system

Abstract

We provide the system. [Solution] A data processing means that receives a photograph and analyzes the shape and physical characteristics of an individual from the photograph, Based on the analyzed characteristics, a proposal method is used to compare them with past data sets and provide personalized clothing suggestions. An information display means that generates and presents purchase instructions for the proposed clothing items, A mechanical means that receives a user's voice command and automatically takes a photograph and makes a suggestion based on the command, A system that includes this.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, many people have difficulty finding the most suitable fashion style for themselves, and there is a problem that they cannot easily discover a style that suits them from a variety of options. As a result, it is necessary to spend time and effort on fashion selection, and the process of making a purchase decision is complicated, so an efficient shopping experience is hindered. Furthermore, there is a problem in the lack of technology for accurately proposing fashion suitable for individual faces and body types. <00​​​​​This invention solves the above problems by providing an information processing means for analyzing facial shape and body shape characteristics based on a person's photograph, and a system that includes a suggestion means for executing personalized fashion suggestions by comparing the analysis results with a past database. Furthermore, it improves shopping efficiency by providing a display means for generating and presenting purchase links corresponding to the suggested fashion items to the user. In addition, it provides highly accurate personalized fashion advice by performing detailed analysis using multimodal technology and utilizing a fashion database that includes past highly-rated examples.

[0006] A "photograph" is image information that records an individual's visual characteristics in digital or analog format.

[0007] "Information processing means" refers to a system consisting of computing devices and software for analyzing digital data and generating a specific output.

[0008] "Facial shape" refers to the outline and distinctive structure of a person's face.

[0009] "Physical characteristics" refer to attributes that describe the overall proportions and physical features of a person.

[0010] A "database" is a system that organizes large amounts of information and enables efficient searching, retrieval, and management of that information.

[0011] "Matching" is the process of finding matches or similarities between certain pieces of information and other pieces of information.

[0012] A "proposal mechanism" is a mechanism for presenting appropriate options to a user based on specific conditions.

[0013] "Fashion items" refer to items and products related to fashion, such as clothing and accessories.

[0014] The "purchase link" is a URL that provides a connection to the web page necessary for purchasing a product online.

[0015] The "display means" is a monitor or display device for visually presenting digital information.

[0016] The "multimodal technology" is a technology that combines multiple data formats (e.g., images, audio, text) for utilization in analysis and generation.

[0017] The "high - evaluation case" refers to past achievements and cases that have been judged to be valuable.

Brief Explanation of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] 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.

[0022] 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.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] The present invention's system analyzes an individual's photograph to suggest the optimal fashion style. This system operates in a network environment including a user terminal, a server, and associated information processing means.

[0040] First, users upload their photos to a device such as a smartphone or computer. The device checks the image format and ensures its integrity before sending it to the server. To protect the privacy of the photos, encryption technology is used during transmission.

[0041] On the server, received photos are input into a high-precision AI analysis model. This AI model identifies the user's facial features, body shape, and other characteristics, and generates analysis results. This process utilizes image recognition technology based on deep learning.

[0042] After the analysis results are obtained, the server uses this information to compare with past databases. These databases contain a variety of fashion examples and trend information. Once the optimal fashion style is selected, this information is provided to the user's device. Each suggested fashion item also includes a link to where it can be purchased.

[0043] Users can visually confirm these suggestions on their device's interface. For items they wish to purchase, they can use the provided link to directly access the e-commerce site and complete the purchase process. This process allows users to efficiently find and purchase fashion items that suit them.

[0044] Furthermore, the server implements multimodal technology, allowing it to utilize data other than photographs for analysis. For example, the user's styling history and preference data may be used. This further improves the accuracy of suggestions and provides a personalized fashion experience. With this invention, users can easily receive highly accurate, personalized fashion suggestions and make purchases quickly.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user launches the application on their device and selects the photo they wish to analyze. The photo should preferably be a full-body image of a person taken from the front.

[0048] Step 2:

[0049] The device checks the format of the selected photo and verifies that it is in the correct format. After verification, it prepares to send the photo to the server. At this time, the image is transmitted encrypted.

[0050] Step 3:

[0051] The server inputs the received photos into an AI analysis model. This model utilizes deep learning technology to identify facial features and body shape characteristics within the photos and generates analysis results.

[0052] Step 4:

[0053] The server compares the analysis results with a historical fashion database. The database includes fashion styles favored by people with similar characteristics and examples of highly-rated fashion trends.

[0054] Step 5:

[0055] The server selects a fashion style optimized for the user based on the matching criteria. The selected fashion items are also accompanied by corresponding purchase links.

[0056] Step 6:

[0057] The terminal displays fashion suggestions and purchase links returned from the server on the user interface. The user reviews the suggested items and clicks the link to purchase them.

[0058] Step 7:

[0059] The user is redirected to the e-commerce site via the provided link and proceeds with the purchase of the displayed items. This completes the process from suggestion to purchase.

[0060] (Example 1)

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

[0062] Conventional fashion suggestion systems have a challenge in that they cannot adequately provide personalized suggestions based on individual users' facial features and body type characteristics. Furthermore, it has been difficult to perform effective analysis while ensuring user privacy. Therefore, there is a need for a system that can suggest the optimal fashion style based on the user's preferences and characteristics.

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

[0064] This invention includes a server that receives an individual's photograph, verifies the image format of the photograph, encrypts it to protect privacy, and transmits it; an information processing means that inputs the received photograph into a deep learning-based analysis model to identify facial shape and body shape features; and a suggestion means that compares the analysis results with a past fashion database and makes optimized fashion suggestions. This makes it possible to suggest highly accurate fashion styles to users based on their individual facial shape and body shape features.

[0065] "Personal photographs" are digital images that are taken or acquired by the user themselves and contain information about the person, including facial features and body shape characteristics.

[0066] "Image format" refers to the data format of a digital image, and specifically to methods of saving image data based on a particular standard, such as JPEG or PNG.

[0067] "Encryption" is a technology that transforms digital data using specific algorithms to protect it from unauthorized access by third parties.

[0068] "Deep learning" is an artificial intelligence (AI) technology that uses large amounts of data to allow computers to automatically learn patterns and improve their recognition and classification performance.

[0069] "Facial and body features" refers to basic morphological information necessary for visually identifying an individual, such as the shape of the subject's face and the contours and proportions of their body.

[0070] An "information processing system" is a combination of hardware and software used to analyze and process received digital data and generate output that corresponds to a specific purpose.

[0071] A "fashion database" is a collection of data containing diverse clothing styles, trend information, and evaluation results, and serves as a source of information used in the proposed system.

[0072] "Multimodal technology" is a technique that integrates and analyzes different types of data (e.g., text, audio, images), making it possible to extract richer information.

[0073] "Evaluation information" refers to data that quantitatively demonstrates recommended performance, such as the extent to which previously proposed fashion styles were supported or used.

[0074] This invention is a system that allows users to upload their own photos and then suggests the most suitable fashion style based on those photos. The system operates via user terminals, a network, and a server.

[0075] Users select their photos using a device such as a smartphone or computer and upload them to the system. Once the photo upload is complete, the device checks the image format to ensure it is correct. After verification, the photos are encrypted using Secure Socket Layer (SSL) or Transport Layer Security (TLS) and sent to the server in a privacy-protected state.

[0076] The server inputs the received photo data into a deep learning-based AI analysis model. This AI model uses a convolutional neural network (CNN) to identify facial features and body shape characteristics. The information analyzed by the AI ​​model is tagged and collected by the server.

[0077] The collected characteristic information is cross-referenced with a fashion database on the server. This database contains various trend information and historical evaluation data, and is used to select the optimal fashion style tailored to the user's characteristics.

[0078] The suggested styles are sent to the user's device along with a purchase link. The user can visually review the suggestions on their device's interface and click on the link for their favorite item to proceed with the purchase on the e-commerce site.

[0079] For example, if a user has a round face and a slim build, the server can analyze these features from the uploaded photo and suggest a combination of a casual shirt and skinny jeans. In this case, the user can input a prompt into the AI ​​model such as, "Please suggest casual fashion suitable for a woman in her 20s with a round face and a slim build," to obtain more specific suggestions.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uploads their photos to the device using a smartphone or computer. The device verifies the image format (such as JPEG or PNG) of the uploaded photos. The input is the user's photo data, and the output is the result of the verification that the format is correct. Specifically, the device parses the image header information to confirm that it is in the correct format.

[0083] Step 2:

[0084] The device sends the formatted photo to the server using encryption technology (SSL / TLS). The input for this step is a properly formatted user photo, and the output is an encrypted data packet. The device securely transmits the photo to the server using a secure communication protocol.

[0085] Step 3:

[0086] The server decrypts the received encrypted photo data and inputs it into a deep learning-based AI analysis model. The input is the decrypted photo data, and the output is the analysis results of the user's facial and body shape features. The server uses a convolutional neural network (CNN) to extract features from the image data.

[0087] Step 4:

[0088] The server uses facial and body shape features obtained from the AI ​​model to match them against a fashion database. The input is the analyzed feature information, and the output is a suggestion of the most suitable fashion style for the user. The server executes SQL queries to extract matching styles from the database.

[0089] Step 5:

[0090] The server generates fashion suggestions based on the matching results, adds purchase links, and sends them to the user's device. The input for this step is optimized style information, and the output is fashion suggestion data sent to the user. The server formats the suggestion content into JSON format and delivers it to the device via a transmission protocol.

[0091] Step 6:

[0092] The user visually reviews fashion suggestions provided through the device's interface. The user selects a suggested item and clicks a provided link to proceed with the purchase on the e-commerce site. The input for this step is fashion suggestion data sent from the server, and the output is the user's purchase action. When the user clicks a link, the device opens a web browser and accesses the online shop.

[0093] (Application Example 1)

[0094] 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."

[0095] There is a need to provide personalized fashion suggestions more intuitively and quickly. Furthermore, an interface is required that allows users to access fashion suggestions and make purchases in a natural way. This includes voice interaction, requiring a system that allows users to easily give commands and receive immediate responses to their requests.

[0096] 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.

[0097] In this invention, the server includes data processing means for receiving photographs and analyzing the shape and physical characteristics of an individual from the photographs; suggestion means for comparing the analyzed characteristics with a past data set and making clothing suggestions optimized for the individual; information display means for generating and displaying purchase instructions for the suggested clothing items; and mechanical means for receiving voice commands from the user and automatically taking photographs and making suggestions based on those commands. This enables the user to receive quick and personalized fashion suggestions by giving voice commands.

[0098] A "photograph" is a digital or analog recording of visual data, and serves as raw material for analyzing an individual's shape and physical characteristics.

[0099] "Data processing means" is a general term for procedures and devices used to analyze input data and extract or transform the desired information.

[0100] A "proposal method" refers to a process or device that presents the user with the optimal options or ideas based on analyzed data.

[0101] "Information display means" refers to methods or devices for visually presenting data or results to a user.

[0102] "Mechanical means" refers to devices or mechanisms that receive instructions from a user via voice or other natural interfaces and perform physical or digital tasks based on those instructions.

[0103] "Voice commands" are orders or requests that a user utters verbally, and they form the basis for a system to perform specific processing based on them.

[0104] A "data set" is a collection of information gathered from the past, which is used by algorithms for future prediction and individual optimization.

[0105] The system that realizes this invention combines a mechanical means for receiving voice commands from the user with a data processing means for receiving and analyzing photographic data and making suggestions. The server analyzes photographs received from the user's smartphone or robot via an AI model using deep learning technology. Through this analysis, the system acquires the individual's shape and physical characteristics and suggests optimal clothing by comparing them with a past data set.

[0106] Next, suggestions derived using the AI ​​model are presented to the user through an information display system. This information display is automatically operated after the user issues a voice command. For example, if the user says, "Suggest some fashion items," a photo is taken via the robot, and suggestions based on the analysis are displayed via voice or on the screen. For each suggested item, the user can receive purchase guidance on the spot.

[0107] The hardware used includes camera devices and voice recognition systems, while the software primarily consists of AI models and secure server connections. This enables intuitive voice control, allowing users to easily receive personalized fashion recommendations.

[0108] For example, by inputting a prompt message such as "Suggest a casual date outfit for a woman in her 20s" into the AI ​​model, the model will display the most suitable outfit for the user.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The user issues a voice command.

[0112] The user issues a voice command such as "Suggest fashion," and the terminal converts this voice into digital data through a voice recognition system. The input is a voice command, and the output is a digitized command. The voice recognition system converts the voice into text, which is then passed on to the next step.

[0113] Step 2:

[0114] The device takes a photo of the user.

[0115] Based on voice commands, the terminal takes photos of the user's face and body. The input is real-time video of the user, and the output is captured photo data. The camera device acquires high-resolution images and prepares them to be sent to the server.

[0116] Step 3:

[0117] The server receives the photo data and begins processing it.

[0118] The server inputs the photo data received from the terminal into an AI model for analysis. The input is the photo data, and the output is the analyzed physical feature information. A deep learning model is used to identify and extract facial and body shape characteristics.

[0119] Step 4:

[0120] The server compares the analysis results with a fashion database.

[0121] The server compares the analyzed feature information with a historical dataset and suggests suitable fashion. The input is the analyzed feature information, and the output is optimized fashion suggestions. Database queries are performed to identify and list fashion examples with similar features.

[0122] Step 5:

[0123] The server sends the fashion suggested by the server to the terminal via an information display device.

[0124] The server organizes the suggested content and sends it to the terminal in a visualized format. The input is fashion suggestion information, and the output is what is displayed on the terminal. The user interface receives the suggested information and displays it visually on the user's screen.

[0125] Step 6:

[0126] The device displays suggestions to the user and presents a purchase link.

[0127] The terminal displays suggestions received from the server to the user and provides links, including purchase instructions. Input is display data from the server, and output is a visual presentation to the user. The information display mechanism plays a role in supporting the user's actions as they select items and proceed with purchase.

[0128] 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.

[0129] This invention is a system that provides individually optimized fashion suggestions by combining an emotion engine that recognizes the user's emotions. This system consists of a user terminal, a server, information processing means, and an emotion engine.

[0130] The user uploads a photo using their device to start the system. After the photo is properly formatted on the device, it is sent to the server. On the server, the photo is passed to an AI analysis model to extract the person's facial and body shape features. In addition, the emotion engine analyzes facial expression and voice data collected from the device to recognize the user's emotions.

[0131] The analysis results and recognized emotional information are compared against the server's fashion database. This database contains a vast amount of fashion information, including past highly-rated examples. In addition to facial shape and body type information, the server adjusts the suggested fashion styles by considering the user's current emotions. For example, if the server determines that the user is in a relaxed mood, it will make suggestions that place more emphasis on casual and comfortable styles.

[0132] The server generates optimized fashion suggestions and creates corresponding purchase links. The terminal displays this suggestion information in the user interface for the user to review. The user reviews the suggested items and, if interested, clicks the link to proceed with the purchase on the e-commerce site.

[0133] For example, when a user takes a photo and launches the app, the system instantly analyzes the facial features, and the emotion engine recognizes a smiling expression. Based on this, the system suggests a casual style with bright colors and provides links to increase purchasing intent. Through these suggestions on the device, users can easily make purchases that perfectly suit their situation.

[0134] This embodiment allows users to easily select and purchase fashion items that suit their situation and emotions. The present invention aims to achieve even more personalized and rapid fashion suggestions by utilizing advanced multimodal technology.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user launches the application on their device, selects a photo of themselves, and uploads it. It is desirable that the photo clearly shows the entire body and face.

[0138] Step 2:

[0139] The device verifies the format and content of the photos and sends them to the server only after confirming their appropriateness. For privacy and security, the transmission is performed using encryption technology.

[0140] Step 3:

[0141] The server receives the photos and inputs them into an AI analysis model. This model utilizes deep learning to analyze facial features and body shape characteristics and extract relevant information.

[0142] Step 4:

[0143] Simultaneously, the emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to recognize the user's emotions. This allows it to acquire emotional information such as joy, sadness, and surprise.

[0144] Step 5:

[0145] The server matches the acquired facial features, body type characteristics, and emotional information against a fashion database. The database includes past highly-rated fashion examples and trends.

[0146] Step 6:

[0147] The server generates optimized fashion suggestions by considering the user's current emotions and analysis results. For example, if the user is feeling relaxed, a comfortable, casual style might be suggested.

[0148] Step 7:

[0149] The server generates purchase links for the suggested fashion styles, formats this information for the user interface, and sends it to the terminal.

[0150] Step 8:

[0151] The device displays the received fashion suggestions and purchase links to the user. The user can review these, and if they are interested in an item, they can click the link to proceed to the e-commerce site and complete the purchase process.

[0152] (Example 2)

[0153] 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".

[0154] Traditional fashion suggestion systems have the problem of only being able to offer uniform suggestions to individuals with diverse personalities and emotional states. Therefore, there is a need for a system that can quickly provide optimal, personalized fashion suggestions. This will allow for meeting diverse individual needs and improving satisfaction with fashion choices.

[0155] 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.

[0156] In this invention, the server includes an information processing device that receives a photograph and analyzes an individual's facial shape and body shape information based on the photograph; an emotion recognition device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions; and a suggestion device that compares the analyzed information and recognized emotion data with past data storage to suggest clothing optimized for the individual. This makes it possible to suggest individually optimized clothing styles based on an individual's facial shape, body shape information, and emotional state.

[0157] An "information processing device" is a device that receives a photograph and analyzes an individual's facial shape and body shape information based on that photograph.

[0158] An "emotion recognition device" is a device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions.

[0159] A "suggestion device" is a device that compares analyzed information and recognized emotional data with past data storage to provide personalized clothing suggestions.

[0160] "Data storage" refers to a database containing clothing data, including past highly-rated examples, used to make new proposals.

[0161] "Clothing style" refers to fashion suggestions optimized based on an individual's facial features, body shape, and emotional state.

[0162] "Multimodal technology" is a technique that obtains information by comprehensively analyzing data in different formats (for example, images, facial expressions, and audio).

[0163] This invention is a system that provides individually optimized fashion suggestions to users. The system consists of a user's terminal, a server, an information processing device, and an emotion recognition device.

[0164] The user starts the system using their device and uploads a photo. This photo is used to obtain facial and body shape information. The device converts the photo to an appropriate format (JPEG, PNG, etc.) and sends it to the server.

[0165] On the server, an information processing device receives photographs and analyzes facial and body shape information using a generation AI model. Libraries such as OpenCV and TENSORFLOW® can be used for the analysis. In addition, by utilizing an emotion recognition device and analyzing facial expression data and voice data received from the terminal, the system recognizes the user's emotions.

[0166] The analyzed facial shape and body shape information, as well as recognized emotion data, are cross-referenced with a data storage system containing fashion data. This data storage system holds a vast amount of clothing data, including past high-rated examples.

[0167] Based on information retrieved from data storage, the server suggests clothing styles optimized for the user based on their facial features, body type, and emotions. For example, if a relaxed emotion is detected, casual and comfortable clothing will be suggested. Related purchase links are also generated and presented to the user.

[0168] For example, if a user uploads a photo of themselves smiling, the emotion recognition device will recognize the expression as "happy," and the server will suggest a casual style with bright colors. This system makes it easy for users to quickly select and purchase fashion items that are best suited to their situation.

[0169] An example of a prompt message might be, "Use the user's photo and sentiment information to suggest a fashion style that matches their current mood." This would enable personalized style suggestions.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user launches the system using their own device and uploads a photo. The input is an image file selected by the user. The device converts the uploaded photo to JPEG or PNG format. This conversion is done to make it suitable for the server to receive and process.

[0173] Step 2:

[0174] The terminal sends the formatted photo data to the server. The server receives the photo data as input and passes it to the information processing unit. The information processing unit extracts face shape and body shape information using an AI analysis model. This uses computer vision tools such as OpenCV and TensorFlow. The output is feature data of the person's face and body.

[0175] Step 3:

[0176] The server uses an emotion recognition device to receive facial expression and voice data transmitted from the terminal as input. The emotion recognition device analyzes this data and outputs the user's emotional state (e.g., "happy" or "relaxed"). This is done by analyzing facial expressions and voice tone using a machine learning model.

[0177] Step 4:

[0178] The server compares the analyzed facial and body shape information, as well as the recognized emotional state, with data storage. This data storage contains a vast amount of fashion data, including past high-rated examples. The server uses this as input to generate the optimal clothing style for the user. The output is an optimized clothing suggestion.

[0179] Step 5:

[0180] The server creates purchase links associated with the generated clothing suggestions. This outputs data that includes links to e-commerce sites, allowing users to easily purchase the suggested items.

[0181] Step 6:

[0182] The device receives clothing suggestions and purchase links sent from the server and displays them to the user. The user interface allows the user to review the suggestions, select items of interest, and proceed with the purchase. This is designed to ensure that information is clearly displayed on the user's screen.

[0183] (Application Example 2)

[0184] 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".

[0185] Conventional fashion recommendation systems make suggestions based solely on the user's body type and facial features, making it difficult to provide personalized recommendations that take into account specific emotions or moods. Furthermore, if the suggested items do not match the user's current emotions or mood, it can diminish their desire to purchase. Therefore, there is a need for a system that can provide clothing recommendations that consider the user's emotional state.

[0186] 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.

[0187] In this invention, the server includes information processing means for receiving photographic and audio data and analyzing an individual's facial features, body shape characteristics, and emotional state from the data; suggestion means for comparing the analyzed features and emotional state with a past database and providing clothing suggestions optimized for the individual's situation and mood; and display means for generating a purchase route to the suggested clothing items and presenting the route. This makes it possible to provide optimal clothing suggestions according to the user's emotions and mood at that time, thereby increasing their willingness to purchase.

[0188] "Photo and audio data" refers to the image and audio information necessary for the system to recognize the user's physical characteristics and emotional state.

[0189] "Analysis of individual facial features, body characteristics, and emotional state" is a process that identifies the user's facial contours, body proportions, and mood and emotions inferred from their facial expressions and voice, based on the received data.

[0190] "Information processing means" refers to a computing device or software program for performing the above analysis, which has the function of analyzing the user's characteristics using photographic and audio data.

[0191] A "past database" is a collection of information that stores fashion-related case studies and user feedback collected in the past.

[0192] "Clothing suggestions optimized for individual circumstances and moods" is a process that considers user analysis data and current emotional state to suggest the most suitable fashion items and styles for that person.

[0193] "Suggestion means" refers to a device or program that has the function of generating optimized fashion suggestions and presenting them to the user.

[0194] "Generating purchase paths" is the process of creating URLs or link information that allow users to easily purchase the suggested clothing items.

[0195] "Display means" refers to a device or program that visually or audibly informs the user of generated clothing suggestions and purchase links.

[0196] The system for implementing this invention consists of a user terminal, a server, information processing means, and an emotion engine. The user first inputs photo and audio data using their terminal. The terminal receives this data, converts it to an appropriate format, and sends it to the server. The server uses the information processing means to analyze the individual's facial features, body type characteristics, and emotional state from the received photo and audio data. This analysis uses an AI image analysis tool (e.g., a custom model using TensorFlow), and emotion analysis utilizes an emotion engine such as Microsoft® Azure®.

[0197] The server compares the analysis results with a past database to provide clothing suggestions optimized for the individual's situation and mood. The database used includes past high-rated examples and user feedback, and is constantly updated to keep up with the latest trends. The server generates a purchase path for the suggested clothing items, which is displayed on the user's device in the form of a link or similar.

[0198] The device displays this suggested information in the user interface, allowing the user to review it and, if interested, proceed with the process on the e-commerce site through the application. An example of a prompt message is: "Input facial image and voice data, analyze the user's emotions in real time, suggest fashion that considers comfort and trends, and generate appropriate e-commerce site links."

[0199] As a concrete example, if a user yawns in front of the robot on a holiday morning, the robot will suggest a relaxed, casual style, explaining that it's perfect for a family picnic. The user can then complete the process via a provided purchase link if they find the style to be ideal. In this way, a system is created that allows users to easily select and purchase fashion that best suits their mood at that moment.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] Users capture photos and audio data using their devices and upload this data to the application. This input data undergoes format conversion and initial processing, and is temporarily stored on the device in preparation for analysis.

[0203] Step 2:

[0204] The terminal sends organized photo and audio data to the server. The server receives this data, converts it to a standard format as input, and processes it into a form usable by information processing tools. This process is important to maintain data consistency.

[0205] Step 3:

[0206] The server processes the data through an AI image analysis tool and uses a generating AI model to extract individual facial and body shape characteristics. In parallel, it runs an emotion engine to analyze the user's emotional state from facial expressions and voice, and generates output data that evaluates individual characteristics.

[0207] Step 4:

[0208] The facial features, body shape characteristics, and emotional state obtained from the input data are compared with a database on the server. Based on past highly-rated fashion examples and trend information, the server uses information processing tools to generate optimal clothing suggestions that are suitable for the user's mood and situation.

[0209] Step 5:

[0210] The server creates a purchase path for the proposed clothing items and generates link information. This makes it possible to generate purchase links that allow the user to directly access the product's purchase page.

[0211] Step 6:

[0212] The server sends the generated clothing suggestions and purchase links to the user's device. The device visually displays this information in a user interface, allowing the user to review the suggestions and click on links to items of interest to proceed with the purchase.

[0213] Step 7:

[0214] Users review the suggested fashion items on their devices, and if necessary, decide to purchase them and retrieve the items via the provided links. The suggestions based on the prompts provided during this process play a role in supporting the user's purchasing intent.

[0215] 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.

[0216] Data generation model 58 is a type of 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.

[0217] 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.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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".

[0231] The present invention's system analyzes an individual's photograph to suggest the optimal fashion style. This system operates in a network environment including a user terminal, a server, and associated information processing means.

[0232] First, users upload their photos to a device such as a smartphone or computer. The device checks the image format and ensures its integrity before sending it to the server. To protect the privacy of the photos, encryption technology is used during transmission.

[0233] On the server, received photos are input into a high-precision AI analysis model. This AI model identifies the user's facial features, body shape, and other characteristics, and generates analysis results. This process utilizes image recognition technology based on deep learning.

[0234] After the analysis results are obtained, the server uses this information to compare with past databases. These databases contain a variety of fashion examples and trend information. Once the optimal fashion style is selected, this information is provided to the user's device. Each suggested fashion item also includes a link to where it can be purchased.

[0235] Users can visually confirm these suggestions on their device's interface. For items they wish to purchase, they can use the provided link to directly access the e-commerce site and complete the purchase process. This process allows users to efficiently find and purchase fashion items that suit them.

[0236] Furthermore, the server implements multimodal technology, allowing it to utilize data other than photographs for analysis. For example, the user's styling history and preference data may be used. This further improves the accuracy of suggestions and provides a personalized fashion experience. With this invention, users can easily receive highly accurate, personalized fashion suggestions and make purchases quickly.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user launches the application on their device and selects the photo they wish to analyze. The photo should preferably be a full-body image of a person taken from the front.

[0240] Step 2:

[0241] The device checks the format of the selected photo and verifies that it is in the correct format. After verification, it prepares to send the photo to the server. At this time, the image is transmitted encrypted.

[0242] Step 3:

[0243] The server inputs the received photos into an AI analysis model. This model utilizes deep learning technology to identify facial features and body shape characteristics within the photos and generates analysis results.

[0244] Step 4:

[0245] The server compares the analysis results with a historical fashion database. The database includes fashion styles favored by people with similar characteristics and examples of highly-rated fashion trends.

[0246] Step 5:

[0247] The server selects a fashion style optimized for the user based on the matching criteria. The selected fashion items are also accompanied by corresponding purchase links.

[0248] Step 6:

[0249] The terminal displays fashion suggestions and purchase links returned from the server on the user interface. The user reviews the suggested items and clicks the link to purchase them.

[0250] Step 7:

[0251] The user is redirected to the e-commerce site via the provided link and proceeds with the purchase of the displayed items. This completes the process from suggestion to purchase.

[0252] (Example 1)

[0253] 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."

[0254] Conventional fashion suggestion systems have a challenge in that they cannot adequately provide personalized suggestions based on individual users' facial features and body type characteristics. Furthermore, it has been difficult to perform effective analysis while ensuring user privacy. Therefore, there is a need for a system that can suggest the optimal fashion style based on the user's preferences and characteristics.

[0255] 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.

[0256] This invention includes a server that receives an individual's photograph, verifies the image format of the photograph, encrypts it to protect privacy, and transmits it; an information processing means that inputs the received photograph into a deep learning-based analysis model to identify facial shape and body shape features; and a suggestion means that compares the analysis results with a past fashion database and makes optimized fashion suggestions. This makes it possible to suggest highly accurate fashion styles to users based on their individual facial shape and body shape features.

[0257] "Personal photographs" are digital images that are taken or acquired by the user themselves and contain information about the person, including facial features and body shape characteristics.

[0258] "Image format" refers to the data format of a digital image, and specifically to methods of saving image data based on a particular standard, such as JPEG or PNG.

[0259] "Encryption" is a technology that transforms digital data using specific algorithms to protect it from unauthorized access by third parties.

[0260] "Deep learning" is an artificial intelligence (AI) technology that uses large amounts of data to allow computers to automatically learn patterns and improve their recognition and classification performance.

[0261] "Facial and body features" refers to basic morphological information necessary for visually identifying an individual, such as the shape of the subject's face and the contours and proportions of their body.

[0262] An "information processing system" is a combination of hardware and software used to analyze and process received digital data and generate output that corresponds to a specific purpose.

[0263] A "fashion database" is a collection of data containing diverse clothing styles, trend information, and evaluation results, and serves as a source of information used in the proposed system.

[0264] "Multimodal technology" is a technique that integrates and analyzes different types of data (e.g., text, audio, images), making it possible to extract richer information.

[0265] "Evaluation information" refers to data that quantitatively demonstrates recommended performance, such as the extent to which previously proposed fashion styles were supported or used.

[0266] This invention is a system that allows users to upload their own photos and then suggests the most suitable fashion style based on those photos. The system operates via user terminals, a network, and a server.

[0267] Users select their photos using a device such as a smartphone or computer and upload them to the system. Once the photo upload is complete, the device checks the image format to ensure it is correct. After verification, the photos are encrypted using Secure Socket Layer (SSL) or Transport Layer Security (TLS) and sent to the server in a privacy-protected state.

[0268] The server inputs the received photo data into a deep learning-based AI analysis model. This AI model uses a convolutional neural network (CNN) to identify facial features and body shape characteristics. The information analyzed by the AI ​​model is tagged and collected by the server.

[0269] The collected characteristic information is cross-referenced with a fashion database on the server. This database contains various trend information and historical evaluation data, and is used to select the optimal fashion style tailored to the user's characteristics.

[0270] The suggested styles are sent to the user's device along with a purchase link. The user can visually review the suggestions on their device's interface and click on the link for their favorite item to proceed with the purchase on the e-commerce site.

[0271] For example, if a user has a round face and a slim build, the server can analyze these features from the uploaded photo and suggest a combination of a casual shirt and skinny jeans. In this case, the user can input a prompt into the AI ​​model such as, "Please suggest casual fashion suitable for a woman in her 20s with a round face and a slim build," to obtain more specific suggestions.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user uploads their photos to the device using a smartphone or computer. The device verifies the image format (such as JPEG or PNG) of the uploaded photos. The input is the user's photo data, and the output is the result of the verification that the format is correct. Specifically, the device parses the image header information to confirm that it is in the correct format.

[0275] Step 2:

[0276] The device sends the formatted photo to the server using encryption technology (SSL / TLS). The input for this step is a properly formatted user photo, and the output is an encrypted data packet. The device securely transmits the photo to the server using a secure communication protocol.

[0277] Step 3:

[0278] The server decrypts the received encrypted photo data and inputs it into a deep learning-based AI analysis model. The input is the decrypted photo data, and the output is the analysis results of the user's facial and body shape features. The server uses a convolutional neural network (CNN) to extract features from the image data.

[0279] Step 4:

[0280] The server uses facial and body shape features obtained from the AI ​​model to match them against a fashion database. The input is the analyzed feature information, and the output is a suggestion of the most suitable fashion style for the user. The server executes SQL queries to extract matching styles from the database.

[0281] Step 5:

[0282] The server generates a fashion proposal based on the matching result, adds a purchase link, and sends it to the user's terminal. The input for this step is the information of the optimized style, and the output is the fashion proposal data sent to the user. The server formats the proposal content in JSON format and distributes it to the terminal via the transmission protocol.

[0283] Step 6:

[0284] The user visually checks the fashion proposal provided through the interface of the terminal. The user selects the proposed item and clicks on the provided link to perform a purchase procedure on the e-commerce site. The input for this step is the fashion proposal data sent from the server, and the output is the user's purchase action. The terminal opens a web browser when the user clicks on the link and accesses the online store.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] There is a demand to provide fashion proposals optimized for individuals in a more intuitive and rapid manner. Also, an interface is required that enables the user to perform fashion proposals and purchase procedures in a natural way. This includes interaction through voice, and a system is needed where the user can easily give commands and receive an immediate response to their requests.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes data processing means for receiving photographs and analyzing the shape and physical characteristics of an individual from the photographs; suggestion means for comparing the analyzed characteristics with a past data set and making clothing suggestions optimized for the individual; information display means for generating and displaying purchase instructions for the suggested clothing items; and mechanical means for receiving voice commands from the user and automatically taking photographs and making suggestions based on those commands. This enables the user to receive quick and personalized fashion suggestions by giving voice commands.

[0290] A "photograph" is a digital or analog recording of visual data, and serves as raw material for analyzing an individual's shape and physical characteristics.

[0291] "Data processing means" is a general term for procedures and devices used to analyze input data and extract or transform the desired information.

[0292] A "proposal method" refers to a process or device that presents the user with the optimal options or ideas based on analyzed data.

[0293] "Information display means" refers to methods or devices for visually presenting data or results to a user.

[0294] "Mechanical means" refers to devices or mechanisms that receive instructions from a user via voice or other natural interfaces and perform physical or digital tasks based on those instructions.

[0295] "Voice commands" are orders or requests that a user utters verbally, and they form the basis for a system to perform specific processing based on them.

[0296] A "data set" is a collection of information gathered from the past, which is used by algorithms for future prediction and individual optimization.

[0297] The system that realizes this invention combines a mechanical means for receiving voice commands from the user with a data processing means for receiving and analyzing photographic data and making suggestions. The server analyzes photographs received from the user's smartphone or robot via an AI model using deep learning technology. Through this analysis, the system acquires the individual's shape and physical characteristics and suggests optimal clothing by comparing them with a past data set.

[0298] Next, suggestions derived using the AI ​​model are presented to the user through an information display system. This information display is automatically operated after the user issues a voice command. For example, if the user says, "Suggest some fashion items," a photo is taken via the robot, and suggestions based on the analysis are displayed via voice or on the screen. For each suggested item, the user can receive purchase guidance on the spot.

[0299] The hardware used includes camera devices and voice recognition systems, while the software primarily consists of AI models and secure server connections. This enables intuitive voice control, allowing users to easily receive personalized fashion recommendations.

[0300] For example, by inputting a prompt message such as "Suggest a casual date outfit for a woman in her 20s" into the AI ​​model, the model will display the most suitable outfit for the user.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The user issues a voice command.

[0304] The user issues a voice command such as "Propose fashion", and the terminal converts this voice into digital data through a voice recognition system. The input is the voice command, and the output is the digitized command. The voice is converted into text by the voice recognition system and passed on to the next step.

[0305] Step 2:

[0306] The terminal takes a photo of the user.

[0307] Based on the voice command, the terminal takes photos of the user's face and body. The input is the user's real-time video, and the output is the captured photo data. Use the camera device to obtain a high-resolution image and prepare to send it to the server.

[0308] Step 3:

[0309] The server receives the photo data and starts processing.

[0310] The server inputs the photo data received from the terminal into an AI model for analysis. The input is the photo data, and the output is the analyzed body feature information. Use a deep learning model to identify and extract the features of the face shape and body type.

[0311] Step 4:

[0312] The server compares the analysis result with the fashion database.

[0313] The server compares the analyzed feature information with the past data set and proposes a suitable fashion. The input is the analyzed feature information, and the output is the optimized fashion proposal. Perform a database query to identify and list fashion examples with similar features.

[0314] Step 5:

[0315] The server sends the fashion suggested by the server to the terminal via an information display device.

[0316] The server organizes the suggested content and sends it to the terminal in a visualized format. The input is fashion suggestion information, and the output is what is displayed on the terminal. The user interface receives the suggested information and displays it visually on the user's screen.

[0317] Step 6:

[0318] The device displays suggestions to the user and presents a purchase link.

[0319] The terminal displays suggestions received from the server to the user and provides links, including purchase instructions. Input is display data from the server, and output is a visual presentation to the user. The information display mechanism plays a role in supporting the user's actions as they select items and proceed with purchase.

[0320] 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.

[0321] This invention is a system that provides individually optimized fashion suggestions by combining an emotion engine that recognizes the user's emotions. This system consists of a user terminal, a server, information processing means, and an emotion engine.

[0322] The user uploads a photo using their device to start the system. After the photo is properly formatted on the device, it is sent to the server. On the server, the photo is passed to an AI analysis model to extract the person's facial and body shape features. In addition, the emotion engine analyzes facial expression and voice data collected from the device to recognize the user's emotions.

[0323] The analysis results and recognized emotional information are compared against the server's fashion database. This database contains a vast amount of fashion information, including past highly-rated examples. In addition to facial shape and body type information, the server adjusts the suggested fashion styles by considering the user's current emotions. For example, if the server determines that the user is in a relaxed mood, it will make suggestions that place more emphasis on casual and comfortable styles.

[0324] The server generates optimized fashion suggestions and creates corresponding purchase links. The terminal displays this suggestion information in the user interface for the user to review. The user reviews the suggested items and, if interested, clicks the link to proceed with the purchase on the e-commerce site.

[0325] For example, when a user takes a photo and launches the app, the system instantly analyzes the facial features, and the emotion engine recognizes a smiling expression. Based on this, the system suggests a casual style with bright colors and provides links to increase purchasing intent. Through these suggestions on the device, users can easily make purchases that perfectly suit their situation.

[0326] This embodiment allows users to easily select and purchase fashion items that suit their situation and emotions. The present invention aims to achieve even more personalized and rapid fashion suggestions by utilizing advanced multimodal technology.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The user launches the application on their device, selects a photo of themselves, and uploads it. It is desirable that the photo clearly shows the entire body and face.

[0330] Step 2:

[0331] The device verifies the format and content of the photos and sends them to the server only after confirming their appropriateness. For privacy and security, the transmission is performed using encryption technology.

[0332] Step 3:

[0333] The server receives the photos and inputs them into an AI analysis model. This model utilizes deep learning to analyze facial features and body shape characteristics and extract relevant information.

[0334] Step 4:

[0335] Simultaneously, the emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to recognize the user's emotions. This allows it to acquire emotional information such as joy, sadness, and surprise.

[0336] Step 5:

[0337] The server matches the acquired facial features, body type characteristics, and emotional information against a fashion database. The database includes past highly-rated fashion examples and trends.

[0338] Step 6:

[0339] The server generates optimized fashion suggestions by considering the user's current emotions and analysis results. For example, if the user is feeling relaxed, a comfortable, casual style might be suggested.

[0340] Step 7:

[0341] The server generates purchase links for the suggested fashion styles, formats this information for the user interface, and sends it to the terminal.

[0342] Step 8:

[0343] The device displays the received fashion suggestions and purchase links to the user. The user can review these, and if they are interested in an item, they can click the link to proceed to the e-commerce site and complete the purchase process.

[0344] (Example 2)

[0345] 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".

[0346] Traditional fashion suggestion systems have the problem of only being able to offer uniform suggestions to individuals with diverse personalities and emotional states. Therefore, there is a need for a system that can quickly provide optimal, personalized fashion suggestions. This will allow for meeting diverse individual needs and improving satisfaction with fashion choices.

[0347] 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.

[0348] In this invention, the server includes an information processing device that receives a photograph and analyzes an individual's facial shape and body shape information based on the photograph; an emotion recognition device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions; and a suggestion device that compares the analyzed information and recognized emotion data with past data storage to suggest clothing optimized for the individual. This makes it possible to suggest individually optimized clothing styles based on an individual's facial shape, body shape information, and emotional state.

[0349] An "information processing device" is a device that receives a photograph and analyzes an individual's facial shape and body shape information based on that photograph.

[0350] An "emotion recognition device" is a device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions.

[0351] A "suggestion device" is a device that compares analyzed information and recognized emotional data with past data storage to provide personalized clothing suggestions.

[0352] "Data storage" refers to a database containing clothing data, including past highly-rated examples, used to make new proposals.

[0353] "Clothing style" refers to fashion suggestions optimized based on an individual's facial features, body shape, and emotional state.

[0354] "Multimodal technology" is a technique that obtains information by comprehensively analyzing data in different formats (for example, images, facial expressions, and audio).

[0355] This invention is a system that provides individually optimized fashion suggestions to users. The system consists of a user's terminal, a server, an information processing device, and an emotion recognition device.

[0356] The user starts the system using their device and uploads a photo. This photo is used to obtain facial and body shape information. The device converts the photo to an appropriate format (JPEG, PNG, etc.) and sends it to the server.

[0357] On the server, an information processing unit receives photos and analyzes facial and body shape information using a generative AI model. Libraries such as OpenCV and TensorFlow can be used for the analysis. In addition, an emotion recognition device is used to analyze facial expression data and voice data received from the terminal to recognize the user's emotions.

[0358] The analyzed facial shape and body shape information, as well as recognized emotion data, are cross-referenced with a data storage system containing fashion data. This data storage system holds a vast amount of clothing data, including past high-rated examples.

[0359] Based on information retrieved from data storage, the server suggests clothing styles optimized for the user based on their facial features, body type, and emotions. For example, if a relaxed emotion is detected, casual and comfortable clothing will be suggested. Related purchase links are also generated and presented to the user.

[0360] For example, if a user uploads a photo of themselves smiling, the emotion recognition device will recognize the expression as "happy," and the server will suggest a casual style with bright colors. This system makes it easy for users to quickly select and purchase fashion items that are best suited to their situation.

[0361] An example of a prompt message might be, "Use the user's photo and sentiment information to suggest a fashion style that matches their current mood." This would enable personalized style suggestions.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The user launches the system using their own device and uploads a photo. The input is an image file selected by the user. The device converts the uploaded photo to JPEG or PNG format. This conversion is done to make it suitable for the server to receive and process.

[0365] Step 2:

[0366] The terminal sends the formatted photo data to the server. The server receives the photo data as input and passes it to the information processing unit. The information processing unit extracts face shape and body shape information using an AI analysis model. This uses computer vision tools such as OpenCV and TensorFlow. The output is feature data of the person's face and body.

[0367] Step 3:

[0368] The server uses an emotion recognition device to receive facial expression and voice data transmitted from the terminal as input. The emotion recognition device analyzes this data and outputs the user's emotional state (e.g., "happy" or "relaxed"). This is done by analyzing facial expressions and voice tone using a machine learning model.

[0369] Step 4:

[0370] The server compares the analyzed facial and body shape information, as well as the recognized emotional state, with data storage. This data storage contains a vast amount of fashion data, including past high-rated examples. The server uses this as input to generate the optimal clothing style for the user. The output is an optimized clothing suggestion.

[0371] Step 5:

[0372] The server creates purchase links associated with the generated clothing suggestions. This outputs data that includes links to e-commerce sites, allowing users to easily purchase the suggested items.

[0373] Step 6:

[0374] The device receives clothing suggestions and purchase links sent from the server and displays them to the user. The user interface allows the user to review the suggestions, select items of interest, and proceed with the purchase. This is designed to ensure that information is clearly displayed on the user's screen.

[0375] (Application Example 2)

[0376] 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."

[0377] Conventional fashion recommendation systems make suggestions based solely on the user's body type and facial features, making it difficult to provide personalized recommendations that take into account specific emotions or moods. Furthermore, if the suggested items do not match the user's current emotions or mood, it can diminish their desire to purchase. Therefore, there is a need for a system that can provide clothing recommendations that consider the user's emotional state.

[0378] 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.

[0379] In this invention, the server includes information processing means for receiving photographic and audio data and analyzing an individual's facial features, body shape characteristics, and emotional state from the data; suggestion means for comparing the analyzed features and emotional state with a past database and providing clothing suggestions optimized for the individual's situation and mood; and display means for generating a purchase route to the suggested clothing items and presenting the route. This makes it possible to provide optimal clothing suggestions according to the user's emotions and mood at that time, thereby increasing their willingness to purchase.

[0380] "Photo and audio data" refers to the image and audio information necessary for the system to recognize the user's physical characteristics and emotional state.

[0381] "Analysis of individual facial features, body characteristics, and emotional state" is a process that identifies the user's facial contours, body proportions, and mood and emotions inferred from their facial expressions and voice, based on the received data.

[0382] "Information processing means" refers to a computing device or software program for performing the above analysis, which has the function of analyzing the user's characteristics using photographic and audio data.

[0383] A "past database" is a collection of information that stores fashion-related case studies and user feedback collected in the past.

[0384] "Clothing suggestions optimized for individual circumstances and moods" is a process that considers user analysis data and current emotional state to suggest the most suitable fashion items and styles for that person.

[0385] "Suggestion means" refers to a device or program that has the function of generating optimized fashion suggestions and presenting them to the user.

[0386] "Generating purchase paths" is the process of creating URLs or link information that allow users to easily purchase the suggested clothing items.

[0387] "Display means" refers to a device or program that visually or audibly informs the user of generated clothing suggestions and purchase links.

[0388] The system for implementing this invention consists of a user terminal, a server, information processing means, and an emotion engine. The user first inputs photo and audio data using their terminal. The terminal receives this data, converts it to an appropriate format, and sends it to the server. The server uses the information processing means to analyze the individual's facial features, body type characteristics, and emotional state from the received photo and audio data. This analysis uses an AI image analysis tool (e.g., a custom model using TensorFlow), and an emotion engine such as Microsoft Azure is used for emotion analysis.

[0389] The server compares the analysis results with a past database to provide clothing suggestions optimized for the individual's situation and mood. The database used includes past high-rated examples and user feedback, and is constantly updated to keep up with the latest trends. The server generates a purchase path for the suggested clothing items, which is displayed on the user's device in the form of a link or similar.

[0390] The device displays this suggested information in the user interface, allowing the user to review it and, if interested, proceed with the process on the e-commerce site through the application. An example of a prompt message is: "Input facial image and voice data, analyze the user's emotions in real time, suggest fashion that considers comfort and trends, and generate appropriate e-commerce site links."

[0391] As a concrete example, if a user yawns in front of the robot on a holiday morning, the robot will suggest a relaxed, casual style, explaining that it's perfect for a family picnic. The user can then complete the process via a provided purchase link if they find the style to be ideal. In this way, a system is created that allows users to easily select and purchase fashion that best suits their mood at that moment.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] Users capture photos and audio data using their devices and upload this data to the application. This input data undergoes format conversion and initial processing, and is temporarily stored on the device in preparation for analysis.

[0395] Step 2:

[0396] The terminal sends organized photo and audio data to the server. The server receives this data, converts it to a standard format as input, and processes it into a form usable by information processing tools. This process is important to maintain data consistency.

[0397] Step 3:

[0398] The server processes the data through an AI image analysis tool and uses a generating AI model to extract individual facial and body shape characteristics. In parallel, it runs an emotion engine to analyze the user's emotional state from facial expressions and voice, and generates output data that evaluates individual characteristics.

[0399] Step 4:

[0400] The facial features, body shape characteristics, and emotional state obtained from the input data are compared with a database on the server. Based on past highly-rated fashion examples and trend information, the server uses information processing tools to generate optimal clothing suggestions that are suitable for the user's mood and situation.

[0401] Step 5:

[0402] The server creates a purchase path for the proposed clothing items and generates link information. This makes it possible to generate purchase links that allow the user to directly access the product's purchase page.

[0403] Step 6:

[0404] The server sends the generated clothing suggestions and purchase links to the user's device. The device visually displays this information in a user interface, allowing the user to review the suggestions and click on links to items of interest to proceed with the purchase.

[0405] Step 7:

[0406] Users review the suggested fashion items on their devices, and if necessary, decide to purchase them and retrieve the items via the provided links. The suggestions based on the prompts provided during this process play a role in supporting the user's purchasing intent.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] The present invention's system analyzes an individual's photograph to suggest the optimal fashion style. This system operates in a network environment including a user terminal, a server, and associated information processing means.

[0424] First, users upload their photos to a device such as a smartphone or computer. The device checks the image format and ensures its integrity before sending it to the server. To protect the privacy of the photos, encryption technology is used during transmission.

[0425] On the server, received photos are input into a high-precision AI analysis model. This AI model identifies the user's facial features, body shape, and other characteristics, and generates analysis results. This process utilizes image recognition technology based on deep learning.

[0426] After the analysis results are obtained, the server uses this information to compare with past databases. These databases contain a variety of fashion examples and trend information. Once the optimal fashion style is selected, this information is provided to the user's device. Each suggested fashion item also includes a link to where it can be purchased.

[0427] Users can visually confirm these suggestions on their device's interface. For items they wish to purchase, they can use the provided link to directly access the e-commerce site and complete the purchase process. This process allows users to efficiently find and purchase fashion items that suit them.

[0428] Furthermore, the server implements multimodal technology, allowing it to utilize data other than photographs for analysis. For example, the user's styling history and preference data may be used. This further improves the accuracy of suggestions and provides a personalized fashion experience. With this invention, users can easily receive highly accurate, personalized fashion suggestions and make purchases quickly.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The user launches the application on their device and selects the photo they wish to analyze. The photo should preferably be a full-body image of a person taken from the front.

[0432] Step 2:

[0433] The device checks the format of the selected photo and verifies that it is in the correct format. After verification, it prepares to send the photo to the server. At this time, the image is transmitted encrypted.

[0434] Step 3:

[0435] The server inputs the received photos into an AI analysis model. This model utilizes deep learning technology to identify facial features and body shape characteristics within the photos and generates analysis results.

[0436] Step 4:

[0437] The server compares the analysis results with a historical fashion database. The database includes fashion styles favored by people with similar characteristics and examples of highly-rated fashion trends.

[0438] Step 5:

[0439] The server selects a fashion style optimized for the user based on the matching criteria. The selected fashion items are also accompanied by corresponding purchase links.

[0440] Step 6:

[0441] The terminal displays fashion suggestions and purchase links returned from the server on the user interface. The user reviews the suggested items and clicks the link to purchase them.

[0442] Step 7:

[0443] The user is redirected to the e-commerce site via the provided link and proceeds with the purchase of the displayed items. This completes the process from suggestion to purchase.

[0444] (Example 1)

[0445] 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."

[0446] Conventional fashion suggestion systems have a challenge in that they cannot adequately provide personalized suggestions based on individual users' facial features and body type characteristics. Furthermore, it has been difficult to perform effective analysis while ensuring user privacy. Therefore, there is a need for a system that can suggest the optimal fashion style based on the user's preferences and characteristics.

[0447] 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.

[0448] This invention includes a server that receives an individual's photograph, verifies the image format of the photograph, encrypts it to protect privacy, and transmits it; an information processing means that inputs the received photograph into a deep learning-based analysis model to identify facial shape and body shape features; and a suggestion means that compares the analysis results with a past fashion database and makes optimized fashion suggestions. This makes it possible to suggest highly accurate fashion styles to users based on their individual facial shape and body shape features.

[0449] "Personal photographs" are digital images that are taken or acquired by the user themselves and contain information about the person, including facial features and body shape characteristics.

[0450] "Image format" refers to the data format of a digital image, and specifically to methods of saving image data based on a particular standard, such as JPEG or PNG.

[0451] "Encryption" is a technology that transforms digital data using specific algorithms to protect it from unauthorized access by third parties.

[0452] "Deep learning" is an artificial intelligence (AI) technology that uses large amounts of data to allow computers to automatically learn patterns and improve their recognition and classification performance.

[0453] "Facial and body features" refers to basic morphological information necessary for visually identifying an individual, such as the shape of the subject's face and the contours and proportions of their body.

[0454] An "information processing system" is a combination of hardware and software used to analyze and process received digital data and generate output that corresponds to a specific purpose.

[0455] A "fashion database" is a collection of data containing diverse clothing styles, trend information, and evaluation results, and serves as a source of information used in the proposed system.

[0456] "Multimodal technology" is a technique that integrates and analyzes different types of data (e.g., text, audio, images), making it possible to extract richer information.

[0457] "Evaluation information" refers to data that quantitatively demonstrates recommended performance, such as the extent to which previously proposed fashion styles were supported or used.

[0458] This invention is a system that allows users to upload their own photos and then suggests the most suitable fashion style based on those photos. The system operates via user terminals, a network, and a server.

[0459] Users select their photos using a device such as a smartphone or computer and upload them to the system. Once the photo upload is complete, the device checks the image format to ensure it is correct. After verification, the photos are encrypted using Secure Socket Layer (SSL) or Transport Layer Security (TLS) and sent to the server in a privacy-protected state.

[0460] The server inputs the received photo data into a deep learning-based AI analysis model. This AI model uses a convolutional neural network (CNN) to identify facial features and body shape characteristics. The information analyzed by the AI ​​model is tagged and collected by the server.

[0461] The collected characteristic information is cross-referenced with a fashion database on the server. This database contains various trend information and historical evaluation data, and is used to select the optimal fashion style tailored to the user's characteristics.

[0462] The suggested styles are sent to the user's device along with a purchase link. The user can visually review the suggestions on their device's interface and click on the link for their favorite item to proceed with the purchase on the e-commerce site.

[0463] For example, if a user has a round face and a slim build, the server can analyze these features from the uploaded photo and suggest a combination of a casual shirt and skinny jeans. In this case, the user can input a prompt into the AI ​​model such as, "Please suggest casual fashion suitable for a woman in her 20s with a round face and a slim build," to obtain more specific suggestions.

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The user uploads their photos to the device using a smartphone or computer. The device verifies the image format (such as JPEG or PNG) of the uploaded photos. The input is the user's photo data, and the output is the result of the verification that the format is correct. Specifically, the device parses the image header information to confirm that it is in the correct format.

[0467] Step 2:

[0468] The device sends the formatted photo to the server using encryption technology (SSL / TLS). The input for this step is a properly formatted user photo, and the output is an encrypted data packet. The device securely transmits the photo to the server using a secure communication protocol.

[0469] Step 3:

[0470] The server decrypts the received encrypted photo data and inputs it into a deep learning-based AI analysis model. The input is the decrypted photo data, and the output is the analysis results of the user's facial and body shape features. The server uses a convolutional neural network (CNN) to extract features from the image data.

[0471] Step 4:

[0472] The server uses facial and body shape features obtained from the AI ​​model to match them against a fashion database. The input is the analyzed feature information, and the output is a suggestion of the most suitable fashion style for the user. The server executes SQL queries to extract matching styles from the database.

[0473] Step 5:

[0474] The server generates fashion suggestions based on the matching results, adds purchase links, and sends them to the user's device. The input for this step is optimized style information, and the output is fashion suggestion data sent to the user. The server formats the suggestion content into JSON format and delivers it to the device via a transmission protocol.

[0475] Step 6:

[0476] The user visually reviews fashion suggestions provided through the device's interface. The user selects a suggested item and clicks a provided link to proceed with the purchase on the e-commerce site. The input for this step is fashion suggestion data sent from the server, and the output is the user's purchase action. When the user clicks a link, the device opens a web browser and accesses the online shop.

[0477] (Application Example 1)

[0478] 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."

[0479] There is a need to provide personalized fashion suggestions more intuitively and quickly. Furthermore, an interface is required that allows users to access fashion suggestions and make purchases in a natural way. This includes voice interaction, requiring a system that allows users to easily give commands and receive immediate responses to their requests.

[0480] 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.

[0481] In this invention, the server includes data processing means for receiving photographs and analyzing the shape and physical characteristics of an individual from the photographs; suggestion means for comparing the analyzed characteristics with a past data set and making clothing suggestions optimized for the individual; information display means for generating and displaying purchase instructions for the suggested clothing items; and mechanical means for receiving voice commands from the user and automatically taking photographs and making suggestions based on those commands. This enables the user to receive quick and personalized fashion suggestions by giving voice commands.

[0482] A "photograph" is a digital or analog recording of visual data, and serves as raw material for analyzing an individual's shape and physical characteristics.

[0483] "Data processing means" is a general term for procedures and devices used to analyze input data and extract or transform the desired information.

[0484] A "proposal method" refers to a process or device that presents the user with the optimal options or ideas based on analyzed data.

[0485] "Information display means" refers to methods or devices for visually presenting data or results to a user.

[0486] "Mechanical means" refers to devices or mechanisms that receive instructions from a user via voice or other natural interfaces and perform physical or digital tasks based on those instructions.

[0487] "Voice commands" are orders or requests that a user utters verbally, and they form the basis for a system to perform specific processing based on them.

[0488] A "data set" is a collection of information gathered from the past, which is used by algorithms for future prediction and individual optimization.

[0489] The system that realizes this invention combines a mechanical means for receiving voice commands from the user with a data processing means for receiving and analyzing photographic data and making suggestions. The server analyzes photographs received from the user's smartphone or robot via an AI model using deep learning technology. Through this analysis, the system acquires the individual's shape and physical characteristics and suggests optimal clothing by comparing them with a past data set.

[0490] Next, suggestions derived using the AI ​​model are presented to the user through an information display system. This information display is automatically operated after the user issues a voice command. For example, if the user says, "Suggest some fashion items," a photo is taken via the robot, and suggestions based on the analysis are displayed via voice or on the screen. For each suggested item, the user can receive purchase guidance on the spot.

[0491] The hardware used includes camera devices and voice recognition systems, while the software primarily consists of AI models and secure server connections. This enables intuitive voice control, allowing users to easily receive personalized fashion recommendations.

[0492] For example, by inputting a prompt message such as "Suggest a casual date outfit for a woman in her 20s" into the AI ​​model, the model will display the most suitable outfit for the user.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] The user issues a voice command.

[0496] The user issues a voice command such as "Suggest fashion," and the terminal converts this voice into digital data through a voice recognition system. The input is a voice command, and the output is a digitized command. The voice recognition system converts the voice into text, which is then passed on to the next step.

[0497] Step 2:

[0498] The device takes a photo of the user.

[0499] Based on voice commands, the terminal takes photos of the user's face and body. The input is real-time video of the user, and the output is captured photo data. The camera device acquires high-resolution images and prepares them to be sent to the server.

[0500] Step 3:

[0501] The server receives the photo data and begins processing it.

[0502] The server inputs the photo data received from the terminal into an AI model for analysis. The input is the photo data, and the output is the analyzed physical feature information. A deep learning model is used to identify and extract facial and body shape characteristics.

[0503] Step 4:

[0504] The server compares the analysis results with a fashion database.

[0505] The server compares the analyzed feature information with a historical dataset and suggests suitable fashion. The input is the analyzed feature information, and the output is optimized fashion suggestions. Database queries are performed to identify and list fashion examples with similar features.

[0506] Step 5:

[0507] The server sends the fashion suggested by the server to the terminal via an information display device.

[0508] The server organizes the suggested content and sends it to the terminal in a visualized format. The input is fashion suggestion information, and the output is what is displayed on the terminal. The user interface receives the suggested information and displays it visually on the user's screen.

[0509] Step 6:

[0510] The device displays suggestions to the user and presents a purchase link.

[0511] The terminal displays suggestions received from the server to the user and provides links, including purchase instructions. Input is display data from the server, and output is a visual presentation to the user. The information display mechanism plays a role in supporting the user's actions as they select items and proceed with purchase.

[0512] 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.

[0513] This invention is a system that provides individually optimized fashion suggestions by combining an emotion engine that recognizes the user's emotions. This system consists of a user terminal, a server, information processing means, and an emotion engine.

[0514] The user uploads a photo using their device to start the system. After the photo is properly formatted on the device, it is sent to the server. On the server, the photo is passed to an AI analysis model to extract the person's facial and body shape features. In addition, the emotion engine analyzes facial expression and voice data collected from the device to recognize the user's emotions.

[0515] The analysis results and recognized emotional information are compared against the server's fashion database. This database contains a vast amount of fashion information, including past highly-rated examples. In addition to facial shape and body type information, the server adjusts the suggested fashion styles by considering the user's current emotions. For example, if the server determines that the user is in a relaxed mood, it will make suggestions that place more emphasis on casual and comfortable styles.

[0516] The server generates optimized fashion suggestions and creates corresponding purchase links. The terminal displays this suggestion information in the user interface for the user to review. The user reviews the suggested items and, if interested, clicks the link to proceed with the purchase on the e-commerce site.

[0517] For example, when a user takes a photo and launches the app, the system instantly analyzes the facial features, and the emotion engine recognizes a smiling expression. Based on this, the system suggests a casual style with bright colors and provides links to increase purchasing intent. Through these suggestions on the device, users can easily make purchases that perfectly suit their situation.

[0518] This embodiment allows users to easily select and purchase fashion items that suit their situation and emotions. The present invention aims to achieve even more personalized and rapid fashion suggestions by utilizing advanced multimodal technology.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The user launches the application on their device, selects a photo of themselves, and uploads it. It is desirable that the photo clearly shows the entire body and face.

[0522] Step 2:

[0523] The device verifies the format and content of the photos and sends them to the server only after confirming their appropriateness. For privacy and security, the transmission is performed using encryption technology.

[0524] Step 3:

[0525] The server receives the photos and inputs them into an AI analysis model. This model utilizes deep learning to analyze facial features and body shape characteristics and extract relevant information.

[0526] Step 4:

[0527] Simultaneously, the emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to recognize the user's emotions. This allows it to acquire emotional information such as joy, sadness, and surprise.

[0528] Step 5:

[0529] The server matches the acquired facial features, body type characteristics, and emotional information against a fashion database. The database includes past highly-rated fashion examples and trends.

[0530] Step 6:

[0531] The server generates optimized fashion suggestions by considering the user's current emotions and analysis results. For example, if the user is feeling relaxed, a comfortable, casual style might be suggested.

[0532] Step 7:

[0533] The server generates purchase links for the suggested fashion styles, formats this information for the user interface, and sends it to the terminal.

[0534] Step 8:

[0535] The device displays the received fashion suggestions and purchase links to the user. The user can review these, and if they are interested in an item, they can click the link to proceed to the e-commerce site and complete the purchase process.

[0536] (Example 2)

[0537] 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."

[0538] Traditional fashion suggestion systems have the problem of only being able to offer uniform suggestions to individuals with diverse personalities and emotional states. Therefore, there is a need for a system that can quickly provide optimal, personalized fashion suggestions. This will allow for meeting diverse individual needs and improving satisfaction with fashion choices.

[0539] 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.

[0540] In this invention, the server includes an information processing device that receives a photograph and analyzes an individual's facial shape and body shape information based on the photograph; an emotion recognition device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions; and a suggestion device that compares the analyzed information and recognized emotion data with past data storage to suggest clothing optimized for the individual. This makes it possible to suggest individually optimized clothing styles based on an individual's facial shape, body shape information, and emotional state.

[0541] An "information processing device" is a device that receives a photograph and analyzes an individual's facial shape and body shape information based on that photograph.

[0542] An "emotion recognition device" is a device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions.

[0543] A "suggestion device" is a device that compares analyzed information and recognized emotional data with past data storage to provide personalized clothing suggestions.

[0544] "Data storage" refers to a database containing clothing data, including past highly-rated examples, used to make new proposals.

[0545] "Clothing style" refers to fashion suggestions optimized based on an individual's facial features, body shape, and emotional state.

[0546] "Multimodal technology" is a technique that obtains information by comprehensively analyzing data in different formats (for example, images, facial expressions, and audio).

[0547] This invention is a system that provides individually optimized fashion suggestions to users. The system consists of a user's terminal, a server, an information processing device, and an emotion recognition device.

[0548] The user starts the system using their device and uploads a photo. This photo is used to obtain facial and body shape information. The device converts the photo to an appropriate format (JPEG, PNG, etc.) and sends it to the server.

[0549] On the server, an information processing unit receives photos and analyzes facial and body shape information using a generative AI model. Libraries such as OpenCV and TensorFlow can be used for the analysis. In addition, an emotion recognition device is used to analyze facial expression data and voice data received from the terminal to recognize the user's emotions.

[0550] The analyzed facial shape and body shape information, as well as recognized emotion data, are cross-referenced with a data storage system containing fashion data. This data storage system holds a vast amount of clothing data, including past high-rated examples.

[0551] Based on information retrieved from data storage, the server suggests clothing styles optimized for the user based on their facial features, body type, and emotions. For example, if a relaxed emotion is detected, casual and comfortable clothing will be suggested. Related purchase links are also generated and presented to the user.

[0552] For example, if a user uploads a photo of themselves smiling, the emotion recognition device will recognize the expression as "happy," and the server will suggest a casual style with bright colors. This system makes it easy for users to quickly select and purchase fashion items that are best suited to their situation.

[0553] An example of a prompt message might be, "Use the user's photo and sentiment information to suggest a fashion style that matches their current mood." This would enable personalized style suggestions.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] The user launches the system using their own device and uploads a photo. The input is an image file selected by the user. The device converts the uploaded photo to JPEG or PNG format. This conversion is done to make it suitable for the server to receive and process.

[0557] Step 2:

[0558] The terminal sends the formatted photo data to the server. The server receives the photo data as input and passes it to the information processing unit. The information processing unit extracts face shape and body shape information using an AI analysis model. This uses computer vision tools such as OpenCV and TensorFlow. The output is feature data of the person's face and body.

[0559] Step 3:

[0560] The server uses an emotion recognition device to receive facial expression and voice data transmitted from the terminal as input. The emotion recognition device analyzes this data and outputs the user's emotional state (e.g., "happy" or "relaxed"). This is done by analyzing facial expressions and voice tone using a machine learning model.

[0561] Step 4:

[0562] The server compares the analyzed facial and body shape information, as well as the recognized emotional state, with data storage. This data storage contains a vast amount of fashion data, including past high-rated examples. The server uses this as input to generate the optimal clothing style for the user. The output is an optimized clothing suggestion.

[0563] Step 5:

[0564] The server creates purchase links associated with the generated clothing suggestions. This outputs data that includes links to e-commerce sites, allowing users to easily purchase the suggested items.

[0565] Step 6:

[0566] The device receives clothing suggestions and purchase links sent from the server and displays them to the user. The user interface allows the user to review the suggestions, select items of interest, and proceed with the purchase. This is designed to ensure that information is clearly displayed on the user's screen.

[0567] (Application Example 2)

[0568] 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."

[0569] Conventional fashion recommendation systems make suggestions based solely on the user's body type and facial features, making it difficult to provide personalized recommendations that take into account specific emotions or moods. Furthermore, if the suggested items do not match the user's current emotions or mood, it can diminish their desire to purchase. Therefore, there is a need for a system that can provide clothing recommendations that consider the user's emotional state.

[0570] 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.

[0571] In this invention, the server includes information processing means for receiving photographic and audio data and analyzing an individual's facial features, body shape characteristics, and emotional state from the data; suggestion means for comparing the analyzed features and emotional state with a past database and providing clothing suggestions optimized for the individual's situation and mood; and display means for generating a purchase route to the suggested clothing items and presenting the route. This makes it possible to provide optimal clothing suggestions according to the user's emotions and mood at that time, thereby increasing their willingness to purchase.

[0572] "Photo and audio data" refers to the image and audio information necessary for the system to recognize the user's physical characteristics and emotional state.

[0573] "Analysis of individual facial features, body characteristics, and emotional state" is a process that identifies the user's facial contours, body proportions, and mood and emotions inferred from their facial expressions and voice, based on the received data.

[0574] "Information processing means" refers to a computing device or software program for performing the above analysis, which has the function of analyzing the user's characteristics using photographic and audio data.

[0575] A "past database" is a collection of information that stores fashion-related case studies and user feedback collected in the past.

[0576] "Clothing suggestions optimized for individual circumstances and moods" is a process that considers user analysis data and current emotional state to suggest the most suitable fashion items and styles for that person.

[0577] "Suggestion means" refers to a device or program that has the function of generating optimized fashion suggestions and presenting them to the user.

[0578] "Generating purchase paths" is the process of creating URLs or link information that allow users to easily purchase the suggested clothing items.

[0579] "Display means" refers to a device or program that visually or audibly informs the user of generated clothing suggestions and purchase links.

[0580] The system for implementing this invention consists of a user terminal, a server, information processing means, and an emotion engine. The user first inputs photo and audio data using their terminal. The terminal receives this data, converts it to an appropriate format, and sends it to the server. The server uses the information processing means to analyze the individual's facial features, body type characteristics, and emotional state from the received photo and audio data. This analysis uses an AI image analysis tool (e.g., a custom model using TensorFlow), and an emotion engine such as Microsoft Azure is used for emotion analysis.

[0581] The server compares the analysis results with a past database to provide clothing suggestions optimized for the individual's situation and mood. The database used includes past high-rated examples and user feedback, and is constantly updated to keep up with the latest trends. The server generates a purchase path for the suggested clothing items, which is displayed on the user's device in the form of a link or similar.

[0582] The device displays this suggested information in the user interface, allowing the user to review it and, if interested, proceed with the process on the e-commerce site through the application. An example of a prompt message is: "Input facial image and voice data, analyze the user's emotions in real time, suggest fashion that considers comfort and trends, and generate appropriate e-commerce site links."

[0583] As a concrete example, if a user yawns in front of the robot on a holiday morning, the robot will suggest a relaxed, casual style, explaining that it's perfect for a family picnic. The user can then complete the process via a provided purchase link if they find the style to be ideal. In this way, a system is created that allows users to easily select and purchase fashion that best suits their mood at that moment.

[0584] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0585] Step 1:

[0586] Users capture photos and audio data using their devices and upload this data to the application. This input data undergoes format conversion and initial processing, and is temporarily stored on the device in preparation for analysis.

[0587] Step 2:

[0588] The terminal sends organized photo and audio data to the server. The server receives this data, converts it to a standard format as input, and processes it into a form usable by information processing tools. This process is important to maintain data consistency.

[0589] Step 3:

[0590] The server processes the data through an AI image analysis tool and uses a generating AI model to extract individual facial and body shape characteristics. In parallel, it runs an emotion engine to analyze the user's emotional state from facial expressions and voice, and generates output data that evaluates individual characteristics.

[0591] Step 4:

[0592] The facial features, body shape characteristics, and emotional state obtained from the input data are compared with a database on the server. Based on past highly-rated fashion examples and trend information, the server uses information processing tools to generate optimal clothing suggestions that are suitable for the user's mood and situation.

[0593] Step 5:

[0594] The server creates a purchase path for the proposed clothing items and generates link information. This makes it possible to generate purchase links that allow the user to directly access the product's purchase page.

[0595] Step 6:

[0596] The server sends the generated clothing suggestions and purchase links to the user's device. The device visually displays this information in a user interface, allowing the user to review the suggestions and click on links to items of interest to proceed with the purchase.

[0597] Step 7:

[0598] Users review the suggested fashion items on their devices, and if necessary, decide to purchase them and retrieve the items via the provided links. The suggestions based on the prompts provided during this process play a role in supporting the user's purchasing intent.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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".

[0616] The present invention's system analyzes an individual's photograph to suggest the optimal fashion style. This system operates in a network environment including a user terminal, a server, and associated information processing means.

[0617] First, users upload their photos to a device such as a smartphone or computer. The device checks the image format and ensures its integrity before sending it to the server. To protect the privacy of the photos, encryption technology is used during transmission.

[0618] On the server, received photos are input into a high-precision AI analysis model. This AI model identifies the user's facial features, body shape, and other characteristics, and generates analysis results. This process utilizes image recognition technology based on deep learning.

[0619] After the analysis results are obtained, the server uses this information to compare with past databases. These databases contain a variety of fashion examples and trend information. Once the optimal fashion style is selected, this information is provided to the user's device. Each suggested fashion item also includes a link to where it can be purchased.

[0620] Users can visually confirm these suggestions on their device's interface. For items they wish to purchase, they can use the provided link to directly access the e-commerce site and complete the purchase process. This process allows users to efficiently find and purchase fashion items that suit them.

[0621] Furthermore, the server implements multimodal technology, allowing it to utilize data other than photographs for analysis. For example, the user's styling history and preference data may be used. This further improves the accuracy of suggestions and provides a personalized fashion experience. With this invention, users can easily receive highly accurate, personalized fashion suggestions and make purchases quickly.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The user launches the application on their device and selects the photo they wish to analyze. The photo should preferably be a full-body image of a person taken from the front.

[0625] Step 2:

[0626] The device checks the format of the selected photo and verifies that it is in the correct format. After verification, it prepares to send the photo to the server. At this time, the image is transmitted encrypted.

[0627] Step 3:

[0628] The server inputs the received photos into an AI analysis model. This model utilizes deep learning technology to identify facial features and body shape characteristics within the photos and generates analysis results.

[0629] Step 4:

[0630] The server compares the analysis results with a historical fashion database. The database includes fashion styles favored by people with similar characteristics and examples of highly-rated fashion trends.

[0631] Step 5:

[0632] The server selects a fashion style optimized for the user based on the matching criteria. The selected fashion items are also accompanied by corresponding purchase links.

[0633] Step 6:

[0634] The terminal displays fashion suggestions and purchase links returned from the server on the user interface. The user reviews the suggested items and clicks the link to purchase them.

[0635] Step 7:

[0636] The user is redirected to the e-commerce site via the provided link and proceeds with the purchase of the displayed items. This completes the process from suggestion to purchase.

[0637] (Example 1)

[0638] 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".

[0639] Conventional fashion suggestion systems have a challenge in that they cannot adequately provide personalized suggestions based on individual users' facial features and body type characteristics. Furthermore, it has been difficult to perform effective analysis while ensuring user privacy. Therefore, there is a need for a system that can suggest the optimal fashion style based on the user's preferences and characteristics.

[0640] 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.

[0641] This invention includes a server that receives an individual's photograph, verifies the image format of the photograph, encrypts it to protect privacy, and transmits it; an information processing means that inputs the received photograph into a deep learning-based analysis model to identify facial shape and body shape features; and a suggestion means that compares the analysis results with a past fashion database and makes optimized fashion suggestions. This makes it possible to suggest highly accurate fashion styles to users based on their individual facial shape and body shape features.

[0642] "Personal photographs" are digital images that are taken or acquired by the user themselves and contain information about the person, including facial features and body shape characteristics.

[0643] "Image format" refers to the data format of a digital image, and specifically to methods of saving image data based on a particular standard, such as JPEG or PNG.

[0644] "Encryption" is a technology that transforms digital data using specific algorithms to protect it from unauthorized access by third parties.

[0645] "Deep learning" is an artificial intelligence (AI) technology that uses large amounts of data to allow computers to automatically learn patterns and improve their recognition and classification performance.

[0646] "Facial and body features" refers to basic morphological information necessary for visually identifying an individual, such as the shape of the subject's face and the contours and proportions of their body.

[0647] An "information processing system" is a combination of hardware and software used to analyze and process received digital data and generate output that corresponds to a specific purpose.

[0648] A "fashion database" is a collection of data containing diverse clothing styles, trend information, and evaluation results, and serves as a source of information used in the proposed system.

[0649] "Multimodal technology" is a technique that integrates and analyzes different types of data (e.g., text, audio, images), making it possible to extract richer information.

[0650] "Evaluation information" refers to data that quantitatively demonstrates recommended performance, such as the extent to which previously proposed fashion styles were supported or used.

[0651] This invention is a system that allows users to upload their own photos and then suggests the most suitable fashion style based on those photos. The system operates via user terminals, a network, and a server.

[0652] Users select their photos using a device such as a smartphone or computer and upload them to the system. Once the photo upload is complete, the device checks the image format to ensure it is correct. After verification, the photos are encrypted using Secure Socket Layer (SSL) or Transport Layer Security (TLS) and sent to the server in a privacy-protected state.

[0653] The server inputs the received photo data into a deep learning-based AI analysis model. This AI model uses a convolutional neural network (CNN) to identify facial features and body shape characteristics. The information analyzed by the AI ​​model is tagged and collected by the server.

[0654] The collected characteristic information is cross-referenced with a fashion database on the server. This database contains various trend information and historical evaluation data, and is used to select the optimal fashion style tailored to the user's characteristics.

[0655] The suggested styles are sent to the user's device along with a purchase link. The user can visually review the suggestions on their device's interface and click on the link for their favorite item to proceed with the purchase on the e-commerce site.

[0656] For example, if a user has a round face and a slim build, the server can analyze these features from the uploaded photo and suggest a combination of a casual shirt and skinny jeans. In this case, the user can input a prompt into the AI ​​model such as, "Please suggest casual fashion suitable for a woman in her 20s with a round face and a slim build," to obtain more specific suggestions.

[0657] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0658] Step 1:

[0659] The user uploads their photos to the device using a smartphone or computer. The device verifies the image format (such as JPEG or PNG) of the uploaded photos. The input is the user's photo data, and the output is the result of the verification that the format is correct. Specifically, the device parses the image header information to confirm that it is in the correct format.

[0660] Step 2:

[0661] The device sends the formatted photo to the server using encryption technology (SSL / TLS). The input for this step is a properly formatted user photo, and the output is an encrypted data packet. The device securely transmits the photo to the server using a secure communication protocol.

[0662] Step 3:

[0663] The server decrypts the received encrypted photo data and inputs it into a deep learning-based AI analysis model. The input is the decrypted photo data, and the output is the analysis results of the user's facial and body shape features. The server uses a convolutional neural network (CNN) to extract features from the image data.

[0664] Step 4:

[0665] The server uses facial and body shape features obtained from the AI ​​model to match them against a fashion database. The input is the analyzed feature information, and the output is a suggestion of the most suitable fashion style for the user. The server executes SQL queries to extract matching styles from the database.

[0666] Step 5:

[0667] The server generates fashion suggestions based on the matching results, adds purchase links, and sends them to the user's device. The input for this step is optimized style information, and the output is fashion suggestion data sent to the user. The server formats the suggestion content into JSON format and delivers it to the device via a transmission protocol.

[0668] Step 6:

[0669] The user visually reviews fashion suggestions provided through the device's interface. The user selects a suggested item and clicks a provided link to proceed with the purchase on the e-commerce site. The input for this step is fashion suggestion data sent from the server, and the output is the user's purchase action. When the user clicks a link, the device opens a web browser and accesses the online shop.

[0670] (Application Example 1)

[0671] 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".

[0672] There is a need to provide personalized fashion suggestions more intuitively and quickly. Furthermore, an interface is required that allows users to access fashion suggestions and make purchases in a natural way. This includes voice interaction, requiring a system that allows users to easily give commands and receive immediate responses to their requests.

[0673] 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.

[0674] In this invention, the server includes data processing means for receiving photographs and analyzing the shape and physical characteristics of an individual from the photographs; suggestion means for comparing the analyzed characteristics with a past data set and making clothing suggestions optimized for the individual; information display means for generating and displaying purchase instructions for the suggested clothing items; and mechanical means for receiving voice commands from the user and automatically taking photographs and making suggestions based on those commands. This enables the user to receive quick and personalized fashion suggestions by giving voice commands.

[0675] A "photograph" is a digital or analog recording of visual data, and serves as raw material for analyzing an individual's shape and physical characteristics.

[0676] "Data processing means" is a general term for procedures and devices used to analyze input data and extract or transform the desired information.

[0677] A "proposal method" refers to a process or device that presents the user with the optimal options or ideas based on analyzed data.

[0678] "Information display means" refers to methods or devices for visually presenting data or results to a user.

[0679] "Mechanical means" refers to devices or mechanisms that receive instructions from a user via voice or other natural interfaces and perform physical or digital tasks based on those instructions.

[0680] "Voice commands" are orders or requests that a user utters verbally, and they form the basis for a system to perform specific processing based on them.

[0681] A "data set" is a collection of information gathered from the past, which is used by algorithms for future prediction and individual optimization.

[0682] The system that realizes this invention combines a mechanical means for receiving voice commands from the user with a data processing means for receiving and analyzing photographic data and making suggestions. The server analyzes photographs received from the user's smartphone or robot via an AI model using deep learning technology. Through this analysis, the system acquires the individual's shape and physical characteristics and suggests optimal clothing by comparing them with a past data set.

[0683] Next, suggestions derived using the AI ​​model are presented to the user through an information display system. This information display is automatically operated after the user issues a voice command. For example, if the user says, "Suggest some fashion items," a photo is taken via the robot, and suggestions based on the analysis are displayed via voice or on the screen. For each suggested item, the user can receive purchase guidance on the spot.

[0684] The hardware used includes camera devices and voice recognition systems, while the software primarily consists of AI models and secure server connections. This enables intuitive voice control, allowing users to easily receive personalized fashion recommendations.

[0685] For example, by inputting a prompt message such as "Suggest a casual date outfit for a woman in her 20s" into the AI ​​model, the model will display the most suitable outfit for the user.

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] The user issues a voice command.

[0689] The user issues a voice command such as "Suggest fashion," and the terminal converts this voice into digital data through a voice recognition system. The input is a voice command, and the output is a digitized command. The voice recognition system converts the voice into text, which is then passed on to the next step.

[0690] Step 2:

[0691] The device takes a photo of the user.

[0692] Based on voice commands, the terminal takes photos of the user's face and body. The input is real-time video of the user, and the output is captured photo data. The camera device acquires high-resolution images and prepares them to be sent to the server.

[0693] Step 3:

[0694] The server receives the photo data and begins processing it.

[0695] The server inputs the photo data received from the terminal into an AI model for analysis. The input is the photo data, and the output is the analyzed physical feature information. A deep learning model is used to identify and extract facial and body shape characteristics.

[0696] Step 4:

[0697] The server compares the analysis results with a fashion database.

[0698] The server compares the analyzed feature information with a historical dataset and suggests suitable fashion. The input is the analyzed feature information, and the output is optimized fashion suggestions. Database queries are performed to identify and list fashion examples with similar features.

[0699] Step 5:

[0700] The server sends the fashion suggested by the server to the terminal via an information display device.

[0701] The server organizes the suggested content and sends it to the terminal in a visualized format. The input is fashion suggestion information, and the output is what is displayed on the terminal. The user interface receives the suggested information and displays it visually on the user's screen.

[0702] Step 6:

[0703] The device displays suggestions to the user and presents a purchase link.

[0704] The terminal displays suggestions received from the server to the user and provides links, including purchase instructions. Input is display data from the server, and output is a visual presentation to the user. The information display mechanism plays a role in supporting the user's actions as they select items and proceed with purchase.

[0705] 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.

[0706] This invention is a system that provides individually optimized fashion suggestions by combining an emotion engine that recognizes the user's emotions. This system consists of a user terminal, a server, information processing means, and an emotion engine.

[0707] The user uploads a photo using their device to start the system. After the photo is properly formatted on the device, it is sent to the server. On the server, the photo is passed to an AI analysis model to extract the person's facial and body shape features. In addition, the emotion engine analyzes facial expression and voice data collected from the device to recognize the user's emotions.

[0708] The analysis results and recognized emotional information are compared against the server's fashion database. This database contains a vast amount of fashion information, including past highly-rated examples. In addition to facial shape and body type information, the server adjusts the suggested fashion styles by considering the user's current emotions. For example, if the server determines that the user is in a relaxed mood, it will make suggestions that place more emphasis on casual and comfortable styles.

[0709] The server generates optimized fashion suggestions and creates corresponding purchase links. The terminal displays this suggestion information in the user interface for the user to review. The user reviews the suggested items and, if interested, clicks the link to proceed with the purchase on the e-commerce site.

[0710] For example, when a user takes a photo and launches the app, the system instantly analyzes the facial features, and the emotion engine recognizes a smiling expression. Based on this, the system suggests a casual style with bright colors and provides links to increase purchasing intent. Through these suggestions on the device, users can easily make purchases that perfectly suit their situation.

[0711] This embodiment allows users to easily select and purchase fashion items that suit their situation and emotions. The present invention aims to achieve even more personalized and rapid fashion suggestions by utilizing advanced multimodal technology.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The user launches the application on their device, selects a photo of themselves, and uploads it. It is desirable that the photo clearly shows the entire body and face.

[0715] Step 2:

[0716] The device verifies the format and content of the photos and sends them to the server only after confirming their appropriateness. For privacy and security, the transmission is performed using encryption technology.

[0717] Step 3:

[0718] The server receives the photos and inputs them into an AI analysis model. This model utilizes deep learning to analyze facial features and body shape characteristics and extract relevant information.

[0719] Step 4:

[0720] Simultaneously, the emotion engine analyzes the user's facial expressions and voice through the device's camera and microphone to recognize the user's emotions. This allows it to acquire emotional information such as joy, sadness, and surprise.

[0721] Step 5:

[0722] The server matches the acquired facial features, body type characteristics, and emotional information against a fashion database. The database includes past highly-rated fashion examples and trends.

[0723] Step 6:

[0724] The server generates optimized fashion suggestions by considering the user's current emotions and analysis results. For example, if the user is feeling relaxed, a comfortable, casual style might be suggested.

[0725] Step 7:

[0726] The server generates purchase links for the suggested fashion styles, formats this information for the user interface, and sends it to the terminal.

[0727] Step 8:

[0728] The device displays the received fashion suggestions and purchase links to the user. The user can review these, and if they are interested in an item, they can click the link to proceed to the e-commerce site and complete the purchase process.

[0729] (Example 2)

[0730] 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".

[0731] Traditional fashion suggestion systems have the problem of only being able to offer uniform suggestions to individuals with diverse personalities and emotional states. Therefore, there is a need for a system that can quickly provide optimal, personalized fashion suggestions. This will allow for meeting diverse individual needs and improving satisfaction with fashion choices.

[0732] 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.

[0733] In this invention, the server includes an information processing device that receives a photograph and analyzes an individual's facial shape and body shape information based on the photograph; an emotion recognition device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions; and a suggestion device that compares the analyzed information and recognized emotion data with past data storage to suggest clothing optimized for the individual. This makes it possible to suggest individually optimized clothing styles based on an individual's facial shape, body shape information, and emotional state.

[0734] An "information processing device" is a device that receives a photograph and analyzes an individual's facial shape and body shape information based on that photograph.

[0735] An "emotion recognition device" is a device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions.

[0736] A "suggestion device" is a device that compares analyzed information and recognized emotional data with past data storage to provide personalized clothing suggestions.

[0737] "Data storage" refers to a database containing clothing data, including past highly-rated examples, used to make new proposals.

[0738] "Clothing style" refers to fashion suggestions optimized based on an individual's facial features, body shape, and emotional state.

[0739] "Multimodal technology" is a technique that obtains information by comprehensively analyzing data in different formats (for example, images, facial expressions, and audio).

[0740] This invention is a system that provides individually optimized fashion suggestions to users. The system consists of a user's terminal, a server, an information processing device, and an emotion recognition device.

[0741] The user starts the system using their device and uploads a photo. This photo is used to obtain facial and body shape information. The device converts the photo to an appropriate format (JPEG, PNG, etc.) and sends it to the server.

[0742] On the server, an information processing unit receives photos and analyzes facial and body shape information using a generative AI model. Libraries such as OpenCV and TensorFlow can be used for the analysis. In addition, an emotion recognition device is used to analyze facial expression data and voice data received from the terminal to recognize the user's emotions.

[0743] The analyzed facial shape and body shape information, as well as recognized emotion data, are cross-referenced with a data storage system containing fashion data. This data storage system holds a vast amount of clothing data, including past high-rated examples.

[0744] Based on information retrieved from data storage, the server suggests clothing styles optimized for the user based on their facial features, body type, and emotions. For example, if a relaxed emotion is detected, casual and comfortable clothing will be suggested. Related purchase links are also generated and presented to the user.

[0745] For example, if a user uploads a photo of themselves smiling, the emotion recognition device will recognize the expression as "happy," and the server will suggest a casual style with bright colors. This system makes it easy for users to quickly select and purchase fashion items that are best suited to their situation.

[0746] An example of a prompt message might be, "Use the user's photo and sentiment information to suggest a fashion style that matches their current mood." This would enable personalized style suggestions.

[0747] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0748] Step 1:

[0749] The user launches the system using their own device and uploads a photo. The input is an image file selected by the user. The device converts the uploaded photo to JPEG or PNG format. This conversion is done to make it suitable for the server to receive and process.

[0750] Step 2:

[0751] The terminal sends the formatted photo data to the server. The server receives the photo data as input and passes it to the information processing unit. The information processing unit extracts face shape and body shape information using an AI analysis model. This uses computer vision tools such as OpenCV and TensorFlow. The output is feature data of the person's face and body.

[0752] Step 3:

[0753] The server uses an emotion recognition device to receive facial expression and voice data transmitted from the terminal as input. The emotion recognition device analyzes this data and outputs the user's emotional state (e.g., "happy" or "relaxed"). This is done by analyzing facial expressions and voice tone using a machine learning model.

[0754] Step 4:

[0755] The server compares the analyzed facial and body shape information, as well as the recognized emotional state, with data storage. This data storage contains a vast amount of fashion data, including past high-rated examples. The server uses this as input to generate the optimal clothing style for the user. The output is an optimized clothing suggestion.

[0756] Step 5:

[0757] The server creates purchase links associated with the generated clothing suggestions. This outputs data that includes links to e-commerce sites, allowing users to easily purchase the suggested items.

[0758] Step 6:

[0759] The device receives clothing suggestions and purchase links sent from the server and displays them to the user. The user interface allows the user to review the suggestions, select items of interest, and proceed with the purchase. This is designed to ensure that information is clearly displayed on the user's screen.

[0760] (Application Example 2)

[0761] 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".

[0762] Conventional fashion recommendation systems make suggestions based solely on the user's body type and facial features, making it difficult to provide personalized recommendations that take into account specific emotions or moods. Furthermore, if the suggested items do not match the user's current emotions or mood, it can diminish their desire to purchase. Therefore, there is a need for a system that can provide clothing recommendations that consider the user's emotional state.

[0763] 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.

[0764] In this invention, the server includes information processing means for receiving photographic and audio data and analyzing an individual's facial features, body shape characteristics, and emotional state from the data; suggestion means for comparing the analyzed features and emotional state with a past database and providing clothing suggestions optimized for the individual's situation and mood; and display means for generating a purchase route to the suggested clothing items and presenting the route. This makes it possible to provide optimal clothing suggestions according to the user's emotions and mood at that time, thereby increasing their willingness to purchase.

[0765] "Photo and audio data" refers to the image and audio information necessary for the system to recognize the user's physical characteristics and emotional state.

[0766] "Analysis of individual facial features, body characteristics, and emotional state" is a process that identifies the user's facial contours, body proportions, and mood and emotions inferred from their facial expressions and voice, based on the received data.

[0767] "Information processing means" refers to a computing device or software program for performing the above analysis, which has the function of analyzing the user's characteristics using photographic and audio data.

[0768] A "past database" is a collection of information that stores fashion-related case studies and user feedback collected in the past.

[0769] "Clothing suggestions optimized for individual circumstances and moods" is a process that considers user analysis data and current emotional state to suggest the most suitable fashion items and styles for that person.

[0770] "Suggestion means" refers to a device or program that has the function of generating optimized fashion suggestions and presenting them to the user.

[0771] "Generating purchase paths" is the process of creating URLs or link information that allow users to easily purchase the suggested clothing items.

[0772] "Display means" refers to a device or program that visually or audibly informs the user of generated clothing suggestions and purchase links.

[0773] The system for implementing this invention consists of a user terminal, a server, information processing means, and an emotion engine. The user first inputs photo and audio data using their terminal. The terminal receives this data, converts it to an appropriate format, and sends it to the server. The server uses the information processing means to analyze the individual's facial features, body type characteristics, and emotional state from the received photo and audio data. This analysis uses an AI image analysis tool (e.g., a custom model using TensorFlow), and an emotion engine such as Microsoft Azure is used for emotion analysis.

[0774] The server compares the analysis results with a past database to provide clothing suggestions optimized for the individual's situation and mood. The database used includes past high-rated examples and user feedback, and is constantly updated to keep up with the latest trends. The server generates a purchase path for the suggested clothing items, which is displayed on the user's device in the form of a link or similar.

[0775] The device displays this suggested information in the user interface, allowing the user to review it and, if interested, proceed with the process on the e-commerce site through the application. An example of a prompt message is: "Input facial image and voice data, analyze the user's emotions in real time, suggest fashion that considers comfort and trends, and generate appropriate e-commerce site links."

[0776] As a concrete example, if a user yawns in front of the robot on a holiday morning, the robot will suggest a relaxed, casual style, explaining that it's perfect for a family picnic. The user can then complete the process via a provided purchase link if they find the style to be ideal. In this way, a system is created that allows users to easily select and purchase fashion that best suits their mood at that moment.

[0777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0778] Step 1:

[0779] Users capture photos and audio data using their devices and upload this data to the application. This input data undergoes format conversion and initial processing, and is temporarily stored on the device in preparation for analysis.

[0780] Step 2:

[0781] The terminal sends organized photo and audio data to the server. The server receives this data, converts it to a standard format as input, and processes it into a form usable by information processing tools. This process is important to maintain data consistency.

[0782] Step 3:

[0783] The server processes the data through an AI image analysis tool and uses a generating AI model to extract individual facial and body shape characteristics. In parallel, it runs an emotion engine to analyze the user's emotional state from facial expressions and voice, and generates output data that evaluates individual characteristics.

[0784] Step 4:

[0785] The facial features, body shape characteristics, and emotional state obtained from the input data are compared with a database on the server. Based on past highly-rated fashion examples and trend information, the server uses information processing tools to generate optimal clothing suggestions that are suitable for the user's mood and situation.

[0786] Step 5:

[0787] The server creates a purchase path for the proposed clothing items and generates link information. This makes it possible to generate purchase links that allow the user to directly access the product's purchase page.

[0788] Step 6:

[0789] The server sends the generated clothing suggestions and purchase links to the user's device. The device visually displays this information in a user interface, allowing the user to review the suggestions and click on links to items of interest to proceed with the purchase.

[0790] Step 7:

[0791] Users review the suggested fashion items on their devices, and if necessary, decide to purchase them and retrieve the items via the provided links. The suggestions based on the prompts provided during this process play a role in supporting the user's purchasing intent.

[0792] 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.

[0793] 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.

[0794] 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 robot 414.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] The following is further disclosed regarding the embodiments described above.

[0814] (Claim 1)

[0815] Information processing means for receiving a photograph and analyzing the facial shape and body characteristics of an individual from the photograph,

[0816] Based on the analyzed characteristics, a proposal method is used to compare them with past databases and provide personalized fashion suggestions.

[0817] A display means that generates a purchase link to the proposed fashion item and presents the link,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein the analysis is performed using multimodal technology.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the database includes fashion data, including past highly-rated examples.

[0823] "Example 1"

[0824] (Claim 1)

[0825] A means for receiving personal photographs, verifying the image format of the photographs, encrypting them to protect privacy, and transmitting them,

[0826] Information processing means that inputs received photographs into an analysis model based on deep learning to identify facial shape and body shape features,

[0827] A proposal method that uses analysis results to compare with past fashion databases and makes optimized fashion suggestions,

[0828] A display means that generates and presents purchase links corresponding to the proposed fashion components,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, which uses multimodal technology to analyze personal preference data other than photographs.

[0832] (Claim 3)

[0833] The system according to claim 1, which includes fashion information in a fashion database that records past evaluation information.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] A data processing means that receives a photograph and analyzes the shape and physical characteristics of an individual from the photograph,

[0837] Based on the analyzed characteristics, a proposal method is used to compare them with past data sets and provide personalized clothing suggestions.

[0838] An information display means that generates and presents purchase instructions for the proposed clothing items,

[0839] A mechanical means that receives a user's voice command and automatically takes a photograph and makes a suggestion based on the command,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, which performs the analysis using multimodal technology and provides appropriate clothing recommendations based on user input.

[0843] (Claim 3)

[0844] The system according to claim 1, wherein the data collection comprises clothing information including past high-rated information, and has a function for analyzing voice commands.

[0845] "Example 2 of combining an emotion engine"

[0846] (Claim 1)

[0847] An information processing device that receives a photograph and analyzes an individual's facial shape and body shape information based on the photograph,

[0848] An emotion recognition device that analyzes facial expression data and voice data obtained from a terminal device to recognize an individual's emotions,

[0849] A suggestion device that compares analyzed information and recognized emotional data with past data storage to provide personalized clothing suggestions,

[0850] A display device that generates a purchase link for the proposed clothing and presents the link,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which performs the analysis and emotion recognition using multimodal technology.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the data storage includes clothing data, including past highly-rated cases.

[0856] "Application example 2 when combining with an emotional engine"

[0857] (Claim 1)

[0858] Information processing means that receives photographic and audio data and analyzes the individual's facial features, body type characteristics, and emotional state from the data,

[0859] Based on analyzed characteristics and emotional states, a suggestion method is provided that compares them with past databases to suggest clothing optimized for the individual's situation and mood.

[0860] A display means that generates a purchase route to the proposed clothing item and presents the route,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, wherein the analysis is performed using multimodal technology.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the database includes fashion data including past highly-rated cases and feedback from purchasing users. [Explanation of Symbols]

[0866] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data processing means that receives a photograph and analyzes the shape and physical characteristics of an individual from the photograph, Based on the analyzed characteristics, a proposal method is used to compare them with past data sets and provide personalized clothing suggestions. An information display means that generates and presents purchase instructions for the proposed clothing items, A mechanical means that receives a user's voice command and automatically takes a photograph and makes a suggestion based on the command, A system that includes this.

2. The system according to claim 1, which performs the analysis using multimodal technology and provides appropriate clothing recommendations based on user input.

3. The system according to claim 1, wherein the data collection comprises clothing information including past high-rated information, and has a function for analyzing voice commands.

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