experience. 【0114】 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. 【0115】 This invention provides a system for highly personalizing the user's fashion selection process and offering style suggestions that take into account their emotional state. This system combines the user's image data and emotional information to enable advanced suggestions that consider a variety of factors. 【0116】 Users upload images of their face or full body from their devices to the system. The emotion engine then recognizes the user's emotions from the image data or real-time data. The recognized emotion data is then reflected in the style desired by the user. 【0117】 The server analyzes the user's image data and extracts features such as face shape, body shape, hair color, and skin tone. This feature information is then compared with a fashion database managed by the server to select the style best suited to the user's characteristics. Simultaneously, the selected style is fine-tuned based on emotional information recognized by the emotion engine. For example, if the user indicates positive emotions, bright colors and relaxed designs will be suggested. 【0118】 The suggested styles are provided to the user along with relevant purchasing information. This includes links to online stores where the items can be purchased, allowing the user to easily acquire the items. 【0119】 For example, if a user uploads an image and the emotion engine recognizes an "excited" state, the server, in addition to its normal analysis process, will generate suggestions for style selection that are appropriate for the user's high energy level, including eye-catching colors and adventurous designs. For instance, these might include items such as red or orange tops or leather jackets. 【0120】 This system provides suggestions that take into account not only the individual characteristics of each user but also their inner emotions, thereby enriching the selection of personalized fashion items. 【0121】 The following describes the processing flow. 【0122】 Step 1: 【0123】 Users access the system's platform using their devices, select their images, and upload them. This provides the system with data for analysis. 【0124】 Step 2: 【0125】 The server sends the image data received from the user to the emotion engine, which analyzes the user's facial expressions and other visual information to recognize their emotions. This allows the server to understand the user's current emotional state. 【0126】 Step 3: 【0127】 The server simultaneously uses image analysis algorithms to extract features such as facial shape, body shape, hair color, and skin tone from the user's image data. The information obtained through this process is used to select a fashion style. 【0128】 Step 4: 【0129】 The server searches its internal fashion database based on the extracted characteristics of the person and selects the most suitable style. Furthermore, based on emotional information from the emotion engine, it fine-tunes the colors and designs of the selected style to generate fashion suggestions that align with the person's emotions. 【0130】 Step 5: 【0131】 The server collects purchasing information related to the suggested style and gathers purchase links for each item. It then organizes this information and converts it into a format that is easily accessible to the user. 【0132】 Step 6: 【0133】 The device displays fashion suggestions and purchase links sent from the server to the user. Based on the displayed information, the user can choose a style that matches their emotional state and purchase items directly from the relevant online store. 【0134】 (Example 2) 【0135】 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". 【0136】 Conventional fashion recommendation systems have faced challenges in providing personalized recommendations that take into account the individual characteristics and emotional states of users. Furthermore, the limited purchasing options available to users often resulted in insufficient user satisfaction. 【0137】 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. 【0138】 In this invention, the server includes means for receiving image information and analyzing the user's characteristics, means for selecting and fine-tuning a style based on the analyzed characteristics and emotional information, and means for integrating and presenting the final style suggestion and purchase information. This enables highly personalized fashion suggestions that reflect the individual characteristics and emotional state of the user. 【0139】 "Image information" refers to digital visual data, including the user's face and entire body. 【0140】 "User characteristics" refers to physical characteristics unique to the user, such as facial features, body shape, hair color, and skin tone. 【0141】 "Emotional information" refers to data that indicates the emotional state inferred from the user's facial expressions and posture, and is classified into categories such as excitement and calmness. 【0142】 "Methods for selecting and fine-tuning styles" refers to the process of choosing the optimal fashion style based on analyzed characteristics and emotional information, and then adjusting that style as needed. 【0143】 "Purchase information for style suggestions" refers to purchase information related to selected fashion items, specifically information that includes links to products that can be purchased. 【0144】 This invention relates to a system that personalizes a user's fashion style and provides style suggestions that take into account their emotional state. The system utilizes a server, a terminal, an emotion recognition engine, and a fashion database. 【0145】 Users upload images of their face or full body to the system using their own devices. During this process, the device converts the image data to a pre-specified format and then prepares it for transmission to the server. Image processing software is used in this process. 【0146】 The server analyzes the received image information and utilizes computer vision technology and machine learning algorithms to extract user features. This allows for the identification of physical characteristics such as facial shape and body shape. 【0147】 A dedicated emotion recognition engine is used to recognize emotional information. The server uses this engine to infer emotional states from facial expressions and postures in images and classify them as "excited," "calm," etc. 【0148】 After all data analysis is complete, the server compares the data with a fashion database and selects the most suitable style based on the user's characteristics and emotional information. Then, it fine-tunes the selected style and finalizes the suggested fashion items. 【0149】 Finally, the server sends the user style suggestions along with relevant purchase information to their device. This includes links to purchasable products, allowing the user to easily access the product purchase page via their mobile device or computer. 【0150】 To give a concrete example, if a user uploads an image and the system recognizes that emotion as "excitement," the server could select fashion items with energetic colors and designs from its database and suggest items such as red or orange tops or leather jackets. 【0151】 An example of a prompt message for a generative AI model would be: "User image data has been uploaded, and the emotion engine has recognized excitement. Based on this, suggest energetic fashion items and generate online purchase links." 【0152】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0153】 Step 1: 【0154】 User image upload 【0155】 The user uploads images of their face or full body to the system using their own device. The device converts the user's selected image file to the specified format (e.g., JPEG, PNG) and prepares it for transfer to the server. It receives the user's image data as input and generates image data ready to be sent to the server as output. 【0156】 Step 2: 【0157】 Recognition of emotional information 【0158】 The server passes the received image data to the emotion recognition engine, which analyzes the user's emotions from their facial expressions. Here, the engine analyzes the facial features in the image at the pixel level and categorizes emotions such as "excitement" and "calmness." It takes image data as input and generates analyzed emotion information as output. 【0159】 Step 3: 【0160】 Image data analysis 【0161】 The server utilizes computer vision technology to extract user features from image data. This includes facial shape, body shape, hair color, and skin tone. Machine learning models are used to quantify specific points within the image and generate feature data. It takes image data as input and generates feature data as output. 【0162】 Step 4: 【0163】 Selecting a Fashion Style 【0164】 The server matches the analyzed feature data and sentiment information with a fashion database to select the optimal style. It uses a similarity calculation algorithm to create a list of fashion items best suited to the user's characteristics. It accepts feature data and sentiment information as input and generates a list of selected styles as output. 【0165】 Step 5: 【0166】 Fine-tuning the proposal style 【0167】 The server reflects emotional information and fine-tunes the selected style. For example, if the user is in an "excited" state, it adds elements with bright, energetic colors and distinctive designs. It receives a selected style list and emotional information as input and creates a final, finely tuned style list as output. 【0168】 Step 6: 【0169】 Providing style and purchasing information 【0170】 The server sends the final style suggestions and associated purchase information to the user's device. This information includes purchase links for wearable fashion items, allowing the user to easily shop. It receives a finely tuned final style list as input and generates style suggestions and purchase information for the user as output. 【0171】 (Application Example 2) 【0172】 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". 【0173】 In today's world, there is a demand for personalized fashion suggestions based on individual users' emotions and characteristics. However, existing systems do not take emotional information into account, making it difficult to provide styles that match a user's momentary emotions. This challenge needs to be addressed to provide users with richer and more appropriate fashion choices. 【0174】 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. 【0175】 In this invention, the server includes means for receiving image data and analyzing the characteristics of a person from the image data; means for selecting a suitable style from a past database based on the analyzed characteristics and emotional information; means for integrating purchase information of the selected style and presenting it to the user; and means for recognizing emotional information from the image and generating style suggestions corresponding to the recognized emotions. This enables highly personalized style suggestions based on the user's characteristics and emotions. 【0176】 "Means for receiving image data" refers to elements that have the function of importing images sent by users into a server or database. 【0177】 "Means for analyzing human characteristics" refers to a function that performs a process of extracting information such as the user's facial shape, body contours, hair color, and skin tone from received image data. 【0178】 A "means for recognizing emotional information" refers to a system that analyzes facial expressions and subtle changes in the user's face from their image to identify the user's emotional state. 【0179】 "Means of selecting a style" refers to the elements responsible for the process of choosing the most suitable fashion style based on the analyzed user characteristics and emotions. 【0180】 "A means of integrating and presenting purchasing information for selected styles" refers to a function that aggregates purchasing information for selected fashion items and presents it to the user in an easy-to-understand manner. 【0181】 "A means of generating style suggestions based on emotions" refers to a function that selects fashion items appropriate to the emotional state expressed by the user and creates suggestions based on that emotional state. 【0182】 The "past database" is a collection of fashion information and user data that has been collected and accumulated to date, and is used as a reference for style selection. 【0183】 The system that realizes this invention involves the coordinated operation of a user's terminal, a server, and a cloud-based database. 【0184】 The user's device can acquire image data using its camera. This image captures the user's face and entire body, and is transmitted to the server through the application. The devices used in this process include smartphones and tablets. 【0185】 The server is located in a cloud environment and processes the received image data. First, it analyzes the features of a person using image processing libraries such as OpenCV, and extracts information such as face shape, body type, hair color, and skin tone. It also recognizes emotional information using Amazon Rekognition and Microsoft Azure's facial recognition APIs to identify the user's instantaneous emotional state. 【0186】 The server further selects a style suitable for the user by comparing the analyzed features and sentiment information with a pre-built fashion database. Using natural language processing tools such as Stanford CORENLP, a generative AI model interprets the prompt text and automatically generates appropriate style suggestions. 【0187】 The generated style suggestions are presented to the user's device along with relevant purchasing information. The user can easily purchase the suggested items through the online store. This entire process allows the user to receive personalized fashion suggestions and make a purchase decision on the spot. 【0188】 As a concrete example, when a user wants to dress up for a weekend event, they launch the app and take a photo of themselves. The server recognizes the emotional information as an excited state and, based on that data, generates and presents adventurous design suggestions, including red and orange items, to the user's device. The user can then review the suggestions and immediately order any items they like. 【0189】 An example of a prompt message is: "Would you like to receive personalized fashion suggestions that blend your emotions and style? Take a photo using your smartphone camera and find items that perfectly match your mood and features at EmoFashion." This prompt helps to focus the user's attention on style selection and facilitates the process of receiving appropriate suggestions. 【0190】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0191】 Step 1: 【0192】 The user launches the application on their device and uses the camera function to acquire an image of themselves. The input is a photograph of the user's face or full body captured by the camera, and this image data is used directly as input to the application. The output is image data stored in the device's memory. This image data is used in the next processing step. 【0193】 Step 2: 【0194】 The terminal sends the acquired image data to the server. The input is image data stored on the user's terminal, and the output is image data sent to the server via the internet. It is recommended to use a secure communication protocol (e.g., HTTPS) during this transmission process. 【0195】 Step 3: 【0196】 The server receives the transmitted image data and uses OpenCV to analyze the user's features from the image. The input is image data received from the terminal, and user feature information such as face shape, body type, hair color, and skin tone is output. This information is also stored as numerical and categorical data. 【0197】 Step 4: 【0198】 The server uses the analyzed feature information to recognize emotional information using Amazon Rekognition or Microsoft Azure's facial recognition API. The input for this step is the feature information and image data obtained in step 3. The output is the user's emotional state (e.g., joy, surprise, excitement), which is also stored as categorical data. 【0199】 Step 5: 【0200】 The server uses the analyzed feature and sentiment information to match it with a fashion database and select the most suitable style for the user. The input is the output data from steps 3 and 4. A generative AI model is used to perform natural language processing based on the prompt text and obtain style suggestions as output. 【0201】 Step 6: 【0202】 The server collects the generated style suggestions and the associated purchase information, and presents them to the user's terminal along with a link to the online store. The input is the style information selected in step 5, and the output is a style suggestion including purchase information. This includes product images, descriptions, and purchase links. 【0203】 Step 7: 【0204】 The user evaluates the presented style suggestions and decides whether to purchase items of interest from the linked online store. The input is style suggestions from the server, and the output is the user's purchase behavior based on their selection. 【0205】 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. 【0206】 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. 【0207】 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. 【0208】 [Second Embodiment] 【0209】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0210】 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. 【0211】 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). 【0212】 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. 【0213】 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. 【0214】 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). 【0215】 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. 【0216】 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. 【0217】 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. 【0218】 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. 【0219】 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. 【0220】 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". 【0221】 This invention relates to a comprehensive system for fashion proposals. This system starts with the user providing their own image data and operates in the following manner. 【0222】 Users upload their own image data to the system using their device. This image data includes the user's face and entire body. The uploaded image data is received by the server and immediately analyzed. 【0223】 The server uses image processing algorithms to analyze the received image data. This extracts features such as the user's facial shape, body shape, hair color, and skin tone. This feature information is then compared against a vast fashion database managed by the server. The database contains historical data on numerous fashion styles, and the style best suited to the analyzed features is selected. 【0224】 After this selection process is complete, the server integrates the details of the resulting style and provides them to the user. Specifically, style suggestions include color harmony, clothing material selection, and accessory recommendations. Furthermore, the server generates purchasing information related to the suggested style. This includes purchase links obtained from online stores and is optimized to allow the user to easily purchase the suggested items. 【0225】 The device displays all of these suggestions and purchase information, allowing users to select according to their preferences and providing a clear path to purchase the suggested styles online. 【0226】 For example, when a user uploads an image of themselves to the system, the server analyzes this image and detects features such as "round face, broad shoulders, brown hair." Comparing this with past data, the server suggests that a style like "light blue jeans, white T-shirt, and navy blue jacket" would be optimal for the user. This suggestion includes purchase links for each item, allowing the user to easily access the designated online store and complete the purchase process by clicking on them. 【0227】 In this way, the present invention achieves a seamless integration of data analysis and online purchasing to support users in making individualized and appropriate fashion choices. 【0228】 The following describes the processing flow. 【0229】 Step 1: 【0230】 Users access the system's platform using their own devices, select image data of their face or full body, and upload it. 【0231】 Step 2: 【0232】 The server receives the image data sent from the user's terminal and prepares to begin the analysis. 【0233】 Step 3: 【0234】 The server uses the received image data to execute image processing algorithms, analyzing and extracting detailed characteristics of the person, such as facial features, body shape, hair color, and skin tone. 【0235】 Step 4: 【0236】 Based on the characteristics obtained through analysis, the server accesses an internal fashion database and uses statistical methods to select the past fashion style that best matches these characteristics. 【0237】 Step 5: 【0238】 Based on the selected style, the server generates specific fashion suggestions, taking into account factors such as color harmony and clothing material selection. 【0239】 Step 6: 【0240】 The server collects purchase links for items related to the suggested style from online stores and organizes this information for the user to receive. 【0241】 Step 7: 【0242】 The terminal displays fashion suggestions and associated purchase links received from the server to the user, helping the user easily purchase the suggested items. 【0243】 (Example 1) 【0244】 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." 【0245】 Modern consumers face the challenge of choosing the perfect fashion style from a vast array of options. This challenge extends beyond simply selecting products; it encompasses the entire process of finding a style that suits their individual physical characteristics and purchasing the right items. Therefore, there is a demand for fashion suggestions tailored to each user's characteristics, along with an efficient and seamless purchasing experience based on those suggestions. 【0246】 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. 【0247】 In this invention, the server includes means for receiving image data and analyzing a person's physical characteristics from the image data; means for selecting the most suitable outfit from a past fashion database based on the analyzed characteristics; and means for integrating purchase information related to the selected outfit and presenting it to the user. This allows the user to easily find a fashion style that suits their physical characteristics and to quickly and efficiently purchase the suggested items. 【0248】 "Image data" refers to still images and videos of people provided by the user, and is fundamental information that forms the basis for analysis. 【0249】 "Physical characteristics" refer to individual outward features such as facial features, body shape, hair color, and skin tone. 【0250】 A "fashion database" is a collection of data that aggregates historical information about past fashion styles, serving as a basis for selecting styles. 【0251】 "Purchase information" refers to information that includes links and details necessary for purchasing selected fashion items. 【0252】 An "online platform" refers to a website or application that enables users to access information and purchase products via the internet. 【0253】 "Method of selection" refers to a computer process that uses analyzed physical characteristics to find the most suitable clothing and style from a database. 【0254】 "Means of integration" refers to the technical process of combining selected styles and purchase information and presenting them to the user in a single, integrated format. 【0255】 This invention is a system primarily composed of a server, a terminal, and a user, each working together to generate fashion suggestions and support the user's purchasing activities. The following details each of the main components and how the system functions. 【0256】 First, the user uploads their image data using their device. Ideally, this image data should include the user's face and full body. The device can be any internet-connected device, such as a smartphone or personal computer. 【0257】 The server receives image data sent by the user and performs analysis using image processing algorithms. This analysis utilizes machine learning libraries and computer vision technologies, specifically OpenCV and TensorFlow. Through this process, physical features such as the user's face shape, body shape, hair color, and skin tone are extracted. 【0258】 The extracted feature information is compared against a large fashion database managed by the server. The database contains information on a diverse range of fashion styles collected in the past, and the server selects the most suitable style based on the analysis results. This selection process uses sophisticated algorithms to determine the style best suited to be suggested to the user. 【0259】 Once the selection is complete, the server generates purchase information based on that style. This purchase information includes purchase links to online stores associated with the suggested items. The server integrates this information and sends it to the user's terminal in a format for them to enjoy. 【0260】 The device will display suggestions and purchase information received from the server. Users can review the options presented on the screen and select their preferred items and styles. The device has a feature that allows users to connect directly to the online store by clicking a link, making the purchasing process smooth. 【0261】 For example, if a user uploads an image with features such as "brown hair, round face, and broad shoulders," the server will analyze it and suggest a style such as "light blue jeans, white T-shirt, and navy blue jacket." An example of a prompt would be, "Suggest a suitable fashion style based on the features in the user's image and generate a purchase link." This prompt initiates a series of processes for the AI ​​model to provide the most suitable suggestions to the user. 【0262】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0263】 Step 1: 【0264】 The user uploads their own image data from their device. The input is an image file selected by the user using their device. The device sends this file to the server via an HTTP request. The output is the image data arriving on the server. Specifically, the user selects a photo from a file browser on their smartphone or PC and presses the system's upload button. 【0265】 Step 2: 【0266】 The server receives image data sent by the user and begins analysis using image processing algorithms. The input is the image data received from the user. Based on this data, the server extracts physical features such as facial shape, body shape, hair color, and skin tone. The output is this feature information. Specifically, it uses libraries such as OpenCV and TensorFlow to apply machine learning algorithms and perform image analysis. 【0267】 Step 3: 【0268】 The server compares extracted physical characteristic information with a fashion database. The inputs are the analyzed characteristic information and the fashion database. The server compares it with past fashion styles in the database and selects the optimal style. The output is the fashion style information deemed most suitable for the user. Specifically, it uses SQL queries or NoSQL searches to access the database and perform similarity evaluations. 【0269】 Step 4: 【0270】 The server generates relevant purchase information based on the selected fashion style. The input is the selected style information. The server uses the APIs of relevant online stores to obtain links to purchasable clothing items and integrates the purchase information. The output is an integrated fashion suggestion and purchase link information to be presented to the user. Specifically, it collects real-time data from each online store and creates purchase options. 【0271】 Step 5: 【0272】 The terminal displays fashion suggestions and purchase information from the server to the user. The input is integrated suggestion information received from the server. The terminal arranges this information on a user interface that is easy to view, allowing the user to check and purchase items. The output is the user's browsing and purchase actions via clicks. Specifically, the suggested items are displayed on the screen, and when the user clicks the purchase link for an item they like, they are directly connected to that store. 【0273】 (Application Example 1) 【0274】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0275】 Traditional fashion recommendation systems presented a cumbersome process for users to find and quickly purchase the style that best suited them. Furthermore, the accuracy and suitability of the recommendations were insufficient, and the purchase process was complicated. Additionally, there was a lack of utilization of interactive devices to enhance the user experience. 【0276】 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. 【0277】 In this invention, the server includes means for receiving image information and analyzing human characteristics from the image information; means for selecting a suitable style from a past information database based on the analyzed characteristics; and means for presenting the selected style on a display device and providing purchase procedure information. This allows users to easily find a fashion style that suits their characteristics and proceed to purchase quickly and seamlessly. As a result, the user experience is improved and sales are promoted. 【0278】 "Image information" refers to data about a person's appearance acquired using devices such as cameras. 【0279】 "Methods for analyzing human characteristics" refer to methods that utilize algorithms and processes to identify features such as facial shape, body shape, hair color, and skin tone from received image information. 【0280】 An "information repository" is a database that stores historical data on numerous fashion styles, providing information from past data that is useful for current choices. 【0281】 "Style" in fashion refers to a specific style or coordination, encompassing a comprehensive proposal that includes color harmony and material selection. 【0282】 A "display device" is an interface that allows users to visually confirm information, and includes, for example, smart mirrors and displays. 【0283】 "Means of providing purchase procedure information" refers to methods of presenting links or purchase options to allow users to easily purchase suggested fashion items. 【0284】 The system for realizing this invention consists of a server for receiving and analyzing the user's image information, a terminal for displaying the proposed content to the user, and an interactive device used by the user. The server analyzes the image information and extracts human features. For this, image processing algorithms such as OpenCV and TensorFlow are utilized. The analyzed feature information is used to select an appropriate format using a cloud-based database such as AWS DynamoDB. 【0285】 The terminal presents the format selected by the server to the user. For this, display devices such as smart mirrors and displays are utilized. The displayed format also includes purchase procedure information, and the user can easily purchase items. This information provision uses integration with an automated e-commerce platform. 【0286】 As a specific example, when the user stands in front of a smart mirror, the mirror automatically captures the user's appearance, and the server analyzes it. As a result, relevant purchase links are displayed on the mirror along with the optimal fashion style in real time. The user can smoothly proceed with the purchase procedure by clicking on it. This invention provides a more personalized purchasing experience. 【0287】 An example of a prompt sentence is "Analyze the user image and propose the optimal fashion style based on the result. Include purchase links in the proposal and smooth the purchase process." By using this prompt, the generative AI model can make effective proposals and provide a satisfactory purchasing experience to the user. 【0288】 The flow of specific processing in Application Example 1 will be described using FIG. 12. 【0289】 Step 1: 【0290】 The user stands in front of the smart mirror, and their appearance is captured by the smart mirror's camera. The image captured by this camera is the input for this step. This image information is then sent to the server for analysis in the next step. 【0291】 Step 2: 【0292】 The server acquires the received image information and uses image processing algorithms such as OpenCV and TensorFlow to analyze features such as human face shape, body shape, hair color, and skin tone. Based on this analysis, specific feature data of the user is output. This creates a foundation for selecting a fashion style that suits the user. 【0293】 Step 3: 【0294】 The server compares the analyzed feature data with an information repository such as AWS DynamoDB and selects the style best suited to the user. This comparison identifies a fashion style that takes color adjustments and material selection into account, and the server generates that style as output. 【0295】 Step 4: 【0296】 The server sends the selected fashion style to the device. This transmitted style information also includes related purchase procedure information, and a purchase link is attached to the user in conjunction with the e-commerce platform. This information is used as input for the next step. 【0297】 Step 5: 【0298】 The device uses a smart mirror display to show the user fashion styles and purchase links received from the server. This allows the user to visually confirm the suggested styles and potentially develop a purchase intent. 【0299】 Step 6: 【0300】 Users can easily purchase recommended items by interacting with the provided purchase link. This interaction results in the output, the order is sent to the e-commerce platform, and the actual purchase process is completed. This allows users to enjoy a seamless shopping experience. 【0301】 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. 【0302】 This invention provides a system for highly personalizing the user's fashion selection process and offering style suggestions that take into account their emotional state. This system combines the user's image data and emotional information to enable advanced suggestions that consider a variety of factors. 【0303】 Users upload images of their face or full body from their devices to the system. The emotion engine then recognizes the user's emotions from the image data or real-time data. The recognized emotion data is then reflected in the style desired by the user. 【0304】 The server analyzes the user's image data and extracts features such as face shape, body shape, hair color, and skin tone. This feature information is then compared with a fashion database managed by the server to select the style best suited to the user's characteristics. Simultaneously, the selected style is fine-tuned based on emotional information recognized by the emotion engine. For example, if the user indicates positive emotions, bright colors and relaxed designs will be suggested. 【0305】 The suggested styles are provided to the user along with relevant purchasing information. This includes links to online stores where the items can be purchased, allowing the user to easily acquire the items. 【0306】 As a specific example, when a user uploads their own image and the emotion engine recognizes an "excited" state, in addition to the normal analysis process, the server generates proposals suitable for the user's high energy level during style selection, including eye-catching colors and adventurous designs. For example, items such as red or orange tops and leather jackets may be included. 【0307】 This system realizes proposals that consider both the individual characteristics of the user and internal emotions, and can further enrich the selection of personal fashion. 【0308】 The following describes the processing flow. 【0309】 Step 1: 【0310】 The user uses the terminal to access the system platform, selects and uploads their own image. Thereby, data for analysis is provided to the system. 【0311】 Step 2: 【0312】 The server sends the image data received from the user to the emotion engine, analyzes the user's expression and other visual information to recognize emotions. Thereby, the user's current emotional state is grasped. 【0313】 Step 3: 【0314】 The server extracts features such as the shape of the face, body shape, hair color, and skin tone from the user's image data using an image analysis algorithm in parallel. The information obtained in this process is used for the selection of fashion styles. 【0315】 Step 4: 【0316】 The server searches its internal fashion database based on the extracted characteristics of the person and selects the most suitable style. Furthermore, based on emotional information from the emotion engine, it fine-tunes the colors and designs of the selected style to generate fashion suggestions that align with the person's emotions. 【0317】 Step 5: 【0318】 The server collects purchasing information related to the suggested style and gathers purchase links for each item. It then organizes this information and converts it into a format that is easily accessible to the user. 【0319】 Step 6: 【0320】 The device displays fashion suggestions and purchase links sent from the server to the user. Based on the displayed information, the user can choose a style that matches their emotional state and purchase items directly from the relevant online store. 【0321】 (Example 2) 【0322】 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". 【0323】 Conventional fashion recommendation systems have faced challenges in providing personalized recommendations that take into account the individual characteristics and emotional states of users. Furthermore, the limited purchasing options available to users often resulted in insufficient user satisfaction. 【0324】 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. 【0325】 In this invention, the server includes means for receiving image information and analyzing the user's characteristics, means for selecting and fine-tuning a style based on the analyzed characteristics and emotional information, and means for integrating and presenting the final style suggestion and purchase information. This enables highly personalized fashion suggestions that reflect the individual characteristics and emotional state of the user. 【0326】 "Image information" refers to digital visual data, including the user's face and entire body. 【0327】 "User characteristics" refers to physical characteristics unique to the user, such as facial features, body shape, hair color, and skin tone. 【0328】 "Emotional information" refers to data that indicates the emotional state inferred from the user's facial expressions and posture, and is classified into categories such as excitement and calmness. 【0329】 "Methods for selecting and fine-tuning styles" refers to the process of choosing the optimal fashion style based on analyzed characteristics and emotional information, and then adjusting that style as needed. 【0330】 "Purchase information for style suggestions" refers to purchase information related to selected fashion items, specifically information that includes links to products that can be purchased. 【0331】 This invention relates to a system that personalizes a user's fashion style and provides style suggestions that take into account their emotional state. The system utilizes a server, a terminal, an emotion recognition engine, and a fashion database. 【0332】 Users upload images of their face or full body to the system using their own devices. During this process, the device converts the image data to a pre-specified format and then prepares it for transmission to the server. Image processing software is used in this process. 【0333】 The server analyzes the received image information and utilizes computer vision technology and machine learning algorithms to extract user features. This allows for the identification of physical characteristics such as facial shape and body shape. 【0334】 A dedicated emotion recognition engine is used to recognize emotional information. The server uses this engine to infer emotional states from facial expressions and postures in images and classify them as "excited," "calm," etc. 【0335】 After all data analysis is complete, the server compares the data with a fashion database and selects the most suitable style based on the user's characteristics and emotional information. Then, it fine-tunes the selected style and finalizes the suggested fashion items. 【0336】 Finally, the server sends the user style suggestions along with relevant purchase information to their device. This includes links to purchasable products, allowing the user to easily access the product purchase page via their mobile device or computer. 【0337】 To give a concrete example, if a user uploads an image and the system recognizes that emotion as "excitement," the server could select fashion items with energetic colors and designs from its database and suggest items such as red or orange tops or leather jackets. 【0338】 An example of a prompt message for a generative AI model would be: "User image data has been uploaded, and the emotion engine has recognized excitement. Based on this, suggest energetic fashion items and generate online purchase links." 【0339】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0340】 Step 1: 【0341】 User image upload 【0342】 The user uploads images of their face or full body to the system using their own device. The device converts the user's selected image file to the specified format (e.g., JPEG, PNG) and prepares it for transfer to the server. It receives the user's image data as input and generates image data ready to be sent to the server as output. 【0343】 Step 2: 【0344】 Recognition of emotional information 【0345】 The server passes the received image data to the emotion recognition engine, which analyzes the user's emotions from their facial expressions. Here, the engine analyzes the facial features in the image at the pixel level and categorizes emotions such as "excitement" and "calmness." It takes image data as input and generates analyzed emotion information as output. 【0346】 Step 3: 【0347】 Image data analysis 【0348】 The server utilizes computer vision technology to extract user features from image data. This includes facial shape, body shape, hair color, and skin tone. Machine learning models are used to quantify specific points within the image and generate feature data. It takes image data as input and generates feature data as output. 【0349】 Step 4: 【0350】 Selecting a Fashion Style 【0351】 The server matches the analyzed feature data and sentiment information with a fashion database to select the optimal style. It uses a similarity calculation algorithm to create a list of fashion items best suited to the user's characteristics. It accepts feature data and sentiment information as input and generates a list of selected styles as output. 【0352】 Step 5: 【0353】 Fine-tuning the proposal style 【0354】 The server reflects emotional information and fine-tunes the selected style. For example, if the user is in an "excited" state, it adds elements with bright, energetic colors and distinctive designs. It receives a selected style list and emotional information as input and creates a final, finely tuned style list as output. 【0355】 Step 6: 【0356】 Providing style and purchasing information 【0357】 The server sends the final style suggestions and associated purchase information to the user's device. This information includes purchase links for wearable fashion items, allowing the user to easily shop. It receives a finely tuned final style list as input and generates style suggestions and purchase information for the user as output. 【0358】 (Application Example 2) 【0359】 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." 【0360】 In today's world, there is a demand for personalized fashion suggestions based on individual users' emotions and characteristics. However, existing systems do not take emotional information into account, making it difficult to provide styles that match a user's momentary emotions. This challenge needs to be addressed to provide users with richer and more appropriate fashion choices. 【0361】 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. 【0362】 In this invention, the server includes means for receiving image data and analyzing the characteristics of a person from the image data; means for selecting a suitable style from a past database based on the analyzed characteristics and emotional information; means for integrating purchase information of the selected style and presenting it to the user; and means for recognizing emotional information from the image and generating style suggestions corresponding to the recognized emotions. This enables highly personalized style suggestions based on the user's characteristics and emotions. 【0363】 "Means for receiving image data" refers to elements that have the function of importing images sent by users into a server or database. 【0364】 "Means for analyzing human characteristics" refers to a function that performs a process of extracting information such as the user's facial shape, body contours, hair color, and skin tone from received image data. 【0365】 A "means for recognizing emotional information" refers to a system that analyzes facial expressions and subtle changes in the user's face from their image to identify the user's emotional state. 【0366】 "Means of selecting a style" refers to the elements responsible for the process of choosing the most suitable fashion style based on the analyzed user characteristics and emotions. 【0367】 "A means of integrating and presenting purchasing information for selected styles" refers to a function that aggregates purchasing information for selected fashion items and presents it to the user in an easy-to-understand manner. 【0368】 "A means of generating style suggestions based on emotions" refers to a function that selects fashion items appropriate to the emotional state expressed by the user and creates suggestions based on that emotional state. 【0369】 The "past database" is a collection of fashion information and user data that has been collected and accumulated to date, and is used as a reference for style selection. 【0370】 The system that realizes this invention involves the coordinated operation of a user's terminal, a server, and a cloud-based database. 【0371】 The user's device can acquire image data using its camera. This image captures the user's face and entire body, and is transmitted to the server through the application. The devices used in this process include smartphones and tablets. 【0372】 The server is located in a cloud environment and processes the received image data. First, it analyzes the features of a person using image processing libraries such as OpenCV, and extracts information such as face shape, body type, hair color, and skin tone. It also recognizes emotional information using Amazon Rekognition or Microsoft Azure's facial recognition API to identify the user's instantaneous emotional state. 【0373】 The server further selects a style suitable for the user by comparing the analyzed features and sentiment information with a pre-built fashion database. Using natural language processing tools such as Stanford CORENLP, a generative AI model interprets the prompt text and automatically generates appropriate style suggestions. 【0374】 The generated style suggestions are presented to the user's device along with relevant purchasing information. The user can easily purchase the suggested items through the online store. This entire process allows the user to receive personalized fashion suggestions and make a purchase decision on the spot. 【0375】 As a concrete example, when a user wants to dress up for a weekend event, they launch the app and take a photo of themselves. The server recognizes the emotional information as an excited state and, based on that data, generates and presents adventurous design suggestions, including red and orange items, to the user's device. The user can then review the suggestions and immediately order any items they like. 【0376】 An example of a prompt message is: "Would you like to receive personalized fashion suggestions that blend your emotions and style? Take a photo using your smartphone camera and find items that perfectly match your mood and features at EmoFashion." This prompt helps to focus the user's attention on style selection and facilitates the process of receiving appropriate suggestions. 【0377】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0378】 Step 1: 【0379】 The user launches the application on their device and uses the camera function to acquire an image of themselves. The input is a photograph of the user's face or full body captured by the camera, and this image data is used directly as input to the application. The output is image data stored in the device's memory. This image data is used in the next processing step. 【0380】 Step 2: 【0381】 The terminal sends the acquired image data to the server. The input is image data stored on the user's terminal, and the output is image data sent to the server via the internet. It is recommended to use a secure communication protocol (e.g., HTTPS) during this transmission process. 【0382】 Step 3: 【0383】 The server receives the transmitted image data and uses OpenCV to analyze the user's features from the image. The input is image data received from the terminal, and user feature information such as face shape, body type, hair color, and skin tone is output. This information is also stored as numerical and categorical data. 【0384】 Step 4: 【0385】 The server uses the analyzed feature information to recognize emotional information using Amazon Rekognition or Microsoft Azure's facial recognition API. The input for this step is the feature information and image data obtained in step 3. The output is the user's emotional state (e.g., joy, surprise, excitement), which is also stored as categorical data. 【0386】 Step 5: 【0387】 The server uses the analyzed feature and sentiment information to match it with a fashion database and select the most suitable style for the user. The input is the output data from steps 3 and 4. A generative AI model is used to perform natural language processing based on the prompt text and obtain style suggestions as output. 【0388】 Step 6: 【0389】 The server collects the generated style suggestions and the associated purchase information, and presents them to the user's terminal along with a link to the online store. The input is the style information selected in step 5, and the output is a style suggestion including purchase information. This includes product images, descriptions, and purchase links. 【0390】 Step 7: 【0391】 The user evaluates the presented style suggestions and decides whether to purchase items of interest from the linked online store. The input is style suggestions from the server, and the output is the user's purchase behavior based on their selection. 【0392】 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. 【0393】 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. 【0394】 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. 【0395】 [Third Embodiment] 【0396】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0397】 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. 【0398】 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). 【0399】 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. 【0400】 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. 【0401】 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). 【0402】 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. 【0403】 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. 【0404】 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. 【0405】 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. 【0406】 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. 【0407】 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". 【0408】 This invention relates to a comprehensive system for fashion proposals. This system starts with the user providing their own image data and operates in the following manner. 【0409】 Users upload their own image data to the system using their device. This image data includes the user's face and entire body. The uploaded image data is received by the server and immediately analyzed. 【0410】 The server uses image processing algorithms to analyze the received image data. This extracts features such as the user's facial shape, body shape, hair color, and skin tone. This feature information is then compared against a vast fashion database managed by the server. The database contains historical data on numerous fashion styles, and the style best suited to the analyzed features is selected. 【0411】 After this selection process is complete, the server integrates the details of the resulting style and provides them to the user. Specifically, style suggestions include color harmony, clothing material selection, and accessory recommendations. Furthermore, the server generates purchasing information related to the suggested style. This includes purchase links obtained from online stores and is optimized to allow the user to easily purchase the suggested items. 【0412】 The device displays all of these suggestions and purchase information, allowing users to select according to their preferences and providing a clear path to purchase the suggested styles online. 【0413】 For example, when a user uploads an image of themselves to the system, the server analyzes this image and detects features such as "round face, broad shoulders, brown hair." Comparing this with past data, the server suggests that a style like "light blue jeans, white T-shirt, and navy blue jacket" would be optimal for the user. This suggestion includes purchase links for each item, allowing the user to easily access the designated online store and complete the purchase process by clicking on them. 【0414】 In this way, the present invention achieves a seamless integration of data analysis and online purchasing to support users in making individualized and appropriate fashion choices. 【0415】 The following describes the processing flow. 【0416】 Step 1: 【0417】 Users access the system's platform using their own devices, select image data of their face or full body, and upload it. 【0418】 Step 2: 【0419】 The server receives the image data sent from the user's terminal and prepares to begin the analysis. 【0420】 Step 3: 【0421】 The server uses the received image data to execute image processing algorithms, analyzing and extracting detailed characteristics of the person, such as facial features, body shape, hair color, and skin tone. 【0422】 Step 4: 【0423】 Based on the characteristics obtained through analysis, the server accesses an internal fashion database and uses statistical methods to select the past fashion style that best matches these characteristics. 【0424】 Step 5: 【0425】 Based on the selected style, the server generates specific fashion suggestions, taking into account factors such as color harmony and clothing material selection. 【0426】 Step 6: 【0427】 The server collects purchase links for items related to the suggested style from online stores and organizes this information for the user to receive. 【0428】 Step 7: 【0429】 The terminal displays fashion suggestions and associated purchase links received from the server to the user, helping the user easily purchase the suggested items. 【0430】 (Example 1) 【0431】 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." 【0432】 Modern consumers face the challenge of choosing the perfect fashion style from a vast array of options. This challenge extends beyond simply selecting products; it encompasses the entire process of finding a style that suits their individual physical characteristics and purchasing the right items. Therefore, there is a demand for fashion suggestions tailored to each user's characteristics, along with an efficient and seamless purchasing experience based on those suggestions. 【0433】 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. 【0434】 In this invention, the server includes means for receiving image data and analyzing a person's physical characteristics from the image data; means for selecting the most suitable outfit from a past fashion database based on the analyzed characteristics; and means for integrating purchase information related to the selected outfit and presenting it to the user. This allows the user to easily find a fashion style that suits their physical characteristics and to quickly and efficiently purchase the suggested items. 【0435】 "Image data" refers to still images and videos of people provided by the user, and is fundamental information that forms the basis for analysis. 【0436】 "Physical characteristics" refer to individual outward features such as facial features, body shape, hair color, and skin tone. 【0437】 A "fashion database" is a collection of data that aggregates historical information about past fashion styles, serving as a basis for selecting styles. 【0438】 "Purchase information" refers to information that includes links and details necessary for purchasing selected fashion items. 【0439】 An "online platform" refers to a website or application that enables users to access information and purchase products via the internet. 【0440】 "Method of selection" refers to a computer process that uses analyzed physical characteristics to find the most suitable clothing and style from a database. 【0441】 "Means of integration" refers to the technical process of combining selected styles and purchase information and presenting them to the user in a single, integrated format. 【0442】 This invention is a system primarily composed of a server, a terminal, and a user, each working together to generate fashion suggestions and support the user's purchasing activities. The following details each of the main components and how the system functions. 【0443】 First, the user uploads their image data using their device. Ideally, this image data should include the user's face and full body. The device can be any internet-connected device, such as a smartphone or personal computer. 【0444】 The server receives image data sent by the user and performs analysis using image processing algorithms. This analysis utilizes machine learning libraries and computer vision technologies, specifically OpenCV and TensorFlow. Through this process, physical features such as the user's face shape, body shape, hair color, and skin tone are extracted. 【0445】 The extracted feature information is compared against a large fashion database managed by the server. The database contains information on a diverse range of fashion styles collected in the past, and the server selects the most suitable style based on the analysis results. This selection process uses sophisticated algorithms to determine the style best suited to be suggested to the user. 【0446】 Once the selection is complete, the server generates purchase information based on that style. This purchase information includes purchase links to online stores associated with the suggested items. The server integrates this information and sends it to the user's terminal in a format for them to enjoy. 【0447】 The device will display suggestions and purchase information received from the server. Users can review the options presented on the screen and select their preferred items and styles. The device has a feature that allows users to connect directly to the online store by clicking a link, making the purchasing process smooth. 【0448】 For example, if a user uploads an image with features such as "brown hair, round face, and broad shoulders," the server will analyze it and suggest a style such as "light blue jeans, white T-shirt, and navy blue jacket." An example of a prompt would be, "Suggest a suitable fashion style based on the features in the user's image and generate a purchase link." This prompt initiates a series of processes for the AI ​​model to provide the most suitable suggestions to the user. 【0449】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0450】 Step 1: 【0451】 The user uploads their own image data from their device. The input is an image file selected by the user using their device. The device sends this file to the server via an HTTP request. The output is the image data arriving on the server. Specifically, the user selects a photo from a file browser on their smartphone or PC and presses the system's upload button. 【0452】 Step 2: 【0453】 The server receives image data sent by the user and begins analysis using image processing algorithms. The input is the image data received from the user. Based on this data, the server extracts physical features such as facial shape, body shape, hair color, and skin tone. The output is this feature information. Specifically, it uses libraries such as OpenCV and TensorFlow to apply machine learning algorithms and perform image analysis. 【0454】 Step 3: 【0455】 The server compares extracted physical characteristic information with a fashion database. The inputs are the analyzed characteristic information and the fashion database. The server compares it with past fashion styles in the database and selects the optimal style. The output is the fashion style information deemed most suitable for the user. Specifically, it uses SQL queries or NoSQL searches to access the database and perform similarity evaluations. 【0456】 Step 4: 【0457】 The server generates relevant purchase information based on the selected fashion style. The input is the selected style information. The server uses the APIs of relevant online stores to obtain links to purchasable clothing items and integrates the purchase information. The output is an integrated fashion suggestion and purchase link information to be presented to the user. Specifically, it collects real-time data from each online store and creates purchase options. 【0458】 Step 5: 【0459】 The terminal displays fashion suggestions and purchase information from the server to the user. The input is integrated suggestion information received from the server. The terminal arranges this information on a user interface that is easy to view, allowing the user to check and purchase items. The output is the user's browsing and purchase actions via clicks. Specifically, the suggested items are displayed on the screen, and when the user clicks the purchase link for an item they like, they are directly connected to that store. 【0460】 (Application Example 1) 【0461】 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." 【0462】 Traditional fashion recommendation systems presented a cumbersome process for users to find and quickly purchase the style that best suited them. Furthermore, the accuracy and suitability of the recommendations were insufficient, and the purchase process was complicated. Additionally, there was a lack of utilization of interactive devices to enhance the user experience. 【0463】 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. 【0464】 In this invention, the server includes means for receiving image information and analyzing human characteristics from the image information; means for selecting a suitable style from a past information database based on the analyzed characteristics; and means for presenting the selected style on a display device and providing purchase procedure information. This allows users to easily find a fashion style that suits their characteristics and proceed to purchase quickly and seamlessly. As a result, the user experience is improved and sales are promoted. 【0465】 "Image information" refers to data about a person's appearance acquired using devices such as cameras. 【0466】 "Methods for analyzing human characteristics" refer to methods that utilize algorithms and processes to identify features such as facial shape, body shape, hair color, and skin tone from received image information. 【0467】 An "information repository" is a database that stores historical data on numerous fashion styles, providing information from past data that is useful for current choices. 【0468】 "Style" in fashion refers to a specific style or coordination, encompassing a comprehensive proposal that includes color harmony and material selection. 【0469】 A "display device" is an interface that allows users to visually confirm information, and includes, for example, smart mirrors and displays. 【0470】 "Means of providing purchase procedure information" refers to methods of presenting links or purchase options to allow users to easily purchase suggested fashion items. 【0471】 The system realizing this invention consists of a server for receiving and analyzing user image information, a terminal for displaying suggestions to the user, and an interactive device for the user's use. The server analyzes the image information and extracts human features. This utilizes image processing algorithms such as OpenCV and TensorFlow. The analyzed feature information is used to select the appropriate format using a cloud-based information repository such as AWS DynamoDB. 【0472】 The terminal presents the user with a format selected by the server. This utilizes display devices such as smart mirrors and displays. The displayed format also includes purchase procedure information, allowing the user to easily purchase items. This information provision is achieved through integration with an automated e-commerce platform. 【0473】 As a concrete example, when a user stands in front of a smart mirror, the mirror automatically captures the user's appearance, and a server analyzes this data. As a result, the mirror displays the optimal fashion style in real time, along with relevant purchase links. The user can then smoothly proceed with the purchase process by clicking on these links. This invention provides a more personalized shopping experience. 【0474】 An example of a prompt would be: "Analyze the user's image and suggest the most suitable fashion style based on the results. Include a purchase link in the suggestion to facilitate the buying process." Using this prompt, the generative AI model can make effective suggestions and provide the user with a satisfying purchasing experience. 【0475】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0476】 Step 1: 【0477】 The user stands in front of the smart mirror, and their appearance is captured by the smart mirror's camera. The image captured by this camera is the input for this step. This image information is then sent to the server for analysis in the next step. 【0478】 Step 2: 【0479】 The server acquires the received image information and uses image processing algorithms such as OpenCV and TensorFlow to analyze features such as human face shape, body shape, hair color, and skin tone. Based on this analysis, specific feature data of the user is output. This creates a foundation for selecting a fashion style that suits the user. 【0480】 Step 3: 【0481】 The server compares the analyzed feature data with an information repository such as AWS DynamoDB and selects the style best suited to the user. This comparison identifies a fashion style that takes color adjustments and material selection into account, and the server generates that style as output. 【0482】 Step 4: 【0483】 The server sends the selected fashion style to the device. This transmitted style information also includes related purchase procedure information, and a purchase link is attached to the user in conjunction with the e-commerce platform. This information is used as input for the next step. 【0484】 Step 5: 【0485】 The device uses a smart mirror display to show the user fashion styles and purchase links received from the server. This allows the user to visually confirm the suggested styles and potentially develop a purchase intent. 【0486】 Step 6: 【0487】 Users can easily purchase recommended items by interacting with the provided purchase link. This interaction results in the output, the order is sent to the e-commerce platform, and the actual purchase process is completed. This allows users to enjoy a seamless shopping experience. 【0488】 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. 【0489】 This invention provides a system for highly personalizing the user's fashion selection process and offering style suggestions that take into account their emotional state. This system combines the user's image data and emotional information to enable advanced suggestions that consider a variety of factors. 【0490】 Users upload images of their face or full body from their devices to the system. The emotion engine then recognizes the user's emotions from the image data or real-time data. The recognized emotion data is then reflected in the style desired by the user. 【0491】 The server analyzes the user's image data and extracts features such as face shape, body shape, hair color, and skin tone. This feature information is then compared with a fashion database managed by the server to select the style best suited to the user's characteristics. Simultaneously, the selected style is fine-tuned based on emotional information recognized by the emotion engine. For example, if the user indicates positive emotions, bright colors and relaxed designs will be suggested. 【0492】 The suggested styles are provided to the user along with relevant purchasing information. This includes links to online stores where the items can be purchased, allowing the user to easily acquire the items. 【0493】 For example, if a user uploads an image and the emotion engine recognizes an "excited" state, the server, in addition to its normal analysis process, will generate suggestions for style selection that are appropriate for the user's high energy level, including eye-catching colors and adventurous designs. For instance, these might include items such as red or orange tops or leather jackets. 【0494】 This system provides suggestions that take into account not only the individual characteristics of each user but also their inner emotions, thereby enriching the selection of personalized fashion items. 【0495】 The following describes the processing flow. 【0496】 Step 1: 【0497】 Users access the system's platform using their devices, select their images, and upload them. This provides the system with data for analysis. 【0498】 Step 2: 【0499】 The server sends the image data received from the user to the emotion engine, which analyzes the user's facial expressions and other visual information to recognize their emotions. This allows the server to understand the user's current emotional state. 【0500】 Step 3: 【0501】 The server simultaneously uses image analysis algorithms to extract features such as facial shape, body shape, hair color, and skin tone from the user's image data. The information obtained through this process is used to select a fashion style. 【0502】 Step 4: 【0503】 The server searches its internal fashion database based on the extracted characteristics of the person and selects the most suitable style. Furthermore, based on emotional information from the emotion engine, it fine-tunes the colors and designs of the selected style to generate fashion suggestions that align with the person's emotions. 【0504】 Step 5: 【0505】 The server collects purchasing information related to the suggested style and gathers purchase links for each item. It then organizes this information and converts it into a format that is easily accessible to the user. 【0506】 Step 6: 【0507】 The device displays fashion suggestions and purchase links sent from the server to the user. Based on the displayed information, the user can choose a style that matches their emotional state and purchase items directly from the relevant online store. 【0508】 (Example 2) 【0509】 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." 【0510】 Conventional fashion recommendation systems have faced challenges in providing personalized recommendations that take into account the individual characteristics and emotional states of users. Furthermore, the limited purchasing options available to users often resulted in insufficient user satisfaction. 【0511】 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. 【0512】 In this invention, the server includes means for receiving image information and analyzing the user's characteristics, means for selecting and fine-tuning a style based on the analyzed characteristics and emotional information, and means for integrating and presenting the final style suggestion and purchase information. This enables highly personalized fashion suggestions that reflect the individual characteristics and emotional state of the user. 【0513】 "Image information" refers to digital visual data, including the user's face and entire body. 【0514】 "User characteristics" refers to physical characteristics unique to the user, such as facial features, body shape, hair color, and skin tone. 【0515】 "Emotional information" refers to data that indicates the emotional state inferred from the user's facial expressions and posture, and is classified into categories such as excitement and calmness. 【0516】 "Methods for selecting and fine-tuning styles" refers to the process of choosing the optimal fashion style based on analyzed characteristics and emotional information, and then adjusting that style as needed. 【0517】 "Purchase information for style suggestions" refers to purchase information related to selected fashion items, specifically information that includes links to products that can be purchased. 【0518】 This invention relates to a system that personalizes a user's fashion style and provides style suggestions that take into account their emotional state. The system utilizes a server, a terminal, an emotion recognition engine, and a fashion database. 【0519】 Users upload images of their face or full body to the system using their own devices. During this process, the device converts the image data to a pre-specified format and then prepares it for transmission to the server. Image processing software is used in this process. 【0520】 The server analyzes the received image information and utilizes computer vision technology and machine learning algorithms to extract user features. This allows for the identification of physical characteristics such as facial shape and body shape. 【0521】 A dedicated emotion recognition engine is used to recognize emotional information. The server uses this engine to infer emotional states from facial expressions and postures in images and classify them as "excited," "calm," etc. 【0522】 After all data analysis is complete, the server compares the data with a fashion database and selects the most suitable style based on the user's characteristics and emotional information. Then, it fine-tunes the selected style and finalizes the suggested fashion items. 【0523】 Finally, the server sends the user style suggestions along with relevant purchase information to their device. This includes links to purchasable products, allowing the user to easily access the product purchase page via their mobile device or computer. 【0524】 To give a concrete example, if a user uploads an image and the system recognizes that emotion as "excitement," the server could select fashion items with energetic colors and designs from its database and suggest items such as red or orange tops or leather jackets. 【0525】 An example of a prompt message for a generative AI model would be: "User image data has been uploaded, and the emotion engine has recognized excitement. Based on this, suggest energetic fashion items and generate online purchase links." 【0526】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0527】 Step 1: 【0528】 User image upload 【0529】 The user uploads images of their face or full body to the system using their own device. The device converts the user's selected image file to the specified format (e.g., JPEG, PNG) and prepares it for transfer to the server. It receives the user's image data as input and generates image data ready to be sent to the server as output. 【0530】 Step 2: 【0531】 Recognition of emotional information 【0532】 The server passes the received image data to the emotion recognition engine, which analyzes the user's emotions from their facial expressions. Here, the engine analyzes the facial features in the image at the pixel level and categorizes emotions such as "excitement" and "calmness." It takes image data as input and generates analyzed emotion information as output. 【0533】 Step 3: 【0534】 Image data analysis 【0535】 The server utilizes computer vision technology to extract user features from image data. This includes facial shape, body shape, hair color, and skin tone. Machine learning models are used to quantify specific points within the image and generate feature data. It takes image data as input and generates feature data as output. 【0536】 Step 4: 【0537】 Selecting a Fashion Style 【0538】 The server matches the analyzed feature data and sentiment information with a fashion database to select the optimal style. It uses a similarity calculation algorithm to create a list of fashion items best suited to the user's characteristics. It accepts feature data and sentiment information as input and generates a list of selected styles as output. 【0539】 Step 5: 【0540】 Fine-tuning the proposal style 【0541】 The server reflects emotional information and fine-tunes the selected style. For example, if the user is in an "excited" state, it adds elements with bright, energetic colors and distinctive designs. It receives a selected style list and emotional information as input and creates a final, finely tuned style list as output. 【0542】 Step 6: 【0543】 Providing style and purchasing information 【0544】 The server sends the final style suggestions and associated purchase information to the user's device. This information includes purchase links for wearable fashion items, allowing the user to easily shop. It receives a finely tuned final style list as input and generates style suggestions and purchase information for the user as output. 【0545】 (Application Example 2) 【0546】 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." 【0547】 In today's world, there is a demand for personalized fashion suggestions based on individual users' emotions and characteristics. However, existing systems do not take emotional information into account, making it difficult to provide styles that match a user's momentary emotions. This challenge needs to be addressed to provide users with richer and more appropriate fashion choices. 【0548】 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. 【0549】 In this invention, the server includes means for receiving image data and analyzing the characteristics of a person from the image data; means for selecting a suitable style from a past database based on the analyzed characteristics and emotional information; means for integrating purchase information of the selected style and presenting it to the user; and means for recognizing emotional information from the image and generating style suggestions corresponding to the recognized emotions. This enables highly personalized style suggestions based on the user's characteristics and emotions. 【0550】 "Means for receiving image data" refers to elements that have the function of importing images sent by users into a server or database. 【0551】 "Means for analyzing human characteristics" refers to a function that performs a process of extracting information such as the user's facial shape, body contours, hair color, and skin tone from received image data. 【0552】 A "means for recognizing emotional information" refers to a system that analyzes facial expressions and subtle changes in the user's face from their image to identify the user's emotional state. 【0553】 "Means of selecting a style" refers to the elements responsible for the process of choosing the most suitable fashion style based on the analyzed user characteristics and emotions. 【0554】 "A means of integrating and presenting purchasing information for selected styles" refers to a function that aggregates purchasing information for selected fashion items and presents it to the user in an easy-to-understand manner. 【0555】 "A means of generating style suggestions based on emotions" refers to a function that selects fashion items appropriate to the emotional state expressed by the user and creates suggestions based on that emotional state. 【0556】 The "past database" is a collection of fashion information and user data that has been collected and accumulated to date, and is used as a reference for style selection. 【0557】 The system that realizes this invention involves the coordinated operation of a user's terminal, a server, and a cloud-based database. 【0558】 The user's device can acquire image data using its camera. This image captures the user's face and entire body, and is transmitted to the server through the application. The devices used in this process include smartphones and tablets. 【0559】 The server is located in a cloud environment and processes the received image data. First, it analyzes the features of a person using image processing libraries such as OpenCV, and extracts information such as face shape, body type, hair color, and skin tone. It also recognizes emotional information using Amazon Rekognition or Microsoft Azure's facial recognition API to identify the user's instantaneous emotional state. 【0560】 The server further selects a style suitable for the user by comparing the analyzed features and sentiment information with a pre-built fashion database. Using natural language processing tools such as Stanford CORENLP, a generative AI model interprets the prompt text and automatically generates appropriate style suggestions. 【0561】 The generated style suggestions are presented to the user's device along with relevant purchasing information. The user can easily purchase the suggested items through the online store. This entire process allows the user to receive personalized fashion suggestions and make a purchase decision on the spot. 【0562】 As a concrete example, when a user wants to dress up for a weekend event, they launch the app and take a photo of themselves. The server recognizes the emotional information as an excited state and, based on that data, generates and presents adventurous design suggestions, including red and orange items, to the user's device. The user can then review the suggestions and immediately order any items they like. 【0563】 An example of a prompt message is: "Would you like to receive personalized fashion suggestions that blend your emotions and style? Take a photo using your smartphone camera and find items that perfectly match your mood and features at EmoFashion." This prompt helps to focus the user's attention on style selection and facilitates the process of receiving appropriate suggestions. 【0564】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0565】 Step 1: 【0566】 The user launches the application on their device and uses the camera function to acquire an image of themselves. The input is a photograph of the user's face or full body captured by the camera, and this image data is used directly as input to the application. The output is image data stored in the device's memory. This image data is used in the next processing step. 【0567】 Step 2: 【0568】 The terminal sends the acquired image data to the server. The input is image data stored on the user's terminal, and the output is image data sent to the server via the internet. It is recommended to use a secure communication protocol (e.g., HTTPS) during this transmission process. 【0569】 Step 3: 【0570】 The server receives the transmitted image data and uses OpenCV to analyze the user's features from the image. The input is image data received from the terminal, and user feature information such as face shape, body type, hair color, and skin tone is output. This information is also stored as numerical and categorical data. 【0571】 Step 4: 【0572】 The server uses the analyzed feature information to recognize emotional information using Amazon Rekognition or Microsoft Azure's facial recognition API. The input for this step is the feature information and image data obtained in step 3. The output is the user's emotional state (e.g., joy, surprise, excitement), which is also stored as categorical data. 【0573】 Step 5: 【0574】 The server uses the analyzed feature and sentiment information to match it with a fashion database and select the most suitable style for the user. The input is the output data from steps 3 and 4. A generative AI model is used to perform natural language processing based on the prompt text and obtain style suggestions as output. 【0575】 Step 6: 【0576】 The server collects the generated style suggestions and the associated purchase information, and presents them to the user's terminal along with a link to the online store. The input is the style information selected in step 5, and the output is a style suggestion including purchase information. This includes product images, descriptions, and purchase links. 【0577】 Step 7: 【0578】 The user evaluates the presented style suggestions and decides whether to purchase items of interest from the linked online store. The input is style suggestions from the server, and the output is the user's purchase behavior based on their selection. 【0579】 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. 【0580】 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. 【0581】 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. 【0582】 [Fourth Embodiment] 【0583】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0584】 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. 【0585】 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). 【0586】 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. 【0587】 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. 【0588】 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). 【0589】 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. 【0590】 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. 【0591】 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. 【0592】 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. 【0593】 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. 【0594】 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. 【0595】 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". 【0596】 This invention relates to a comprehensive system for fashion proposals. This system starts with the user providing their own image data and operates in the following manner. 【0597】 Users upload their own image data to the system using their device. This image data includes the user's face and entire body. The uploaded image data is received by the server and immediately analyzed. 【0598】 The server uses image processing algorithms to analyze the received image data. This extracts features such as the user's facial shape, body shape, hair color, and skin tone. This feature information is then compared against a vast fashion database managed by the server. The database contains historical data on numerous fashion styles, and the style best suited to the analyzed features is selected. 【0599】 After this selection process is complete, the server integrates the details of the resulting style and provides them to the user. Specifically, style suggestions include color harmony, clothing material selection, and accessory recommendations. Furthermore, the server generates purchasing information related to the suggested style. This includes purchase links obtained from online stores and is optimized to allow the user to easily purchase the suggested items. 【0600】 The device displays all of these suggestions and purchase information, allowing users to select according to their preferences and providing a clear path to purchase the suggested styles online. 【0601】 For example, when a user uploads an image of themselves to the system, the server analyzes this image and detects features such as "round face, broad shoulders, brown hair." Comparing this with past data, the server suggests that a style like "light blue jeans, white T-shirt, and navy blue jacket" would be optimal for the user. This suggestion includes purchase links for each item, allowing the user to easily access the designated online store and complete the purchase process by clicking on them. 【0602】 In this way, the present invention achieves a seamless integration of data analysis and online purchasing to support users in making individualized and appropriate fashion choices. 【0603】 The following describes the processing flow. 【0604】 Step 1: 【0605】 Users access the system's platform using their own devices, select image data of their face or full body, and upload it. 【0606】 Step 2: 【0607】 The server receives the image data sent from the user's terminal and prepares to begin the analysis. 【0608】 Step 3: 【0609】 The server uses the received image data to execute image processing algorithms, analyzing and extracting detailed characteristics of the person, such as facial features, body shape, hair color, and skin tone. 【0610】 Step 4: 【0611】 Based on the characteristics obtained through analysis, the server accesses an internal fashion database and uses statistical methods to select the past fashion style that best matches these characteristics. 【0612】 Step 5: 【0613】 Based on the selected style, the server generates specific fashion suggestions, taking into account factors such as color harmony and clothing material selection. 【0614】 Step 6: 【0615】 The server collects purchase links for items related to the suggested style from online stores and organizes this information for the user to receive. 【0616】 Step 7: 【0617】 The terminal displays fashion suggestions and associated purchase links received from the server to the user, helping the user easily purchase the suggested items. 【0618】 (Example 1) 【0619】 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". 【0620】 Modern consumers face the challenge of choosing the perfect fashion style from a vast array of options. This challenge extends beyond simply selecting products; it encompasses the entire process of finding a style that suits their individual physical characteristics and purchasing the right items. Therefore, there is a demand for fashion suggestions tailored to each user's characteristics, along with an efficient and seamless purchasing experience based on those suggestions. 【0621】 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. 【0622】 In this invention, the server includes means for receiving image data and analyzing a person's physical characteristics from the image data; means for selecting the most suitable outfit from a past fashion database based on the analyzed characteristics; and means for integrating purchase information related to the selected outfit and presenting it to the user. This allows the user to easily find a fashion style that suits their physical characteristics and to quickly and efficiently purchase the suggested items. 【0623】 "Image data" refers to still images and videos of people provided by the user, and is fundamental information that forms the basis for analysis. 【0624】 "Physical characteristics" refer to individual outward features such as facial features, body shape, hair color, and skin tone. 【0625】 A "fashion database" is a collection of data that aggregates historical information about past fashion styles, serving as a basis for selecting styles. 【0626】 "Purchase information" refers to information that includes links and details necessary for purchasing selected fashion items. 【0627】 An "online platform" refers to a website or application that enables users to access information and purchase products via the internet. 【0628】 "Method of selection" refers to a computer process that uses analyzed physical characteristics to find the most suitable clothing and style from a database. 【0629】 "Means of integration" refers to the technical process of combining selected styles and purchase information and presenting them to the user in a single, integrated format. 【0630】 This invention is a system primarily composed of a server, a terminal, and a user, each working together to generate fashion suggestions and support the user's purchasing activities. The following details each of the main components and how the system functions. 【0631】 First, the user uploads their image data using their device. Ideally, this image data should include the user's face and full body. The device can be any internet-connected device, such as a smartphone or personal computer. 【0632】 The server receives image data sent by the user and performs analysis using image processing algorithms. This analysis utilizes machine learning libraries and computer vision technologies, specifically OpenCV and TensorFlow. Through this process, physical features such as the user's face shape, body shape, hair color, and skin tone are extracted. 【0633】 The extracted feature information is compared against a large fashion database managed by the server. The database contains information on a diverse range of fashion styles collected in the past, and the server selects the most suitable style based on the analysis results. This selection process uses sophisticated algorithms to determine the style best suited to be suggested to the user. 【0634】 Once the selection is complete, the server generates purchase information based on that style. This purchase information includes purchase links to online stores associated with the suggested items. The server integrates this information and sends it to the user's terminal in a format for them to enjoy. 【0635】 The device will display suggestions and purchase information received from the server. Users can review the options presented on the screen and select their preferred items and styles. The device has a feature that allows users to connect directly to the online store by clicking a link, making the purchasing process smooth. 【0636】 For example, if a user uploads an image with features such as "brown hair, round face, and broad shoulders," the server will analyze it and suggest a style such as "light blue jeans, white T-shirt, and navy blue jacket." An example of a prompt would be, "Suggest a suitable fashion style based on the features in the user's image and generate a purchase link." This prompt initiates a series of processes for the AI ​​model to provide the most suitable suggestions to the user. 【0637】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0638】 Step 1: 【0639】 The user uploads their own image data from their device. The input is an image file selected by the user using their device. The device sends this file to the server via an HTTP request. The output is the image data arriving on the server. Specifically, the user selects a photo from a file browser on their smartphone or PC and presses the system's upload button. 【0640】 Step 2: 【0641】 The server receives image data sent by the user and begins analysis using image processing algorithms. The input is the image data received from the user. Based on this data, the server extracts physical features such as facial shape, body shape, hair color, and skin tone. The output is this feature information. Specifically, it uses libraries such as OpenCV and TensorFlow to apply machine learning algorithms and perform image analysis. 【0642】 Step 3: 【0643】 The server compares extracted physical characteristic information with a fashion database. The inputs are the analyzed characteristic information and the fashion database. The server compares it with past fashion styles in the database and selects the optimal style. The output is the fashion style information deemed most suitable for the user. Specifically, it uses SQL queries or NoSQL searches to access the database and perform similarity evaluations. 【0644】 Step 4: 【0645】 The server generates relevant purchase information based on the selected fashion style. The input is the selected style information. The server uses the APIs of relevant online stores to obtain links to purchasable clothing items and integrates the purchase information. The output is an integrated fashion suggestion and purchase link information to be presented to the user. Specifically, it collects real-time data from each online store and creates purchase options. 【0646】 Step 5: 【0647】 The terminal displays fashion suggestions and purchase information from the server to the user. The input is integrated suggestion information received from the server. The terminal arranges this information on a user interface that is easy to view, allowing the user to check and purchase items. The output is the user's browsing and purchase actions via clicks. Specifically, the suggested items are displayed on the screen, and when the user clicks the purchase link for an item they like, they are directly connected to that store. 【0648】 (Application Example 1) 【0649】 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". 【0650】 Traditional fashion recommendation systems presented a cumbersome process for users to find and quickly purchase the style that best suited them. Furthermore, the accuracy and suitability of the recommendations were insufficient, and the purchase process was complicated. Additionally, there was a lack of utilization of interactive devices to enhance the user experience. 【0651】 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. 【0652】 In this invention, the server includes means for receiving image information and analyzing human characteristics from the image information; means for selecting a suitable style from a past information database based on the analyzed characteristics; and means for presenting the selected style on a display device and providing purchase procedure information. This allows users to easily find a fashion style that suits their characteristics and proceed to purchase quickly and seamlessly. As a result, the user experience is improved and sales are promoted. 【0653】 "Image information" refers to data about a person's appearance acquired using devices such as cameras. 【0654】 "Methods for analyzing human characteristics" refer to methods that utilize algorithms and processes to identify features such as facial shape, body shape, hair color, and skin tone from received image information. 【0655】 An "information repository" is a database that stores historical data on numerous fashion styles, providing information from past data that is useful for current choices. 【0656】 "Style" in fashion refers to a specific style or coordination, encompassing a comprehensive proposal that includes color harmony and material selection. 【0657】 A "display device" is an interface that allows users to visually confirm information, and includes, for example, smart mirrors and displays. 【0658】 "Means of providing purchase procedure information" refers to methods of presenting links or purchase options to allow users to easily purchase suggested fashion items. 【0659】 The system realizing this invention consists of a server for receiving and analyzing user image information, a terminal for displaying suggestions to the user, and an interactive device for the user's use. The server analyzes the image information and extracts human features. This utilizes image processing algorithms such as OpenCV and TensorFlow. The analyzed feature information is used to select the appropriate format using a cloud-based information repository such as AWS DynamoDB. 【0660】 The terminal presents the user with a format selected by the server. This utilizes display devices such as smart mirrors and displays. The displayed format also includes purchase procedure information, allowing the user to easily purchase items. This information provision is achieved through integration with an automated e-commerce platform. 【0661】 As a concrete example, when a user stands in front of a smart mirror, the mirror automatically captures the user's appearance, and a server analyzes this data. As a result, the mirror displays the optimal fashion style in real time, along with relevant purchase links. The user can then smoothly proceed with the purchase process by clicking on these links. This invention provides a more personalized shopping experience. 【0662】 An example of a prompt would be: "Analyze the user's image and suggest the most suitable fashion style based on the results. Include a purchase link in the suggestion to facilitate the buying process." Using this prompt, the generative AI model can make effective suggestions and provide the user with a satisfying purchasing experience. 【0663】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0664】 Step 1: 【0665】 The user stands in front of the smart mirror, and their appearance is captured by the smart mirror's camera. The image captured by this camera is the input for this step. This image information is then sent to the server for analysis in the next step. 【0666】 Step 2: 【0667】 The server acquires the received image information and uses image processing algorithms such as OpenCV and TensorFlow to analyze features such as human face shape, body shape, hair color, and skin tone. Based on this analysis, specific feature data of the user is output. This creates a foundation for selecting a fashion style that suits the user. 【0668】 Step 3: 【0669】 The server compares the analyzed feature data with an information repository such as AWS DynamoDB and selects the style best suited to the user. This comparison identifies a fashion style that takes color adjustments and material selection into account, and the server generates that style as output. 【0670】 Step 4: 【0671】 The server sends the selected fashion style to the device. This transmitted style information also includes related purchase procedure information, and a purchase link is attached to the user in conjunction with the e-commerce platform. This information is used as input for the next step. 【0672】 Step 5: 【0673】 The device uses a smart mirror display to show the user fashion styles and purchase links received from the server. This allows the user to visually confirm the suggested styles and potentially develop a purchase intent. 【0674】 Step 6: 【0675】 Users can easily purchase recommended items by interacting with the provided purchase link. This interaction results in the output, the order is sent to the e-commerce platform, and the actual purchase process is completed. This allows users to enjoy a seamless shopping experience. 【0676】 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. 【0677】 This invention provides a system for highly personalizing the user's fashion selection process and offering style suggestions that take into account their emotional state. This system combines the user's image data and emotional information to enable advanced suggestions that consider a variety of factors. 【0678】 Users upload images of their face or full body from their devices to the system. The emotion engine then recognizes the user's emotions from the image data or real-time data. The recognized emotion data is then reflected in the style desired by the user. 【0679】 The server analyzes the user's image data and extracts features such as face shape, body shape, hair color, and skin tone. This feature information is then compared with a fashion database managed by the server to select the style best suited to the user's characteristics. Simultaneously, the selected style is fine-tuned based on emotional information recognized by the emotion engine. For example, if the user indicates positive emotions, bright colors and relaxed designs will be suggested. 【0680】 The suggested styles are provided to the user along with relevant purchasing information. This includes links to online stores where the items can be purchased, allowing the user to easily acquire the items. 【0681】 For example, if a user uploads an image and the emotion engine recognizes an "excited" state, the server, in addition to its normal analysis process, will generate suggestions for style selection that are appropriate for the user's high energy level, including eye-catching colors and adventurous designs. For instance, these might include items such as red or orange tops or leather jackets. 【0682】 This system provides suggestions that take into account not only the individual characteristics of each user but also their inner emotions, thereby enriching the selection of personalized fashion items. 【0683】 The following describes the processing flow. 【0684】 Step 1: 【0685】 Users access the system's platform using their devices, select their images, and upload them. This provides the system with data for analysis. 【0686】 Step 2: 【0687】 The server sends the image data received from the user to the emotion engine, which analyzes the user's facial expressions and other visual information to recognize their emotions. This allows the server to understand the user's current emotional state. 【0688】 Step 3: 【0689】 The server simultaneously uses image analysis algorithms to extract features such as facial shape, body shape, hair color, and skin tone from the user's image data. The information obtained through this process is used to select a fashion style. 【0690】 Step 4: 【0691】 The server searches its internal fashion database based on the extracted characteristics of the person and selects the most suitable style. Furthermore, based on emotional information from the emotion engine, it fine-tunes the colors and designs of the selected style to generate fashion suggestions that align with the person's emotions. 【0692】 Step 5: 【0693】 The server collects purchasing information related to the suggested style and gathers purchase links for each item. It then organizes this information and converts it into a format that is easily accessible to the user. 【0694】 Step 6: 【0695】 The device displays fashion suggestions and purchase links sent from the server to the user. Based on the displayed information, the user can choose a style that matches their emotional state and purchase items directly from the relevant online store. 【0696】 (Example 2) 【0697】 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". 【0698】 Conventional fashion recommendation systems have faced challenges in providing personalized recommendations that take into account the individual characteristics and emotional states of users. Furthermore, the limited purchasing options available to users often resulted in insufficient user satisfaction. 【0699】 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. 【0700】 In this invention, the server includes means for receiving image information and analyzing the user's characteristics, means for selecting and fine-tuning a style based on the analyzed characteristics and emotional information, and means for integrating and presenting the final style suggestion and purchase information. This enables highly personalized fashion suggestions that reflect the individual characteristics and emotional state of the user. 【0701】 "Image information" refers to digital visual data, including the user's face and entire body. 【0702】 "User characteristics" refers to physical characteristics unique to the user, such as facial features, body shape, hair color, and skin tone. 【0703】 "Emotional information" refers to data that indicates the emotional state inferred from the user's facial expressions and posture, and is classified into categories such as excitement and calmness. 【0704】 "Methods for selecting and fine-tuning styles" refers to the process of choosing the optimal fashion style based on analyzed characteristics and emotional information, and then adjusting that style as needed. 【0705】 "Purchase information for style suggestions" refers to purchase information related to selected fashion items, specifically information that includes links to products that can be purchased. 【0706】 This invention relates to a system that personalizes a user's fashion style and provides style suggestions that take into account their emotional state. The system utilizes a server, a terminal, an emotion recognition engine, and a fashion database. 【0707】 Users upload images of their face or full body to the system using their own devices. During this process, the device converts the image data to a pre-specified format and then prepares it for transmission to the server. Image processing software is used in this process. 【0708】 The server analyzes the received image information and utilizes computer vision technology and machine learning algorithms to extract user features. This allows for the identification of physical characteristics such as facial shape and body shape. 【0709】 A dedicated emotion recognition engine is used to recognize emotional information. The server uses this engine to infer emotional states from facial expressions and postures in images and classify them as "excited," "calm," etc. 【0710】 After all data analysis is complete, the server compares the data with a fashion database and selects the most suitable style based on the user's characteristics and emotional information. Then, it fine-tunes the selected style and finalizes the suggested fashion items. 【0711】 Finally, the server sends the user style suggestions along with relevant purchase information to their device. This includes links to purchasable products, allowing the user to easily access the product purchase page via their mobile device or computer. 【0712】 To give a concrete example, if a user uploads an image and the system recognizes that emotion as "excitement," the server could select fashion items with energetic colors and designs from its database and suggest items such as red or orange tops or leather jackets. 【0713】 An example of a prompt message for a generative AI model would be: "User image data has been uploaded, and the emotion engine has recognized excitement. Based on this, suggest energetic fashion items and generate online purchase links." 【0714】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0715】 Step 1: 【0716】 User image upload 【0717】 The user uploads images of their face or full body to the system using their own device. The device converts the user's selected image file to the specified format (e.g., JPEG, PNG) and prepares it for transfer to the server. It receives the user's image data as input and generates image data ready to be sent to the server as output. 【0718】 Step 2: 【0719】 Recognition of emotional information 【0720】 The server passes the received image data to the emotion recognition engine, which analyzes the user's emotions from their facial expressions. Here, the engine analyzes the facial features in the image at the pixel level and categorizes emotions such as "excitement" and "calmness." It takes image data as input and generates analyzed emotion information as output. 【0721】 Step 3: 【0722】 Image data analysis 【0723】 The server utilizes computer vision technology to extract user features from image data. This includes facial shape, body shape, hair color, and skin tone. Machine learning models are used to quantify specific points within the image and generate feature data. It takes image data as input and generates feature data as output. 【0724】 Step 4: 【0725】 Selecting a Fashion Style 【0726】 The server matches the analyzed feature data and sentiment information with a fashion database to select the optimal style. It uses a similarity calculation algorithm to create a list of fashion items best suited to the user's characteristics. It accepts feature data and sentiment information as input and generates a list of selected styles as output. 【0727】 Step 5: 【0728】 Fine-tuning the proposal style 【0729】 The server reflects emotional information and fine-tunes the selected style. For example, if the user is in an "excited" state, it adds elements with bright, energetic colors and distinctive designs. It receives a selected style list and emotional information as input and creates a final, finely tuned style list as output. 【0730】 Step 6: 【0731】 Providing style and purchasing information 【0732】 The server sends the final style suggestions and associated purchase information to the user's device. This information includes purchase links for wearable fashion items, allowing the user to easily shop. It receives a finely tuned final style list as input and generates style suggestions and purchase information for the user as output. 【0733】 (Application Example 2) 【0734】 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". 【0735】 In today's world, there is a demand for personalized fashion suggestions based on individual users' emotions and characteristics. However, existing systems do not take emotional information into account, making it difficult to provide styles that match a user's momentary emotions. This challenge needs to be addressed to provide users with richer and more appropriate fashion choices. 【0736】 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. 【0737】 In this invention, the server includes means for receiving image data and analyzing the characteristics of a person from the image data; means for selecting a suitable style from a past database based on the analyzed characteristics and emotional information; means for integrating purchase information of the selected style and presenting it to the user; and means for recognizing emotional information from the image and generating style suggestions corresponding to the recognized emotions. This enables highly personalized style suggestions based on the user's characteristics and emotions. 【0738】 "Means for receiving image data" refers to elements that have the function of importing images sent by users into a server or database. 【0739】 "Means for analyzing human characteristics" refers to a function that performs a process of extracting information such as the user's facial shape, body contours, hair color, and skin tone from received image data. 【0740】 A "means for recognizing emotional information" refers to a system that analyzes facial expressions and subtle changes in the user's face from their image to identify the user's emotional state. 【0741】 "Means of selecting a style" refers to the elements responsible for the process of choosing the most suitable fashion style based on the analyzed user characteristics and emotions. 【0742】 "A means of integrating and presenting purchasing information for selected styles" refers to a function that aggregates purchasing information for selected fashion items and presents it to the user in an easy-to-understand manner. 【0743】 "A means of generating style suggestions based on emotions" refers to a function that selects fashion items appropriate to the emotional state expressed by the user and creates suggestions based on that emotional state. 【0744】 The "past database" is a collection of fashion information and user data that has been collected and accumulated to date, and is used as a reference for style selection. 【0745】 The system that realizes this invention involves the coordinated operation of a user's terminal, a server, and a cloud-based database. 【0746】 The user's device can acquire image data using its camera. This image captures the user's face and entire body, and is transmitted to the server through the application. The devices used in this process include smartphones and tablets. 【0747】 The server is located in a cloud environment and processes the received image data. First, it analyzes the features of a person using image processing libraries such as OpenCV, and extracts information such as face shape, body type, hair color, and skin tone. It also recognizes emotional information using Amazon Rekognition or Microsoft Azure's facial recognition API to identify the user's instantaneous emotional state. 【0748】 The server further selects a style suitable for the user by comparing the analyzed features and sentiment information with a pre-built fashion database. Using natural language processing tools such as Stanford CORENLP, a generative AI model interprets the prompt text and automatically generates appropriate style suggestions. 【0749】 The generated style suggestions are presented to the user's device along with relevant purchasing information. The user can easily purchase the suggested items through the online store. This entire process allows the user to receive personalized fashion suggestions and make a purchase decision on the spot. 【0750】 As a concrete example, when a user wants to dress up for a weekend event, they launch the app and take a photo of themselves. The server recognizes the emotional information as an excited state and, based on that data, generates and presents adventurous design suggestions, including red and orange items, to the user's device. The user can then review the suggestions and immediately order any items they like. 【0751】 An example of a prompt message is: "Would you like to receive personalized fashion suggestions that blend your emotions and style? Take a photo using your smartphone camera and find items that perfectly match your mood and features at EmoFashion." This prompt helps to focus the user's attention on style selection and facilitates the process of receiving appropriate suggestions. 【0752】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0753】 Step 1: 【0754】 The user launches the application on their device and uses the camera function to acquire an image of themselves. The input is a photograph of the user's face or full body captured by the camera, and this image data is used directly as input to the application. The output is image data stored in the device's memory. This image data is used in the next processing step. 【0755】 Step 2: 【0756】 The terminal sends the acquired image data to the server. The input is image data stored on the user's terminal, and the output is image data sent to the server via the internet. It is recommended to use a secure communication protocol (e.g., HTTPS) during this transmission process. 【0757】 Step 3: 【0758】 The server receives the transmitted image data and uses OpenCV to analyze the user's features from the image. The input is image data received from the terminal, and user feature information such as face shape, body type, hair color, and skin tone is output. This information is also stored as numerical and categorical data. 【0759】 Step 4: 【0760】 The server uses the analyzed feature information to recognize emotional information using Amazon Rekognition or Microsoft Azure's facial recognition API. The input for this step is the feature information and image data obtained in step 3. The output is the user's emotional state (e.g., joy, surprise, excitement), which is also stored as categorical data. 【0761】 Step 5: 【0762】 The server uses the analyzed feature and sentiment information to match it with a fashion database and select the most suitable style for the user. The input is the output data from steps 3 and 4. A generative AI model is used to perform natural language processing based on the prompt text and obtain style suggestions as output. 【0763】 Step 6: 【0764】 The server collects the generated style suggestions and the associated purchase information, and presents them to the user's terminal along with a link to the online store. The input is the style information selected in step 5, and the output is a style suggestion including purchase information. This includes product images, descriptions, and purchase links. 【0765】 Step 7: 【0766】 The user evaluates the presented style suggestions and decides whether to purchase items of interest from the linked online store. The input is style suggestions from the server, and the output is the user's purchase behavior based on their selection. 【0767】 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. 【0768】 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. 【0769】 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. 【0770】 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. 【0771】 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. 【0772】 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. 【0773】 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. 【0774】 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. 【0775】 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." 【0776】 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. 【0777】 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. 【0778】 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. 【0779】 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. 【0780】 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. 【0781】 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. 【0782】 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. 【0783】 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. 【0784】 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. 【0785】 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. 【0786】 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. 【0787】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0788】 The following is further disclosed regarding the embodiments described above. 【0789】 (Claim 1) 【0790】 A means for receiving image data and analyzing the characteristics of a person from said image data, 【0791】 Based on the analyzed features, a means of selecting a suitable style from past databases, 【0792】 A means of integrating and presenting the selected style's purchase information to the user, 【0793】 A system that includes this. 【0794】 (Claim 2) 【0795】 The system according to claim 1, which generates a coordinate based on analyzed features, taking into account color harmony and material selection. 【0796】 (Claim 3) 【0797】 The system according to claim 1, which collects the best purchasing options from multiple online stores related to the selected style. 【0798】 "Example 1" 【0799】 (Claim 1) 【0800】 A means for receiving image data and analyzing the physical characteristics of a person from said image data, 【0801】 Based on the analyzed characteristics, a method for selecting the optimal outfit from a past fashion database, 【0802】 A means of integrating and presenting purchase information related to the selected outfit to the user, 【0803】 A means to generate a purchase link for the proposed costume and allow the user to easily complete the purchase process, 【0804】 A means of providing a coordinated look that takes into account suitable color tones and materials through database matching, 【0805】 A system that includes this. 【0806】 (Claim 2) 【0807】 The system according to claim 1, which collects and integrates optimal online purchase options based on analyzed physical characteristics. 【0808】 (Claim 3) 【0809】 The system according to claim 1, which displays suggested outfits tailored to the user's preferences on an online platform and optimizes the process up to purchase. 【0810】 "Application Example 1" 【0811】 (Claim 1) 【0812】 A means for receiving image information and analyzing human characteristics from said image information, 【0813】 A means of selecting a suitable format from past data based on the analyzed features, 【0814】 A means of displaying the selected format on a display device and providing purchase procedure information, 【0815】 A system that includes this. 【0816】 (Claim 2) 【0817】 The system according to claim 1, which generates combinations based on analyzed features, taking into account color adjustment and material selection. 【0818】 (Claim 3) 【0819】 The system according to claim 1, which collects and presents the best purchase options from multiple e-commerce platforms related to the selected format. 【0820】 "Example 2 of combining an emotion engine" 【0821】 (Claim 1) 【0822】 A means for receiving image information and analyzing the user's characteristics from said image information, 【0823】 A method for selecting a suitable style from a past data collection based on analyzed features and emotional information, 【0824】 A means of fine-tuning the selected style based on emotional information to generate a final style proposal, 【0825】 A means of integrating and presenting the final style suggestion and purchasing information to the user, 【0826】 A system that includes this. 【0827】 (Claim 2) 【0828】 The system according to claim 1, which recognizes the user's emotional information from image information and makes fine adjustments to the style according to the emotional state. 【0829】 (Claim 3) 【0830】 The system according to claim 1, which collects the optimal purchasing options from multiple purchasing sources related to the selected style. 【0831】 "Application example 2 of combining emotional engines" 【0832】 (Claim 1) 【0833】 A means for receiving image data and analyzing the characteristics of a person from said image data, 【0834】 A means of selecting a suitable style from a past database based on analyzed features and sentiment information, 【0835】 A means of integrating and presenting the selected style's purchase information to the user, 【0836】 A means for recognizing emotional information from an image and generating style suggestions corresponding to the recognized emotion, 【0837】 A system that includes this. 【0838】 (Claim 2) 【0839】 The system according to claim 1, which generates a coordinate based on analyzed features and emotional information, taking into account color harmony and material selection. 【0840】 (Claim 3) 【0841】 The system according to claim 1, which collects and displays to the user the best purchasing options from multiple online purchasing locations related to the selected style. [Explanation of Symbols] 【0842】 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

[Claim 1] A means for receiving image data and analyzing the characteristics of a person from said image data, Based on the analyzed features, a means of selecting a suitable style from past databases, A means of integrating and presenting the selected style's purchase information to the user, A system that includes this. [Claim 2] The system according to claim 1, which generates a coordinate based on analyzed features, taking into account color harmony and material selection. [Claim 3] The system according to claim 1, which collects the best purchasing options from multiple online stores related to the selected style.

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